diff --git a/.gitignore b/.gitignore index 46c93d3..ebad973 100644 --- a/.gitignore +++ b/.gitignore @@ -80,7 +80,13 @@ # csv files in selected input folders !08_2pager/081_data/cleaned_*.csv !08_2pager/081_data/hosted_in_repo/*.csv +!05_working_paper/053_outputs/*correlations.csv +!02_simulation/023_outputs/*.csv +!02_simulation/023_outputs/*/*.csv +# excel for working paper +!05_working_paper/051_rawdata/LPV_*Template.xlsx +!05_working_paper/053_outputs/LPV_*PAPER.xlsx ################## #Include images used in the 2pager template diff --git a/00_documentation/001_technical_note/Technical_Note.md b/00_documentation/001_technical_note/Technical_Note.md index 4ad02af..213ced0 100644 --- a/00_documentation/001_technical_note/Technical_Note.md +++ b/00_documentation/001_technical_note/Technical_Note.md @@ -69,21 +69,21 @@ PIRLS is the anchor assessment used in our dataset. Of the major multi-country a ##### **International**: Trends in International Mathematics and Science Study ([TIMSS](https://nces.ed.gov/timss/)) -For some countries we use proficiency data from TIMSS international math and science assessment in grades 3-6 (most countries participated in grade 4). For these countries, we use the science scores as a proxy for reading scores, counting children as proficient if they exceeded the Low International Benchmark of 400 (Minimum Proficiency Level 2). We have two rationales for using this proxy. The first is conceptual: the ability to decipher science questions requires reading proficiency, since most science questions are word problems. The second is empirical: across the countries for which we have data for both subject areas, proficiency on science is highly correlated with reading proficiency. Within the PISA assessment, the science-reading correlation is 0.97, and for countries that have participated in both TIMSS and PIRLS, the correlation between the two is 0.99. Only in the case of Jordan, for which had no science scores, we are using mathematics proficiency as a proxy for reading proficiency (same MPL of 2, Low International Benchmark, 400 points). +For some countries we use proficiency data from TIMSS international math and science assessment in grades 3-6 (most countries participated in grade 4). For these countries, we use the science scores as a proxy for reading scores, counting children as proficient if they exceeded the Low International Benchmark of 400 points (Minimum Proficiency Level 2). We have two rationales for using this proxy. The first is conceptual: the ability to decipher science questions requires reading proficiency, since most science questions are word problems. The second is empirical: across the countries for which we have data for both subject areas, proficiency on science is highly correlated with reading proficiency. Within the PISA assessment, the science-reading correlation is 0.97, and for countries that have participated in both TIMSS and PIRLS, the correlation between the two is 0.99. Only in the case of Jordan, for which had no science scores, we are using mathematics proficiency as a proxy for reading proficiency (same MPL of 2, Low International Benchmark, a score of 400 points). ##### **Regional**: Latin American Laboratory for Assessment of the Quality of Education ([LLECE](http://www.unesco.org/new/en/santiago/education/education-assessment-llece/)) -LLECE has implemented three rounds of regional assessments in Latin America (PERCE, SERCE and TERCE). The most recent for which we have data, the TERCE, was carried out in 2013 and covered 15 countries. The TERCE scores were reported in two scales: we choose to use the SERCE-compatible reporting scale, for historical comparability. In the SERCE scale, we defined minimum proficiency as reaching Level 3 in language. +LLECE has implemented three rounds of regional assessments in Latin America (PERCE, SERCE and TERCE). The most recent for which we have data, the TERCE, was carried out in 2013 and covered 15 countries. The TERCE scores were reported in two scales: we choose to use the SERCE-compatible reporting scale, for historical comparability. In the SERCE scale, we defined minimum proficiency as reaching Level 3 in language (a score of 513.66 points). ##### **Regional**: CONFEMEN Education Systems Analysis Program ([PASEC](http://www.pasec.confemen.org)) -PASEC has carried out several rounds of data collection in Francophone African countries. The most recent round was carried out in 2014, and we used data from that round of PASEC to provide estimates for 10 countries, considering minimum reading proficiency as reaching Level 4 in language for 6th graders. +PASEC has carried out several rounds of data collection in Francophone African countries. The most recent round was carried out in 2014, and we used data from that round of PASEC to provide estimates for 10 countries, considering minimum reading proficiency as reaching Level 4 in language for 6th graders (a score of 595.1 points). Other rounds of PASEC were also included in our dataset, with proficiency data extracted from official reports instead of generated from the harmonized microdata in the Global Learning Assessment Database ([GLAD](https://github.com/worldbank/GLAD)). From all the countries included in the global number, this is the case for 3: Dem. Rep. of Congo, Madagascar and Mali (COD MLI MDG). ##### **Regional**: Southern and Eastern Africa Consortium for Monitoring Educational Quality ([SACMEQ](http://www.sacmeq.org/)) -SACMEQ has carried out several rounds of data collection for Eastern and Southern African countries. The latest round of the SACMEQ assessment was carried out in 2013 (SACMEQ IV). Due to concerns on the quality of the data for this round, however, we do not use this to establish levels. We do however, use the earlier rounds of data for estimating changes in proficiency over time. We considered as minimum proficiency reaching Level 5 in reading. +SACMEQ has carried out several rounds of data collection for Eastern and Southern African countries. The latest round of the SACMEQ assessment was carried out in 2013 (SACMEQ IV). Due to concerns on the quality of the data for this round, however, we do not use this to establish levels. We do, however, use this data for estimating changes in proficiency over time. We considered as minimum proficiency reaching Level 5 in reading (a score of 509 points). ##### **Country-specific**: National Learning Assessments (NLA) @@ -101,14 +101,14 @@ For each new assessment incorporated into the database, the harmonization proces The GAML created an initial mappings between three regional assessments (PASEC, LLECE, and SACMEQ) as part of the SDG monitoring process ([2018a](http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2018/12/4.1.1_29_Consensus-building-meeting-package.pdf), [2018b](http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2018/10/Final-Report-of-September-2018-Paris-Consensus-Meeting.pdf)). These mappings, however, are provisional. These have been updated during GAML workshops in 2019, and there is a process underway to validate them using analysis of individual items, but it will be some time before that process reaches any conclusions. While we typically use these thresholds, summarized in the table below, we triangulate these with other data where possible. -|Assessment| Minimum Proficiency Level (MPL) | Grade(s) assessed | Most recent year | Number of countries | +|Assessment| Minimum Proficiency Level (MPL) | Grade(s) assessed | Most recent year | Number of low- and middle-income countries w/ data after 2011 | |---|---|---|---|---| -|PIRLS|Level 2 (Low international benchmark)|4|2016|59| -|TIMSS|Level 2 (Low international benchmark)|4|2015|14| -|LLECE|Level 3|6|2013|13| -|PASEC|Level 4|5 and 6|2014|15| -|SACMEQ|Level 5|6|2007|11| -|NLAs|Varies by country|4, 5 and 6|2017|9|| +|PIRLS|Level 2 (Low international benchmark, 400 points)|4|2016|15| +|TIMSS|Level 2 (Low international benchmark, 400 points)|4|2015|7| +|LLECE|Level 3 (513.66 points)|6|2013|15| +|PASEC|Level 4 (595.1 points)|5 and 6|2014|13| +|SACMEQ|Level 5 (509 points) |6|2007|-| +|NLAs|Varies by country|4, 5 and 6|2017|12|| ##### Preferred Learning Assessments Order @@ -152,24 +152,9 @@ The regional focal points and country TTLs have validated the enrollment data an #### Population Source Our source of population is the official United Nations populations estimates and projections (2017 Revision), prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. The 2017 Revision is the twenty-fifth round of global population estimates and projections produced by the Population Division since 1951. The detailed description of the methodology on the way that country estimates have been prepared, including the assumptions that were used to project fertility, mortality and international migration up to -the year 2100 can be found on the [website of the Population Division](www.unpopulation.org). +the year 2100 can be found on the [website of the Population Division](www.unpopulation.org). -This work uses the **10-14 age group** UN Population Division’s “medium fertility” scenario. The data can be also found at [HealthStats' Population Estimates and Projection database](https://databank.worldbank.org/data/source/health-nutrition-and-population-statistics:-population-estimates-and-projections). See also: [graphs](https://esa.un.org/unpd/wpp/Graphs/Probabilistic/) and [methodology](https://population.un.org/wpp/Publications/Files/WPP2017_Methodology.pdf). - -#### Population by Region and Income level - -The table below shows the target population, 10-14 years old, by region and country income level classification in 2015, our anchor year. - -|region | HIC | UMC | LMC | LIC | **Total**| -|---|---|---|---|---|---| -|EAS|10,448,241 | 87,689,307 | 49,371,244 | 1,885,239 | 149,394,032 | -|ECS|25,422,323 | 19,022,549 | 5,238,264 | 823,390 | 50,506,528 | -|LCN|2,240,453 | 47,456,826 | 3,444,678 | 1,178,250 | 54,320,208 | -|MEA|4,184,567 | 14,847,722 | 13,116,378 | 5,070,562 | 37,219,228 | -|NAC|22,578,317 | - | -| -| 22,578,316| -|SAS|- | 1,733,106 | 164,939,900 | 7,974,831 | 174,647,840 | -|SSF| 6,448 | 5,769,214 | 53,027,261 | 64,356,101 | 123,159,024 | -|**Total**|64,880,349|176,518,724 | 289,137,725 | 81,288,373 | 611,825,176 || +This work uses the **10-14 age group** UN Population Division’s “medium fertility” scenario. The data can be also found at [HealthStats' Population Estimates and Projection database](https://databank.worldbank.org/data/source/health-nutrition-and-population-statistics:-population-estimates-and-projections). See also: [graphs](https://esa.un.org/unpd/wpp/Graphs/Probabilistic/) and [methodology](https://population.un.org/wpp/Publications/Files/WPP2017_Methodology.pdf). *** @@ -199,11 +184,13 @@ A second empirical goal is to measure how _learning poverty_ has improved over ### Calculation of Standard Errors -We calculate standard errors that are reported in the tables of the technical paper in the following way. In short, we use a bootstrapping technique, which captures sampling error in the proportion of students minimally proficient at the country-year-assessment level. +Our Learning Poverty measure is a weighted average of two indicators, namely the share of learners below a minimum proficiency threshold and the share of out-of-school children. The first indicator is estimated using sample-based learning assessments, the latter is estimated in virtually all cases using administrative records from national Education Management Information System and the population census. Both measures have an associated error term, however they are of very different nature. + +Our learning data is sample-based, and as such their error term reflect the sample error and the psychometrical procedure used to estimate the latent learning variable; our out-of-school measure is a population measure estimated using administrative records and census data, as such does not have a sample error. Both measures, however, are also affected by non-sampling error, such as questionnaire or measurement error, implementation challenges, and behavior effects. Unfortunately, we have no basis to capture this later term, hence we use bootstrap for error propagation of the sample error associated with our learning measure. -Using our student level assessment microdata, we form an indicator for whether each student is above the minimum proficiency level defined above and estimate the mean to produce the proportion proficient in each country, along with the standard error of that mean estimate for each country-year-assessment combination. Applying the Central Limit Theorem, which can be justified because the assessment databases typically contain several hundred student observations, our estimator of the proportion above minimum proficiency in each country follows an asymptotically Normal distribution. To produce standard errors for our final numbers, which are based on these country-year-assessment level proficiency numbers, we take 100 bootstrap random draws of our country-year-assessment level proficiency database, where each individual observation in our database is drawn from the Normal distribution with the mean of this distribution being our estimate of the proportion minimally proficient and the variance being the squared standard error of that estimated proportion. Then our final global and regional numbers are calculated in each of these 100 bootstrap simulated databases, and our standard error is the standard deviation of our estimate across those 100 bootstrap datasets. +Using our student level assessment microdata, we form an indicator for whether each student is above the minimum proficiency level defined above and estimate the mean to produce the proportion proficient in each country, along with the standard error of that mean estimate for each country-year-assessment combination. Applying the Central Limit Theorem, which can be justified because the assessment databases typically contain several hundred student observations, our estimator of the proportion above minimum proficiency in each country follows an asymptotically Normal distribution. To produce standard errors for our final numbers, which are based on these country-year-assessment level proficiency numbers, we take 100 bootstrap random draws of our country-year-assessment level proficiency database, where each individual observation in our database is drawn from the Normal distribution with the mean of this distribution being our estimate of the proportion minimally proficient and the variance being the squared standard error of that estimated proportion. Then our final global and regional numbers are calculated in each of these 100 bootstrap simulated databases, and our standard error is the standard deviation of our estimate across those 100 bootstrap datasets. -We assume that enrollment numbers, which also feed into our adjusted non-proficiency measure, are reported without error, as they are typically calculated using administrative records. We acknowledge that in some cases, even using administrative records can lead to inaccurate counts of students enrolled in schools. These types of misreporting errors are difficult to account for, and we are unable to incorporate this type of error into our standard error calculations. We acknowledge this as a limitation. +As discussed, our enrollment numbers, which also feed into our Learning Poverty measure, are population measures based on administrative records without an associated sample error. We acknowledge that this indicator might suffer from non-sample errors, which can lead to inaccurate counts of students enrolled in schools. Non-sample errors affect both of our measures, as they have a direct impact on the out-of-school measure, and an indirect effect in our learning estimates as they impact the sample frame. These types of misreporting errors are difficult to account for, and we are unable to incorporate this type of error into our standard error calculations. We acknowledge this as a limitation. #### Special Cases for Standard Error Calculations -In some cases, where we are using country-year observations based on national assessments, we do not have a standard error associated with this observation. When no standard error is available, we use a value of 0.5, which is approximately the median standard error across all country-year-assessment combinations used. +In some cases, where we are using country-year observations based on national assessments, we do not have a standard error associated with this observation. When no standard error is available, we use a value of 1.2pp, which is approximately the median standard error across all country-year-assessment combinations used. diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/dyntext_LP.txt b/00_documentation/002_repo_structure/0022_dataset_tables/dyntext_LP.txt index 71c2d5e..8913b29 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/dyntext_LP.txt +++ b/00_documentation/002_repo_structure/0022_dataset_tables/dyntext_LP.txt @@ -69,7 +69,6 @@ Documentation of <> ~~~~ <>: <> -<>: <> ~~~~ diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/enrollment.md b/00_documentation/002_repo_structure/0022_dataset_tables/enrollment.md index b4b1b34..91d37e4 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/enrollment.md +++ b/00_documentation/002_repo_structure/0022_dataset_tables/enrollment.md @@ -10,7 +10,6 @@ Dataset of enrollment. Long in countrycode and year, wide in enrollment definiti ~~~~ sources: Multiple enrollment definitions were combined according to a ranking. Original data from World Bank (country team validation, ANER) and UIS (TNER, NET, GER) -lastsave: 16 Oct 2019 21:57:15 by wb255520 ~~~~ @@ -24,23 +23,23 @@ idvars: countrycode year valuevars: enrollment_validated_all enrollment_validated_fe enrollment_validated_ma enrollment_validated_flag enrollment_interpolated_all enrollment_interpolated_fe enrollment_interpolated_ma enrollment_interpolated_flag traitvars: enrollment_source enrollment_definition year_enrollment -. codebook, compact - -Variable Obs Unique Mean Min Max Label ----------------------------------------------------------------------------------------------------------------------------------------- -countrycode 6293 217 . . . WB country code (3 letters) -year 6293 29 2004 1990 2018 Year -en~dated_all 5771 2908 87.21567 19.10539 100 Validated % of children enrolled in school (using closest year, both genders) -enr~dated_fe 5348 2742 86.29536 15.50506 100 Validated % of children enrolled in school (using closest year, female only) -enr~dated_ma 5348 2743 87.94001 22.14 100.4548 Validated % of children enrolled in school (using closest year, male only) -e~dated_flag 6293 2 .194184 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) -en~lated_all 5771 3755 87.24924 19.10539 100 Validated % of children enrolled in school (using interpolation, both genders) -enr~lated_fe 4739 2857 87.20877 15.50506 100 Validated % of children enrolled in school (using interpolation, female only) -enr~lated_ma 4739 2858 88.61073 22.14 100.4548 Validated % of children enrolled in school (using interpolation, male only) -e~lated_flag 6293 2 .1978389 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) -enrollmen~ce 6293 5 . . . The source used for this enrollment value -enrollment~n 6293 5 . . . The definition used for this enrollment value -year_enrol~t 5771 29 2005.041 1990 2018 The year that the enrollment value is from ----------------------------------------------------------------------------------------------------------------------------------------- - +. codebook, compact + +Variable Obs Unique Mean Min Max Label +--------------------------------------------------------------------------------------------------------------------------------------- +countrycode 6293 217 . . . WB country code (3 letters) +year 6293 29 2004 1990 2018 Year +en~dated_all 5771 2908 87.21567 19.10539 100 Validated % of children enrolled in school (using closest year, both genders) +enr~dated_fe 5348 2742 86.29536 15.50506 100 Validated % of children enrolled in school (using closest year, female only) +enr~dated_ma 5348 2743 87.94001 22.14 100.4548 Validated % of children enrolled in school (using closest year, male only) +e~dated_flag 6293 2 .194184 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) +en~lated_all 5771 3755 87.24924 19.10539 100 Validated % of children enrolled in school (using interpolation, both genders) +enr~lated_fe 4739 2857 87.20877 15.50506 100 Validated % of children enrolled in school (using interpolation, female only) +enr~lated_ma 4739 2858 88.61073 22.14 100.4548 Validated % of children enrolled in school (using interpolation, male only) +e~lated_flag 6293 2 .1978389 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) +enrollmen~ce 6293 4 . . . The source used for this enrollment value +enrollment~n 6293 6 . . . The definition used for this enrollment value +year_enrol~t 5771 29 2005.041 1990 2018 The year that the enrollment value is from +--------------------------------------------------------------------------------------------------------------------------------------- + ~~~~ diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/population.md b/00_documentation/002_repo_structure/0022_dataset_tables/population.md index cc52654..29095e2 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/population.md +++ b/00_documentation/002_repo_structure/0022_dataset_tables/population.md @@ -10,7 +10,6 @@ Dataset of late primary aged population. Long in countrycode and year, wide in p ~~~~ sources: World Bank staff estimates using the World Bank's total population and age distributions of the United Nations Population Division's World Population Prospects. -lastsave: 16 Oct 2019 21:57:14 by wb255520 ~~~~ @@ -24,25 +23,25 @@ idvars: countrycode year_population valuevars: population_fe_10 population_fe_primary population_fe_9plus population_ma_10 population_ma_primary population_ma_9plus population_all_10 population_all_primary population_all_9plus population_fe_1014 population_ma_1014 population_all_1014 population_source traitvars: population_source -. codebook, compact - -Variable Obs Unique Mean Min Max Label ----------------------------------------------------------------------------------------------------------------------------------------- -countrycode 13237 217 . . . WB country code (3 letters) -year_popul~n 13237 61 2020 1990 2050 Year of population -populat~e_10 11795 6667 320676.7 479 1.33e+07 Female population aged 10 (WB API) -po~e_primary 10514 7901 2282059 3477 7.53e+07 Female population primary age, country specific (WB API) -popu~e_9plus 11722 8056 1192931 967 5.14e+07 Female population aged 9 to end of primary, country specific (WB API) -populat~a_10 11795 6671 339959 492 1.42e+07 Male population aged 10 (WB API) -po~a_primary 10514 7939 2419477 3858 8.08e+07 Male population primary age, country specific (WB API) -popu~a_9plus 11722 8043 1259789 1007 5.52e+07 Male population aged 9 to end of primary, country specific (WB API) -populat~l_10 11795 7305 660635.7 971 2.75e+07 Total population aged 10 (WB API) -po~l_primary 10514 8655 4701535 7335 1.56e+08 Total population primary age, country specific (WB API) -popu~l_9plus 11722 8815 2452720 1974 1.07e+08 Total population aged 9 to end of primary, country specific (WB API) -popul~e_1014 11792 8026 1582075 2300 6.21e+07 Female population between ages 10 to 14 (WB API) -popul~a_1014 11792 8055 1676621 2300 6.72e+07 Male population between ages 10 to 14 (WB API) -popul~l_1014 11792 8758 3258696 4600 1.29e+08 Total population between ages 10 to 14 (WB API) -population~e 13237 1 . . . The source used for population variables ----------------------------------------------------------------------------------------------------------------------------------------- - +. codebook, compact + +Variable Obs Unique Mean Min Max Label +--------------------------------------------------------------------------------------------------------------------------------------- +countrycode 13237 217 . . . WB country code (3 letters) +year_popul~n 13237 61 2020 1990 2050 Year of population +populat~e_10 11795 6667 320676.7 479 1.33e+07 Female population aged 10 (WB API) +po~e_primary 10514 7901 2282059 3477 7.53e+07 Female population primary age, country specific (WB API) +popu~e_9plus 11722 8056 1192931 967 5.14e+07 Female population aged 9 to end of primary, country specific (WB API) +populat~a_10 11795 6671 339959 492 1.42e+07 Male population aged 10 (WB API) +po~a_primary 10514 7939 2419477 3858 8.08e+07 Male population primary age, country specific (WB API) +popu~a_9plus 11722 8043 1259789 1007 5.52e+07 Male population aged 9 to end of primary, country specific (WB API) +populat~l_10 11795 7305 660635.7 971 2.75e+07 Total population aged 10 (WB API) +po~l_primary 10514 8655 4701535 7335 1.56e+08 Total population primary age, country specific (WB API) +popu~l_9plus 11722 8815 2452720 1974 1.07e+08 Total population aged 9 to end of primary, country specific (WB API) +popul~e_1014 11792 8026 1582075 2300 6.21e+07 Female population between ages 10 to 14 (WB API) +popul~a_1014 11792 8055 1676621 2300 6.72e+07 Male population between ages 10 to 14 (WB API) +popul~l_1014 11792 8758 3258696 4600 1.29e+08 Total population between ages 10 to 14 (WB API) +population~e 13237 1 . . . The source used for population variables +--------------------------------------------------------------------------------------------------------------------------------------- + ~~~~ diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/proficiency.md b/00_documentation/002_repo_structure/0022_dataset_tables/proficiency.md index c3dfb8d..91ce8ca 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/proficiency.md +++ b/00_documentation/002_repo_structure/0022_dataset_tables/proficiency.md @@ -10,39 +10,44 @@ Dataset of proficiency. One country may have multiple or no observations at all. ~~~~ sources: Compilation of proficiency measures from 3 sources: CLO (Country Level Outcomes from GLAD), National Learning Assessment (from UIS), HAD (Harmonized Assessment Database) -lastsave: 16 Oct 2019 21:57:14 by wb255520 ~~~~ -About the **15 variables** in this dataset: +About the **21 variables** in this dataset: ~~~~ The variables belong to the following variable classifications: idvars valuevars traitvars idvars: countrycode year idgrade test nla_code subject -valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe +valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe fgt1_all fgt1_fe fgt1_ma fgt2_all fgt2_fe fgt2_ma traitvars: min_proficiency_threshold source_assessment surveyid -. codebook, compact - -Variable Obs Unique Mean Min Max Label ----------------------------------------------------------------------------------------------------------------------------------------- -countrycode 697 146 . . . WB country code (3 letters) -year 697 20 2009.898 1996 2017 Year of assessment -idgrade 697 4 4.308465 3 6 Grade ID -test 697 7 . . . Assessment -nla_code 697 22 . . . Reference code for NLA in markdown documentation -subject 697 3 . . . Subject -nonprof_all 697 697 30.44718 .2252221 99.89659 % pupils below minimum proficiency (all) -se_nonprof~l 559 559 .9498702 .1218972 3.419903 SE of pupils below minimum proficiency (all) -nonprof_ma 559 559 24.92177 .1586974 97.96137 % pupils below minimum proficiency (ma) -se_nonprof~a 559 559 1.199997 .1287481 3.848194 SE of pupils below minimum proficiency (ma) -nonprof_fe 559 559 22.02045 .1284599 97.83222 % pupils below minimum proficiency (fe) -se_nonprof~e 559 559 1.142743 .1141958 3.566816 SE of pupils below minimum proficiency (fe) -min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific) -source_ass~t 697 3 . . . Source of assessment data -surveyid 697 503 . . . SurveyID (countrycode_year_assessment) ----------------------------------------------------------------------------------------------------------------------------------------- - +. codebook, compact + +Variable Obs Unique Mean Min Max Label +--------------------------------------------------------------------------------------------------------------------------------------- +countrycode 697 146 . . . WB country code (3 letters) +year 697 20 2009.898 1996 2017 Year of assessment +idgrade 697 4 4.308465 3 6 Grade ID +test 697 7 . . . Assessment +nla_code 697 22 . . . Reference code for NLA in markdown documentation +subject 697 3 . . . Subject +nonprof_all 697 697 30.44595 .2252221 99.89659 % pupils below minimum proficiency (all) +se_nonprof~l 559 559 1.05663 .1218972 3.419903 SE of pupils below minimum proficiency (all) +nonprof_ma 561 561 25.08579 .1586974 97.96137 % pupils below minimum proficiency (ma) +se_nonprof~a 559 559 1.297817 .1287481 3.848194 SE of pupils below minimum proficiency (ma) +nonprof_fe 561 561 22.18714 .1284599 97.83222 % pupils below minimum proficiency (fe) +se_nonprof~e 559 559 1.245255 .1141958 4.098772 SE of pupils below minimum proficiency (fe) +fgt1_all 559 559 .1708391 .0308948 .7537994 Avg gap to minimum proficiency (all, FGT1) +fgt1_fe 559 559 .1641004 .0257019 .7643428 Avg gap to minimum proficiency (fe, FGT1) +fgt1_ma 559 559 .1757896 .0298228 .7457443 Avg gap to minimum proficiency (ma, FGT1) +fgt2_all 559 559 .0610742 .001687 .6159182 Avg gap squared to minimum proficiency (all, FGT2) +fgt2_fe 559 559 .0572421 .0011683 .6308874 Avg gap squared to minimum proficiency (fe, FGT2) +fgt2_ma 559 559 .0639653 .0017091 .6044815 Avg gap squared to minimum proficiency (ma, FGT2) +min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific) +source_ass~t 697 3 . . . Source of assessment data +surveyid 697 503 . . . SurveyID (countrycode_year_assessment) +--------------------------------------------------------------------------------------------------------------------------------------- + ~~~~ diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/rawfull.md b/00_documentation/002_repo_structure/0022_dataset_tables/rawfull.md index 5d960be..1d96977 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/rawfull.md +++ b/00_documentation/002_repo_structure/0022_dataset_tables/rawfull.md @@ -10,78 +10,83 @@ Dataset of proficiency merged with enrollment and population. Not a timeseries, ~~~~ sources: All population, enrollment and proficiency sources combined. -lastsave: 16 Oct 2019 21:57:15 by wb255520 ~~~~ -About the **54 variables** in this dataset: +About the **60 variables** in this dataset: ~~~~ The variables belong to the following variable classifications: idvars valuevars traitvars idvars: countrycode year_assessment idgrade test nla_code subject -valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe enrollment_validated_all enrollment_validated_fe enrollment_validated_ma enrollment_validated_flag enrollment_interpolated_all enrollment_interpolated_fe enrollment_interpolated_ma enrollment_interpolated_flag population_fe_10 population_fe_primary population_fe_9plus population_ma_10 population_ma_primary population_ma_9plus population_all_10 population_all_primary population_all_9plus population_fe_1014 population_ma_1014 population_all_1014 population_source +valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe fgt1_all fgt1_fe fgt1_ma fgt2_all fgt2_fe fgt2_ma enrollment_validated_all enrollment_validated_fe enrollment_validated_ma enrollment_validated_flag enrollment_interpolated_all enrollment_interpolated_fe enrollment_interpolated_ma enrollment_interpolated_flag population_fe_10 population_fe_primary population_fe_9plus population_ma_10 population_ma_primary population_ma_9plus population_all_10 population_all_primary population_all_9plus population_fe_1014 population_ma_1014 population_all_1014 population_source traitvars: year_enrollment year_population source_assessment enrollment_source population_source enrollment_definition min_proficiency_threshold surveyid countryname region region_iso2 regionname adminregion adminregion_iso2 adminregionname incomelevel incomelevel_iso2 incomelevelname lendingtype lendingtype_iso2 lendingtypename cmu -. codebook, compact - -Variable Obs Unique Mean Min Max Label ----------------------------------------------------------------------------------------------------------------------------------------- -countrycode 914 217 . . . WB country code (3 letters) -year_asses~t 914 20 2011.109 1996 2017 Year of assessment -idgrade 914 5 -233.895 -999 6 Grade ID -test 914 8 . . . Assessment -nla_code 914 22 . . . Reference code for NLA in markdown documentation -subject 914 4 . . . Subject -nonprof_all 697 697 30.44718 .2252221 99.89659 % pupils below minimum proficiency (all) -se_nonprof~l 559 559 .9498702 .1218972 3.419903 SE of pupils below minimum proficiency (all) -nonprof_ma 559 559 24.92177 .1586974 97.96137 % pupils below minimum proficiency (ma) -se_nonprof~a 559 559 1.199997 .1287481 3.848194 SE of pupils below minimum proficiency (ma) -nonprof_fe 559 559 22.02045 .1284599 97.83222 % pupils below minimum proficiency (fe) -se_nonprof~e 559 559 1.142743 .1141958 3.566816 SE of pupils below minimum proficiency (fe) -en~dated_all 896 503 92.41767 23.54786 100 Validated % of children enrolled in school (using closest year, both genders) -enr~dated_fe 855 481 92.32435 16.75778 100 Validated % of children enrolled in school (using closest year, female only) -enr~dated_ma 855 481 92.87754 30.29106 100.2015 Validated % of children enrolled in school (using closest year, male only) -e~dated_flag 914 2 .1345733 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) -en~lated_all 896 531 92.438 23.54786 100 Validated % of children enrolled in school (using interpolation, both genders) -enr~lated_fe 773 445 93.02931 16.75778 100 Validated % of children enrolled in school (using interpolation, female only) -enr~lated_ma 773 445 93.36419 30.29106 100.2015 Validated % of children enrolled in school (using interpolation, male only) -e~lated_flag 914 2 .1444201 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) -populat~e_10 890 193 293630.7 671 1.21e+07 Female population aged 10 (WB API) -po~e_primary 832 172 2052513 4599 7.23e+07 Female population primary age, country specific (WB API) -popu~e_9plus 873 191 1093833 1374 3.62e+07 Female population aged 9 to end of primary, country specific (WB API) -populat~a_10 890 193 310736.6 696 1.35e+07 Male population aged 10 (WB API) -po~a_primary 832 172 2170324 4773 8.04e+07 Male population primary age, country specific (WB API) -popu~a_9plus 873 191 1153568 1425 4.04e+07 Male population aged 9 to end of primary, country specific (WB API) -populat~l_10 890 193 604367.3 1370 2.55e+07 Total population aged 10 (WB API) -po~l_primary 832 172 4222837 9372 1.53e+08 Total population primary age, country specific (WB API) -popu~l_9plus 873 191 2247401 2799 7.65e+07 Total population aged 9 to end of primary, country specific (WB API) -popul~e_1014 890 193 1438254 3162 5.98e+07 Female population between ages 10 to 14 (WB API) -popul~a_1014 890 193 1521794 3286 6.71e+07 Male population between ages 10 to 14 (WB API) -popul~l_1014 890 193 2960048 6448 1.27e+08 Total population between ages 10 to 14 (WB API) -population~e 914 1 . . . The source used for population variables -year_enrol~t 896 24 2010.494 1991 2017 The year that the enrollment value is from -year_popul~n 914 1 2015 2015 2015 Year of population -source_ass~t 697 3 . . . Source of assessment data -enrollmen~ce 914 5 . . . The source used for this enrollment value -enrollment~n 914 5 . . . The definition used for this enrollment value -min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific) -surveyid 697 503 . . . SurveyID (countrycode_year_assessment) -countryname 914 217 . . . Country Name -region 914 7 . . . Region Code -region_iso2 914 7 . . . Region Code (ISO 2 digits) -regionname 914 7 . . . Region Name -adminregion 475 6 . . . Administrative Region Code -adminregio~2 475 6 . . . Administrative Region Code (ISO 2 digits) -adminregio~e 475 6 . . . Administrative Region Name -incomelevel 914 4 . . . Income Level Code -incomeleve~2 914 4 . . . Income Level Code (ISO 2 digits) -incomeleve~e 914 4 . . . Income Level Name -lendingtype 914 4 . . . Lending Type Code -lendingtyp~2 914 4 . . . Lending Type Code (ISO 2 digits) -lendingty~me 911 4 . . . Lending Type Name -cmu 680 48 . . . WB Country Management Unit ----------------------------------------------------------------------------------------------------------------------------------------- - +. codebook, compact + +Variable Obs Unique Mean Min Max Label +--------------------------------------------------------------------------------------------------------------------------------------- +countrycode 914 217 . . . WB country code (3 letters) +year_asses~t 914 20 2011.109 1996 2017 Year of assessment +idgrade 914 5 -233.895 -999 6 Grade ID +test 914 8 . . . Assessment +nla_code 914 22 . . . Reference code for NLA in markdown documentation +subject 914 4 . . . Subject +nonprof_all 697 697 30.44595 .2252221 99.89659 % pupils below minimum proficiency (all) +se_nonprof~l 559 559 1.05663 .1218972 3.419903 SE of pupils below minimum proficiency (all) +nonprof_ma 561 561 25.08579 .1586974 97.96137 % pupils below minimum proficiency (ma) +se_nonprof~a 559 559 1.297817 .1287481 3.848194 SE of pupils below minimum proficiency (ma) +nonprof_fe 561 561 22.18714 .1284599 97.83222 % pupils below minimum proficiency (fe) +se_nonprof~e 559 559 1.245255 .1141958 4.098772 SE of pupils below minimum proficiency (fe) +fgt1_all 559 559 .1708391 .0308948 .7537994 Avg gap to minimum proficiency (all, FGT1) +fgt1_fe 559 559 .1641004 .0257019 .7643428 Avg gap to minimum proficiency (fe, FGT1) +fgt1_ma 559 559 .1757896 .0298228 .7457443 Avg gap to minimum proficiency (ma, FGT1) +fgt2_all 559 559 .0610742 .001687 .6159182 Avg gap squared to minimum proficiency (all, FGT2) +fgt2_fe 559 559 .0572421 .0011683 .6308874 Avg gap squared to minimum proficiency (fe, FGT2) +fgt2_ma 559 559 .0639653 .0017091 .6044815 Avg gap squared to minimum proficiency (ma, FGT2) +en~dated_all 896 503 92.41767 23.54786 100 Validated % of children enrolled in school (using closest year, both genders) +enr~dated_fe 855 481 92.32435 16.75778 100 Validated % of children enrolled in school (using closest year, female only) +enr~dated_ma 855 481 92.87754 30.29106 100.2015 Validated % of children enrolled in school (using closest year, male only) +e~dated_flag 914 2 .1345733 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) +en~lated_all 896 531 92.438 23.54786 100 Validated % of children enrolled in school (using interpolation, both genders) +enr~lated_fe 773 445 93.02931 16.75778 100 Validated % of children enrolled in school (using interpolation, female only) +enr~lated_ma 773 445 93.36419 30.29106 100.2015 Validated % of children enrolled in school (using interpolation, male only) +e~lated_flag 914 2 .1444201 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) +populat~e_10 890 193 293630.7 671 1.21e+07 Female population aged 10 (WB API) +po~e_primary 832 172 2052513 4599 7.23e+07 Female population primary age, country specific (WB API) +popu~e_9plus 873 191 1093833 1374 3.62e+07 Female population aged 9 to end of primary, country specific (WB API) +populat~a_10 890 193 310736.6 696 1.35e+07 Male population aged 10 (WB API) +po~a_primary 832 172 2170324 4773 8.04e+07 Male population primary age, country specific (WB API) +popu~a_9plus 873 191 1153568 1425 4.04e+07 Male population aged 9 to end of primary, country specific (WB API) +populat~l_10 890 193 604367.3 1370 2.55e+07 Total population aged 10 (WB API) +po~l_primary 832 172 4222837 9372 1.53e+08 Total population primary age, country specific (WB API) +popu~l_9plus 873 191 2247401 2799 7.65e+07 Total population aged 9 to end of primary, country specific (WB API) +popul~e_1014 890 193 1438254 3162 5.98e+07 Female population between ages 10 to 14 (WB API) +popul~a_1014 890 193 1521794 3286 6.71e+07 Male population between ages 10 to 14 (WB API) +popul~l_1014 890 193 2960048 6448 1.27e+08 Total population between ages 10 to 14 (WB API) +population~e 914 1 . . . The source used for population variables +year_enrol~t 896 24 2010.494 1991 2017 The year that the enrollment value is from +year_popul~n 914 1 2015 2015 2015 Year of population +source_ass~t 697 3 . . . Source of assessment data +enrollmen~ce 914 4 . . . The source used for this enrollment value +enrollment~n 914 6 . . . The definition used for this enrollment value +min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific) +surveyid 697 503 . . . SurveyID (countrycode_year_assessment) +countryname 914 217 . . . Country Name +region 914 7 . . . Region Code +region_iso2 914 7 . . . Region Code (ISO 2 digits) +regionname 914 7 . . . Region Name +adminregion 475 6 . . . Administrative Region Code +adminregio~2 475 6 . . . Administrative Region Code (ISO 2 digits) +adminregio~e 475 6 . . . Administrative Region Name +incomelevel 914 4 . . . Income Level Code +incomeleve~2 914 4 . . . Income Level Code (ISO 2 digits) +incomeleve~e 914 4 . . . Income Level Name +lendingtype 914 4 . . . Lending Type Code +lendingtyp~2 914 4 . . . Lending Type Code (ISO 2 digits) +lendingty~me 914 4 . . . Lending Type Name +cmu 680 48 . . . WB Country Management Unit +--------------------------------------------------------------------------------------------------------------------------------------- + ~~~~ diff --git a/00_documentation/002_repo_structure/0022_dataset_tables/rawlatest.md b/00_documentation/002_repo_structure/0022_dataset_tables/rawlatest.md index 42cb115..5bcb6cc 100644 --- a/00_documentation/002_repo_structure/0022_dataset_tables/rawlatest.md +++ b/00_documentation/002_repo_structure/0022_dataset_tables/rawlatest.md @@ -10,71 +10,76 @@ Preference 1005 dataset. Contains one observation for each of the 217 countries, ~~~~ sources: All population, enrollment and proficiency sources combined. -lastsave: 16 Oct 2019 21:57:15 by wb255520 ~~~~ -About the **47 variables** in this dataset: +About the **53 variables** in this dataset: ~~~~ The variables belong to the following variable classifications: idvars valuevars traitvars idvars: countrycode preference -valuevars: adj_nonprof_all adj_nonprof_fe adj_nonprof_ma nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe enrollment_all enrollment_ma enrollment_fe population_2015_fe population_2015_ma population_2015_all population_source anchor_population anchor_population_w_assessment +valuevars: adj_nonprof_all adj_nonprof_fe adj_nonprof_ma nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe fgt1_all fgt1_fe fgt1_ma fgt2_all fgt2_fe fgt2_ma enrollment_all enrollment_ma enrollment_fe population_2015_fe population_2015_ma population_2015_all population_source anchor_population anchor_population_w_assessment traitvars: idgrade test nla_code subject year_assessment year_enrollment enrollment_flag enrollment_source enrollment_definition min_proficiency_threshold surveyid countryname region region_iso2 regionname adminregion adminregion_iso2 adminregionname incomelevel incomelevel_iso2 incomelevelname lendingtype lendingtype_iso2 lendingtypename cmu preference_description lp_by_gender_is_available -. codebook, compact - -Variable Obs Unique Mean Min Max Label ----------------------------------------------------------------------------------------------------------------------------------------- -countrycode 217 217 . . . WB country code (3 letters) -preference 217 1 . . . Preference -adj_nonpro~l 116 116 38.49645 1.640046 98.72119 Learning Poverty (adjusted non-proficiency, all) -adj_nonpro~e 92 92 31.41049 1.199595 98.7845 Learning Poverty (adjusted non-proficiency, fe) -adj_nonpro~a 92 92 35.47869 2.114902 98.6577 Learning Poverty (adjusted non-proficiency, ma) -nonprof_all 116 116 36.10672 .3021896 97.90538 % pupils below minimum proficiency (all) -se_nonprof~l 97 97 .9926926 .1344 2.819569 SE of pupils below minimum proficiency (all) -nonprof_ma 97 97 34.16343 .2807498 97.96137 % pupils below minimum proficiency (ma) -se_nonprof~a 97 97 1.271574 .1502485 3.470551 SE of pupils below minimum proficiency (ma) -nonprof_fe 97 97 29.91959 .3251851 97.83222 % pupils below minimum proficiency (fe) -se_nonprof~e 97 97 1.182721 .1886169 2.936706 SE of pupils below minimum proficiency (fe) -enrollment~l 199 194 90.18949 23.54786 100 Validated % of children enrolled in school (using closest year, both genders) -enrollment~a 190 185 90.59003 30.29106 100 Validated % of children enrolled in school (using closest year, male only) -enrollmen~fe 190 185 89.86092 16.75778 100 Validated % of children enrolled in school (using closest year, female only) -populatio~fe 193 193 1529768 3162 5.98e+07 Female population between ages 10 to 14 (WB API) -population~a 193 193 1640311 3286 6.71e+07 Male population between ages 10 to 14 (WB API) -population~l 193 193 3170079 6448 1.27e+08 Total population between ages 10 to 14 (WB API) -populatio~ce 217 1 . . . The source used for population variables -anchor_pop~n 193 193 3170079 6448 1.27e+08 Total population between ages 10 to 14 (WB API) -anchor_pop~t 193 117 2667970 0 1.27e+08 Anchor population * has data dummy -idgrade 217 4 -462.4931 -999 6 Grade ID -test 217 6 . . . Assessment -nla_code 217 16 . . . Reference code for NLA in markdown documentation -subject 217 4 . . . Subject -year_asses~t 217 13 2014.134 2001 2017 Year of assessment -year_enrol~t 199 19 2012.739 1993 2017 The year that the enrollment value is from -enrollment~g 217 2 .1751152 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) -enrollmen~ce 217 5 . . . The source used for this enrollment value -enrollment~n 217 5 . . . The definition used for this enrollment value -min_profic~d 114 13 . . . Minimum Proficiency Threshold (assessment-specific) -surveyid 116 116 . . . SurveyID (countrycode_year_assessment) -countryname 217 217 . . . Country Name -region 217 7 . . . Region Code -region_iso2 217 7 . . . Region Code (ISO 2 digits) -regionname 217 7 . . . Region Name -adminregion 138 6 . . . Administrative Region Code -adminregio~2 138 6 . . . Administrative Region Code (ISO 2 digits) -adminregio~e 138 6 . . . Administrative Region Name -incomelevel 217 4 . . . Income Level Code -incomeleve~2 217 4 . . . Income Level Code (ISO 2 digits) -incomeleve~e 217 4 . . . Income Level Name -lendingtype 217 4 . . . Lending Type Code -lendingtyp~2 217 4 . . . Lending Type Code (ISO 2 digits) -lendingty~me 216 4 . . . Lending Type Name -cmu 169 48 . . . WB Country Management Unit -preference~n 217 1 . . . Preference description -lp_by_gend~e 217 2 .4239631 0 1 Dummy for availibility of Learning Poverty gender disaggregated ----------------------------------------------------------------------------------------------------------------------------------------- - +. codebook, compact + +Variable Obs Unique Mean Min Max Label +--------------------------------------------------------------------------------------------------------------------------------------- +countrycode 217 217 . . . WB country code (3 letters) +preference 217 1 . . . Preference +adj_nonpro~l 116 116 38.49149 1.640046 98.72119 Learning Poverty (adjusted non-proficiency, all) +adj_nonpro~e 94 94 32.27536 1.199595 98.7845 Learning Poverty (adjusted non-proficiency, fe) +adj_nonpro~a 94 94 36.28663 2.114902 98.6577 Learning Poverty (adjusted non-proficiency, ma) +nonprof_all 116 116 36.09931 .3021896 97.90538 % pupils below minimum proficiency (all) +se_nonprof~l 97 97 1.141696 .1344 3.357331 SE of pupils below minimum proficiency (all) +nonprof_ma 99 99 34.90619 .2807498 97.96137 % pupils below minimum proficiency (ma) +se_nonprof~a 97 97 1.420192 .1502485 3.741401 SE of pupils below minimum proficiency (ma) +nonprof_fe 99 99 30.70454 .3251851 97.83222 % pupils below minimum proficiency (fe) +se_nonprof~e 97 97 1.33035 .1886169 4.098772 SE of pupils below minimum proficiency (fe) +fgt1_all 97 97 .2326854 .0527193 .7537994 Avg gap to minimum proficiency (all, FGT1) +fgt1_fe 97 97 .2243604 .0453513 .7643428 Avg gap to minimum proficiency (fe, FGT1) +fgt1_ma 97 97 .2387256 .0490034 .7457443 Avg gap to minimum proficiency (ma, FGT1) +fgt2_all 97 97 .1026644 .0041254 .6159182 Avg gap squared to minimum proficiency (all, FGT2) +fgt2_fe 97 97 .0976484 .0032002 .6308874 Avg gap squared to minimum proficiency (fe, FGT2) +fgt2_ma 97 97 .1065394 .0041836 .6044815 Avg gap squared to minimum proficiency (ma, FGT2) +enrollment~l 199 194 90.18949 23.54786 100 Validated % of children enrolled in school (using closest year, both genders) +enrollment~a 190 185 90.59003 30.29106 100 Validated % of children enrolled in school (using closest year, male only) +enrollmen~fe 190 185 89.86092 16.75778 100 Validated % of children enrolled in school (using closest year, female only) +populatio~fe 193 193 1529768 3162 5.98e+07 Female population between ages 10 to 14 (WB API) +population~a 193 193 1640311 3286 6.71e+07 Male population between ages 10 to 14 (WB API) +population~l 193 193 3170079 6448 1.27e+08 Total population between ages 10 to 14 (WB API) +populatio~ce 217 1 . . . The source used for population variables +anchor_pop~n 193 193 3170079 6448 1.27e+08 Total population between ages 10 to 14 (WB API) +anchor_pop~t 193 117 2667970 0 1.27e+08 Anchor population * has data dummy +idgrade 217 4 -462.4931 -999 6 Grade ID +test 217 6 . . . Assessment +nla_code 217 16 . . . Reference code for NLA in markdown documentation +subject 217 4 . . . Subject +year_asses~t 217 13 2014.134 2001 2017 Year of assessment +year_enrol~t 199 19 2012.739 1993 2017 The year that the enrollment value is from +enrollment~g 217 2 .1751152 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%) +enrollmen~ce 217 4 . . . The source used for this enrollment value +enrollment~n 217 6 . . . The definition used for this enrollment value +min_profic~d 114 13 . . . Minimum Proficiency Threshold (assessment-specific) +surveyid 116 116 . . . SurveyID (countrycode_year_assessment) +countryname 217 217 . . . Country Name +region 217 7 . . . Region Code +region_iso2 217 7 . . . Region Code (ISO 2 digits) +regionname 217 7 . . . Region Name +adminregion 138 6 . . . Administrative Region Code +adminregio~2 138 6 . . . Administrative Region Code (ISO 2 digits) +adminregio~e 138 6 . . . Administrative Region Name +incomelevel 217 4 . . . Income Level Code +incomeleve~2 217 4 . . . Income Level Code (ISO 2 digits) +incomeleve~e 217 4 . . . Income Level Name +lendingtype 217 4 . . . Lending Type Code +lendingtyp~2 217 4 . . . Lending Type Code (ISO 2 digits) +lendingty~me 217 4 . . . Lending Type Name +cmu 169 48 . . . WB Country Management Unit +preference~n 217 1 . . . Preference description +lp_by_gend~e 217 2 .4331797 0 1 Dummy for availibility of Learning Poverty gender disaggregated +--------------------------------------------------------------------------------------------------------------------------------------- + ~~~~ diff --git a/00_documentation/002_repo_structure/Repo_Structure.md b/00_documentation/002_repo_structure/Repo_Structure.md index 61bcbb5..4cbbe43 100644 --- a/00_documentation/002_repo_structure/Repo_Structure.md +++ b/00_documentation/002_repo_structure/Repo_Structure.md @@ -1,148 +1,164 @@ -# How this repo is structured -back to the [README](https://github.com/worldbank/LearningPoverty/blob/master/README.md) :leftwards_arrow_with_hook: - -### Table of Contents -1. [Folder and file structure](#folder-and-file-structure) -1. [Task details](#task-details) - 2.0. [Task 00_documentation](#task-00_documentation) - 2.1. [Task 01_data](#task-01_data) - 2.2. [Task 02_simulation](#task-02_simulation) - 2.3. [Task 03_export_tables](#task-03_export_tables) - 2.4. [Task 04_repo_update](#task-04_repo_update) -1. [Generating flowcharts](#generating-flowcharts) - -## Folder and file structure - -Each folder starting with exactly two digits is a task folder. A task folder is organized as a small project to separate tasks like generating the dataset, running the simulation, producing the learning poverty briefs. Whenever possible, we strive for a consistent naming of subfolders, with most tasks 0i having the subfolders 0i1_rawdata, 0i2_programs, 0i3_outputs. - -All code needed to process and copy input data sets to the local clone, and to generate all datasets from them are shared through this repository. The code can always be found in the task sub folder programs and it is numbered with a prefix that matches the folder number. This allows to immediately identify where each code fits the workflow by looking at its name. - -Some files that are nor code nor datasets (PDFs, presentations etc.) are shared directly over the OneDrive folder as such files are not suitable to share over GitHub. This folder is restricted to contributors in the World Bank. - -Folders that would start empty - without any files that we wish to track in the repo - will have a placeholder markdown file, just to synchronize the folder structure, for GitHub would ignore a folder if completely empty. - -## Task details - -### Task 00_documentation - -This folder contains only markdown files that document this project, plus accompanying images. - -| Sub-Folder Name | Usage | -|---|---| -|**001_technical_note**|Information on how the data was calculated and which sources were used| -|**002_repo_structure**|Guide to the folder structure and data flow in the project| -|**003_contribution_and_replication**|Guidelines for contributing to and replicating this repo|| - -### Task 01_data - -In this task folder we generate a "picture" of learning poverty in 2015 (rawlatest), and all files needed to project learning poverty, which will be used in the simulation task. This task runs exclusively in Stata. - -| Sub-Folder Name | Usage | -|---|---| -|**011_rawdata**|This folder starts empty, except for the subfolder `hosted_in_repo`, which contains 13 .csv and .md files.| -|**012_programs**|Programs that compile all data on the recent history and a current picture of learning poverty| -|**013_outputs**|This folder should start empty. It will store the outputs for the data task.| - -For each relevant file in 013_outputs, we generate a mardown documentation, accessible through the links below: -* [Documentation of population](./0022_dataset_tables/population.md) -* [Documentation of enrollment](./0022_dataset_tables/enrollment.md) -* [Documentation of proficiency](./0022_dataset_tables/proficiency.md) -* [Documentation of rawfull](./0022_dataset_tables/rawfull.md) -* [Documentation of rawlatest](./0022_dataset_tables/rawlatest.md) - -All the data needed for this project comes from thirteen .csv and .md files in `01_data/011_rawdata/hosted_in_repo/`. Those files are first imported into `01_data/011_rawdata/` as .dta files, then combined into intermediate datasets. Population, enrollment and proficiency datasets are created in `01_data/013_outputs/`, then combined in an exhaustive manner into rawfull, also stored in `01_data/013_outputs/`. - -![00211](./0021_flowcharts/00211_data_mermaid.png) - -From rawfull, we construct multiple _preference_ datasets, each being the result of trimming down rawfull through the _preferred_list_ program. A useful analogy is that each _preference_ is a "picture" of the world, with different camera adjustments and angles. Then, we display the global and regional numbers that each preferences represent. Lastly, we choose one preference that we baptize as rawlatest, which should be understood as the chosen "picture" for learning poverty in 2015. - -![00212](./0021_flowcharts/00212_data_mermaid.png) - -### Task 02_simulation - -In this task folder we project the proficiency scores in 2030. It runs partly in Stata, partly in R. - -| Sub-Folder Name | Usage | -|---|---| -|**021_rawdata**|This folder should have no data on learning poverty - nor enrollment, proficiency, population - the do-files in `022_program` should only read learning poverty data from `013_outputs`. If those outputs need to be modified for any purpose for the `02_simulation` task, then that should be done in the do-files in `012_program`. This folder only contains inputs for generating the spells, that is to compare assessments over time.| -|**022_programs**|Programs that run all the simulations| -|**023_outputs**|This folder should start empty. It will store the outputs for all simulations.| - -### Task 03_export_tables - -In this task folder we generate tables and graphs for the Learning Poverty technical paper. We also export the data in this project as indicators to the World Bank API. It runs exclusively in Stata. - -| Sub-Folder Name | Usage | -|---|---| -|**031_rawdata**|Contains only one csv, with the metadata of WB API indicators produced by this project| -|**032_programs**|Programs that export all tables and graphs| -|**033_outputs**|Starts empty, will receive all tables and graphs that went into the paper, plus the series of _learning poverty_ indicators for the WB API| - -### Task 04_repo_update - -For reproducibility purposes, we 'froze' the data gathered from multiple APIs and data sources in `01_data/011_rawdata/hosted_in_repo`. In this task, we update those .md and .csv input files, by running the queries to those APIs and updating the sources. It runs exclusively in Stata. Parts of this task may require access to the World Bank network. - -| Sub-Folder Name | Usage | -|---|---| -|**041_rawdata**|Raw data that does not come from APIs| -|**042_programs**|Programs that update all input data files| -|**043_outputs**|Starts empty, will receive updated files that may be transfered to 011_rawdata| - -## Generating flowcharts - -All the diagrams and flowcharts were generated from text in a similar manner as markdown, through _mermaid_. - -To update the charts, you can use the [mermaid live editor](https://mermaidjs.github.io/mermaid-live-editor/). Pasting the code in this page in the live editor will render the images displayed in this page. - -As of now, the GitHub markdown renderer does not support _mermaid_, which is why the rendering can only be done by statically saving the _.png_ files in the repo. But this is a [feature that has been requested](https://github.community/t5/How-to-use-Git-and-GitHub/Feature-Request-Support-Mermaid-markdown-graph-diagrams-in-md/td-p/21967) and may one day be added to GitHub. - -#### Flowchart code of 01_data task -```mermaid -graph LR - - subgraph "011_rawdata: *.csv and *.md to *.dta" - - raw_cty[country_metadata] - raw_pop["population_1014
population_by_age
primary_school_age"] - raw_pro["proficiency_from_GLAD
proficiency_from_NLA
proficiency_no_microdata"] - raw_enr["enrollment_edulit_uis
enrollment_tenr_wbopendata
enrollment_validated"] - - raw_cty-->|0120_import_rawdata|raw_cty - raw_pop-->|0120_import_rawdata|raw_pop - raw_pro-->|0120_import_rawdata|raw_pro - raw_enr-->|0120_import_rawdata|raw_enr - - end - - - subgraph "basic files in 013_output" - - raw_pop-->|0121_combine_population_data|pop[population] - raw_pro-->|0122_combine_proficiency_data|pro[proficiency] - raw_enr-->|"0123_combine_enrollment_data
0124_enrollment_extrapolation"|enr[enrollment] - - end - - - subgraph "final files in 013_output" - - rawfull["rawfull
long on proficiency
wide on enrollment
wide on population"] - raw_cty-->|0125_create_rawfull|rawfull - pop-->|0125_create_rawfull|rawfull - enr-->|0125_create_rawfull|rawfull - pro-->|0125_create_rawfull|rawfull - - preferred{"keep 1 obs by cty for proficiency
keep 1 set of vars for enrollment
keep 1 set of vars for population"} - rawfull-->|0126_create_rawlatest|preferred - preferred-->|01261_preferred_list|pref1[preference1000] - preferred-->|01261_preferred_list|pref2[preference2000] - preferred-->|01261_preferred_list|pref3[preference3000] - - disp(("display global and
regional aggregates")) - pref1-->|01262_global_number|disp - pref2-->|01262_global_number|disp - pref3-->|01262_global_number|disp - - end - -``` +# How this repo is structured +back to the [README](https://github.com/worldbank/LearningPoverty/blob/master/README.md) :leftwards_arrow_with_hook: + +### Table of Contents +1. [Folder and file structure](#folder-and-file-structure) +1. [Task details](#task-details) + 2.0. [Task 00_documentation](#task-00_documentation) + 2.1. [Task 01_data](#task-01_data) + 2.2. [Task 02_simulation](#task-02_simulation) + 2.3. [Task 03_export_tables](#task-03_export_tables) + 2.4. [Task 04_repo_update](#task-04_repo_update) +1. [Generating flowcharts](#generating-flowcharts) + +## Folder and file structure + +Each folder starting with exactly two digits is a task folder. A task folder is organized as a small project to separate tasks like generating the dataset, running the simulation, producing the learning poverty briefs. Whenever possible, we strive for a consistent naming of subfolders, with most tasks 0i having the subfolders 0i1_rawdata, 0i2_programs, 0i3_outputs. + +All code needed to process and copy input data sets to the local clone, and to generate all datasets from them are shared through this repository. The code can always be found in the task sub folder programs and it is numbered with a prefix that matches the folder number. This allows to immediately identify where each code fits the workflow by looking at its name. + +Some files that are nor code nor datasets (PDFs, presentations etc.) are shared directly over the OneDrive folder as such files are not suitable to share over GitHub. This folder is restricted to contributors in the World Bank. + +Folders that would start empty - without any files that we wish to track in the repo - will have a placeholder markdown file, just to synchronize the folder structure, for GitHub would ignore a folder if completely empty. + +## Task details + +### Task 00_documentation + +This folder contains only markdown files that document this project, plus accompanying images. + +| Sub-Folder Name | Usage | +|---|---| +|**001_technical_note**|Information on how the data was calculated and which sources were used| +|**002_repo_structure**|Guide to the folder structure and data flow in the project| +|**003_contribution_and_replication**|Guidelines for contributing to and replicating this repo|| + +### Task 01_data + +In this task folder we generate a "picture" of learning poverty in 2015 (rawlatest), and all files needed to project learning poverty, which will be used in the simulation task. This task runs exclusively in Stata. + +| Sub-Folder Name | Usage | +|---|---| +|**011_rawdata**|This folder starts empty, except for the subfolder `hosted_in_repo`, which contains 13 .csv and .md files.| +|**012_programs**|Programs that compile all data on the recent history and a current picture of learning poverty| +|**013_outputs**|This folder should start empty. It will store the outputs for the data task.| + +For each relevant file in 013_outputs, we generate a mardown documentation, accessible through the links below: +* [Documentation of population](./0022_dataset_tables/population.md) +* [Documentation of enrollment](./0022_dataset_tables/enrollment.md) +* [Documentation of proficiency](./0022_dataset_tables/proficiency.md) +* [Documentation of rawfull](./0022_dataset_tables/rawfull.md) +* [Documentation of rawlatest](./0022_dataset_tables/rawlatest.md) + +All the data needed for this project comes from thirteen .csv and .md files in `01_data/011_rawdata/hosted_in_repo/`. Those files are first imported into `01_data/011_rawdata/` as .dta files, then combined into intermediate datasets. Population, enrollment and proficiency datasets are created in `01_data/013_outputs/`, then combined in an exhaustive manner into rawfull, also stored in `01_data/013_outputs/`. + +![00211](./0021_flowcharts/00211_data_mermaid.png) + +From rawfull, we construct multiple _preference_ datasets, each being the result of trimming down rawfull through the _preferred_list_ program. A useful analogy is that each _preference_ is a "picture" of the world, with different camera adjustments and angles. Then, we display the global and regional numbers that each preferences represent. Lastly, we choose one preference that we baptize as rawlatest, which should be understood as the chosen "picture" for learning poverty in 2015. + +![00212](./0021_flowcharts/00212_data_mermaid.png) + +### Task 02_simulation + +In this task folder we project the proficiency scores in 2030. It contains code in Stata and in R. + +| Sub-Folder Name | Usage | +|---|---| +|**021_rawdata**|This folder should have no data on learning poverty - nor enrollment, proficiency, population - the do-files in `022_program` should only read learning poverty data from `013_outputs`. If those outputs need to be modified for any purpose for the `02_simulation` task, then that should be done in the do-files in `012_program`. This folder only contains inputs for generating the spells, that is to compare assessments over time.| +|**022_programs**|Programs that run all the simulations| +|**023_outputs**|This folder should start empty. It will store the outputs for all simulations.| + +In this task, first, all valid spells are created (0220), then, they are aggregated according to various rules into markdown files (0221). Those markdown files are inputs of growth rates to the simulations (0222), which use an ado file to allow for flexibility in the simulation. This part runs exclusively in Stata. + +Though not incorporated in the technical paper, there are also files in R to allow for users to play around with the simulation. + +### Task 03_export_tables + +In this task folder we manipulate results from the previous tasks into summary tables. We also export the data in this project as indicators to the World Bank API. It runs exclusively in Stata. + +| Sub-Folder Name | Usage | +|---|---| +|**031_rawdata**|Contains only one csv, with the metadata of WB API indicators produced by this project| +|**032_programs**|Programs that export all tables and graphs| +|**033_outputs**|Starts empty, will receive several tables, plus the series of _learning poverty_ indicators for the WB API| + +### Task 04_repo_update + +For reproducibility purposes, we 'froze' the data gathered from multiple APIs and data sources in `01_data/011_rawdata/hosted_in_repo`. In this task, we update those .md and .csv input files, by running the queries to those APIs and updating the sources. It runs exclusively in Stata. Parts of this task may require access to the World Bank network. + +| Sub-Folder Name | Usage | +|---|---| +|**041_rawdata**|Raw data that does not come from APIs| +|**042_programs**|Programs that update all input data files| +|**043_outputs**|Starts empty, will receive updated files that may be transferred to 011_rawdata| + +### Task 05_working_paper + +In this task folder we generate tables and graphs for the Learning Poverty technical paper. This includes some validation of the Learning Poverty measure using other assessments, such as PISA. It runs exclusively in Stata. + +| Sub-Folder Name | Usage | +|---|---| +|**051_rawdata**|Contains one Excel file that is the structure of all tables and graphs in the technical paper, plus inputs to the validation performed in the task| +|**052_programs**|Programs that export all tables and graphs| +|**053_outputs**|Starts with a ready-to-use copy of the Excel with tables and figures, and one correlation analysis that require access to microdata that is only available to WBG users. More files are added.| + +If you execute this task, you will have two Excel files in 053_outputs. One contains ready-to-use tables and figures (LPV_Tables_Figures_PAPER.xlsx) while the other is created on the fly (LPV_Tables_Figures.xlsx) based on the empty template and the results produced in the local clone of this repository. Unless the task 04_repo_update is run or any input file is changed by the user, both files will be identical. + +## Generating flowcharts + +All the diagrams and flowcharts were generated from text in a similar manner as markdown, through _mermaid_. + +To update the charts, you can use the [mermaid live editor](https://mermaidjs.github.io/mermaid-live-editor/). Pasting the code in this page in the live editor will render the images displayed in this page. + +As of now, the GitHub markdown renderer does not support _mermaid_, which is why the rendering can only be done by statically saving the _.png_ files in the repo. But this is a [feature that has been requested](https://github.community/t5/How-to-use-Git-and-GitHub/Feature-Request-Support-Mermaid-markdown-graph-diagrams-in-md/td-p/21967) and may one day be added to GitHub. + +#### Flowchart code of 01_data task +```mermaid +graph LR + + subgraph "011_rawdata: *.csv and *.md to *.dta" + + raw_cty[country_metadata] + raw_pop["population_1014
population_by_age
primary_school_age"] + raw_pro["proficiency_from_GLAD
proficiency_from_NLA
proficiency_no_microdata"] + raw_enr["enrollment_edulit_uis
enrollment_tenr_wbopendata
enrollment_validated"] + + raw_cty-->|0120_import_rawdata|raw_cty + raw_pop-->|0120_import_rawdata|raw_pop + raw_pro-->|0120_import_rawdata|raw_pro + raw_enr-->|0120_import_rawdata|raw_enr + + end + + + subgraph "basic files in 013_output" + + raw_pop-->|0121_combine_population_data|pop[population] + raw_pro-->|0122_combine_proficiency_data|pro[proficiency] + raw_enr-->|"0123_combine_enrollment_data
0124_enrollment_extrapolation"|enr[enrollment] + + end + + + subgraph "final files in 013_output" + + rawfull["rawfull
long on proficiency
wide on enrollment
wide on population"] + raw_cty-->|0125_create_rawfull|rawfull + pop-->|0125_create_rawfull|rawfull + enr-->|0125_create_rawfull|rawfull + pro-->|0125_create_rawfull|rawfull + + preferred{"keep 1 obs by cty for proficiency
keep 1 set of vars for enrollment
keep 1 set of vars for population"} + rawfull-->|0126_create_rawlatest|preferred + preferred-->|01261_preferred_list|pref1[preference1000] + preferred-->|01261_preferred_list|pref2[preference2000] + preferred-->|01261_preferred_list|pref3[preference3000] + + disp(("display global and
regional aggregates")) + pref1-->|01262_global_number|disp + pref2-->|01262_global_number|disp + pref3-->|01262_global_number|disp + + end + +``` diff --git a/01_data/011_rawdata/hosted_in_repo/country_metadata.csv b/01_data/011_rawdata/hosted_in_repo/country_metadata.csv index de7e13e..d5db06a 100644 --- a/01_data/011_rawdata/hosted_in_repo/country_metadata.csv +++ b/01_data/011_rawdata/hosted_in_repo/country_metadata.csv @@ -1,219 +1,219 @@ -countrycode,countryname,region,region_iso2,regionname,adminregion,adminregion_iso2,adminregionname,incomelevel,incomelevel_iso2,incomelevelname,lendingtype,lendingtype_iso2,lendingtypename,cmu -ABW,Aruba,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -AFG,Afghanistan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,SACAF -AGO,Angola,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,AFCC1 -ALB,Albania,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB -AND,Andorra,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -ARE,United Arab Emirates,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -ARG,Argentina,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC7C -ARM,Armenia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC -ASM,American Samoa,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,LNX,XX,Not classified,EACNF -ATG,Antigua and Barbuda,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C -AUS,Australia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNF -AUT,Austria,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -AZE,Azerbaijan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC -BDI,Burundi,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 -BEL,Belgium,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -BEN,Benin,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 -BFA,Burkina Faso,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 -BGD,Bangladesh,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,SACBB -BGR,Bulgaria,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEU -BHR,Bahrain,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -BHS,"Bahamas, The",LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C -BIH,Bosnia and Herzegovina,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB -BLR,Belarus,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEE -BLZ,Belize,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C -BMU,Bermuda,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, -BOL,Bolivia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,LCC6C -BRA,Brazil,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC5C -BRB,Barbados,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C -BRN,Brunei Darussalam,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACPF -BTN,Bhutan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,SACBB -BWA,Botswana,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 -CAF,Central African Republic,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 -CAN,Canada,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, -CHE,Switzerland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -CHI,Channel Islands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -CHL,Chile,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC6C -CHN,China,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACCF -CIV,Cote d'Ivoire,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF2 -CMR,Cameroon,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCC1 -COD,"Congo, Dem. Rep.",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 -COG,"Congo, Rep.",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCC2 -COL,Colombia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC4C -COM,Comoros,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCS2 -CPV,Cabo Verde,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCF1 -CRI,Costa Rica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC2C -CUB,Cuba,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,LNX,XX,Not classified, -CUW,Curacao,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -CYM,Cayman Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -CYP,Cyprus,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -CZE,Czech Republic,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -DEU,Germany,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -DJI,Djibouti,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,MNC03 -DMA,Dominica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C -DNK,Denmark,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -DOM,Dominican Republic,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C -DZA,Algeria,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC01 -ECU,Ecuador,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC6C -EGY,"Egypt, Arab Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC03 -ERI,Eritrea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 -ESP,Spain,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -EST,Estonia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -ETH,Ethiopia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 -FIN,Finland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -FJI,Fiji,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,EACNF -FRA,France,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -FRO,Faroe Islands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -FSM,"Micronesia, Fed. Sts.",EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF -GAB,Gabon,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCC1 -GBR,United Kingdom,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -GEO,Georgia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC -GHA,Ghana,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCW1 -GIB,Gibraltar,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -GIN,Guinea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 -GMB,"Gambia, The",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF1 -GNB,Guinea-Bissau,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF1 -GNQ,Equatorial Guinea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCC1 -GRC,Greece,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -GRD,Grenada,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C -GRL,Greenland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -GTM,Guatemala,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC2C -GUM,Guam,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNF -GUY,Guyana,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,LCC3C -HKG,"Hong Kong SAR, China",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, -HND,Honduras,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,LCC2C -HRV,Croatia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,IBD,XF,IBRD,ECCEU -HTI,Haiti,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LIC,XM,Low income,IDX,XI,IDA,LCC8C -HUN,Hungary,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -IDN,Indonesia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACIF -IMN,Isle of Man,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -IND,India,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,SACIN -IRL,Ireland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -IRN,"Iran, Islamic Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 -IRQ,Iraq,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 -ISL,Iceland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -ISR,Israel,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified, -ITA,Italy,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -JAM,Jamaica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C -JOR,Jordan,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 -JPN,Japan,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, -KAZ,Kazakhstan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCCA -KEN,Kenya,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCE2 -KGZ,Kyrgyz Republic,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,ECCCA -KHM,Cambodia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACMM -KIR,Kiribati,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF -KNA,St. Kitts and Nevis,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C -KOR,"Korea, Rep.",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACCF -KWT,Kuwait,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -LAO,Lao PDR,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACLF -LBN,Lebanon,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 -LBR,Liberia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW1 -LBY,Libya,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC01 -LCA,St. Lucia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C -LIE,Liechtenstein,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -LKA,Sri Lanka,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,SACSN -LSO,Lesotho,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCS1 -LTU,Lithuania,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -LUX,Luxembourg,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -LVA,Latvia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -MAC,"Macao SAR, China",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, -MAF,St. Martin (French part),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -MAR,Morocco,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC01 -MCO,Monaco,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -MDA,Moldova,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,ECCEE -MDG,Madagascar,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCS2 -MDV,Maldives,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,SACSN -MEX,Mexico,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC1C -MHL,Marshall Islands,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF -MKD,North Macedonia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB -MLI,Mali,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 -MLT,Malta,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC01 -MMR,Myanmar,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACMM -MNE,Montenegro,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB -MNG,Mongolia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACCF -MNP,Northern Mariana Islands,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, -MOZ,Mozambique,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCS2 -MRT,Mauritania,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF1 -MUS,Mauritius,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS2 -MWI,Malawi,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE1 -MYS,Malaysia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACPF -NAM,Namibia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 -NCL,New Caledonia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, -NER,Niger,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 -NGA,Nigeria,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCW2 -NIC,Nicaragua,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,LCC2C -NLD,Netherlands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -NOR,Norway,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -NPL,Nepal,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,SACSN -NRU,Nauru,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACNQ -NZL,New Zealand,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNQ -OMN,Oman,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -PAK,Pakistan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,SACPK -PAN,Panama,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC2C -PER,Peru,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC6C -PHL,Philippines,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACPF -PLW,Palau,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,IBD,XF,IBRD,EACNF -PNG,Papua New Guinea,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACNQ -POL,Poland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,IBD,XF,IBRD,ECCEU -PRI,Puerto Rico,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -PRK,"Korea, Dem. People’s Rep.",EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LIC,XM,Low income,LNX,XX,Not classified,EACCF -PRT,Portugal,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -PRY,Paraguay,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC7C -PSE,West Bank and Gaza,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,LNX,XX,Not classified,MNC04 -PYF,French Polynesia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EAPSG -QAT,Qatar,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -ROU,Romania,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEU -RUS,Russian Federation,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCRU -RWA,Rwanda,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 -SAU,Saudi Arabia,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 -SDN,Sudan,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCE3 -SEN,Senegal,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF1 -SGP,Singapore,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EAPSG -SLB,Solomon Islands,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF -SLE,Sierra Leone,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW1 -SLV,El Salvador,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,LCC2C -SMR,San Marino,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -SOM,Somalia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 -SRB,Serbia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB -SSD,South Sudan,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 -STP,Sao Tome and Principe,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCC1 -SUR,Suriname,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C -SVK,Slovak Republic,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -SVN,Slovenia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU -SWE,Sweden,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, -SWZ,Eswatini,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,AFCS1 -SXM,Sint Maarten (Dutch part),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C -SYC,Seychelles,SSF,ZG,Sub-Saharan Africa,,,,HIC,XD,High income,IBD,XF,IBRD,AFCS2 -SYR,Syrian Arab Republic,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,MNC02 -TCA,Turks and Caicos Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -TCD,Chad,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 -TGO,Togo,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 -THA,Thailand,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACPF -TJK,Tajikistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,ECCCA -TKM,Turkmenistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD, -TLS,Timor-Leste,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACDF -TON,Tonga,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF -TTO,Trinidad and Tobago,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C -TUN,Tunisia,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC01 -TUR,Turkey,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCTR -TUV,Tuvalu,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF -TZA,Tanzania,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE1 -UGA,Uganda,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 -UKR,Ukraine,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,ECCEE -URY,Uruguay,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC7C -USA,United States,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, -UZB,Uzbekistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,ECCCA -VCT,St. Vincent and the Grenadines,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C -VEN,"Venezuela, RB",LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC4C -VGB,British Virgin Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -VIR,Virgin Islands (U.S.),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, -VNM,Vietnam,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACVF -VUT,Vanuatu,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF -WSM,Samoa,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF -XKX,Kosovo,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,ECCWB -YEM,"Yemen, Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,MNC03 -ZAF,South Africa,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 -ZMB,Zambia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCE1 -ZWE,Zimbabwe,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,,AFCE1 -Country Code,Country Name,Region Code,Region Code (ISO 2 digits),Region Name,Administrative Region Code,Administrative Region Code (ISO 2 digits),Administrative Region Name,Income Level Code,Income Level Code (ISO 2 digits),Income Level Name,Lending Type Code,Lending Type Code (ISO 2 digits),Lending Type Name,WB Country Management Unit +countrycode,countryname,region,region_iso2,regionname,adminregion,adminregion_iso2,adminregionname,incomelevel,incomelevel_iso2,incomelevelname,lendingtype,lendingtype_iso2,lendingtypename,cmu +ABW,Aruba,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +AFG,Afghanistan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,SACAF +AGO,Angola,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,AFCC1 +ALB,Albania,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB +AND,Andorra,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +ARE,United Arab Emirates,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +ARG,Argentina,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC7C +ARM,Armenia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC +ASM,American Samoa,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,LNX,XX,Not classified,EACNF +ATG,Antigua and Barbuda,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C +AUS,Australia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNF +AUT,Austria,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +AZE,Azerbaijan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC +BDI,Burundi,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 +BEL,Belgium,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +BEN,Benin,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 +BFA,Burkina Faso,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 +BGD,Bangladesh,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,SACBB +BGR,Bulgaria,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEU +BHR,Bahrain,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +BHS,"Bahamas, The",LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C +BIH,Bosnia and Herzegovina,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB +BLR,Belarus,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEE +BLZ,Belize,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C +BMU,Bermuda,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, +BOL,Bolivia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,LCC6C +BRA,Brazil,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC5C +BRB,Barbados,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C +BRN,Brunei Darussalam,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACPF +BTN,Bhutan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,SACBB +BWA,Botswana,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 +CAF,Central African Republic,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 +CAN,Canada,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, +CHE,Switzerland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +CHI,Channel Islands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +CHL,Chile,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC6C +CHN,China,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACCF +CIV,Cote d'Ivoire,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF2 +CMR,Cameroon,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCC1 +COD,"Congo, Dem. Rep.",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCC2 +COG,"Congo, Rep.",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCC2 +COL,Colombia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC4C +COM,Comoros,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCS2 +CPV,Cabo Verde,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCF1 +CRI,Costa Rica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC2C +CUB,Cuba,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,LNX,XX,Not classified, +CUW,Curacao,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +CYM,Cayman Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +CYP,Cyprus,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +CZE,Czech Republic,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +DEU,Germany,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +DJI,Djibouti,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,MNC03 +DMA,Dominica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C +DNK,Denmark,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +DOM,Dominican Republic,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C +DZA,Algeria,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC01 +ECU,Ecuador,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC6C +EGY,"Egypt, Arab Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC03 +ERI,Eritrea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 +ESP,Spain,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +EST,Estonia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +ETH,Ethiopia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 +FIN,Finland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +FJI,Fiji,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,EACNF +FRA,France,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +FRO,Faroe Islands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +FSM,"Micronesia, Fed. Sts.",EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF +GAB,Gabon,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCC1 +GBR,United Kingdom,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +GEO,Georgia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCSC +GHA,Ghana,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCW1 +GIB,Gibraltar,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +GIN,Guinea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 +GMB,"Gambia, The",SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF1 +GNB,Guinea-Bissau,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF1 +GNQ,Equatorial Guinea,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCC1 +GRC,Greece,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +GRD,Grenada,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C +GRL,Greenland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +GTM,Guatemala,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC2C +GUM,Guam,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNF +GUY,Guyana,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,LCC3C +HKG,"Hong Kong SAR, China",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, +HND,Honduras,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,LCC2C +HRV,Croatia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,IBD,XF,IBRD,ECCEU +HTI,Haiti,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LIC,XM,Low income,IDX,XI,IDA,LCC8C +HUN,Hungary,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +IDN,Indonesia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACIF +IMN,Isle of Man,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +IND,India,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,SACIN +IRL,Ireland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +IRN,"Iran, Islamic Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 +IRQ,Iraq,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 +ISL,Iceland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +ISR,Israel,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified, +ITA,Italy,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +JAM,Jamaica,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C +JOR,Jordan,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 +JPN,Japan,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, +KAZ,Kazakhstan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCCA +KEN,Kenya,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCE2 +KGZ,Kyrgyz Republic,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,ECCCA +KHM,Cambodia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACMM +KIR,Kiribati,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF +KNA,St. Kitts and Nevis,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C +KOR,"Korea, Rep.",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACCF +KWT,Kuwait,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +LAO,Lao PDR,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACLF +LBN,Lebanon,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC02 +LBR,Liberia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW1 +LBY,Libya,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,MNC01 +LCA,St. Lucia,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C +LIE,Liechtenstein,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +LKA,Sri Lanka,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,SACSN +LSO,Lesotho,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCS1 +LTU,Lithuania,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +LUX,Luxembourg,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +LVA,Latvia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +MAC,"Macao SAR, China",EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, +MAF,St. Martin (French part),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +MAR,Morocco,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC01 +MCO,Monaco,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +MDA,Moldova,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,ECCEE +MDG,Madagascar,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCS2 +MDV,Maldives,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,SACSN +MEX,Mexico,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC1C +MHL,Marshall Islands,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF +MKD,North Macedonia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB +MLI,Mali,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 +MLT,Malta,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC01 +MMR,Myanmar,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACMM +MNE,Montenegro,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB +MNG,Mongolia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACCF +MNP,Northern Mariana Islands,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, +MOZ,Mozambique,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCS2 +MRT,Mauritania,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF1 +MUS,Mauritius,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS2 +MWI,Malawi,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE1 +MYS,Malaysia,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACPF +NAM,Namibia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 +NCL,New Caledonia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified, +NER,Niger,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 +NGA,Nigeria,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCW2 +NIC,Nicaragua,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,LCC2C +NLD,Netherlands,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +NOR,Norway,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +NPL,Nepal,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,SACSN +NRU,Nauru,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACNQ +NZL,New Zealand,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EACNQ +OMN,Oman,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +PAK,Pakistan,SAS,8S,South Asia,SAS,8S,South Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,SACPK +PAN,Panama,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC2C +PER,Peru,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC6C +PHL,Philippines,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACPF +PLW,Palau,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,IBD,XF,IBRD,EACNF +PNG,Papua New Guinea,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACNQ +POL,Poland,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,IBD,XF,IBRD,ECCEU +PRI,Puerto Rico,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +PRK,"Korea, Dem. People’s Rep.",EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LIC,XM,Low income,LNX,XX,Not classified,EACCF +PRT,Portugal,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +PRY,Paraguay,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC7C +PSE,West Bank and Gaza,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,LNX,XX,Not classified,MNC04 +PYF,French Polynesia,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EAPSG +QAT,Qatar,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +ROU,Romania,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCEU +RUS,Russian Federation,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCRU +RWA,Rwanda,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 +SAU,Saudi Arabia,MEA,ZQ,Middle East and North Africa,,,,HIC,XD,High income,LNX,XX,Not classified,MNC05 +SDN,Sudan,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCE3 +SEN,Senegal,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCF1 +SGP,Singapore,EAS,Z4,East Asia and Pacific,,,,HIC,XD,High income,LNX,XX,Not classified,EAPSG +SLB,Solomon Islands,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF +SLE,Sierra Leone,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW1 +SLV,El Salvador,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,LCC2C +SMR,San Marino,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +SOM,Somalia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 +SRB,Serbia,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCWB +SSD,South Sudan,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE3 +STP,Sao Tome and Principe,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCC1 +SUR,Suriname,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC3C +SVK,Slovak Republic,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +SVN,Slovenia,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified,ECCEU +SWE,Sweden,ECS,Z7,Europe and Central Asia,,,,HIC,XD,High income,LNX,XX,Not classified, +SWZ,Eswatini,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,AFCS1 +SXM,Sint Maarten (Dutch part),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified,LCC3C +SYC,Seychelles,SSF,ZG,Sub-Saharan Africa,,,,HIC,XD,High income,IBD,XF,IBRD,AFCS2 +SYR,Syrian Arab Republic,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,MNC02 +TCA,Turks and Caicos Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +TCD,Chad,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCW3 +TGO,Togo,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCF2 +THA,Thailand,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,EACPF +TJK,Tajikistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LIC,XM,Low income,IDX,XI,IDA,ECCCA +TKM,Turkmenistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD, +TLS,Timor-Leste,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,EACDF +TON,Tonga,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF +TTO,Trinidad and Tobago,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC3C +TUN,Tunisia,MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,MNC01 +TUR,Turkey,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,ECCTR +TUV,Tuvalu,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF +TZA,Tanzania,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE1 +UGA,Uganda,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,AFCE2 +UKR,Ukraine,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,ECCEE +URY,Uruguay,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,IBD,XF,IBRD,LCC7C +USA,United States,NAC,XU,North America,,,,HIC,XD,High income,LNX,XX,Not classified, +UZB,Uzbekistan,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,ECCCA +VCT,St. Vincent and the Grenadines,LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IDB,XH,Blend,LCC3C +VEN,"Venezuela, RB",LCN,ZJ,Latin America and Caribbean,LAC,XJ,Latin America and Caribbean (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,LCC4C +VGB,British Virgin Islands,LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +VIR,Virgin Islands (U.S.),LCN,ZJ,Latin America and Caribbean,,,,HIC,XD,High income,LNX,XX,Not classified, +VNM,Vietnam,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IBD,XF,IBRD,EACVF +VUT,Vanuatu,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,EACNF +WSM,Samoa,EAS,Z4,East Asia and Pacific,EAP,4E,East Asia and Pacific (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,EACNF +XKX,Kosovo,ECS,Z7,Europe and Central Asia,ECA,7E,Europe and Central Asia (excluding high income),UMC,XT,Upper middle income,IDX,XI,IDA,ECCWB +YEM,"Yemen, Rep.",MEA,ZQ,Middle East and North Africa,MNA,XQ,Middle East and North Africa (excluding high income),LIC,XM,Low income,IDX,XI,IDA,MNC03 +ZAF,South Africa,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),UMC,XT,Upper middle income,IBD,XF,IBRD,AFCS1 +ZMB,Zambia,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDX,XI,IDA,AFCE1 +ZWE,Zimbabwe,SSF,ZG,Sub-Saharan Africa,SSA,ZF,Sub-Saharan Africa (excluding high income),LMC,XN,Lower middle income,IDB,XH,Blend,AFCE1 +Country Code,Country Name,Region Code,Region Code (ISO 2 digits),Region Name,Administrative Region Code,Administrative Region Code (ISO 2 digits),Administrative Region Name,Income Level Code,Income Level Code (ISO 2 digits),Income Level Name,Lending Type Code,Lending Type Code (ISO 2 digits),Lending Type Name,WB Country Management Unit diff --git a/01_data/011_rawdata/hosted_in_repo/proficiency_from_GLAD.csv b/01_data/011_rawdata/hosted_in_repo/proficiency_from_GLAD.csv index 9cc86ce..6fd1300 100644 --- a/01_data/011_rawdata/hosted_in_repo/proficiency_from_GLAD.csv +++ b/01_data/011_rawdata/hosted_in_repo/proficiency_from_GLAD.csv @@ -1,560 +1,560 @@ -countrycode,year,test,idgrade,subject,nonprof_all,se_nonprof_all,nonprof_ma,se_nonprof_ma,nonprof_fe,se_nonprof_fe -ARE,2011,PIRLS,4,read,36.017685,.8808257,42.150146,1.4214823,29.855675,1.1456658 -ARE,2011,TIMSS,4,math,36.141468,.97319388,38.809471,1.5059434,33.462761,1.2600484 -ARE,2011,TIMSS,4,science,39.358074,1.0403529,43.469112,1.5790539,35.230553,1.4192994 -ARE,2015,TIMSS,4,math,31.665915,.85144204,32.954182,1.3370425,30.250454,1.6114295 -ARE,2015,TIMSS,4,science,33.224003,.93746746,35.835308,1.4489555,30.354858,1.6378882 -ARE,2016,PIRLS,4,read,32.428772,1.3028113,38.499516,1.7644614,26.029068,1.6799361 -ARG,2001,PIRLS,4,read,40.235104,2.7008476,44.358452,3.0494652,36.24559,2.9624629 -ARG,2006,LLECE,6,read,55.296715,.63825852,57.913132,.90317416,52.624977,.90514821 -ARG,2013,LLECE,6,read,53.578964,.94843376,59.282379,1.3507472,49.011475,1.3413248 -ARM,2003,TIMSS,4,math,25.028837,1.4846536,27.53191,1.7162535,22.400993,1.5977393 -ARM,2003,TIMSS,4,science,34.457951,1.7659854,36.311317,2.0276687,32.512207,1.9867963 -ARM,2007,TIMSS,4,math,13.261837,1.1947109,14.053506,1.2131757,12.413817,1.6255416 -ARM,2007,TIMSS,4,science,23.421991,1.5546815,26.122242,1.6673526,20.529539,1.9306825 -ARM,2011,TIMSS,4,math,28.494602,1.4271125,29.590267,1.5639182,27.277542,1.8726581 -ARM,2011,TIMSS,4,science,42.30035,1.8219091,43.347752,2.1434176,41.136909,2.3086748 -ARM,2015,TIMSS,4,math,16.116714,1.2635183,17.039865,1.4937094,15.142399,1.4093491 -ARM,2015,TIMSS,4,science,29.995728,1.6847034,33.155674,1.7214665,26.660656,2.0458755 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+ZMB,2007,SACMEQ,6,read,87.565964,1.4153329,86.795685,1.4983078,88.376846,1.72483,.18911655,.19331199,.18505862,.044074237,.045670912,.042529892 +ZWE,2007,SACMEQ,6,read,57.897732,2.3851306,60.068321,2.6243873,56.220715,2.6018646,.14654744,.1386627,.1560991,.030454958,.027578944,.033938993 diff --git a/01_data/011_rawdata/hosted_in_repo/proficiency_from_NLA_md.csv b/01_data/011_rawdata/hosted_in_repo/proficiency_from_NLA_md.csv index 7e1eeec..06b3d18 100644 --- a/01_data/011_rawdata/hosted_in_repo/proficiency_from_NLA_md.csv +++ b/01_data/011_rawdata/hosted_in_repo/proficiency_from_NLA_md.csv @@ -1,23 +1,23 @@ -year,idgrade,nla_code,countrycode,nonprof_all -2013,5,AFG_2,AFG,87 -2016,4,CHN,CHN,18.200001 -2015,5,BGD_3,BGD,55 -2017,5,BGD_3,BGD,56 -2017,5,IND_4,IND,53.700001 -2014,6,UGA_1,UGA,61.700001 -2014,6,UGA_2,UGA,81.099998 -2014,4,PAK_3,PAK,65 -2015,4,LKA_3,LKA,14 -2011,5,VNM_1,VNM,1.08 -2015,4,ETH_1,ETH,42.68 -2015,4,ETH_2,ETH,55.099998 -2015,4,ETH_3,ETH,88.720001 -2011,5,COD,COD,62.019001 -2013,6,KHM_1,KHM,49.799999 -2017,6,MYS_1,MYS,11.7 -2016,5,ALB_1,ALB,4.9000001 -2014,4,KGZ_1,KGZ,63.799999 -2016,6,GHA_1,GHA,28 -2015,5,MDG_1,MDG,95.800003 -2012,5,MLI_1,MLI,86.599998 -2013,6,HND_1,HND,69.400002 +year,idgrade,nla_code,countrycode,nonprof_all,nonprof_fe,nonprof_ma +2013,5,AFG_2,AFG,87,, +2016,4,CHN,CHN,18.200001,, +2015,5,BGD_3,BGD,55,, +2017,5,BGD_3,BGD,56,, +2017,5,IND_4,IND,53.700001,53,55 +2014,6,UGA_1,UGA,61.700001,, +2014,6,UGA_2,UGA,81.099998,, +2014,4,PAK_3,PAK,65,, +2015,4,LKA_3,LKA,14,, +2011,5,VNM_1,VNM,1.08,, +2015,4,ETH_1,ETH,42.68,, +2015,4,ETH_2,ETH,55.099998,, +2015,4,ETH_3,ETH,88.720001,, +2011,5,COD,COD,62.019001,, +2013,6,KHM_1,KHM,49.799999,, +2017,6,MYS_1,MYS,11.7,, +2016,5,ALB_1,ALB,4.9000001,, +2014,4,KGZ_1,KGZ,63.799999,, +2016,6,GHA_1,GHA,28,, +2015,5,MDG_1,MDG,95.800003,, +2012,5,MLI_1,MLI,85.739998,84.550003,86.860001 +2013,6,HND_1,HND,69.400002,, diff --git a/01_data/011_rawdata/hosted_in_repo/region_metadata.csv b/01_data/011_rawdata/hosted_in_repo/region_metadata.csv new file mode 100644 index 0000000..0539f9a --- /dev/null +++ b/01_data/011_rawdata/hosted_in_repo/region_metadata.csv @@ -0,0 +1,47 @@ +countrycode,countryname +ARB,Arab World +CSS,Caribbean small states +CEB,Central Europe and the Baltics +EAR,Early-demographic dividend +EAS,East Asia & Pacific +TEA,East Asia & Pacific (IDA & IBRD countries) +EAP,East Asia & Pacific (excluding high income) +EMU,Euro area +ECS,Europe & Central Asia +TEC,Europe & Central Asia (IDA & IBRD countries) +ECA,Europe & Central Asia (excluding high income) +EUU,European Union +FCS,Fragile and conflict affected situations +HPC,Heavily indebted poor countries (HIPC) +HIC,High income +IBD,IBRD only +IBT,IDA & IBRD total +IDB,IDA blend +IDX,IDA only +IDA,IDA total +LTE,Late-demographic dividend +LCN,Latin America & Caribbean +LAC,Latin America & Caribbean (excluding high income) +TLA,Latin America & the Caribbean (IDA & IBRD countries) +LDC,Least developed countries: UN classification +LMY,Low & middle income +LIC,Low income +LMC,Lower middle income +MEA,Middle East & North Africa +TMN,Middle East & North Africa (IDA & IBRD countries) +MNA,Middle East & North Africa (excluding high income) +MIC,Middle income +NAC,North America +OED,OECD members +OSS,Other small states +PSS,Pacific island small states +PST,Post-demographic dividend +PRE,Pre-demographic dividend +SST,Small states +SAS,South Asia +TSA,South Asia (IDA & IBRD) +SSF,Sub-Saharan Africa +TSS,Sub-Saharan Africa (IDA & IBRD countries) +SSA,Sub-Saharan Africa (excluding high income) +UMC,Upper middle income +WLD,World diff --git a/01_data/012_programs/0120_import_rawdata.do b/01_data/012_programs/0120_import_rawdata.do index 4d72e61..24ab1e3 100644 --- a/01_data/012_programs/0120_import_rawdata.do +++ b/01_data/012_programs/0120_import_rawdata.do @@ -1,429 +1,431 @@ -*==============================================================================* -* 0120 SUBTASK: IMPORT ALL RAWDATA CSV/MD FILES HOSTED IN REPO -*==============================================================================* -qui { - - /* This do file manipulates all the CSV and MD files hosted in the repo, - importing each one into an equivalent dta: - - country_metadata.csv - - proficiency_from_GLAD.csv - - proficiency_no_microdata.csv - - population_1014.csv - - population_by_age.csv - - primary_school_age.csv - - enrollment_tenr_wbopendata.csv - - enrollment_edulit_uis.csv - - enrollment_validated.md - - primary_expenditure_wbopendata.csv (spending in education) - - hci_indicators_wbopendata.csv (HCI, LAYS, EYRS, HLO) - - poverty_gdp_indicators (Poverty @ ILP; LMCL; UMCL; GDP per capita PPP$) - */ - - * Directory where to find the CSVs or MDs (from the repo) - local input_dir "${clone}/01_data/011_rawdata/hosted_in_repo" - * Directory where to save the newly created DTAs - local output_dir "${clone}/01_data/011_rawdata" - - noi di "" - noi di "{phang}Importing rawdata from CSV and MD files hosted in the repo...{p_end}" - - - - /************************** - Country Metadata - **************************/ - * Prepare local to create file - local clonefile "country_metadata" - - * Open the raw csv with both data and labels (in the last observation) - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear - - * Loop through all variables, labelling them per last observation - foreach thisvar of varlist _all { - label var `thisvar' "`= `thisvar'[_N]'" - } - - * Drop last observation, which only had the var labels - drop if _n == _N - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - - /************************** - Proficiency from GLAD_CLO - **************************/ - * Prepare local to create file - local clonefile "proficiency_from_GLAD" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Beautify: format, order and label - format %4.1fc nonprof* se_nonprof* - foreach subgroup in all fe ma { - label var nonprof_`subgroup' "% pupils below minimum proficiency (`subgroup')" - label var se_nonprof_`subgroup' "SE of pupils below minimum proficiency (`subgroup')" - } - label var countrycode "WB country code (3 letters)" - label var idgrade "Grade ID" - label var test "Assessment" - label var subject "Subject" - label var year "Year of assessment" - - *Only relevant variables kept - order countrycode idgrade test year subject *nonprof* - keep countrycode idgrade test year subject *nonprof* - - * Add source for CLO file - gen source_assessment = "CLO (Country Level Outcomes from GLAD)" - label var source_assessment "Source of assessment data" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - - /************************** - Proficiency from NLA_md - **************************/ - * Prepare local to create file - local clonefile "proficiency_from_NLA_md" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Small needed adjustments (fix variables in this dataset) - gen test = "NLA" - gen subject = "read" - gen source_assessment = "National Learning Assessment (from UIS)" - label var nla_code "Reference code for NLA in markdown documentation" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************** - Proficiency no microdata - **************************/ - * Prepare local to create file - local clonefile "proficiency_no_microdata" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************************* - Population from WB Opendata 10-14 - *************************************/ - * Prepare local to create file - local clonefile "population_1014" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Calculate aggregate variable - gen pop1014_all = pop1014_fe + pop1014_ma - - * Rename and label variables - rename pop1014_fe population_fe_1014 - label var population_fe_1014 "Female population between ages 10 to 14 (WB API)" - rename pop1014_ma population_ma_1014 - label var population_ma_1014 "Male population between ages 10 to 14 (WB API)" - rename pop1014_all population_all_1014 - label var population_all_1014 "Total population between ages 10 to 14 (WB API)" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************************* - Population from WB Opendata by age - *************************************/ - * Prepare local to create file - local clonefile "population_by_age" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear - - * Calculate aggregate variable (rowtotal handles missing values better) - egen pop_ALL = rowtotal(pop_FE pop_MA), missing - - * Rename and label variables - label var age "Age cohort (exact year) for population data" - label var pop_FE "Female population in this age" - label var pop_MA "Male population in this age" - label var pop_ALL "Total population in this age" - rename pop_* population_* - rename population_*, lower - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /***************************************** - Primary entrance age and duration data - *****************************************/ - - * Prepare local to create file - local clonefile "primary_school_age" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Create the end age and start age for our definition of 5 last years of primary. - gen primary_start_age = uis_ceage_1 - gen primary_end_age = uis_ceage_1 + se_prm_durs - - * Rename and label variables - label var uis_ceage_1 "Official entrance age to each ISCED level of education" - label var se_prm_durs "Primary education, duration (years)" - label var primary_start_age "Age primary school start (Compulsory starting age)" - label var primary_end_age "Age primary school end (Compulsory starting age + duration of primary)" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - - /*********************************** - Enrollment (TENR) from WB opendata - ***********************************/ - - * Prepare local to create file - local clonefile "enrollment_tenr_wbopendata" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear - - * Rename and label variables - rename se_prm_tenr enrollment_ANER_ALL - label var enrollment_ANER_ALL "Adjusted Net Enrollment Rate (ANER), Primary, Both Sexes" - rename se_prm_tenr_fe enrollment_ANER_FE - label var enrollment_ANER_FE "Adjusted Net Enrollment Rate (ANER), Primary, Female" - rename se_prm_tenr_ma enrollment_ANER_MA - label var enrollment_ANER_MA "Adjusted Net Enrollment Rate (ANER), Primary, Male" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - - /******************************* - Enrollment from UIS csv - *******************************/ - * Prepare local to create file - local clonefile "enrollment_edulit_uis" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear - - * Standardize varnames to those used in wbopendata - rename (ïedulit_ind location time) (series countrycode year) - keep series countrycode year value - - * Bring from long to wide - reshape wide value, i(countrycode year) j(series) string - - * Only keep year after 1990 - keep if year >= 1990 - - * Rename and label variables we will use from UIS. - * Adjusted Net Enrollment is intentially omitted as it will come from wbopendata - - * Gross Enrollment - rename valueGER_1 enrollment_GER_ALL - label var enrollment_GER_ALL "Gross Enrollment Rate (GER), Primary, Both Sexes" - rename valueGER_1_F enrollment_GER_FE - label var enrollment_GER_FE "Gross Enrollment Rate (GER), Primary, Female" - rename valueGER_1_M enrollment_GER_MA - label var enrollment_GER_MA "Gross Enrollment Rate (GER), Primary, Male" - - * Total Net Enrollment - rename valueNERT_1_CP enrollment_TNER_ALL - label var enrollment_TNER_ALL "Total Net Enrollment Rate (TNER), Primary, Both Sexes" - rename valueNERT_1_F_CP enrollment_TNER_FE - label var enrollment_TNER_FE "Total Net Enrollment Rate (TNER), Primary, Female" - rename valueNERT_1_M_CP enrollment_TNER_MA - label var enrollment_TNER_MA "Total Net Enrollment Rate (TNER), Primary, Male" - - * Net Enrollment - rename valueNER_1_CP enrollment_NER_ALL - label var enrollment_NER_ALL "Net Enrollment Rate (NER), Primary, Both Sexes" - rename valueNER_1_F_CP enrollment_NER_FE - label var enrollment_NER_FE "Net Enrollment Rate (NER), Primary, Female" - rename valueNER_1_M_CP enrollment_NER_MA - label var enrollment_NER_MA "Net Enrollment Rate (NER), Primary, Male" - - * Keep only relevant variables - keep countrycode year enrollment_* - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /******************************* - Enrollment validated md - *******************************/ - * Prepare local to create file - local clonefile "enrollment_validated" - - * Import the md rawdata in the repo - import delimited using "`input_dir'/`clonefile'.md", delimiter("|") varnames(1) clear - - * Corrections/problems that come with the md importing - keep countrycode year suggested_enrollment decision - drop if countrycode=="---" - destring year, replace - - * Destring suggested enrollment after fixing string missing values - replace suggested_enrollment = "" if suggested_enrollment == "NA" - destring suggested_enrollment, replace - - * This line of code make sure that within a country there is only one type of decision - by countrycode (decision), sort : gen same = (decision[1] == decision[_N]) - assert same == 1 - drop same - - * Keep countries that has a to-use value and then drop variable - keep if decision == "use" - drop decision - - * Standardize variable name - rename suggested_enrollment enrollment_VALID_ALL - label var enrollment_VALID_ALL "Enrollment value validated by country team" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************************* - Primary Expenditure from WB Opendata - *************************************/ - - * Prepare local to create file - local clonefile "primary_expenditure" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear - - * Rename and label variables used in the enrollment-method - rename uis_xunit_pppconst_1 exp_pri_perstu_raw - rename se_prm_enrr gross_enrol_pri_raw - label var gross_enrol_pri_raw "School enrollment, primary (% gross)" - label var exp_pri_perstu_raw "Initial government funding per primary student, constant PPP$" - - * Rename and label variables used in the total-method - rename uis_x_pppconst_1_fsg exp_pri_total_raw - rename sp_prm_totl_in children_pri_age - label var exp_pri_total_raw "Government expenditure on primary education, constant PPP$ (millions)" - label var children_pri_age "School age population, primary education, both sexes (number)" - - - * Calculating Spending per child using the enrollment-method - * using per student primary expenditure multiplied with gross enrollment (setting Spending - * per child equal to per student expenditure if gross enrollement is higher than 100%) - gen exp_pri_perchild_enrol = exp_pri_perstu_raw * gross_enrol_pri_raw / 100 - replace exp_pri_perchild_enrol = exp_pri_perstu_raw if exp_pri_perchild_enrol > exp_pri_perstu_raw - lab var exp_pri_perchild_enrol "Spending per child, enrollment-method (per student spending * gross enrollment)" - - * Spending per child - using total expenditure and primary school age population - * Calculating Spending per child using the total-method - * using total primary expediture divided by number of children in primary age (multiplied - * by 1,000,000 to go from millions of dollars to dollars) - gen exp_pri_perchild_total = 1000000 * exp_pri_total_raw / children_pri_age - lab var exp_pri_perchild_total "Spending per child, total-method (total spending / children primary age)" - - * Restore the sort order - sort countrycode year - - * TODO DOUBLE CHECK IF THIS IS NEEDED HERE OR THIS ADJUSTMENT COULD SIT AT TWO PAGER TASK ONLY - * Replace regional codes for prefered codes from a communication purpose - replace region = "EAP" if region == "EAS" - replace region = "ECA" if region == "ECS" - replace region = "LAC" if region == "LCN" - replace region = "MNA" if region == "MEA" - replace region = "SAR" if region == "SAS" - replace region = "SSA" if region == "SSF" - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************************* - HCI Indicators from WB Opendata - *************************************/ - - * Prepare local to create file - local clonefile "hci_indicators" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear - - * Rename file names to convention used when creating the 2pagers - rename hd_hci_lays_ma LAYS_m - rename hd_hci_lays_fe LAYS_f - rename hd_hci_lays LAYS_mf - rename hd_hci_eyrs_ma ExpYS_m - rename hd_hci_eyrs_fe ExpYS_f - rename hd_hci_eyrs ExpYS_mf - rename hd_hci_hlos_ma HarmTS_m - rename hd_hci_hlos_fe HarmTS_f - rename hd_hci_hlos HarmTS_mf - rename hd_hci_ovrl HCI_mf - rename hd_hci_ovrl_fe HCI_f - rename hd_hci_ovrl_ma HCI_m - - - * Make sure that there are only one obs per country - isid countrycode - - * WB API is not up to date with most recent country names - replace countryname = "North Macedonia" if countrycode == "MKD" - - * Restore the sort order - sort countrycode year - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - /************************************* - Poverty and GDP from WB Opendata - *************************************/ - - * Prepare local to create file - local clonefile "poverty_gdp_indicators" - - * Open the raw csv with data - import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear - - *Restore the sort order - sort countrycode year - - * Compress and save in rawdata - compress - noi save "`output_dir'/`clonefile'.dta", replace - - - - * Display message to end this do file - noi di as res "{phang}Concluded importing rawdata.{p_end}" - -} +*==============================================================================* +* 0120 SUBTASK: IMPORT ALL RAWDATA CSV/MD FILES HOSTED IN REPO +*==============================================================================* +qui { + + /* This do file manipulates all the CSV and MD files hosted in the repo, + importing each one into an equivalent dta: + - country_metadata.csv + - proficiency_from_GLAD.csv + - proficiency_no_microdata.csv + - population_1014.csv + - population_by_age.csv + - primary_school_age.csv + - enrollment_tenr_wbopendata.csv + - enrollment_edulit_uis.csv + - enrollment_validated.md + - primary_expenditure_wbopendata.csv (spending in education) + - hci_indicators_wbopendata.csv (HCI, LAYS, EYRS, HLO) + - poverty_gdp_indicators (Poverty @ ILP; LMCL; UMCL; GDP per capita PPP$) + */ + + * Directory where to find the CSVs or MDs (from the repo) + local input_dir "${clone}/01_data/011_rawdata/hosted_in_repo" + * Directory where to save the newly created DTAs + local output_dir "${clone}/01_data/011_rawdata" + + noi di "" + noi di "{phang}Importing rawdata from CSV and MD files hosted in the repo...{p_end}" + + + + /************************** + Country Metadata + **************************/ + * Prepare local to create file + local clonefile "country_metadata" + + * Open the raw csv with both data and labels (in the last observation) + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear + + * Loop through all variables, labelling them per last observation + foreach thisvar of varlist _all { + label var `thisvar' "`= `thisvar'[_N]'" + } + + * Drop last observation, which only had the var labels + drop if _n == _N + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + + /************************** + Proficiency from GLAD_CLO + **************************/ + * Prepare local to create file + local clonefile "proficiency_from_GLAD" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Beautify: format, order and label + format %4.1fc nonprof* se_nonprof* + foreach subgroup in all fe ma { + label var nonprof_`subgroup' "% pupils below minimum proficiency (`subgroup')" + label var se_nonprof_`subgroup' "SE of pupils below minimum proficiency (`subgroup')" + label var fgt1_`subgroup' "Avg gap to minimum proficiency (`subgroup', FGT1)" + label var fgt2_`subgroup' "Avg gap squared to minimum proficiency (`subgroup', FGT2)" + } + label var countrycode "WB country code (3 letters)" + label var idgrade "Grade ID" + label var test "Assessment" + label var subject "Subject" + label var year "Year of assessment" + + *Only relevant variables kept + order countrycode idgrade test year subject *nonprof* fgt1* fgt2* + keep countrycode idgrade test year subject *nonprof* fgt1* fgt2* + + * Add source for CLO file + gen source_assessment = "CLO (Country Level Outcomes from GLAD)" + label var source_assessment "Source of assessment data" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + + /************************** + Proficiency from NLA_md + **************************/ + * Prepare local to create file + local clonefile "proficiency_from_NLA_md" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Small needed adjustments (fix variables in this dataset) + gen test = "NLA" + gen subject = "read" + gen source_assessment = "National Learning Assessment (from UIS)" + label var nla_code "Reference code for NLA in markdown documentation" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************** + Proficiency no microdata + **************************/ + * Prepare local to create file + local clonefile "proficiency_no_microdata" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************************* + Population from WB Opendata 10-14 + *************************************/ + * Prepare local to create file + local clonefile "population_1014" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Calculate aggregate variable + gen pop1014_all = pop1014_fe + pop1014_ma + + * Rename and label variables + rename pop1014_fe population_fe_1014 + label var population_fe_1014 "Female population between ages 10 to 14 (WB API)" + rename pop1014_ma population_ma_1014 + label var population_ma_1014 "Male population between ages 10 to 14 (WB API)" + rename pop1014_all population_all_1014 + label var population_all_1014 "Total population between ages 10 to 14 (WB API)" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************************* + Population from WB Opendata by age + *************************************/ + * Prepare local to create file + local clonefile "population_by_age" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear + + * Calculate aggregate variable (rowtotal handles missing values better) + egen pop_ALL = rowtotal(pop_FE pop_MA), missing + + * Rename and label variables + label var age "Age cohort (exact year) for population data" + label var pop_FE "Female population in this age" + label var pop_MA "Male population in this age" + label var pop_ALL "Total population in this age" + rename pop_* population_* + rename population_*, lower + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /***************************************** + Primary entrance age and duration data + *****************************************/ + + * Prepare local to create file + local clonefile "primary_school_age" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Create the end age and start age for our definition of 5 last years of primary. + gen primary_start_age = uis_ceage_1 + gen primary_end_age = uis_ceage_1 + se_prm_durs + + * Rename and label variables + label var uis_ceage_1 "Official entrance age to each ISCED level of education" + label var se_prm_durs "Primary education, duration (years)" + label var primary_start_age "Age primary school start (Compulsory starting age)" + label var primary_end_age "Age primary school end (Compulsory starting age + duration of primary)" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + + /*********************************** + Enrollment (TENR) from WB opendata + ***********************************/ + + * Prepare local to create file + local clonefile "enrollment_tenr_wbopendata" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(preserve) clear + + * Rename and label variables + rename se_prm_tenr enrollment_ANER_ALL + label var enrollment_ANER_ALL "Adjusted Net Enrollment Rate (ANER), Primary, Both Sexes" + rename se_prm_tenr_fe enrollment_ANER_FE + label var enrollment_ANER_FE "Adjusted Net Enrollment Rate (ANER), Primary, Female" + rename se_prm_tenr_ma enrollment_ANER_MA + label var enrollment_ANER_MA "Adjusted Net Enrollment Rate (ANER), Primary, Male" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + + /******************************* + Enrollment from UIS csv + *******************************/ + * Prepare local to create file + local clonefile "enrollment_edulit_uis" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'.csv", varnames(1) case(lower) clear + + * Standardize varnames to those used in wbopendata + rename (ïedulit_ind location time) (series countrycode year) + keep series countrycode year value + + * Bring from long to wide + reshape wide value, i(countrycode year) j(series) string + + * Only keep year after 1990 + keep if year >= 1990 + + * Rename and label variables we will use from UIS. + * Adjusted Net Enrollment is intentially omitted as it will come from wbopendata + + * Gross Enrollment + rename valueGER_1 enrollment_GER_ALL + label var enrollment_GER_ALL "Gross Enrollment Rate (GER), Primary, Both Sexes" + rename valueGER_1_F enrollment_GER_FE + label var enrollment_GER_FE "Gross Enrollment Rate (GER), Primary, Female" + rename valueGER_1_M enrollment_GER_MA + label var enrollment_GER_MA "Gross Enrollment Rate (GER), Primary, Male" + + * Total Net Enrollment + rename valueNERT_1_CP enrollment_TNER_ALL + label var enrollment_TNER_ALL "Total Net Enrollment Rate (TNER), Primary, Both Sexes" + rename valueNERT_1_F_CP enrollment_TNER_FE + label var enrollment_TNER_FE "Total Net Enrollment Rate (TNER), Primary, Female" + rename valueNERT_1_M_CP enrollment_TNER_MA + label var enrollment_TNER_MA "Total Net Enrollment Rate (TNER), Primary, Male" + + * Net Enrollment + rename valueNER_1_CP enrollment_NER_ALL + label var enrollment_NER_ALL "Net Enrollment Rate (NER), Primary, Both Sexes" + rename valueNER_1_F_CP enrollment_NER_FE + label var enrollment_NER_FE "Net Enrollment Rate (NER), Primary, Female" + rename valueNER_1_M_CP enrollment_NER_MA + label var enrollment_NER_MA "Net Enrollment Rate (NER), Primary, Male" + + * Keep only relevant variables + keep countrycode year enrollment_* + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /******************************* + Enrollment validated md + *******************************/ + * Prepare local to create file + local clonefile "enrollment_validated" + + * Import the md rawdata in the repo + import delimited using "`input_dir'/`clonefile'.md", delimiter("|") varnames(1) clear + + * Corrections/problems that come with the md importing + keep countrycode year suggested_enrollment decision + drop if countrycode=="---" + destring year, replace + + * Destring suggested enrollment after fixing string missing values + replace suggested_enrollment = "" if suggested_enrollment == "NA" + destring suggested_enrollment, replace + + * This line of code make sure that within a country there is only one type of decision + by countrycode (decision), sort : gen same = (decision[1] == decision[_N]) + assert same == 1 + drop same + + * Keep countries that has a to-use value and then drop variable + keep if decision == "use" + drop decision + + * Standardize variable name + rename suggested_enrollment enrollment_VALID_ALL + label var enrollment_VALID_ALL "Enrollment value validated by country team" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************************* + Primary Expenditure from WB Opendata + *************************************/ + + * Prepare local to create file + local clonefile "primary_expenditure" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear + + * Rename and label variables used in the enrollment-method + rename uis_xunit_pppconst_1 exp_pri_perstu_raw + rename se_prm_enrr gross_enrol_pri_raw + label var gross_enrol_pri_raw "School enrollment, primary (% gross)" + label var exp_pri_perstu_raw "Initial government funding per primary student, constant PPP$" + + * Rename and label variables used in the total-method + rename uis_x_pppconst_1_fsg exp_pri_total_raw + rename sp_prm_totl_in children_pri_age + label var exp_pri_total_raw "Government expenditure on primary education, constant PPP$ (millions)" + label var children_pri_age "School age population, primary education, both sexes (number)" + + + * Calculating Spending per child using the enrollment-method + * using per student primary expenditure multiplied with gross enrollment (setting Spending + * per child equal to per student expenditure if gross enrollement is higher than 100%) + gen exp_pri_perchild_enrol = exp_pri_perstu_raw * gross_enrol_pri_raw / 100 + replace exp_pri_perchild_enrol = exp_pri_perstu_raw if exp_pri_perchild_enrol > exp_pri_perstu_raw + lab var exp_pri_perchild_enrol "Spending per child, enrollment-method (per student spending * gross enrollment)" + + * Spending per child - using total expenditure and primary school age population + * Calculating Spending per child using the total-method + * using total primary expediture divided by number of children in primary age (multiplied + * by 1,000,000 to go from millions of dollars to dollars) + gen exp_pri_perchild_total = 1000000 * exp_pri_total_raw / children_pri_age + lab var exp_pri_perchild_total "Spending per child, total-method (total spending / children primary age)" + + * Restore the sort order + sort countrycode year + + * TODO DOUBLE CHECK IF THIS IS NEEDED HERE OR THIS ADJUSTMENT COULD SIT AT TWO PAGER TASK ONLY + * Replace regional codes for prefered codes from a communication purpose + replace region = "EAP" if region == "EAS" + replace region = "ECA" if region == "ECS" + replace region = "LAC" if region == "LCN" + replace region = "MNA" if region == "MEA" + replace region = "SAR" if region == "SAS" + replace region = "SSA" if region == "SSF" + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************************* + HCI Indicators from WB Opendata + *************************************/ + + * Prepare local to create file + local clonefile "hci_indicators" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear + + * Rename file names to convention used when creating the 2pagers + rename hd_hci_lays_ma LAYS_m + rename hd_hci_lays_fe LAYS_f + rename hd_hci_lays LAYS_mf + rename hd_hci_eyrs_ma ExpYS_m + rename hd_hci_eyrs_fe ExpYS_f + rename hd_hci_eyrs ExpYS_mf + rename hd_hci_hlos_ma HarmTS_m + rename hd_hci_hlos_fe HarmTS_f + rename hd_hci_hlos HarmTS_mf + rename hd_hci_ovrl HCI_mf + rename hd_hci_ovrl_fe HCI_f + rename hd_hci_ovrl_ma HCI_m + + + * Make sure that there are only one obs per country + isid countrycode + + * WB API is not up to date with most recent country names + replace countryname = "North Macedonia" if countrycode == "MKD" + + * Restore the sort order + sort countrycode year + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + /************************************* + Poverty and GDP from WB Opendata + *************************************/ + + * Prepare local to create file + local clonefile "poverty_gdp_indicators" + + * Open the raw csv with data + import delimited using "`input_dir'/`clonefile'_wbopendata.csv", varnames(1) case(preserve) clear + + *Restore the sort order + sort countrycode year + + * Compress and save in rawdata + compress + noi save "`output_dir'/`clonefile'.dta", replace + + + + * Display message to end this do file + noi di as res "{phang}Concluded importing rawdata.{p_end}" + +} diff --git a/01_data/012_programs/0121_combine_population_data.do b/01_data/012_programs/0121_combine_population_data.do index f9c1d27..76b6b50 100644 --- a/01_data/012_programs/0121_combine_population_data.do +++ b/01_data/012_programs/0121_combine_population_data.do @@ -44,6 +44,7 @@ qui { foreach popvar in population_fe population_ma population_all { * Age: 10 only gen `popvar'_10 = `popvar' if age == 10 + gen `popvar'_0516 = `popvar' if age >= 5 & age <= 16 **** NOTE: THIS OPTION IS COMMENTED OUT FOR IT IS LESS ADEQUATE THAN THE SP.POP.1014 SERIES //* Age: 10-14 only //gen `popvar'_api1014 = `popvar' if age >= 10 & age <= 14 @@ -72,6 +73,7 @@ qui { if "`aggregation'" == "all" local agg_label "Total population" label var population_`aggregation'_10 "`agg_label' aged 10 (WB API)" + label var population_`aggregation'_0516 "`agg_label' aged 05-16 (WB API)" label var population_`aggregation'_primary "`agg_label' primary age, country specific (WB API)" label var population_`aggregation'_9plus "`agg_label' aged 9 to end of primary, country specific (WB API)" } diff --git a/01_data/012_programs/0122_combine_proficiency_data.do b/01_data/012_programs/0122_combine_proficiency_data.do index 34fd770..8c5a816 100644 --- a/01_data/012_programs/0122_combine_proficiency_data.do +++ b/01_data/012_programs/0122_combine_proficiency_data.do @@ -64,7 +64,7 @@ qui { local sources "Compilation of proficiency measures from 3 sources: CLO (Country Level Outcomes from GLAD), National Learning Assessment (from UIS), HAD (Harmonized Assessment Database)" edukit_save, filename(proficiency) path("${clone}/01_data/013_outputs/") /// idvars(countrycode year idgrade test nla_code subject) /// - varc("value *nonprof*; trait *threshold source_assessment surveyid") /// + varc("value *nonprof* fgt*; trait *threshold source_assessment surveyid") /// metadata("description `description'; sources `sources'; filename Proficiency") } else save "${clone}/01_data/013_outputs/proficiency.dta", replace diff --git a/01_data/012_programs/0123_combine_enrollment_data.do b/01_data/012_programs/0123_combine_enrollment_data.do index 3e69cf9..ae72f75 100644 --- a/01_data/012_programs/0123_combine_enrollment_data.do +++ b/01_data/012_programs/0123_combine_enrollment_data.do @@ -25,8 +25,6 @@ qui { gen enrollment_GER_source = "UIS" gen enrollment_VALID_source = "Country Team Validation" - replace enrollment_VALID_source = "National household survey" if countrycode == "AFG" & enrollment_VALID_source == "Country Team Validation" - * Label source variables label var enrollment_ANER_source "Source used for the ANER indicator" label var enrollment_TNER_source "Source used for the TNER indicator" diff --git a/01_data/012_programs/0124_enrollment_extrapolation.do b/01_data/012_programs/0124_enrollment_extrapolation.do index 6c2739a..bfb2e8a 100644 --- a/01_data/012_programs/0124_enrollment_extrapolation.do +++ b/01_data/012_programs/0124_enrollment_extrapolation.do @@ -169,6 +169,8 @@ qui { replace enrollment_definition = enrollment_definition + " (capped at 100%)" if enrollment_validated > 100 & !missing(enrollment_validated) replace enrollment_validated = 100 if enrollment_validated > 100 & !missing(enrollment_validated) + * Correct metadata for Afghanistan + replace enrollment_definition = "National household survey" if countrycode == "AFG" & enrollment_definition == "Country Team Validation" *********************************** *In some cases, we could not interpolate enrollment, because there was only one value for the country. In this case, use the carry forward value diff --git a/01_data/012_programs/0125_create_rawfull.do b/01_data/012_programs/0125_create_rawfull.do index 0258678..c433a4e 100644 --- a/01_data/012_programs/0125_create_rawfull.do +++ b/01_data/012_programs/0125_create_rawfull.do @@ -1,94 +1,94 @@ -*==============================================================================* -* 0125 SUBTASK: MERGE PROFICIENCY, ENROLLMENT AND POPULATION INTO RAWFULL -*==============================================================================* -qui { - - noi di _newline "{phang}Creating rawfull with $anchor_year as anchor year...{p_end}" - - /* The anchor year is set as a global in the 012_run.do, for it affects - several files. When Learning Poverty numbers were first released - in Sep 2019, the chosen anchor year was 2015. */ - - *-----------------------------* - * Dataset WITHOUT proficiency * - *-----------------------------* - - * Open population for anchor year only - * Note that population.dta is LONG on year, WIDE on population definitions and gender - use "${clone}/01_data/013_outputs/population.dta" if year_population == $anchor_year, clear - - * To be able to merge with enrollment - clonevar year = year_population - - * Brings in Enrollment - * Note that enrollment.dta is LONG on year, WIDE on enrollment definiions and gender - merge m:1 countrycode year using "${clone}/01_data/013_outputs/enrollment.dta", keep(master match) nogen - - * This dataset purposefully will be saved without proficiency data - gen str test = "None" - gen str nla_code = "N.A." - gen str subject = "N.A." - gen int idgrade = -999 - - * Save the dataset with only population and enrollment, which still has 1 obs = 1 cty, all for 2015 - save "${clone}/01_data/013_outputs/population_enrollment.dta", replace - - - *-------------------------------* - * Dataset WITH proficiency data * - *-------------------------------* - * Open population for anchor year only - * Note that population.dta is LONG on year, WIDE on population definitions and gender - use "${clone}/01_data/013_outputs/population.dta" if year_population==$anchor_year, clear - - - *** Brings in Proficiency (LONG) - merge 1:m countrycode using "${clone}/01_data/013_outputs/proficiency.dta", keep(match) nogen - //* TODO Investigate two cases (CHI and BIH) - - * Brings in Enrollment (WIDE) - merge m:1 countrycode year using "${clone}/01_data/013_outputs/enrollment.dta", keep(master match) nogen - - - - *---------------------------------* - * Appends both, bring in metadata * - *---------------------------------* - - * This would be rawfull, except that it doesn't have all the countries. - * So appends the 1st dataset, population_enrollment.dta (without proficiency), - * so every country has 1 obs with test="None" plus as many others as there are assessments - append using "${clone}/01_data/013_outputs/population_enrollment.dta" - rename year year_assessment - - * Brings in country metadata. Assert all match (metadata has chosen 217 countries) - merge m:1 countrycode using "${clone}/01_data/011_rawdata/country_metadata.dta", assert(match) nogen - - - *-------------------------------* - * Unit of Observation Explainer * - *-------------------------------* - * Observations are uniquely identified by: - isid countrycode test nla_code subject idgrade year_assessment - - * Organizing the dataset - local assessment_vars "test nla_code subject idgrade year_assessment *nonprof* min_proficiency_threshold source_assessment" - unab enrollment_vars : *enrollment* - unab population_vars : *population* - order countrycode `assessment_vars' `enrollment_vars' `population_vars' - - * Compress and save - compress - noi di "{phang}Saving file: ${clone}/01_data/013_outputs/rawfull.dta{p_end}" - * If global is one, saves with metadata through edukitsave. Otherwise use regular save - if $use_edukit_save { - local description "Dataset of proficiency merged with enrollment and population. Not a timeseries, rather a collection of observations, from which subsets of time series may be extracted. Long in proficiency, wide on population and enrollment." - local sources "All population, enrollment and proficiency sources combined." - edukit_save, filename(rawfull) path("${clone}/01_data/013_outputs/") /// - idvars(countrycode year_assessment idgrade test nla_code subject) /// - varc("value *nonprof* enrollment_val* enrollment_inte* population_*; trait year_enrollment year_population *source* *definition* *threshold* surveyid countryname region* adminregion* incomelevel* lendingtype* cmu") /// - metadata("description `description'; sources `sources'; filename Rawfull") - } - else save "${clone}/01_data/013_outputs/rawfull.dta", replace - -} +*==============================================================================* +* 0125 SUBTASK: MERGE PROFICIENCY, ENROLLMENT AND POPULATION INTO RAWFULL +*==============================================================================* +qui { + + noi di _newline "{phang}Creating rawfull with $anchor_year as anchor year...{p_end}" + + /* The anchor year is set as a global in the 012_run.do, for it affects + several files. When Learning Poverty numbers were first released + in Sep 2019, the chosen anchor year was 2015. */ + + *-----------------------------* + * Dataset WITHOUT proficiency * + *-----------------------------* + + * Open population for anchor year only + * Note that population.dta is LONG on year, WIDE on population definitions and gender + use "${clone}/01_data/013_outputs/population.dta" if year_population == $anchor_year, clear + + * To be able to merge with enrollment + clonevar year = year_population + + * Brings in Enrollment + * Note that enrollment.dta is LONG on year, WIDE on enrollment definiions and gender + merge m:1 countrycode year using "${clone}/01_data/013_outputs/enrollment.dta", keep(master match) nogen + + * This dataset purposefully will be saved without proficiency data + gen str test = "None" + gen str nla_code = "N.A." + gen str subject = "N.A." + gen int idgrade = -999 + + * Save the dataset with only population and enrollment, which still has 1 obs = 1 cty, all for 2015 + save "${clone}/01_data/013_outputs/population_enrollment.dta", replace + + + *-------------------------------* + * Dataset WITH proficiency data * + *-------------------------------* + * Open population for anchor year only + * Note that population.dta is LONG on year, WIDE on population definitions and gender + use "${clone}/01_data/013_outputs/population.dta" if year_population==$anchor_year, clear + + + *** Brings in Proficiency (LONG) + merge 1:m countrycode using "${clone}/01_data/013_outputs/proficiency.dta", keep(match) nogen + //* TODO Investigate two cases (CHI and BIH) + + * Brings in Enrollment (WIDE) + merge m:1 countrycode year using "${clone}/01_data/013_outputs/enrollment.dta", keep(master match) nogen + + + + *---------------------------------* + * Appends both, bring in metadata * + *---------------------------------* + + * This would be rawfull, except that it doesn't have all the countries. + * So appends the 1st dataset, population_enrollment.dta (without proficiency), + * so every country has 1 obs with test="None" plus as many others as there are assessments + append using "${clone}/01_data/013_outputs/population_enrollment.dta" + rename year year_assessment + + * Brings in country metadata. Assert all match (metadata has chosen 217 countries) + merge m:1 countrycode using "${clone}/01_data/011_rawdata/country_metadata.dta", assert(match) nogen + + + *-------------------------------* + * Unit of Observation Explainer * + *-------------------------------* + * Observations are uniquely identified by: + isid countrycode test nla_code subject idgrade year_assessment + + * Organizing the dataset + local assessment_vars "test nla_code subject idgrade year_assessment *nonprof* min_proficiency_threshold source_assessment" + unab enrollment_vars : *enrollment* + unab population_vars : *population* + order countrycode `assessment_vars' `enrollment_vars' `population_vars' + + * Compress and save + compress + noi di "{phang}Saving file: ${clone}/01_data/013_outputs/rawfull.dta{p_end}" + * If global is one, saves with metadata through edukitsave. Otherwise use regular save + if $use_edukit_save { + local description "Dataset of proficiency merged with enrollment and population. Not a timeseries, rather a collection of observations, from which subsets of time series may be extracted. Long in proficiency, wide on population and enrollment." + local sources "All population, enrollment and proficiency sources combined." + edukit_save, filename(rawfull) path("${clone}/01_data/013_outputs/") /// + idvars(countrycode year_assessment idgrade test nla_code subject) /// + varc("value *nonprof* fgt* enrollment_val* enrollment_inte* population_*; trait year_enrollment year_population *source* *definition* *threshold* surveyid countryname region* adminregion* incomelevel* lendingtype* cmu") /// + metadata("description `description'; sources `sources'; filename Rawfull") + } + else save "${clone}/01_data/013_outputs/rawfull.dta", replace + +} diff --git a/01_data/012_programs/01261_preferred_list.do b/01_data/012_programs/01261_preferred_list.do index fadd1fb..9df15f0 100644 --- a/01_data/012_programs/01261_preferred_list.do +++ b/01_data/012_programs/01261_preferred_list.do @@ -1,281 +1,301 @@ -*==============================================================================* -* PROGRAM: SELECTS DATABASE FROM RAWFULL ACCORDING TO PREFERENCE -*==============================================================================* - -/* In rawfull, isidvars = countrycode idgrade year_assessment test nla_code subject - that is, proficiency is in the long format. Meanwhile, enrollment and population - are in the wide format. - - To get to a "photo" of learning poverty in the world, we need to pick a single - proficiency from each country (drop non-chosen proficiency observations), and - pair it with a single enrollment method (drop other enrollment variables) and - a single population metric (drop other population variables). - - This is what this program does - select a 'runname' out of rawfull, based on - specified preferences. -*/ - -cap program drop preferred_list -program define preferred_list, rclass - syntax , /// - RUNNAME(string) /// - TIMSS_subject(string) /// - [ /// - NLA_keep(string) /// - DROP_assessment(string) /// - ENROLLment(string) /// - POPulation(string) /// - EXCEPTION(string) /// - TIMEwindow(string) /// - COUNTRYfilter(string) /// - WORLDalso /// - ] - -/* The user must specify a number of options -(1) RUNNAME() - dictates the name of the run, and the resulting rawlatest file (i.e. preference1000) -(2) TIMSS_subject() - dictates either math or science for TIMSS. Either enter string "math" or "science" -(3) NLA_keep() - dictates that the countries in the list are to use the National Learning Assessment. This option takes nla_codes or countrycodes -(4) DROP_assessment() - dictates which assessments to disregard when calculating proficiency levels. This option takes assessment names (ie: SACMEQ) -(5) ENROLLment() - dictates which enrollment to use (options: "validated" or "interpolated" -(6) POPulation() - dictates which population to use (options: "10" "1014" "primary" "9plus") -(7) EXCEPTION() - takes assessments (ie: HND_2013_LLECE) that will trump preferred order to ease adding exceptions to the rule -(8) TIMEwindow() - option to be passed to population_weight program, to display a global number -(9) COUNTRYfilter() - option to be passed to population_weight program, to display a global number -(10) WORLDalso - option that displays table for WORLD also, when countryfilter is used -*/ - -qui { - - * Load rawfull.dta - use "${clone}/01_data/013_outputs/rawfull.dta", clear - - * Check TIMSS_SUBJECT() option - * Display error and exit if option not allowed - if inlist("`timss_subject'","math","science")==0 { - noi dis as error "TIMSS_SUBJECT must be either math or science. Try again." - break - } - else if "`timss_subject'" == "math" { - * Math is kept and science is dropped for TIMSS - drop if subject=="science" & test=="TIMSS" - } - else if "`timss_subject'" == "science" { - * Jordan is one exeption: always keep math, even if science is specified - * because it has no science data - drop if subject=="math" & test=="TIMSS" & countrycode!="JOR" - } - - * Keep only NLAs passed in NLA_KEEP option, dropping all others - levelsof nla_code if test == "NLA", local(all_nlas) - foreach this_nla_code of local all_nlas { - * If cannot find this nla_code in the list to keep - if strmatch("`nla_keep'", "*`this_nla_code'*")==0 { - drop if nla_code == "`this_nla_code'" - } - } - - * Drop assessments listed in DROP_ASSESSMENT option - * First, check if the option was used - if "`drop_assessment'" != "" { - * For each test found in rawfull - levelsof test, local(all_assessments) - foreach this_test of local all_assessments { - * Drop observations with this_test if it belongs to drop list - if strmatch("`drop_assessment'", "*`this_test'*") == 1 { - drop if test == "`this_test'" - } - } - } - - * Check ENROLLMENT() option - * Must be one of enrollment methods supported - * Assume "validated" as default if not specified - if "`enrollment'" == "validated" | "`enrollment'" == "" { - drop enrollment_interpolated* - rename enrollment_validated_* enrollment_* - } - else if "`enrollment'" == "interpolated" { - drop enrollment_validated* - rename enrollment_interpolated_* enrollment_* - } - else { - noi dis as error `"ENROLLMENT must be either "interpolated" or "validated". Try again."' - break - } - - * Check POPULATION() option - * Assume "1014" as default if not specified - if "`population'"=="" local population == "1014" - * Give error if option specified does not exist - if inlist("`population'","10","1014","primary","9plus") == 0 { - noi dis as error `"POPULATION method not supported. Try again (use: "10", "1014", "primary" or "9plus")."' - break - } - else { - foreach method in 10 1014 primary 9plus { - * Drop population variables that were not specified - if "`population'" != "`method'" drop population_*_`method' - } - } - * Rename the population variable - rename population_*_`population' population_${anchor_year}_* - * Given that this is simply always = $anchor_year and now appears in var name - drop year_population - - * Check EXCEPTION() option - * For as long as it's not empty, will read each surveyid in it and only - * keep that observation for that country - while "`exception'" != "" { - * Parsing out multiple surveyid passed as exceptions - gettoken this_surveyid exception : exception, parse(" ") - * Splitting countrycode from surveyid (first 3 letters) - local this_countrycode = substr("`this_surveyid'",1,3) - * Drop observations from this country that are not the given exception - drop if countrycode == "`this_countrycode'" & surveyid != "`this_surveyid'" - * Remove trailing characters after the parsing - local exception = trim("`exception'") - } - - *----------------- - * Grade Window - *----------------- - * Only assessments of grade 3-6 are considered, so drop all other grades that made it so far - keep if (idgrade>=3 & idgrade<=6) | missing(idgrade) | idgrade==-999 - * But after considering the assessment hierarchy, we will re-consider grade hierarchy - - *----------------- - * Time Window - *----------------- - * For multiple instances of the same test, the one closest to the anchor_year - * is preferred, any other is dropped. - * When tied, chose the least recent (ie: anchor_year=2015, 2015 > 2014 > 2016) - * which is why we subtract the .01 in the aux variable below - gen years_from_anchor = abs($anchor_year - year_assessment - .01) - bysort countrycode test: egen min_years_from_anchor = min(years_from_anchor) - * Will only keep the preferred year for each test (including test = "None") - keep if (years_from_anchor == min_years_from_anchor) - * Drop aux variables - drop *years_from_anchor - - *---------------------- - * Assessment Hierarchy - *---------------------- - * General rule: ILAs > RLAs > EGRA - * Exception: NLAs are treated as special case, since they trump all other selections - - * Dummies for each assessment (just to make the code more readable) - foreach assessment in NLA PIRLS TIMSS EGRA { - gen byte is_`assessment' = (test == "`assessment'") - } - * Regional Learning Assessments are bundled together - gen byte is_RLA = inlist(test,"LLECE","LLECE-T","PASEC","SACMEQ") - - * Preferred ranking of assessments: - gen int assessment_ranking = . - * 0. Countries without assessment data should be kept, as well as NLA observations - replace assessment_ranking = 0 if is_NLA - * 1. PIRLS from 2010 or more recent - replace assessment_ranking = 1 if (is_PIRLS & year_assessment >= 2010) - * 2. TIMSS from 2010 or more recent - replace assessment_ranking = 2 if (is_TIMSS & year_assessment >= 2010) - * 3. Regional Learning Assessment from 2010 or more recent - replace assessment_ranking = 3 if (is_RLA & year_assessment >= 2010) - * 4. PIRLS older than 2010 - replace assessment_ranking = 4 if (is_PIRLS & year_assessment < 2010) - * 5. TIMSS older than 2010 - replace assessment_ranking = 5 if (is_TIMSS & year_assessment < 2010) - * 6. Regional Learning Assessment older than 2010 - replace assessment_ranking = 6 if (is_RLA & year_assessment < 2010) - * 7. EGRAs - replace assessment_ranking = 7 if (is_EGRA) - * 8. No assessment data - replace assessment_ranking = 8 if test == "None" - - * Keep only the preferred assessment - bysort countrycode: egen min_assessment_ranking = min(assessment_ranking) - keep if (assessment_ranking == min_assessment_ranking) - * Drop aux variables - drop is_* *assessment_ranking - - * NOTE: there may be more than one grade assessed, which is taken care of in next step - - *----------------- - * Grade Hierarchy - *----------------- - * Grade 4 > Grade 5 > Grade 6 > Grade 3 - gen idgrade_ranking = . - replace idgrade_ranking = 1 if idgrade == 4 - replace idgrade_ranking = 2 if idgrade == 5 - replace idgrade_ranking = 3 if idgrade == 6 - replace idgrade_ranking = 4 if idgrade == 3 - - * Keep only the preferred grade - bysort countrycode: egen min_idgrade_ranking = min(idgrade_ranking) - keep if (idgrade_ranking == min_idgrade_ranking) - * Drop aux variables - drop *idgrade_ranking - - *------------------------------* - * Learning poverty calculation - *------------------------------* - * Adjusts non-proficiency by out-of school - foreach subgroup in all fe ma { - gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) - label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" - } - gen byte lp_by_gender_is_available = !missing(adj_nonprof_fe) & !missing(adj_nonprof_ma) - label var lp_by_gender_is_available "Dummy for availibility of Learning Poverty gender disaggregated" - - *----------------- - * Final touches - *----------------- - * Double check that each country appears only once by now - duplicates tag countrycode, gen(duplicates_countrycode) - * Will break here if not one observation per countrycode - assert duplicates_countrycode == 0 - * Now can drop this auxiliary variable - drop duplicates_countrycode - - * Order - order countrycode-year_assessment adj_nonprof* - - * Label the preference and creates a description - gen str preference = "`runname'" - gen preference_description = "`runname': TIMSS(`timss_subject')+NLA(`nla_keep')+Drop(`drop_assessment')+Population(`population')+Enrollment(`enrollment')" - label var preference "Preference" - label var preference_description "Preference description" - - * Auxiliary variables for generating weights - clonevar anchor_population = population_${anchor_year}_all - gen anchor_population_w_assessment = anchor_population * !missing(nonprof_all) - label var anchor_population_w_assessment "Anchor population * has data dummy" - - * Save - compress - - * If global is one, saves with metadata through edukitsave. Otherwise use regular save - if $use_edukit_save { - local description "Preference `runname' dataset. Contains one observation for each of the 217 countries, with corresponding proficiency, enrollment and population. Should be interpreted as a picture of learning poverty in the world, for a chosen angle - that is, rules for selecting the preferred assessment, enrollment and population definitions." - local sources "All population, enrollment and proficiency sources combined." - edukit_save, filename("preference`runname'") path("${clone}/01_data/013_outputs/") /// - idvars(countrycode preference) /// - varc("value *nonprof* enrollment_all enrollment_ma enrollment_fe population_* anchor_*; trait idgrade test nla_code subject *year* enrollment_flag enrollment_*source* *definition* *threshold* surveyid countryname region* adminregion* incomelevel* lendingtype* cmu preference_description lp_by_gender_is_available") /// - metadata("description `description'; sources `sources'; filename Rawlatest") - } - else save "${clone}/01_data/013_outputs/preference`runname'.dta", replace - - - *-------------------------------- - * Display number by region - *-------------------------------- - * NOTE: this section is only for display (makes QA easier), but will not be saved in the preference dataset - - * Displays global number based on population weights for given options - noi population_weights, preference(`runname') timewindow(`timewindow') countryfilter(`countryfilter') - - * Because most often we want to see both PART2 countries and WORLD, does worldalso when option is specified - if "`worldalso'" == "worldalso" noi population_weights, preference(`runname') timewindow(`timewindow') - -} - -end +*==============================================================================* +* PROGRAM: SELECTS DATABASE FROM RAWFULL ACCORDING TO PREFERENCE +*==============================================================================* + +/* In rawfull, isidvars = countrycode idgrade year_assessment test nla_code subject + that is, proficiency is in the long format. Meanwhile, enrollment and population + are in the wide format. + + To get to a "photo" of learning poverty in the world, we need to pick a single + proficiency from each country (drop non-chosen proficiency observations), and + pair it with a single enrollment method (drop other enrollment variables) and + a single population metric (drop other population variables). + + This is what this program does - select a 'runname' out of rawfull, based on + specified preferences. +*/ + +cap program drop preferred_list +program define preferred_list, rclass + syntax , /// + RUNNAME(string) /// + TIMSS_subject(string) /// + [ /// + NLA_keep(string) /// + DROP_assessment(string) /// + DROP_round(string) /// + ENROLLment(string) /// + POPulation(string) /// + EXCEPTION(string) /// + TIMEwindow(string) /// + COUNTRYfilter(string) /// + WORLDalso /// + ] + +/* The user must specify a number of options +(1) RUNNAME() - dictates the name of the run, and the resulting rawlatest file (i.e. preference1000) +(2) TIMSS_subject() - dictates either math or science for TIMSS. Either enter string "math" or "science" +(3) NLA_keep() - dictates that the countries in the list are to use the National Learning Assessment. This option takes nla_codes or countrycodes +(4) DROP_assessment() - dictates which assessments to disregard when calculating proficiency levels. This option takes assessment names (ie: SACMEQ) +(4) DROP_round() - dictates which rounds to disregard when calculating proficiency levels. This option takes assessment_year (ie: TIMSS_2011) +(5) ENROLLment() - dictates which enrollment to use (options: "validated" or "interpolated" +(6) POPulation() - dictates which population to use (options: "10" "1014" "primary" "9plus") +(7) EXCEPTION() - takes assessments (ie: HND_2013_LLECE) that will trump preferred order to ease adding exceptions to the rule +(8) TIMEwindow() - option to be passed to population_weight program, to display a global number +(9) COUNTRYfilter() - option to be passed to population_weight program, to display a global number +(10) WORLDalso - option that displays table for WORLD also, when countryfilter is used +*/ + +qui { + + * Load rawfull.dta + use "${clone}/01_data/013_outputs/rawfull.dta", clear + + * Check TIMSS_SUBJECT() option + * Display error and exit if option not allowed + if inlist("`timss_subject'","math","science")==0 { + noi dis as error "TIMSS_SUBJECT must be either math or science. Try again." + break + } + else if "`timss_subject'" == "math" { + * Math is kept and science is dropped for TIMSS + drop if subject=="science" & test=="TIMSS" + } + else if "`timss_subject'" == "science" { + * Jordan is one exeption: always keep math, even if science is specified + * because it has no science data + drop if subject=="math" & test=="TIMSS" & countrycode!="JOR" + } + + * Keep only NLAs passed in NLA_KEEP option, dropping all others + levelsof nla_code if test == "NLA", local(all_nlas) + foreach this_nla_code of local all_nlas { + * If cannot find this nla_code in the list to keep + if strmatch("`nla_keep'", "*`this_nla_code'*")==0 { + drop if nla_code == "`this_nla_code'" + } + } + + * Drop assessments listed in DROP_ASSESSMENT option + * First, check if the option was used + if "`drop_assessment'" != "" { + * For each test found in rawfull + levelsof test, local(all_assessments) + foreach this_test of local all_assessments { + * Drop observations with this_test if it belongs to drop list + if strmatch("`drop_assessment'", "*`this_test'*") == 1 { + drop if test == "`this_test'" + } + } + } + + * Drop surveys listed in DROP_ROUND option + * First, check if the option was used + if "`drop_round'" != "" { + * For each round found in rawfull + gen round = test + "_" + strofreal(year_assessment) + levelsof round, local(all_rounds) + foreach this_round of local all_rounds { + * Drop observations with this_round if it belongs to drop list + if strmatch("`drop_round'", "*`this_round'*") == 1 { + drop if round == "`this_round'" + } + } + } + + * Check ENROLLMENT() option + * Must be one of enrollment methods supported + * Assume "validated" as default if not specified + if "`enrollment'" == "validated" | "`enrollment'" == "" { + drop enrollment_interpolated* + rename enrollment_validated_* enrollment_* + } + else if "`enrollment'" == "interpolated" { + drop enrollment_validated* + rename enrollment_interpolated_* enrollment_* + } + else { + noi dis as error `"ENROLLMENT must be either "interpolated" or "validated". Try again."' + break + } + + * Check POPULATION() option + * Assume "1014" as default if not specified + if "`population'"=="" local population == "1014" + * Give error if option specified does not exist + if inlist("`population'","10","1014","primary","9plus") == 0 { + noi dis as error `"POPULATION method not supported. Try again (use: "10", "1014", "primary" or "9plus")."' + break + } + else { + foreach method in 10 1014 primary 9plus { + * Drop population variables that were not specified + if "`population'" != "`method'" drop population_*_`method' + } + } + * Rename the population variable + rename population_*_`population' population_${anchor_year}_* + * Given that this is simply always = $anchor_year and now appears in var name + drop year_population + + * Check EXCEPTION() option + * For as long as it's not empty, will read each surveyid in it and only + * keep that observation for that country + while "`exception'" != "" { + * Parsing out multiple surveyid passed as exceptions + gettoken this_surveyid exception : exception, parse(" ") + * Splitting countrycode from surveyid (first 3 letters) + local this_countrycode = substr("`this_surveyid'",1,3) + * Drop observations from this country that are not the given exception + drop if countrycode == "`this_countrycode'" & surveyid != "`this_surveyid'" + * Remove trailing characters after the parsing + local exception = trim("`exception'") + } + + *----------------- + * Grade Window + *----------------- + * Only assessments of grade 3-6 are considered, so drop all other grades that made it so far + keep if (idgrade>=3 & idgrade<=6) | missing(idgrade) | idgrade==-999 + * But after considering the assessment hierarchy, we will re-consider grade hierarchy + + *----------------- + * Time Window + *----------------- + * For multiple instances of the same test, the one closest to the anchor_year + * is preferred, any other is dropped. + * When tied, chose the most recent (ie: anchor_year=2015, 2015 > 2016 > 2014) + * which is why we add the .01 in the aux variable below + gen years_from_anchor = abs($anchor_year - year_assessment + .01) + bysort countrycode test: egen min_years_from_anchor = min(years_from_anchor) + * Will only keep the preferred year for each test (including test = "None") + keep if (years_from_anchor == min_years_from_anchor) + * Drop aux variables + drop *years_from_anchor + + *---------------------- + * Assessment Hierarchy + *---------------------- + * General rule: ILAs > RLAs > EGRA + * Exception: NLAs are treated as special case, since they trump all other selections + + * Dummies for each assessment (just to make the code more readable) + foreach assessment in NLA PIRLS TIMSS EGRA { + gen byte is_`assessment' = (test == "`assessment'") + } + * Regional Learning Assessments are bundled together + gen byte is_RLA = inlist(test,"LLECE","LLECE-T","PASEC","SACMEQ","SEA-PLM") + + * Originally anchor year of 2015, the exceptions were defined in relation to 2010 + * To make it flexible for future updates + local year_limit = $anchor_year - 5 + + * Preferred ranking of assessments: + gen int assessment_ranking = . + * 0. Countries without assessment data should be kept, as well as NLA observations + replace assessment_ranking = 0 if is_NLA + * 1. PIRLS from 2010 or more recent + replace assessment_ranking = 1 if (is_PIRLS & year_assessment >= `year_limit') + * 2. TIMSS from 2010 or more recent + replace assessment_ranking = 2 if (is_TIMSS & year_assessment >= `year_limit') + * 3. Regional Learning Assessment from 2010 or more recent + replace assessment_ranking = 3 if (is_RLA & year_assessment >= `year_limit') + * 4. PIRLS older than 2010 + replace assessment_ranking = 4 if (is_PIRLS & year_assessment < `year_limit') + * 5. TIMSS older than 2010 + replace assessment_ranking = 5 if (is_TIMSS & year_assessment < `year_limit') + * 6. Regional Learning Assessment older than 2010 + replace assessment_ranking = 6 if (is_RLA & year_assessment < `year_limit') + * 7. EGRAs + replace assessment_ranking = 7 if (is_EGRA) + * 8. No assessment data + replace assessment_ranking = 8 if test == "None" + + * Keep only the preferred assessment + bysort countrycode: egen min_assessment_ranking = min(assessment_ranking) + keep if (assessment_ranking == min_assessment_ranking) + * Drop aux variables + drop is_* *assessment_ranking + + * NOTE: there may be more than one grade assessed, which is taken care of in next step + + *----------------- + * Grade Hierarchy + *----------------- + * Grade 4 > Grade 5 > Grade 6 > Grade 3 + gen idgrade_ranking = . + replace idgrade_ranking = 1 if idgrade == 4 + replace idgrade_ranking = 2 if idgrade == 5 + replace idgrade_ranking = 3 if idgrade == 6 + replace idgrade_ranking = 4 if idgrade == 3 + + * Keep only the preferred grade + bysort countrycode: egen min_idgrade_ranking = min(idgrade_ranking) + keep if (idgrade_ranking == min_idgrade_ranking) + * Drop aux variables + drop *idgrade_ranking + + *------------------------------* + * Learning poverty calculation + *------------------------------* + * Adjusts non-proficiency by out-of school + foreach subgroup in all fe ma { + gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) + label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" + } + gen byte lp_by_gender_is_available = !missing(adj_nonprof_fe) & !missing(adj_nonprof_ma) + label var lp_by_gender_is_available "Dummy for availibility of Learning Poverty gender disaggregated" + + *----------------- + * Final touches + *----------------- + * Double check that each country appears only once by now + duplicates tag countrycode, gen(duplicates_countrycode) + * Will break here if not one observation per countrycode + assert duplicates_countrycode == 0 + * Now can drop this auxiliary variable + drop duplicates_countrycode + + * Order + order countrycode-year_assessment adj_nonprof* + + * Label the preference and creates a description + gen str preference = "`runname'" + gen preference_description = "`runname': TIMSS(`timss_subject')+NLA(`nla_keep')+Drop(`drop_assessment')+Population(`population')+Enrollment(`enrollment')" + label var preference "Preference" + label var preference_description "Preference description" + + * Auxiliary variables for generating weights + clonevar anchor_population = population_${anchor_year}_all + gen anchor_population_w_assessment = anchor_population * !missing(nonprof_all) + label var anchor_population_w_assessment "Anchor population * has data dummy" + + * Save + compress + + * If global is one, saves with metadata through edukitsave. Otherwise use regular save + if $use_edukit_save { + local description "Preference `runname' dataset. Contains one observation for each of the 217 countries, with corresponding proficiency, enrollment and population. Should be interpreted as a picture of learning poverty in the world, for a chosen angle - that is, rules for selecting the preferred assessment, enrollment and population definitions." + local sources "All population, enrollment and proficiency sources combined." + edukit_save, filename("preference`runname'") path("${clone}/01_data/013_outputs/") /// + idvars(countrycode preference) /// + varc("value *nonprof* fgt* enrollment_all enrollment_ma enrollment_fe population_* anchor_*; trait idgrade test nla_code subject *year* enrollment_flag enrollment_*source* *definition* *threshold* surveyid countryname region* adminregion* incomelevel* lendingtype* cmu preference_description lp_by_gender_is_available") /// + metadata("description `description'; sources `sources'; filename Rawlatest") + } + else save "${clone}/01_data/013_outputs/preference`runname'.dta", replace + + + *-------------------------------- + * Display number by region + *-------------------------------- + * NOTE: this section is only for display (makes QA easier), but will not be saved in the preference dataset + + * Displays global number based on population weights for given options + noi population_weights, preference(`runname') timewindow(`timewindow') countryfilter(`countryfilter') + + * Because most often we want to see both PART2 countries and WORLD, does worldalso when option is specified + if "`worldalso'" == "worldalso" noi population_weights, preference(`runname') timewindow(`timewindow') + +} + +end diff --git a/01_data/012_programs/01262_population_weights.do b/01_data/012_programs/01262_population_weights.do index 41abd6f..15a3d7e 100644 --- a/01_data/012_programs/01262_population_weights.do +++ b/01_data/012_programs/01262_population_weights.do @@ -1,136 +1,141 @@ -*==============================================================================* -* PROGRAM: SELECTS DATABASE FROM RAWFULL ACCORDING TO PREFERENCE -*==============================================================================* - -cap program drop population_weights -program define population_weights, rclass - syntax , [ /// - PREFERENCE(string) /// - TIMEwindow(string) /// - COUNTRYfilter(string) /// - ] - - qui { - - *----------------- - * Check options - *----------------- - * If no preference is specified, uses the file in memory - if "`preference'" == "" { - * Check that the file contains a unique preference, or the code should break - tab preference - assert `r(r)'==1 - noi disp as txt "Applying weights to the already loaded file" - } - * If preference is specified, open that file from outputs - else { - use "${clone}/01_data/013_outputs/preference`preference'.dta", clear - } - - * Generate dummy on whether assessment data in LP is inside TIMEWINDOW() - * note that missing data on LP is considered outside the TIMEWINDOW() - cap drop include_assessment - if "`timewindow'" == "" { - * If not specified, this becomes a simple test for whether LP data is available - gen byte include_assessment = !missing(adj_nonprof_all) - } - else { - * If specified, apply the condition to create the dummy - cap gen byte include_assessment = (`timewindow') & !missing(adj_nonprof_all) - if _rc != 0 { - noi di as err `"The option TIMEWINDOW() is incorrectly specified. Good example: timewindow(year_assessment>=2011)"' - break - } - } - - * Generate dummy on whether country is inside COUNTRYFILTER() - cap drop include_country - if "`countryfilter'" == "" { - * If not specified, all observations are included - gen byte include_country = 1 - } - else { - * If specified, apply the condition to create a dummy - cap gen byte include_country = (`countryfilter') - if _rc != 0 { - noi di as err `"The option COUNTRYFILTER() is incorrectly specified. Good example: countryfilter(incomelevel!="HIC" & lendingtype!="LNX")"' - break - } - } - - * A country learning poverty number is included only if it satisfies both the TIMEWINDOWN() and COUNTRYFILTER() - cap drop included_in_weights - gen byte included_in_weights = include_country * include_assessment - label var included_in_weights "Observation is considered for aggregation weights" - - * Before we can create weights for each aggregation, we need this aux var - * so that global is as much as a group as 'region' or 'incomelevel' - * and we can do a single loop - gen str global = "TOTAL" - - - *-------------------- - * Aggregation weights - *-------------------- - * For each possible aggregation level, the same calculation is performed - local possible_aggregations "global region adminregion incomelevel lendingtype" - foreach aggregation of local possible_aggregations { - - * Preemptly drop variable that will be created if they existed - foreach ending in n_countries total_population population_w_data coverage weight { - cap drop `aggregation'_`ending' - } - - * The number of countries that will be used in the aggregation - egen int `aggregation'_n_countries = total(included_in_weights), by(`aggregation') - label var `aggregation'_n_countries "Number of countries included in aggregation by `aggregation'" - - * Total population in the aggregation (ie: not excluded in the country filter) - egen `aggregation'_total_population = total(anchor_population * include_country), by(`aggregation') - label var `aggregation'_total_population "Total population represented in aggregation by `aggregation'" - - * Population in the aggregation for which we have and will use learning poverty data (ie: also in the time windown) - egen `aggregation'_population_w_data = total(anchor_population * included_in_weights), by(`aggregation') - label var `aggregation'_population_w_data "Population with learning poverty data in aggregation by `aggregation'" - - * The coverage is the ratio of population with data over total population - gen `aggregation'_coverage = `aggregation'_population_w_data / `aggregation'_total_population - label var `aggregation'_coverage "Population coverage in aggregation by `aggregation'" - - * The weight we want is the population included, scaled by coverage - * It is rounded to an integer number so it can be used as frequency weights - * and interpreted as number of late primary age children - gen long `aggregation'_weight = round(included_in_weights * anchor_population / `aggregation'_coverage) - label var `aggregation'_weight "Population scaled as weights for aggregation by `aggregation'" - - } - - * For global_weight, it was decided that we should use region_weights, - * ie: a country with missing data in SSA is proxied by SSA average, - * to avoid regional bias according to region _coverage - replace global_weight = region_weight - - * Drop excessive amount of auxiliary variables created - drop include_country include_assessment global - - - - *-------------------------------- - * Display number by region - *-------------------------------- - local aggregation "region" - if `"`countryfilter'"' == "" local countryfilter "none (WORLD)" - sum included_in_weights - local n_included_countries = r(sum) - - noi di "" - noi di as res "Learning Poverty Global Number" - noi di as res " preference: `preference'" - noi di as res `" time window: `timewindow'"' - noi di as res `" cty filters: `countryfilter'"' - noi di as res " # countries: `n_included_countries'" - noi tabstat adj_nonprof_all `aggregation'_population_w_data `aggregation'_total_population [fw = `aggregation'_weight], by(`aggregation') format(%20.1fc) - -} - -end +*==============================================================================* +* PROGRAM: SELECTS DATABASE FROM RAWFULL ACCORDING TO PREFERENCE +*==============================================================================* + +cap program drop population_weights +program define population_weights, rclass + syntax , [ /// + PREFERENCE(string) /// + TIMEwindow(string) /// + COUNTRYfilter(string) /// + combine_ida_blend /// + ] + + qui { + + *----------------- + * Check options + *----------------- + * If no preference is specified, uses the file in memory + if "`preference'" == "" { + * Check that the file contains a unique preference, or the code should break + tab preference + assert `r(r)'==1 + noi disp as txt "Applying weights to the already loaded file" + } + * If preference is specified, open that file from outputs + else { + use "${clone}/01_data/013_outputs/preference`preference'.dta", clear + } + + if "`combine_ida_blend'" == "combine_ida_blend" { + replace lendingtype = "IDXB" if inlist(lendingtype, "IDX", "IDB") + } + + * Generate dummy on whether assessment data in LP is inside TIMEWINDOW() + * note that missing data on LP is considered outside the TIMEWINDOW() + cap drop include_assessment + if "`timewindow'" == "" { + * If not specified, this becomes a simple test for whether LP data is available + gen byte include_assessment = !missing(adj_nonprof_all) + } + else { + * If specified, apply the condition to create the dummy + cap gen byte include_assessment = (`timewindow') & !missing(adj_nonprof_all) + if _rc != 0 { + noi di as err `"The option TIMEWINDOW() is incorrectly specified. Good example: timewindow(year_assessment>=2011)"' + break + } + } + + * Generate dummy on whether country is inside COUNTRYFILTER() + cap drop include_country + if "`countryfilter'" == "" { + * If not specified, all observations are included + gen byte include_country = 1 + } + else { + * If specified, apply the condition to create a dummy + cap gen byte include_country = (`countryfilter') + if _rc != 0 { + noi di as err `"The option COUNTRYFILTER() is incorrectly specified. Good example: countryfilter(incomelevel!="HIC" & lendingtype!="LNX")"' + break + } + } + + * A country learning poverty number is included only if it satisfies both the TIMEWINDOWN() and COUNTRYFILTER() + cap drop included_in_weights + gen byte included_in_weights = include_country * include_assessment + label var included_in_weights "Observation is considered for aggregation weights" + + * Before we can create weights for each aggregation, we need this aux var + * so that global is as much as a group as 'region' or 'incomelevel' + * and we can do a single loop + gen str global = "TOTAL" + + + *-------------------- + * Aggregation weights + *-------------------- + * For each possible aggregation level, the same calculation is performed + local possible_aggregations "global region adminregion incomelevel lendingtype" + foreach aggregation of local possible_aggregations { + + * Preemptly drop variable that will be created if they existed + foreach ending in n_countries total_population population_w_data coverage weight { + cap drop `aggregation'_`ending' + } + + * The number of countries that will be used in the aggregation + egen int `aggregation'_n_countries = total(included_in_weights), by(`aggregation') + label var `aggregation'_n_countries "Number of countries included in aggregation by `aggregation'" + + * Total population in the aggregation (ie: not excluded in the country filter) + egen `aggregation'_total_population = total(anchor_population * include_country), by(`aggregation') + label var `aggregation'_total_population "Total population represented in aggregation by `aggregation'" + + * Population in the aggregation for which we have and will use learning poverty data (ie: also in the time windown) + egen `aggregation'_population_w_data = total(anchor_population * included_in_weights), by(`aggregation') + label var `aggregation'_population_w_data "Population with learning poverty data in aggregation by `aggregation'" + + * The coverage is the ratio of population with data over total population + gen `aggregation'_coverage = `aggregation'_population_w_data / `aggregation'_total_population + label var `aggregation'_coverage "Population coverage in aggregation by `aggregation'" + + * The weight we want is the population included, scaled by coverage + * It is rounded to an integer number so it can be used as frequency weights + * and interpreted as number of late primary age children + gen long `aggregation'_weight = round(included_in_weights * anchor_population / `aggregation'_coverage) + label var `aggregation'_weight "Population scaled as weights for aggregation by `aggregation'" + + } + + * For global_weight, it was decided that we should use region_weights, + * ie: a country with missing data in SSA is proxied by SSA average, + * to avoid regional bias according to region _coverage + replace global_weight = region_weight + + * Drop excessive amount of auxiliary variables created + drop include_assessment global + + + + *-------------------------------- + * Display number by region + *-------------------------------- + local aggregation "region" + if `"`countryfilter'"' == "" local countryfilter "none (WORLD)" + sum included_in_weights + local n_included_countries = r(sum) + + noi di "" + noi di as res "Learning Poverty Global Number" + noi di as res " preference: `preference'" + noi di as res `" time window: `timewindow'"' + noi di as res `" cty filters: `countryfilter'"' + noi di as res " # countries: `n_included_countries'" + noi tabstat adj_nonprof_all `aggregation'_population_w_data `aggregation'_total_population [fw = `aggregation'_weight], by(`aggregation') format(%20.1fc) + +} + +end diff --git a/01_data/012_programs/0126_create_rawlatest.do b/01_data/012_programs/0126_create_rawlatest.do index 1cc5f49..d959f0e 100644 --- a/01_data/012_programs/0126_create_rawlatest.do +++ b/01_data/012_programs/0126_create_rawlatest.do @@ -57,7 +57,7 @@ qui { timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") worldalso - noi disp as err _newline "Sensitivity analysis: change population definitions (1014, 10, primary, 9plus)" + noi disp as err _newline "Sensitivity analysis: change population definitions (1014, 10, 0516, primary, 9plus)" * Preference = 1005_1014 noi preferred_list, runname("1005_1014") timss_subject(science) drop_assessment(SACMEQ EGRA) /// @@ -71,6 +71,12 @@ qui { enrollment(validated) population(10) exception("HND_2013_LLECE CHL_2013_LLECE COL_2013_LLECE") /// timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") + * Preference = 1005_0516 + noi preferred_list, runname("1005_0516") timss_subject(science) drop_assessment(SACMEQ EGRA) /// + nla_keep(AFG_2 CHN BGD_3 IND_4 PAK_3 LKA_3 VNM_1 UGA_2 ETH_3 COD KHM_1 MYS_1 KGZ_1 MDG_1 MLI_1) /// + enrollment(validated) population(0516) exception("HND_2013_LLECE CHL_2013_LLECE COL_2013_LLECE") /// + timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") + * Preference = 1005_primary noi preferred_list, runname("1005_primary") timss_subject(science) drop_assessment(SACMEQ EGRA) /// nla_keep(AFG_2 CHN BGD_3 IND_4 PAK_3 LKA_3 VNM_1 UGA_2 ETH_3 COD KHM_1 MYS_1 KGZ_1 MDG_1 MLI_1) /// @@ -84,9 +90,9 @@ qui { timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") - noi disp as err _newline "Sensitivity analysis: change reporting window (8, 6 and 4 years)" + noi disp as err _newline "Sensitivity analysis: change reporting window (Latest available, 8, 6 and 4 years)" - foreach year in 2011 2013 2015 { + foreach year in 2001 2011 2013 2015 { * Displays output for chosen preferences for PART2 countries (`year') noi population_weights, preference(1005) timewindow(year_assessment>=`year') countryfilter(lendingtype!="LNX") diff --git a/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv b/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv index 9eb91c8..6343867 100644 --- a/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv +++ b/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv @@ -1,298 +1,311 @@ -countrycode,country,test,idgrade,n_res,comparable,spell,note -ARE,United Arab Emirates,PIRLS,4,1,1,2011-2016, -ARE,United Arab Emirates,TIMSS,4,1,1,2011-2015, -ARM,Armenia,TIMSS,4,1,1,2003-2011,2007 results not comparable to 2003 or 2011 -ARM,Armenia,TIMSS,4,1,0,2003-2007, -ARM,Armenia,TIMSS,4,1,0,2011-2015, -ARM,Armenia,TIMSS,4,1,0,2007-2011, -AUS,Australia,PIRLS,4,1,1,2011-2016, -AUS,Australia,TIMSS,4,1,1,2003-2015, -AUS,Australia,TIMSS,4,1,1,2011-2015, -AUS,Australia,TIMSS,4,1,1,2003-2007, -AUS,Australia,TIMSS,4,1,1,2003-2011, -AUS,Australia,TIMSS,4,1,1,2007-2015, -AUS,Australia,TIMSS,4,1,1,2007-2011, -AUT,Austria,PIRLS,4,1,1,2006-2011, -AUT,Austria,PIRLS,4,1,1,2006-2016, -AUT,Austria,PIRLS,4,1,1,2011-2016, -AUT,Austria,TIMSS,4,1,1,2007-2011, -AZE,Azerbaijan,PIRLS,4,1,1,2011-2016, -BGR,Bulgaria,PIRLS,4,1,1,2001-2011, -BGR,Bulgaria,PIRLS,4,1,1,2006-2011, -BGR,Bulgaria,PIRLS,4,1,1,2001-2006, -BGR,Bulgaria,PIRLS,4,1,1,2011-2016, -BGR,Bulgaria,PIRLS,4,1,1,2001-2016, -BGR,Bulgaria,PIRLS,4,1,1,2006-2016, -BHR,Bahrain,TIMSS,4,1,1,2011-2015, -CAN,Canada,PIRLS,4,1,1,2011-2016, -CHL,Chile,TIMSS,4,1,1,2011-2015, -CZE,Czech Republic,PIRLS,4,1,1,2011-2016, -CZE,Czech Republic,TIMSS,4,1,1,2007-2011, -CZE,Czech Republic,TIMSS,4,1,1,2007-2015, -CZE,Czech Republic,TIMSS,4,1,1,2011-2015, -DEU,Germany,PIRLS,4,1,1,2006-2016, -DEU,Germany,PIRLS,4,1,1,2001-2006, -DEU,Germany,PIRLS,4,1,1,2001-2011, -DEU,Germany,PIRLS,4,1,1,2001-2016, -DEU,Germany,PIRLS,4,1,1,2011-2016, -DEU,Germany,PIRLS,4,1,1,2006-2011, -DEU,Germany,TIMSS,4,1,1,2007-2015, -DEU,Germany,TIMSS,4,1,1,2011-2015, -DEU,Germany,TIMSS,4,1,1,2007-2011, -DNK,Denmark,PIRLS,4,1,1,2006-2011, -DNK,Denmark,PIRLS,4,1,1,2011-2016, -DNK,Denmark,PIRLS,4,1,1,2006-2016, -DNK,Denmark,TIMSS,4,1,1,2007-2011, -DNK,Denmark,TIMSS,4,1,1,2011-2015, -DNK,Denmark,TIMSS,4,1,1,2007-2015, -ESP,Spain,PIRLS,4,1,1,2006-2011, -ESP,Spain,PIRLS,4,1,1,2011-2016, -ESP,Spain,PIRLS,4,1,1,2006-2016, -ESP,Spain,TIMSS,4,1,1,2011-2015, -FIN,Finland,PIRLS,4,1,1,2011-2016, -FIN,Finland,TIMSS,4,1,1,2011-2015, -FRA,France,PIRLS,4,1,1,2001-2006, -FRA,France,PIRLS,4,1,1,2011-2016, -FRA,France,PIRLS,4,1,1,2001-2011, -FRA,France,PIRLS,4,1,1,2006-2016, -FRA,France,PIRLS,4,1,1,2006-2011, -FRA,France,PIRLS,4,1,1,2001-2016, -GEO,Georgia,PIRLS,4,1,1,2006-2011, -GEO,Georgia,PIRLS,4,1,1,2011-2016, -GEO,Georgia,PIRLS,4,1,1,2006-2016, -GEO,Georgia,TIMSS,4,1,1,2007-2015, -GEO,Georgia,TIMSS,4,1,1,2011-2015, -GEO,Georgia,TIMSS,4,1,1,2007-2011, -HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2011, -HKG,Hong Kong SAR,PIRLS,4,1,1,2006-2011, -HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2016, -HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2006, -HKG,Hong Kong SAR,PIRLS,4,1,1,2011-2016, -HKG,Hong Kong SAR,PIRLS,4,1,1,2006-2016, -HKG,Hong Kong SAR,TIMSS,4,1,1,2007-2011, -HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2007, -HKG,Hong Kong SAR,TIMSS,4,1,1,2011-2015, -HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2015, -HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2011, -HKG,Hong Kong SAR,TIMSS,4,1,1,2007-2015, -HRV,Croatia,TIMSS,4,1,1,2011-2015, -HUN,Hungary,PIRLS,4,1,1,2001-2006, -HUN,Hungary,PIRLS,4,1,1,2001-2016, -HUN,Hungary,PIRLS,4,1,1,2011-2016, -HUN,Hungary,PIRLS,4,1,1,2006-2016, -HUN,Hungary,PIRLS,4,1,1,2001-2011, -HUN,Hungary,PIRLS,4,1,1,2006-2011, -HUN,Hungary,TIMSS,4,1,1,2003-2011, -HUN,Hungary,TIMSS,4,1,1,2011-2015, -HUN,Hungary,TIMSS,4,1,1,2007-2011, -HUN,Hungary,TIMSS,4,1,1,2003-2007, -HUN,Hungary,TIMSS,4,1,1,2007-2015, -HUN,Hungary,TIMSS,4,1,1,2003-2015, -IDN,Indonesia,PIRLS,4,1,1,2006-2011, -IRL,Ireland,PIRLS,4,1,1,2011-2016, -IRL,Ireland,TIMSS,4,1,1,2011-2015, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2006-2011, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2006-2016, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2001-2006, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2011-2016, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2001-2016, -IRN,"Iran, Islamic Rep. of",PIRLS,4,1,1,2001-2011, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2007-2015, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2011-2015, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2003-2015, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2007-2011, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2003-2007, -IRN,"Iran, Islamic Rep. of",TIMSS,4,1,1,2003-2011, -ISL,Iceland,PIRLS,4,1,1,2001-2006, -ISR,Israel,PIRLS,4,1,0,2006-2011, -ISR,Israel,PIRLS,4,1,0,2001-2011, -ISR,Israel,PIRLS,4,1,1,2011-2016, -ISR,Israel,PIRLS,4,1,1,2001-2006, -ITA,Italy,PIRLS,4,1,1,2001-2011, -ITA,Italy,PIRLS,4,1,1,2001-2016, -ITA,Italy,PIRLS,4,1,1,2006-2011, -ITA,Italy,PIRLS,4,1,1,2006-2016, -ITA,Italy,PIRLS,4,1,1,2001-2006, -ITA,Italy,PIRLS,4,1,1,2011-2016, -ITA,Italy,TIMSS,4,1,1,2011-2015, -ITA,Italy,TIMSS,4,1,1,2007-2015, -ITA,Italy,TIMSS,4,1,1,2007-2011, -ITA,Italy,TIMSS,4,1,1,2003-2015, -ITA,Italy,TIMSS,4,1,1,2003-2011, -ITA,Italy,TIMSS,4,1,1,2003-2007, -JPN,Japan,TIMSS,4,1,1,2007-2015, -JPN,Japan,TIMSS,4,1,1,2011-2015, -JPN,Japan,TIMSS,4,1,1,2003-2007, -JPN,Japan,TIMSS,4,1,1,2007-2011, -JPN,Japan,TIMSS,4,1,1,2003-2011, -JPN,Japan,TIMSS,4,1,1,2003-2015, -KAZ,Kazakhstan,TIMSS,4,1,1,2011-2015, -KAZ,Kazakhstan,TIMSS,4,1,0,2007-2011, -KOR,"Korea, Rep. of",TIMSS,4,1,1,2011-2015, -KWT,Kuwait,PIRLS,4,1,1,2001-2006, -KWT,Kuwait,TIMSS,4,1,0,2007-2011, -KWT,Kuwait,TIMSS,4,1,1,2011-2015, -LTU,Lithuania,PIRLS,4,1,1,2001-2016, -LTU,Lithuania,PIRLS,4,1,1,2001-2011, -LTU,Lithuania,PIRLS,4,1,1,2006-2016, -LTU,Lithuania,PIRLS,4,1,1,2011-2016, -LTU,Lithuania,PIRLS,4,1,1,2001-2006, -LTU,Lithuania,PIRLS,4,1,1,2006-2011, -LTU,Lithuania,TIMSS,4,1,1,2007-2015, -LTU,Lithuania,TIMSS,4,1,1,2007-2011, -LTU,Lithuania,TIMSS,4,1,1,2011-2015, -LTU,Lithuania,TIMSS,4,1,1,2003-2011, -LTU,Lithuania,TIMSS,4,1,1,2003-2007, -LTU,Lithuania,TIMSS,4,1,1,2003-2015, -LVA,Latvia,PIRLS,4,1,1,2001-2006, -MAR,Morocco,PIRLS,4,1,1,2011-2016, -MAR,Morocco,PIRLS,4,1,1,2001-2006, -MAR,Morocco,PIRLS,4,1,0,2001-2011, -MAR,Morocco,PIRLS,4,1,0,2006-2011, -MAR,Morocco,TIMSS,4,1,0,2003-2011, -MAR,Morocco,TIMSS,4,1,1,2011-2015, -MAR,Morocco,TIMSS,4,1,0,2007-2011, -MAR,Morocco,TIMSS,4,1,1,2003-2007, -MDA,"Moldova, Rep. of",PIRLS,4,1,1,2001-2006, -MKD,"Macedonia, Rep. of",PIRLS,4,1,1,2001-2006, -MLT,Malta,PIRLS,4,1,1,2011-2016, -NLD,Netherlands,PIRLS,4,1,1,2001-2006, -NLD,Netherlands,PIRLS,4,1,1,2006-2016, -NLD,Netherlands,PIRLS,4,1,1,2011-2016, -NLD,Netherlands,PIRLS,4,1,1,2001-2016, -NLD,Netherlands,PIRLS,4,1,1,2001-2011, -NLD,Netherlands,PIRLS,4,1,1,2006-2011, -NLD,Netherlands,TIMSS,4,1,1,2011-2015, -NLD,Netherlands,TIMSS,4,1,1,2003-2011, -NLD,Netherlands,TIMSS,4,1,1,2003-2015, -NLD,Netherlands,TIMSS,4,1,1,2007-2011, -NLD,Netherlands,TIMSS,4,1,1,2007-2015, -NLD,Netherlands,TIMSS,4,1,1,2003-2007, -NOR,Norway,PIRLS,4,1,1,2006-2016, -NOR,Norway,PIRLS,4,1,1,2001-2016, -NOR,Norway,PIRLS,4,1,1,2001-2006, -NOR,Norway,PIRLS,4,1,1,2011-2016, -NOR,Norway,PIRLS,4,1,1,2006-2011, -NOR,Norway,PIRLS,4,1,1,2001-2011, -NOR,Norway,TIMSS,4,1,1,2011-2015, -NOR,Norway,TIMSS,4,1,1,2003-2007, -NOR,Norway,TIMSS,4,1,1,2007-2011, -NOR,Norway,TIMSS,4,1,1,2003-2011, -NOR,Norway,TIMSS,4,1,1,2003-2015, -NOR,Norway,TIMSS,4,1,1,2007-2015, -NZL,New Zealand,PIRLS,4,1,1,2006-2016, -NZL,New Zealand,PIRLS,4,1,1,2011-2016, -NZL,New Zealand,PIRLS,4,1,1,2006-2011, -NZL,New Zealand,PIRLS,4,1,1,2001-2011, -NZL,New Zealand,PIRLS,4,1,1,2001-2016, -NZL,New Zealand,PIRLS,4,1,1,2001-2006, -NZL,New Zealand,TIMSS,4,1,1,2007-2015, -NZL,New Zealand,TIMSS,4,1,1,2003-2011, -NZL,New Zealand,TIMSS,4,1,1,2003-2015, -NZL,New Zealand,TIMSS,4,1,1,2003-2007, -NZL,New Zealand,TIMSS,4,1,1,2007-2011, -NZL,New Zealand,TIMSS,4,1,1,2011-2015, -OMN,Oman,PIRLS,4,1,1,2011-2016, -OMN,Oman,TIMSS,4,1,1,2011-2015, -POL,Poland,PIRLS,4,1,0,2011-2016, -POL,Poland,PIRLS,4,1,1,2006-2011, -POL,Poland,PIRLS,4,1,1,2006-2016, -POL,Poland,TIMSS,4,1,0,2011-2015, -PRT,Portugal,PIRLS,4,1,1,2011-2016, -PRT,Portugal,TIMSS,4,1,1,2011-2015, -QAT,Qatar,PIRLS,4,1,0,2006-2011, -QAT,Qatar,PIRLS,4,1,1,2011-2016, -QAT,Qatar,TIMSS,4,1,0,2007-2011, -QAT,Qatar,TIMSS,4,1,1,2011-2015, -ROU,Romania,PIRLS,4,1,1,2006-2011, -ROU,Romania,PIRLS,4,1,1,2001-2011, -ROU,Romania,PIRLS,4,1,1,2001-2006, -RUS,Russian Federation,PIRLS,4,1,1,2006-2016, -RUS,Russian Federation,PIRLS,4,1,1,2001-2016, -RUS,Russian Federation,PIRLS,4,1,1,2001-2011, -RUS,Russian Federation,PIRLS,4,1,1,2006-2011, -RUS,Russian Federation,PIRLS,4,1,1,2011-2016, -RUS,Russian Federation,PIRLS,4,1,1,2001-2006, -RUS,Russian Federation,TIMSS,4,1,1,2003-2007, -RUS,Russian Federation,TIMSS,4,1,1,2007-2011, -RUS,Russian Federation,TIMSS,4,1,1,2003-2015, -RUS,Russian Federation,TIMSS,4,1,1,2011-2015, -RUS,Russian Federation,TIMSS,4,1,1,2007-2015, -RUS,Russian Federation,TIMSS,4,1,1,2003-2011, -SAU,Saudi Arabia,PIRLS,4,1,1,2011-2016, -SAU,Saudi Arabia,TIMSS,4,1,1,2011-2015, -SGP,Singapore,PIRLS,4,1,1,2006-2011, -SGP,Singapore,PIRLS,4,1,1,2001-2011, -SGP,Singapore,PIRLS,4,1,1,2001-2016, -SGP,Singapore,PIRLS,4,1,1,2011-2016, 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+ARE,United Arab Emirates,TIMSS,4,1,1,2011-2015, +ARM,Armenia,TIMSS,4,1,1,2003-2011,2007 results not comparable to 2003 or 2011 +ARM,Armenia,TIMSS,4,1,0,2003-2007, +ARM,Armenia,TIMSS,4,1,0,2011-2015, +ARM,Armenia,TIMSS,4,1,0,2007-2011, +ARM,Armenia,TIMSS,4,1,0,2007-2015, +AUS,Australia,PIRLS,4,1,1,2011-2016, +AUS,Australia,TIMSS,4,1,1,2003-2015, +AUS,Australia,TIMSS,4,1,1,2011-2015, +AUS,Australia,TIMSS,4,1,1,2003-2007, +AUS,Australia,TIMSS,4,1,1,2003-2011, +AUS,Australia,TIMSS,4,1,1,2007-2015, +AUS,Australia,TIMSS,4,1,1,2007-2011, +AUT,Austria,PIRLS,4,1,1,2006-2011, +AUT,Austria,PIRLS,4,1,1,2006-2016, +AUT,Austria,PIRLS,4,1,1,2011-2016, +AUT,Austria,TIMSS,4,1,1,2007-2011, +AZE,Azerbaijan,PIRLS,4,1,1,2011-2016, +BGR,Bulgaria,PIRLS,4,1,1,2001-2011, +BGR,Bulgaria,PIRLS,4,1,1,2006-2011, +BGR,Bulgaria,PIRLS,4,1,1,2001-2006, +BGR,Bulgaria,PIRLS,4,1,1,2011-2016, +BGR,Bulgaria,PIRLS,4,1,1,2001-2016, +BGR,Bulgaria,PIRLS,4,1,1,2006-2016, +BHR,Bahrain,TIMSS,4,1,1,2011-2015, 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+FRA,France,PIRLS,4,1,1,2001-2006, +FRA,France,PIRLS,4,1,1,2011-2016, +FRA,France,PIRLS,4,1,1,2001-2011, +FRA,France,PIRLS,4,1,1,2006-2016, +FRA,France,PIRLS,4,1,1,2006-2011, +FRA,France,PIRLS,4,1,1,2001-2016, +GEO,Georgia,PIRLS,4,1,1,2006-2011, +GEO,Georgia,PIRLS,4,1,1,2011-2016, +GEO,Georgia,PIRLS,4,1,1,2006-2016, +GEO,Georgia,TIMSS,4,1,1,2007-2015, +GEO,Georgia,TIMSS,4,1,1,2011-2015, +GEO,Georgia,TIMSS,4,1,1,2007-2011, +HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2011, +HKG,Hong Kong SAR,PIRLS,4,1,1,2006-2011, +HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2016, +HKG,Hong Kong SAR,PIRLS,4,1,1,2001-2006, +HKG,Hong Kong SAR,PIRLS,4,1,1,2011-2016, +HKG,Hong Kong SAR,PIRLS,4,1,1,2006-2016, +HKG,Hong Kong SAR,TIMSS,4,1,1,2007-2011, +HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2007, +HKG,Hong Kong SAR,TIMSS,4,1,1,2011-2015, +HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2015, +HKG,Hong Kong SAR,TIMSS,4,1,1,2003-2011, +HKG,Hong Kong SAR,TIMSS,4,1,1,2007-2015, +HRV,Croatia,TIMSS,4,1,1,2011-2015, 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+JPN,Japan,TIMSS,4,1,1,2003-2011, +JPN,Japan,TIMSS,4,1,1,2003-2015, +KAZ,Kazakhstan,TIMSS,4,1,1,2011-2015, +KAZ,Kazakhstan,TIMSS,4,1,0,2007-2011, +KAZ,Kazakhstan,TIMSS,4,1,0,2007-2015, +KOR,"Korea, Rep. of",TIMSS,4,1,1,2011-2015, +KWT,Kuwait,PIRLS,4,1,1,2001-2006, +KWT,Kuwait,TIMSS,4,1,0,2007-2011, +KWT,Kuwait,TIMSS,4,1,0,2007-2015, +KWT,Kuwait,TIMSS,4,1,1,2011-2015, +LTU,Lithuania,PIRLS,4,1,1,2001-2016, +LTU,Lithuania,PIRLS,4,1,1,2001-2011, +LTU,Lithuania,PIRLS,4,1,1,2006-2016, +LTU,Lithuania,PIRLS,4,1,1,2011-2016, +LTU,Lithuania,PIRLS,4,1,1,2001-2006, +LTU,Lithuania,PIRLS,4,1,1,2006-2011, +LTU,Lithuania,TIMSS,4,1,1,2007-2015, +LTU,Lithuania,TIMSS,4,1,1,2007-2011, +LTU,Lithuania,TIMSS,4,1,1,2011-2015, +LTU,Lithuania,TIMSS,4,1,1,2003-2011, +LTU,Lithuania,TIMSS,4,1,1,2003-2007, +LTU,Lithuania,TIMSS,4,1,1,2003-2015, +LVA,Latvia,PIRLS,4,1,1,2001-2006, +MAR,Morocco,PIRLS,4,1,1,2011-2016, +MAR,Morocco,PIRLS,4,1,1,2001-2006, +MAR,Morocco,PIRLS,4,1,0,2001-2011, +MAR,Morocco,PIRLS,4,1,0,2001-2016, +MAR,Morocco,PIRLS,4,1,0,2006-2011, +MAR,Morocco,PIRLS,4,1,0,2006-2016, +MAR,Morocco,TIMSS,4,1,1,2003-2007, +MAR,Morocco,TIMSS,4,1,0,2003-2011, +MAR,Morocco,TIMSS,4,1,0,2003-2015, +MAR,Morocco,TIMSS,4,1,0,2007-2011, +MAR,Morocco,TIMSS,4,1,0,2007-2015, +MAR,Morocco,TIMSS,4,1,1,2011-2015, +MDA,"Moldova, Rep. of",PIRLS,4,1,1,2001-2006, +MKD,"Macedonia, Rep. of",PIRLS,4,1,1,2001-2006, +MLT,Malta,PIRLS,4,1,1,2011-2016, +NLD,Netherlands,PIRLS,4,1,1,2001-2006, +NLD,Netherlands,PIRLS,4,1,1,2006-2016, +NLD,Netherlands,PIRLS,4,1,1,2011-2016, +NLD,Netherlands,PIRLS,4,1,1,2001-2016, +NLD,Netherlands,PIRLS,4,1,1,2001-2011, +NLD,Netherlands,PIRLS,4,1,1,2006-2011, +NLD,Netherlands,TIMSS,4,1,1,2011-2015, +NLD,Netherlands,TIMSS,4,1,1,2003-2011, +NLD,Netherlands,TIMSS,4,1,1,2003-2015, +NLD,Netherlands,TIMSS,4,1,1,2007-2011, +NLD,Netherlands,TIMSS,4,1,1,2007-2015, +NLD,Netherlands,TIMSS,4,1,1,2003-2007, +NOR,Norway,PIRLS,4,1,1,2006-2016, +NOR,Norway,PIRLS,4,1,1,2001-2016, +NOR,Norway,PIRLS,4,1,1,2001-2006, +NOR,Norway,PIRLS,4,1,1,2011-2016, +NOR,Norway,PIRLS,4,1,1,2006-2011, +NOR,Norway,PIRLS,4,1,1,2001-2011, +NOR,Norway,TIMSS,4,1,1,2011-2015, +NOR,Norway,TIMSS,4,1,1,2003-2007, +NOR,Norway,TIMSS,4,1,1,2007-2011, +NOR,Norway,TIMSS,4,1,1,2003-2011, +NOR,Norway,TIMSS,4,1,1,2003-2015, +NOR,Norway,TIMSS,4,1,1,2007-2015, +NZL,New Zealand,PIRLS,4,1,1,2006-2016, +NZL,New Zealand,PIRLS,4,1,1,2011-2016, +NZL,New Zealand,PIRLS,4,1,1,2006-2011, +NZL,New Zealand,PIRLS,4,1,1,2001-2011, +NZL,New Zealand,PIRLS,4,1,1,2001-2016, +NZL,New Zealand,PIRLS,4,1,1,2001-2006, +NZL,New Zealand,TIMSS,4,1,1,2007-2015, +NZL,New Zealand,TIMSS,4,1,1,2003-2011, +NZL,New Zealand,TIMSS,4,1,1,2003-2015, +NZL,New Zealand,TIMSS,4,1,1,2003-2007, +NZL,New Zealand,TIMSS,4,1,1,2007-2011, +NZL,New Zealand,TIMSS,4,1,1,2011-2015, +OMN,Oman,PIRLS,4,1,1,2011-2016, +OMN,Oman,TIMSS,4,1,1,2011-2015, +POL,Poland,PIRLS,4,1,0,2011-2016, +POL,Poland,PIRLS,4,1,1,2006-2011, +POL,Poland,PIRLS,4,1,0,2006-2016, +POL,Poland,TIMSS,4,1,0,2011-2015, +PRT,Portugal,PIRLS,4,1,1,2011-2016, +PRT,Portugal,TIMSS,4,1,1,2011-2015, +QAT,Qatar,PIRLS,4,1,0,2006-2011, +QAT,Qatar,PIRLS,4,1,0,2006-2016, +QAT,Qatar,PIRLS,4,1,1,2011-2016, +QAT,Qatar,TIMSS,4,1,0,2007-2011, +QAT,Qatar,TIMSS,4,1,0,2007-2015, +QAT,Qatar,TIMSS,4,1,1,2011-2015, +ROU,Romania,PIRLS,4,1,1,2006-2011, +ROU,Romania,PIRLS,4,1,1,2001-2011, +ROU,Romania,PIRLS,4,1,1,2001-2006, +RUS,Russian Federation,PIRLS,4,1,1,2006-2016, +RUS,Russian Federation,PIRLS,4,1,1,2001-2016, +RUS,Russian Federation,PIRLS,4,1,1,2001-2011, +RUS,Russian Federation,PIRLS,4,1,1,2006-2011, +RUS,Russian Federation,PIRLS,4,1,1,2011-2016, +RUS,Russian Federation,PIRLS,4,1,1,2001-2006, +RUS,Russian Federation,TIMSS,4,1,1,2003-2007, +RUS,Russian Federation,TIMSS,4,1,1,2007-2011, +RUS,Russian Federation,TIMSS,4,1,1,2003-2015, +RUS,Russian Federation,TIMSS,4,1,1,2011-2015, +RUS,Russian Federation,TIMSS,4,1,1,2007-2015, +RUS,Russian Federation,TIMSS,4,1,1,2003-2011, +SAU,Saudi Arabia,PIRLS,4,1,1,2011-2016, +SAU,Saudi Arabia,TIMSS,4,1,1,2011-2015, +SGP,Singapore,PIRLS,4,1,1,2006-2011, +SGP,Singapore,PIRLS,4,1,1,2001-2011, +SGP,Singapore,PIRLS,4,1,1,2001-2016, +SGP,Singapore,PIRLS,4,1,1,2011-2016, +SGP,Singapore,PIRLS,4,1,1,2001-2006, +SGP,Singapore,PIRLS,4,1,1,2006-2016, +SGP,Singapore,TIMSS,4,1,1,2007-2015, +SGP,Singapore,TIMSS,4,1,1,2003-2011, +SGP,Singapore,TIMSS,4,1,1,2003-2007, +SGP,Singapore,TIMSS,4,1,1,2003-2015, +SGP,Singapore,TIMSS,4,1,1,2011-2015, +SGP,Singapore,TIMSS,4,1,1,2007-2011, +SRB,Serbia,TIMSS,4,1,1,2011-2015, +SVK,Slovak Republic,PIRLS,4,1,1,2001-2016, +SVK,Slovak Republic,PIRLS,4,1,1,2001-2006, +SVK,Slovak Republic,PIRLS,4,1,1,2001-2011, +SVK,Slovak Republic,PIRLS,4,1,1,2011-2016, +SVK,Slovak Republic,PIRLS,4,1,1,2006-2011, +SVK,Slovak Republic,PIRLS,4,1,1,2006-2016, +SVK,Slovak Republic,TIMSS,4,1,1,2007-2011, +SVK,Slovak Republic,TIMSS,4,1,1,2007-2015, +SVK,Slovak Republic,TIMSS,4,1,1,2011-2015, +SVN,Slovenia,PIRLS,4,1,1,2006-2011, +SVN,Slovenia,PIRLS,4,1,1,2001-2016, +SVN,Slovenia,PIRLS,4,1,1,2011-2016, +SVN,Slovenia,PIRLS,4,1,1,2001-2006, +SVN,Slovenia,PIRLS,4,1,1,2006-2016, +SVN,Slovenia,PIRLS,4,1,1,2001-2011, +SVN,Slovenia,TIMSS,4,1,1,2003-2011, +SVN,Slovenia,TIMSS,4,1,1,2003-2015, +SVN,Slovenia,TIMSS,4,1,1,2007-2011, +SVN,Slovenia,TIMSS,4,1,1,2007-2015, +SVN,Slovenia,TIMSS,4,1,1,2011-2015, +SVN,Slovenia,TIMSS,4,1,1,2003-2007, +SWE,Sweden,PIRLS,4,1,1,2001-2006, +SWE,Sweden,PIRLS,4,1,1,2011-2016, +SWE,Sweden,PIRLS,4,1,1,2001-2016, +SWE,Sweden,PIRLS,4,1,1,2006-2016, +SWE,Sweden,PIRLS,4,1,1,2001-2011, +SWE,Sweden,PIRLS,4,1,1,2006-2011, +SWE,Sweden,TIMSS,4,1,1,2007-2015, +SWE,Sweden,TIMSS,4,1,1,2007-2011, +SWE,Sweden,TIMSS,4,1,1,2011-2015, +TTO,Trinidad and Tobago,PIRLS,4,1,1,2011-2016, +TTO,Trinidad and Tobago,PIRLS,4,1,1,2006-2011, +TTO,Trinidad and Tobago,PIRLS,4,1,1,2006-2016, +TUN,Tunisia,TIMSS,4,1,1,2003-2007, +TUN,Tunisia,TIMSS,4,1,1,2007-2011, +TUN,Tunisia,TIMSS,4,1,1,2003-2011, +TUR,Turkey,TIMSS,4,1,1,2011-2015, +TWN,Chinese Taipei,PIRLS,4,1,1,2006-2016, +TWN,Chinese Taipei,PIRLS,4,1,1,2011-2016, +TWN,Chinese Taipei,PIRLS,4,1,1,2006-2011, +TWN,Chinese Taipei,TIMSS,4,1,1,2007-2011, +TWN,Chinese Taipei,TIMSS,4,1,1,2011-2015, +TWN,Chinese Taipei,TIMSS,4,1,1,2007-2015, +TWN,Chinese Taipei,TIMSS,4,1,1,2003-2007, +TWN,Chinese Taipei,TIMSS,4,1,1,2003-2015, +TWN,Chinese Taipei,TIMSS,4,1,1,2003-2011, +USA,United States,PIRLS,4,1,1,2011-2016, +USA,United States,PIRLS,4,1,1,2001-2011, +USA,United States,PIRLS,4,1,1,2006-2016, +USA,United States,PIRLS,4,1,1,2006-2011, +USA,United States,PIRLS,4,1,1,2001-2006, +USA,United States,PIRLS,4,1,1,2001-2016, +USA,United States,TIMSS,4,1,1,2007-2015, +USA,United States,TIMSS,4,1,1,2007-2011, +USA,United States,TIMSS,4,1,1,2003-2011, +USA,United States,TIMSS,4,1,1,2011-2015, +USA,United States,TIMSS,4,1,1,2003-2007, +USA,United States,TIMSS,4,1,1,2003-2015, +YEM,Yemen,TIMSS,4,1,0,2003-2007, +YEM,Yemen,TIMSS,4,1,0,2003-2011, +YEM,Yemen,TIMSS,4,1,1,2007-2011, +ZAF,Republic of South Africa,PIRLS,5,1,1,2011-2016, +ZAF,Republic of South Africa,PIRLS,5,1,0,2006-2011, +ZAF,Republic of South Africa,PIRLS,5,1,0,2006-2016, diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005.md diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005_incomelevel.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_incomelevel.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005_incomelevel.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_incomelevel.md diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005_initial_poverty_level.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_initial_poverty_level.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_unweigthed_pref1005_initial_poverty_level.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_initial_poverty_level.md diff --git a/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005.md new file mode 100644 index 0000000..a56a25d --- /dev/null +++ b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005.md @@ -0,0 +1,8 @@ +region|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|--- +EAS|.9928978 |-.3658836186|-.0824895129|.1592597663|.6605101824| .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 +ECS|.5349326134|-.5838775635|.0302856453|.3472499847|.4202709198|.580406189|.7594089508|.993560791|1.250891089|1.509056091 +LCN|.8755936027|-.1170823202|.1996558011|.348995477|.5060367584|.6157662272|.6422293782|1.290894747|1.808432937|2.01589489 +MEA|1.167431593|-1.07779026|-1.07779026|-.8095169067|.8876209259|1.019738555|1.163887024|1.74887085|1.836900711|2.795577049 +SAS|.9928978 |-.3658836186|-.0824895129|.1592597663|.6605101824| .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 +SSF|1.548801184|-.0847494975|-.050043378|.1894073486|.8552856445|1.01135397|1.907785177|2.14191103|2.655576229|3.458876371 diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_incomelevel.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_incomelevel.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_incomelevel.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_incomelevel.md diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_initial_poverty_level.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_initial_poverty_level.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_initial_poverty_level.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_initial_poverty_level.md diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_newsimnum_no_ISR.md b/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_newsimnum_no_ISR.md similarity index 100% rename from 02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_newsimnum_no_ISR.md rename to 02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_newsimnum_no_ISR.md diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_incomelevel.md new file mode 100644 index 0000000..30792df --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.043361526|.1098833084|.3254736066|.4754783809|.5318005085|.6033495069|.6557021141|2.009296656|3.399290323 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|14|1.378473878|-.4393558502|-.050043378|.8876209259|.9247283936|1.130933285|1.250891089|1.907785177|2.919117689|3.224466324 +UMC|41|.9880903363|-.2613609433|.0741177574|.2364507467|.5226565599|.7594101429|1.056338549|1.509055614|2.138032198|2.494049072 +Overall|70|1.010739446|-.289137274|.0222196989|.3507395983|.5500851274|.7409020066|.9560632706|1.367117047|2.196035862|2.729081392 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_initial_poverty_level.md new file mode 100644 index 0000000..bd0dad6 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|19|.5455414653|-.2613609433|.0302852634|.0741177574|.214461863|.4615398347|.5804069638|.7594101429|.9935610294|1.405039191 +25-50% Learning Poverty|22|1.427832127|-.4284128547|-.1170823202|.3472485542|.6422293782|1.305986047|1.928851843|2.722119093|3.224466324|3.339704037 +50-75% Learning Poverty|18|.9704443812|-.5878426433|.2364507467|.4894169271|.6157662272|.7845796943|.8876209259|1.163887024|1.907785177|2.283068895 +75-100% Learning Poverty|11|1.046015263|-.1648273468|-.050043378|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.494049072 +Overall|70|1.010739446|-.3826458454|.0243606456|.2727313638|.4497689605|.8827914|1.224389315|1.658524394|2.109634399|2.409986019 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_region.md new file mode 100644 index 0000000..f42cbbd --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_unweighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|70|1.010739446|-.3174599409|-.0095117679|.2822549343|.6538476348|.8624457121|1.211952686|1.562487721|2.152261019|2.418192625 +ECS|23|.6218341589|-.2613609433|-.1330456734|.0741177574|.3472485542|.5741840601|.6788892746|.9935610294|1.250891089|1.509055614 +LCN|18|.8755936027|-.1170823202|.1996558011|.4615398347|.5226565599|.6241406202|.6422293782|1.102916479|1.808432937|2.138032198 +MEA|10|1.377310038|-.6244363785|-.3020915985|.3613967896|1.025753975|1.500394583|2.059985638|2.539322376|2.857347012|3.109872818 +SAS|70|1.010739446|-.3174599409|-.0095117679|.2822549343|.6538476348|.8624457121|1.211952686|1.562487721|2.152261019|2.418192625 +SSF|18|1.344092727|-.5878426433|-.050043378|.1894073486|.8552856445|1.01135397|1.907785177|2.14191103|3.224466324|3.45887661 +Overall|70|1.010739446|-.3174599409|-.0095117679|.2822549343|.6538476348|.8624457121|1.211952686|1.562487721|2.152261019|2.418192625 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_incomelevel.md new file mode 100644 index 0000000..cdfdef3 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.1170823202|.0303592682|.4615398347|.4894169271|.5318005085|.5741840601|.6325149536|.6788892746|3.339704037 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|14|1.378473878|-.050043378|.6157662272|.8876209259|.9247283936|1.097979426|1.250891089|1.907785177|2.722119093|3.553827763 +UMC|41|.9880903363|-.4284128547|.0302852634|.2364507467|.5804069638|.8552856445|1.079627514|1.509055614|2.138032198|2.494049072 +Overall|70|1.010739446|-.3196510077|.1183477268|.3701776564|.5859015584|.790466845|.9655374289|1.363804579|1.966578007|2.786441326 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_initial_poverty_level.md new file mode 100644 index 0000000..2a3a202 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|19|.5455414653|-.1330456734|.0303592682|.214461863|.5741840601|.5804069638|.6788892746|.8586306572|1.250891089|3.098399878 +25-50% Learning Poverty|22|1.427832127|-.5838775635|-.1444602013|-.1170823202|.6325149536|1.056338549|1.509055614|1.928851843|2.722119093|3.339704037 +50-75% Learning Poverty|18|.9704443812|-.0847494975|.2364507467|.4894169271|.5226565599|.6157662272|.8552856445|1.097979426|1.808432937|2.14191103 +75-100% Learning Poverty|11|1.046015263|-.1648273468|-.050043378|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.494049072 +Overall|70|1.010739446|-.2673109472|.0157762561|.1648921967|.5204122663|.7931854725|1.110869527|1.419195533|1.996052861|2.833314657 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_region.md new file mode 100644 index 0000000..c094079 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|70|1.010739446|-.3969896138|.0063469228|.1969453543|.5540554523|.7848812342|.9347607493|1.476319551|1.955615044|2.601178646 +ECS|23|.6218341589|-.5838775635|-.1444602013|.1917898208|.5741840601|.6788892746|.8586306572|1.056338549|1.250891089|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.3489952981|.5060367584|.6157662272|.6422293782|1.290894747|1.808432937|2.01589489 +MEA|10|1.377310038|-.4393558502|-.1648273468|-.1648273468|-.1648273468|.8876209259|1.163887024|1.836902142|2.795576096|2.919117689 +SAS|70|1.010739446|-.3969896138|.0063469228|.1969453543|.5540554523|.7848812342|.9347607493|1.476319551|1.955615044|2.601178646 +SSF|18|1.344092727|-.5878426433|-.050043378|.1121804342|.8552856445|.9247283936|1.097979426|1.928851843|2.494049072|3.45887661 +Overall|70|1.010739446|-.3969896138|.0063469228|.1969453543|.5540554523|.7848812342|.9347607493|1.476319551|1.955615044|2.601178646 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_incomelevel.md new file mode 100644 index 0000000..fbc9196 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|9|.6976592541|-.1170823202|.0303592682|.1894073486|.4615398347|.4894169271|.5741840601|.6325149536|.6788892746|3.339704037 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|14|1.161264658|-.4757461548|-.4393558502|.6157662272|.8876209259|1.01135397|1.163887024|1.250891089|2.722119093|2.919117689 +UMC|42|.8916711807|-.4284128547|.0302852634|.214461863|.4158546329|.7366419435|1.056338549|1.509055614|2.138032198|2.494049072 +Overall|70|.8741492033|-.4092391431|-.0926777497|.2644093633|.4774643481|.7011274695|.9413838983|1.244947791|1.987422943|2.647418737 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_initial_poverty_level.md new file mode 100644 index 0000000..011f206 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.5597435236|-.0513802059|.0522385128|.2031258345|.4386972189|.5772955418|.7191497087|.9260958433|1.32796514 +25-50% Learning Poverty|20|1.196086407|-.5061452389|-.1307712644|.2683279514|.6373721361|1.079627514|1.672978878|2.086672068|2.758847713|3.320166111 +50-75% Learning Poverty|18|.7282813191|-.5878426433|-.0847494975|.3427832425|.5226565599|.6648199558|.8552856445|1.097979426|1.808432937|2.14191103 +75-100% Learning Poverty|12|1.337386727|-.1648273468|-.050043378|.1121804342|.1996558011|1.201800823|1.893757463|2.138032198|2.494049072|2.919117689 +Overall|70|.8741492033|-.4839552939|-.0824148729|.1989656091|.4087663889|.8107837439|1.187510252|1.450520754|1.945417881|2.379234791 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_region.md new file mode 100644 index 0000000..21c00bc --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_unweighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|70|.8741492033|-.5220160484|-.121817179|.1557680815|.5140486956|.7655034661|.9704791903|1.529829025|1.879181623|2.362168312 +ECS|24|.7740775943|-.2613609433|-.1330456734|.1917898208|.3472485542|.5772955418|.7594101429|.9935610294|1.405039191|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.4615398347|.5226565599|.6241406202|.6422293782|1.102916479|1.808432937|2.138032198 +MEA|13|1.025630474|-2.254277468|-.8095169067|-.4393558502|.8876209259|1.163887024|1.836902142|2.795576096|2.919117689|3.300628185 +SAS|70|.8741492033|-.5220160484|-.121817179|.1557680815|.5140486956|.7655034661|.9704791903|1.529829025|1.879181623|2.362168312 +SSF|13|.8671020269|-.0847494975|-.050043378|.1121804342|.3427832425|.8552856445|.9247283936|1.907785177|1.928851843|2.14191103 +Overall|70|.8741492033|-.5220160484|-.121817179|.1557680815|.5140486956|.7655034661|.9704791903|1.529829025|1.879181623|2.362168312 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_incomelevel.md new file mode 100644 index 0000000..d0eee8b --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|9|.6976592541|-.1170823202|.0303592682|.1894073486|.4615398347|.4894169271|.5741840601|.5741840601|.6788892746|.6788892746 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|14|1.161264658|-.4757461548|-.050043378|.6157662272|.9061746597|1.01135397|1.130933285|1.250891089|1.907785177|2.722119093 +UMC|42|.8916711807|-.4284128547|.0522015095|.2364507467|.5226565599|.8552856445|1.056338549|1.509055614|2.01589489|2.494049072 +Overall|70|.8741492033|-.4092391431|-.0016655065|.2776026726|.5452562571|.7723136544|.9347931147|1.237448096|1.751273751|2.265914202 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_initial_poverty_level.md new file mode 100644 index 0000000..449d5b5 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.2613609433|.0303592682|.1917898208|.5741840601|.5804069638|.6788892746|.8586306572|1.250891089|3.098399878 +25-50% Learning Poverty|20|1.196086407|-.4284128547|-.1444602013|-.1170823202|.2683279514|.6422293782|1.056338549|1.509055614|1.928851843|2.722119093 +50-75% Learning Poverty|18|.7282813191|-.4757461548|-.0847494975|.3427832425|.4894169271|.6157662272|.8552856445|1.097979426|1.808432937|1.907785177 +75-100% Learning Poverty|12|1.337386727|-.1648273468|-.050043378|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.494049072 +Overall|70|.8741492033|-.347669065|-.0629718602|.1287201941|.4007945061|.6661894321|.9692310691|1.283463478|1.740043402|2.581130028 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_region.md new file mode 100644 index 0000000..9ee3f21 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_rmlastsacmeq_sim_weighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|70|.8741492033|-.3642804921|-.0569865331|.1778119206|.3921268284|.7468756437|.8795090914|1.296661973|1.856238127|2.330761433 +ECS|24|.7740775943|-.5838775635|-.1444602013|.1917898208|.5741840601|.6788892746|.8586306572|1.056338549|1.405039191|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.3489952981|.5060367584|.6157662272|.6422293782|1.290894747|1.808432937|2.01589489 +MEA|13|1.025630474|-.8095169067|-.4393558502|-.1648273468|-.1648273468|.8876209259|1.163887024|1.163887024|2.795576096|3.300628185 +SAS|70|.8741492033|-.3642804921|-.0569865331|.1778119206|.3921268284|.7468756437|.8795090914|1.296661973|1.856238127|2.330761433 +SSF|13|.8671020269|-.0847494975|-.050043378|.1121804342|.3427832425|.8552856445|.9247283936|1.907785177|1.928851843|2.14191103 +Overall|70|.8741492033|-.3642804921|-.0569865331|.1778119206|.3921268284|.7468756437|.8795090914|1.296661973|1.856238127|2.330761433 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_incomelevel.md new file mode 100644 index 0000000..b966490 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.043361526|.1098833084|.3254736066|.4754783809|.5318005085|.6033495069|.6557021141|2.009296656|3.399290323 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|15|1.254859209|-.4757461548|-.2446996123|.6157662272|.9061746597|1.097979426|1.207389116|1.907785177|2.820618391|3.224466324 +UMC|42|.8704405427|-.4284128547|.0302852634|.214461863|.4158546329|.7366419435|1.056338549|1.509055614|2.138032198|2.494049072 +Overall|72|.9211502075|-.3958661258|-.0441854857|.287145704|.48874107|.7264293432|.952487886|1.376597762|2.184752464|2.732697487 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_initial_poverty_level.md new file mode 100644 index 0000000..3160bd5 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.5597435236|-.0513802059|.0522385128|.2031258345|.4386972189|.5772955418|.7191497087|.9260958433|1.32796514 +25-50% Learning Poverty|22|1.35060823|-.4284128547|-.1170823202|.3472485542|.6422293782|1.305986047|1.928851843|2.722119093|3.224466324|3.339704037 +50-75% Learning Poverty|19|.894329071|-.5878426433|-.0847494975|.3427832425|.5226565599|.713873744|.8876209259|1.163887024|1.907785177|2.283068895 +75-100% Learning Poverty|11|1.046015263|-.1648273468|-.050043378|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.494049072 +Overall|72|.9211502075|-.4666953385|-.0800573975|.2282097936|.421086818|.8505729437|1.209903479|1.627983332|2.072589636|2.37285614 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_region.md new file mode 100644 index 0000000..ca5524b --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_unweighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|72|.9211502075|-.4772844613|-.0698195621|.1779700518|.4472616017|.8008588552|1.170198083|1.510556817|2.110159636|2.350965977 +ECS|23|.6218341589|-.2613609433|-.1330456734|.0741177574|.3472485542|.5741840601|.6788892746|.9935610294|1.250891089|1.509055614 +LCN|18|.8755936027|-.1170823202|.1996558011|.4615398347|.5226565599|.6241406202|.6422293782|1.102916479|1.808432937|2.138032198 +MEA|12|.732560277|-2.254277468|-.8095169067|-.4393558502|-.1648273468|1.025753975|1.836902142|2.283068895|2.795576096|2.919117689 +SAS|72|.9211502075|-.4772844613|-.0698195621|.1779700518|.4472616017|.8008588552|1.170198083|1.510556817|2.110159636|2.350965977 +SSF|17|1.483696818|-.0847494975|.1121804342|.3427832425|.8552856445|1.097979426|1.928851843|2.14191103|3.224466324|3.45887661 +Overall|72|.9211502075|-.4772844613|-.0698195621|.1779700518|.4472616017|.8008588552|1.170198083|1.510556817|2.110159636|2.350965977 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.md new file mode 100644 index 0000000..e329f08 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.1170823202|.0303592682|.4615398347|.4894169271|.5318005085|.5741840601|.6325149536|.6788892746|3.339704037 +LIC|5|.2407225519|-.5878426433|-.376334995|-.1648273468|-.1247884184|-.0847494975|.0137154683|.1121804342|1.020516157|1.928851843 +LMC|15|1.254859209|-.4757461548|-.050043378|.6157662272|.9247283936|1.097979426|1.163887024|1.907785177|2.722119093|3.224466324 +UMC|42|.8704405427|-.4284128547|.0522015095|.2364507467|.5226565599|.8552856445|1.056338549|1.509055614|1.893757463|2.494049072 +Overall|72|.9211502075|-.4061051309|-.0018926328|.3188706338|.5568434596|.795638144|.9393742085|1.373377323|1.836959362|2.724421501 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.md new file mode 100644 index 0000000..253a91d --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.2613609433|.0303592682|.214461863|.5741840601|.5804069638|.6788892746|.8586306572|1.250891089|3.098399878 +25-50% Learning Poverty|22|1.35060823|-.4284128547|-.1444602013|-.1170823202|.6422293782|1.056338549|1.509055614|1.928851843|2.722119093|3.339704037 +50-75% Learning Poverty|19|.894329071|-.4757461548|-.0847494975|.3427832425|.4894169271|.6157662272|.8552856445|1.097979426|1.808432937|2.14191103 +75-100% Learning Poverty|11|1.046015263|-.1648273468|-.050043378|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.494049072 +Overall|72|.9211502075|-.3542302847|-.0657174513|.1313929558|.5153869987|.787766099|1.101320028|1.406948566|1.983097553|2.827393532 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_incomelevel.md new file mode 100644 index 0000000..b66751b --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.043361526|.1098833084|.3254736066|.4754783809|.5318005085|.6033495069|.6557021141|2.009296656|3.399290323 +LIC|7|1.923312664|-.5878426433|-.1648273468|-.0847494975|-.0847494975|.1121804342|1.928851843|1.928851843|6.005954266|6.253621578 +LMC|19|2.089959383|-.4757461548|-.050043378|.8876209259|1.097979426|1.250891089|2.722119093|3.224466324|4.267488003|4.595072269 +UMC|46|1.286958933|-.4284128547|.0741177574|.2364507467|.5804069638|.8569581509|1.405039191|1.893757463|2.283068895|3.383987665 +Overall|82|1.489150405|-.4060327113|.0293126144|.3707685471|.6307544708|.8450033665|1.657164931|2.054105997|3.027293682|3.911439657 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_initial_poverty_level.md new file mode 100644 index 0000000..b6c6e84 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.5597435236|-.0513802059|.0522385128|.2031258345|.4386972189|.5772955418|.7191497087|.9260958433|1.32796514 +25-50% Learning Poverty|23|1.47778511|-.4284128547|-.1170823202|.3472485542|1.056338549|1.509055614|1.928851843|2.795576096|3.300628185|3.383987665 +50-75% Learning Poverty|24|1.914967179|-.4757461548|.2364507467|.5226565599|.713873744|.9928001761|1.808432937|2.14191103|4.595072269|5.881882191 +75-100% Learning Poverty|15|2.270070314|-.1648273468|.0310685281|.1996558011|1.201800823|1.893757463|2.316040516|2.919117689|4.404980659|6.253621578 +Overall|82|1.489150405|-.4260815084|.0295164436|.2996351421|.7746132612|1.167265773|1.634786606|2.120413065|3.302351236|4.138542652 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_region.md new file mode 100644 index 0000000..5cfbbe3 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_unweighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_u_10|delta_reg_u_20|delta_reg_u_30|delta_reg_u_40|delta_reg_u_50|delta_reg_u_60|delta_reg_u_70|delta_reg_u_80|delta_reg_u_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|82|1.489150405|-.4474446774|-.0149299586|.3971174359|.9693316221|1.278265357|1.724974036|2.304561377|2.972390175|3.583505869 +ECS|24|.7740775943|-.2613609433|-.1330456734|.1917898208|.3472485542|.5772955418|.7594101429|.9935610294|1.405039191|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.4615398347|.5226565599|.6241406202|.6422293782|1.102916479|1.808432937|2.138032198 +MEA|13|1.025630474|-2.254277468|-.8095169067|-.4393558502|.8876209259|1.163887024|1.836902142|2.795576096|2.919117689|3.300628185 +SAS|82|1.489150405|-.4474446774|-.0149299586|.3971174359|.9693316221|1.278265357|1.724974036|2.304561377|2.972390175|3.583505869 +SSF|25|2.887688875|-.050043378|.2660952806|.9247283936|1.91831851|2.494049072|3.421432018|4.267488003|5.4907341|6.253621578 +Overall|82|1.489150405|-.4474446774|-.0149299586|.3971174359|.9693316221|1.278265357|1.724974036|2.304561377|2.972390175|3.583505869 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_incomelevel.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_incomelevel.md new file mode 100644 index 0000000..bd98154 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_incomelevel.md @@ -0,0 +1,7 @@ +incomelevel|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +HIC|10|.9737809896|-.1170823202|.0303592682|.4615398347|.4894169271|.5318005085|.5741840601|.6325149536|.6788892746|3.339704037 +LIC|7|1.923312664|-.5878426433|-.376334995|-.1648273468|-.1247884184|.0137154683|1.020516157|1.928851843|3.967402935|6.129787922 +LMC|19|2.089959383|-.4757461548|.6157662272|.8876209259|1.097979426|1.250891089|2.722119093|2.919117689|3.88318944|4.542472839 +UMC|46|1.286958933|-.4284128547|.0522015095|.2364507467|.6422293782|.8586306572|1.478873134|1.808432937|2.139971733|3.098399878 +Overall|82|1.489150405|-.4150230587|.1435375065|.3805260956|.6637172103|.837536037|1.617486358|1.932661772|2.521707535|3.721206427 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_initial_poverty_level.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_initial_poverty_level.md new file mode 100644 index 0000000..2bf4734 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_initial_poverty_level.md @@ -0,0 +1,7 @@ +initial_poverty_level|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +0-25% Learning Poverty|20|.4055505395|-.2613609433|.0303592682|.1917898208|.5741840601|.5804069638|.6788892746|.8586306572|1.250891089|3.098399878 +25-50% Learning Poverty|23|1.47778511|-.4284128547|-.1444602013|.1894073486|.6422293782|1.056338549|1.509055614|1.928851843|2.722119093|3.339704037 +50-75% Learning Poverty|24|1.914967179|-.4757461548|.2364507467|.4894169271|.5226565599|.713873744|1.130933285|1.907785177|3.88318944|5.09958601 +75-100% Learning Poverty|15|2.270070314|-.1648273468|.1121804342|.1996558011|.9247283936|1.478873134|1.893757463|2.138032198|2.919117689|6.253621578 +Overall|82|1.489150405|-.3533052802|.0566112809|.2796708345|.6423127055|.9173169732|1.266278386|1.699921131|2.739144802|4.328970432 diff --git a/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_region.md b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_region.md new file mode 100644 index 0000000..cc35851 --- /dev/null +++ b/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_withoutliers_sim_weighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|82|1.489150405|-.3233734965|.0170328077|.4159353375|.8684633374|1.208683968|1.635900736|1.98835206|2.833551884|3.556695223 +ECS|24|.7740775943|-.5838775635|-.1444602013|.1917898208|.5741840601|.6788892746|.8586306572|1.056338549|1.405039191|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.3489952981|.5060367584|.6157662272|.6422293782|1.290894747|1.808432937|2.01589489 +MEA|13|1.025630474|-.8095169067|-.4393558502|-.1648273468|-.1648273468|.8876209259|1.163887024|1.163887024|2.795576096|3.300628185 +SAS|82|1.489150405|-.3233734965|.0170328077|.4159353375|.8684633374|1.208683968|1.635900736|1.98835206|2.833551884|3.556695223 +SSF|25|2.887688875|-.0847494975|.1894073486|.9247283936|1.928851843|2.317980051|3.383987665|3.88318944|5.09958601|6.253621578 +Overall|82|1.489150405|-.3233734965|.0170328077|.4159353375|.8684633374|1.208683968|1.635900736|1.98835206|2.833551884|3.556695223 diff --git a/02_simulation/021_rawdata/simulation_spells_weighted_region.md b/02_simulation/021_rawdata/simulation_spells_weighted_region.md new file mode 100644 index 0000000..165507e --- /dev/null +++ b/02_simulation/021_rawdata/simulation_spells_weighted_region.md @@ -0,0 +1,9 @@ +region|n_spells|delta_adj_pct|delta_reg_w_10|delta_reg_w_20|delta_reg_w_30|delta_reg_w_40|delta_reg_w_50|delta_reg_w_60|delta_reg_w_70|delta_reg_w_80|delta_reg_w_90 +---|---|---|---|---|---|---|---|---|---|---|--- +EAS|72|.9211502075|-.3395171762|-.0500752069|.1969645917|.515599966|.68058002|.873223722|1.340769291|1.852282286|2.555362701 +ECS|23|.6218341589|-.5838775635|-.1444602013|.1917898208|.5741840601|.6788892746|.8586306572|1.056338549|1.250891089|2.244492292 +LCN|18|.8755936027|-.1170823202|.1996558011|.3489952981|.5060367584|.6157662272|.6422293782|1.290894747|1.808432937|2.01589489 +MEA|12|.732560277|-.8095169067|-.4393558502|-.1648273468|-.1648273468|.3613967896|.8876209259|1.163887024|2.283068895|2.919117689 +SAS|72|.9211502075|-.3395171762|-.0500752069|.1969645917|.515599966|.68058002|.873223722|1.340769291|1.852282286|2.555362701 +SSF|17|1.483696818|-.0847494975|-.050043378|.1894073486|.8552856445|.9247283936|1.097979426|1.928851843|2.494049072|3.45887661 +Overall|72|.9211502075|-.3395171762|-.0500752069|.1969645917|.515599966|.68058002|.873223722|1.340769291|1.852282286|2.555362701 diff --git a/02_simulation/022_program/022_simulations.do b/02_simulation/022_program/022_simulations.do deleted file mode 100644 index c171f05..0000000 --- a/02_simulation/022_program/022_simulations.do +++ /dev/null @@ -1,65 +0,0 @@ -/*====================================================== -Author: Brian Stacy -Modified: Joao Pedro Azevedo - -This do file: -Simulations using both Growth Rates Calculated from Spells -=======================================================* -*/ -*produce simulation dataset using ado. - - -cd "${clone}\02_simulation\022_program - -/*Execute an ado file to produce the dataset for the simulations. The configuration for the ado file matches closesly -the configuration for the _preferred_list ado used to produce the raw latest. This is intentional as -1)Develops database for preferred list -The user must specify a number of options. -(1) preference() - which dictates which preference to use for the adjusted proficiency levels. Current options are 0,1,2,...,926. -(2) specialincludeassess() - which dictate which assessments to specifically include in spell calculations. This option takes assessment names -(3) specialincludegrade() - which dictate which grades to specifically include in spell calculations. This option takes grade names -(4) dropgrade() - which dictate which grades to not calculate proficiency levels. This option takes assessment names -(5) filename() - which dictates the name of the file produced to be used in the simulation. -(6) TIMSS_SUBJECT()-dictates either math or science for TIMSS. either enter string "math" or "science" -(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated for the spells -(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. -(9) ifspell() - if option to keep these units. Use regular stata if syntax -(10)ifsim() - if option to keep these units. Use regular stata if syntax -(11)POPULATION_2015() - enter Yes to fix population at 2015 levels. e.g. _simulation_dataset ,population_2015(Yes) /// - -*/ - -*Run simulation with tabulations done by region and growth rates calculated using regional growth -_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// - ifwindow(if assess_year>=2011) /// - ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1001) /// - specialincludeassess(PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// - filename(simfile_preference_1001_regional_growth) /// - usefile("${clone}/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a.md") /// - timss(science) enrollment(validated) population_2015(No) /// - groupingsim(region) groupingspells(region) growthdynamics(Yes) /// - percentile(50(10)90) - - -*Run simulation with tabulations done by region and growth rates calculated using growth rates by initial poverty level -_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// - ifwindow(if assess_year>=2011) /// - ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1001) /// - specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// - filename(simfile_preference_1001_initial_poverty_level_growth) /// - usefile("${clone}/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_initial_poverty_level.md") /// - timss(science) enrollment(validated) population_2015(No) /// - groupingsim(region) groupingspells(initial_poverty_level) growthdynamics(Yes) /// - percentile(50(10)90) - - -*Run simulation with tabulations done by region and growth rates calculated using growth rates by income level -_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// - ifwindow(if assess_year>=2011) /// - ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1001) /// - specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// - filename(simfile_preference_1001_incomelevel_growth) /// - usefile("${clone}/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_incomelevel.md") /// - timss(science) enrollment(validated) population_2015(No) /// - groupingsim(region) groupingspells(incomelevel) growthdynamics(Yes) /// - percentile(50(10)90) diff --git a/02_simulation/022_program/0231_custom_spells_no_ISR.do b/02_simulation/022_program/0231_custom_spells_no_ISR.do deleted file mode 100644 index f4685b8..0000000 --- a/02_simulation/022_program/0231_custom_spells_no_ISR.do +++ /dev/null @@ -1,283 +0,0 @@ -qui { - - cd "${clone}\02_simulation\022_program - - loc preference 1005 - loc enrollment validated - loc inputfolder clone - - use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear - gen year=year_assessment - *nla_code should be distributed over s rather than being available in each . - - *temporarily rename test to assessment and idgrade to grade, change back after merge. - rename test assessment - rename idgrade grade - - *------------------------------* - * Learning poverty calculation - *------------------------------* - * Adjusts non-proficiency by out-of school - foreach subgroup in all fe ma { - gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_validated_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) - label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" - } - - - gen adj_pct_reading_low_rawfull= 100-adj_nonprof_all - - merge m:1 countrycode using "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", keepusing(test idgrade incomelevel lendingtype nonprof_all) - gen adj_pct_reading_low= 100-adj_nonprof_all - *change name in rawlatest of assessment to test_rawlatest, and revert back to test from assessment - - rename adj_pct_reading_low adj_pct_reading_low_rawlatest - rename adj_pct_reading_low_rawfull adj_pct_reading_low - - - gen initial_poverty_level_temp=100-adj_pct_reading_low_rawlatest - cap gen initial_poverty_level="0-25% Learning Poverty" - cap replace initial_poverty_level="25-50% Learning Poverty" if initial_poverty_level_temp>=25 - cap replace initial_poverty_level="50-75% Learning Poverty" if initial_poverty_level_temp>=50 - cap replace initial_poverty_level="75-100% Learning Poverty" if initial_poverty_level_temp>=75 - - - rename test test_rawlatest - rename assessment test - *same for grade - rename idgrade idgrade_rawlatest - rename grade idgrade - - drop if _merge==1 - - gen enrollment=enrollment_validated_all - drop if subject!="science" & test=="TIMSS" & countrycode!="JOR" - - sort countrycode nla_code idgrade test subject - count - - - * Cleaning the data file - keep region regionname countrycode countryname incomelevel incomelevelname lendingtype /// - lendingtypename year_population year_assessment idgrade test source_assessment /// - enrollment /// - adj_pct_reading_low* subject nla_code initial_poverty_level - - * Generating all possible combinations of forward spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen spell_c1 = string(year_assessment[_n-1]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c2 = string(year_assessment[_n-2]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c3 = string(year_assessment[_n-3]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c4 = string(year_assessment[_n-4]) + "-" + string(year_assessment) - - reshape long spell_c, i(countrycode nla_code idgrade test subject year_assessment subject) j(lag) - ren spell_c spell - - *tag if actual spell: - gen spell_exists=(length(spell) == 9 ) - - ********************************************** - * Preparing the data for simulations: - ********************************************** - * The data should be restructured for unique identifiers: - sort countrycode nla_code idgrade test subject year_assessment spell lag - - * Rules for cleaning the spell data: - * Bringing in the list of countries and spells for which the data is not comparable: - - merge m:1 countrycode idgrade test year_assessment spell using "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", assert(master match using) keep(master match) keepusing(comparable) nogen - drop if comparable == 0 - - * Generating preferred consecutive spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : egen lag_min = min(lag) - * Keeping the comparable consecutive spells - keep if lag == lag_min - - * All comparable spells for TIMSS/PIRLS - assert comparable == 1 if !missing(comparable) - - * Annual change in enrollment, adjusted proficiency and proficiency - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen delta_adj_pct = (adj_pct_reading_low-adj_pct_reading_low[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen initial_adj_pct = adj_pct_reading_low[_n-1] - bysort countrycode nla_code idgrade test subject : gen final_adj_pct = adj_pct_reading_low - - - * Drop observations specified by [if] [in]. - if `"`ifspell'"'!="" { - di `"`ifspell'"' - keep `ifspell' - } - - /* weights */ - - if ("`weight'" == "") { - cap tempname wtg - cap gen `wtg' = 1 - local weight2 "" - loc weight "fw" - loc exp "=`wtg'" - } - - * Generating deltas in terms of reduction of gap to the frontier - gen gap_to_frontier = 100-adj_pct_reading_low - bysort countrycode nla_code idgrade test subject : gen red_gap_frontier = -1*(gap_to_frontier-gap_to_frontier[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen pct_red_gap = (red_gap_frontier/gap_to_frontier[_n-1]) - gen pct_red_gap_100 = pct_red_gap*100 - - - *Following threshold IIB specification as the baseline file to be used will be threshold IIB. - *Not using spell data for SACMEQ 2007 - SACMEQ 2013: - *replace delta_adj_pct = . if test == "SACMEQ" & year == 2013 - *replace pct_red_gap_100 = . if test == "SACMEQ" & year == 2013 - - * Generating categories of countries - gen catinitial = . - foreach var in 25 50 75 100 { - replace catinitial= `var' if initial_adj_pct <= `var' & catinitial== . - } - - gen initial_learning_poverty = 100-initial_adj_pct - - - **************************************************************** - * Identify only selected spells (n=71) - - keep if test != "no assessment" & test != "EGRA" & delta_adj_pct != . & delta_adj_pct > -2 & delta_adj_pct < 4 & test != "PASEC" & year_assessment>2000 & countrycode!="ISR" - bysort countrycode : gen tot = _N - gen wtg = 1/tot - **************************************************************** - * Results by Assessment - * Original - tabstat delta_adj_pct if test != "no assessment" & test != "EGRA" , by(test) stat(mean median min max N) - - -*********************************************' - - - *Calculating regional 90th, 8th and 70th percentiles with weights - gen delta_reg_50 = . - gen delta_reg_60 = . - gen delta_reg_70 = . - gen delta_reg_80 = . - gen delta_reg_90 = . - - gen delta_reg_w_50 = . - gen delta_reg_w_60 = . - gen delta_reg_w_70 = . - gen delta_reg_w_80 = . - gen delta_reg_w_85 = . - gen delta_reg_w_90 = . - - gen delta_reg_50_noSQ = . - gen delta_reg_60_noSQ = . - gen delta_reg_70_noSQ = . - gen delta_reg_80_noSQ = . - gen delta_reg_90_noSQ = . - - gen delta_reg_w_50_noSQ = . - gen delta_reg_w_60_noSQ = . - gen delta_reg_w_70_noSQ = . - gen delta_reg_w_80_noSQ = . - gen delta_reg_w_85_noSQ = . - gen delta_reg_w_90_noSQ = . - - gen threshold="III" - levelsof threshold, local(tr) - - foreach t of local tr { - - levelsof region, local(reg) - foreach r of local reg { - - count if !missing(delta_adj_pct ) & threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - local count=`r(N)' - - *Only make change to regional if we have at least 3 regional spells - - /* no weights */ - _pctile delta_adj_pct if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & countrycode!="ISR", /// - percentiles(50(10)90) - - replace delta_reg_50 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_60 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_70 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_80 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_90 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - - /* with weights */ - _pctile delta_adj_pct [aw = wtg] if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & countrycode!="ISR" , /// - percentiles(50(10)90) - - replace delta_reg_w_50 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_w_60 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_w_70 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_w_80 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - replace delta_reg_w_90 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR" - - /* no weights + NO SAQMEC */ - _pctile delta_adj_pct if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & countrycode!="ISR"& test != "SACMEQ", /// - percentiles(50(10)90) - - replace delta_reg_50_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_60_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_70_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_80_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_90_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - - /* with weights + NO SAQMEC*/ - _pctile delta_adj_pct [aw = wtg] if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & countrycode!="ISR"& test != "SACMEQ", /// - percentiles(50(10)90) - - replace delta_reg_w_50_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_w_60_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_w_70_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_w_80_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - replace delta_reg_w_90_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ" - - } - - } - - - ** with SACMEQ (no PASEC) - tabstat delta_adj_pct delta_reg_50 delta_reg_60 delta_reg_70 delta_reg_80 delta_reg_90 if test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR", by( region ) - - tabstat delta_adj_pct delta_reg_50_noSQ delta_reg_60_noSQ delta_reg_70_noSQ delta_reg_80_noSQ delta_reg_90_noSQ if test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ", by( region ) stat(mean) - - - ** No SAQMEC (no PASEC) - tabstat delta_adj_pct delta_reg_w_50 delta_reg_w_60 delta_reg_w_70 delta_reg_w_80 delta_reg_w_90 if test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR", by( region ) stat(mean N) - - tabstat delta_adj_pct delta_reg_w_50_noSQ delta_reg_w_60_noSQ delta_reg_w_70_noSQ delta_reg_w_80_noSQ delta_reg_w_90_noSQ if test != "EGRA" /// - & test != "PASEC" & test != "no assessment" & year_assessment>2000 & countrycode!="ISR"& test != "SACMEQ", by( region ) stat(mean) - -} diff --git a/02_simulation/022_program/_simulation_dataset.ado b/02_simulation/022_program/_simulation_dataset.ado deleted file mode 100644 index 563856e..0000000 --- a/02_simulation/022_program/_simulation_dataset.ado +++ /dev/null @@ -1,1061 +0,0 @@ -* v.0.4 JPAzevedo -** option SAVECTRYFILE created -* v.0.3.1 BStacy -** time region collumn prior to merge -* v.0.3 JPAzevedo -** create INPUTFOLDER Option default value CLONE -** add 25 as a CATINITIAL -* v.0.2 JPAzevedo -** add NOSIMULATION OPTION -** remove drop year_population from code (drop year_population is now a condition -** option all year_populations - - -/*Execute an ado file to produce the dataset for the simulations. The configuration for the ado file matches closesly -the configuration for the _preferred_list ado used to produce the raw latest. This is intentional as -1)Develops database for preferred list -The user must specify a number of options. -(1) preference() - which dictates which preference to use for the adjusted proficiency levels. Current options are 0,1,2,...,926. -(3) dropassess() - which dictate which assessments to not calculate proficiency levels. This option takes assessment names -(4) dropgrade() - which dictate which grades to not calculate proficiency levels. This option takes assessment names -(5) filename() - which dictates the name of the file produced to be used in the simulation. -(6) TIMSS_SUBJECT()-dictates either math or science for TIMSS. either enter string "math" or "science" -(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated enrollment for the spells -(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. -As an example: _simulation_dataset, preference(926) dropassess(SACMEQ) dropgrade(3) filename(simulation_926) timss(science) enrollment(validated) -Specifies that Bangladesh, China, India, and Pakistan use National Learning Assessments. Preference 926 is applied for all assessments. -*/ - - - -cap program drop _simulation_dataset -program define _simulation_dataset, rclass - version 15 - syntax [varlist] [, /// - IFSPELL(string) /// - IFSIM(string) /// - IFWINDOW(string) /// - WEIGHT(string) /// - PREFERENCE(string) /// - SPECIALINCLUDEASSESS(string) /// - SPECIALINCLUDEGRADE(string) /// - DROPGRADE(string) /// - FILENAME(string) /// - USEFILE(string) /// - TIMSS(string) /// - ENROLLMENT(string) /// - EGRADROP(string) /// - QUIET /// - PERCENTILE(string) /// - NOSIMULATION /// - ALLyear_populationS /// - POPULATION_2015(string) /// - INPUTFOLDER(string) /// - SAVECTRYFILE(string) /// - GROUPINGSPELLS(string) /// - GROUPINGSIM(string) /// - GROWTHDYNAMICS(string) /// - ] - - if ("`inputfolder'" == "") { - loc inputfolder clone - } - - if "`QUIET'" == "" { - loc qui "noi " - } - - if "`percentile'" == "" { - loc percentile "50(10)90" - } - - noi di - noi di in r "ATTENTION: " in y "_simulation_dataset" in g " is pulling the data from " in y "`inputfolder'" - noi di - - qui { - - di "preference: `preference'" - clear - import delimited "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS.csv", - *Correcting idgrade for ZAF "2006-11" - replace idgrade = 5 if countrycode == "ZAF" & test == "PIRLS" & spell == "2006-2011" - gen year_assessment_i = substr(spell,1,4) - destring year_assessment_i, replace - gen year_assessment = substr(spell,6,4) - destring year_assessment, replace - - save "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", replace - - - use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear - gen year=year_assessment - *nla_code should be distributed over s rather than being available in each . - - *temporarily rename test to assessment and idgrade to grade, change back after merge. - rename test assessment - rename idgrade grade - - *------------------------------* - * Learning poverty calculation - *------------------------------* - * Adjusts non-proficiency by out-of school - foreach subgroup in all fe ma { - gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_validated_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) - label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" - } - - - gen adj_pct_reading_low_rawfull= 100-adj_nonprof_all - - merge m:1 countrycode using "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", keepusing(test idgrade incomelevel lendingtype nonprof_all) - gen adj_pct_reading_low= 100-adj_nonprof_all - *change name in rawlatest of assessment to test_rawlatest, and revert back to test from assessment - - rename adj_pct_reading_low adj_pct_reading_low_rawlatest - rename adj_pct_reading_low_rawfull adj_pct_reading_low - - - gen initial_poverty_level_temp=100-adj_pct_reading_low_rawlatest - cap gen initial_poverty_level="0-25% Learning Poverty" - cap replace initial_poverty_level="25-50% Learning Poverty" if initial_poverty_level_temp>=25 - cap replace initial_poverty_level="50-75% Learning Poverty" if initial_poverty_level_temp>=50 - cap replace initial_poverty_level="75-100% Learning Poverty" if initial_poverty_level_temp>=75 - - - - - rename test test_rawlatest - rename assessment test - *same for grade - rename idgrade idgrade_rawlatest - rename grade idgrade - - drop if _merge==1 - - gen enrollment=enrollment_validated_all - drop if subject!="`timss'" & test=="TIMSS" & countrycode!="JOR" - - - *Keep assessments listed in specialincludeassess option - levelsof test, local(list_alltest) - foreach tst in `list_alltest' { - if strmatch("`specialincludeassess'", "*`tst'*")==0 { - drop if test=="`tst'" - di "`tst'" - } - } - - *Keep grades listed in specialincludegrades option - levelsof idgrade, local(list_grd) - foreach grd in `list_grd' { - if strmatch("`specialincludegrade'", "*`grd'*")==0 { - drop if idgrade==`grd' & idgrade!=idgrade_rawlatest - di "`grd'" - } - } - - sort countrycode nla_code idgrade test subject - count - - - *Cleaning the data file - keep region regionname countrycode countryname incomelevel incomelevelname lendingtype /// - lendingtypename year_population year_assessment idgrade test source_assessment /// - enrollment /// - adj_pct_reading_low* subject nla_code initial_poverty_level - - *Generating all possible combinations of forward spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen spell_c1 = string(year_assessment[_n-1]) + "-" + string(year_assessment) - - bysort countrycode nla_code idgrade test subject : gen spell_c2 = string(year_assessment[_n-2]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c3 = string(year_assessment[_n-3]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c4 = string(year_assessment[_n-4]) + "-" + string(year_assessment) - - reshape long spell_c, i(countrycode nla_code idgrade test subject year_assessment subject) j(lag) - ren spell_c spell - - *tag if actual spell: - gen spell_exists=(length(spell) == 9 ) - - ********************************************** - *Preparing the data for simulations: - ********************************************** - *The data should be restructured for unique identifiers: - sort countrycode nla_code idgrade test subject year_assessment spell lag - - *Rules for cleaning the spell data: - *Bringing in the list of countries and spells for which the data is not comparable: - - merge m:1 countrycode idgrade test year_assessment spell using "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", assert(master match using) keep(master match) keepusing(comparable) nogen - drop if comparable == 0 - - *Generating preferred consecutive spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : egen lag_min = min(lag) - *Keeping the comparable consecutive spells - keep if lag == lag_min - - *All comparable spells for TIMSS/PIRLS - assert comparable == 1 if !missing(comparable) - - *Annual change in enrollment, adjusted proficiency and proficiency - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen delta_adj_pct = (adj_pct_reading_low-adj_pct_reading_low[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen initial_adj_pct = adj_pct_reading_low[_n-1] - bysort countrycode nla_code idgrade test subject : gen final_adj_pct = adj_pct_reading_low - - - *drop observatoins specified by [if] [in]. - if `"`ifspell'"'!="" { - di `"`ifspell'"' - keep `ifspell' - } - - - - - /* weights */ - - if ("`weight'" == "") { - cap tempname wtg - cap gen `wtg' = 1 - local weight2 "" - loc weight "fw" - loc exp "=`wtg'" - } - - - - *Generating deltas in terms of reduction of gap to the frontier - gen gap_to_frontier = 100-adj_pct_reading_low - bysort countrycode nla_code idgrade test subject : gen red_gap_frontier = -1*(gap_to_frontier-gap_to_frontier[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen pct_red_gap = (red_gap_frontier/gap_to_frontier[_n-1]) - gen pct_red_gap_100 = pct_red_gap*100 - - - *Generating categories of countries - gen catinitial = . - foreach var in 25 50 75 100 { - replace catinitial= `var' if initial_adj_pct <= `var' & catinitial== . - } - - *Developing country weights to give each country equal weight despite the number of observations: - *Counting the country observations where delta exists: - - *Set weights to unity if no weights specified - if "`weight2'"=="" { - gen wgt=1 - } - - if "`weight'"!="" { - local weight2 "`weight'" - bysort countrycode: gen delta_exists = !missing(delta_adj_pct) - bysort countrycode delta_exists: gen w = _N - cap gen wgt = . - replace wgt = 1/w - } - - *Calculating global 90th, 80th and 70th percentiles: - - forvalues i = `percentile' { - egen delta_global_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') - } - gsort -delta_adj_pct - list countryname initial_adj_pct final_adj_pct delta_adj_pct test spell if delta_adj_pct > delta_global_90 & !missing(delta_adj_pct) - tabstat delta_global_90 , by(region) - - *Calculating global 90th, 80th and 70th percentiles with weights - forvalues i = `percentile' { - gen delta_global_w_`i' = . - } - gen threshold="III" - levelsof threshold, local(tr) - foreach t of local tr { - _pctile delta_adj_pct [weight = wgt] if threshold == "`t'" & test != "EGRA", percentiles(`percentile') - local counter=1 - forvalues i = `percentile' { - - replace delta_global_w_`i' = r(r`counter') if threshold == "`t'" & test != "EGRA" - local counter=`counter' + 1 - } - } - - - - - `qui' di "Number of spells per `groupingspells'" - `qui' tab `groupingspells' if spell_exists == 1 - - `qui' di "Number of spells per test" - `qui' tab test if spell_exists == 1 - - `qui' di "Spells per `groupingspells'" - `qui' tab test if spell_exists == 1 - - `qui' tab spell if spell_exists == 1 - - *Comparing percentiles with and without weight - `qui' di "Comparing output by with and without weights (90th percentile)" - `qui' tabstat delta_adj_pct delta_global_90 delta_global_w_90 [`weight'`exp'] if spell_exists == 1, /// - by() stat(mean N) - - encode `groupingspells', gen(reg_n) - - egen group = group( reg_n) - - *Percentiles by wbregion: - - forvalues i = `percentile' { - bysort reg_n: egen delta_reg_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') - - } - `qui' di "Results by `groupingspells'" - `qui' tabstat delta_adj_pct delta_reg* [`weight'`exp'], by(reg_n) - - - - cap list countryname initial_adj_pct final_adj_pct delta_adj_pct test idgrade spell if delta_adj_pct > delta_reg_90 & !missing(delta_adj_pct) & region == "SSF" - - *Calculating regional 90th, 8th and 70th percentiles with weights - forvalues i = `percentile' { - gen delta_reg_w_`i' = . - } - - levelsof threshold, local(tr) - foreach t of local tr { - levelsof `groupingspells', local(reg) - foreach r of local reg { - count if !missing(delta_adj_pct) & threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" - local count=`r(N)' - *Only make change to regional if we have at least 3 regional spells - if `count'<3 { - forvalues i = `percentile' { - replace delta_reg_`i' = . if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" - } - - } - if `count'>=3 { - _pctile delta_adj_pct [weight = w] if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA", percentiles(`percentile') - local counter=1 - forvalues i = `percentile' { - replace delta_reg_w_`i' = r(r`counter') if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" - local counter=`counter' + 1 - } - } - } - } - - - *Comparison of regional percentiles with and without weights - `qui' tabstat initial_adj_pct [`weight'`exp'] if spell_exists == 1, /// - by(reg_n) stat(mean p50 min max N) - - *Comparison of regional percentiles with and without weights - `qui' tabstat delta_adj_pct delta_reg* [`weight'`exp'] if spell_exists == 1, /// - by(reg_n) stat(mean N) - - - *Percentiles by initial values: - - forvalues i = `percentile' { - bysort catinitial: egen delta_ini_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') - } - - `qui' tabstat delta_adj_pct delta_ini* [`weight'`exp'] if spell_exists == 1, /// - by(catinitial) stat(mean N) - - - *Average and percentile percentage changes in gap to frontier: - forvalues i = `percentile' { - *drop red_gap_`i'_irsat red_gap_`i'_irsat_extend red_gap_`i'_sas red_gap_`i' red_gap_global_`i' red_gap_global_`i'_extend /* try tp create a variable does alreayd exist in your database */ - bysort `groupingspells': egen red_gap_`i' = pctile(pct_red_gap) if test != "EGRA" , p(`i') - - /*bysort region: egen red_gap_`i'_irsat_extend = max(red_gap_`i'_irsat) - replace red_gap_`i'_irsat = red_gap_`i'_irsat_extend if missing(red_gap_`i'_irsat) - *South Asia does not have any nationally representative tests - bysort region: egen red_gap_`i'_sas = pctile(pct_red_gap) if region == "SAS", p(`i') - gen red_gap_`i' = red_gap_`i'_irsat - replace red_gap_`i' = red_gap_`i'_sas if missing(red_gap_`i') - */ - -egen red_gap_global_`i' = pctile(pct_red_gap) if test != "EGRA", p(`i') - *egen red_gap_global_`i'_extend = max(red_gap_global_`i') - *replace red_gap_global_`i' = red_gap_global_`i'_extend if missing(red_gap_global_`i') - } - - - save "${clone}\02_simulation\023_outputs\\`filename'_spells.dta", replace - - egen count_ctry_spells = count(delta_adj_pct) , by(countrycode) - - *Obtaining data for projection: - collapse (first) test countryname `groupingspells' (mean) delta_adj_pct (lastnm) delta_adj_pct_r = delta_adj_pct (max) delta_adj_pct_m = delta_adj_pct (mean) count_ctry_spells pct_red_gap red_gap_* delta_reg_* delta_global_* [`weight'`exp'], by(countrycode ) - - foreach var of varlist pct_red_gap red_gap_70 red_gap_80 red_gap_90 { - replace `var' = 0 if `var' < 0 - } - - egen count_n = count(delta_adj_pct) - egen count_reg_n = count(delta_adj_pct) , by(`groupingspells') - - - local preference="`preference'" - - save "${clone}\02_simulation\023_outputs\\`filename'.dta", replace - - - - - - ********************************************************************* - * SIMULATION: - ********************************************************************* - - if ("`nosimulation'" == "") { - - *Import growth rates defined in a markdown file if the usefile() option is specified - - if ("`usefile'" != "") { - import delimited "`usefile'", delimiter("|") varnames(1) clear - - drop v1 - drop v9 - drop if _n==1 - *dropping delta_adj_pct, because it is dangerous to merge with this. - drop delta_adj_pct - replace `groupingspells'=strtrim(`groupingspells') - replace `groupingspells'=subinstr(`groupingspells', "`=char(9)'", "", .) - - describe delta_reg*, varlist - foreach var in `r(varlist)' { - destring `var', replace - } - local usefile2=subinstr("`usefile'", ".md", ".dta",.) - save "`usefile2'", replace - } - - - - use "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", replace - merge 1:1 countrycode using "${clone}\02_simulation\023_outputs\\`filename'.dta", - - *Create learning level if not created - cap replace initial_poverty_level="0-25% Learning Poverty" if !missing(adj_nonprof) - cap replace initial_poverty_level="25-50% Learning Poverty" if adj_nonprof>=25 & !missing(adj_nonprof) - cap replace initial_poverty_level="50-75% Learning Poverty" if adj_nonprof>=50 & !missing(adj_nonprof) - cap replace initial_poverty_level="75-100% Learning Poverty" if adj_nonprof>=75 & !missing(adj_nonprof) - - cap gen adj_pct_reading_low= 100-adj_nonprof_all - - ********************************************************************* - *Replace missing deltas: Historical - ********************************************************************* - *Replace with values in markdown file if specified - if ("`usefile'" != "") { - cap drop _merge - noi merge m:1 `groupingspells' using "`usefile2'", update replace - noi di "Update growth rates with values from markdown file." - - assert(1 3 4 5) - - - drop if _merge==2 - } - - replace pct_red_gap = red_gap_50 if pct_red_gap == . - - - - - if "`weight2'"=="" { - *replace with median if missing - levelsof `groupingspells', local(rgn) - foreach var in `rgn' { - di "`var'" - count if !missing(delta_adj_pct) & `groupingspells'=="`var'" - local count=`r(N)' - su delta_reg_50 if `groupingspells'=="`var'" - *Only make change to regional if we have at least 3 regional spells - if `count'>=3 { - cap replace delta_adj_pct = `r(mean)' if delta_adj_pct == . & `groupingspells'=="`var'" - } - } - su delta_global_50 - replace delta_adj_pct = `r(mean)' if delta_adj_pct == . - - } - di "`weight2'" - if "`weight2'"!="" { - levelsof `groupingspells', local(rgn) - foreach var in `rgn' { - di "`var'" - count if !missing(delta_adj_pct) & `groupingspells'=="`var'" - local count=`r(N)' - - su delta_reg_w_50 if `groupingspells'=="`var'" - *Only make change to regional if we have at least 3 regional spells - if `count'>=3 { - cap replace delta_adj_pct = `r(mean)' if delta_adj_pct == . & `groupingspells'=="`var'" - } - } - su delta_global_w_50 - replace delta_adj_pct = `r(mean)' if delta_adj_pct == . - - } - - forval i=`percentile' { - levelsof `groupingspells', local(rgn) - foreach var in `rgn' { - count if !missing(delta_reg_w_`i') & `groupingspells'=="`var'" - local count=`r(N)' - su delta_reg_w_`i' if `groupingspells'=="`var'" - *Only make change to regional if we have at least 3 regional spells - if `count'>=3 { - cap replace delta_reg_w_`i' = `r(mean)' if delta_reg_w_`i' == . & `groupingspells'=="`var'" - } - - count if !missing(delta_reg_`i') & `groupingspells'=="`var'" - local count=`r(N)' - su delta_reg_`i' if `groupingspells'=="`var'" - if `count'>=3 { - cap replace delta_reg_`i' = `r(mean)' if delta_reg_`i' == . & `groupingspells'=="`var'" - } - - } - } - - forval i=`percentile' { - su delta_global_w_`i' - replace delta_reg_w_`i' = `r(mean)' if delta_reg_w_`i' == . - - su delta_global_`i' - replace delta_reg_`i' = `r(mean)' if delta_reg_`i' == . - - su delta_global_`i' - replace delta_global_`i' = `r(mean)' if delta_global_`i' == . - - su delta_global_w_`i' - replace delta_global_w_`i' = `r(mean)' if delta_global_w_`i' == . - } - - *Use own country spell only if there are two or more spells, so that slight changes don't throw off simulations - if "`weight2'"!="" { - replace delta_adj_pct = delta_reg_w_50 if !missing(count_ctry_spells) & count_ctry_spells<2 - } - if "`weight2'"=="" { - replace delta_adj_pct = delta_reg_50 if !missing(count_ctry_spells) & count_ctry_spells<2 - } - - *Don't slow down countries that have max growth rates higher than the regional percentage - forval i=70(10)90 { - replace delta_reg_w_`i' = delta_adj_pct_m if delta_reg_w_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) - replace delta_reg_`i' = delta_adj_pct_m if delta_reg_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) - replace delta_global_w_`i' = delta_adj_pct_m if delta_global_w_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) - replace delta_global_`i' = delta_adj_pct_m if delta_global_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) - } - *save dataset - save "${clone}\02_simulation\023_outputs\\`filename'.dta", replace - - - - - save "${clone}\02_simulation\023_outputs\\`filename'.dta", replace - * Drop trait/useless variables because it's too confusing - rename year_assessment assess_year - drop count_* _merge adminregion* /// - *source* cmu regionname test idgrade /// - pop* enrollment* /// - nla_code pct_* - - - * Rename remaining variables in a consistent logic to be able to reshape long - rename adj_pct_reading_low baseline - *rename delta_adj_pct_r reduct_own // WARNING!!! NOT SURE THIS INTERPRETATION IS CORRECT. BRIAN CAN YOU CHECK? - rename delta_adj_pct growth_own - generate growei_own = growth_own - forvalues i = `percentile' { - rename red_gap_`i' reduct_r`i' - cap rename red_gap_global_`i' reduct_g`i' //WARNING!!! WHY ONLY EXISTS FOR G90 ON YOUR FILE, BRIAN? - rename delta_reg_w_`i' growei_r`i' - rename delta_reg_`i' growth_r`i' - rename delta_global_w_`i' growei_g`i' - rename delta_global_`i' growth_g`i' - } - - /* From my understanding, as of now, there are: - 7 possible "benchmark" - - own = own country historical, business as usual - - r70, r80, r90 = region percentiles 70, 80, 90 - - g70, g80, g90 = global percentiles 70, 80, 90 - 3 possible "rateflavor" - - reduct = rate applied in (100 - baseline), hopefully a negative rate - - growth = rate applied in baseline, hopefully a positive rate - - growei = rate applied in baseline (same as above), but constructed from weighted something - */ - - * Transform the dataset in long to reflect those combinations of benchmarks and rate_flavors - - *save time in simulations by dropping some combinations we don't use - if "`weight2'"=="" { - drop growei_* - drop reduct_* - reshape long growth , i(countrycode `groupingspells' baseline) j(benchmark) string - rename ( growth ) ( rategrowth ) - - } - if "`weight2'"!="" { - drop growth_* - drop reduct_* - reshape long growei, i(countrycode `groupingspells' baseline) j(benchmark) string - rename ( growei) ( rategrowei) - - } - - reshape long rate, i(countrycode `groupingspells' baseline benchmark) j(rate_flavor) string - - order countrycode `groupingspells' rate_flavor benchmark rate baseline - format rate baseline %10.2f - - * Describes input flavor - gen str50 input_flavor = "preference: " + preference - replace input_flavor = input_flavor + " | rate_flavor: " + rate_flavor - replace input_flavor = input_flavor + " | benchmark: " + benchmark - - - *************************************************** - *Dynamically calculated growth rates (particularly for growth rates based on initial learning poverty categories) - *************************************************** - if "`groupingspells'"=="initial_poverty_level" { - levelsof initial_poverty_level, local(pov_levels) - levelsof input_flavor , local(flavors) - - local counter=1 - foreach var in `pov_levels' { - gen rate_`counter'=. - label var rate_`counter' "`var'" - foreach flav in `flavors' { - qui su rate if input_flavor == "`flav'" & initial_poverty_level == "`var'" - replace rate_`counter'=`r(mean)' if input_flavor == "`flav'" - } - replace rate_`counter'=rate if benchmark=="_own" - - local counter=`counter'+1 - } -} - - drop rate_flavor benchmark - - - - - save "${clone}\02_simulation\023_outputs\\`filename'_long.dta", replace - - - // Delete those lines if input vars are already labelled - label var rate "Rate (growth or delta) used in simulation" - label var baseline "Adjusted percentage of test takers with minimum reading proficiency at baseline" - label var input_flavor "Short for: Benchmark Scenario | Rate Method | Latest Prefence" - * Benchmark Scenario: own/r70/r80/r90/g70/g80/g90 - * Rate Method: reduce/growth/growei - * Latest Preference: 926 - *label var input_flavor_des "Long description: Benchmark Scenario | Rate Method | Latest Prefence" - - - * Step 1. Simulate future adjusted proficiency - *================================================== - tempname tmp_adjpro - // Advances adjusted proficiency (adjpro) for all year_populations to simulate - // Each adjpro_year_population is created as column, then it is reshaped to long - quietly forvalues i=2015/2050 { // CHANGE HERE FOR A LONGER HORIZON - gen adjpro`i' = . - gen rate`i'=rate - replace adjpro`i' = 100 - (100-baseline)*((1-rate)^(`i'-2015)) if ( strpos(input_flavor, "reduct") & !missing(baseline)) - replace adjpro`i' = baseline + rate*(`i'-2015) if (!strpos(input_flavor, "reduct") & !missing(baseline)) - - *Add dynamics to simulations based on initial poverty level - if "`groupingspells'"=="initial_poverty_level" & "`growthdynamics'"=="Yes"{ - if `i'>2015 { - local j=`i'-1 - replace adjpro`i' = adjpro`j' + rate_4 if (!strpos(input_flavor, "reduct") & !missing(baseline)) - replace adjpro`i' = adjpro`j' + rate_3 if adjpro`j'>=25 & (!strpos(input_flavor, "reduct") & !missing(baseline)) - replace adjpro`i' = adjpro`j' + rate_2 if adjpro`j'>=50 & (!strpos(input_flavor, "reduct") & !missing(baseline)) - replace adjpro`i' = adjpro`j' + rate_1 if adjpro`j'>=75 & (!strpos(input_flavor, "reduct") & !missing(baseline)) - - } - } - - replace adjpro`i' = 100 if ( adjpro`i' > 100 & !missing(adjpro`i') ) // Upper bound is 100 - replace adjpro`i' = 0 if ( adjpro`i' < 0 & !missing(adjpro`i') ) // Lower bound is 0 - } - - - reshape long adjpro, i(countrycode input_flavor baseline rate) j(year_population) - - // Housekeeping and save temp - label var adjpro "Adjusted percentage of test takers with minimum reading proficiency simulated" - order countrycode year_population input_flavor rate baseline adjpro - format adjpro %10.2f - save "${clone}\02_simulation\023_outputs\\`filename'_long.dta", replace - - - ********************************************************************* - * Merge Population projections (1960-2050 ) - ********************************************************************* - - use "${`inputfolder'}/01_data/013_outputs/population.dta" , clear - cap g year_population = year - - *Choose population type - if "$population" == "" { - local population = "population_all_1014" - gen pop = `population' - } - else { - cap confirm var $population - if _rc!=0 { - di "You messed up the population option, try: (empty=xls) | pop_TOT_10 | pop_TOT_10_14 | pop_TOT_primary | pop_TOT_9_plus" - di "You specified population as: $population. Is that what you want?" - error 2222 - } - else { - gen pop = $population - } - } - - - *Use population in 2015 for the simulations if specified - if "$pop_sim"=="Yes" { - gen tmp1_2015_pop=pop if year_population==2015 - egen tmp2_2015_pop=mean(tmp1_2015_pop), by(countrycode) - replace pop=tmp2_2015_pop - } - - - g source_population = "World Bank" - keep countrycode year_population* pop - - - - //TWN does not have population data - drop if countrycode == "TWN" - - _countrymetadata, match(countrycode) - tempfile popdata - save `popdata', replace - -/* - tempname tmp - import excel using "${clone}\01_data\011_rawdata\population\Pop by 5 year_population age groups for JP v2.xlsx", /// - sheet("Data") firstrow clear - gen id = _n - reshape long YR , i(id CountryName CountryCode SeriesName SeriesCode) j(year_population) string - tolower CountryName CountryCode SeriesName SeriesCode YR - replace seriescode = subinstr(seriescode,".","_",.) - drop id seriesname - drop if seriescode == "" - destring yr, replace force - destring year_population , replace - reshape wide yr, i(countryname countrycode year_population) j(seriescode) string - renpfix yr - tolower SP* - foreach var in 0004 0509 1014 1519 { - gen double sp_pop_`var' = sp_pop_`var'_fe + sp_pop_`var'_ma - } - sort countrycode year_population - save `tmp', replace -*/ - - /*** Merge population ***/ - - use "${clone}/02_simulation/023_outputs//`filename'_long.dta", replace - - sort countrycode year_population - cap drop _merge - merge m:1 countrycode year_population using `popdata', update - - drop if _merge == 2 - - rename _merge merge_population - - label define merge_population 1 "Yrs with no population" 3 "Yrs with population" - - label values merge_population merge_population - - order countrycode year_population input_flavor rate baseline adjpro pop* - - - - * Step 3. Compare simulated versus target - *================================================== - // Targets specification - label define ltarget /// - 0 "100 percent adjusted proficiency" /// - 1 "98 percent adjusted proficiency" /// - 2 "95 percent adjusted proficiency" /// - 3 "Reducing the gap to frontier by 1/2" /// - 4 "Reducing the gap to frontier by 2/3" /// - 5 "Reducing the gap to frontier by 1/3" - - // Each target specification is a column at first but reshaped to long after dummies - // Generate target dummies - gen byte dtarget0 = adjpro>=100 if !missing(adjpro) - gen byte dtarget1 = adjpro>=98 if !missing(adjpro) - gen byte dtarget2 = adjpro>=95 if !missing(adjpro) - // Aux variable to contruct targets based on gap reduction - gen gap_to_frontier_ratio = (100-adjpro)/(100-baseline) - gen byte dtarget3 = 1 if (gap_to_frontier_ratio<=1/2 & !missing(gap_to_frontier_ratio)) - gen byte dtarget4 = 1 if (gap_to_frontier_ratio<=2/3 & !missing(gap_to_frontier_ratio)) - gen byte dtarget5 = 1 if (gap_to_frontier_ratio<=1/3 & !missing(gap_to_frontier_ratio)) - drop gap_to_frontier_ratio - - - // Reshape to long - reshape long dtarget, i(countrycode year_population input_flavor baseline rate adjpro) j(target) - label var target "Description of target" - order countrycode year_population input_flavor target baseline rate adjpro dtarget pop* - - // Housekeeping - label var dtarget "Dummy for whether a country met the target" - label values target ltarget - - // Save long dataset with all countries - // Change to _save_metadata - - // Simulation ID and Simulation Descriptor - gen str5 sim_id = "`filename'" - gen str250 sim_describe="filename(`filename') + ifspell(`ifspell') + ifsim(`ifsim') + ifwindow(`ifwindow') weight(`weight') preference(`preference') specialincludeassess(`specialincludeassess') specialincludegrade(`specialincludegrade') timss(`timss') + enrollment(`enrollment') + population_sim_2015(`population_2015')" - - cap gen dropped_spell_sample_id="`incomegroupdrop'" - cap gen dropped_simulation_sample_id="`simincomegroupdrop'" - cap replace dropped_spell_sample_id="Full Sample Used" if dropped_spell_sample_id=="" - cap replace dropped_simulation_sample_id="Full Sample Used" if dropped_simulation_sample_id=="" - - save "${clone}/02_simulation/023_outputs//`filename'_long.dta", replace - - *Generate population for countries used in simulation - gen pop_sim=pop - - if `"`ifsim'"'!="" { - di `"`ifsim'"' - tempvar simkeep - gen `simkeep'=0 - replace `simkeep'=1 `ifsim' - replace rate=. if `simkeep'==0 - replace adjpro=. if `simkeep'==0 - replace dtarget=. if `simkeep'==0 - replace pop_sim=. if `simkeep'==0 - } - - *drop countries without recent assessments at baseline - if `"`ifwindow'"'!="" { - tempvar simwindow - gen `simwindow'=0 - replace `simwindow'=1 `ifwindow' - - replace rate=. if `simwindow'==0 - replace adjpro=. if `simwindow'==0 - replace dtarget=. if `simwindow'==0 - } - - * Step 4. Display regions overall achievement - *================================================== - // Percentage of proficient kids by region-year_population-specs - local which_pop_wgt = "pop" - gen wgt_included = `which_pop_wgt' if !missing(adjpro) - replace rate=. if missing(adjpro) - gen aux_adjpro = `which_pop_wgt'*adjpro - gen aux_rate = `which_pop_wgt'*rate - gen country_count=!missing(adjpro) - - - preserve - - *********** - *Produce formatted table at country level that is reshaped - *********** - keep if target==1 & year_population>=2015 & year_population<=2030 //doesnt matter target, just reduce to 1 copy in 2030 - - *generate variable indicating whether own growth rate or regional 90 - gen growth_type=substr(input_flavor, -3,.) - - *generate preference variable - - keep if year_population>=2015 & year_population<=2030 - keep countrycode region year_population growth_type preference pop wgt_included adjpro country_count - - gen learning_poverty=100-adjpro - - - egen rgn_mn=mean(learning_poverty), by(region year_population growth_type) - cap replace learning_poverty = rgn_mn if learning_poverty == . - drop rgn_mn - - drop region - - drop adjpro - - _countrymetadata, match(countrycode) - - tempvar simkeep - gen `simkeep'=0 - replace `simkeep'=1 `ifsim' - replace learning_poverty=. if `simkeep'==0 - - *reshape dataset by year_population - reshape wide pop wgt_included learning_poverty country_count , i(countrycode growth_type) j(year_population) - - *reshape dataset by growth_type - reshape wide pop* wgt_included* learning_poverty* country_count* , i(countrycode) j(growth_type) string - - foreach var in pop wgt_included country_count { - forval i=2015/2030 { - rename `var'`i'own `var'_`i' - drop `var'`i'?* - } - } - - - - save "${clone}/02_simulation/023_outputs//`filename'_country_sim_table.dta", replace - - restore - - - - if ("`savectryfile'" != "") { - - preserve - - - collapse (first) sim_id sim_describe dropped_simulation_sample_id dropped_spell_sample_id (sum) country_count num_countries_meeting_target=dtarget /// - aux_adjpro aux_rate pop_total=pop pop_with_data=wgt_included pop_sim , /// - by(`groupingsim' year_population input_flavor target) - gen wgt_adjpro = aux_adjpro/pop_with_data - gen wgt_growth_rate = aux_rate/pop_with_data - - label var wgt_adjpro "Weighted adjusted proficiency for each `groupingsim'" - label var wgt_growth_rate "Weighted mean growth rate for each `groupingsim'" - label var pop_with_data "Population with data in `groupingsim'" - label var pop_total "Total regional population" - label var pop_total "Total regional population Among Countries in Simulation" - label var num_countries_meeting_target "Number of countries in `groupingsim' meeting specified proficiency target" - - save "${`inputfolder'}/02_simulation/023_outputs//dta_ctry_`savectryfile'.dta", replace - - restore - - - - } - - collapse (first) sim_id sim_describe dropped_simulation_sample_id dropped_spell_sample_id (sum) country_count num_countries_meeting_target=dtarget /// - aux_adjpro aux_rate pop_total=pop pop_with_data=wgt_included pop_sim , /// - by(`groupingsim' year_population input_flavor target) - gen wgt_adjpro = aux_adjpro/pop_with_data - gen wgt_growth_rate = aux_rate/pop_with_data - - drop aux_adjpro aux_rate - - label var wgt_adjpro "Weighted adjusted proficiency for each `groupingsim'" - label var wgt_growth_rate "Weighted mean growth rate for each `groupingsim'" - label var pop_with_data "Population with data in `groupingsim'" - label var pop_total "Total regional population" - label var pop_total "Total regional population Among Countries in Simulation" - - label var num_countries_meeting_target "Number of countries in `groupingsim' meeting specified proficiency target" - - if ("`savectryfile'" != "") { - - preserve - - save "${`inputfolder'}/02_simulation/023_outputs//dta_`groupingsim'_`savectryfile'.dta", replace - - restore - - } - - if ("`allyear_populations'" == "") { - - // ON THE FLY FOR EMAIL REQUEST - keep if target==1 & year_population>=2015 & year_population<=2030 //doesnt matter target, just reduce to 1 copy in 2030 - - // Q1 - preserve - if "`weight2'"=="" { - keep if input_flavor =="preference: `preference' | rate_flavor: growth | benchmark: _own" - } - if "`weight2'"!="" { - keep if input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _own" - } - - save "${clone}/02_simulation/023_outputs//`filename'_sim_numbers.dta", replace - - - // Q2 - restore - if "`weight2'"=="" { - keep if input_flavor =="preference: `preference' | rate_flavor: growth | benchmark: _r90" - } - if "`weight2'"!="" { - keep if input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r50" /// - | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r60" /// - | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r70" /// - | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r80" /// - | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r90" /// - - } - - append using "${clone}/02_simulation/023_outputs//`filename'_sim_numbers.dta", - gen specification="`filename'" - - *generate variable indicating whether own growth rate or regional 90 - gen growth_type=substr(input_flavor, -3,.) - - *generate preference variable - gen preference=substr(input_flavor, 13,4) - - *Add in global number - preserve - tempfile globalfile - collapse wgt_adjpro* [aw=pop_sim], by(growth_type year_population) - gen `groupingsim'="Global" - save `globalfile' - restore - append using `globalfile' - - rename year_population year - save "${clone}/02_simulation/023_outputs//`filename'_sim_numbers.dta", replace - - - *********** - *Produce formatted table that is reshaped - *********** - replace `groupingsim'="Z_Global" if `groupingsim'=="Global" - - keep if year>=2015 & year<=2030 - keep year `groupingsim' growth_type preference pop_total pop_with_data pop_sim wgt_adjpro country_count - - gen learning_poverty=100-wgt_adjpro - drop wgt_adjpro - *reshape dataset by year - reshape wide pop_total pop_with_data pop_sim learning_poverty country_count , i(`groupingsim' growth_type) j(year) - - *reshape dataset by growth_type - reshape wide pop_total* pop_with_data* pop_sim* learning_poverty* country_count* , i(`groupingsim') j(growth_type) string - - foreach var in pop_total pop_with_data pop_sim country_count { - forval i=2015/2030 { - rename `var'`i'own `var'_`i' - drop `var'`i'?* - } - } - replace `groupingsim'="Global" if `groupingsim'=="Z_Global" - - save "${clone}/02_simulation/023_outputs//`filename'_sim_table.dta", replace - } - } - } - - end diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a.md deleted file mode 100644 index ad84401..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a.md +++ /dev/null @@ -1,8 +0,0 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| 1.024168 |.799059 |1.108933 | 1.444374 | 1.704331 | 2.397043 | -|ECS| .5257723 | .5804062 | .8586311 | 1.056339 | 1.250893 | 1.509056 | -|LCN| .9103526 | .6422299 | 1.102917 | 1.478872 | 1.808432 | 1.893757 | -|MEA| .9802885 | .8876209 | .8876209 | 1.163887 | 1.163887 | 2.283069 | -|SAS| 1.024168 |.799059 | 1.108933 | 1.444374 | 1.704331 | 2.397043 | -|SSF| 1.552273 | 1.00963 | 1.449121 | 1.947759 | 2.382817 | 3.651614 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_incomelevel.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_incomelevel.md deleted file mode 100644 index 75cc1e7..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_incomelevel.md +++ /dev/null @@ -1,6 +0,0 @@ -|incomelevel | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|LIC | -0.37 | -0.96 | -0.47 | 0.01 | 0.90 | 1.78 | -|LMC | 0.72 | 0.41 | 0.62 | 0.89 | 1.15 | 1.16 | -|UMC | 0.75 | 0.71 | 0.99 | 1.41 | 1.84 | 2.24 | -|HIC | 0.71 | 0.57 | 0.61 | 0.63 | 1.09 | 1.09 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_initial_poverty_level.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_initial_poverty_level.md deleted file mode 100644 index 04f26a3..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_initial_poverty_level.md +++ /dev/null @@ -1,6 +0,0 @@ -|initial_poverty_level | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -| 0-25% Learning Poverty | .7810273 | .4615402 | .632515 | .8586311 | 1.093158 | 1.405041 | -| 25-50% Learning Poverty | .6911094 | 1.04144 | 1.102917 | 2.283069 | 2.722118 | 2.795575 | -| 50-75% Learning Poverty | .7334087 | .5514922 | .774477 | 1.151856 | 1.68804 | 2.068671 | -| 75-100% Learning Poverty | -.0852622 | .4622571 | .7935333 | 1.136203 | 1.478872 | 1.627544 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna.md deleted file mode 100644 index 8fac854..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna.md +++ /dev/null @@ -1,8 +0,0 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| 1.024168 |.799059 |1.108933 | 1.444374 | 1.704331 | 2.397043 | -|ECS| .5257723 | .5804062 | .8586311 | 1.056339 | 1.250893 | 1.509056 | -|LCN| .9103526 | .6422299 | 1.102917 | 1.478872 | 1.808432 | 1.893757 | -|MEA| .9802885 | 1.4121 | 1.748871 | 1.836901 | 2.295264 | 2.795577 | -|SAS| 1.024168 |.799059 | 1.108933 | 1.444374 | 1.704331 | 2.397043 | -|SSF| 1.552273 | 1.00963 | 1.449121 | 1.947759 | 2.382817 | 3.651614 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna_noISR.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna_noISR.md deleted file mode 100644 index c478048..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1000a_mna_noISR.md +++ /dev/null @@ -1,8 +0,0 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| 1.024168 |.799059 |1.108933 | 1.444374 | 1.704331 | 2.397043 | -|ECS| .5257723 | .5804062 | .8586311 | 1.056339 | 1.250893 | 1.509056 | -|LCN| .9103526 | .6422299 | 1.102917 | 1.478872 | 1.808432 | 1.893757 | -|MEA| .9802885 | 1.672584 | 1.792886| 2.283069 | 2.393407 | 3.048103 | -|SAS| 1.024168 |.799059 | 1.108933 | 1.444374 | 1.704331 | 2.397043 | -|SSF| 1.552273 | 1.00963 | 1.449121 | 1.947759 | 2.382817 | 3.651614 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_lower_ci.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_lower_ci.md deleted file mode 100644 index c46578f..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_lower_ci.md +++ /dev/null @@ -1,8 +0,0 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| 0.895790384 |0.636984692 |0.873266504 |1.312120888| 1.636801296 |2.060018516| -|ECS| 0.368216764 |0.381345072 |0.51229318 |0.650816384 |0.883058584 |0.94650562 | -|LCN| 0.75242034 |0.468141352 |0.482920992| 0.998325408 |1.575809812 |1.839990488 | -|MEA|0.798584696 |0.401339008 |0.559501988| 1.128388116 |1.390347536 |2.035339648 | -|SAS| 0.895790384 |0.636984692 |0.873266504| 1.312120888 |1.636801296 |2.060018516| -|SSF| 1.436569244 |0.844299868 |1.277981336| 1.87720614 |2.224923232 |3.07999134 | diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_upper_ci.md b/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_upper_ci.md deleted file mode 100644 index b14abbc..0000000 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005_upper_ci.md +++ /dev/null @@ -1,8 +0,0 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| 1.090005216| 0.926975708| 1.314237496| 1.649367112| 2.034298704| 2.580625484 | -|ECS| 0.701648436| 0.779467328| 1.00652482| 1.336305216| 1.618723416| 2.07160638 | -|LCN| 0.99876686| 0.763391048 |0.801537808| 1.583464592| 2.041056188| 2.191799512 | -|MEA|1.536279304| 1.638138992 |1.768272012| 2.369353884| 2.283454464| 3.555814352 | -|SAS| 1.090005216| 0.926975708| 1.314237496| 1.649367112| 2.034298704| 2.580625484 | -|SSF| 1.661032756| 1.178408132| 2.537588664| 2.40661586| 3.086228768| 3.83776066 | diff --git a/02_simulation/022_programs/0220_create_all_spells.do b/02_simulation/022_programs/0220_create_all_spells.do new file mode 100644 index 0000000..86dc82d --- /dev/null +++ b/02_simulation/022_programs/0220_create_all_spells.do @@ -0,0 +1,267 @@ +*==============================================================================* +* 0220 SUBTASK: CREATE ALL LEARNING POVERTY SPELLS (PLUS DUMMIES OF USE) +*==============================================================================* + +local chosen_preference = 1005 +local enrollment_def "validated" + +quietly { + + * Ensure comparability file is up to date + import delimited "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv", clear + gen year_assessment_i = substr(spell,1,4) + gen year_assessment = substr(spell,6,4) + destring year_assessment_i year_assessment, replace + save "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta", replace + + *-----------------------------------------------------------------------------* + * All possible spells = all combinations of points in time equivalent measures + *-----------------------------------------------------------------------------* + + * Starts with rawfull + use "${clone}/01_data/013_outputs/rawfull.dta", clear + + * Correct the few of PASEC desguised as NLAs + replace nla_code = "N.A." if inlist(countrycode,"MLI","MDG","COD") & test == "NLA" + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") & test == "NLA" + + * More intuitive/shorter names for key variables + rename (enrollment_`enrollment_def'_all nonprof_all) (enrollment bmp) + + * Calculates learning poverty + gen learningpoverty = 100 * ( 1 - (enrollment/100) * (1 - bmp/100)) + label var learningpoverty "Learning Poverty (adjusted non-proficiency, all)" + + * Keep only essential variables + local idvars "countrycode idgrade test nla_code subject" + local values "year_assessment enrollment bmp learningpoverty" + local traits "region incomelevel lendingtype countryname" + keep `idvars' `values' `traits' + + * Will save two almost identical copies of the file, with year sufix in variables + tempfile rawfull_y1 rawfull_y2 + + * Rename value vars as year 1 + rename (year_assessment enrollment bmp learningpoverty) (y1 enrollment_y1 bmp_y1 learningpoverty_y1) + save `rawfull_y1', replace + + * Rename value vars as year 2* + rename (y1 enrollment_y1 bmp_y1 learningpoverty_y1) (y2 enrollment_y2 bmp_y2 learningpoverty_y2) + save `rawfull_y2', replace + + * Join both datasets, that is, all possible combinations that match in idvars + use `rawfull_y1', clear + joinby `idvars' using `rawfull_y2' + + * No sense in keeping if y2>y1 because it already appears with flipped years + drop if y2 <= y1 + + * Spell is identified by those two years plus other idvars + gen str9 spell = string(y1) + "-" + string(y2) + label var spell "Spell" + + * Concatenate a full spell id + gen spell_id = countrycode + "_" + test + "_" + subject + "_" + spell + "_grade" + string(idgrade) + label var spell_id "Full spell identification" + + * Lenght in years could be used for weighting + gen int spell_lenght = y2 - y1 + label var spell_lenght "Spell lenght (years)" + + *-----------------------------------------------------------------------------* + * Several dummies to map on spells characteristics and usage + *-----------------------------------------------------------------------------* + + * Will use the same label for all dummies + label define ny 0 "no" 1 "yes" + + * For TIMSS, only keeps science, except for Jordan, should be equivalent to + * if a country has TIMMS spells for a given year interval, keep the science only + gen byte excess_timss: ny = (subject!= "science" & test == "TIMSS" & countrycode != "JOR") + label var excess_timss "Redundant TIMSS spell (least preferred subject)" + + * SACMEQ round IV (2013) has some quality issues, mark those as doubtful + gen byte quality_control: ny = 1 - (test == "SACMEQ" & (y1 == 2013 | y2 == 2013) ) + label var quality_control "Flags out SACMEQ low quality spell (with 2013)" + + * Mark only the spells that correspond to the preferred specification of LP + merge m:1 `idvars' using "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", keep(master match) keepusing(`traits') gen(preferred) + recode preferred (1 = 0) (3 = 1) + label var preferred "Same assessment, subject and grade as preferred in rawlatest" + label values preferred ny + + * Mark whether it is a comparable spell + merge m:1 countrycode idgrade test spell using "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta", keep(master match) keepusing(comparable) nogen + replace comparable = 0 if inlist(test, "EGRA", "PASEC") + replace comparable = 1 if test == "PASEC" & y1 >= 2014 + replace comparable = 1 if missing(comparable) + label var comparable "Spell is comparable" + label values comparable ny + + + * Up to this point , if a country participated in 4 PIRLS, + * it would have 3 stepwise spells, 2 skipping spells, 1 overarching spell. + * Will only keep the stepwise spells, that is, of consecutive years. + + * As of now, a single loop run is called, but for robustness uses a while + local move_on = 0 + while `move_on' != 1 { + + * Identifies the minimum and maximum lag (when both are 1, it's unique) + sort `idvars' y1 y2 + by `idvars' y1 : gen min_lag = (_n == 1) + by `idvars' y1 : gen max_lag = (_n == _N) + + * The min_lag must be comparable, unless a single non-comparable exists + count if min_lag == 1 & comparable == 0 & max_lag !=1 + if `r(N)' == 0 local move_on = 1 + + drop if min_lag == 1 & comparable == 0 & max_lag !=1 + drop min_lag max_lag + } + + * Now we can actually keep only the minimum lag + sort `idvars' y1 y2 + by `idvars' y1 : keep if _n == 1 + + + * What actually matters: the value of the spell + gen delta_lp = (learningpoverty_y2 - learningpoverty_y1) / (y1 - y2) + label var delta_lp "Annualized change in Learning Poverty in this spell (pp)" + + * Adds filter of spells neither too big nor small + gen byte range_old: ny = (delta_lp > -2 & delta_lp < 4) + label var range_old "Spell is between -2 and 4 annual change in Learning Poverty" + gen byte range_new: ny = (delta_lp > -4 & delta_lp < 4) + label var range_new "Spell is between -4 and 4 annual change in Learning Poverty" + + * What was used for the simulation in the glossy (Part2 only) + gen byte glossy_sim: ny = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0 & test != "NLA" & range_old == 1) + label var glossy_sim "Spell was used in the simulation for the Glossy (N=70)" + + * What we are actually using in the tehnical paper (Part2 only) + * - change the range from [-2, 4] to [-4,4] + * - removes the South Africa spell which does not seem comparable + gen byte used_sim: ny = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0 & range_new == 1 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5") + label var used_sim "Spell was used in the simulation (N=72)" + * Almost equal to the above, but without the outliers exclusion, because it's needed for a table later + gen byte almostused_sim: ny = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5") + label var almostused_sim "Spell was ALMOST used in the simulation (without range condition)" + + * In some tables we report an analogous of "used_sim", but including Part1 countries + gen byte potential_sim: ny = (comparable == 1 & y1 >= 2000 & excess_timss == 0 & range_new == 1 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5") + label var potential_sim "Spell would be used in the simulation if Part 1 was included (N=207)" + * Almost equal to the above, but without the outliers exclusion, because it's needed for a table later + gen byte almostpotential_sim: ny = (comparable == 1 & y1 >= 2000 & excess_timss == 0 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5") + label var almostpotential_sim "Spell was ALMOST potential simulation (without range condition)" + + * Alternatively, we could use spells without the range rule + gen byte withoutliers_sim: ny = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5") + label var withoutliers_sim "Spell could be used in the simulation (N=82)" + + * Alternatively, we could use spells without the range rule but drop SACMEQ 2013 + gen byte rmlastsacmeq_sim: ny = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0 & spell_id != "ZAF_PIRLS_read_2011-2016_grade5" & quality_control == 1) + label var rmlastsacmeq_sim "Spell could be used in the simulation (N=70)" + + * Creates initial learning poverty level + gen initial_poverty_level = "" + replace initial_poverty_level = "0-25% Learning Poverty" if !missing(learningpoverty_y1) + replace initial_poverty_level = "25-50% Learning Poverty" if learningpoverty_y1 >= 25 & !missing(learningpoverty_y1) + replace initial_poverty_level = "50-75% Learning Poverty" if learningpoverty_y1 >= 50 & !missing(learningpoverty_y1) + replace initial_poverty_level = "75-100% Learning Poverty" if learningpoverty_y1 >= 75 & !missing(learningpoverty_y1) + label var initial_poverty_level "Categorical string variable on initial Learning Poverty (y1 of spell)" + + * Beautify + sort countrycode + order `idvars' spell* delta_lp *y1 *y2 preferred comparable range_* *sim `traits' + format %4.1f enrollment* bmp* learningpoverty* delta_lp + format %3.0g preferred comparable *sim + + compress + save "${clone}/02_simulation/023_outputs/all_spells.dta", replace + + noi disp as result _n "Saved all spells." _n +} + +* Display summary stats of spells to double check results (copied below) + +tab test if glossy_sim + +tab test if used_sim + +tab test if potential_sim + +tab test if withoutliers_sim + +tab test if rmlastsacmeq_sim + +exit +*------------------------------------------------------------------------------* +/* + +* Results as of June 26, 2020 +. +. tab test if glossy_sim + + Assessment | Freq. Percent Cum. +------------+----------------------------------- + LLECE | 14 20.00 20.00 + PIRLS | 23 32.86 52.86 + SACMEQ | 17 24.29 77.14 + TIMSS | 16 22.86 100.00 +------------+----------------------------------- + Total | 70 100.00 + +. +. tab test if used_sim + + Assessment | Freq. Percent Cum. +------------+----------------------------------- + LLECE | 14 19.44 19.44 + NLA | 1 1.39 20.83 + PIRLS | 23 31.94 52.78 + SACMEQ | 17 23.61 76.39 + TIMSS | 17 23.61 100.00 +------------+----------------------------------- + Total | 72 100.00 + +. +. tab test if potential_sim + + Assessment | Freq. Percent Cum. +------------+----------------------------------- + LLECE | 14 6.76 6.76 + NLA | 1 0.48 7.25 + PIRLS | 95 45.89 53.14 + SACMEQ | 17 8.21 61.35 + TIMSS | 80 38.65 100.00 +------------+----------------------------------- + Total | 207 100.00 + +. +. tab test if withoutliers_sim + + Assessment | Freq. Percent Cum. +------------+----------------------------------- + LLECE | 14 17.07 17.07 + NLA | 1 1.22 18.29 + PIRLS | 23 28.05 46.34 + SACMEQ | 25 30.49 76.83 + TIMSS | 19 23.17 100.00 +------------+----------------------------------- + Total | 82 100.00 + +. +. tab test if rmlastsacmeq_sim + + Assessment | Freq. Percent Cum. +------------+----------------------------------- + LLECE | 14 20.00 20.00 + NLA | 1 1.43 21.43 + PIRLS | 23 32.86 54.29 + SACMEQ | 13 18.57 72.86 + TIMSS | 19 27.14 100.00 +------------+----------------------------------- + Total | 70 100.00 + +*/ diff --git a/02_simulation/022_programs/0221_aggregates_spells.do b/02_simulation/022_programs/0221_aggregates_spells.do new file mode 100644 index 0000000..0c78920 --- /dev/null +++ b/02_simulation/022_programs/0221_aggregates_spells.do @@ -0,0 +1,134 @@ +*==============================================================================* +* 0221 SUBTASK: AGGREGATE SPELLS AND EXPORT TO MARKDOWNS FOR SIMULATION +*==============================================================================* + +quietly { + + + *-----------------------------------------------------------------------------* + * Generate alternative markdown inputs for simulations + *-----------------------------------------------------------------------------* + + foreach marker in glossy_sim used_sim withoutliers_sim rmlastsacmeq_sim { + + local aggregations "region incomelevel initial_poverty_level" + foreach aggregation of local aggregations { + + * Open the spells file and prepare it for collapse + *---------------------------------------------------- + + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + + * Will only keep the observations marked as used in simulation file_marker + * for example if used_sim == 1 means N spells = 71 + keep if `marker' == 1 + + * To avoid changing names later, when those mds are called + * Will only add the marker to the name of the md files that are new + if "`marker'" == "used_sim" local mdprefix "simulation_spells" + else local mdprefix "simulation_spells_`marker'" + + + * Weighted version will consider each country only once, that is, if the same + * country has multiple spells, they are averaged + bysort countrycode : gen n_spells_country = _N + gen spells_wgt = 1 / n_spells_country + + * Placeholder variables for weighted and un-weighted spells + * note that the name "reg" was for region but is used for all aggregations + forvalues p = 10(10)90 { + gen delta_reg_u_`p' = . + gen delta_reg_w_`p' = . + } + + * Substitute summary stats for each aggregation (weighted and unweighted) + *---------------------------------------------------- + + levelsof `aggregation', local(this_aggregation_values) + foreach agg_value of local this_aggregation_values { + + count if `aggregation' == "`agg_value'" + local count = `r(N)' + + * By Region, no weights + _pctile delta_lp if `aggregation' == "`agg_value'", percentiles(10(10)90) + forvalues p = 10(10)90 { + local d = `p'/10 + replace delta_reg_u_`p' = r(r`d') if `aggregation' == "`agg_value'" + } + + * By Region, with weights + _pctile delta_lp [aw = spells_wgt] if `aggregation' == "`agg_value'", percentiles(10(10)90) + forvalues p = 10(10)90 { + local d = `p'/10 + replace delta_reg_w_`p' = r(r`d') if `aggregation' == "`agg_value'" + } + } + + * Creates a file with Global Overall line + preserve + collapse (count) n_spells = delta_lp (mean) delta_adj_pct = delta_lp delta_reg_u_* delta_reg_w_* + gen `aggregation' = "Overall" + tempfile global + save `global' + restore + + * Collapse into regions (a mean of all equal values, since the calculation actually happened in pctile) + collapse (count) n_spells = delta_lp (mean) delta_adj_pct = delta_lp delta_reg_u_* delta_reg_w_*, by(`aggregation') + + * Replace value for EAS and SAS with the global, because of lack of spells + if "`aggregation'" == "region" { + drop if inlist(region, "EAS", "SAS") + append using `global' + replace region = "EAS" if region == "Overall" + append using `global' + replace region = "SAS" if region == "Overall" + } + + sort `aggregation' + + * In any aggregation case, add the global line at the bottom + append using `global' + + gen aux_sort = _n +1 + + * Save final version to markdown file, compatible with Github + + * Do some extra formatting for markdown file + * Which is adding a blank line with --- at the first observation + set obs `=_N+1' + replace aux_sort = 1 if _n == _N + sort aux_sort + foreach var of varlist `aggregation' n_spells delta_* { + tostring `var', replace force + replace `var'="---" if _n==1 + } + drop aux_sort + + * At this point, both weighted and unweighted variables exist. + * Keep only one group at a time, in separate files + * Since we decided to use the WEIGHTED file by REGION, all others are saved in sensitivity_checks + + preserve + drop delta_reg_w_* + export delimited using "${clone}/02_simulation/021_rawdata/sensitivity_checks/`mdprefix'_unweighted_`aggregation'.md", delimiter("|") replace + restore + + * Weighted file + drop delta_reg_u_* + if "`marker'" == "used_sim" & "`aggregation'" == "region" export delimited using "${clone}/02_simulation/021_rawdata/`mdprefix'_weighted_`aggregation'.md", delimiter("|") replace + else export delimited using "${clone}/02_simulation/021_rawdata/sensitivity_checks/`mdprefix'_weighted_`aggregation'.md", delimiter("|") replace + + * Note that out of the 9 combinations ( 3 markers * 3 aggregations ) + * only one md file is saved in the main folder + * others go to a subfolder for easy identification of our preferred one + + * next aggregation + } + + * next marker + } + + noisily disp as result _n "Exported special simulation md files" + +} diff --git a/02_simulation/022_programs/0222_simulations.do b/02_simulation/022_programs/0222_simulations.do new file mode 100644 index 0000000..dc490a0 --- /dev/null +++ b/02_simulation/022_programs/0222_simulations.do @@ -0,0 +1,64 @@ +*==============================================================================* +* 0222 SUBTASK: RUN SIMULATIONS USING GROWTH RATES FROM SPELLS +*==============================================================================* + +/* +Mandatory parameters: + - filename : prefix for which resulting dtas will be named (saved in 023_outputs) + - groupingspells : group on which benchmarking spells are assigned (region | incomelevel | initial_poverty_level) + - usefile : an md with spells provided per groups as defined in groupingspells (full path should be used) + - preference : starting point for the simulation + +Optional parameters (no default): + - countryfilter : filter on the aggregation provided, applied to the starting point as in population_weights (ie: lendingtype!="LNX") + - timewindow : filter on the assessments at starting point considered, as in population_weights (ie: year_assessment>=2011) + - ifspell : filter on all_spells.dta (ie: if used_sim == 1) + +Optional parameters (with default): + - minspell : default to 1 if ommitted. The minimum number of country own spells to avoid overwritting with group value + - percentile : default to 50(10)90 if omitted. All values must be available in the usefile + - groupingsim : default is region. Does not need to match the groupingspells, and it only matters for displaying results + - quiet : default is to display main results, use this option to quiet it fully +*/ + + +* PREFERRED SIMULATION + +* Run simulation with tabulations done by region and growth rates calculated using regional growth +simulate_learning_poverty, ifspell(if used_sim == 1) /// + preference(1005) timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") /// + filename(simfile_preference_1005_regional_growth) groupingspells(region) /// + usefile("${clone}/02_simulation/021_rawdata/simulation_spells_weighted_region.md") /// + + +* SENSITIVITY CHECKS ON SPELLS SELECTION + +* Same as main one, but with minspell = 2 instead of 1 +simulate_learning_poverty, ifspell(if used_sim == 1) minspell(2) /// + preference(1005) timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") /// + filename(simfile_preference_1005_regional_growth_min2) groupingspells(region) /// + usefile("${clone}/02_simulation/021_rawdata/simulation_spells_weighted_region.md") + +* Same as above, but using the spells that were in the Glossy +simulate_learning_poverty, ifspell(if glossy_sim == 1) minspell(2) /// + preference(1005) timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") /// + filename(simfile_preference_1005_regional_growth_glossy) groupingspells(region) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_glossy_sim_weighted_region.md") + + +* SENSITIVITY CHECKS ON MECHANICS OF GROWTH RATE + +* Run simulation with growth rates calculated using income level growth +simulate_learning_poverty, ifspell(if used_sim == 1) /// + preference(1005) timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") /// + filename(simfile_preference_1005_income_level) groupingspells(incomelevel) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.md") + +* Run simulation with growth rates calculated using initial learning poverty +simulate_learning_poverty, ifspell(if used_sim == 1) /// + preference(1005) timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") /// + filename(simfile_preference_1005_initial_poverty_level) groupingspells(initial_poverty_level) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.md") + + +exit diff --git a/02_simulation/022_program/022_run.do b/02_simulation/022_programs/022_run.do similarity index 69% rename from 02_simulation/022_program/022_run.do rename to 02_simulation/022_programs/022_run.do index 44fc631..2f91fb0 100644 --- a/02_simulation/022_program/022_run.do +++ b/02_simulation/022_programs/022_run.do @@ -24,9 +24,17 @@ global master_seed 17893 // Ensures reproducibility *------------------------------------------------------------------------------- * Subroutines for this task *------------------------------------------------------------------------------- -* Make sure stata simulation_dataset.ado file is loaded -do "${clone}/02_simulation/022_program/_simulation_dataset.ado" +* Get all possible learning poverty spells (plus dummies of comparability and use) +do "${clone}/02_simulation/022_programs/0220_create_all_spells.do" + +* Generate alternative markdown inputs for simulations +do "${clone}/02_simulation/022_programs/0221_aggregates_spells.do" * Run simulations to produce final datasets -do "${clone}/02_simulation/022_program/022_simulations.do" +do "${clone}/02_simulation/022_programs/0222_simulations.do" + +/* To generate spells that were saved as markdown in the 021_rawdata/old_version +do "${clone}/02_simulation/022_programs/022x_custom_spells.do" +do "${clone}/02_simulation/022_programs/022y_custom_spells_bootstrap.do" +*/ *------------------------------------------------------------------------------- diff --git a/02_simulation/022_programs/022x_spells_ssf_attempts.do b/02_simulation/022_programs/022x_spells_ssf_attempts.do new file mode 100644 index 0000000..09067e4 --- /dev/null +++ b/02_simulation/022_programs/022x_spells_ssf_attempts.do @@ -0,0 +1,65 @@ +use "${clone}/02_simulation/023_outputs/all_spells.dta", clear +tab lendingtype +tab test +tab spell if test == "PASEC" + +qui { + + * GLOSSY version (below) is equivalent to attempt0 + gen attempt0 = (lendingtype != "LNX" & range_ok == 1 & comparable == 1 & y1 >= 2000 & excess_timss == 0) & region == "SSF" + + * Without the "drop outliers" (masked as range_ok) + gen attempt1 = (lendingtype != "LNX" & comparable == 1 & y1 >= 2000 & excess_timss == 0) & region == "SSF" + * This should be equivalent in SSF only to: + //gen attempt1 = inlist(test, "SACMEQ", "PIRLS") & comparable == 1 & region == "SSF" + + * Version that drops the SACMEQ 2013 + gen attempt2 = inlist(test, "SACMEQ", "PIRLS") & comparable == 1 & y2 != 2013 & region == "SSF" + + * Version with only SACMEQ 2000-2007 + gen attempt3 = inlist(test, "SACMEQ") & y2 != 2013 & region == "SSF" + + * Version that keeps PASEC (any) but not the 2013 SACMEQ + gen attempt4 = inlist(test, "PASEC", "SACMEQ") & y2 != 2013 & region == "SSF" + + * Version that keeps PASEC (not too old), but not the 2013 SACMEQ + gen attempt5 = inlist(test, "PASEC", "SACMEQ") & y1 >= 2000 & y2 != 2013 & region == "SSF" + + * Version that keeps PASEC (any) and SACMEQ (any) + gen attempt6 = inlist(test, "PASEC", "SACMEQ") & region == "SSF" + + * Version that keeps PASEC (not too old) and SACMEQ (any) + gen attempt7 = inlist(test, "PASEC", "SACMEQ") & y1 >= 2000 & region == "SSF" + + * Masks North Africa as Africa + gen attempt8 = inlist(test, "SACMEQ", "PIRLS") & comparable == 1 & (region == "SSF" | inlist(countrycode , "MAR", "TUN")) + + * Same as glossy, but outlier range -4 to 4 (instead of -2 to 4) + gen attempt9 = (lendingtype != "LNX" & delta_lp<=4 & delta_lp>=-4 & comparable == 1 & y1 >= 2000 & excess_timss == 0) & region == "SSF" + + + noi disp as res _n "Attempt | Weight | N spells | p50 | p80" + + forvalues i=0/9 { + + preserve + + keep if attempt`i' + + * Weighted version will consider each country only once, that is, if the same + * country has multiple spells, they are averaged + bysort countrycode : gen n_spells_country = _N + gen spells_wgt = 1 / n_spells_country + gen spells_wgt2 = spells_wgt * spell_lenght + + _pctile delta_lp [aw = spells_wgt] , percentiles(50 80) + + noi disp as txt " `i' | reg | `=_N' | `: di %3.2fc r(r1)' | `: di %3.2fc r(r2)'" + + _pctile delta_lp [aw = spells_wgt2] , percentiles(50 80) + + // noi disp as txt " `i' | new | `=_N' | `: di %3.2fc r(r1)' | `: di %3.2fc r(r2)'" + + restore + } +} \ No newline at end of file diff --git a/02_simulation/022_programs/country_simulation_bubbleplot/app.R b/02_simulation/022_programs/country_simulation_bubbleplot/app.R new file mode 100644 index 0000000..31c6ca7 --- /dev/null +++ b/02_simulation/022_programs/country_simulation_bubbleplot/app.R @@ -0,0 +1,252 @@ +library(shiny) +library(DT) +library(plotly) +library(crosstalk) +library(tidyverse) +library(knitr) +library(kableExtra) + + +ui <- fluidPage( + titlePanel("Learning Poverty - Country Simulation"), + sidebarLayout( + sidebarPanel( img( src="https://user-images.githubusercontent.com/43160181/66895172-211fc400-efc0-11e9-998e-c5090e51730d.png", width="200", height="200", align="Center"), + h1(" Simulation"), + + p(" This tool shows how much progress can be made reducing Learning Poverty over time under different growth scenarios. + Scenarios are presented where countries grow at their regional 50th, 60th, 70th, 80th, 90th, 95th, and 99th percentiles. + To learn more about Learning Poverty, view the World Bank topic page on the subject. ", style = "font-size:20px"), + a(href="https://www.worldbank.org/en/topic/education/brief/learning-poverty", "Learning Poverty Page", style = "font-size:20px"), + h2("Start Simulation"), + + p("Click on the play button in the bottom right corner to launch the simulation and to see how quickly learning poverty can be reduced.. ", style = "font-size:20px"), + + width = 2 + ), + mainPanel( + selectizeInput("country", "Select Countries", + choices=NULL, + selected=c("All"), + multiple=T), + plotlyOutput("x2", height = 800), + htmlOutput("x1", width='60%',) , + sliderInput("year", "Year", + min=2015, + max=2030, + step=1, + value=2015, + sep="", + width='100%', + animate= + animationOptions(interval = 800, loop = FALSE) ), + includeMarkdown('lpov_desc.md'), + + + + tags$head(tags$style(type='text/css', ".slider-animate-button { font-size: 20pt !important; }")) + + ) +) +) +server <- function(input, output, session) { + + load(file = 'pref1005_p99_sim_numbers.RData') + + #add country choices + choice <- unique(as.character(tab_country1$countryname)) + choice<-append('All',choice) + + updateSelectizeInput(session, 'country', choices = choice, selected=c("All"), server=TRUE) + + + #list of variables to keep + keep_list<-c("countrycode", "countryname", "region", "regionname", "adminregion", + "adminregionname", "incomelevel", "incomelevelname") + + + tab_country <- reactive({ + if (!("All" %in% input$country)) + tab_country1 %>% + filter(countryname %in% input$country) + else ( + tab_country1 + ) + + }) + + country_long <- reactive ({ + tab_country() %>% + filter(!is.na(learning_poverty2015r50)) %>% + select(keep_list, starts_with("learning_poverty"), starts_with("pop")) %>% + pivot_longer( #pivot population variables from wide to long + cols=starts_with('pop'), + names_to= "year_pop", + values_to = "population" + ) %>% + pivot_longer( #Pivot learning poverty from wide to long + cols=starts_with("learning_poverty"), + names_to = "type", + values_to = "learning_poverty" + ) %>% + mutate(year_pop=substring(year_pop, 5,9), + year=substring(type, nchar("learning_poverty")+1,nchar("learning_poverty")+4), + growth_type=substring(type, nchar("learning_poverty")+5,nchar("learning_poverty")+9)) %>% + filter(year_pop==year) %>% #because we reshaped wide to long twice, need to remove duplicates + filter(substring(growth_type,1,1)=="r") %>% + mutate(growth_type=factor(growth_type, levels=c("r50", "r60", "r70", "r80", "r90", "r95", "r99"), + labels=c("50th Percentile", + "60th Percentile", + "70th Percentile", + "80th Percentile", + "90th Percentile", + "95th Percentile", + "99th Percentile"))) + }) + + global_pop <- reactive({ + #add in global population + tab1 %>% + filter(region!="Global") %>% + select(starts_with("pop_total")) %>% + summarise_all(~sum(.)) %>% + pivot_longer( #pivot population variables from wide to long + cols=starts_with('pop'), + names_to= "year", + values_to = "population_global" + ) %>% + mutate(year=substring(year, nchar(year)-3, nchar(year))) %>% + filter(year==input$year) + + }) + + + # create global number database too + global_long <- reactive({ + tab1 %>% + filter(region=="Global") %>% + select(region, starts_with("learning_poverty")) %>% + pivot_longer( #Pivot learning poverty from wide to long + cols=starts_with("learning_poverty"), + names_to = "type", + values_to = "learning_poverty_global" + ) %>% + mutate(year=substring(type, nchar("learning_poverty")+1,nchar("learning_poverty")+4), + growth_type=substring(type, nchar("learning_poverty")+5,nchar("learning_poverty")+9)) %>% + filter(substring(growth_type,1,1)=="r") %>% + mutate(growth_type=factor(growth_type, levels=c("r50", "r60", "r70", "r80", "r90", "r95", "r99"), + labels=c("50th Percentile", + "60th Percentile", + "70th Percentile", + "80th Percentile", + "90th Percentile", + "95th Percentile", + "99th Percentile"))) %>% + select(growth_type, learning_poverty_global, year) %>% + filter(year==input$year) + + + }) + + country_long_gl<- reactive({ + country_long() %>% + left_join(global_pop()) %>% + left_join(global_long()) + + }) + + country_sim_df<- reactive( { + + country_long_gl() %>% + filter(year==input$year) + }) + + + millions_df<- reactive( { + + global_long() %>% + left_join(global_pop()) %>% + filter(year==input$year) + }) + + + + + + + + # highlight selected rows in the scatterplot + output$x2 <- renderPlotly({ + + d_plot<-country_sim_df() %>% + mutate(pop_pov=(learning_poverty_global/100)*population_global) + + country_sim <-plot_ly(d_plot, + x = ~growth_type, + y =~learning_poverty, + size = ~population, + color = ~regionname, + text = ~countryname, + hovertemplate = paste( + "%{text}

", + "%{yaxis.title.text}: %{y}
", + "" + ), + hoverinfo="text", + type="scatter", + mode = "markers", + #Choosing the range of the bubbles' sizes: + sizes = c(10, 50), + marker = list(opacity = 0.5, sizemode = 'diameter') ) %>% + layout(title = 'Simulation of Learning Poverty Overtime by Country', + xaxis = list(title="Regional Growth Rate"), + yaxis = list(title="Learning Poverty", + range=c(0,100))) + + country_sim + + }) + + + # highlight selected rows in the table + output$x1 <- renderText({ + + country_long_gl_text <- millions_df() %>% + mutate(pop_pov=(learning_poverty_global/100)*population_global) %>% + select(year, growth_type, learning_poverty_global, population_global, pop_pov) %>% + group_by(growth_type) %>% + summarise(pop_pov=round(mean(pop_pov),0)) %>% + mutate(pop_pov= formatC(pop_pov, format = "f", big.mark = ",", drop0trailing = TRUE)) %>% + pivot_wider( + names_from=growth_type, + values_from=c("pop_pov")) %>% + mutate(Label=factor(row_number(), labels=c("Number of Children in Learning Poverty"))) + + + country_long_gl_text2 <- millions_df() %>% + group_by(growth_type) %>% + summarise(learning_poverty_global=paste(round(mean(learning_poverty_global),1),"%", sep="")) %>% + pivot_wider( + names_from=growth_type, + values_from=c("learning_poverty_global")) %>% + mutate(Label=factor(row_number()+1, labels=c("Learning Poverty %"))) + + + country_long_gl_text <- country_long_gl_text %>% + bind_rows(country_long_gl_text2) %>% + select( Label, everything()) + + kable(country_long_gl_text, + row.names = T, + caption="Glboal Number of Children in Learning Poverty Under Different Growth Scenarios", + format="html") %>% + column_spec(1, width = "10em") %>% + kable_styling(bootstrap_options = c("striped", "hover")) + + + }) + + + +} + +shinyApp(ui, server) \ No newline at end of file diff --git a/02_simulation/022_programs/country_simulation_bubbleplot/lpov_desc.md b/02_simulation/022_programs/country_simulation_bubbleplot/lpov_desc.md new file mode 100644 index 0000000..230a9af --- /dev/null +++ b/02_simulation/022_programs/country_simulation_bubbleplot/lpov_desc.md @@ -0,0 +1,31 @@ +## What is Learning Poverty? +Learning Poverty means being unable to read and understand a short, age-appropriate text by age 10. All foundational skills are important, but we focus on reading because: (i) reading proficiency is an easily understood measure of learning; (ii) reading is a student’s gateway to learning in every other area; and, (iii) reading proficiency can serve as a proxy for foundational learning in other subjects, in the same way that the absence child stunting is a marker of healthy early childhood development. + +### How is Learning Poverty measured? +This indicator brings together schooling and learning. It starts with the share of children in school who haven’t achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. Formally, we do this by calculating Learning Poverty as: + +_

LP = [BMP * (1-OoS)] + [1 * OoS]

_ + +where, _LP_ is Learning Poverty; _BMP_ is share of children in school below minimum proficiency; and _OoS_ is the percentage of out-of-school children. + +Our decision was to treat out-of-school children as non-proficient in reading. This means that Learning Poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low Out-of-School population, the share of pupils Below Minimum Proficiency will be very close to the reported Learning Poverty. Given that this measure is intended to motivate action by governments and societies more generally, discounting for Out-of-School population avoids giving countries an incentive to improve their rate by encouraging dropout of marginal students. + +### Estimating growth + +In order to simulate reductions in learning poverty, a second empirical challenge is to measure how reading proficiency has improved in recent years, to better understand how quickly countries will improve in the future. Doing this is even more challenging than estimating levels, because of the lack of data and thresholds that are comparable over time. For any individual country, there may be multiple estimates of proficiency rates available for the past 15 years, but simply calculating growth using those estimates would be misleading. Often, those estimates are based on data from different assessments—PIRLS and PASEC, for example. Although the equating process described in our technical documentation aims to harmonize the proficiency levels across assessments to allow calculation of proficiency levels, this process is too imprecise to be used for growth estimates. Given that growth rates are typically much smaller than the levels (e.g., 1% annual growth rate on a baseline of 50%), the noise introduced by mixing assessments is likely to swamp the signal (the actual change in proficiency). See the technical report for more details on how we calculate growth. + +### Regional aggregation +The Regional number is the population weighted aggregation for the countries from a specific region with assessment data following the Reporting Rule. Regional numbers are only reported if at least 40% of the 10-14 population is covered by an actual learning assessment. + +### Global aggregation +The Global number is the population weighted regional number weighted by the actual 10-14 population from each region. In this case, the implicit assumption is that the population from countries with no reading poverty, are assigned with their respective regional averages. + +### Growth Rates +|
  Region
| 
  Average Growth
| 
   50th Percentile
| 
  60th Percentile
| 
  70th Percentile
| 
   80th Percentile
| 
  90th Percentile
| 
   95th Percentile
| 
   99th Percentile
| +| ----- | :--------------: | :-------------: | :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | +|
 East Asia & Pacific
| .993 | .782 | 1.094 | 1.481 | 1.836 | 2.320 | 2.580 | 3.120 | +|
 Europe and Central Asia
| .535 | .580 | .759 | .994 | 1.251 | 1.509 | 1.509 | 2.244 | +|
 Latin America and Caribbean
| .876 | .616 | .642 | 1.291 | 1.8081 | 2.016 | 2.138 | 3.340 | +|
 Middle East and North Africa
| 1.167 | 1.020 | 1.164 | 1.749 | 1.837 | 2.796 | 3.301 | 3.301 | +|
 South Asia
| .9928978 | .782 | 1.094 | 1.481 | 1.836 | 2.320 | 2.5807 | 3.120 | +|
 Sub-Saharan Africa
| 1.549 | 1.011 | 1.908 | 2.141 | 2.656 | 3.459| 3.883 | 3.883 | diff --git a/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_growth_rates.csv b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_growth_rates.csv new file mode 100644 index 0000000..79daa68 --- /dev/null +++ b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_growth_rates.csv @@ -0,0 +1,1025 @@ +year,input_flavor,target,region,sim_id,sim_describe,dropped_simulation_sample_id,dropped_spell_sample_id,country_count,num_countries_meeting_target,pop_total,pop_with_data,pop_sim,wgt_adjpro,wgt_growth_rate,specification,growth_type,preference +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149394031,119064332,137060551,78.835296630859375,0.7819802165031433,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149394031,119064332,137060551,78.835296630859375,1.0937520265579224,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149394031,119064332,137060551,78.835296630859375,1.7252427339553833,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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Used,6,1,150019000,119749000,137891600,85.577667236328125,1.7188411951065063,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150019000,119749000,137891600,86.64201354980469,2.0055949687957764,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150019000,119749000,137891600,88.096221923828125,2.3973870277404785,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150019000,119749000,137891600,88.76670837402344,2.5801470279693604,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150019000,119749000,137891600,90.80139923095703,3.1202850341796875,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150628600,120324000,138522300,82.74730682373047,0.7819801568984985,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150628600,120324000,138522300,84.214202880859375,1.0937520265579224,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150628600,120324000,138522300,87.22069549560547,1.717878818511963,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150628600,120324000,138522300,88.55119323730469,2.0049076080322266,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150628600,120324000,138522300,90.3690414428711,2.397075653076172,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150628600,120324000,138522300,91.207763671875,2.5801470279693604,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150628600,120324000,138522300,93.69490051269531,3.1202850341796875,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,151525800,121126000,139400600,83.48543548583984,0.7819802165031433,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,151525800,121126000,139400600,85.24474334716797,1.0937520265579224,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,151525800,121126000,139400600,88.85245513916016,1.718071460723877,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,151525800,121126000,139400600,90.447601318359375,2.005045175552368,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,151525800,121126000,139400600,92.60713195800781,2.3971378803253174,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,151525800,121126000,139400600,93.58100128173828,2.5801470279693604,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,151525800,121126000,139400600,96.20792388916016,3.1202850341796875,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152505100,122012000,140362900,84.21009826660156,0.7819802165031433,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152505100,122012000,140362900,86.261871337890625,1.0937520265579224,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152505100,122012000,140362900,90.47777557373047,1.7191866636276245,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,152505100,122012000,140362900,92.335693359375,2.0058417320251465,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,152505100,122012000,140362900,94.80777740478516,2.3974990844726562,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,152505100,122012000,140362900,95.9409408569336,2.5801470279693604,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,152505100,122012000,140362900,96.93843841552734,3.1202850341796875,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,153467300,122892000,141321100,84.92542266845703,0.7819802165031433,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,153467300,122892000,141321100,87.26976776123047,1.0937520265579224,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,153467300,122892000,141321100,92.10126495361328,1.7209339141845703,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,153467300,122892000,141321100,94.18486785888672,2.007089614868164,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,153467300,122892000,141321100,96.755218505859375,2.398064613342285,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,153467300,122892000,141321100,96.63121032714844,2.5801470279693604,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,153467300,122892000,141321100,97.61820220947266,3.1202850341796875,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154176900,123576000,142048000,85.64177703857422,0.7819802165031433,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154176900,123576000,142048000,88.27849578857422,1.0937520265579224,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,154176900,123576000,142048000,93.72050476074219,1.7226381301879883,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,154176900,123576000,142048000,96.034637451171875,2.0083067417144775,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,154176900,123576000,142048000,97.37415313720703,2.398616075515747,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154176900,123576000,142048000,97.23149871826172,2.5801470279693604,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154176900,123576000,142048000,98.2542953491211,3.1202850341796875,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154377900,123869000,142317300,86.368560791015625,0.7819802165031433,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,154377900,123869000,142317300,89.29685974121094,1.0937520265579224,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,154377900,123869000,142317300,95.322509765625,1.723629355430603,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,154377900,123869000,142317300,97.78339385986328,2.00901460647583,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154377900,123869000,142317300,98.00169372558594,2.3989369869232178,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154377900,123869000,142317300,97.76848602294922,2.5801470279693604,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154377900,123869000,142317300,98.901092529296875,3.1202850341796875,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154150700,123833000,142169200,87.1080551147461,0.7819802165031433,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,154150700,123833000,142169200,90.326812744140625,1.0937520265579224,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,154150700,123833000,142169200,96.92671966552734,1.7237801551818848,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,154150700,123833000,142169200,98.3966064453125,2.009122371673584,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154150700,123833000,142169200,98.57213592529297,2.3989856243133545,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,154150700,123833000,142169200,98.306732177734375,2.5801470279693604,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,154150700,123833000,142169200,99.55382537841797,3.1202850341796875,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,153406800,123380000,141555400,87.85364532470703,0.7819802165031433,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,153406800,123380000,141555400,91.357818603515625,1.0937520265579224,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,153406800,123380000,141555400,98.52839660644531,1.7234752178192139,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,153406800,123380000,141555400,99.00982666015625,2.008904457092285,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,153406800,123380000,141555400,99.13616180419922,2.3988869190216064,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,153406800,123380000,141555400,98.84735870361328,2.5801470279693604,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,153406800,123380000,141555400,99.80583190917969,3.1202850341796875,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152239200,122572000,140546600,88.60054779052734,0.7819802165031433,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,152239200,122572000,140546600,92.37004089355469,1.0937520265579224,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,152239200,122572000,140546600,99.41512298583984,1.7229382991790771,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,152239200,122572000,140546600,99.60838317871094,2.00852108001709,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,152239200,122572000,140546600,99.69876861572266,2.3987131118774414,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,152239200,122572000,140546600,99.38712310791016,2.5801470279693604,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,152239200,122572000,140546600,99.8488540649414,3.1202850341796875,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150797500,121481000,139277700,89.340545654296875,0.7819802165031433,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,150797500,121481000,139277700,93.37425231933594,1.0937520265579224,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,4,150797500,121481000,139277700,99.4784164428711,1.7227426767349243,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,150797500,121481000,139277700,99.63236999511719,2.0083813667297363,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,150797500,121481000,139277700,99.73058319091797,2.3986499309539795,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,150797500,121481000,139277700,99.7832260131836,2.5801470279693604,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,150797500,121481000,139277700,99.89265441894531,3.1202850341796875,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,2,149187800,120142000,137827900,90.06428527832031,0.7819802165031433,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,3,149187800,120142000,137827900,94.36186981201172,1.0937520265579224,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,149187800,120142000,137827900,99.54048156738281,1.7234869003295898,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,149187800,120142000,137827900,99.65571594238281,2.008913040161133,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,149187800,120142000,137827900,99.7619400024414,2.398890972137451,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,149187800,120142000,137827900,99.8188705444336,2.5801470279693604,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,5,149187800,120142000,137827900,99.937225341796875,3.1202850341796875,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,0.58040618896484375,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,0.7594090104103088,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,1.3240408897399902,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,1.401034951210022,pref1005_p99,r80,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,1.5165796279907227,pref1005_p99,r90,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,1.5090559720993042,pref1005_p99,r95,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,2.244493007659912,pref1005_p99,r99,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,1,51161440,20368622,27500412,87.40476989746094,0.58040618896484375,pref1005_p99,r50,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,1,51161440,20368622,27500412,87.583770751953125,0.7594090104103088,pref1005_p99,r60,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51161440,20368622,27500412,88.14778137207031,1.3234162330627441,pref1005_p99,r70,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51161440,20368622,27500412,88.22484588623047,1.4004809856414795,pref1005_p99,r80,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51161440,20368622,27500412,88.34090423583984,1.5165427923202515,pref1005_p99,r90,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51161440,20368622,27500412,88.33341979980469,1.5090559720993042,pref1005_p99,r95,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,51161440,20368622,27500412,89.06517028808594,2.244493007659912,pref1005_p99,r99,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,1,51999312,20796720,28116485,88.08836364746094,0.58040618896484375,pref1005_p99,r50,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51999312,20796720,28116485,88.44636535644531,0.7594090104103088,pref1005_p99,r60,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51999312,20796720,28116485,89.57111358642578,1.3217852115631104,pref1005_p99,r70,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,51999312,20796720,28116485,89.70685577392578,1.3995375633239746,pref1005_p99,r80,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,51999312,20796720,28116485,89.9090576171875,1.5165796279907227,pref1005_p99,r90,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,51999312,20796720,28116485,89.894012451171875,1.5090559720993042,pref1005_p99,r95,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,51999312,20796720,28116485,90.8409652709961,2.244493007659912,pref1005_p99,r99,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,52970060,21270042,28835805,88.76300048828125,0.58040618896484375,pref1005_p99,r50,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,52970060,21270042,28835805,89.29369354248047,0.7594090104103088,pref1005_p99,r60,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,52970060,21270042,28835805,90.595458984375,1.319471836090088,pref1005_p99,r70,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,52970060,21270042,28835805,90.78160858154297,1.3983386754989624,pref1005_p99,r80,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,52970060,21270042,28835805,90.96920013427734,1.516677737236023,pref1005_p99,r90,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,52970060,21270042,28835805,90.94633483886719,1.5090559720993042,pref1005_p99,r95,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,52970060,21270042,28835805,92.15925598144531,2.244493007659912,pref1005_p99,r99,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,53885500,21744000,29559000,89.422393798828125,0.58040618896484375,pref1005_p99,r50,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,53885500,21744000,29559000,90.089141845703125,0.7594090104103088,pref1005_p99,r60,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,53885500,21744000,29559000,91.41535186767578,1.3173645734786987,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,53885500,21744000,29559000,91.65443420410156,1.3972100019454956,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,53885500,21744000,29559000,91.90187072753906,1.5167675018310547,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,53885500,21744000,29559000,91.87101745605469,1.5090559720993042,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,53885500,21744000,29559000,93.26789093017578,2.244493007659912,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,54676500,22192000,30238000,90.03575897216797,0.58040618896484375,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,54676500,22192000,30238000,90.68089294433594,0.7594090104103088,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,54676500,22192000,30238000,92.19527435302734,1.315942645072937,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,54676500,22192000,30238000,92.492828369140625,1.3963607549667358,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,54676500,22192000,30238000,92.69126892089844,1.5168770551681519,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,54676500,22192000,30238000,92.65216827392578,1.5090559720993042,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,54676500,22192000,30238000,94.31110382080078,2.244493007659912,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,55346600,22587000,30861000,90.57080841064453,0.58040618896484375,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,55346600,22587000,30861000,91.1632080078125,0.7594090104103088,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,55346600,22587000,30861000,92.9499740600586,1.31514310836792,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55346600,22587000,30861000,93.199005126953125,1.3956950902938843,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55346600,22587000,30861000,93.41460418701172,1.5169355869293213,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,55346600,22587000,30861000,93.36732482910156,1.5090560913085938,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,55346600,22587000,30861000,95.291717529296875,2.244493007659912,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,55868600,22933000,31418000,90.93553924560547,0.58040618896484375,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,55868600,22933000,31418000,91.61125183105469,0.7594090104103088,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55868600,22933000,31418000,93.5890121459961,1.3149197101593018,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55868600,22933000,31418000,93.83207702636719,1.395302414894104,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,55868600,22933000,31418000,94.07195281982422,1.5170412063598633,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55868600,22933000,31418000,94.03630065917969,1.5090559720993042,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,55868600,22933000,31418000,96.20403289794922,2.244493007659912,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56237800,23210000,31884000,91.280731201171875,0.58040618896484375,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56237800,23210000,31884000,92.04154205322266,0.7594090104103088,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,56237800,23210000,31884000,94.17284393310547,1.3151849508285522,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56237800,23210000,31884000,94.4307861328125,1.395117998123169,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56237800,23210000,31884000,94.698822021484375,1.5171359777450562,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56237800,23210000,31884000,94.6793212890625,1.5090559720993042,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56237800,23210000,31884000,97.08821105957031,2.244493007659912,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56462900,23423000,32250000,91.60951232910156,0.58040618896484375,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56462900,23423000,32250000,92.41141510009766,0.7594090104103088,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,56462900,23423000,32250000,94.72614288330078,1.3154358863830566,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56462900,23423000,32250000,95.0128173828125,1.394957423210144,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56462900,23423000,32250000,95.2931137084961,1.5172195434570313,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56462900,23423000,32250000,95.29016876220703,1.5090559720993042,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,9,56462900,23423000,32250000,97.94510650634766,2.244493007659912,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56537000,23548000,32476000,91.93651580810547,0.58040618896484375,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56537000,23548000,32476000,92.7625503540039,0.7594090104103088,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56537000,23548000,32476000,95.27684020996094,1.315455436706543,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56537000,23548000,32476000,95.58295440673828,1.3947283029556274,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56537000,23548000,32476000,95.88795471191406,1.5172386169433594,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56537000,23548000,32476000,95.88795471191406,1.5090559720993042,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,56537000,23548000,32476000,98.5130844116211,2.244493007659912,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56500000,23610000,32582000,92.25457000732422,0.58040618896484375,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56500000,23610000,32582000,93.10938262939453,0.7594090104103088,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56500000,23610000,32582000,95.831329345703125,1.3155853748321533,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56500000,23610000,32582000,96.15087127685547,1.3946235179901123,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56500000,23610000,32582000,96.48621368408203,1.517248272895813,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56500000,23610000,32582000,96.48621368408203,1.5090559720993042,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,56500000,23610000,32582000,98.62217712402344,2.244493007659912,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56300000,23583000,32538000,92.53021240234375,0.58040618896484375,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56300000,23583000,32538000,93.448211669921875,0.7594090104103088,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56300000,23583000,32538000,96.38892364501953,1.3155779838562012,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,56300000,23583000,32538000,96.724945068359375,1.3944718837738037,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,56300000,23583000,32538000,97.0897445678711,1.5171329975128174,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,56300000,23583000,32538000,97.0897445678711,1.5090559720993042,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,56300000,23583000,32538000,98.71533203125,2.244493007659912,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,55951000,23486000,32376000,92.79772186279297,0.58040618896484375,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55951000,23486000,32376000,93.78114318847656,0.7594090104103088,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,55951000,23486000,32376000,96.94055938720703,1.3157628774642944,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,7,55951000,23486000,32376000,97.303192138671875,1.3945189714431763,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,55951000,23486000,32376000,97.69673919677734,1.5170724391937256,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,55951000,23486000,32376000,97.69673919677734,1.5090559720993042,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,55951000,23486000,32376000,98.81554412841797,2.244493007659912,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,55498000,23319000,32115000,93.05045318603516,0.58040618896484375,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55498000,23319000,32115000,94.10212707519531,0.7594090104103088,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,55498000,23319000,32115000,97.49143981933594,1.3159857988357544,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,55498000,23319000,32115000,97.880615234375,1.3946012258529663,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,9,55498000,23319000,32115000,98.25565338134766,1.5169405937194824,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,9,55498000,23319000,32115000,98.25565338134766,1.5090559720993042,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,55498000,23319000,32115000,98.91439056396484,2.244493007659912,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,54950900,23082000,31761000,93.28130340576172,0.58040618896484375,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,54950900,23082000,31761000,94.412109375,0.7594090104103088,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,8,54950900,23082000,31761000,97.782562255859375,1.316437005996704,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,9,54950900,23082000,31761000,98.19807434082031,1.3948633670806885,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,54950900,23082000,31761000,98.540313720703125,1.5168622732162476,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,10,54950900,23082000,31761000,98.540313720703125,1.5090559720993042,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,11,54950900,23082000,31761000,99.01049041748047,2.244493007659912,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,0.6157662272453308,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,0.6422293782234192,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,1.3822258710861206,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,1.8257224559783936,pref1005_p99,r80,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,2.0224344730377197,pref1005_p99,r90,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,2.1380319595336914,pref1005_p99,r95,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,3.3397040367126465,pref1005_p99,r99,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,49.82139587402344,0.6157662272453308,pref1005_p99,r50,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,49.847862243652344,0.6422293782234192,pref1005_p99,r60,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,50.58823013305664,1.3826000690460205,pref1005_p99,r70,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,51.03154373168945,1.8259117603302002,pref1005_p99,r80,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,51.22815704345703,2.022526264190674,pref1005_p99,r90,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,51.34366226196289,2.1380319595336914,pref1005_p99,r95,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,52.54533386230469,3.3397040367126465,pref1005_p99,r99,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,50.41950607299805,0.6157662272453308,pref1005_p99,r50,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,50.47243118286133,0.6422293782234192,pref1005_p99,r60,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,51.95417022705078,1.3830974102020264,pref1005_p99,r70,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,52.840179443359375,1.8261020183563232,pref1005_p99,r80,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,53.23322677612305,2.0226247310638428,pref1005_p99,r90,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,53.46403884887695,2.1380319595336914,pref1005_p99,r95,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,55.86738204956055,3.3397040367126465,pref1005_p99,r99,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,51.01521682739258,0.615766167640686,pref1005_p99,r50,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,51.094608306884766,0.6422293782234192,pref1005_p99,r60,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,53.319114685058594,1.3837323188781738,pref1005_p99,r70,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,54.64677429199219,1.826284408569336,pref1005_p99,r80,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,55.23609161376953,2.022724151611328,pref1005_p99,r90,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,55.58201599121094,2.1380319595336914,pref1005_p99,r95,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,59.18703079223633,3.3397040367126465,pref1005_p99,r99,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,51.611141204833984,0.6157662272453308,pref1005_p99,r50,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,51.71699523925781,0.642229437828064,pref1005_p99,r60,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,54.68538284301758,1.3843258619308472,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,56.45383071899414,1.826438546180725,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,57.23929214477539,2.022803783416748,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,57.7002067565918,2.1380319595336914,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,62.506893157958984,3.3397040367126465,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,52.212440490722656,0.6157662272453308,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,52.344757080078125,0.6422293782234192,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,56.05699157714844,1.3846768140792847,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,58.26658630371094,1.826595425605774,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,59.24802780151367,2.022883653640747,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,59.823768615722656,2.1380319595336914,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,65.8321304321289,3.3397040367126465,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52140500,45197000,51234000,52.81887435913086,0.6157662272453308,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52140500,45197000,51234000,52.977657318115234,0.6422293782234192,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52140500,45197000,51234000,57.435821533203125,1.3852568864822388,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52140500,45197000,51234000,60.08477020263672,1.8267481327056885,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52140500,45197000,51234000,61.262020111083984,2.0229570865631104,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52140500,45197000,51234000,61.95247268676758,2.1380319595336914,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52140500,45197000,51234000,69.162506103515625,3.3397040367126465,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52070700,45089000,51167900,53.42720413208008,0.6157662272453308,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52070700,45089000,51167900,53.61244583129883,0.6422293782234192,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52070700,45089000,51167900,58.80955123901367,1.3855013847351074,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52070700,45089000,51167900,61.89918899536133,1.826878309249878,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52070700,45089000,51167900,63.27204895019531,2.023001194000244,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52070700,45089000,51167900,64.08306121826172,2.1380319595336914,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52070700,45089000,51167900,72.48896789550781,3.3397040367126465,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52046300,45020000,51154200,54.03981018066406,0.6157662272453308,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52046300,45020000,51154200,54.25151824951172,0.642229437828064,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52046300,45020000,51154200,60.18497085571289,1.3855364322662354,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52046300,45020000,51154200,63.71669387817383,1.8270025253295898,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52046300,45020000,51154200,65.28498840332031,2.0230391025543213,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52046300,45020000,51154200,66.21793365478516,2.1380319595336914,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52046300,45020000,51154200,75.81830596923828,3.3397040367126465,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52035600,44980000,51155200,54.64735412597656,0.6157662272453308,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52035600,44980000,51155200,54.885520935058594,0.642229437828064,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52035600,44980000,51155200,61.55580139160156,1.385617971420288,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52035600,44980000,51155200,65.52937316894531,1.827126145362854,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52035600,44980000,51155200,67.29292297363281,2.0230753421783447,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52035600,44980000,51155200,68.347747802734375,2.1380319595336914,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,52035600,44980000,51155200,79.142578125,3.3397040367126465,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51986100,44949000,51122500,55.24446487426758,0.6157662272453308,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51986100,44949000,51122500,55.509098052978516,0.6422293782234192,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51986100,44949000,51122500,62.91929626464844,1.3859647512435913,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51986100,44949000,51122500,67.33184051513672,1.8272196054458618,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51986100,44949000,51122500,69.29048156738281,2.023083448410034,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51986100,44949000,51122500,70.46562957763672,2.1380319595336914,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,2,51986100,44949000,51122500,82.449188232421875,3.3397040367126465,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51910700,44944000,51070600,55.8343391418457,0.615766167640686,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51910700,44944000,51070600,56.12543487548828,0.6422293782234192,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51910700,44944000,51070600,64.2794189453125,1.3865854740142822,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51910700,44944000,51070600,69.1272964477539,1.8273022174835205,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51910700,44944000,51070600,71.28093719482422,2.023087501525879,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51910700,44944000,51070600,72.5732650756836,2.1380319595336914,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,3,51910700,44944000,51070600,85.72962188720703,3.3397040367126465,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51808600,44948000,50997200,56.41539001464844,0.615766167640686,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51808600,44948000,50997200,56.73295211791992,0.642229437828064,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51808600,44948000,50997200,65.63484191894531,1.3873977661132813,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51808600,44948000,50997200,70.91416931152344,1.8273415565490723,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51808600,44948000,50997200,73.26276397705078,2.023057699203491,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51808600,44948000,50997200,74.67229461669922,2.1380319595336914,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,4,51808600,44948000,50997200,88.91285705566406,3.3397040367126465,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51674300,44948000,50895100,56.98871994018555,0.6157662272453308,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51674300,44948000,50895100,57.33274459838867,0.6422293782234192,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51674300,44948000,50895100,66.98697662353516,1.3884036540985107,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51674300,44948000,50895100,72.6938247680664,1.8273917436599731,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51674300,44948000,50895100,75.23714447021484,2.02303147315979,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51674300,44948000,50895100,76.76377868652344,2.1380319595336914,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,5,51674300,44948000,50895100,92.02042388916016,3.3397040367126465,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51508700,44904000,50758900,57.56480026245117,0.615766167640686,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51508700,44904000,50758900,57.93528366088867,0.6422293782234192,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51508700,44904000,50758900,68.34404754638672,1.3894453048706055,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51508700,44904000,50758900,74.47637939453125,1.8274688720703125,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51508700,44904000,50758900,77.21427917480469,2.023033380508423,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51508700,44904000,50758900,78.85797882080078,2.1380319595336914,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,7,51508700,44904000,50758900,94.37144470214844,3.3397040367126465,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51308300,44792000,50581800,58.1425895690918,0.6157662272453308,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,51308300,44792000,50581800,58.5395393371582,0.6422293782234192,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51308300,44792000,50581800,69.70313262939453,1.3903212547302246,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51308300,44792000,50581800,76.26109313964844,1.8275189399719238,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51308300,44792000,50581800,79.19344329833984,2.0230085849761963,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,2,51308300,44792000,50581800,80.9542007446289,2.1380319595336914,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,7,51308300,44792000,50581800,96.04894256591797,3.3397040367126465,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,1.019739031791687,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,1.1638870239257812,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,2.1350514888763428,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,2.201173782348633,pref1005_p99,r80,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,2.921267509460449,pref1005_p99,r90,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,3.300628900527954,pref1005_p99,r95,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,3.300628900527954,pref1005_p99,r99,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,37.74507141113281,1.0197389125823975,pref1005_p99,r50,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,37.88922119140625,1.1638870239257812,pref1005_p99,r60,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,38.861820220947266,2.1364850997924805,pref1005_p99,r70,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,38.92786407470703,2.202526092529297,pref1005_p99,r80,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,39.64706802368164,2.921734094619751,pref1005_p99,r90,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,40.025962829589844,3.300628900527954,pref1005_p99,r95,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,40.025962829589844,3.300628900527954,pref1005_p99,r99,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,38.80068588256836,1.019739031791687,pref1005_p99,r50,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,39.08898162841797,1.1638870239257812,pref1005_p99,r60,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,41.0379753112793,2.138383150100708,pref1005_p99,r70,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,41.16984176635742,2.2043163776397705,pref1005_p99,r80,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,42.60590744018555,2.922351837158203,pref1005_p99,r90,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,43.362464904785156,3.300628900527954,pref1005_p99,r95,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,43.362464904785156,3.300628900527954,pref1005_p99,r99,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,39.86252212524414,1.019739031791687,pref1005_p99,r50,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,40.29496383666992,1.1638870239257812,pref1005_p99,r60,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,43.224952697753906,2.140549659729004,pref1005_p99,r70,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,43.422386169433594,2.20635986328125,pref1005_p99,r80,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,45.57247543334961,2.9230568408966064,pref1005_p99,r90,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,46.70519256591797,3.300628900527954,pref1005_p99,r95,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,46.70519256591797,3.300628900527954,pref1005_p99,r99,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,40.91185760498047,1.019739031791687,pref1005_p99,r50,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,41.48844909667969,1.1638870239257812,pref1005_p99,r60,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,45.40095138549805,2.142012357711792,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,45.66386032104492,2.2077395915985107,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,48.527034759521484,2.9235329627990723,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,50.03541564941406,3.300628900527954,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,50.03541564941406,3.300628900527954,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,41.924957275390625,1.019739031791687,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,42.64569854736328,1.1638870239257812,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,47.53407669067383,2.1415627002716064,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,47.86284255981445,2.207315683364868,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,51.44319534301758,2.923386812210083,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,53.32940673828125,3.300629138946533,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,53.32940673828125,3.300629138946533,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,42.878509521484375,1.019739031791687,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,43.7433967590332,1.1638870239257812,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,49.58864974975586,2.1380960941314697,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,49.98434829711914,2.204045534133911,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,54.29362487792969,2.9222583770751953,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,56.56385040283203,3.300628900527954,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,56.56385040283203,3.300628900527954,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,43.80560302734375,1.019739031791687,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,44.814640045166016,1.1638870239257812,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,51.60076141357422,2.1333327293395996,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,52.064300537109375,2.199552536010742,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,57.112388610839844,2.920707941055298,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,59.77183532714844,3.300629138946533,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,59.77183532714844,3.300629138946533,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,44.72196578979492,1.019739031791687,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,45.87514877319336,1.1638870239257812,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,53.588958740234375,2.128113269805908,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,54.12108612060547,2.194629192352295,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,59.91613006591797,2.919009208679199,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,62.96908187866211,3.300628900527954,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,62.96908187866211,3.300628900527954,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,45.665016174316406,1.019739031791687,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,46.96234893798828,1.1638870239257812,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,55.606773376464844,2.1243786811828613,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,56.20732498168945,2.1911063194274902,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,62.74751281738281,2.9177937507629395,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,66.193023681640625,3.300628900527954,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,66.193023681640625,3.300628900527954,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,46.66773223876953,1.019739031791687,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,48.10921096801758,1.1638870239257812,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,57.71088409423828,2.124053955078125,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,58.37834548950195,2.190800189971924,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,65.64722442626953,2.9176881313323975,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,69.47663116455078,3.300628900527954,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,69.47663116455078,3.300628900527954,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,48604000,29086000,42574000,47.739768981933594,1.019739031791687,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,48604000,29086000,42574000,49.32539749145508,1.1638870239257812,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,48604000,29086000,42574000,59.76468276977539,2.127340793609619,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,48604000,29086000,42574000,60.4968376159668,2.1939003467559814,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,48604000,29086000,42574000,68.47026824951172,2.918757915496826,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,48604000,29086000,42574000,72.67085266113281,3.300628900527954,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,48604000,29086000,42574000,72.67085266113281,3.300628900527954,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49178000,29370000,43122000,48.85088348388672,1.019739031791687,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49178000,29370000,43122000,50.580657958984375,1.1638870239257812,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49178000,29370000,43122000,61.231693267822266,2.1329798698425293,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49178000,29370000,43122000,62.026573181152344,2.1992197036743164,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49178000,29370000,43122000,70.68305206298828,2.92059326171875,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49178000,29370000,43122000,75.24348449707031,3.300628900527954,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49178000,29370000,43122000,75.24348449707031,3.300628900527954,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49529000,29504000,43471000,49.9882926940918,1.019739031791687,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49529000,29504000,43471000,51.86221694946289,1.1638870239257812,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49529000,29504000,43471000,62.724822998046875,2.1401238441467285,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49529000,29504000,43471000,63.58066940307617,2.205958366394043,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49529000,29504000,43471000,72.901153564453125,2.9229183197021484,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49529000,29504000,43471000,77.81139373779297,3.300629138946533,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49529000,29504000,43471000,77.81139373779297,3.300629138946533,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49727000,29537000,43673000,51.10077667236328,1.019739031791687,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49727000,29537000,43673000,53.11885070800781,1.1638870239257812,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,64.16999816894531,2.146306276321411,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,65.0867691040039,2.211790084838867,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,75.07073974609375,2.9249305725097656,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,80.33051300048828,3.300628900527954,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,80.33051300048828,3.300628900527954,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49793000,29506000,43753000,52.143157958984375,1.019739031791687,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49793000,29506000,43753000,54.305381774902344,1.1638870239257812,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,65.511138916015625,2.149195671081543,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,66.490936279296875,2.214515447616577,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,77.16127014160156,2.9258711338043213,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,82.78263854980469,3.300629138946533,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,82.78263854980469,3.300629138946533,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r80,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r90,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r95,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,r99,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r50,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r60,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r70,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r80,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r90,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r95,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,r99,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r50,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r60,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r70,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r80,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r90,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r95,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,r99,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r50,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r60,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r70,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r80,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r90,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r95,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,r99,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r50,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r60,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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_r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22223000,0,0,NA,NA,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22223000,0,0,NA,NA,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22223000,0,0,NA,NA,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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_r90,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22193000,0,0,NA,NA,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22193000,0,0,NA,NA,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22193000,0,0,NA,NA,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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Used,5,1,174676000,171645000,174676000,54.469947814941406,2.5801470279693604,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,174676000,171645000,174676000,57.16233825683594,3.1202850341796875,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,173463000,170479000,173463000,46.18730926513672,0.7819802165031433,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,173463000,170479000,173463000,48.05793762207031,1.0937520265579224,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,173463000,170479000,173463000,50.3798942565918,1.4807440042495728,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,173463000,170479000,173463000,52.508731842041016,1.83555006980896,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,173463000,170479000,173463000,55.417362213134766,2.320322036743164,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,173463000,170479000,173463000,56.96923065185547,2.5801470279693604,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,173463000,170479000,173463000,60.176849365234375,3.1202850341796875,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,171907000,168958000,171907000,46.8828125,0.7819802165031433,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,171907000,168958000,171907000,49.06521987915039,1.0937520265579224,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,171907000,168958000,171907000,51.7741584777832,1.4807440042495728,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,171907000,168958000,171907000,54.2578010559082,1.83555006980896,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,171907000,168958000,171907000,57.63628387451172,2.320322036743164,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,171907000,168958000,171907000,59.436370849609375,2.5801470279693604,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,171907000,168958000,171907000,63.17849349975586,3.1202850341796875,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,170127000,167209000,170127000,47.56884765625,0.7819801568984985,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,170127000,167209000,170127000,50.063018798828125,1.0937520265579224,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,170127000,167209000,170127000,53.158958435058594,1.4807440042495728,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,170127000,167209000,170127000,55.99740219116211,1.83555006980896,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,170127000,167209000,170127000,59.83673095703125,2.320322036743164,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,170127000,167209000,170127000,61.893924713134766,2.5801470279693604,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,170127000,167209000,170127000,66.1705322265625,3.1202850341796875,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,168476000,165585000,168476000,48.25315475463867,0.7819802165031433,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,168476000,165585000,168476000,51.05910110473633,1.0937520265579224,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,168476000,165585000,168476000,54.542030334472656,1.4807440042495728,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,168476000,165585000,168476000,57.717464447021484,1.83555006980896,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,168476000,165585000,168476000,62.035457611083984,2.320322036743164,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,168476000,165585000,168476000,64.34979248046875,2.5801470279693604,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,168476000,165585000,168476000,69.16094970703125,3.1202850341796875,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167302000,164441000,167302000,48.94497299194336,0.7819802165031433,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167302000,164441000,167302000,52.06269073486328,1.0937520265579224,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167302000,164441000,167302000,55.93243408203125,1.4807440042495728,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167302000,164441000,167302000,59.444007873535156,1.83555006980896,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167302000,164441000,167302000,64.24187469482422,2.320322036743164,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167302000,164441000,167302000,66.81340789794922,2.5801470279693604,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167302000,164441000,167302000,72.15924072265625,3.1202850341796875,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166688000,163852000,166688000,49.64801788330078,0.7819802165031433,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166688000,163852000,166688000,53.077510833740234,1.0937520265579224,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166688000,163852000,166688000,57.319087982177734,1.4807440042495728,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166688000,163852000,166688000,61.1820068359375,1.8355499505996704,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166688000,163852000,166688000,66.45992279052734,2.320322036743164,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166688000,163852000,166688000,69.28874969482422,2.5801470279693604,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166688000,163852000,166688000,75.1694564819336,3.1202850341796875,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166557000,163740000,166557000,50.36064910888672,0.7819802165031433,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,54.10190963745117,1.0937520265579224,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,58.715545654296875,1.4807440042495728,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,62.92995071411133,1.83555006980896,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,68.68809509277344,2.320322036743164,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,71.77430725097656,2.5801470279693604,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166557000,163740000,166557000,78.190093994140625,3.1202850341796875,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166771000,163973000,166771000,51.08197784423828,0.7819801568984985,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,55.135013580322266,1.0937520265579224,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,60.12097930908203,1.4807440042495728,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,64.6869888305664,1.8355499505996704,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,70.92552947998047,2.320322036743164,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,74.26922607421875,2.5801470279693604,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,166771000,163973000,166771000,81.22027587890625,3.1202850341796875,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167057000,164274000,167057000,51.81028366088867,0.7819801568984985,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,56.16987991333008,1.0937520265579224,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,61.53374481201172,1.4807440042495728,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,66.45149993896484,1.83555006980896,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,73.1706314086914,2.320322036743164,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,76.77191925048828,2.5801470279693604,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167057000,164274000,167057000,84.2584457397461,3.1202850341796875,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167187000,164410000,167187000,52.545291900634766,0.7819802165031433,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,57.20595932006836,1.0937520265579224,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,62.95367431640625,1.4807440042495728,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,68.223358154296875,1.83555006980896,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,75.42333221435547,2.320322036743164,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,79.2823257446289,2.5801470279693604,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,1,167187000,164410000,167187000,87.30461120605469,3.1202850341796875,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,1.0113539695739746,pref1005_p99,r50,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,1.9077850580215454,pref1005_p99,r60,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,2.18613338470459,pref1005_p99,r70,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,2.65557599067688,pref1005_p99,r80,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,3.458876132965088,pref1005_p99,r90,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,3.8831889629364014,pref1005_p99,r95,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,3.8831889629364014,pref1005_p99,r99,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,14.32271671295166,1.0113539695739746,pref1005_p99,r50,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,15.219147682189941,1.9077850580215454,pref1005_p99,r60,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,15.497187614440918,2.185825824737549,pref1005_p99,r70,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,15.966938018798828,2.65557599067688,pref1005_p99,r80,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,16.77023696899414,3.458875894546509,pref1005_p99,r90,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,17.194551467895508,3.8831889629364014,pref1005_p99,r95,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,17.194551467895508,3.8831889629364014,pref1005_p99,r99,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,15.340529441833496,1.0113539695739746,pref1005_p99,r50,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,17.133390426635742,1.9077850580215454,pref1005_p99,r60,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,17.689205169677734,2.185692548751831,pref1005_p99,r70,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,18.62897300720215,2.65557599067688,pref1005_p99,r80,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,20.235572814941406,3.458875894546509,pref1005_p99,r90,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,21.084197998046875,3.8831889629364014,pref1005_p99,r95,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,130017851,59929514,130017851,21.084197998046875,3.8831889629364014,pref1005_p99,r99,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,16.360254287719727,1.0113539695739746,pref1005_p99,r50,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,19.049549102783203,1.9077849388122559,pref1005_p99,r60,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,19.8831844329834,2.1856634616851807,pref1005_p99,r70,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,21.29292106628418,2.65557599067688,pref1005_p99,r80,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,23.702821731567383,3.458876132965088,pref1005_p99,r90,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,24.975759506225586,3.8831889629364014,pref1005_p99,r95,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,133576484,61586844,133576484,24.975759506225586,3.8831889629364014,pref1005_p99,r99,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,17.378076553344727,1.0113539695739746,pref1005_p99,r50,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,20.96379852294922,1.9077850580215454,pref1005_p99,r60,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,22.075109481811523,2.185612201690674,pref1005_p99,r70,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,23.95496368408203,2.65557599067688,pref1005_p99,r80,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,27.168163299560547,3.458875894546509,pref1005_p99,r90,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,28.86541748046875,3.8831889629364014,pref1005_p99,r95,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,137130200,63260000,137130200,28.86541748046875,3.8831889629364014,pref1005_p99,r99,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,18.392385482788086,1.0113539695739746,pref1005_p99,r50,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,22.874540328979492,1.9077850580215454,pref1005_p99,r60,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,24.26266098022461,2.1854090690612793,pref1005_p99,r70,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,26.613496780395508,2.65557599067688,pref1005_p99,r80,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,30.629995346069336,3.458875894546509,pref1005_p99,r90,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,32.75156021118164,3.8831889629364014,pref1005_p99,r95,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,140587400,64902000,140587400,32.75156021118164,3.8831889629364014,pref1005_p99,r99,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,19.401735305786133,1.0113539695739746,pref1005_p99,r50,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,24.78032112121582,1.9077850580215454,pref1005_p99,r60,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,26.444488525390625,2.1851460933685303,pref1005_p99,r70,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,29.267066955566406,2.65557599067688,pref1005_p99,r80,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,34.08686828613281,3.458875894546509,pref1005_p99,r90,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,36.63274383544922,3.8831889629364014,pref1005_p99,r95,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,36.63274383544922,3.8831889629364014,pref1005_p99,r99,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,20.406539916992187,1.0113539695739746,pref1005_p99,r50,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,26.681556701660156,1.9077849388122559,pref1005_p99,r60,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,28.620140075683594,2.184725284576416,pref1005_p99,r70,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,31.916093826293945,2.65557599067688,pref1005_p99,r80,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,37.53919219970703,3.458876132965088,pref1005_p99,r90,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,40.50938415527344,3.8831889629364014,pref1005_p99,r95,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,40.50938415527344,3.8831889629364014,pref1005_p99,r99,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,21.406600952148438,1.0113539695739746,pref1005_p99,r50,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,28.578046798706055,1.9077849388122559,pref1005_p99,r60,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,30.78922462463379,2.1841821670532227,pref1005_p99,r70,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,34.56037902832031,2.65557599067688,pref1005_p99,r80,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,40.986778259277344,3.458876132965088,pref1005_p99,r90,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,44.381282806396484,3.8831889629364014,pref1005_p99,r95,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,150620600,69775000,150620600,44.381282806396484,3.8831889629364014,pref1005_p99,r99,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,22.402851104736328,1.0113539695739746,pref1005_p99,r50,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,30.470731735229492,1.9077849388122559,pref1005_p99,r60,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,32.95268249511719,2.1835575103759766,pref1005_p99,r70,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,37.20085144042969,2.65557599067688,pref1005_p99,r80,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,44.43054962158203,3.458875894546509,pref1005_p99,r90,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,48.249366760253906,3.8831889629364014,pref1005_p99,r95,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,153830800,71364000,153830800,48.249366760253906,3.8831889629364014,pref1005_p99,r99,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,23.394878387451172,1.0113539695739746,pref1005_p99,r50,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,32.359188079833984,1.9077849388122559,pref1005_p99,r60,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,35.1100959777832,2.182875871658325,pref1005_p99,r70,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,39.83709716796875,2.65557599067688,pref1005_p99,r80,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,47.87009811401367,3.458875894546509,pref1005_p99,r90,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,52.11322784423828,3.8831889629364014,pref1005_p99,r95,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,156898800,72890000,156898800,52.11322784423828,3.8831889629364014,pref1005_p99,r99,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,24.383419036865234,1.0113539695739746,pref1005_p99,r50,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,34.24415969848633,1.9077849388122559,pref1005_p99,r60,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,37.262210845947266,2.1821532249450684,pref1005_p99,r70,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,42.4698600769043,2.65557599067688,pref1005_p99,r80,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,51.30615997314453,3.458876132965088,pref1005_p99,r90,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,55.97360610961914,3.8831889629364014,pref1005_p99,r95,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,159815800,74365000,159815800,55.97360610961914,3.8831889629364014,pref1005_p99,r99,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,162662800,75802000,162662800,25.368595123291016,1.0113539695739746,pref1005_p99,r50,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,162662800,75802000,162662800,36.12576675415039,1.9077850580215454,pref1005_p99,r60,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,162662800,75802000,162662800,39.40870666503906,2.181363344192505,pref1005_p99,r70,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,162662800,75802000,162662800,45.09926223754883,2.65557599067688,pref1005_p99,r80,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,162662800,75802000,162662800,54.738861083984375,3.458875894546509,pref1005_p99,r90,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,162662800,75802000,162662800,59.83061218261719,3.8831889629364014,pref1005_p99,r95,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,162662800,75802000,162662800,59.83061218261719,3.8831889629364014,pref1005_p99,r99,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,165439800,77204000,165439800,26.34986114501953,1.0113539695739746,pref1005_p99,r50,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,165439800,77204000,165439800,38.00346374511719,1.9077849388122559,pref1005_p99,r60,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,165439800,77204000,165439800,41.548851013183594,2.180507183074951,pref1005_p99,r70,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,165439800,77204000,165439800,47.72474670410156,2.65557599067688,pref1005_p99,r80,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,165439800,77204000,165439800,58.16764831542969,3.458875894546509,pref1005_p99,r90,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,165439800,77204000,165439800,63.67603302001953,3.8831889629364014,pref1005_p99,r95,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,165439800,77204000,165439800,63.67603302001953,3.8831889629364014,pref1005_p99,r99,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,168218800,78584000,168218800,27.330652236938477,1.0113539695739746,pref1005_p99,r50,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,168218800,78584000,168218800,39.88068389892578,1.9077849388122559,pref1005_p99,r60,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,168218800,78584000,168218800,43.686744689941406,2.1796462535858154,pref1005_p99,r70,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,168218800,78584000,168218800,50.34975814819336,2.65557599067688,pref1005_p99,r80,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,168218800,78584000,168218800,61.59550094604492,3.458875894546509,pref1005_p99,r90,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,168218800,78584000,168218800,67.51539611816406,3.8831889629364014,pref1005_p99,r95,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,168218800,78584000,168218800,67.51539611816406,3.8831889629364014,pref1005_p99,r99,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r50,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,171013600,79938000,171013600,28.31182289123535,1.0113539695739746,pref1005_p99,r50,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r60,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,171013600,79938000,171013600,41.7582893371582,1.9077850580215454,pref1005_p99,r60,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r70,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,171013600,79938000,171013600,45.82392120361328,2.1788272857666016,pref1005_p99,r70,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r80,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,171013600,79938000,171013600,52.975154876708984,2.65557599067688,pref1005_p99,r80,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r90,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,171013600,79938000,171013600,65.012481689453125,3.458875894546509,pref1005_p99,r90,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r95,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,171013600,79938000,171013600,71.35559844970703,3.8831889629364014,pref1005_p99,r95,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _r99,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,1,171013600,79938000,171013600,71.35559844970703,3.8831889629364014,pref1005_p99,r99,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149394031,119064332,137060551,78.835296630859375,0.699194610118866,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149077719,118834295,136859941,79.55028533935547,0.6991451382637024,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149194897,118974717,137035063,80.27864837646484,0.6990070939064026,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149550605,119310987,137409419,80.99278259277344,0.6988229155540466,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150019000,119749000,137891600,81.683807373046875,0.6986629962921143,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150628600,120324000,138522300,82.36073303222656,0.6985830664634705,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,151525800,121126000,139400600,83.02196502685547,0.698599100112915,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152505100,122012000,140362900,83.6702651977539,0.6986916661262512,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,153467300,122892000,141321100,84.309814453125,0.6988368034362793,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154176900,123576000,142048000,84.95072937011719,0.6989783644676208,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154377900,123869000,142317300,85.60200500488281,0.6990606188774109,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,154150700,123833000,142169200,86.26575469970703,0.6990731954574585,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,153406800,123380000,141555400,86.93553161621094,0.699047863483429,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,152239200,122572000,140546600,87.60658264160156,0.699003279209137,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,150797500,121481000,139277700,88.27112579345703,0.6989870071411133,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,EAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,149187800,120142000,137827900,88.920379638671875,0.6990488171577454,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,0,50506526,20018286,27044897,86.713775634765625,0.5586810111999512,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,1,51161440,20368622,27500412,87.38346099853516,0.5590953230857849,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,51999312,20796720,28116485,88.046112060546875,0.5592811703681946,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,2,52970060,21270042,28835805,88.69981384277344,0.5593435168266296,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,53885500,21744000,29559000,89.33927917480469,0.559627890586853,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,54676500,22192000,30238000,89.93632507324219,0.5605195164680481,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,55346600,22587000,30861000,90.32106018066406,0.5621610879898071,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,3,55868600,22933000,31418000,90.65601348876953,0.5642195343971252,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56237800,23210000,31884000,90.97667694091797,0.5665426254272461,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56462900,23423000,32250000,91.28301239013672,0.5686290860176086,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,4,56537000,23548000,32476000,91.58580780029297,0.5701121091842651,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,5,56500000,23610000,32582000,91.87733459472656,0.5710886716842651,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,56300000,23583000,32538000,92.12187194824219,0.5714664459228516,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55951000,23486000,32376000,92.35631561279297,0.571571409702301,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,55498000,23319000,32115000,92.562164306640625,0.57135409116745,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,ECS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,12,6,54950900,23082000,31761000,92.74585723876953,0.571153461933136,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,54320207,47141204,53347001,49.220916748046875,0.6367214918136597,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53874523,46769142,52924235,49.842281341552734,0.6366537809371948,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,53419696,46387233,52487704,50.4609489440918,0.6364877820014954,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52972224,46009003,52056859,51.076629638671875,0.6362367868423462,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52585700,45658000,51674100,51.69180679321289,0.6359319686889648,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52293900,45361000,51382800,52.31181716918945,0.635641872882843,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52140500,45197000,51234000,52.93614196777344,0.6353102922439575,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52070700,45089000,51167900,53.561710357666016,0.6349815726280212,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52046300,45020000,51154200,54.19137954711914,0.63471221923828125,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,0,52035600,44980000,51155200,54.81608581542969,0.6345139741897583,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51986100,44949000,51122500,55.43092346191406,0.6344120502471924,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51910700,44944000,51070600,56.03928756713867,0.6344410181045532,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51808600,44948000,50997200,56.63621520996094,0.6345345377922058,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51674300,44948000,50895100,57.22663116455078,0.6346940994262695,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51508700,44904000,50758900,57.82044219970703,0.6348749399185181,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,LCN,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,16,1,51308300,44792000,50581800,58.41462707519531,0.6349273324012756,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37219229,22375764,32533278,36.700706481933594,1.3200181722640991,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,37709998,22649117,32944277,38.04752731323242,1.3221938610076904,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,38364182,23003601,33506663,39.41108703613281,1.324939250946045,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,39183069,23427353,34198154,40.787384033203125,1.3280260562896729,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,40154000,23939000,35018000,42.15468978881836,1.3304471969604492,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,41268000,24571000,35973000,43.48115158081055,1.3309776782989502,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,42549000,25348000,37076000,44.731258392333984,1.3285300731658936,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,43918000,26215000,38301000,45.940982818603516,1.3247932195663452,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,45327000,27120000,39581000,47.12851333618164,1.3205573558807373,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,46661000,27960000,40792000,48.34754943847656,1.3177978992462158,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,47753000,28617000,41789000,49.654762268066406,1.3184419870376587,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,48604000,29086000,42574000,51.072715759277344,1.3227343559265137,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49178000,29370000,43122000,52.56622314453125,1.3293507099151611,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,0,49529000,29504000,43471000,54.118560791015625,1.3374519348144531,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49727000,29537000,43673000,55.62118148803711,1.3444616794586182,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,MEA,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,6,1,49793000,29506000,43753000,56.38143539428711,1.3479437828063965,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22578317,0,0,NA,NA,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22737231,0,0,NA,NA,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22894988,0,0,NA,NA,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23045167,0,0,NA,NA,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23143000,0,0,NA,NA,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23127000,0,0,NA,NA,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,23120000,0,0,NA,NA,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22992000,0,0,NA,NA,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22774000,0,0,NA,NA,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22527000,0,0,NA,NA,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22316000,0,0,NA,NA,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22261000,0,0,NA,NA,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22223000,0,0,NA,NA,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22193000,0,0,NA,NA,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22171000,0,0,NA,NA,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,NAC,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,0,0,22167000,0,0,NA,NA,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,174647837,171290911,174647837,41.794525146484375,0.6788902282714844,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,175077905,171816252,175077905,42.442386627197266,0.6788902282714844,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,175352123,172166136,175352123,43.08539962768555,0.6788902282714844,pref1005_p99,own,1005 +2018,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,175469465,172344232,175469465,43.72254943847656,0.6788902282714844,pref1005_p99,own,1005 +2019,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,175307000,172233000,175307000,44.34861755371094,0.6788902282714844,pref1005_p99,own,1005 +2020,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,174676000,171645000,174676000,44.963661193847656,0.6788902282714844,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,173463000,170479000,173463000,45.56877136230469,0.6788902282714844,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,171907000,168958000,171907000,46.16118240356445,0.6788902282714844,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,170127000,167209000,170127000,46.7441291809082,0.6788902282714844,pref1005_p99,own,1005 +2024,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,168476000,165585000,168476000,47.325347900390625,0.6788902282714844,pref1005_p99,own,1005 +2025,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167302000,164441000,167302000,47.9140739440918,0.6788902282714844,pref1005_p99,own,1005 +2026,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166688000,163852000,166688000,48.5140266418457,0.6788902282714844,pref1005_p99,own,1005 +2027,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166557000,163740000,166557000,49.12356948852539,0.6788902282714844,pref1005_p99,own,1005 +2028,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,166771000,163973000,166771000,49.7418098449707,0.6788902282714844,pref1005_p99,own,1005 +2029,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167057000,164274000,167057000,50.36702346801758,0.6788902282714844,pref1005_p99,own,1005 +2030,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SAS,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,5,0,167187000,164410000,167187000,50.99894714355469,0.6788902282714844,pref1005_p99,own,1005 +2015,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,123159024,56789447,123159024,13.309395790100098,1.0533517599105835,pref1005_p99,own,1005 +2016,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,126528932,58321977,126528932,14.364421844482422,1.053059697151184,pref1005_p99,own,1005 +2017,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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Used,17,0,140587400,64902000,140587400,18.598936080932617,1.052663803100586,pref1005_p99,own,1005 +2021,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,143980500,66532000,143980500,19.648096084594727,1.0524142980575562,pref1005_p99,own,1005 +2022,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample Used,17,0,147325600,68158000,147325600,20.691162109375,1.0520144701004028,pref1005_p99,own,1005 +2023,preference: 1005 | rate_flavor: growei | benchmark: _own,1,SSF,pref1,filename(pref1005_p99) + ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!=,Full Sample Used,Full Sample 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a/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_numbers_country_level.csv b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_numbers_country_level.csv new file mode 100644 index 0000000..6744689 --- /dev/null +++ b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/pref1005_p99_sim_numbers_country_level.csv @@ -0,0 +1,218 @@ 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 +AND,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Andorra,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +ARE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,357716,NA,0,NA,376794,NA,0,NA,391950,NA,0,NA,410445,NA,0,NA,432000,NA,0,NA,453000,NA,0,NA,475000,NA,0,NA,488000,NA,0,NA,501000,NA,0,NA,515000,NA,0,NA,525000,NA,0,NA,531000,NA,0,NA,528000,NA,0,NA,522000,NA,0,NA,518000,NA,0,NA,517000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,United Arab Emirates,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,1005,0 +ARG,53.878196716308594,53.19930648803711,52.520416259765625,51.84152603149414,51.162635803222656,50.48374557495117,49.80485534667969,49.1259651184082,48.44707489013672,47.768184661865234,47.08929443359375,46.410404205322266,45.73151397705078,45.0526237487793,44.37373352050781,43.69484329223633,53.878196716308594,52.82185745239258,51.76551818847656,50.70918273925781,49.6528434753418,48.59650421142578,47.540164947509766,46.48382568359375,45.427490234375,44.371150970458984,43.31481170654297,42.25847244262695,41.20213317871094,40.14579391479492,39.08945846557617,38.033119201660156,53.878196716308594,52.399322509765625,50.92045211791992,49.44157791137695,47.962703704833984,46.483829498291016,45.00495910644531,43.526084899902344,42.047210693359375,40.56834030151367,39.0894660949707,37.610591888427734,36.131717681884766,34.65284729003906,33.173973083496094,31.695098876953125,53.878196716308594,51.97041320800781,50.062625885009766,48.154842376708984,46.24705505371094,44.339271545410156,42.43148422241211,40.52370071411133,38.61591339111328,36.7081298828125,34.80034637451172,32.89256286621094,30.984771728515625,29.076988220214844,27.169204711914063,25.26142120361328,53.878196716308594,51.22262191772461,48.56704330444336,45.911468505859375,43.255889892578125,40.60031509399414,37.944740295410156,35.289161682128906,32.63359069824219,29.978012084960938,27.322433471679688,24.666854858398438,22.01128387451172,19.35570526123047,16.70012664794922,14.0445556640625,53.878196716308594,50.53849411010742,47.198787689208984,43.85908508300781,40.519378662109375,37.1796760559082,33.83997344970703,30.500267028808594,27.160568237304688,23.82086181640625,20.481155395507813,17.141448974609375,13.801750183105469,10.462043762207031,7.122337341308594,3.7826385498046875,53.878196716308594,49.9950065612793,46.11181640625,42.22863006591797,38.34543991088867,34.462249755859375,30.579063415527344,26.69586944580078,22.81268310546875,18.929489135742187,15.046302795410156,11.163116455078125,7.2799224853515625,3.3967361450195312,0,0,3549320,3549320,1,53.878196716308594,3561847,3561847,1,53.26243209838867,3573119,3573119,1,52.646663665771484,3583581,3583581,1,52.03089904785156,3593000,3593000,1,51.415130615234375,3606000,3606000,1,50.79936599731445,3625000,3625000,1,50.183597564697266,3648000,3648000,1,49.567832946777344,3673000,3673000,1,48.95206832885742,3695000,3695000,1,48.336299896240234,3712000,3712000,1,47.72053527832031,3724000,3724000,1,47.104766845703125,3731000,3731000,1,46.4890022277832,3734000,3734000,1,45.87323760986328,3733000,3733000,1,45.257469177246094,3731000,3731000,1,44.64170455932617,53.878196716308594,53.26243209838867,52.646663665771484,52.03089904785156,51.415130615234375,50.79936599731445,50.18360137939453,49.567832946777344,48.95206832885742,48.336299896240234,47.72053527832031,47.104766845703125,46.4890022277832,45.87323760986328,45.257469177246094,44.64170455932617,53.878196716308594,53.235965728759766,52.5937385559082,51.951507568359375,51.30928039550781,50.667049407958984,50.024818420410156,49.382591247558594,48.740360260009766,48.0981330871582,47.455902099609375,46.81367492675781,46.171443939208984,45.529212951660156,44.886985778808594,44.244754791259766,53.878196716308594,52.587303161621094,51.29640579223633,50.00551223754883,48.71461486816406,47.42372131347656,46.13282775878906,44.8419303894043,43.5510368347168,42.2601432800293,40.96924591064453,39.67835235595703,38.387454986572266,37.096561431884766,35.8056640625,34.5147705078125,53.878196716308594,52.06976318359375,50.261329650878906,48.45289611816406,46.644466400146484,44.83603286743164,43.0275993347168,41.21916580200195,39.41073226928711,37.602298736572266,35.79386901855469,33.985435485839844,32.177001953125,30.368568420410156,28.560134887695313,26.75170135498047,53.878196716308594,51.862300872802734,49.846405029296875,47.83051300048828,45.81461715698242,43.79872131347656,41.7828254699707,39.76693344116211,37.75103759765625,35.735137939453125,33.71924591064453,31.703353881835938,29.687454223632812,27.67156219482422,25.655670166015625,23.6397705078125,53.878196716308594,51.74016571044922,49.60213088989258,47.4640998840332,45.32606887817383,43.18803787231445,41.05000305175781,38.91197204589844,36.77394104003906,34.63591003417969,32.49787902832031,30.359848022460937,28.22180938720703,26.083778381347656,23.94574737548828,21.807716369628906,53.878196716308594,50.53849411010742,47.198787689208984,43.85908508300781,40.51938247680664,37.1796760559082,33.83997344970703,30.500267028808594,27.160568237304688,23.82086181640625,20.481155395507813,17.141448974609375,13.801750183105469,10.462043762207031,7.122337341308594,3.7826385498046875,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 +ASM,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,American Samoa,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,LNX,Not classified,1005,0 +ATG,54.847354888916016,54.16846466064453,53.48957443237305,52.81068420410156,52.13179397583008,51.452903747558594,50.77401351928711,50.095123291015625,49.41623306274414,48.737342834472656,48.05845260620117,47.37956237792969,46.7006721496582,46.02178192138672,45.342891693115234,44.66400146484375,54.847354888916016,53.791015625,52.734676361083984,51.678340911865234,50.62200164794922,49.5656623840332,48.50932312011719,47.45298385620117,46.39664840698242,45.340309143066406,44.28396987915039,43.227630615234375,42.17129135131836,41.114952087402344,40.058616638183594,39.00227737426758,54.847354888916016,53.164451599121094,51.48155212402344,49.79865264892578,48.115753173828125,46.43285369873047,44.74995422363281,43.23556137084961,41.761390686035156,40.28722381591797,38.813053131103516,37.33888626098633,35.864715576171875,34.39054870605469,32.9163818359375,31.442211151123047,54.847354888916016,52.83568572998047,50.824012756347656,48.81234359741211,46.8006706237793,44.78900146484375,42.7773323059082,40.93416976928711,39.131229400634766,37.32829284667969,35.525352478027344,33.722415924072266,31.91947364807129,30.116535186767578,28.313596725463867,26.510658264160156,54.847354888916016,52.14902114868164,49.45068359375,46.75235366821289,44.05401611328125,41.355682373046875,38.6573486328125,36.12752151489258,33.63792037963867,31.148319244384766,28.658714294433594,26.169111251831055,23.67951202392578,21.319091796875,19.020614624023438,16.86296272277832,54.847354888916016,51.507652282714844,48.167945861816406,44.828243255615234,41.4885368347168,38.148834228515625,34.80913162231445,31.6379337310791,28.506961822509766,25.375991821289062,22.303861618041992,19.38161849975586,16.66568374633789,14.07374382019043,11.77769660949707,9.6861572265625,54.847354888916016,50.96416473388672,47.08097457885742,43.19778823852539,39.314598083496094,35.4314079284668,31.711803436279297,28.071311950683594,24.430824279785156,20.94615364074707,17.676881790161133,14.583698272705078,11.886035919189453,9.469087600708008,7.315194606781006,5.5359110832214355,7063,NA,0,54.847354888916016,6971,NA,0,54.1507682800293,6899,NA,0,53.45417785644531,6845,NA,0,52.757591247558594,6800,NA,0,52.06100082397461,6800,NA,0,51.364410400390625,6900,NA,0,50.667823791503906,6900,NA,0,49.97123718261719,7100,NA,0,49.2746467590332,7100,NA,0,48.57805633544922,7300,NA,0,47.8814697265625,7300,NA,0,47.19894790649414,7300,NA,0,46.62114715576172,7300,NA,0,46.04335021972656,7300,NA,0,45.46554946899414,7300,NA,0,44.88774871826172,54.847354888916016,54.231590270996094,53.615821838378906,53.000057220458984,52.3842887878418,51.768524169921875,51.15275955200195,50.536991119384766,49.921226501464844,49.305458068847656,48.689693450927734,48.07392501831055,47.458160400390625,46.8423957824707,46.226627349853516,45.610862731933594,54.847354888916016,54.20512390136719,53.562896728515625,52.9206657409668,52.278438568115234,51.636207580566406,50.99397659301758,50.351749420166016,49.70951843261719,49.067291259765625,48.4250602722168,47.782833099365234,47.140602111816406,46.49837112426758,45.856143951416016,45.21391296386719,54.847354888916016,53.2936897277832,51.74002456665039,50.18635940551758,48.6326904296875,47.07902908325195,45.52536392211914,44.14020538330078,42.79527282714844,41.450340270996094,40.105403900146484,38.76047134399414,37.41553497314453,36.07060623168945,34.725669860839844,33.3807373046875,54.847354888916016,52.91728210449219,50.987213134765625,49.05714416503906,47.1270751953125,45.19700241088867,43.26693344116211,41.50537109375,39.784034729003906,38.06269454956055,36.34135437011719,34.620018005371094,32.898677825927734,31.177339553833008,29.45600128173828,27.734663009643555,54.847354888916016,52.7410888671875,50.63481903076172,48.52855682373047,46.42228698730469,44.31602096557617,42.209754943847656,40.271995544433594,38.37446212768555,36.476924896240234,34.57939147949219,32.68185806274414,30.784320831298828,28.88678550720215,26.9892520904541,25.09171485900879,54.847354888916016,52.70932388305664,50.5712890625,48.433258056640625,46.29522705078125,44.157196044921875,42.019161224365234,39.88113021850586,37.743099212646484,35.60506820678711,33.51069259643555,31.506288528442383,29.501882553100586,27.497478485107422,25.493072509765625,23.48866844177246,54.847354888916016,51.507652282714844,48.167945861816406,44.828243255615234,41.4885368347168,38.148834228515625,34.80913162231445,31.6379337310791,28.5069637298584,25.375991821289062,22.303861618041992,19.38161849975586,16.66568374633789,14.07374382019043,11.77769660949707,9.6861572265625,Antigua and Barbuda,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 +AUS,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1418447,NA,0,NA,1453571,NA,0,NA,1497278,NA,0,NA,1543775,NA,0,NA,1584000,NA,0,NA,1617000,NA,0,NA,1647000,NA,0,NA,1669000,NA,0,NA,1687000,NA,0,NA,1702000,NA,0,NA,1719000,NA,0,NA,1744000,NA,0,NA,1767000,NA,0,NA,1791000,NA,0,NA,1808000,NA,0,NA,1819000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,1005,0 +AUT,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,414655,NA,0,NA,418339,NA,0,NA,419718,NA,0,NA,420054,NA,0,NA,419000,NA,0,NA,418000,NA,0,NA,417000,NA,0,NA,417000,NA,0,NA,418000,NA,0,NA,419000,NA,0,NA,422000,NA,0,NA,427000,NA,0,NA,432000,NA,0,NA,437000,NA,0,NA,443000,NA,0,NA,447000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +AZE,23.281028747558594,22.602142333984375,21.923248291015625,21.244354248046875,20.565467834472656,19.886581420898438,19.207687377929688,18.528793334960938,17.84990692138672,17.1710205078125,16.49212646484375,15.813232421875,15.134346008300781,14.455459594726563,13.776565551757812,13.097671508789062,23.281028747558594,22.224693298339844,21.168350219726563,20.112014770507812,19.05567169189453,17.99933624267578,16.94300079345703,15.88665771484375,14.830322265625,13.773979187011719,12.717643737792969,11.661308288574219,10.604965209960938,9.5486297607421875,8.492286682128906,7.435951232910156,23.281028747558594,21.802154541015625,20.323280334472656,18.844406127929688,17.36553955078125,15.886665344238281,14.407791137695313,12.928916931152344,11.450042724609375,9.971168518066406,8.4922943115234375,7.013427734375,5.534553527832031,4.0556793212890625,2.5768051147460937,1.097930908203125,23.281028747558594,21.373245239257813,19.46546173095703,17.55767059326172,15.649887084960938,13.742103576660156,11.834320068359375,9.9265289306640625,8.018745422363281,6.1109619140625,4.203178405761719,2.2953948974609375,0.387603759765625,0,0,0,23.281028747558594,20.625450134277344,17.969879150390625,15.314300537109375,12.658721923828125,10.003150939941406,7.347572326660156,4.691993713378906,2.0364227294921875,0,0,0,0,0,0,0,23.281028747558594,19.941322326660156,16.60162353515625,13.261917114257812,9.922210693359375,6.582511901855469,3.2428054809570313,0,0,0,0,0,0,0,0,0,23.281028747558594,19.397842407226563,15.5146484375,11.631462097167969,7.748268127441406,3.865081787109375,0,0,0,0,0,0,0,0,0,0,639852,639852,1,23.281028747558594,639428,639428,1,22.70062255859375,645233,645233,1,22.120216369628906,656388,656388,1,21.539810180664062,673000,673000,1,20.95940399169922,696000,696000,1,20.378997802734375,728000,728000,1,19.79859161376953,764000,764000,1,19.218185424804687,801000,801000,1,18.637779235839844,835000,835000,1,18.057373046875,856000,856000,1,17.476966857910156,866000,866000,1,16.896560668945312,864000,864000,1,16.31615447998047,853000,853000,1,15.735748291015625,836000,836000,1,15.155342102050781,817000,817000,1,14.574935913085937,23.281028747558594,22.70062255859375,22.120216369628906,21.539810180664062,20.95940399169922,20.378997802734375,19.79859161376953,19.218185424804687,18.637779235839844,18.057373046875,17.476966857910156,16.896560668945312,16.31615447998047,15.735748291015625,15.155342102050781,14.574935913085937,23.281028747558594,22.521621704101563,21.76220703125,21.00279998779297,20.243392944335938,19.483985900878906,18.724571228027344,17.965164184570313,17.20575714111328,16.44635009765625,15.686935424804688,14.927528381347656,14.168121337890625,13.408714294433594,12.649299621582031,11.889892578125,23.281028747558594,22.224693298339844,21.168350219726563,20.112014770507812,19.05567169189453,17.99933624267578,16.94300079345703,15.88665771484375,14.830322265625,13.773979187011719,12.717643737792969,11.661308288574219,10.604965209960938,9.5486297607421875,8.492286682128906,7.435951232910156,23.281028747558594,22.030136108398438,20.77924346923828,19.528358459472656,18.2774658203125,17.026573181152344,15.775680541992188,14.524795532226562,13.273902893066406,12.02301025390625,10.772117614746094,9.5212249755859375,8.2703399658203125,7.019447326660156,5.7685546875,4.517662048339844,23.281028747558594,21.77197265625,20.262916564941406,18.753860473632813,17.24480438232422,15.735748291015625,14.226692199707031,12.717636108398438,11.208580017089844,9.69952392578125,8.190467834472656,6.6814117431640625,5.172355651855469,3.663299560546875,2.1542434692382812,0.6451873779296875,23.281028747558594,21.77197265625,20.262916564941406,18.753860473632813,17.24480438232422,15.735748291015625,14.226692199707031,12.717636108398438,11.208580017089844,9.69952392578125,8.190467834472656,6.6814117431640625,5.172355651855469,3.663299560546875,2.1542434692382812,0.6451873779296875,23.281028747558594,21.036537170410156,18.79204559326172,16.54754638671875,14.303054809570313,12.058563232421875,9.8140716552734375,7.569580078125,5.3250885009765625,3.0805892944335937,0.83609771728515625,0,0,0,0,0,Azerbaijan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia,SAS,South Asia (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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and Herzegovina,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 +BMU,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Bermuda,NAC,North America,,,HIC,High income,LNX,Not classified,1005,0 +BOL,54.847354888916016,54.16846466064453,53.48957443237305,52.81068420410156,52.13179397583008,51.452903747558594,50.77401351928711,50.095123291015625,49.41623306274414,48.737342834472656,48.05845260620117,47.37956237792969,46.7006721496582,46.02178192138672,45.342891693115234,44.66400146484375,54.847354888916016,53.791015625,52.734676361083984,51.678340911865234,50.62200164794922,49.5656623840332,48.50932312011719,47.45298385620117,46.39664840698242,45.340309143066406,44.28396987915039,43.227630615234375,42.17129135131836,41.114952087402344,40.058616638183594,39.00227737426758,54.847354888916016,53.164451599121094,51.48155212402344,49.79865264892578,48.115753173828125,46.43285369873047,44.74995422363281,43.23556137084961,41.761390686035156,40.28722381591797,38.813053131103516,37.33888626098633,35.864715576171875,34.39054870605469,32.9163818359375,31.442211151123047,54.847354888916016,52.83568572998047,50.824012756347656,48.81234359741211,46.8006706237793,44.78900146484375,42.7773323059082,40.93416976928711,39.131229400634766,37.32829284667969,35.525352478027344,33.722415924072266,31.91947364807129,30.116535186767578,28.313596725463867,26.510658264160156,54.847354888916016,52.14902114868164,49.45068359375,46.75235366821289,44.05401611328125,41.355682373046875,38.6573486328125,36.12752151489258,33.63792037963867,31.148319244384766,28.658714294433594,26.169111251831055,23.67951202392578,21.319091796875,19.020614624023438,16.86296272277832,54.847354888916016,51.507652282714844,48.167945861816406,44.828243255615234,41.4885368347168,38.148834228515625,34.80913162231445,31.6379337310791,28.506961822509766,25.375991821289062,22.303861618041992,19.38161849975586,16.66568374633789,14.07374382019043,11.77769660949707,9.6861572265625,54.847354888916016,50.96416473388672,47.08097457885742,43.19778823852539,39.314598083496094,35.4314079284668,31.711803436279297,28.071311950683594,24.430824279785156,20.94615364074707,17.676881790161133,14.583698272705078,11.886035919189453,9.469087600708008,7.315194606781006,5.5359110832214355,1152997,NA,0,54.847354888916016,1156399,NA,0,54.1507682800293,1158441,NA,0,53.45417785644531,1159405,NA,0,52.757591247558594,1160000,NA,0,52.06100082397461,1161000,NA,0,51.364410400390625,1163000,NA,0,50.667823791503906,1165000,NA,0,49.97123718261719,1167000,NA,0,49.2746467590332,1169000,NA,0,48.57805633544922,1169000,NA,0,47.8814697265625,1168000,NA,0,47.19894790649414,1166000,NA,0,46.62114715576172,1164000,NA,0,46.04335021972656,1162000,NA,0,45.46554946899414,1162000,NA,0,44.88774871826172,54.847354888916016,54.231590270996094,53.615821838378906,53.000057220458984,52.3842887878418,51.768524169921875,51.15275955200195,50.536991119384766,49.921226501464844,49.305458068847656,48.689693450927734,48.07392501831055,47.458160400390625,46.8423957824707,46.226627349853516,45.610862731933594,54.847354888916016,54.20512390136719,53.562896728515625,52.9206657409668,52.278438568115234,51.636207580566406,50.99397659301758,50.351749420166016,49.70951843261719,49.067291259765625,48.4250602722168,47.782833099365234,47.140602111816406,46.49837112426758,45.856143951416016,45.21391296386719,54.847354888916016,53.2936897277832,51.74002456665039,50.18635940551758,48.6326904296875,47.07902908325195,45.52536392211914,44.14020538330078,42.79527282714844,41.450340270996094,40.105403900146484,38.76047134399414,37.41553497314453,36.07060623168945,34.725669860839844,33.3807373046875,54.847354888916016,52.91728210449219,50.987213134765625,49.05714416503906,47.1270751953125,45.19700241088867,43.26693344116211,41.50537109375,39.784034729003906,38.06269454956055,36.34135437011719,34.620018005371094,32.898677825927734,31.177339553833008,29.45600128173828,27.734663009643555,54.847354888916016,52.7410888671875,50.63481903076172,48.52855682373047,46.42228698730469,44.31602096557617,42.209754943847656,40.271995544433594,38.37446212768555,36.476924896240234,34.57939147949219,32.68185806274414,30.784320831298828,28.88678550720215,26.9892520904541,25.09171485900879,54.847354888916016,52.70932388305664,50.5712890625,48.433258056640625,46.29522705078125,44.157196044921875,42.019161224365234,39.88113021850586,37.743099212646484,35.60506820678711,33.51069259643555,31.506288528442383,29.501882553100586,27.497478485107422,25.493072509765625,23.48866844177246,54.847354888916016,51.507652282714844,48.167945861816406,44.828243255615234,41.4885368347168,38.148834228515625,34.80913162231445,31.6379337310791,28.5069637298584,25.375991821289062,22.303861618041992,19.38161849975586,16.66568374633789,14.07374382019043,11.77769660949707,9.6861572265625,Bolivia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Asia,SAS,South Asia (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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African Republic,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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classified,1005,0 +CHE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,396082,NA,0,NA,400202,NA,0,NA,403418,NA,0,NA,405535,NA,0,NA,408000,NA,0,NA,409000,NA,0,NA,415000,NA,0,NA,421000,NA,0,NA,427000,NA,0,NA,432000,NA,0,NA,436000,NA,0,NA,443000,NA,0,NA,449000,NA,0,NA,455000,NA,0,NA,458000,NA,0,NA,459000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +CHI,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,8607,NA,0,NA,8628,NA,0,NA,8615,NA,0,NA,8567,NA,0,NA,8500,NA,0,NA,8500,NA,0,NA,8600,NA,0,NA,8600,NA,0,NA,8800,NA,0,NA,8900,NA,0,NA,9000,NA,0,NA,9000,NA,0,NA,9000,NA,0,NA,9000,NA,0,NA,9000,NA,0,NA,8900,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Channel Islands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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d'Ivoire,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Verde,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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Rica,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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high income),UMC,Upper middle income,LNX,Not classified,1005,0 +CUW,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,10863,NA,0,NA,10886,NA,0,NA,10794,NA,0,NA,10589,NA,0,NA,10400,NA,0,NA,10400,NA,0,NA,10400,NA,0,NA,10400,NA,0,NA,10500,NA,0,NA,10500,NA,0,NA,10400,NA,0,NA,10200,NA,0,NA,10000,NA,0,NA,9800,NA,0,NA,9400,NA,0,NA,9200,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Curacao,LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 +CYM,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Cayman Islands,LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 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classified,1005,0 +DEU,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3803743,NA,0,NA,3805429,NA,0,NA,3793064,NA,0,NA,3783767,NA,0,NA,3765000,NA,0,NA,3761000,NA,0,NA,3740000,NA,0,NA,3731000,NA,0,NA,3733000,NA,0,NA,3746000,NA,0,NA,3774000,NA,0,NA,3824000,NA,0,NA,3884000,NA,0,NA,3947000,NA,0,NA,3999000,NA,0,NA,4026000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +DJI,63.83265686035156,63.15376281738281,62.474876403808594,61.79598617553711,61.117095947265625,60.438201904296875,59.759315490722656,59.08042526245117,58.40153503417969,57.7226448059082,57.04375457763672,56.364864349365234,55.68597412109375,55.007083892822266,54.32819366455078,53.6493034362793,63.83265686035156,62.77631759643555,61.71997833251953,60.66364288330078,59.6072998046875,58.55096435546875,57.494625091552734,56.43828582763672,55.38195037841797,54.32560729980469,53.26927185058594,52.21293258666992,51.156593322753906,50.10025405883789,49.043914794921875,47.987579345703125,63.83265686035156,62.005157470703125,60.17765808105469,58.35015869140625,56.52265930175781,54.695159912109375,52.8676643371582,51.040164947509766,49.21266555786133,47.385169982910156,45.55767059326172,43.838619232177734,42.56122589111328,41.28383255004883,40.006439208984375,38.729042053222656,63.83265686035156,61.69272994995117,59.55280303955078,57.412879943847656,55.272953033447266,53.133026123046875,50.993099212646484,48.85317611694336,46.71324920654297,44.57332229614258,42.43339920043945,40.40192413330078,38.81209945678711,37.2222785949707,35.6324577331543,34.04263687133789,63.83265686035156,61.06957244873047,58.30648422241211,55.543399810791016,52.78031539916992,50.01723098754883,47.254146575927734,44.491058349609375,41.72797775268555,38.96489334106445,36.201805114746094,33.54716873168945,31.334192276000977,29.1212100982666,26.908227920532227,24.69525146484375,63.83265686035156,60.492950439453125,57.15324783325195,53.81354522705078,50.473838806152344,47.13413619995117,43.79443359375,40.45472717285156,37.11502456665039,33.77532196044922,30.43561363220215,27.275997161865234,24.4929141998291,21.70982551574707,18.92673683166504,16.143653869628906,63.83265686035156,59.949466705322266,56.06627655029297,52.18309020996094,48.29990005493164,44.416709899902344,40.53351974487305,36.65032958984375,32.76714324951172,28.883949279785156,25.530040740966797,22.294052124023438,19.05805778503418,15.822068214416504,12.976293563842773,10.38750171661377,90908,NA,0,63.83265686035156,90289,NA,0,62.657325744628906,89556,NA,0,61.48199462890625,88776,NA,0,60.306663513183594,88000,NA,0,59.1313362121582,89000,NA,0,57.95600891113281,90000,NA,0,56.780677795410156,92000,NA,0,55.6053466796875,94000,NA,0,54.430015563964844,96000,NA,0,53.25468826293945,97000,NA,0,52.07936096191406,98000,NA,0,50.904029846191406,98000,NA,0,49.72869873046875,98000,NA,0,48.553367614746094,98000,NA,0,47.394771575927734,97000,NA,0,46.64511489868164,63.83265686035156,62.81291961669922,61.79317855834961,60.7734375,59.753700256347656,58.73396301269531,57.7142219543457,56.694480895996094,55.67474365234375,54.655006408691406,53.6352653503418,52.61552810668945,51.595787048339844,50.5760498046875,49.556312561035156,48.53657150268555,63.83265686035156,62.66876983642578,61.5048828125,60.34099578857422,59.17710876464844,58.013221740722656,56.849334716796875,55.685447692871094,54.52156066894531,53.35767364501953,52.19378662109375,51.02989959716797,49.86601257324219,48.702125549316406,47.538238525390625,46.374351501464844,63.83265686035156,61.825157165527344,59.81766128540039,57.81016540527344,55.80266571044922,53.795169830322266,51.78767395019531,49.780174255371094,47.77267837524414,45.76518249511719,43.75768280029297,41.85863494873047,40.4012451171875,38.94384765625,37.48645782470703,36.0290641784668,63.83265686035156,61.751800537109375,59.67094421386719,57.590087890625,55.50923538208008,53.42837905883789,51.3475227355957,49.266666412353516,47.18581008911133,45.10495376586914,43.02409744262695,41.051692962646484,39.52094268798828,37.99019241333008,36.459442138671875,34.928688049316406,63.83265686035156,60.952903747558594,58.073150634765625,55.193397521972656,52.31364822387695,49.433895111083984,46.554141998291016,43.67438888549805,40.79463577270508,37.91488265991211,35.035133361816406,32.26382827758789,29.93418312072754,27.604536056518555,25.27488899230957,22.945240020751953,63.83265686035156,60.53202819824219,57.23139953613281,53.93077087402344,50.63014221191406,47.32951354980469,44.02888107299805,40.72825241088867,37.4276237487793,34.12699508666992,30.82636833190918,27.63418960571289,24.88365936279297,22.133136749267578,19.382612228393555,16.63208770751953,63.83265686035156,60.53202819824219,57.23139953613281,53.93077087402344,50.63014221191406,47.32951354980469,44.02888107299805,40.72825241088867,37.4276237487793,34.12699508666992,30.82636833190918,27.63418960571289,24.88365936279297,22.133136749267578,19.382612228393555,16.63208770751953,Djibouti,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IDB,Blend,1005,1 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Republic,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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classified,1005,0 +EST,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,64096,NA,0,NA,66470,NA,0,NA,69017,NA,0,NA,71584,NA,0,NA,74000,NA,0,NA,74000,NA,0,NA,76000,NA,0,NA,76000,NA,0,NA,75000,NA,0,NA,74000,NA,0,NA,74000,NA,0,NA,72000,NA,0,NA,72000,NA,0,NA,70000,NA,0,NA,69000,NA,0,NA,68000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 +FIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,291555,NA,0,NA,294414,NA,0,NA,298272,NA,0,NA,302728,NA,0,NA,307000,NA,0,NA,311000,NA,0,NA,313000,NA,0,NA,315000,NA,0,NA,315000,NA,0,NA,314000,NA,0,NA,311000,NA,0,NA,306000,NA,0,NA,299000,NA,0,NA,291000,NA,0,NA,283000,NA,0,NA,276000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IDB,Blend,1005,1 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classified,1005,0 +FRO,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Faroe Islands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Fed Sts",EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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income,LNX,Not classified,1005,0 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 +GIB,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Gibraltar,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IDB,Blend,1005,1 +GRL,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Greenland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IDX,IDA,1005,1 +HKG,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,260966,NA,0,NA,257806,NA,0,NA,261464,NA,0,NA,270266,NA,0,NA,281000,NA,0,NA,288000,NA,0,NA,296000,NA,0,NA,301000,NA,0,NA,302000,NA,0,NA,304000,NA,0,NA,308000,NA,0,NA,320000,NA,0,NA,334000,NA,0,NA,349000,NA,0,NA,362000,NA,0,NA,372000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,1005,0 +HND,74.68135070800781,74.00245666503906,73.32357025146484,72.644683837890625,71.965789794921875,71.286895751953125,70.6080093383789,69.92912292480469,69.25022888183594,68.57133483886719,67.89244842529297,67.21356201171875,66.53466796875,65.85577392578125,65.17688751220703,64.49800109863281,74.68135070800781,73.62501525878906,72.56867218017578,71.51233673095703,70.45599365234375,69.399658203125,68.34332275390625,67.28697967529297,66.23064422607422,65.17430114746094,64.11796569824219,63.06162643432617,62.005287170410156,60.94894790649414,59.89261245727539,58.836273193359375,74.68135070800781,73.20247650146484,71.723602294921875,70.24473571777344,68.76585388183594,67.2869873046875,65.80811309814453,64.32923889160156,62.850364685058594,61.37149429321289,59.89262008666992,58.41374588012695,56.934871673583984,55.45600128173828,53.97712707519531,52.498252868652344,74.68135070800781,72.77356719970703,70.86578369140625,68.95799255371094,67.05020904541016,65.142425537109375,63.23463821411133,61.32685470581055,59.4190673828125,57.51128387451172,55.60350036621094,53.69571304321289,51.78792953491211,49.88014221191406,47.97235870361328,46.064571380615234,74.68135070800781,72.02577209472656,69.37019348144531,66.7146224975586,64.05904388427734,61.40346908569336,58.747894287109375,56.092315673828125,53.43674087524414,50.781166076660156,48.125587463378906,45.47001266479492,42.81443786621094,40.15885925292969,37.5032844543457,34.84770965576172,74.68135070800781,71.341644287109375,68.00193786621094,64.66223907470703,61.322532653808594,57.98283004760742,54.64312744140625,51.30342102050781,47.96371841430664,44.62401580810547,41.28430938720703,37.94460678100586,34.60490417480469,31.26519775390625,27.925491333007813,24.585792541503906,74.68135070800781,70.79815673828125,66.91497039794922,63.03178405761719,59.14859390258789,55.265403747558594,51.3822135925293,47.4990234375,43.61583709716797,39.73264694213867,35.849456787109375,31.966270446777344,28.08307647705078,24.19989013671875,20.316696166992188,16.433509826660156,1049126,1049126,1,74.68135070800781,1046078,1046078,1,74.065582275390625,1041865,1041865,1,73.44981384277344,1036801,1036801,1,72.83405303955078,1031000,1031000,1,72.2182846069336,1024000,1024000,1,71.60252380371094,1014000,1014000,1,70.98675537109375,1005000,1005000,1,70.37098693847656,996000,996000,1,69.755218505859375,989000,989000,1,69.13945007324219,985000,985000,1,68.52368927001953,985000,985000,1,67.90792083740234,989000,989000,1,67.29216003417969,996000,996000,1,66.6763916015625,1002000,1002000,1,66.06062316894531,1007000,1007000,1,65.444854736328125,74.68135070800781,74.065582275390625,73.44981384277344,72.83405303955078,72.2182846069336,71.60252380371094,70.98675537109375,70.37098693847656,69.755218505859375,69.13945770263672,68.52368927001953,67.90792083740234,67.29216003417969,66.6763916015625,66.06062316894531,65.444854736328125,74.68135070800781,74.03912353515625,73.39689636230469,72.7546615600586,72.11243438720703,71.47019958496094,70.827972412109375,70.18574523925781,69.54351806640625,68.90128326416016,68.2590560913086,67.61682891845703,66.97459411621094,66.332366943359375,65.69013977050781,65.04791259765625,74.68135070800781,73.39045715332031,72.09956359863281,70.80867004394531,69.51776885986328,68.22687530517578,66.93598175048828,65.64508056640625,64.35418701171875,63.063297271728516,61.77239990234375,60.48150634765625,59.190608978271484,57.899715423583984,56.608821868896484,55.31792449951172,74.68135070800781,72.87291717529297,71.064483642578125,69.25605010986328,67.44761657714844,65.639190673828125,63.830753326416016,62.02231979370117,60.21388626098633,58.405452728271484,56.59701919555664,54.78858947753906,52.98015594482422,51.171722412109375,49.36328887939453,47.55485534667969,74.68135070800781,72.66545104980469,70.6495590209961,68.6336669921875,66.617767333984375,64.60187530517578,62.58597946166992,60.57008743286133,58.55419158935547,56.53829574584961,54.52239990234375,52.50650405883789,50.4906120300293,48.47471618652344,46.45882034301758,44.44292449951172,74.68135070800781,72.54331970214844,70.40528869628906,68.26725769042969,66.12922668457031,63.99119186401367,61.85315704345703,59.715126037597656,57.57709503173828,55.439064025878906,53.301029205322266,51.16299819946289,49.024967193603516,46.88693618774414,44.7489013671875,42.610870361328125,74.68135070800781,71.341644287109375,68.00194549560547,64.66223907470703,61.32253646850586,57.98283004760742,54.64312744140625,51.30342102050781,47.96371841430664,44.62401580810547,41.28430938720703,37.94460678100586,34.60490417480469,31.26519775390625,27.925491333007813,24.585792541503906,Honduras,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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and Central Asia,,,HIC,High income,IBD,IBRD,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),LIC,Low income,IDX,IDA,1005,1 +HUN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,486169,NA,0,NA,486405,NA,0,NA,487283,NA,0,NA,488692,NA,0,NA,488000,NA,0,NA,485000,NA,0,NA,480000,NA,0,NA,471000,NA,0,NA,461000,NA,0,NA,453000,NA,0,NA,447000,NA,0,NA,447000,NA,0,NA,450000,NA,0,NA,454000,NA,0,NA,458000,NA,0,NA,460000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 +IMN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Isle of Man,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Asia,SAS,South Asia (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 +IRL,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,310443,NA,0,NA,320185,NA,0,NA,330799,NA,0,NA,341265,NA,0,NA,351000,NA,0,NA,357000,NA,0,NA,362000,NA,0,NA,364000,NA,0,NA,365000,NA,0,NA,363000,NA,0,NA,359000,NA,0,NA,353000,NA,0,NA,346000,NA,0,NA,336000,NA,0,NA,326000,NA,0,NA,317000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Islamic Rep",MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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classified,1005,0 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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classified,1005,0 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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Republic,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Kitts and Nevis,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 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classified,1005,0 +KWT,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,231315,NA,0,NA,241145,NA,0,NA,254903,NA,0,NA,272429,NA,0,NA,291000,NA,0,NA,308000,NA,0,NA,318000,NA,0,NA,321000,NA,0,NA,321000,NA,0,NA,321000,NA,0,NA,320000,NA,0,NA,317000,NA,0,NA,312000,NA,0,NA,306000,NA,0,NA,300000,NA,0,NA,293000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Kuwait,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,1005,0 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PDR,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Lucia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IDB,Blend,1005,1 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Lanka,SAS,South Asia,SAS,South Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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+MAF,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,St Martin (French part),LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 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East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 +MCO,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Monaco,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia,SAS,South Asia (excluding high income),UMC,Upper middle income,IDX,IDA,1005,1 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Islands,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 +MNP,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Northern Mariana Islands,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,1005,0 +MOZ,84.23503875732422,83.55614471435547,82.87725830078125,82.1983642578125,81.51947784423828,80.84058380126953,80.16169738769531,79.48280334472656,78.80391693115234,78.1250228881836,77.446136474609375,76.767242431640625,76.0883560180664,75.40946197509766,74.73057556152344,74.05168151855469,84.23503875732422,83.17869567871094,82.12236022949219,81.06602478027344,80.00968170166016,78.9533462524414,77.897003173828125,76.840667724609375,75.784332275390625,74.72798919677734,73.6716537475586,72.61531066894531,71.55897521972656,70.50263214111328,69.44629669189453,68.38996124267578,84.23503875732422,82.68694305419922,81.13885498046875,79.59075927734375,78.04267120361328,76.49458312988281,74.94648742675781,73.39839935302734,71.85030364990234,70.302215576171875,68.7541275024414,67.2060317993164,65.65794372558594,64.10985565185547,62.561763763427734,61.013671875,84.23503875732422,82.28326416015625,80.33148956298828,78.37971496582031,76.42794036865234,74.4761734008789,72.52439880371094,70.57262420654297,68.620849609375,66.66908264160156,64.7173080444336,62.765533447265625,60.81376266479492,58.86198806762695,56.91021728515625,54.95844268798828,84.23503875732422,81.57946014404297,78.92388153076172,76.268310546875,73.61273193359375,70.9571533203125,68.30158233642578,65.64600372314453,62.99042510986328,60.3348503112793,57.67927169799805,55.02369689941406,52.36812210083008,49.71254348754883,47.056968688964844,44.401390075683594,84.23503875732422,80.89533233642578,77.55562591552734,74.21592712402344,70.876220703125,67.53651428222656,64.19681549072266,60.85710525512695,57.51740264892578,54.17770004272461,50.83799362182617,47.498291015625,44.15858840942383,40.81888198852539,37.47917938232422,34.24557876586914,84.23503875732422,80.35184478759766,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.93589782714844,57.05270767211914,53.16952133178711,49.28633117675781,45.403141021728516,41.51995086669922,37.63676071166992,33.88237762451172,30.22760772705078,26.572843551635742,3567033,NA,0,84.23503875732422,3669769,NA,0,83.19498443603516,3768478,NA,0,82.154937744140625,3864270,NA,0,81.11488342285156,3959000,NA,0,80.07483673095703,4054000,NA,0,79.0347900390625,4148000,NA,0,77.99473571777344,4244000,NA,0,76.9546890258789,4340000,NA,0,75.91463470458984,4437000,NA,0,74.87458801269531,4537000,NA,0,73.83454132080078,4637000,NA,0,72.79448699951172,4742000,NA,0,71.75444030761719,4848000,NA,0,70.71439361572266,4956000,NA,0,69.6743392944336,5067000,NA,0,68.63429260253906,84.23503875732422,83.22368621826172,82.21232604980469,81.20097351074219,80.18962097167969,79.17826843261719,78.16691589355469,77.15556335449219,76.14420318603516,75.13285064697266,74.12149810791016,73.11014556884766,72.098785400390625,71.087432861328125,70.076080322265625,69.064727783203125,84.23503875732422,82.32725524902344,80.419464111328125,78.51168060302734,76.60389709472656,74.69611358642578,72.788330078125,70.88054656982422,68.9727554321289,67.064971923828125,65.15718841552734,63.2494010925293,61.341617584228516,59.43383026123047,57.52604675292969,55.61825942993164,84.23503875732422,82.06291198730469,79.89078521728516,77.7186508178711,75.5465316772461,73.37440490722656,71.20227813720703,69.0301513671875,66.85802459716797,64.68589782714844,62.513771057128906,60.341644287109375,58.169517517089844,55.99739074707031,53.825260162353516,51.65313720703125,84.23503875732422,81.57946014404297,78.92388916015625,76.268310546875,73.61273193359375,70.95716094970703,68.30158233642578,65.64600372314453,62.99042892456055,60.3348503112793,57.67927551269531,55.02370071411133,52.36812210083008,49.712547302246094,47.05697250366211,44.40139389038086,84.23503875732422,80.77616119384766,77.3172836303711,73.85840606689453,70.3995361328125,66.94065856933594,63.48177719116211,60.02290344238281,56.56402587890625,53.10515213012695,49.64627456665039,46.187400817871094,42.72852325439453,39.26964569091797,35.818565368652344,32.563148498535156,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,Mozambique,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 +NCL,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,21253,NA,0,NA,21289,NA,0,NA,21479,NA,0,NA,21779,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,21000,NA,0,NA,21000,NA,0,NA,21000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,New Caledonia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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classified,1005,0 +NOR,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,306025,NA,0,NA,309220,NA,0,NA,312861,NA,0,NA,316313,NA,0,NA,320000,NA,0,NA,321000,NA,0,NA,323000,NA,0,NA,323000,NA,0,NA,321000,NA,0,NA,319000,NA,0,NA,315000,NA,0,NA,315000,NA,0,NA,313000,NA,0,NA,311000,NA,0,NA,309000,NA,0,NA,307000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Asia,SAS,South Asia (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Asia,SAS,South Asia (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 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New Guinea,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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and Central Asia,,,HIC,High income,IBD,IBRD,1005,1 +PRI,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,245126,NA,0,NA,236643,NA,0,NA,231088,NA,0,NA,225531,NA,0,NA,230000,NA,0,NA,234000,NA,0,NA,230000,NA,0,NA,221000,NA,0,NA,205000,NA,0,NA,189000,NA,0,NA,171000,NA,0,NA,154000,NA,0,NA,136000,NA,0,NA,119000,NA,0,NA,104000,NA,0,NA,95000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Puerto Rico,LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 +PRK,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1885239,NA,0,NA,1849266,NA,0,NA,1805994,NA,0,NA,1758830,NA,0,NA,1716000,NA,0,NA,1685000,NA,0,NA,1667000,NA,0,NA,1661000,NA,0,NA,1664000,NA,0,NA,1672000,NA,0,NA,1679000,NA,0,NA,1691000,NA,0,NA,1702000,NA,0,NA,1716000,NA,0,NA,1727000,NA,0,NA,1735000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Korea, Dem People’s Rep",EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LIC,Low income,LNX,Not classified,1005,0 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa (excluding high income),LMC,Lower middle income,LNX,Not classified,1005,0 +PYF,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,21906,NA,0,NA,21984,NA,0,NA,22221,NA,0,NA,22567,NA,0,NA,23000,NA,0,NA,23000,NA,0,NA,22000,NA,0,NA,22000,NA,0,NA,21000,NA,0,NA,19700,NA,0,NA,19400,NA,0,NA,19300,NA,0,NA,19300,NA,0,NA,19500,NA,0,NA,19700,NA,0,NA,19800,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,French Polynesia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,1005,0 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Islands,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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Leone,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Salvador,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 +SMR,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,San Marino,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Sudan,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Tome and Principe,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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classified,1005,0 +SVN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,90503,NA,0,NA,92401,NA,0,NA,95119,NA,0,NA,98334,NA,0,NA,101000,NA,0,NA,104000,NA,0,NA,106000,NA,0,NA,107000,NA,0,NA,109000,NA,0,NA,109000,NA,0,NA,109000,NA,0,NA,107000,NA,0,NA,107000,NA,0,NA,105000,NA,0,NA,103000,NA,0,NA,101000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +SWE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,521357,NA,0,NA,541160,NA,0,NA,560538,NA,0,NA,578051,NA,0,NA,590000,NA,0,NA,599000,NA,0,NA,607000,NA,0,NA,611000,NA,0,NA,612000,NA,0,NA,611000,NA,0,NA,611000,NA,0,NA,614000,NA,0,NA,618000,NA,0,NA,622000,NA,0,NA,625000,NA,0,NA,626000,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,1005,0 +SWZ,84.23503875732422,83.55614471435547,82.87725830078125,82.1983642578125,81.51947784423828,80.84058380126953,80.16169738769531,79.48280334472656,78.80391693115234,78.1250228881836,77.446136474609375,76.767242431640625,76.0883560180664,75.40946197509766,74.73057556152344,74.05168151855469,84.23503875732422,83.17869567871094,82.12236022949219,81.06602478027344,80.00968170166016,78.9533462524414,77.897003173828125,76.840667724609375,75.784332275390625,74.72798919677734,73.6716537475586,72.61531066894531,71.55897521972656,70.50263214111328,69.44629669189453,68.38996124267578,84.23503875732422,82.68694305419922,81.13885498046875,79.59075927734375,78.04267120361328,76.49458312988281,74.94648742675781,73.39839935302734,71.85030364990234,70.302215576171875,68.7541275024414,67.2060317993164,65.65794372558594,64.10985565185547,62.561763763427734,61.013671875,84.23503875732422,82.28326416015625,80.33148956298828,78.37971496582031,76.42794036865234,74.4761734008789,72.52439880371094,70.57262420654297,68.620849609375,66.66908264160156,64.7173080444336,62.765533447265625,60.81376266479492,58.86198806762695,56.91021728515625,54.95844268798828,84.23503875732422,81.57946014404297,78.92388153076172,76.268310546875,73.61273193359375,70.9571533203125,68.30158233642578,65.64600372314453,62.99042510986328,60.3348503112793,57.67927169799805,55.02369689941406,52.36812210083008,49.71254348754883,47.056968688964844,44.401390075683594,84.23503875732422,80.89533233642578,77.55562591552734,74.21592712402344,70.876220703125,67.53651428222656,64.19681549072266,60.85710525512695,57.51740264892578,54.17770004272461,50.83799362182617,47.498291015625,44.15858840942383,40.81888198852539,37.47917938232422,34.24557876586914,84.23503875732422,80.35184478759766,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.93589782714844,57.05270767211914,53.16952133178711,49.28633117675781,45.403141021728516,41.51995086669922,37.63676071166992,33.88237762451172,30.22760772705078,26.572843551635742,137633,NA,0,84.23503875732422,138630,NA,0,83.19498443603516,140545,NA,0,82.154937744140625,143172,NA,0,81.11488342285156,146000,NA,0,80.07483673095703,148000,NA,0,79.0347900390625,147000,NA,0,77.99473571777344,146000,NA,0,76.9546890258789,144000,NA,0,75.91463470458984,143000,NA,0,74.87458801269531,142000,NA,0,73.83454132080078,141000,NA,0,72.79448699951172,141000,NA,0,71.75444030761719,141000,NA,0,70.71439361572266,141000,NA,0,69.6743392944336,141000,NA,0,68.63429260253906,84.23503875732422,83.22368621826172,82.21232604980469,81.20097351074219,80.18962097167969,79.17826843261719,78.16691589355469,77.15556335449219,76.14420318603516,75.13285064697266,74.12149810791016,73.11014556884766,72.098785400390625,71.087432861328125,70.076080322265625,69.064727783203125,84.23503875732422,82.32725524902344,80.419464111328125,78.51168060302734,76.60389709472656,74.69611358642578,72.788330078125,70.88054656982422,68.9727554321289,67.064971923828125,65.15718841552734,63.2494010925293,61.341617584228516,59.43383026123047,57.52604675292969,55.61825942993164,84.23503875732422,82.06291198730469,79.89078521728516,77.7186508178711,75.5465316772461,73.37440490722656,71.20227813720703,69.0301513671875,66.85802459716797,64.68589782714844,62.513771057128906,60.341644287109375,58.169517517089844,55.99739074707031,53.825260162353516,51.65313720703125,84.23503875732422,81.57946014404297,78.92388916015625,76.268310546875,73.61273193359375,70.95716094970703,68.30158233642578,65.64600372314453,62.99042892456055,60.3348503112793,57.67927551269531,55.02370071411133,52.36812210083008,49.712547302246094,47.05697250366211,44.40139389038086,84.23503875732422,80.77616119384766,77.3172836303711,73.85840606689453,70.3995361328125,66.94065856933594,63.48177719116211,60.02290344238281,56.56402587890625,53.10515213012695,49.64627456665039,46.187400817871094,42.72852325439453,39.26964569091797,35.818565368652344,32.563148498535156,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,Eswatini,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 +SXM,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Sint Maarten (Dutch part),LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 +SYC,84.23503875732422,83.55614471435547,82.87725830078125,82.1983642578125,81.51947784423828,80.84058380126953,80.16169738769531,79.48280334472656,78.80391693115234,78.1250228881836,77.446136474609375,76.767242431640625,76.0883560180664,75.40946197509766,74.73057556152344,74.05168151855469,84.23503875732422,83.17869567871094,82.12236022949219,81.06602478027344,80.00968170166016,78.9533462524414,77.897003173828125,76.840667724609375,75.784332275390625,74.72798919677734,73.6716537475586,72.61531066894531,71.55897521972656,70.50263214111328,69.44629669189453,68.38996124267578,84.23503875732422,82.68694305419922,81.13885498046875,79.59075927734375,78.04267120361328,76.49458312988281,74.94648742675781,73.39839935302734,71.85030364990234,70.302215576171875,68.7541275024414,67.2060317993164,65.65794372558594,64.10985565185547,62.561763763427734,61.013671875,84.23503875732422,82.28326416015625,80.33148956298828,78.37971496582031,76.42794036865234,74.4761734008789,72.52439880371094,70.57262420654297,68.620849609375,66.66908264160156,64.7173080444336,62.765533447265625,60.81376266479492,58.86198806762695,56.91021728515625,54.95844268798828,84.23503875732422,81.57946014404297,78.92388153076172,76.268310546875,73.61273193359375,70.9571533203125,68.30158233642578,65.64600372314453,62.99042510986328,60.3348503112793,57.67927169799805,55.02369689941406,52.36812210083008,49.71254348754883,47.056968688964844,44.401390075683594,84.23503875732422,80.89533233642578,77.55562591552734,74.21592712402344,70.876220703125,67.53651428222656,64.19681549072266,60.85710525512695,57.51740264892578,54.17770004272461,50.83799362182617,47.498291015625,44.15858840942383,40.81888198852539,37.47917938232422,34.24557876586914,84.23503875732422,80.35184478759766,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.93589782714844,57.05270767211914,53.16952133178711,49.28633117675781,45.403141021728516,41.51995086669922,37.63676071166992,33.88237762451172,30.22760772705078,26.572843551635742,6448,NA,0,84.23503875732422,6596,NA,0,83.19498443603516,6795,NA,0,82.154937744140625,7018,NA,0,81.11488342285156,7200,NA,0,80.07483673095703,7400,NA,0,79.0347900390625,7500,NA,0,77.99473571777344,7600,NA,0,76.9546890258789,7600,NA,0,75.91463470458984,7800,NA,0,74.87458801269531,7800,NA,0,73.83454132080078,7800,NA,0,72.79448699951172,7800,NA,0,71.75444030761719,7800,NA,0,70.71439361572266,7800,NA,0,69.6743392944336,7600,NA,0,68.63429260253906,84.23503875732422,83.22368621826172,82.21232604980469,81.20097351074219,80.18962097167969,79.17826843261719,78.16691589355469,77.15556335449219,76.14420318603516,75.13285064697266,74.12149810791016,73.11014556884766,72.098785400390625,71.087432861328125,70.076080322265625,69.064727783203125,84.23503875732422,82.32725524902344,80.419464111328125,78.51168060302734,76.60389709472656,74.69611358642578,72.788330078125,70.88054656982422,68.9727554321289,67.064971923828125,65.15718841552734,63.2494010925293,61.341617584228516,59.43383026123047,57.52604675292969,55.61825942993164,84.23503875732422,82.06291198730469,79.89078521728516,77.7186508178711,75.5465316772461,73.37440490722656,71.20227813720703,69.0301513671875,66.85802459716797,64.68589782714844,62.513771057128906,60.341644287109375,58.169517517089844,55.99739074707031,53.825260162353516,51.65313720703125,84.23503875732422,81.57946014404297,78.92388916015625,76.268310546875,73.61273193359375,70.95716094970703,68.30158233642578,65.64600372314453,62.99042892456055,60.3348503112793,57.67927551269531,55.02370071411133,52.36812210083008,49.712547302246094,47.05697250366211,44.40139389038086,84.23503875732422,80.77616119384766,77.3172836303711,73.85840606689453,70.3995361328125,66.94065856933594,63.48177719116211,60.02290344238281,56.56402587890625,53.10515213012695,49.64627456665039,46.187400817871094,42.72852325439453,39.26964569091797,35.818565368652344,32.563148498535156,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,84.23503875732422,80.35185241699219,76.468658447265625,72.5854721069336,68.70227813720703,64.819091796875,60.9359016418457,57.052711486816406,53.16952133178711,49.28633499145508,45.40314483642578,41.519954681396484,37.63676834106445,33.88237762451172,30.22761344909668,26.572851181030273,Seychelles,SSF,Sub-Saharan Africa,,,HIC,High income,IBD,IBRD,1005,1 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Arab Republic,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 +TCA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Turks and Caicos Islands,LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IDX,IDA,1005,1 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and Tobago,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 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East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IDX,IDA,1005,1 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Africa,SSA,Sub-Saharan Africa (excluding high income),LIC,Low income,IDX,IDA,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 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America and Caribbean,,,HIC,High income,IBD,IBRD,1005,1 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and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDB,Blend,1005,1 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Vincent and the Grenadines,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IDB,Blend,1005,1 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RB",LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,1005,1 +VGB,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,British Virgin Islands,LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 +VIR,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,7217,NA,0,NA,7216,NA,0,NA,7249,NA,0,NA,7304,NA,0,NA,7400,NA,0,NA,7400,NA,0,NA,7400,NA,0,NA,7400,NA,0,NA,7300,NA,0,NA,7200,NA,0,NA,7000,NA,0,NA,6900,NA,0,NA,6600,NA,0,NA,6400,NA,0,NA,6200,NA,0,NA,6100,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Virgin Islands (US),LCN,Latin America and Caribbean,,,HIC,High income,LNX,Not classified,1005,0 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,1005,1 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Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IDX,IDA,1005,1 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diff --git a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005.md b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/special_simulation_spells_nopasec_weigthed_pref1005_p99.md similarity index 58% rename from 02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005.md rename to 02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/special_simulation_spells_nopasec_weigthed_pref1005_p99.md index 2abd9b2..07a442c 100644 --- a/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005.md +++ b/02_simulation/022_programs/country_simulation_bubbleplot/shiny_rawdata/special_simulation_spells_nopasec_weigthed_pref1005_p99.md @@ -1,8 +1,8 @@ -|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | -|----- |------------ |----------- |----------- |----------- |----------- |----------- | -|EAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | -|ECS| .5349326 | .5804062 | .759409 | .9935608 | 1.250891 | 1.509056 | -|LCN| .8755936 | .6157662 | .6422294 | 1.290895 | 1.808433 | 2.015895 | -|MEA| 1.167432 | 1.019739 | 1.163887 | 1.748871 | 1.836901 | 2.795577 | -|SAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | -|SSF| 1.548801 | 1.011354 | 1.907785 | 2.141911 | 2.655576 | 3.458876| +|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | delta_reg_w_95 | delta_reg_w_99 +|----- |------------ |----------- |----------- |----------- |----------- |----------- | ----------- | ----------- +|EAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | 2.580147 | 3.120285 +|ECS| .5349326 | .5804062 | .759409 | .9935608 | 1.250891 | 1.509056 | 1.509056 | 2.244493 +|LCN| .8755936 | .6157662 | .6422294 | 1.290895 | 1.808433 | 2.015895 | 2.138032 | 3.339704 +|MEA| 1.167432 | 1.019739 | 1.163887 | 1.748871 | 1.836901 | 2.795577 | 3.300629 | 3.300629 +|SAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | 2.580147 | 3.120285 +|SSF| 1.548801 | 1.011354 | 1.907785 | 2.141911 | 2.655576 | 3.458876| 3.883189 | 3.883189 diff --git a/02_simulation/022_programs/country_simulation_bubbleplot/special_simulation_spells_nopasec_weigthed_pref1005_p99.md b/02_simulation/022_programs/country_simulation_bubbleplot/special_simulation_spells_nopasec_weigthed_pref1005_p99.md new file mode 100644 index 0000000..07a442c --- /dev/null +++ b/02_simulation/022_programs/country_simulation_bubbleplot/special_simulation_spells_nopasec_weigthed_pref1005_p99.md @@ -0,0 +1,8 @@ +|region | delta_adj_pct | delta_reg_w_50 | delta_reg_w_60 | delta_reg_w_70 | delta_reg_w_80 | delta_reg_w_90 | delta_reg_w_95 | delta_reg_w_99 +|----- |------------ |----------- |----------- |----------- |----------- |----------- | ----------- | ----------- +|EAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | 2.580147 | 3.120285 +|ECS| .5349326 | .5804062 | .759409 | .9935608 | 1.250891 | 1.509056 | 1.509056 | 2.244493 +|LCN| .8755936 | .6157662 | .6422294 | 1.290895 | 1.808433 | 2.015895 | 2.138032 | 3.339704 +|MEA| 1.167432 | 1.019739 | 1.163887 | 1.748871 | 1.836901 | 2.795577 | 3.300629 | 3.300629 +|SAS| .9928978 | .7819802 | 1.093752 | 1.480744 | 1.83555 | 2.320322 | 2.580147 | 3.120285 +|SSF| 1.548801 | 1.011354 | 1.907785 | 2.141911 | 2.655576 | 3.458876| 3.883189 | 3.883189 diff --git a/02_simulation/022_programs/old_version/0222_simulations.do b/02_simulation/022_programs/old_version/0222_simulations.do new file mode 100644 index 0000000..93c2aa2 --- /dev/null +++ b/02_simulation/022_programs/old_version/0222_simulations.do @@ -0,0 +1,180 @@ +/*====================================================== +Author: Brian Stacy +Modified: Joao Pedro Azevedo + +This do file: +Simulations using both Growth Rates Calculated from Spells +=======================================================* +*/ +*produce simulation dataset using ado. + + +cd "${clone}/02_simulation/022_programs + +/*Execute an ado file to produce the dataset for the simulations. The configuration for the ado file matches closesly +the configuration for the _preferred_list ado used to produce the raw latest. This is intentional as +1)Develops database for preferred list +The user must specify a number of options. +(1) preference() - which dictates which preference to use for the adjusted proficiency levels. Current options are 0,1,2,...,926. +(2) specialincludeassess() - which dictate which assessments to specifically include in spell calculations. This option takes assessment names +(3) specialincludegrade() - which dictate which grades to specifically include in spell calculations. This option takes grade names +(4) dropgrade() - which dictate which grades to not calculate proficiency levels. This option takes assessment names +(5) filename() - which dictates the name of the file produced to be used in the simulation. +(6) TIMSS_SUBJECT()-dictates either math or science for TIMSS. either enter string "math" or "science" +(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated for the spells +(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. +(9) ifspell() - if option to keep these units. Use regular stata if syntax +(10)ifsim() - if option to keep these units. Use regular stata if syntax +(11)POPULATION_2015() - enter Yes to fix population at 2015 levels. e.g. _simulation_dataset ,population_2015(Yes) /// + +*/ + +* Run simulation with tabulations done by region and growth rates calculated using regional growth (new spells sunmmary file) +_simulation_dataset, ifspell(if year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_regional_growth) /// + usefile("${clone}/02_simulation/021_rawdata/simulation_spells_weighted_region.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(region) growthdynamics(Yes) /// + percentile(10(10)90) quiet + +* Sensitivity Checks: growth by Income Level and Initial Learning Poverty + +* Run simulation with tabulations done by region and growth rates calculated using income level growth (new spells sunmmary file) +_simulation_dataset, ifspell(if year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_income_level) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(incomelevel) growthdynamics(Yes) /// + percentile(50(10)90) quiet + +* Run simulation with tabulations done by region and growth rates calculated using initial learning poverty (new spells sunmmary file) +_simulation_dataset, ifspell(if year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_initial_poverty_level) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(initial_poverty_level) growthdynamics(Yes) /// + percentile(50(10)90) quiet + +* Sensitivity Checks: growth by Region using all spells, including outliers +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_regional_growth_oldused) /// + usefile("${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_oldused_sim_weighted_region.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(region) growthdynamics(Yes) /// + percentile(10(10)90) quiet + + +exit + + +/* Calls the old special_simulation_spells markdowns + +*Run simulation with tabulations done by region and growth rates calculated using regional growth +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_regional_growth) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(region) growthdynamics(Yes) /// + percentile(10(10)90) + + + +*Run simulation with tabulations done by region and growth rates calculated using growth rates by initial poverty level +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_initial_poverty_level_growth) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_initial_poverty_level.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(initial_poverty_level) growthdynamics(Yes) /// + percentile(50(10)90) + + +*Run simulation with tabulations done by region and growth rates calculated using growth rates by income level +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_incomelevel_growth) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_incomelevel.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(incomelevel) growthdynamics(Yes) /// + percentile(50(10)90) + + + +********************************************************** +* Sensitivity Analysis +********************************************************** + +********* +* Unweighted spells (weighted results weight by the number of spells) +********* + +*Run simulation with tabulations done by region and growth rates calculated using regional growth + +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_regional_growth_growth_unweighted_spells) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(region) growthdynamics(Yes) /// + percentile(50(10)90) + + + +*Run simulation with tabulations done by region and growth rates calculated using growth rates by initial poverty level +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_initial_poverty_level_growth_growth_unweighted_spells) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_initial_poverty_level.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(initial_poverty_level) growthdynamics(Yes) /// + percentile(50(10)90) + + +*Run simulation with tabulations done by region and growth rates calculated using growth rates by income level +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_incomelevel_growth_unweighted_spells) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_unweigthed_pref1005_incomelevel.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(incomelevel) growthdynamics(Yes) /// + percentile(50(10)90) + +****** +*No ISR in MNA growth rates +****** +*Run simulation with tabulations done by region and growth rates calculated using regional growth + +_simulation_dataset, ifspell(if delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX") /// + ifwindow(if assess_year>=2011) /// + ifsim(if lendingtype!="LNX" ) weight(aw=wgt) preference(1005) /// + specialincludeassess( PIRLS LLECE TIMSS SACMEQ ) specialincludegrade(3 4 5 6) /// + filename(simfile_preference_1005_regional_growth_noISR) /// + usefile("${clone}/02_simulation/021_rawdata/old_version/special_simulation_spells_nopasec_weigthed_pref1005_newsimnum_no_ISR.md") /// + timss(science) enrollment(validated) population_2015(No) /// + groupingsim(region) groupingspells(region) growthdynamics(Yes) /// + percentile(50(10)90) diff --git a/02_simulation/022_program/0231_custom_spells.do b/02_simulation/022_programs/old_version/022x_custom_spells.do similarity index 56% rename from 02_simulation/022_program/0231_custom_spells.do rename to 02_simulation/022_programs/old_version/022x_custom_spells.do index 24b8557..0da1fad 100644 --- a/02_simulation/022_program/0231_custom_spells.do +++ b/02_simulation/022_programs/old_version/022x_custom_spells.do @@ -1,14 +1,29 @@ qui { - cd "${clone}\02_simulation\022_program + cd "${clone}\02_simulation\022_programs loc preference 1005 loc enrollment validated loc inputfolder clone - + loc lendingtypes & lendingtype!="LNX" + use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear gen year=year_assessment *nla_code should be distributed over s rather than being available in each . + clear + import delimited "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS.csv", + *Correcting idgrade for ZAF "2006-11" + replace idgrade = 5 if countrycode == "ZAF" & test == "PIRLS" & spell == "2006-2011" + gen year_assessment_i = substr(spell,1,4) + destring year_assessment_i, replace + gen year_assessment = substr(spell,6,4) + destring year_assessment, replace + + save "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", replace + + use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear + gen year=year_assessment + *nla_code should be distributed over s rather than being available in each . *temporarily rename test to assessment and idgrade to grade, change back after merge. rename test assessment @@ -143,7 +158,8 @@ qui { **************************************************************** * Identify only selected spells (n=71) - keep if test != "no assessment" & test != "EGRA" & delta_adj_pct != . & delta_adj_pct > -2 & delta_adj_pct < 4 & test != "PASEC" & year_assessment>2000 & lendingtype!="LNX" & test!="NLA" + keep if test != "no assessment" & test != "EGRA" & delta_adj_pct != . & delta_adj_pct > -2 & delta_adj_pct < 4 & test != "PASEC" & year_assessment>2000 `lendingtypes' & test!="NLA" + bysort countrycode : gen tot = _N gen wtg = 1/tot **************************************************************** @@ -156,12 +172,20 @@ qui { *Calculating regional 90th, 8th and 70th percentiles with weights + gen delta_reg_10 = . + gen delta_reg_20 = . + gen delta_reg_30 = . + gen delta_reg_40 = . gen delta_reg_50 = . gen delta_reg_60 = . gen delta_reg_70 = . gen delta_reg_80 = . gen delta_reg_90 = . + gen delta_reg_w_10 = . + gen delta_reg_w_20 = . + gen delta_reg_w_30 = . + gen delta_reg_w_40 = . gen delta_reg_w_50 = . gen delta_reg_w_60 = . gen delta_reg_w_70 = . @@ -169,12 +193,20 @@ qui { gen delta_reg_w_85 = . gen delta_reg_w_90 = . + gen delta_reg_10_noSQ = . + gen delta_reg_20_noSQ = . + gen delta_reg_30_noSQ = . + gen delta_reg_40_noSQ = . gen delta_reg_50_noSQ = . gen delta_reg_60_noSQ = . gen delta_reg_70_noSQ = . gen delta_reg_80_noSQ = . gen delta_reg_90_noSQ = . + gen delta_reg_w_10_noSQ = . + gen delta_reg_w_20_noSQ = . + gen delta_reg_w_30_noSQ = . + gen delta_reg_w_40_noSQ = . gen delta_reg_w_50_noSQ = . gen delta_reg_w_60_noSQ = . gen delta_reg_w_70_noSQ = . @@ -199,65 +231,94 @@ qui { /* no weights */ _pctile delta_adj_pct if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & delta_adj_pct > -2 & delta_adj_pct < 4 & year_assessment>2000 & lendingtype!="LNX", /// - percentiles(50(10)90) + percentiles(10(10)90) - replace delta_reg_50 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_10 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_20 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_30 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_40 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_50 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_60 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_60 = r(r6) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_70 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_70 = r(r7) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_80 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_80 = r(r8) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_90 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_90 = r(r9) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" /* with weights */ _pctile delta_adj_pct [aw = wtg] if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & lendingtype!="LNX" , /// - percentiles(50(10)90) - - replace delta_reg_w_50 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + percentiles(10(10)90) + replace delta_reg_w_10 = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_w_20 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_w_30 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_w_60 = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_40 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_w_70 = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_50 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_w_80 = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_60 = r(r6) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" - replace delta_reg_w_90 = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_70 = r(r7) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_w_80 = r(r8) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + replace delta_reg_w_90 = r(r9) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" /* no weights + NO SAQMEC */ _pctile delta_adj_pct if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & lendingtype!="LNX"& test != "SACMEQ", /// - percentiles(50(10)90) - - replace delta_reg_50_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + percentiles(10(10)90) + replace delta_reg_10_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_60_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_20_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_70_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_30_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_80_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_40_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_90_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_50_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_60_noSQ = r(r6) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_70_noSQ = r(r7) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_80_noSQ = r(r8) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_90_noSQ = r(r9) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" /* with weights + NO SAQMEC*/ _pctile delta_adj_pct [aw = wtg] if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & delta_adj_pct > -2 & delta_adj_pct < 4 & lendingtype!="LNX"& test != "SACMEQ", /// - percentiles(50(10)90) - - replace delta_reg_w_50_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + percentiles(10(10)90) + replace delta_reg_w_10_noSQ = r(r1) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_w_20_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_w_30_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_w_60_noSQ = r(r2) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_40_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_w_70_noSQ = r(r3) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_50_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_w_80_noSQ = r(r4) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_60_noSQ = r(r6) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" - replace delta_reg_w_90_noSQ = r(r5) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + replace delta_reg_w_70_noSQ = r(r7) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_w_80_noSQ = r(r8) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" + replace delta_reg_w_90_noSQ = r(r9) if threshold == "`t'" & region == "`r'" & test != "EGRA" /// & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ" } @@ -281,3 +342,112 @@ qui { & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ", by( region ) stat(mean) } + + +** with SACMEQ (no PASEC) +tabstat delta_adj_pct delta_reg_50 delta_reg_60 delta_reg_70 delta_reg_80 delta_reg_90 if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX", by( region ) + +tabstat delta_adj_pct delta_reg_50_noSQ delta_reg_60_noSQ delta_reg_70_noSQ delta_reg_80_noSQ delta_reg_90_noSQ if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ", by( region ) stat(mean) + + +** No SAQMEC (no PASEC) + +tabstat delta_adj_pct delta_reg_w_50 delta_reg_w_60 delta_reg_w_70 delta_reg_w_80 delta_reg_w_90 if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX", by( region ) + +tabstat delta_adj_pct delta_reg_w_50_noSQ delta_reg_w_60_noSQ delta_reg_w_70_noSQ delta_reg_w_80_noSQ delta_reg_w_90_noSQ if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX"& test != "SACMEQ", by( region ) stat(mean) + + + +*export summary statistics to excel +putexcel set "${clone}\02_simulation\023_outputs\preference_`preference'_spell_stats.xlsx", replace + +preserve +tabstat delta_adj_pct , by(test) stat(mean p50 min max n) columns(statistics) save +return list +*save tabstat output to excel +putexcel B1="Annualized Change (p.p.)" +putexcel A2="Test" +putexcel B2="Mean" +putexcel C2="p50" +putexcel D2="min" +putexcel E2="max" +putexcel F2="N" + +mat ATT = r(Stat1)' +putexcel A3= "`r(name1)'", +putexcel B3= matrix(ATT) +mat ATT = r(Stat2)' +putexcel A4= "`r(name2)'", +putexcel B4= matrix(ATT) +mat ATT = r(Stat3)' +putexcel A5= "`r(name3)'", +putexcel B5= matrix(ATT) +mat ATT = r(Stat4)' +putexcel A6= "`r(name4)'", +putexcel B6= matrix(ATT) +mat ATT = r(StatTotal)' +putexcel A7= "Total", +putexcel B7= matrix(ATT) + +*Now for initital poverty level +tabstat initial_learning_poverty , by(test) stat(mean p50 min max n) columns(statistics) save +return list +*save tabstat output to excel +putexcel H1="Initial conditions" +putexcel H2="Mean" +putexcel I2="p50" +putexcel J2="min" +putexcel K2="max" +putexcel L2="N" + +mat ATT = r(Stat1)' +putexcel H3= matrix(ATT) +mat ATT = r(Stat2)' +putexcel H4= matrix(ATT) +mat ATT = r(Stat3)' +putexcel H5= matrix(ATT) +mat ATT = r(Stat4)' +putexcel H6= matrix(ATT) +mat ATT = r(StatTotal)' +putexcel H7= matrix(ATT) +restore + + +*save final version to markdown file, compatible with github + +preserve +collapse delta_adj_pct delta_reg_w_10 delta_reg_w_20 delta_reg_w_30 delta_reg_w_40 delta_reg_w_50 delta_reg_w_60 delta_reg_w_70 delta_reg_w_80 delta_reg_w_90 if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX" + +tempfile global +gen region="Overall" + +save `global' +restore + +collapse delta_adj_pct delta_reg_w_10 delta_reg_w_20 delta_reg_w_30 delta_reg_w_40 delta_reg_w_50 delta_reg_w_60 delta_reg_w_70 delta_reg_w_80 delta_reg_w_90 if test != "EGRA" /// + & test != "PASEC" & test != "no assessment" & year_assessment>2000 & lendingtype!="LNX", by(region) + +append using `global' + +*Replace value for EAS and SAS, because of lack of spells +drop if region=="EAS" | region=="SAS" +expand 2 if region=="Overall" , gen( expand) +replace region="EAS" if region=="Overall" & expand==0 +replace region="SAS" if region=="Overall" +drop expand + +*Do some extra formatting for markdown file +set obs `=_N+1' +sort region + +foreach var in region delta_adj_pct delta_reg_w_10 delta_reg_w_20 delta_reg_w_30 delta_reg_w_40 delta_reg_w_50 delta_reg_w_60 delta_reg_w_70 delta_reg_w_80 delta_reg_w_90 { + tostring `var', replace force + replace `var'="---" if _n==1 +} + +export delimited "${clone}\02_simulation\023_outputs\preference_`preference'_weighted_no_pasec.md", delimiter("|") replace diff --git a/02_simulation/022_program/0231_custom_spells_bootstrap.do b/02_simulation/022_programs/old_version/022y_custom_spells_bootstrap.do similarity index 99% rename from 02_simulation/022_program/0231_custom_spells_bootstrap.do rename to 02_simulation/022_programs/old_version/022y_custom_spells_bootstrap.do index b5acb30..351ab00 100644 --- a/02_simulation/022_program/0231_custom_spells_bootstrap.do +++ b/02_simulation/022_programs/old_version/022y_custom_spells_bootstrap.do @@ -1,6 +1,6 @@ qui { - cd "${clone}\02_simulation\022_program + cd "${clone}\02_simulation\022_programs loc preference 1005 loc enrollment validated diff --git a/02_simulation/022_programs/old_version/_simulation_dataset.ado b/02_simulation/022_programs/old_version/_simulation_dataset.ado new file mode 100644 index 0000000..7eaadfe --- /dev/null +++ b/02_simulation/022_programs/old_version/_simulation_dataset.ado @@ -0,0 +1,1052 @@ +* v.0.4 JPAzevedo +** option SAVECTRYFILE created +* v.0.3.1 BStacy +** time region collumn prior to merge +* v.0.3 JPAzevedo +** create INPUTFOLDER Option default value CLONE +** add 25 as a CATINITIAL +* v.0.2 JPAzevedo +** add NOSIMULATION OPTION +** remove drop year_population from code (drop year_population is now a condition +** option all year_populations + + +/*Execute an ado file to produce the dataset for the simulations. The configuration for the ado file matches closesly +the configuration for the _preferred_list ado used to produce the raw latest. This is intentional as +1)Develops database for preferred list +The user must specify a number of options. +(1) preference() - which dictates which preference to use for the adjusted proficiency levels. Current options are 0,1,2,...,926. +(3) dropassess() - which dictate which assessments to not calculate proficiency levels. This option takes assessment names +(4) dropgrade() - which dictate which grades to not calculate proficiency levels. This option takes assessment names +(5) filename() - which dictates the name of the file produced to be used in the simulation. +(6) TIMSS_SUBJECT()-dictates either math or science for TIMSS. either enter string "math" or "science" +(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated enrollment for the spells +(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. +As an example: _simulation_dataset, preference(926) dropassess(SACMEQ) dropgrade(3) filename(simulation_926) timss(science) enrollment(validated) +Specifies that Bangladesh, China, India, and Pakistan use National Learning Assessments. Preference 926 is applied for all assessments. +*/ + + + +cap program drop _simulation_dataset +program define _simulation_dataset, rclass + + version 15 + syntax [varlist] [, /// + IFSPELL(string) /// + IFSIM(string) /// + IFWINDOW(string) /// + WEIGHT(string) /// + PREFERENCE(string) /// + SPECIALINCLUDEASSESS(string) /// + SPECIALINCLUDEGRADE(string) /// + DROPGRADE(string) /// + FILENAME(string) /// + USEFILE(string) /// + TIMSS(string) /// + ENROLLMENT(string) /// + EGRADROP(string) /// + QUIET /// + PERCENTILE(string) /// + NOSIMULATION /// + ALLyear_populationS /// + POPULATION_2015(string) /// + INPUTFOLDER(string) /// + SAVECTRYFILE(string) /// + GROUPINGSPELLS(string) /// + GROUPINGSIM(string) /// + GROWTHDYNAMICS(string) /// + ] + + if ("`inputfolder'" == "") { + loc inputfolder clone + } + + if "`quiet'" == "" { + loc qui "noi " + } + + if "`percentile'" == "" { + loc percentile "50(10)90" + } + + noi di _n in r "ATTENTION: " in y "_simulation_dataset" in g " is pulling the data from " in y "`inputfolder'" + noi di _n as text "preference: `preference' ; groupingspells : `groupingspells'" + + quietly { + + ************************************************************************ + ********** FIRST PART - CREATES THE SIMULATION GROWTH RATES ************ + *************** only needed if no usefile is provided ****************** + * Not clear why needed if using 0220 & 0221 to create and aggreg spells* + ************************************************************************ + + clear + import delimited "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS.csv", + *Correcting idgrade for ZAF "2006-11" + replace idgrade = 5 if countrycode == "ZAF" & test == "PIRLS" & spell == "2006-2011" + gen year_assessment_i = substr(spell,1,4) + destring year_assessment_i, replace + gen year_assessment = substr(spell,6,4) + destring year_assessment, replace + + save "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta", replace + + + use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear + gen year=year_assessment + *nla_code should be distributed over s rather than being available in each . + + *temporarily rename test to assessment and idgrade to grade, change back after merge. + rename test assessment + rename idgrade grade + + *------------------------------* + * Learning poverty calculation + *------------------------------* + * Adjusts non-proficiency by out-of school + foreach subgroup in all fe ma { + gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_validated_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) + label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" + } + + + gen adj_pct_reading_low_rawfull= 100-adj_nonprof_all + + merge m:1 countrycode using "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", keepusing(test idgrade incomelevel lendingtype nonprof_all) + gen adj_pct_reading_low= 100-adj_nonprof_all + *change name in rawlatest of assessment to test_rawlatest, and revert back to test from assessment + + rename adj_pct_reading_low adj_pct_reading_low_rawlatest + rename adj_pct_reading_low_rawfull adj_pct_reading_low + + + gen initial_poverty_level_temp = 100 - adj_pct_reading_low_rawlatest + cap gen initial_poverty_level = "0-25% Learning Poverty" + cap replace initial_poverty_level = "25-50% Learning Poverty" if initial_poverty_level_temp >= 25 + cap replace initial_poverty_level = "50-75% Learning Poverty" if initial_poverty_level_temp >= 50 + cap replace initial_poverty_level = "75-100% Learning Poverty" if initial_poverty_level_temp >= 75 + + rename test test_rawlatest + rename assessment test + *same for grade + rename idgrade idgrade_rawlatest + rename grade idgrade + + drop if _merge==1 + + gen enrollment=enrollment_validated_all + drop if subject!="`timss'" & test=="TIMSS" & countrycode!="JOR" + + + *Keep assessments listed in specialincludeassess option + levelsof test, local(list_alltest) + foreach tst in `list_alltest' { + if strmatch("`specialincludeassess'", "*`tst'*")==0 { + drop if test=="`tst'" + di "`tst'" + } + } + + *Keep grades listed in specialincludegrades option + levelsof idgrade, local(list_grd) + foreach grd in `list_grd' { + if strmatch("`specialincludegrade'", "*`grd'*")==0 { + drop if idgrade==`grd' & idgrade!=idgrade_rawlatest + di "`grd'" + } + } + + sort countrycode nla_code idgrade test subject + count + + + *Cleaning the data file + keep region regionname countrycode countryname incomelevel incomelevelname lendingtype /// + lendingtypename year_population year_assessment idgrade test source_assessment /// + enrollment adj_pct_reading_low* subject nla_code initial_poverty_level + + *Generating all possible combinations of forward spells: + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : gen spell_c1 = string(year_assessment[_n-1]) + "-" + string(year_assessment) + bysort countrycode nla_code idgrade test subject : gen spell_c2 = string(year_assessment[_n-2]) + "-" + string(year_assessment) + bysort countrycode nla_code idgrade test subject : gen spell_c3 = string(year_assessment[_n-3]) + "-" + string(year_assessment) + bysort countrycode nla_code idgrade test subject : gen spell_c4 = string(year_assessment[_n-4]) + "-" + string(year_assessment) + + reshape long spell_c, i(countrycode nla_code idgrade test subject year_assessment subject) j(lag) + ren spell_c spell + + *tag if actual spell: + gen spell_exists=(length(spell) == 9 ) + + ********************************************** + *Preparing the data for simulations: + ********************************************** + *The data should be restructured for unique identifiers: + sort countrycode nla_code idgrade test subject year_assessment spell lag + + *Rules for cleaning the spell data: + *Bringing in the list of countries and spells for which the data is not comparable: + + merge m:1 countrycode idgrade test year_assessment spell using "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta", assert(master match using) keep(master match) keepusing(comparable) nogen + drop if comparable == 0 + + *Generating preferred consecutive spells: + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : egen lag_min = min(lag) + *Keeping the comparable consecutive spells + keep if lag == lag_min + + *All comparable spells for TIMSS/PIRLS + assert comparable == 1 if !missing(comparable) + + *Annual change in enrollment, adjusted proficiency and proficiency + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : gen delta_adj_pct = (adj_pct_reading_low-adj_pct_reading_low[_n-1])/(year_assessment-year_assessment[_n-1]) + bysort countrycode nla_code idgrade test subject : gen initial_adj_pct = adj_pct_reading_low[_n-1] + bysort countrycode nla_code idgrade test subject : gen final_adj_pct = adj_pct_reading_low + + + *drop observatoins specified by [if] [in]. + if `"`ifspell'"'!="" { + di `"`ifspell'"' + keep `ifspell' + } + + /* weights */ + + if ("`weight'" == "") { + cap tempname wtg + cap gen `wtg' = 1 + local weight2 "" + loc weight "fw" + loc exp "=`wtg'" + } + + *Generating deltas in terms of reduction of gap to the frontier + gen gap_to_frontier = 100-adj_pct_reading_low + bysort countrycode nla_code idgrade test subject : gen red_gap_frontier = -1*(gap_to_frontier-gap_to_frontier[_n-1])/(year_assessment-year_assessment[_n-1]) + bysort countrycode nla_code idgrade test subject : gen pct_red_gap = (red_gap_frontier/gap_to_frontier[_n-1]) + gen pct_red_gap_100 = pct_red_gap*100 + + *Generating categories of countries + gen catinitial = . + foreach var in 25 50 75 100 { + replace catinitial= `var' if initial_adj_pct <= `var' & catinitial== . + } + + *Developing country weights to give each country equal weight despite the number of observations: + *Counting the country observations where delta exists: + + *Set weights to unity if no weights specified + if "`weight2'"=="" { + gen wgt=1 + } + + if "`weight'"!="" { + local weight2 "`weight'" + bysort countrycode: gen delta_exists = !missing(delta_adj_pct) + bysort countrycode delta_exists: gen w = _N + cap gen wgt = . + replace wgt = 1/w + } + + *Calculating global 90th, 80th and 70th percentiles: + + forvalues i = `percentile' { + egen delta_global_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') + } + gsort -delta_adj_pct + list countryname initial_adj_pct final_adj_pct delta_adj_pct test spell if delta_adj_pct > delta_global_90 & !missing(delta_adj_pct) + tabstat delta_global_90 , by(region) + + *Calculating global 90th, 80th and 70th percentiles with weights + forvalues i = `percentile' { + gen delta_global_w_`i' = . + } + gen threshold="III" + levelsof threshold, local(tr) + foreach t of local tr { + _pctile delta_adj_pct [weight = wgt] if threshold == "`t'" & test != "EGRA", percentiles(`percentile') + local counter=1 + forvalues i = `percentile' { + replace delta_global_w_`i' = r(r`counter') if threshold == "`t'" & test != "EGRA" + local counter=`counter' + 1 + } + } + + + + `qui' di "Number of spells per `groupingspells'" + `qui' tab `groupingspells' if spell_exists == 1 + + `qui' di "Number of spells per test" + `qui' tab test if spell_exists == 1 + + `qui' di "Spells per `groupingspells'" + `qui' tab test if spell_exists == 1 + + `qui' tab spell if spell_exists == 1 + + *Comparing percentiles with and without weight + `qui' di "Comparing output by with and without weights (90th percentile)" + `qui' tabstat delta_adj_pct delta_global_90 delta_global_w_90 [`weight'`exp'] if spell_exists == 1, /// + by() stat(mean N) + + encode `groupingspells', gen(reg_n) + + egen group = group( reg_n) + + *Percentiles by wbregion: + + forvalues i = `percentile' { + bysort reg_n: egen delta_reg_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') + } + `qui' di "Results by `groupingspells'" + `qui' tabstat delta_adj_pct delta_reg* [`weight'`exp'], by(reg_n) + + + cap list countryname initial_adj_pct final_adj_pct delta_adj_pct test idgrade spell if delta_adj_pct > delta_reg_90 & !missing(delta_adj_pct) & region == "SSF" + + *Calculating regional 90th, 8th and 70th percentiles with weights + forvalues i = `percentile' { + gen delta_reg_w_`i' = . + } + + levelsof threshold, local(tr) + foreach t of local tr { + levelsof `groupingspells', local(reg) + foreach r of local reg { + count if !missing(delta_adj_pct) & threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" + local count=`r(N)' + *Only make change to regional if we have at least 3 regional spells + if `count'<3 { + forvalues i = `percentile' { + replace delta_reg_`i' = . if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" + } + } + if `count'>=3 { + _pctile delta_adj_pct [weight = w] if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA", percentiles(`percentile') + local counter=1 + forvalues i = `percentile' { + replace delta_reg_w_`i' = r(r`counter') if threshold == "`t'" & `groupingspells' == "`r'" & test != "EGRA" + local counter=`counter' + 1 + } + } + } + } + + + *Comparison of regional percentiles with and without weights + `qui' tabstat initial_adj_pct [`weight'`exp'] if spell_exists == 1, /// + by(reg_n) stat(mean p50 min max N) + + *Comparison of regional percentiles with and without weights + `qui' tabstat delta_adj_pct delta_reg* [`weight'`exp'] if spell_exists == 1, /// + by(reg_n) stat(mean N) + + + *Percentiles by initial values: + forvalues i = `percentile' { + bysort catinitial: egen delta_ini_`i' = pctile(delta_adj_pct) if test != "EGRA", p(`i') + } + + `qui' tabstat delta_adj_pct delta_ini* [`weight'`exp'] if spell_exists == 1, /// + by(catinitial) stat(mean N) + + + *Average and percentile percentage changes in gap to frontier: + forvalues i = `percentile' { + *drop red_gap_`i'_irsat red_gap_`i'_irsat_extend red_gap_`i'_sas red_gap_`i' red_gap_global_`i' red_gap_global_`i'_extend /* try tp create a variable does alreayd exist in your database */ + bysort `groupingspells': egen red_gap_`i' = pctile(pct_red_gap) if test != "EGRA" , p(`i') + + /* bysort region: egen red_gap_`i'_irsat_extend = max(red_gap_`i'_irsat) + replace red_gap_`i'_irsat = red_gap_`i'_irsat_extend if missing(red_gap_`i'_irsat) + *South Asia does not have any nationally representative tests + bysort region: egen red_gap_`i'_sas = pctile(pct_red_gap) if region == "SAS", p(`i') + gen red_gap_`i' = red_gap_`i'_irsat + replace red_gap_`i' = red_gap_`i'_sas if missing(red_gap_`i') + */ + + egen red_gap_global_`i' = pctile(pct_red_gap) if test != "EGRA", p(`i') + *egen red_gap_global_`i'_extend = max(red_gap_global_`i') + *replace red_gap_global_`i' = red_gap_global_`i'_extend if missing(red_gap_global_`i') + } + + + * save "${clone}/02_simulation/023_outputs/`filename'_spells.dta", replace + + egen count_ctry_spells = count(delta_adj_pct) , by(countrycode) + + *Obtaining data for projection: + collapse (first) test countryname `groupingspells' (mean) delta_adj_pct (lastnm) delta_adj_pct_r = delta_adj_pct (max) delta_adj_pct_m = delta_adj_pct (mean) count_ctry_spells pct_red_gap red_gap_* delta_reg_* delta_global_* [`weight'`exp'], by(countrycode ) + + foreach var of varlist pct_red_gap red_gap_70 red_gap_80 red_gap_90 { + replace `var' = 0 if `var' < 0 + } + + egen count_n = count(delta_adj_pct) + egen count_reg_n = count(delta_adj_pct) , by(`groupingspells') + + + local preference="`preference'" + + save "${clone}/02_simulation/023_outputs/`filename'.dta", replace + + * End of FIRST PART + + + + ********************************************************************* + ****************** SECOND PART - ACTUAL SIMULATION ****************** + ****** Will not run if no_simulation is specified ******* + ********************************************************************* + + if ("`nosimulation'" == "") { + + *Import growth rates defined in a markdown file if the usefile() option is specified + + if ("`usefile'" != "") { + import delimited "`usefile'", delimiter("|") varnames(1) clear + + cap drop v1 + cap drop v9 + drop if _n==1 + *dropping delta_adj_pct, because it is dangerous to merge with this. + drop delta_adj_pct + replace `groupingspells'=strtrim(`groupingspells') + replace `groupingspells'=subinstr(`groupingspells', "`=char(9)'", "", .) + + describe delta_reg*, varlist + foreach var in `r(varlist)' { + destring `var', replace + } + local usefile2=subinstr("`usefile'", ".md", ".dta",.) + save "`usefile2'", replace + } + + + + use "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", replace + merge 1:1 countrycode using "${clone}/02_simulation/023_outputs/`filename'.dta", + + *Create learning level if not created + cap replace initial_poverty_level = "0-25% Learning Poverty" if !missing(adj_nonprof) + cap replace initial_poverty_level = "25-50% Learning Poverty" if adj_nonprof >= 25 & !missing(adj_nonprof) + cap replace initial_poverty_level = "50-75% Learning Poverty" if adj_nonprof >= 50 & !missing(adj_nonprof) + cap replace initial_poverty_level = "75-100% Learning Poverty" if adj_nonprof >= 75 & !missing(adj_nonprof) + + cap gen adj_pct_reading_low= 100-adj_nonprof_all + + ********************************************************************* + *Replace missing deltas: Historical + ********************************************************************* + *Replace with values in markdown file if specified + if ("`usefile'" != "") { + cap drop _merge + `qui' merge m:1 `groupingspells' using "`usefile2'", update replace + noi di "Update growth rates with values from markdown file." + + assert(1 3 4 5) + + drop if _merge==2 + } + + replace pct_red_gap = red_gap_50 if pct_red_gap == . + + + if "`weight2'"=="" { + *replace with median if missing + levelsof `groupingspells', local(rgn) + foreach var in `rgn' { + di "`var'" + count if !missing(delta_adj_pct) & `groupingspells'=="`var'" + local count=`r(N)' + su delta_reg_50 if `groupingspells'=="`var'" + *Only make change to regional if we have at least 3 regional spells + if `count'>=3 { + cap replace delta_adj_pct = `r(mean)' if delta_adj_pct == . & `groupingspells'=="`var'" + } + } + su delta_global_50 + replace delta_adj_pct = `r(mean)' if delta_adj_pct == . + } + + + if "`weight2'"!="" { + di "`weight2'" + levelsof `groupingspells', local(rgn) + foreach var in `rgn' { + di "`var'" + count if !missing(delta_adj_pct) & `groupingspells'=="`var'" + local count=`r(N)' + + su delta_reg_w_50 if `groupingspells'=="`var'" + *Only make change to regional if we have at least 3 regional spells + if `count'>=3 { + cap replace delta_adj_pct = `r(mean)' if delta_adj_pct == . & `groupingspells'=="`var'" + } + } + su delta_global_w_50 + replace delta_adj_pct = `r(mean)' if delta_adj_pct == . + } + + forval i=`percentile' { + levelsof `groupingspells', local(rgn) + foreach var in `rgn' { + count if !missing(delta_reg_w_`i') & `groupingspells'=="`var'" + local count=`r(N)' + su delta_reg_w_`i' if `groupingspells'=="`var'" + *Only make change to regional if we have at least 3 regional spells + if `count'>=3 { + cap replace delta_reg_w_`i' = `r(mean)' if delta_reg_w_`i' == . & `groupingspells'=="`var'" + } + + count if !missing(delta_reg_`i') & `groupingspells'=="`var'" + local count=`r(N)' + su delta_reg_`i' if `groupingspells'=="`var'" + if `count'>=3 { + cap replace delta_reg_`i' = `r(mean)' if delta_reg_`i' == . & `groupingspells'=="`var'" + } + + } + } + + forval i=`percentile' { + su delta_global_w_`i' + replace delta_reg_w_`i' = `r(mean)' if delta_reg_w_`i' == . + + su delta_global_`i' + replace delta_reg_`i' = `r(mean)' if delta_reg_`i' == . + + su delta_global_`i' + replace delta_global_`i' = `r(mean)' if delta_global_`i' == . + + su delta_global_w_`i' + replace delta_global_w_`i' = `r(mean)' if delta_global_w_`i' == . + } + + *Use own country spell only if there are two or more spells, so that slight changes don't throw off simulations + if "`weight2'"!="" { + replace delta_adj_pct = delta_reg_w_50 if !missing(count_ctry_spells) & count_ctry_spells<2 + } + if "`weight2'"=="" { + replace delta_adj_pct = delta_reg_50 if !missing(count_ctry_spells) & count_ctry_spells<2 + } + + *Don't slow down countries that have max growth rates higher than the regional percentage + forval i=70(10)90 { + replace delta_reg_w_`i' = delta_adj_pct_m if delta_reg_w_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) + replace delta_reg_`i' = delta_adj_pct_m if delta_reg_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) + replace delta_global_w_`i' = delta_adj_pct_m if delta_global_w_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) + replace delta_global_`i' = delta_adj_pct_m if delta_global_`i' < delta_adj_pct_m & !missing(delta_adj_pct_m) + } + *save dataset + save "${clone}/02_simulation/023_outputs/`filename'.dta", replace + + + save "${clone}/02_simulation/023_outputs/`filename'.dta", replace + + * Drop trait/useless variables because it's too confusing + rename year_assessment assess_year + drop count_* _merge adminregion* /// + *source* cmu regionname test idgrade /// + pop* enrollment* /// + nla_code pct_* + + + * Rename remaining variables in a consistent logic to be able to reshape long + rename adj_pct_reading_low baseline + *rename delta_adj_pct_r reduct_own // WARNING!!! NOT SURE THIS INTERPRETATION IS CORRECT. BRIAN CAN YOU CHECK? + rename delta_adj_pct growth_own + generate growei_own = growth_own + forvalues i = `percentile' { + rename red_gap_`i' reduct_r`i' + cap rename red_gap_global_`i' reduct_g`i' //WARNING!!! WHY ONLY EXISTS FOR G90 ON YOUR FILE, BRIAN? + rename delta_reg_w_`i' growei_r`i' + rename delta_reg_`i' growth_r`i' + rename delta_global_w_`i' growei_g`i' + rename delta_global_`i' growth_g`i' + } + + /* From my understanding, as of now, there are: + 7 possible "benchmark" + - own = own country historical, business as usual + - r70, r80, r90 = region percentiles 70, 80, 90 + - g70, g80, g90 = global percentiles 70, 80, 90 + 3 possible "rateflavor" + - reduct = rate applied in (100 - baseline), hopefully a negative rate + - growth = rate applied in baseline, hopefully a positive rate + - growei = rate applied in baseline (same as above), but constructed from weighted something + */ + + * Transform the dataset in long to reflect those combinations of benchmarks and rate_flavors + + *save time in simulations by dropping some combinations we don't use + if "`weight2'"=="" { + drop growei_* + drop reduct_* + reshape long growth , i(countrycode `groupingspells' baseline) j(benchmark) string + rename ( growth ) ( rategrowth ) + } + if "`weight2'"!="" { + drop growth_* + drop reduct_* + reshape long growei, i(countrycode `groupingspells' baseline) j(benchmark) string + rename ( growei) ( rategrowei) + } + + reshape long rate, i(countrycode `groupingspells' baseline benchmark) j(rate_flavor) string + + order countrycode `groupingspells' rate_flavor benchmark rate baseline + format rate baseline %10.2f + + * Describes input flavor + gen str50 input_flavor = "preference: " + preference + replace input_flavor = input_flavor + " | rate_flavor: " + rate_flavor + replace input_flavor = input_flavor + " | benchmark: " + benchmark + + + *************************************************** + *Dynamically calculated growth rates (particularly for growth rates based on initial learning poverty categories) + *************************************************** + if "`groupingspells'"=="initial_poverty_level" { + levelsof initial_poverty_level, local(pov_levels) + levelsof input_flavor , local(flavors) + + local counter=1 + foreach var in `pov_levels' { + gen rate_`counter'=. + label var rate_`counter' "`var'" + foreach flav in `flavors' { + qui su rate if input_flavor == "`flav'" & initial_poverty_level == "`var'" + replace rate_`counter'=`r(mean)' if input_flavor == "`flav'" + } + replace rate_`counter'=rate if benchmark=="_own" + + local counter=`counter'+1 + } + } + + drop rate_flavor benchmark + + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + + // Delete those lines if input vars are already labelled + label var rate "Rate (growth or delta) used in simulation" + label var baseline "Adjusted percentage of test takers with minimum reading proficiency at baseline" + label var input_flavor "Short for: Benchmark Scenario | Rate Method | Latest Prefence" + * Benchmark Scenario: own/r70/r80/r90/g70/g80/g90 + * Rate Method: reduce/growth/growei + * Latest Preference: 926 + *label var input_flavor_des "Long description: Benchmark Scenario | Rate Method | Latest Prefence" + + + * Step 1. Simulate future adjusted proficiency + *================================================== + tempname tmp_adjpro + // Advances adjusted proficiency (adjpro) for all year_populations to simulate + // Each adjpro_year_population is created as column, then it is reshaped to long + quietly forvalues i=2015/2050 { // CHANGE HERE FOR A LONGER HORIZON + gen adjpro`i' = . + gen rate`i'=rate + replace adjpro`i' = 100 - (100-baseline)*((1-rate)^(`i'-2015)) if ( strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = baseline + rate*(`i'-2015) if (!strpos(input_flavor, "reduct") & !missing(baseline)) + + *Add dynamics to simulations based on initial poverty level + if "`groupingspells'"=="initial_poverty_level" & "`growthdynamics'"=="Yes"{ + if `i'>2015 { + local j=`i'-1 + replace adjpro`i' = adjpro`j' + rate_4 if (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_3 if adjpro`j'>=25 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_2 if adjpro`j'>=50 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_1 if adjpro`j'>=75 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + } + } + + replace adjpro`i' = 100 if ( adjpro`i' > 100 & !missing(adjpro`i') ) // Upper bound is 100 + replace adjpro`i' = 0 if ( adjpro`i' < 0 & !missing(adjpro`i') ) // Lower bound is 0 + } + + + reshape long adjpro, i(countrycode input_flavor baseline rate) j(year_population) + + // Housekeeping and save temp + label var adjpro "Adjusted percentage of test takers with minimum reading proficiency simulated" + order countrycode year_population input_flavor rate baseline adjpro + format adjpro %10.2f + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + + ********************************************************************* + * Merge Population projections (1960-2050 ) + ********************************************************************* + + use "${`inputfolder'}/01_data/013_outputs/population.dta" , clear + cap g year_population = year + + *Choose population type + if "$population" == "" { + local population = "population_all_1014" + gen pop = `population' + } + else { + cap confirm var $population + if _rc!=0 { + di "You messed up the population option, try: (empty=xls) | pop_TOT_10 | pop_TOT_10_14 | pop_TOT_primary | pop_TOT_9_plus" + di "You specified population as: $population. Is that what you want?" + error 2222 + } + else { + gen pop = $population + } + } + + + *Use population in 2015 for the simulations if specified + if "$pop_sim"=="Yes" { + gen tmp1_2015_pop=pop if year_population==2015 + egen tmp2_2015_pop=mean(tmp1_2015_pop), by(countrycode) + replace pop=tmp2_2015_pop + } + + + g source_population = "World Bank" + keep countrycode year_population* pop + + + + //TWN does not have population data + drop if countrycode == "TWN" + + merge m:1 countrycode using "${`inputfolder'}/01_data/011_rawdata/country_metadata.dta", keep(master match) nogen + tempfile popdata + save `popdata', replace + + /* + tempname tmp + import excel using "${clone}\01_data\011_rawdata\population\Pop by 5 year_population age groups for JP v2.xlsx", /// + sheet("Data") firstrow clear + gen id = _n + reshape long YR , i(id CountryName CountryCode SeriesName SeriesCode) j(year_population) string + tolower CountryName CountryCode SeriesName SeriesCode YR + replace seriescode = subinstr(seriescode,".","_",.) + drop id seriesname + drop if seriescode == "" + destring yr, replace force + destring year_population , replace + reshape wide yr, i(countryname countrycode year_population) j(seriescode) string + renpfix yr + tolower SP* + foreach var in 0004 0509 1014 1519 { + gen double sp_pop_`var' = sp_pop_`var'_fe + sp_pop_`var'_ma + } + sort countrycode year_population + save `tmp', replace + */ + + /*** Merge population ***/ + + use "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + sort countrycode year_population + cap drop _merge + merge m:1 countrycode year_population using `popdata', update + + drop if _merge == 2 + + rename _merge merge_population + + label define merge_population 1 "Yrs with no population" 3 "Yrs with population" + + label values merge_population merge_population + + order countrycode year_population input_flavor rate baseline adjpro pop* + + + + * Step 3. Compare simulated versus target + *================================================== + // Targets specification + label define ltarget /// + 0 "100 percent adjusted proficiency" /// + 1 "98 percent adjusted proficiency" /// + 2 "95 percent adjusted proficiency" /// + 3 "Reducing the gap to frontier by 1/2" /// + 4 "Reducing the gap to frontier by 2/3" /// + 5 "Reducing the gap to frontier by 1/3" + + // Each target specification is a column at first but reshaped to long after dummies + // Generate target dummies + gen byte dtarget0 = adjpro>=100 if !missing(adjpro) + gen byte dtarget1 = adjpro>=98 if !missing(adjpro) + gen byte dtarget2 = adjpro>=95 if !missing(adjpro) + // Aux variable to contruct targets based on gap reduction + gen gap_to_frontier_ratio = (100-adjpro)/(100-baseline) + gen byte dtarget3 = 1 if (gap_to_frontier_ratio<=1/2 & !missing(gap_to_frontier_ratio)) + gen byte dtarget4 = 1 if (gap_to_frontier_ratio<=2/3 & !missing(gap_to_frontier_ratio)) + gen byte dtarget5 = 1 if (gap_to_frontier_ratio<=1/3 & !missing(gap_to_frontier_ratio)) + drop gap_to_frontier_ratio + + + // Reshape to long + reshape long dtarget, i(countrycode year_population input_flavor baseline rate adjpro) j(target) + label var target "Description of target" + order countrycode year_population input_flavor target baseline rate adjpro dtarget pop* + + // Housekeeping + label var dtarget "Dummy for whether a country met the target" + label values target ltarget + + // Save long dataset with all countries + // Change to _save_metadata + + // Simulation ID and Simulation Descriptor + gen str5 sim_id = "`filename'" + gen str250 sim_describe="filename(`filename') + ifspell(`ifspell') + ifsim(`ifsim') + ifwindow(`ifwindow') weight(`weight') preference(`preference') specialincludeassess(`specialincludeassess') specialincludegrade(`specialincludegrade') timss(`timss') + enrollment(`enrollment') + population_sim_2015(`population_2015')" + + cap gen dropped_spell_sample_id="`incomegroupdrop'" + cap gen dropped_simulation_sample_id="`simincomegroupdrop'" + cap replace dropped_spell_sample_id="Full Sample Used" if dropped_spell_sample_id=="" + cap replace dropped_simulation_sample_id="Full Sample Used" if dropped_simulation_sample_id=="" + + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + *Generate population for countries used in simulation + gen pop_sim=pop + + if `"`ifsim'"'!="" { + di `"`ifsim'"' + tempvar simkeep + gen `simkeep'=0 + replace `simkeep'=1 `ifsim' + replace rate=. if `simkeep'==0 + replace adjpro=. if `simkeep'==0 + replace dtarget=. if `simkeep'==0 + replace pop_sim=. if `simkeep'==0 + } + + *drop countries without recent assessments at baseline + if `"`ifwindow'"'!="" { + tempvar simwindow + gen `simwindow'=0 + replace `simwindow'=1 `ifwindow' + + replace rate=. if `simwindow'==0 + replace adjpro=. if `simwindow'==0 + replace dtarget=. if `simwindow'==0 + } + + * Step 4. Display regions overall achievement + *================================================== + // Percentage of proficient kids by region-year_population-specs + local which_pop_wgt = "pop" + gen wgt_included = `which_pop_wgt' if !missing(adjpro) + replace rate=. if missing(adjpro) + gen aux_adjpro = `which_pop_wgt'*adjpro + gen aux_rate = `which_pop_wgt'*rate + gen country_count=!missing(adjpro) + + + preserve + + *********** + *Produce formatted table at country level that is reshaped + *********** + keep if target==1 & year_population>=2015 & year_population<=2030 //doesnt matter target, just reduce to 1 copy in 2030 + + *generate variable indicating whether own growth rate or regional 90 + gen growth_type=substr(input_flavor, -3,.) + + *generate preference variable + + keep if year_population>=2015 & year_population<=2030 + keep countrycode region year_population growth_type preference pop wgt_included adjpro country_count + + gen learning_poverty=100-adjpro + + + egen rgn_mn=mean(learning_poverty), by(region year_population growth_type) + cap replace learning_poverty = rgn_mn if learning_poverty == . + drop rgn_mn + + drop region + + drop adjpro + + merge m:1 countrycode using "${`inputfolder'}/01_data/011_rawdata/country_metadata.dta", keep(master match) nogen + + tempvar simkeep + gen `simkeep'=0 + replace `simkeep'=1 `ifsim' + replace learning_poverty=. if `simkeep'==0 + + *reshape dataset by year_population + reshape wide pop wgt_included learning_poverty country_count , i(countrycode growth_type) j(year_population) + + *reshape dataset by growth_type + reshape wide pop* wgt_included* learning_poverty* country_count* , i(countrycode) j(growth_type) string + + foreach var in pop wgt_included country_count { + forval i=2015/2030 { + rename `var'`i'own `var'_`i' + drop `var'`i'?* + } + } + + + + save "${clone}/02_simulation/023_outputs/`filename'_country_sim_table.dta", replace + + restore + + + + if ("`savectryfile'" != "") { + + preserve + + + collapse (first) sim_id sim_describe dropped_simulation_sample_id dropped_spell_sample_id (sum) country_count num_countries_meeting_target=dtarget /// + aux_adjpro aux_rate pop_total=pop pop_with_data=wgt_included pop_sim , /// + by(`groupingsim' year_population input_flavor target) + gen wgt_adjpro = aux_adjpro/pop_with_data + gen wgt_growth_rate = aux_rate/pop_with_data + + label var wgt_adjpro "Weighted adjusted proficiency for each `groupingsim'" + label var wgt_growth_rate "Weighted mean growth rate for each `groupingsim'" + label var pop_with_data "Population with data in `groupingsim'" + label var pop_total "Total regional population" + label var pop_total "Total regional population Among Countries in Simulation" + label var num_countries_meeting_target "Number of countries in `groupingsim' meeting specified proficiency target" + + save "${`inputfolder'}/02_simulation/023_outputs/dta_ctry_`savectryfile'.dta", replace + + restore + + } + + collapse (first) sim_id sim_describe dropped_simulation_sample_id dropped_spell_sample_id (sum) country_count num_countries_meeting_target=dtarget /// + aux_adjpro aux_rate pop_total=pop pop_with_data=wgt_included pop_sim , /// + by(`groupingsim' year_population input_flavor target) + gen wgt_adjpro = aux_adjpro/pop_with_data + gen wgt_growth_rate = aux_rate/pop_with_data + + drop aux_adjpro aux_rate + + label var wgt_adjpro "Weighted adjusted proficiency for each `groupingsim'" + label var wgt_growth_rate "Weighted mean growth rate for each `groupingsim'" + label var pop_with_data "Population with data in `groupingsim'" + label var pop_total "Total regional population" + label var pop_total "Total regional population Among Countries in Simulation" + + label var num_countries_meeting_target "Number of countries in `groupingsim' meeting specified proficiency target" + + if ("`savectryfile'" != "") { + + preserve + + save "${`inputfolder'}/02_simulation/023_outputs/dta_`groupingsim'_`savectryfile'.dta", replace + + restore + + } + + if ("`allyear_populations'" == "") { + + // ON THE FLY FOR EMAIL REQUEST + keep if target==1 & year_population>=2015 & year_population<=2030 //doesnt matter target, just reduce to 1 copy in 2030 + + // Q1 + preserve + if "`weight2'"=="" { + keep if input_flavor =="preference: `preference' | rate_flavor: growth | benchmark: _own" + } + if "`weight2'"!="" { + keep if input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _own" + } + + save "${clone}/02_simulation/023_outputs/`filename'_sim_numbers.dta", replace + + + // Q2 + restore + if "`weight2'"=="" { + keep if input_flavor =="preference: `preference' | rate_flavor: growth | benchmark: _r90" + } + if "`weight2'"!="" { + keep if input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r50" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r10" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r20" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r30" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r40" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r60" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r70" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r80" /// + | input_flavor =="preference: `preference' | rate_flavor: growei | benchmark: _r90" + } + + append using "${clone}/02_simulation/023_outputs//`filename'_sim_numbers.dta", + gen specification="`filename'" + + *generate variable indicating whether own growth rate or regional 90 + gen growth_type=substr(input_flavor, -3,.) + + *generate preference variable + gen preference=substr(input_flavor, 13,4) + + *Add in global number + preserve + tempfile globalfile + collapse wgt_adjpro* [aw=pop_sim], by(growth_type year_population) + gen `groupingsim'="Global" + save `globalfile' + restore + + append using `globalfile' + + rename year_population year + save "${clone}/02_simulation/023_outputs/`filename'_sim_numbers.dta", replace + + + *********** + *Produce formatted table that is reshaped + *********** + replace `groupingsim'="Z_Global" if `groupingsim'=="Global" + + keep if year>=2015 & year<=2030 + keep year `groupingsim' growth_type preference pop_total pop_with_data pop_sim wgt_adjpro country_count + + gen learning_poverty=100-wgt_adjpro + drop wgt_adjpro + + *reshape dataset by year + reshape wide pop_total pop_with_data pop_sim learning_poverty country_count , i(`groupingsim' growth_type) j(year) + + *reshape dataset by growth_type + reshape wide pop_total* pop_with_data* pop_sim* learning_poverty* country_count* , i(`groupingsim') j(growth_type) string + + foreach var in pop_total pop_with_data pop_sim country_count { + forval i=2015/2030 { + rename `var'`i'own `var'_`i' + drop `var'`i'?* + } + } + replace `groupingsim'="Global" if `groupingsim'=="Z_Global" + + save "${clone}/02_simulation/023_outputs/`filename'_sim_table.dta", replace + export delimited using "${clone}/02_simulation/023_outputs/`filename'_sim_table.csv", replace + + * end if allyear_populations + } + + noi di _n as res "This simulation concluded." + + * Close the if nosimulation + } + + * Close the quietly + } + +end diff --git a/02_simulation/022_program/_simulation_dataset.sthlp b/02_simulation/022_programs/old_version/_simulation_dataset.sthlp similarity index 100% rename from 02_simulation/022_program/_simulation_dataset.sthlp rename to 02_simulation/022_programs/old_version/_simulation_dataset.sthlp diff --git a/02_simulation/022_program/public_simulation_app/app.R b/02_simulation/022_programs/public_simulation_app/app.R similarity index 100% rename from 02_simulation/022_program/public_simulation_app/app.R rename to 02_simulation/022_programs/public_simulation_app/app.R diff --git a/02_simulation/022_program/public_simulation_app/lpov_desc.md b/02_simulation/022_programs/public_simulation_app/lpov_desc.md similarity index 100% rename from 02_simulation/022_program/public_simulation_app/lpov_desc.md rename to 02_simulation/022_programs/public_simulation_app/lpov_desc.md diff --git a/02_simulation/022_program/public_simulation_app/special_simulation_spells_nopasec_weigthed_pref1005.md b/02_simulation/022_programs/public_simulation_app/special_simulation_spells_nopasec_weigthed_pref1005.md similarity index 100% rename from 02_simulation/022_program/public_simulation_app/special_simulation_spells_nopasec_weigthed_pref1005.md rename to 02_simulation/022_programs/public_simulation_app/special_simulation_spells_nopasec_weigthed_pref1005.md diff --git a/02_simulation/022_programs/simulate_learning_poverty.ado b/02_simulation/022_programs/simulate_learning_poverty.ado new file mode 100644 index 0000000..3570fdc --- /dev/null +++ b/02_simulation/022_programs/simulate_learning_poverty.ado @@ -0,0 +1,399 @@ +/* This ADO was inspired by the _simulation_dataset that can be found + in old_version. But it has far fewer options, with the upside of a + more transparent calculation proceess. It only works from a prior + calculation and classification of all spells (0220_create_all_spells) + and a MANDATORY usefile option. Preference is also MANDATORY. +*/ + +cap program drop simulate_learning_poverty +program define simulate_learning_poverty, rclass + + version 15 + syntax , /// + FILENAME(string) /// + GROUPINGSPELLS(string) /// + USEFILE(string) /// + PREFERENCE(string) /// + [ /// + COUNTRYFILTER(string) /// + TIMEWINDOW(string) /// + IFSPELL(string) /// + MINSPELL(integer 1) /// + PERCENTILE(string) /// + GROUPINGSIM(string) /// + quiet /// + ] + + quietly { + + *********************************************** + ********** OPTION CHECKING & SETUP ************ + *********************************************** + + if "`quiet'" == "" { + local qui "noi " + } + + if "`percentile'" == "" { + local percentile "50(10)90" + } + + * Valid options are: region incomelevel initial_poverty_level + if "`groupingspells'" == "initial_poverty_level" { + local growthdynamics "Yes" + } + + * Valid options are: region adminregion incomelevel lendingtype + if "`groupingsim'" == "" { + local groupingsim "region" + } + + * Import growth rates defined in a markdown file if the usefile() option is specified + *------------------------------------------------------ + if "`usefile'" != "" { + + import delimited "`usefile'", delimiter("|") varnames(1) clear + + cap drop v1 + cap drop v9 + drop if _n==1 + + replace `groupingspells' = strtrim(`groupingspells') + replace `groupingspells' = subinstr(`groupingspells', "`=char(9)'", "", .) + + describe delta_reg_?_??, varlist + foreach var in `r(varlist)' { + destring `var', replace + label var `var' "Annualized learning poverty change for group and percentile" + } + destring n_spells, replace + label var n_spells "N spells in the group" + + * Weighted and unweighted variables had been given different names + cap rename delta_reg_w_* delta_reg_* + cap rename delta_reg_u_* delta_reg_* + + local usefile2 = subinstr("`usefile'", ".md", ".dta",.) + save "`usefile2'", replace + + * Display values that will be used + cap replace `groupingspells' = "_Overall" if `groupingspells' == "Overall" + `qui' display _n "Spells assigned in simulation (N countries, BaU and High)" + `qui' tabstat n_spells delta_reg_50 delta_reg_80, by(`groupingspells') nototal format(%4.2fc) + } + + * Gets country own rates from full spell database + *------------------------------------------------------ + * Starts with all the spells + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + + * Keep only observations specified by. Ie: "if used sim == 1" + if `"`ifspell'"' != "" keep `ifspell' + + * Does not need to be weighted, as it collapses by country + collapse (mean) delta_lp_own = delta_lp (count) n_spells_own = delta_lp , by(countrycode) + + * Country own number will be discarded if composed by less than the minimum number of spells + drop if n_spells_own < `minspell' + + `qui' disp as result _n "N countries with own spell used in BaU `=_N' (n_spells_own >= `minspell')" + + * Save list of all countries with some own spell + label var n_spells_own "N own country spells" + label var delta_lp_own "Average own country spells" + save "${clone}/02_simulation/023_outputs/`filename'_ownspells.dta", replace emptyok + + + *********************************************** + ********** PART 1 - BASELINE 2015 ************ + *********************************************** + + * Starting point is a given preference with the time and countryfilters + * Note that this ado already opens the preference file + population_weights, preference(`preference') timewindow(`timewindow') countryfilter(`countryfilter') combine_ida_blend + + * Creates initial learning poverty level + gen initial_poverty_level = "" + replace initial_poverty_level = "0-25% Learning Poverty" if !missing(adj_nonprof_all) + replace initial_poverty_level = "25-50% Learning Poverty" if adj_nonprof_all >= 25 & !missing(adj_nonprof_all) + replace initial_poverty_level = "50-75% Learning Poverty" if adj_nonprof_all >= 50 & !missing(adj_nonprof_all) + replace initial_poverty_level = "75-100% Learning Poverty" if adj_nonprof_all >= 75 & !missing(adj_nonprof_all) + label var initial_poverty_level "Categorical string variable on initial Learning Poverty (2015)" + + * Complement from learning poverty + gen baseline = 100 - adj_nonprof_all + label var baseline "Adjusted proficiency at baseline" + rename adj_nonprof_all lpv_baseline + label var lpv_baseline "Learning poverty at baseline" + format %3.1f *baseline + + * Only relevant variables + order countrycode baseline lpv_baseline included_in_weights include_country /// + region incomelevel lendingtype initial_poverty_level preference + keep countrycode - preference + + * Brings in own spells (delta_lp_own) + merge 1:1 countrycode using "${clone}/02_simulation/023_outputs/`filename'_ownspells.dta", keep(master match) nogen + + * Brings in grouping spells from md (delta_reg_??) + merge m:1 `groupingspells' using "`usefile2'", keep(master match) keepusing(delta_reg*) nogen + + * Combines both spells info + forvalues i = `percentile' { + * Replaces group by own value if available (regardless of value) + if `i'<=50 replace delta_reg_`i' = delta_lp_own if !missing(delta_lp_own) + * Replaces group by own value only if bigger, that is, don't slow down + * countries that have growth rates higher than the group average + else replace delta_reg_`i' = delta_lp_own if delta_lp_own > delta_reg_`i' & !missing(delta_lp_own) + } + * Replaces own value for p50 of group if own is not available + replace delta_lp_own = delta_reg_50 if missing(delta_lp_own) + + * Save baseline simulation file + compress + save "${clone}/02_simulation/023_outputs/`filename'_baseline.dta", replace + + **************************************************************************** + + * Some wild renaming. See if could rather change names in the start. + * Rename variables in a consistent logic to be able to reshape long + rename delta_lp_own growei_own + forvalues i = `percentile' { + rename delta_reg_`i' growei_r`i' + } + drop n_spells_own + + * Transform the dataset in long + reshape long growei, i(countrycode `groupingspells' baseline) j(benchmark) string + rename ( growei ) ( rategrowei ) + label var benchmark "Benchmark (ie: own, r80)" + + * This is reminescent of a time when there were 7 rate_flavors + reshape long rate, i(countrycode `groupingspells' baseline benchmark) j(rate_flavor) string + label var rate "Rate used in simulation" + label var rate_flavor "Rate flavor (ie: growth, growei, reduct) " + + * Describes input flavor + gen str50 input_flavor = "preference: " + preference + replace input_flavor = input_flavor + " | rate_flavor: " + rate_flavor + replace input_flavor = input_flavor + " | benchmark: " + benchmark + label var input_flavor "Short for: Benchmark Scenario | Rate Method | Latest Prefence" + + + /* At some point we had 21 different versions of simulation methods + 7 possible "benchmark" + - own = own country historical, business as usual + - r70, r80, r90 = region percentiles 70, 80, 90 + - g70, g80, g90 = global percentiles 70, 80, 90 + 3 possible "rateflavor" + - reduct = rate applied in (100 - baseline), hopefully a negative rate + - growth = rate applied in baseline, hopefully a positive rate + - growei = rate applied in baseline (same as above), but constructed from weighted something + + But now we have set into 2 benchmarks (own = BaU and r80 = High) + and a single rateflavor = growei + */ + + * Dynamically calculated growth rates (particularly for growth rates based on initial learning poverty categories) + *================================================== + if "`groupingspells'" == "initial_poverty_level" { + levelsof initial_poverty_level, local(pov_levels) + levelsof input_flavor, local(flavors) + local counter=1 + foreach pov_lev of local pov_levels { + gen rate_`counter' = . + label var rate_`counter' "`pov_lev'" + foreach flav of local flavors { + sum rate if input_flavor == "`flav'" & initial_poverty_level == "`pov_lev'" + replace rate_`counter'=`r(mean)' if input_flavor == "`flav'" + } + replace rate_`counter'=rate if benchmark=="_own" + local counter = `counter' + 1 + } + } + + + *********************************************** + ********** PART 2 - NOW SIMULATE ************* + *********************************************** + + + * Step 1. Simulate future adjusted proficiency + *================================================== + + * Advances adjusted proficiency (adjpro) for all year_populations to simulate + * Each adjpro_year_population is created as column, then it is reshaped to long + + forvalues i = 2015/2050 { // CHANGE HERE FOR A LONGER HORIZON + + gen adjpro`i' = . + gen rate`i' = rate + * Flavor that uses a MULTIPLICATIVE rate (reduct) + replace adjpro`i' = 100 - (100-baseline)*((1-rate)^(`i'-2015)) if ( strpos(input_flavor, "reduct") & !missing(baseline)) + * Flavors that use an ADDITIVE rate (growth or growei) + replace adjpro`i' = baseline + rate*(`i'-2015) if (!strpos(input_flavor, "reduct") & !missing(baseline)) + + *Add dynamics to simulations based on initial poverty level + if "`groupingspells'" == "initial_poverty_level" & "`growthdynamics'" == "Yes"{ + if `i'>2015 { + local j=`i'-1 + replace adjpro`i' = adjpro`j' + rate_4 if (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_3 if adjpro`j'>=25 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_2 if adjpro`j'>=50 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + replace adjpro`i' = adjpro`j' + rate_1 if adjpro`j'>=75 & (!strpos(input_flavor, "reduct") & !missing(baseline)) + } + } + + replace adjpro`i' = 100 if ( adjpro`i' > 100 & !missing(adjpro`i') ) // Upper bound is 100 + replace adjpro`i' = 0 if ( adjpro`i' < 0 & !missing(adjpro`i') ) // Lower bound is 0 + } + + reshape long adjpro, i(countrycode input_flavor baseline rate) j(year) + label var year "Year" + + * Housekeeping and save intermediate file + label var adjpro "Share of non learning poor" + gen lpv = 100 - adjpro + label var lpv "Share of learning poor" + format lpv adjpro %4.2f + + compress + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + + * Step 2. Merge Population projections (1960-2050) + *================================================== + use "${clone}/01_data/013_outputs/population.dta" , clear + clonevar population = population_all_1014 + clonevar year = year_population + keep countrycode year population + tempfile popdata + save `popdata', replace + + * Open long learning poverty rates to merge population + use "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + merge m:1 countrycode year using `popdata', keep(master match) nogen + + * Save long dataset with all countries and population info + order countrycode year population included_in_weights include_country /// + adjpro lpv baseline lpv_baseline rate /// + region incomelevel lendingtype initial_poverty_level /// + rate_flavor benchmark preference input_flavor rate???? + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + + * Step 3. Add weights to enable groupingspells (ie: regions) overall achievement + *=============================================================================== + + * Adapted from 01262_population_weights + + * Error check - the below tables will only be correct if this is true + isid countrycode year input_flavor + + * Truly total population (regardless of filter) + egen group_unfiltered_population = total(population), by(`groupingsim' year input_flavor) + * Total population in the aggregation (ie: not excluded in the country filter) + egen group_total_population = total(population * include_country), by(`groupingsim' year input_flavor) + * Population in the aggregation for which we have and will use learning poverty data (ie: also in the time windown) + egen group_population_w_data = total(population * included_in_weights), by(`groupingsim' year input_flavor) + * The coverage is the ratio of population with data over total population + gen group_coverage = group_population_w_data / group_total_population + + * The weight we want is the population included, scaled by coverage + * It is rounded to an integer number so it can be used as frequency weights + * and interpreted as number of late primary age children + gen long `groupingsim'_weight = round(included_in_weights * population / group_coverage) + + * Save the long file with weights + save "${clone}/02_simulation/023_outputs/`filename'_long.dta", replace + + + + + * Step 4. Collapse values by groupingspells (ie: regions) into new file + *=============================================================================== + * As of now the code is only creating growei, but just to be safe + keep if (preference == "`preference'" & rate_flavor == "growei") + isid countrycode year benchmark + + collapse (mean) lpv (first) total_population = group_total_population /// + [fw = `groupingsim'_weight], /// + by(`groupingsim' year benchmark) + + gen learning_poor = total_population * lpv / 100 + replace benchmark = benchmark + "_" + + * Creates line with goverall + tempfile overall + + preserve + collapse (mean) lpv [fw = total_population], by(year benchmark) + save `overall', replace + restore + preserve + collapse (sum) total_population learning_poor, by(year benchmark) + merge 1:1 year benchmark using "`overall'", nogen + gen `groupingsim' = "_Overall" + save `overall', replace + restore + + append using `overall' + + save "${clone}/02_simulation/023_outputs/`filename'_fulltable.dta", replace + + + * Step 5. Creates a summary of file with just what is displayed in the paper + *=============================================================================== + + * Only keep years and scenarios (what will actually be displayed in paper) + keep if year==2015 | year==2030 + keep if inlist(benchmark, "_own_", "_r80_") + reshape wide lpv learning_poor , i(`groupingsim' total_population year) j(benchmark) string + reshape wide lpv* learning_poor* total_population, i(`groupingsim') j(year) + + * Population in millions + gen pop_2015 = total_population2015 / 1E6 + gen pop_2030 = total_population2030 / 1E6 + label var pop_2015 "Population 2015 (millions)" + label var pop_2030 "Population 2030 (millions)" + + label var lpv_own_2015 "Learning Poverty 2015, base (%)" + label var lpv_own_2030 "Learning Poverty 2030, BaU (%)" + label var lpv_r80_2030 "Learning Poverty 2030, High (%)" + + * Learning poor as share of total + foreach snapshot in own_2015 own_2030 r80_2030 { + sum learning_poor_`snapshot' if region == "_Overall" + gen lps_`snapshot' = 100 * learning_poor_`snapshot' / `r(sum)' + } + label var lps_own_2015 "Share of Learning Poor 2015, base (%)" + label var lps_own_2030 "Share of Learning Poor 2030, BaU (%)" + label var lps_r80_2030 "Share of Learning Poor, High (%)" + + order `groupingsim' pop_2015 pop_2030 lpv_own_2015 lpv_own_2030 lpv_r80_2030 lps_own_2015 lps_own_2030 lps_r80_2030 + keep `groupingsim' - lps_r80_2030 + + * Saves this summary version as a csv and dta + save "${clone}/02_simulation/023_outputs/`filename'_summarytable.dta", replace + export delimited using "${clone}/02_simulation/023_outputs/`filename'_summarytable.csv", replace + + * Display results for 2030 + drop if `groupingsim' == "_Overall" // since this line will be calculated with tabstats + + `qui' di "" + `qui' di as res "Learning Poverty Simulated Global Numbers" + `qui' di as txt " preference: `preference'" + `qui' di as txt `" time window: `timewindow'"' + `qui' di as txt `" cty filters: `countryfilter'"' + `qui' di as res _n "Baseline (2015)" + `qui' tabstat lpv_own_2015 [aw = pop_2015], by(`groupingsim') format(%4.2fc) + `qui' di as res _n "BaU (2030) | High (2030)" + `qui' tabstat lpv_own_2030 lpv_r80_2030 [aw = pop_2030], by(`groupingsim') format(%4.2fc) + + + noi di _n as res "This simulation concluded." + + * Close the quietly + } + +end diff --git a/02_simulation/022_program/simulations/app.R b/02_simulation/022_programs/simulations/app.R similarity index 99% rename from 02_simulation/022_program/simulations/app.R rename to 02_simulation/022_programs/simulations/app.R index 3b362ae..842d9ce 100644 --- a/02_simulation/022_program/simulations/app.R +++ b/02_simulation/022_programs/simulations/app.R @@ -57,7 +57,7 @@ ui <- fluidPage( textInput("specialincludeassess", "Include any extra assessments in the spells database", value="PIRLS LLECE TIMSS SACMEQ"), textInput("specialincludegrade", "Include any extra grades in the spells database", value="3 4 5 6"), textInput("enrollment", "which enrollment variable to use", value="validated"), - textInput("usefile", "Name of Markdown file with Regional Growth Rates", value='"${clone}/02_simulation/022_program/special_simulation_spells_nopasec_weigthed_pref1005.md"'), + textInput("usefile", "Name of Markdown file with Regional Growth Rates", value='"${clone}/02_simulation/022_programs/special_simulation_spells_nopasec_weigthed_pref1005.md"'), textInput("timss", "TIMSS: Science or Math", value='science'), textInput("population_2015", "Keep population fixed at 2015?", value='No'), textInput("savectryfile", "Save Country File with this name:", value='special_spells_nopasec_weigthed'), diff --git a/02_simulation/022_program/simulations/sim_prep.do b/02_simulation/022_programs/simulations/sim_prep.do similarity index 92% rename from 02_simulation/022_program/simulations/sim_prep.do rename to 02_simulation/022_programs/simulations/sim_prep.do index 173b68f..6e6add1 100644 --- a/02_simulation/022_program/simulations/sim_prep.do +++ b/02_simulation/022_programs/simulations/sim_prep.do @@ -9,7 +9,7 @@ gl master_seed 17893 // User-dependant paths for local repo clone *Brian if inlist("`c(username)'","wb469649","WB469649") { -/*Local repo clone */ global clone "C:/Users/`c(username)'/Documents/GitHub/LearningPoverty" +/*Local repo clone */ global clone "C:/Users/`c(username)'/Documents/GitHub/LearningPoverty-Production" } *Diana if inlist("`c(username)'","wb552057","WB552057","diana") { @@ -42,6 +42,6 @@ if inlist("`c(username)'","wb462869","WB462869") { *------------------------------------------------------------------------------- * Make sure stata simulation_dataset.ado file is loaded -cd "${clone}/02_simulation/022_program/" +cd "${clone}/02_simulation/022_programs/" -do "${clone}/02_simulation/022_program/_simulation_dataset.ado" +do "${clone}/02_simulation/022_programs/_simulation_dataset.ado" diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_sim_table.csv new file mode 100644 index 0000000..8071779 --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_unweighted_spells_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_unweighted_spells_sim_table.csv new file mode 100644 index 0000000..13f38d4 --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_incomelevel_growth_unweighted_spells_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_growth_unweighted_spells_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_growth_unweighted_spells_sim_table.csv new file mode 100644 index 0000000..daea9f3 --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_growth_unweighted_spells_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_sim_table.csv new file mode 100644 index 0000000..635bb46 --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_initial_poverty_level_growth_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_NEW_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_NEW_sim_table.csv new file mode 100644 index 0000000..086a22d --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_NEW_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_growth_unweighted_spells_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_growth_unweighted_spells_sim_table.csv new file mode 100644 index 0000000..3b3763e --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_growth_unweighted_spells_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_noISR_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_noISR_sim_table.csv new file mode 100644 index 0000000..129076a --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_noISR_sim_table.csv @@ -0,0 +1,9 @@ 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diff --git a/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_sim_table.csv b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_sim_table.csv new file mode 100644 index 0000000..f6e38ef --- /dev/null +++ b/02_simulation/023_outputs/old_version/simfile_preference_1005_regional_growth_sim_table.csv @@ -0,0 +1,9 @@ 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+SAS,5,174647837,171290911,174647837,58.205475,5,175077905,171816252,175077905,57.557613,5,175352123,172166136,175352123,56.9146,5,175469465,172344232,175469465,56.277451,5,175307000,172233000,175307000,55.651382,5,174676000,171645000,174676000,55.036339,5,173463000,170479000,173463000,54.431229,5,171907000,168958000,171907000,53.838818,5,170127000,167209000,170127000,53.255871,5,168476000,165585000,168476000,52.674652,5,167302000,164441000,167302000,52.085926,5,166688000,163852000,166688000,51.485973,5,166557000,163740000,166557000,50.876431,5,166771000,163973000,166771000,50.25819,5,167057000,164274000,167057000,49.632977,5,167187000,164410000,167187000,49.001053,58.205475,58.634602,59.068577,59.508419,59.959339,60.421284,60.893162,61.377739,61.871784,62.367558,62.85582,63.332851,63.800301,64.259048,64.710823,65.155884,58.205475,58.236942,58.273266,58.315449,58.368713,58.433002,58.507229,58.594147,58.690536,58.788654,58.879261,58.958637,59.028431,59.089523,59.143642,59.191055,58.205475,58.04903,57.897442,57.751709,57.617062,57.493431,57.379745,57.278751,57.187225,57.097431,57.000118,56.891582,56.773464,56.646641,56.512848,56.372345,58.205475,57.696949,57.193283,56.695465,56.20874,55.733032,55.267265,54.81419,54.370586,53.928707,53.479317,53.018703,52.5485,52.069599,51.583721,51.091141,58.205475,57.469379,56.738132,56.012745,55.298435,54.595158,53.901814,53.221165,52.549988,51.880535,51.203571,50.515381,49.817604,49.111126,48.397678,47.677517,58.205475,57.321609,56.442596,55.569435,54.707367,53.856316,53.015202,52.186787,51.36784,50.550617,49.725883,48.889927,48.04438,47.190136,46.328918,45.460991,58.205475,56.787678,55.374733,53.967644,52.57164,51.186657,49.811615,48.449268,47.096386,45.745239,44.386574,43.028423,41.663586,40.28978,38.908657,37.520386,58.205475,56.315132,54.429646,52.550007,50.681461,48.823933,46.976353,45.141457,43.322014,41.518108,39.706612,37.883629,36.050678,34.208588,32.358994,30.502014,58.205475,55.678661,53.1567,50.640594,48.135574,45.641579,43.163227,40.718159,38.282677,35.848892,33.407356,30.954102,28.490631,26.017815,23.537247,21.048958,1005 +SSF,17,123159024,56789447,123159024,86.667374,17,126528932,58321977,126528932,85.691284,17,130017851,59929514,130017851,84.710999,17,133576484,61586844,133576484,83.72876,17,137130200,63260000,137130200,82.748619,17,140587400,64902000,140587400,81.773033,17,143980500,66532000,143980500,80.803352,17,147325600,68158000,147325600,79.840096,17,150620600,69775000,150620600,78.883636,17,153830800,71364000,153830800,77.933029,17,156898800,72890000,156898800,76.988647,17,159815800,74365000,159815800,76.049805,17,162662800,75802000,162662800,75.116814,17,165439800,77204000,165439800,74.19043,17,168218800,78584000,168218800,73.266495,17,171013600,79938000,171013600,72.343399,86.667374,87.25296,87.834061,88.389557,88.941116,89.475731,90.0019,90.51474,91.031654,91.551651,92.074646,92.599823,93.126678,93.642838,94.155785,94.666618,86.667374,86.715164,86.758469,86.799858,86.843201,86.890121,86.942093,86.998665,87.059998,87.125153,87.194489,87.267342,87.343529,87.423584,87.504112,87.584244,86.667374,86.55294,86.434021,86.313187,86.194305,86.079002,85.96875,85.863098,85.762207,85.665138,85.572258,85.48288,85.396843,85.314674,85.232979,85.150887,86.667374,85.809837,84.947807,84.083878,83.221878,82.363472,81.510117,80.661362,79.817368,78.977188,78.141205,77.308723,76.479584,75.654305,74.829506,74.004311,86.667374,85.740395,84.808922,83.875549,82.944107,82.016258,81.09346,80.175262,79.261826,78.352203,77.446777,76.544853,75.646271,74.751556,73.857307,72.962669,86.667374,85.567139,84.462418,83.355789,82.251106,81.150009,80.053955,78.962502,77.875816,76.792946,75.714264,74.639091,73.567253,72.49929,71.431793,70.363907,86.667374,84.674141,82.676788,80.677475,78.680305,76.687943,74.701714,72.722382,70.750404,68.784805,66.825974,64.873215,62.926991,60.988083,59.052158,57.117393,86.667374,84.157265,81.642746,79.126312,76.611862,74.101265,71.595963,69.095772,66.600922,64.110458,61.624748,59.143124,56.665543,54.192604,51.720692,49.24873,86.667374,83.206245,79.740623,76.273102,72.807518,69.34552,65.888573,62.436222,58.98864,55.544872,52.105289,48.66922,45.236488,41.807625,38.379688,34.962624,1005 +Global,,,,,52.698315,,,,,52.120811,,,,,51.50938,,,,,50.882389,,,,,50.253639,,,,,49.616104,,,,,48.979069,,,,,48.345615,,,,,47.7169,,,,,47.09861,,,,,46.497997,,,,,45.907238,,,,,45.33894,,,,,44.789845,,,,,44.262215,,,,,43.802238,52.698315,53.3246,53.923065,54.501728,55.076973,55.643085,56.199371,56.756973,57.322891,57.902847,58.50198,59.113861,59.751316,60.403294,61.076321,61.772018,52.698315,52.907837,53.088139,53.252953,53.414814,53.571926,53.722305,53.878456,54.043129,54.221916,54.419804,54.630398,54.866379,55.124489,55.404041,55.706081,52.698315,52.733341,52.739235,52.729752,52.717606,52.70121,52.678825,52.663094,52.656815,52.665573,52.696312,52.739967,52.809731,52.902054,53.016018,53.152729,52.698315,52.329819,51.928486,51.508366,51.08989,50.667542,50.240921,49.82827,49.424454,49.033924,48.660213,48.296593,47.956566,47.633884,47.327148,47.037014,52.698315,52.106682,51.482544,50.848312,50.211796,49.570374,48.931091,48.295795,47.666946,47.050343,46.453457,45.868858,45.307949,44.767517,44.245544,43.742226,52.698315,51.955441,51.181461,50.399696,49.615597,48.837257,48.050034,47.265877,46.49012,45.727303,44.98204,44.248245,43.537876,42.847183,42.174706,41.524593,52.698315,51.229786,49.743744,48.260525,46.77906,45.288136,43.784309,42.285698,40.788963,39.301689,37.836845,36.397446,35.03022,33.785049,32.80471,31.84333,52.698315,50.782185,48.855087,46.92717,45.000618,43.063366,41.117702,39.174019,37.239033,35.314674,33.571068,31.993881,30.479774,28.988319,27.628197,26.280045,52.698315,50.177883,47.66951,45.159481,42.648834,40.128189,37.609486,35.097305,32.957184,30.868996,28.817993,26.804077,24.842278,22.879471,21.033188,19.176765, diff --git a/02_simulation/023_outputs/simfile_preference_1005_income_level_summarytable.csv b/02_simulation/023_outputs/simfile_preference_1005_income_level_summarytable.csv new file mode 100644 index 0000000..93d7470 --- /dev/null +++ b/02_simulation/023_outputs/simfile_preference_1005_income_level_summarytable.csv @@ -0,0 +1,8 @@ +region,pop_2015,pop_2030,lpv_own_2015,lpv_own_2030,lpv_r80_2030,lps_own_2015,lps_own_2030,lps_r80_2030 +EAS,137.06055,137.82791,21.1647,4.4420638,.15002166,10.048742,2.2604487,.12031931 +ECS,27.044895,31.761,13.286224,3.1984007,.80880296,1.2447268,.37505913,.14947933 +LCN,53.347,50.581799,50.779083,39.416515,22.83053,9.3838701,7.3611417,6.7197719 +MEA,32.533279,43.752998,63.299294,51.211834,29.414488,7.1336837,8.272768,7.4888196 +SAS,174.64784,167.18701,58.205475,45.391834,19.47456,35.213905,28.01902,18.945864 +SSF,123.15903,171.0136,86.667374,85.06765,66.902313,36.975071,53.711563,66.575745 +_Overall,547.7926,602.12433,52.698311,44.982246,28.541027,100,100,100 diff --git a/02_simulation/023_outputs/simfile_preference_1005_initial_poverty_level_summarytable.csv b/02_simulation/023_outputs/simfile_preference_1005_initial_poverty_level_summarytable.csv new file mode 100644 index 0000000..2635447 --- /dev/null +++ b/02_simulation/023_outputs/simfile_preference_1005_initial_poverty_level_summarytable.csv @@ -0,0 +1,8 @@ +region,pop_2015,pop_2030,lpv_own_2015,lpv_own_2030,lpv_r80_2030,lps_own_2015,lps_own_2030,lps_r80_2030 +EAS,137.06055,137.82791,21.1647,7.5375896,2.3660009,10.048742,3.8551843,1.9108526 +ECS,27.044895,31.761,13.286224,3.4277914,1.809603,1.2447268,.40400252,.33678487 +LCN,53.347,50.581799,50.779083,39.579136,19.666872,9.3838701,7.4290981,5.8291426 +MEA,32.533279,43.752998,63.299294,54.419418,33.368847,7.1336837,8.8356237,8.5550814 +SAS,174.64784,167.18701,58.205475,51.210705,26.354261,35.213905,31.771578,25.81835 +SSF,123.15903,171.0136,86.667374,75.171516,57.42989,36.975071,47.704514,57.549789 +_Overall,547.7926,602.12433,52.698311,44.754665,28.342537,100,100,100 diff --git a/02_simulation/023_outputs/simfile_preference_1005_regional_growth_glossy_summarytable.csv b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_glossy_summarytable.csv new file mode 100644 index 0000000..02b4151 --- /dev/null +++ b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_glossy_summarytable.csv @@ -0,0 +1,8 @@ +region,pop_2015,pop_2030,lpv_own_2015,lpv_own_2030,lpv_r80_2030,lps_own_2015,lps_own_2030,lps_r80_2030 +EAS,137.06055,137.82791,21.1647,9.8950253,1.5025971,10.048742,5.1918259,1.2968612 +ECS,27.044895,31.761,13.286224,6.7269106,2.6292293,1.2447268,.81334698,.52292138 +LCN,53.347,50.581799,50.779083,41.585373,23.980068,9.3838701,8.0075731,7.5955367 +MEA,32.533279,43.752998,63.299294,44.559193,22.838743,7.1336837,7.4218335,6.2573981 +SAS,174.64784,167.18701,58.205475,47.41119,29.993401,35.213905,30.17515,31.400892 +SSF,123.15903,171.0136,86.667374,74.329521,49.422859,36.975071,48.39027,52.926392 +_Overall,547.7926,602.12433,52.698311,43.62624,26.521622,100,100,100 diff --git a/02_simulation/023_outputs/simfile_preference_1005_regional_growth_min2_summarytable.csv b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_min2_summarytable.csv new file mode 100644 index 0000000..7e7bb87 --- /dev/null +++ b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_min2_summarytable.csv @@ -0,0 +1,8 @@ +region,pop_2015,pop_2030,lpv_own_2015,lpv_own_2030,lpv_r80_2030,lps_own_2015,lps_own_2030,lps_r80_2030 +EAS,137.06055,137.82791,21.1647,11.358292,1.8283305,10.048742,5.8070078,1.5238901 +ECS,27.044895,31.761,13.286224,6.7269106,2.6292293,1.2447268,.79252338,.50499189 +LCN,53.347,50.581799,50.779083,41.585373,23.980068,9.3838701,7.8025603,7.3351073 +MEA,32.533279,43.752998,63.299294,55.088978,28.906893,7.1336837,8.9407673,7.6484079 +SAS,174.64784,167.18701,58.205475,48.975708,31.528131,35.213905,30.372849,31.875906 +SSF,123.15903,171.0136,86.667374,72.962669,49.422859,36.975071,46.28429,51.111694 +_Overall,547.7926,602.12433,52.698311,44.772522,27.463259,100,100,100 diff --git a/02_simulation/023_outputs/simfile_preference_1005_regional_growth_summarytable.csv b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_summarytable.csv new file mode 100644 index 0000000..015db47 --- /dev/null +++ b/02_simulation/023_outputs/simfile_preference_1005_regional_growth_summarytable.csv @@ -0,0 +1,8 @@ +region,pop_2015,pop_2030,lpv_own_2015,lpv_own_2030,lpv_r80_2030,lps_own_2015,lps_own_2030,lps_r80_2030 +EAS,137.06055,137.82791,21.1647,6.433795,.34061652,10.048742,3.3183558,.28814289 +ECS,27.044895,31.761,13.286224,3.3977602,1.8019266,1.2447268,.4038364,.35126615 +LCN,53.347,50.581799,50.779083,39.579128,23.738907,9.3838701,7.4916773,7.369873 +MEA,32.533279,43.752998,63.299294,56.171352,28.906893,7.1336837,9.1968966,7.7627263 +SAS,174.64784,167.18701,58.205475,50.474407,31.528131,35.213905,31.578558,32.352345 +SSF,123.15903,171.0136,86.667374,75.021996,49.422859,36.975071,48.010677,51.875645 +_Overall,547.7926,602.12433,52.698311,44.380817,27.058819,100,100,100 diff --git a/03_export_tables/031_rawdata/lpv_metadata.csv b/03_export_tables/031_rawdata/lpv_metadata.csv index 0808e65..4b3198d 100644 --- a/03_export_tables/031_rawdata/lpv_metadata.csv +++ b/03_export_tables/031_rawdata/lpv_metadata.csv @@ -1,10 +1,10 @@ -Indicator,Indicator Name,Source,Source Note,Source Organization -SE.LPV.PRIM,Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of  children who haven’t achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see [CITE WORKING PAPER].",Word Bank and UIS -SE.LPV.PRIM.FE,Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of  children who haven’t achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see [CITE WORKING PAPER].",Word Bank and UIS -SE.LPV.PRIM.MA,Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of  children who haven’t achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see [CITE WORKING PAPER].",Word Bank and UIS -SE.LPV.PRIM.OOS,Primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS -SE.LPV.PRIM.OOS.FE,Female primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS -SE.LPV.PRIM.OOS.MA,Male primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by UIS. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS -SE.LPV.PRM.BMP,Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS -SE.LPV.PRIM.BMP.FE,Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS -SE.LPV.PRIM.BMP.MA,Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses on Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper series. Washington, DC: World Bank.",Word Bank and UIS +Indicator,Indicator Name,Source,Source Note,Source Organization +SE.LPV.PRIM,Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.FE,Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.MA,Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%),Education Statistics,"This indicator brings together schooling and learning. It starts with the share of children who have not achieved minimum reading proficiency and adjusts it by the proportion of children who are out of school. The data used to calculate Learning Poverty has been made possible thanks to the work of the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS), which established Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.OOS,Primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.OOS.FE,Female primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.OOS.MA,Male primary school age children out-of-school (%),Education Statistics,"The Out-of-School adjustment in our Learning Poverty indicator relies on enrollment data. Our preferred definition is the adjusted net primary enrollment as reported by the UNESCO Institute for Statistics (UIS). For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.BMP,Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.BMP.FE,Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS +SE.LPV.PRIM.BMP.MA,Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold,Education Statistics,"This indicator uses the Minimum Proficiency Levels (MPLs) defined by the Global Alliance to Monitor Learning (GAML) led by the UNESCO Institute for Statistics (UIS) in the context of the SDG 4.1.1 monitoring, which established learning benchmarks across different cross-national and national assessments. For more information please see Azevedo, Joao Pedro, and others. 2019. Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It. World Bank Policy Research Working Paper series. Washington, DC: World Bank.",World Bank and UIS diff --git a/03_export_tables/032_programs/0320_spells_statistics.do b/03_export_tables/032_programs/0320_spells_statistics.do new file mode 100644 index 0000000..5674d8b --- /dev/null +++ b/03_export_tables/032_programs/0320_spells_statistics.do @@ -0,0 +1,137 @@ +*==============================================================================* +* 0320 SUBTASK: PRODUCE SPELLS STATISTICS SUMMARY TABLES +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + + *-----------------------------------------------------------------------------* + * Statistics on spells used and potentially used (for Table 22) + *-----------------------------------------------------------------------------* + + foreach filter in potential_sim used_sim { + + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + + clonevar delta_adj_pct = delta_lp + clonevar initial_learning_poverty = learningpoverty_y1 + + * Will only keep the observations marked for the simulation + keep if `filter' == 1 + + preserve + + collapse (mean) meand = delta_adj_pct meanlp = initial_learning_poverty /// + (p50) p50d = delta_adj_pct p50lp = initial_learning_poverty /// + (min) mind = delta_adj_pct minlp = initial_learning_poverty /// + (max) maxd = delta_adj_pct maxlp = initial_learning_poverty /// + (count) nd = delta_adj_pct nlp = initial_learning_poverty + + gen test = "Total" + + tempfile all_tests + save `all_tests', replace + + restore + + collapse (mean) meand = delta_adj_pct meanlp = initial_learning_poverty /// + (p50) p50d = delta_adj_pct p50lp = initial_learning_poverty /// + (min) mind = delta_adj_pct minlp = initial_learning_poverty /// + (max) maxd = delta_adj_pct maxlp = initial_learning_poverty /// + (count) nd = delta_adj_pct nlp = initial_learning_poverty /// + , by(test) + + append using `all_tests' + + gen filter = "`filter'" + + tempfile `filter' + save ``filter'', replace + + } + + use `potential_sim' + append using `used_sim' + + * Save stats file + save "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_assessment.dta", replace + + + *-----------------------------------------------------------------------------* + * Statistics on spells used and potentially used (for Table 23) + *-----------------------------------------------------------------------------* + + foreach weigth_mode in not_weighted weighted { + + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + + clonevar delta_adj_pct = delta_lp + clonevar initial_learning_poverty = learningpoverty_y1 + + * Will only keep the observations marked for the simulation + keep if used_sim == 1 + + * Addendum to the collapse + if "`weigth_mode'" == "weighted" { + * Weighted version will consider each country only once, that is, if the same + * country has multiple spells, they are averaged + bysort countrycode : gen n_spells_country = _N + gen spells_wgt = 1 / n_spells_country + + local wgt_opt "[aw = spells_wgt]" + } + else { + local wgt_opt "" + } + + preserve + + collapse (mean) meand = delta_adj_pct meanlp = initial_learning_poverty /// + (p50) p50d = delta_adj_pct p50lp = initial_learning_poverty /// + (min) mind = delta_adj_pct minlp = initial_learning_poverty /// + (max) maxd = delta_adj_pct maxlp = initial_learning_poverty /// + (count) nd = delta_adj_pct nlp = initial_learning_poverty /// + `wgt_opt' + + gen region = "Total" + + tempfile all_tests + save `all_tests', replace + + restore + + collapse (mean) meand = delta_adj_pct meanlp = initial_learning_poverty /// + (p50) p50d = delta_adj_pct p50lp = initial_learning_poverty /// + (min) mind = delta_adj_pct minlp = initial_learning_poverty /// + (max) maxd = delta_adj_pct maxlp = initial_learning_poverty /// + (count) nd = delta_adj_pct nlp = initial_learning_poverty /// + `wgt_opt', by(region) + + append using `all_tests' + + gen weights = "`weigth_mode'" + + tempfile `weigth_mode' + save ``weigth_mode'', replace + + } + + use `not_weighted' + append using `weighted' + + * For those two regions with a single spell, won't report values + foreach var of varlist mean* p50* min* max* { + replace `var' = . if inlist(region, "EAS", "SAS") + } + + * Save stats file + save "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_region.dta", replace + + + noi disp as res _newline "Finished exporting spells statistics." + +} diff --git a/03_export_tables/032_programs/03211_outline_tables.do b/03_export_tables/032_programs/03211_outline_tables.do new file mode 100644 index 0000000..00beae0 --- /dev/null +++ b/03_export_tables/032_programs/03211_outline_tables.do @@ -0,0 +1,314 @@ +*==============================================================================* +* PROGRAMS: CREATES TWO TABLES FOR TECHNICAL PAPER +*==============================================================================* + +* This do contains two programs that create tables called in 0321 to outline +* the current learning poverty situation. The first does it aggregated, the +* second program does it with a gender breakdown. + + +cap program drop outline_current_lp +program define outline_current_lp, rclass + + syntax , filename(string) [REPetitions(string)] + + qui { + + * Check if specified filename is ".csv" + if substr("`filename'",-4,4)!=".csv" { + noi disp as err "Filename should end in .csv" + break + } + local filename_csv = "`filename'" + local filename_dta = subinstr("`filename'", ".csv", ".dta", 1) + local filename_bs_dta = subinstr("`filename'", ".csv", "_bs.dta", 1) + + * Marks which filename it is to make it easier when importing to excel + local end_of_path = strrpos("`filename'","/") + local total_lenght = strlen("`filename'") + local file_marker = substr("`filename'", `end_of_path' + 1, `total_lenght' - `end_of_path' - 4) + + * Creates empty files where all will be appended + preserve + clear + save "`filename_dta'" , emptyok replace + if "`repetitions'"!="" save "`filename_bs_dta'", emptyok replace + restore + + * Aggregations we want to create tables for + local possible_aggregations "incomelevel lendingtype adminregion region global" + gen str global = "TOTAL" + + * Aux variable to have average year of included assessments + gen year2avg = year_assessment if included_in_weights == 1 + + * Loop through each aggregation + foreach aggregation of local possible_aggregations { + + preserve + + bys `aggregation': egen total_countries = sum(include_country) + + * Collapse the measures in tables being generated + collapse (mean) mean_lp = adj_nonprof_all (min) min_lp = adj_nonprof_all (max) max_lp = adj_nonprof_all /// + (mean) mean_fgt1 = fgt1_all mean_fgt2 = fgt1_all /// + (mean) n_countries = `aggregation'_n_countries (mean) coverage = `aggregation'_coverage /// + (mean) population_w_data = `aggregation'_population_w_data /// + (mean) total_population = `aggregation'_total_population /// + (mean) avg_assess_year = year2avg (mean) total_countries /// + (min) min_assess_year = year2avg (max) max_assess_year = year2avg /// + [fw = `aggregation'_weight], by(`aggregation') + + * Makes all the files compatible for appending later + rename `aggregation' group + gen aggregated_by = "`aggregation'" + + * Appends to table file being created + append using "`filename_dta'" + save "`filename_dta'", replace + + restore + + + * Bootstrap approach for std errors + if "`repetitions'"!="" { + + * Replaces the SE with median value for assessments without this information + replace se_nonprof_all = 1.2 if missing(se_nonprof_all) & !missing(nonprof_all) + + forvalues i = 1/`repetitions' { + preserve + + * Bootstrap value for learning poverty + gen adj_nonprof_bs_all = 100 * ( 1 - (enrollment_all/100) * (1 - rnormal(nonprof_all, se_nonprof_all)/100)) + + collapse (mean) adj_nonprof_all adj_nonprof_bs_all [fw = `aggregation'_weight], by(`aggregation') + + * Makes all the files compatible for appending later + rename `aggregation' group + gen aggregated_by = "`aggregation'" + gen repetition = `i' + + * Appends to table file being created + append using "`filename_bs_dta'" + save "`filename_bs_dta'", replace + + restore + } + } + + } + + + * Final touches in the created files from multiple appends + + if "`repetitions'"!="" { + + * Open the bootstrap SE with all appended repetitions + use "`filename_bs_dta'", clear + + collapse (sd) se_lp = adj_nonprof_bs_all, by(group aggregated_by) + + tempfile se + save `se' + + } + + * Open the table with all appended aggregations + use "`filename_dta'", clear + + if "`repetitions'"!="" { + merge 1:1 group aggregated_by using `se', keep(master match) nogen + label var se_lp "S.E. Learning Poverty (%)" + } + + * Beautify and label + gen file = "`file_marker'" + + * Coverage in percentage points + replace coverage = coverage * 100 + + * Population in millions + replace total_population = total_population / 1E6 + replace population_w_data = population_w_data / 1E6 + + * Learning poor in millions + gen learning_poor = mean_lp * total_population / 100 + + * Label variables + label var aggregated_by "Aggregation group" + label var group "Group" + label var mean_lp "Learning Poverty (%)" + label var min_lp "Minimum Learning Poverty" + label var max_lp "Maximum Learning Poverty" + label var mean_fgt1 "Average Learning Gap (%, FGT1)" + label var mean_fgt2 "Average Learning Gap Squared (%, FGT2)" + label var coverage "Population Coverage (%)" + label var population_w_data "Population w/ Assessment (in millions)" + label var total_population "Regional Population (in millions)" + label var learning_poor "Learning Poor (in millions)" + label var n_countries "Number of countries w/ Assessment" + label var total_countries "Total number of countries" + label var avg_assess_year "Avg. Year" + label var min_assess_year "Min Year" + label var max_assess_year "Max Year" + label var file "File marker (table creation)" + + order aggregated_by group *lp* *fgt* learning_poor *population* coverage *countries* *year file + sort aggregated_by group + + save "`filename_dta'", replace + + //* Export final csv as well + //noi export delimited "`filename_csv'", replace + + + } + + +end + + +****** GENDER TABLE ****** + +cap program drop outline_gender_lp +program define outline_gender_lp, rclass + + syntax , filename(string) [REPetitions(string)] + + qui { + + * Check if specified filename is ".csv" + if substr("`filename'",-4,4)!=".csv" { + noi disp as err "Filename should end in .csv" + break + } + local filename_csv = "`filename'" + local filename_dta = subinstr("`filename'", ".csv", ".dta", 1) + local filename_bs_dta = subinstr("`filename'", ".csv", "_bs.dta", 1) + + * Marks which filename it is to make it easier when importing to excel + local end_of_path = strrpos("`filename'","/") + local total_lenght = strlen("`filename'") + local file_marker = substr("`filename'", `end_of_path' + 1, `total_lenght' - `end_of_path' - 4) + + * Creates empty files where all will be appended + preserve + clear + save "`filename_dta'" , emptyok replace + if "`repetitions'"!="" save "`filename_bs_dta'", emptyok replace + restore + + * Aggregations we want to create tables for + local possible_aggregations "incomelevel lendingtype adminregion region global" + gen str global = "TOTAL" + + * Given that we'll print out gender split, only keep LP data if have the gender split + clonevar adj_nonprof_all_compatible = adj_nonprof_all + replace adj_nonprof_all_compatible = . if lp_by_gender_is_available == 0 + + * Loop through each aggregation + foreach aggregation of local possible_aggregations { + + preserve + + * Collapse the measures in tables being generated + collapse (mean) mean_lp_allcomp = adj_nonprof_all_compatible /// + (mean) mean_lp_ma = adj_nonprof_ma (mean) mean_lp_fe = adj_nonprof_fe /// + (rawsum) n_countries = lp_by_gender_is_available /// + [fw = `aggregation'_weight], by(`aggregation') + + * Makes all the files compatible for appending later + rename `aggregation' group + gen aggregated_by = "`aggregation'" + + * Appends to table file being created + append using "`filename_dta'" + save "`filename_dta'", replace + + restore + + + * Bootstrap approach for std errors + if "`repetitions'"!="" { + + * Replaces the SE with median value for assessments without this information + foreach subgroup in all ma fe { + replace se_nonprof_`subgroup' = 1.2 if missing(se_nonprof_`subgroup') & !missing(nonprof_`subgroup') + } + + forvalues i = 1/`repetitions' { + preserve + + * Bootstrap value for learning poverty + foreach subgroup in all ma fe { + gen adj_nonprof_bs_`subgroup' =100 * ( 1 - (enrollment_`subgroup'/100) * (1 - rnormal(nonprof_`subgroup', se_nonprof_`subgroup')/100)) + } + + collapse (mean) adj_nonprof_all_compatible adj_nonprof_ma adj_nonprof_fe adj_nonprof_bs_* /// + [fw = `aggregation'_weight], by(`aggregation') + + * Makes all the files compatible for appending later + rename `aggregation' group + gen aggregated_by = "`aggregation'" + gen repetition = `i' + + * Appends to table file being created + append using "`filename_bs_dta'" + save "`filename_bs_dta'", replace + + restore + } + } + + } + + * Final touches in the created files from multiple appends + + if "`repetitions'"!="" { + + * Open the bootstrap SE with all appended repetitions + use "`filename_bs_dta'", clear + + collapse (sd) adj_nonprof_bs*, by(group aggregated_by) + rename (adj_nonprof_bs_all adj_nonprof_bs_ma adj_nonprof_bs_fe) /// + (se_lp_allcomp se_lp_ma se_lp_fe) + + tempfile se + save `se' + + } + + * Open the table with all appended aggregations + use "`filename_dta'", clear + + if "`repetitions'"!="" { + merge 1:1 group aggregated_by using `se', keep(master match) nogen + label var se_lp_ma "S.E. male" + label var se_lp_fe "S.E. female" + label var se_lp_allcomp "S.E. pooled (comparable)" + } + + * Beautify and label + gen file = "`file_marker'" + + * Label variables + label var aggregated_by "Aggregation group" + label var group "Group" + label var mean_lp_ma "LP male (%)" + label var mean_lp_fe "LP female (%)" + label var mean_lp_allcomp "LP pooled (%, comparable)" + label var n_countries "Number of countries w/ breakdown" + label var file "File marker (table creation)" + + order aggregated_by group *lp* *countries* file + sort aggregated_by group + + save "`filename_dta'", replace + + //* Export final csv as well + //noi export delimited "`filename_csv'", replace + + } + +end diff --git a/03_export_tables/032_programs/0321_create_outlines.do b/03_export_tables/032_programs/0321_create_outlines.do new file mode 100644 index 0000000..d265007 --- /dev/null +++ b/03_export_tables/032_programs/0321_create_outlines.do @@ -0,0 +1,149 @@ +*==============================================================================* +* 0321 SUBTASK: GENERATE TABLES OUTLINING THE CURRENT SITUATION +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + * Where all files created during this do will be saved + local outputs_folder "${clone}/03_export_tables/033_outputs/individual_tables" + + * Option of bootstrap repetitions for SE calculation + * (will be applied to all outlines) + local reps "repetitions(100)" + noi dis as txt _n "All tables calculated with bootstrap `reps' (may take a long time)" + + *----------------------------------------------------------------------------- + * Outline of Current Situation + *----------------------------------------------------------------------------- + + * Part 2 countries only + population_weights, preference(`chosen_preference') timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") combine_ida_blend + outline_current_lp, filename("`outputs_folder'/outline_current_lp_part2.csv") `reps' + + * All countries in the world + population_weights, preference(`chosen_preference') timewindow(year_assessment>=2011) combine_ida_blend + outline_current_lp, filename("`outputs_folder'/outline_current_lp_world.csv") `reps' + + * Repeats the above without the restriction on year_assessment (witouth BS repetitions) + + * Part 2 countries only + population_weights, preference(`chosen_preference') countryfilter(lendingtype!="LNX") combine_ida_blend + outline_current_lp, filename("`outputs_folder'/outline_current_lp_old_part2.csv") + + * All countries in the world + population_weights, preference(`chosen_preference') combine_ida_blend + outline_current_lp, filename("`outputs_folder'/outline_current_lp_old_world.csv") + + + noi disp as res _newline "Finished exporting outline of Current Situation." + + + *----------------------------------------------------------------------------- + * Sensitivity Analysis: for the chosen preference ("picture"), + * varies options to gauge how that influences the global numbers + *----------------------------------------------------------------------------- + + * Sensitivity analysis: changing reporting window (8, 6 and 4 years) + + foreach year in 2001 2011 2013 2015 { + + * Part 2 countries only + population_weights, preference(`chosen_preference') timewindow(year_assessment>=`year') countryfilter(lendingtype!="LNX") + outline_current_lp, filename("`outputs_folder'/outline_SA_lp_`year'_part2.csv") `reps' + + * All countries in the world + population_weights, preference(`chosen_preference') timewindow(year_assessment>=`year') + outline_current_lp, filename("`outputs_folder'/outline_SA_lp_`year'_world.csv") `reps' + + } + + noi disp as res _newline "Finished exporting Time Window Sensitivity Tables." + + * Sensitivity analysis: changing the population definitions + + * NOTE: the list of preferences generated as sensitivity analysis is hard numbered + local preferences_in_SA "1005_1014 1005_10 1005_0516 1005_primary 1005_9plus" + foreach preference of local preferences_in_SA { + + * Part 2 countries only + population_weights, preference(`preference') timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") + outline_current_lp, filename("`outputs_folder'/outline_SA_lp_`preference'_part2.csv") `reps' + + * All countries in the world + population_weights, preference(`preference') timewindow(year_assessment>=2011) + outline_current_lp, filename("`outputs_folder'/outline_SA_lp_`preference'_world.csv") `reps' + + } + + noi disp as res _newline "Finished exporting Population Sensitivity Tables." + + + *----------------------------------------------------------------------------- + * Outline of Current Situation with Gender Breakdown + *----------------------------------------------------------------------------- + + * Part 2 countries only + * there was a decision to drop MNG and PHL + population_weights, preference(`chosen_preference') countryfilter(lendingtype!="LNX" & inlist(countrycode,"MNG","PHL")==0) combine_ida_blend + replace lp_by_gender_is_available = 0 if inlist(countrycode,"MNG","PHL") + outline_gender_lp, filename("`outputs_folder'/outline_gender_lp_part2.csv") `reps' + + * All countries in the world + * there was a decision to drop MNG and PHL + population_weights, preference(`chosen_preference') countryfilter(inlist(countrycode,"MNG","PHL")==0) combine_ida_blend + replace lp_by_gender_is_available = 0 if inlist(countrycode,"MNG","PHL") + outline_gender_lp, filename("`outputs_folder'/outline_gender_lp_world.csv") `reps' + + noi disp as res _newline "Finished exporting outline with Gender Breakdown." + + + *----------------------------------------------------------------------------- + * Quick check on number of countries + *----------------------------------------------------------------------------- + + * Number of countries we should expect for two pagers + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", clear + gen byte should_have_2pgr = !missing(nonprof_all) + keep countrycode should_have_2pgr lp_by_gender_is_available + + + *----------------------------------------------------------------------------- + * Combine the stubs to have less files + *----------------------------------------------------------------------------- + foreach stub in SA_lp gender_lp current_lp { + + * Retrieves list of stub dtas in the 033_outputs + local stub_files : dir "`outputs_folder'" files "outline_`stub'_*.dta", respectcase + + * Append all those sensitivity tables in one single dta while erasing them + clear + foreach stub_file of local stub_files { + + * Will only keep the bootstrap file, just in case (and not append it) + if substr("`stub_file'", -7, .) != "_bs.dta" { + append using "`outputs_folder'/`stub_file'" + } + erase "`outputs_folder'/`stub_file'" + + } + + * Add concatenations + if "`stub'" == "current_lp" gen byte old = strpos(file, "_old_") != 0 + gen byte part2_only = strpos(file, "part2") != 0 + gen str agg_file = aggregated_by + "_" + file + gen str concatenated = group + "_" + aggregated_by + "_" + file + + * Save combined file + save "`outputs_folder'/outline_all_`stub'.dta", replace + + * Export table as csv + // export delimited "`outputs_folder'/outline_all_`stub'.csv", replace + + } + + +} diff --git a/03_export_tables/032_programs/03221_tables_for_paper.do b/03_export_tables/032_programs/03221_tables_for_paper.do deleted file mode 100644 index 9f76258..0000000 --- a/03_export_tables/032_programs/03221_tables_for_paper.do +++ /dev/null @@ -1,135 +0,0 @@ -*==============================================================================* -* PROGRAMS: CREATES TWO TABLES FOR TECHNICAL PAPER -*==============================================================================* - -cap program drop table56 -program define table56, rclass - - syntax , filename(string) - - qui { - - * Check if specified filename is ".csv" - if substr("`filename'",-4,4)!=".csv" { - noi disp as err "Filename should end in .csv" - break - } - - * Creates empty tempfile where all will be appended - tempfile this_table - touch `this_table' - - * Aggregations we want to create tables for - local possible_aggregations "incomelevel lendingtype adminregion region global" - gen str global = "TOTAL" - - * Loop through each aggregation - foreach aggregation of local possible_aggregations { - - preserve - - * Collapse the measures in tables being generated - collapse (mean) mean_lp = adj_nonprof_all (min) min_lp = adj_nonprof_all (max) max_lp = adj_nonprof_all /// - (mean) n_countries = `aggregation'_n_countries (mean) coverage = `aggregation'_coverage /// - (mean) population_w_data = `aggregation'_population_w_data /// - (mean) total_population = `aggregation'_total_population /// - [fw = `aggregation'_weight], by(`aggregation') - - * Makes all the files compatible for appending later - rename `aggregation' group - gen aggregated_by = "`aggregation'" - - * Appends to table file being created - append using `this_table' - save `this_table', replace - - restore - - } - - * Open the table with all appended aggregations - use `this_table', clear - - * Marks which filename it is to make it easier when importing to excel - local end_of_path = strrpos("`filename'","/") - local total_lenght = strlen("`filename'") - gen file = substr("`filename'", `end_of_path' + 1, `total_lenght' - `end_of_path' - 4) - - * Export final csv - noi export delimited "`filename'", replace - - * Save as dta as well - local filename = subinstr("`filename'", ".csv", ".dta", 1) - save "`filename'", replace - - } - - -end - - - - -cap program drop table7 -program define table7, rclass - - syntax , filename(string) - - qui { - - * Check if specified filename is ".csv" - if substr("`filename'",-4,4)!=".csv" { - noi disp as err "Filename should end in .csv" - break - } - - * Creates empty tempfile where all will be appended - tempfile this_table - touch `this_table' - - * Aggregations we want to create tables for - local possible_aggregations "incomelevel lendingtype adminregion region global" - gen str global = "TOTAL" - - * Given that we'll print out gender split, only keep LP data if have the gender split - clonevar adj_nonprof_all_compatible = adj_nonprof_all - replace adj_nonprof_all_compatible = . if lp_by_gender_is_available == 0 - - * Loop through each aggregation - foreach aggregation of local possible_aggregations { - - preserve - - * Collapse the measures in tables being generated - collapse (mean) mean_lp = adj_nonprof_all_compatible (mean) mean_lp_ma = adj_nonprof_ma (mean) mean_lp_fe = adj_nonprof_fe (rawsum) n = lp_by_gender_is_available [fw = `aggregation'_weight], by(`aggregation') - - * Makes all the files compatible for appending later - rename `aggregation' group - gen aggregated_by = "`aggregation'" - - * Appends to table file being created - append using `this_table' - save `this_table', replace - - restore - - } - - * Open the table with all appended aggregations - use `this_table', clear - - * Marks which filename it is to make it easier when importing to excel - local end_of_path = strrpos("`filename'","/") - local total_lenght = strlen("`filename'") - gen file = substr("`filename'", `end_of_path' + 1, `total_lenght' - `end_of_path' - 4) - - * Export final csv - noi export delimited "`filename'", replace - - * Save as dta as well - local filename = subinstr("`filename'", ".csv", ".dta", 1) - save "`filename'", replace - - } - -end diff --git a/03_export_tables/032_programs/0322_decomposition.do b/03_export_tables/032_programs/0322_decomposition.do new file mode 100644 index 0000000..af240f2 --- /dev/null +++ b/03_export_tables/032_programs/0322_decomposition.do @@ -0,0 +1,395 @@ +*==============================================================================* +* 0322 SUBTASK: DECOMPOSITION OF LPV LEVELS AND CHANGE +*==============================================================================* + +quietly { + + tempfile tmp1 tmp2 + + *----------------------------------------------------------------------------- + * Decomposition of Learning Poverty Levels + *----------------------------------------------------------------------------- + foreach gender in all ma fe { + + foreach type in reg inc len { + + foreach filter in `"countryfilter(lendingtype!="LNX")"' `""' { + + if "`filter'" == "" { + local f b + } + else { + local f a + } + + ** prepare dataset + if "`gender'" == "all" /// + population_weights, preference(1005) timewindow(year_assessment>=2011) combine_ida_blend `filter' + else { + if "`filter'" == "" local aux_filter `"countryfilter(inlist(countrycode,"MNG","PHL")==0)"' + else local aux_filter `"countryfilter(lendingtype!="LNX" & inlist(countrycode,"MNG","PHL")==0)"' + population_weights, preference(1005) combine_ida_blend `aux_filter' + drop if inlist(countrycode,"MNG","PHL") + } + + keep if adj_nonprof_`gender' != . + keep if global_weight != . + + * More intuitive/shorter names for key variables + rename (adj_nonprof_`gender' enrollment_`gender' nonprof_`gender') (learningpoverty enrollment bmp) + gen oos = 100 - enrollment + + tabstat learningpoverty [aw = region_weight], by(region) + tabstat learningpoverty [aw = global_weight], by(region) + + gen exp = 2 + + sum learningpoverty oos bmp + + * countrycode + encode countrycode, gen(ctry) + *regional + encode region , gen(reg) + * income levels + encode incomelevel , gen(inc) + * lending type (creating IDA/BLED) + encode lendingtype , gen(len) + + * turn all indicators to indexes + foreach var in learningpoverty oos bmp { + replace `var' = `var'/100 + } + + * create complementary variable + gen oos_complement = 1-oos + + expand exp , gen(time) + + * create counterfactural / NO LEARNING POVERTY + foreach var in learningpoverty oos bmp oos_complement { + replace `var' = 0 if time == 1 + } + + *----------------------------------------------------------------------------- + * prepare decomposition by category + *----------------------------------------------------------------------------- + + preserve + + adecomp learningpoverty bmp oos_complement oos [aw = region_weight], /// + equation((c1*c2)+(c3)) by(time) id(ctry) indicator(mean) group(`type') + + + mat a = r(b) + mat a = a' + + svmat double a, names(col) + + keep r1-r4 + drop in 1/2 + gen `type' = _n + order `type' + + label value `type' `type' + + gen bmp = r1 + r2 + gen oos = r3 + gen total = r4 + + drop r1 - r4 + + keep if bmp != . + + decode `type' , generate(category) + drop `type' + order category + + save `tmp1', replace + + restore + + *----------------------------------------------------------------------------- + * prepare global decomposition + *----------------------------------------------------------------------------- + + preserve + + adecomp learningpoverty bmp oos_complement oos [aw = region_weight], /// + equation((c1*c2)+(c3)) by(time) id(ctry) indicator(mean) + + + mat a = r(b) + mat a = a' + + svmat double a, names(col) + + keep r1-r4 + drop in 1/2 + gen `type' = 0 + order `type' + label value `type' `type' + + gen bmp = r1 + r2 + gen oos = r3 + gen total = r4 + + drop r1 - r4 + + keep if bmp != . + + decode `type' , generate(category) + drop `type' + order category + + replace category = "WLD" if category == "" + + save `tmp2', replace + + restore + + *----------------------------------------------------------------------------- + * append decomposition by categor and global + *----------------------------------------------------------------------------- + + use `tmp1', clear + append using `tmp2' + + gen double shr_bmp = bmp/total + gen double shr_oos = oos/total + + order category total + + * flip sign of absolute value + foreach var in total bmp oos { + replace `var' = `var'*(-1) + } + + * multiply all results by 100 and format output to one decimal + foreach var in total bmp oos shr_bmp shr_oos { + replace `var' = `var'*100 + format `var' %16.1f + } + + + gen panel = "`type'" + if ("`f'" == "a") gen filter = "low- and middle- income" + else gen filter = "all ctrys" + + order panel filter category total + + save "${clone}/03_export_tables/033_outputs/individual_tables/panel_`type'_`f'.dta", replace + + * next filter + } + + * next type + } + + + *----------------------------------------------------------------------------- + ** Final Output + *----------------------------------------------------------------------------- + clear + foreach filename in panel_reg_a panel_inc_a panel_len_a panel_reg_b panel_inc_b panel_len_b { + append using "${clone}/03_export_tables/033_outputs/individual_tables/`filename'.dta" + erase "${clone}/03_export_tables/033_outputs/individual_tables/`filename'.dta" + } + + label var category "Group" + label var total "Learning Poverty" + label var bmp "Below Minimum Proficiency (BMP)" + label var oos "Out of School (OOS)" + label var shr_bmp "Percentage of Learning Poverty Explained by BMP" + label var shr_oos "Percentage of Learning Poverty Explained by OOS" + + save "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_lpv_`gender'.dta", replace + + * next gender + } + + noi disp as res _n "Decomposition of Learning Poverty Levels done." + + + + *----------------------------------------------------------------------------- + * Decomposition of Learning Poverty Spells + *----------------------------------------------------------------------------- + + clear + use "${clone}/02_simulation/023_outputs/all_spells.dta" + keep if used_sim == 1 + keep countrycode y1 bmp_y1 enrollment_y1 learningpoverty_y1 y2 bmp_y2 enrollment_y2 learningpoverty_y2 region incomelevel lendingtype + rename (y1 y2) (year_y1 year_y2) + sort countrycode year_y1 + bysort countrycode : gen seq = _n + + reshape long year bmp enrollment learningpoverty , i(countrycode region incomelevel lendingtype seq) j(time) string + + * countrycode + encode countrycode, gen(ctry) + *regional + encode region , gen(reg) + * income levels + encode incomelevel , gen(inc) + * lending type (creating IDA/BLED) + encode lendingtype , gen(len) + recode len 3=2 + + * time + encode time, gen(t) + drop time + rename t time + + * turn all indicators to indexes + foreach var in learningpoverty bmp enrollment { + replace `var' = `var'/100 + } + + * create complementary variable + gen oos = 1-enrollment + gen oos_complement = 1-oos + + bysort countrycode : gen tot = _N + gen wtg = 1/tot + + sum learningpoverty oos bmp + + sort seq countrycode year + bysort seq countrycode : gen diff =year[2]-year[1] + + foreach var in learningpoverty bmp oos_complement oos { + replace `var' = `var'/diff + } + + *----------------------------------------------------------------------------- + * prepare decomposition by category + *----------------------------------------------------------------------------- + + local type reg + + preserve + + adecomp learningpoverty bmp oos_complement oos [aw=wtg] , /// + equation((c1*c2)+(c3)) by(time) id(ctry) indicator(mean) group(`type') stats() + + + mat a = r(b) + mat a = a' + + svmat double a, names(col) + + keep r1-r4 + drop in 1/2 + gen `type' = _n + order `type' + + label value `type' `type' + + gen bmp = r1 + r2 + gen oos = r3 + gen total = r4 + + drop r1 - r4 + + keep if bmp != . + + decode `type' , generate(category) + drop `type' + order category + + save `tmp1', replace + + restore + + + *----------------------------------------------------------------------------- + * prepare decomposition by WLD + *----------------------------------------------------------------------------- + + local type reg + + preserve + + adecomp learningpoverty bmp oos_complement oos [aw=wtg] , /// + equation((c1*c2)+(c3)) by(time) id(ctry) indicator(mean) + + + mat a = r(b) + mat a = a' + + svmat double a, names(col) + + keep r1-r4 + drop in 1/2 + gen `type' = 0 + order `type' + + label value `type' `type' + + gen bmp = r1 + r2 + gen oos = r3 + gen total = r4 + + drop r1 - r4 + + keep if bmp != . + + decode `type' , generate(category) + drop `type' + order category + + replace category = "WLD" if category == "" + + save `tmp2', replace + + restore + + *----------------------------------------------------------------------------- + * append decomposition by categor and global + *----------------------------------------------------------------------------- + + use `tmp1', clear + append using `tmp2' + + gen double shr_bmp = bmp/total + gen double shr_oos = oos/total + + order category total + + * flip sign of absolute value + *foreach var in total bmp oos { + * replace `var' = `var'*(-1) + *} + + * multiply all results by 100 and format output to one decimal + foreach var in total bmp oos shr_bmp shr_oos { + replace `var' = `var'*100 + format `var' %16.2f + } + + * rescale decomposition if share of both components surpasses 100 in module + forvalues obs = 1/`=_N' { + while `=shr_bmp[`obs']' > 100 & `=shr_oos[`obs']' < -100 { + replace shr_bmp = shr_bmp - 100 if _n == `obs' + replace shr_oos = shr_oos + 100 if _n == `obs' + } + while `=shr_bmp[`obs']' < -100 & `=shr_oos[`obs']' > 100 { + replace shr_bmp = shr_bmp + 100 if _n == `obs' + replace shr_oos = shr_oos - 100 if _n == `obs' + } + } + + order category + + label var category "Region" + label var total "Annualized Change in Learning Poverty" + label var bmp "Annualized Change in Below Minimum Proficiency (BMP)" + label var oos "Annualized Change in Out of School (OOS)" + label var shr_bmp "Percentage of Annualized Change in Learning Poverty Explained by BMP" + label var shr_oos "Percentage of Annualized Change in Learning Poverty Explained by OOS" + + save "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_spells.dta", replace + + noi disp as res _n "Decomposition of Learning Poverty Spells done." + +} diff --git a/03_export_tables/032_programs/0322_gender_tables.do b/03_export_tables/032_programs/0322_gender_tables.do deleted file mode 100644 index 791035c..0000000 --- a/03_export_tables/032_programs/0322_gender_tables.do +++ /dev/null @@ -1,103 +0,0 @@ -*==============================================================================* -* 0322 SUBTASK: GENERATE GENDER TABLES FOR TECHNICAL PAPER -*==============================================================================* -qui { - - * Change here only if wanting to use a different preference - * than what is being passed in the global in 032_run - * But don't commit any change here (only commit in global 032_run) - local chosen_preference = $chosen_preference - - - *----------------------------------------------------------------------------- - * Creates CSV sheets for Excel with Tables for Glossy - * (as a cross-check with put_to_excel) - *----------------------------------------------------------------------------- - - * Tables 5 and 6 - PART2 - population_weights, preference(`chosen_preference') timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") - table56, filename("${clone}/03_export_tables/033_outputs/viz_tab56_part2.csv") - - * Tables 5 and 6 - WORLD - population_weights, preference(`chosen_preference') timewindow(year_assessment>=2011) - table56, filename("${clone}/03_export_tables/033_outputs/viz_tab56_world.csv") - - * Table 7 (gender) - PART2 - population_weights, preference(`chosen_preference') countryfilter(lendingtype!="LNX") - table7, filename("${clone}/03_export_tables/033_outputs/viz_tab7_part2.csv") - - * Table 7 (gender) - PART2 DROPPING MNG and PHL - population_weights, preference(`chosen_preference') countryfilter(lendingtype!="LNX" & inlist(countrycode,"MNG","PHL")==0) - replace lp_by_gender_is_available = 0 if inlist(countrycode,"MNG","PHL") - table7, filename("${clone}/03_export_tables/033_outputs/viz_tab7_part2_no_MNG_PHL.csv") - - * Table 7 (gender) - WORLD - population_weights, preference(`chosen_preference') - table7, filename("${clone}/03_export_tables/033_outputs/viz_tab7_world.csv") - - * Table 7 (gender) - WORLD DROPPING MNG and PHL - population_weights, preference(`chosen_preference') countryfilter(inlist(countrycode,"MNG","PHL")==0) - replace lp_by_gender_is_available = 0 if inlist(countrycode,"MNG","PHL") - noi tab countrycode if enrollment_flag == 1 & lp_by_gender_is_available == 1 - table7, filename("${clone}/03_export_tables/033_outputs/viz_tab7_world_no_MNG_PHL.csv") - - * Number of countries we should expect for two pagers - use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", clear - gen byte should_have_2pgr = !missing(nonprof_all) - keep countrycode should_have_2pgr lp_by_gender_is_available - export delimited "${clone}/03_export_tables/033_outputs/viz_countrylist.csv", replace - - noi disp as res _newline "Finished exporting Tables 5/6 and 7 to csv." - - *----------------------------------------------------------------------------- - * Sensitivity Analysis: for the chosen preference ("picture"), - * varies options to gauge how that influences the global numbers - *----------------------------------------------------------------------------- - - noi disp as res _newline "Sensitivity analysis: changing reporting window (8, 6 and 4 years)" - - foreach year in 2011 2013 2015 { - - * Displays output for chosen preferences for PART2 countries (`year') - noi population_weights, preference(`chosen_preference') timewindow(year_assessment>=`year') countryfilter(lendingtype!="LNX") - table56, filename("${clone}/03_export_tables/033_outputs/viz_SA_1005_`year'_part2.csv") - - * Displays output for chosen preferences for WORLD (`year') - noi population_weights, preference(`chosen_preference') timewindow(year_assessment>=`year') - table56, filename("${clone}/03_export_tables/033_outputs/viz_SA_1005_`year'_world.csv") - - } - - * NOTE: the list of preferences generated as sensitivity analysis is hard numbered - local preferences_in_SA "1005 1005b 1005_1014 1005_10 1005_primary 1005_9plus" - foreach preference of local preferences_in_SA { - - * Tables 5 and 6 - PART2 - population_weights, preference(`preference') timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") - table56, filename("${clone}/03_export_tables/033_outputs/viz_SA_`preference'_part2.csv") - - * Tables 5 and 6 - WORLD - population_weights, preference(`preference') timewindow(year_assessment>=2011) - table56, filename("${clone}/03_export_tables/033_outputs/viz_SA_`preference'_world.csv") - - } - - * Retrieves list of sensitivity dtas in the 033_outputs - local SA_files : dir "${clone}/03_export_tables/033_outputs/" files "viz_SA_*.dta", respectcase - * Append all those sensitivity tables in one single dta - touch "${clone}/03_export_tables/033_outputs/viz_SA_all.dta", replace - foreach SA_file of local SA_files { - append using "${clone}/03_export_tables/033_outputs/`SA_file'" - save "${clone}/03_export_tables/033_outputs/viz_SA_all.dta", replace - } - - * Add concatenation - gen str concatenated = group + "-" + aggregated_by + "-" + file - - * Export table as csv - export delimited "${clone}/03_export_tables/033_outputs/viz_SA_all.csv", replace - - noi disp as res _newline "Finished exporting Sensitivity Tables." - - -} diff --git a/03_export_tables/032_programs/0323_country_annex.do b/03_export_tables/032_programs/0323_country_annex.do index 9d53523..193bca9 100644 --- a/03_export_tables/032_programs/0323_country_annex.do +++ b/03_export_tables/032_programs/0323_country_annex.do @@ -1,85 +1,92 @@ -*==============================================================================* -* 0323 SUBTASK: COUNTRY ANNEX NUMBERS -*==============================================================================* -qui { - - * Change here only if wanting to use a different preference - * than what is being passed in the global in 032_run - * But don't commit any change here (only commit in global 032_run) - local chosen_preference = $chosen_preference - - - use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace - population_weights, timewindow(year_assessment>=2011) countryfilter() - - keep if year_assessment>=2011 - - *FIRST DO WITH ALL 98 Countries - *Export output for preference 1000 to excel for country spreadsheet - - gen pct_reading_low_target=100-nonprof_all - - - *Build sub-totals and totals - *regional sub-totals - preserve - collapse adj_nonprof_all population_2015_all [fw=region_weight], by(region) - gen countryname="ZZZ" - tempfile rgn_98 - save `rgn_98' - restore - - preserve - collapse adj_nonprof_all [fw=region_weight] - gen region="Z_Global" - gen countryname="ZZZ" - tempfile glob_98 - save `glob_98' - restore - - - *Now DO WITH no LNX countries - - use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace - population_weights, timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") - gen pct_reading_low_target=100-nonprof_all - - keep if year_assessment>=2011 - - *regional sub-totals using just the !LNX countries - preserve - drop if incomelevel=="LNX" - collapse adj_nonprof_all [fw=region_weight], by(region) - gen countryname="ZZZ_!LNX" - tempfile rgn - save `rgn' - restore - - preserve - collapse adj_nonprof_all [fw=region_weight] - gen region="Z_Global" - gen countryname="ZZZ_!LNX" - tempfile glob - save `glob' - restore - - - append using `rgn_98' - append using `glob_98' - append using `rgn' - append using `glob' - sort region countryname - replace region="Global" if region=="Z_Global" - replace countryname="Group Total" if countryname=="ZZZ" - replace countryname="Group Total w/out LNX" if countryname=="ZZZ_!LNX" - - drop if test=="None" - - keep region adminregion countrycode countryname adj_nonprof_all enrollment_all pct_reading_low_target population_2015_all incomelevel lendingtype test year_assessment - order region adminregion countrycode countryname adj_nonprof_all enrollment_all pct_reading_low_target population_2015_all incomelevel lendingtype test year_assessment - - export excel using "${clone}/03_export_tables/033_outputs/rawlatest_cntry_file.xlsx", replace firstrow(varl) - - noi disp as res _newline "Finished exporting excel for country annex." - -} +*==============================================================================* +* 0323 SUBTASK: COUNTRY ANNEX NUMBERS +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace + population_weights, timewindow(year_assessment>=2011) countryfilter() + + keep if year_assessment>=2011 + + *FIRST DO WITH ALL 98 Countries + *Export output for preference 1000 to excel for country spreadsheet + + gen pct_reading_low_target=100-nonprof_all + + + *Build sub-totals and totals + *regional sub-totals + preserve + collapse adj_nonprof_all population_2015_all [fw=region_weight], by(region) + gen countryname="ZZZ" + tempfile rgn_98 + save `rgn_98' + restore + + preserve + collapse adj_nonprof_all [fw=region_weight] + gen region="Z_Global" + gen countryname="ZZZ" + tempfile glob_98 + save `glob_98' + restore + + + *Now DO WITH no LNX countries + + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace + population_weights, timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") + gen pct_reading_low_target=100-nonprof_all + + keep if year_assessment>=2011 + + *regional sub-totals using just the !LNX countries + preserve + drop if incomelevel=="LNX" + collapse adj_nonprof_all [fw=region_weight], by(region) + gen countryname="ZZZ_!LNX" + tempfile rgn + save `rgn' + restore + + preserve + collapse adj_nonprof_all [fw=region_weight] + gen region="Z_Global" + gen countryname="ZZZ_!LNX" + tempfile glob + save `glob' + restore + + + append using `rgn_98' + append using `glob_98' + append using `rgn' + append using `glob' + sort region countryname + replace region="Global" if region=="Z_Global" + replace countryname="Group Total" if countryname=="ZZZ" + replace countryname="Group Total w/out LNX" if countryname=="ZZZ_!LNX" + + drop if test=="None" + + keep region adminregion countrycode countryname adj_nonprof_all enrollment_all pct_reading_low_target population_2015_all incomelevel lendingtype test year_assessment + order region adminregion countrycode countryname adj_nonprof_all enrollment_all pct_reading_low_target population_2015_all incomelevel lendingtype test year_assessment + + *----------------------------------------------------------------------------* + * Manual corrections that need to be done to rawlatest wrt "exceptions" + * that were disguised as NLAs (Mali Madagascar) and with recent year (Congo) + replace year_assessment = 2010 if countrycode == "COD" + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + *----------------------------------------------------------------------------* + + export excel using "${clone}/03_export_tables/033_outputs/rawlatest_cntry_file.xlsx", replace firstrow(varl) + + noi disp as res _newline "Finished exporting excel for country annex." + +} diff --git a/03_export_tables/032_programs/0324_export_WDI.do b/03_export_tables/032_programs/0324_export_WDI.do new file mode 100644 index 0000000..dd8fd54 --- /dev/null +++ b/03_export_tables/032_programs/0324_export_WDI.do @@ -0,0 +1,276 @@ +*==============================================================================* +* 0324 SUBTASK: EXPORT INDICATOR SERIES FOR WDI (WB API) +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + * Countries we don't want to report + local countries_not_reported "CUB PHL" + + *-------------------- + * Aggregates + *-------------------- + + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", clear + + * Excludes any countries that we want to avoid reporting + * NOTE: they are being dropped both from the aggregate and cty level + foreach country of local countries_not_reported { + drop if countrycode == "`country'" + } + + * Get proper regional and incomelevel averages - PART2 countries + population_weights, timewindow(year_assessment>=2011) countryfilter(lendingtype!="LNX") + gen part2 = "WBC" + + * Creates aggregated values of Learning Poverty, Below Minimum Prof and Enrollment by gender subgroups + local possible_subgroups "all fe ma" + foreach subgroup of local possible_subgroups { + egen part2_lpov_`subgroup' = wtmean(adj_nonprof_`subgroup'), weight(global_weight) + egen part2_bmp_`subgroup' = wtmean(nonprof_`subgroup'), weight(global_weight) + egen part2_enr_`subgroup' = wtmean(enrollment_`subgroup'), weight(global_weight) + * From Enrollment to Out-of-School + gen part2_oos_`subgroup' = 100 - part2_enr_`subgroup' + } + + + * Get proper regional and incomelevel averages - WORLD + population_weights, timewindow(year_assessment>=2011) + gen global = "WLD" + + * Creates aggregated values of Learning Poverty, Below Minimum Prof and Enrollment by gender subgroups + local possible_subgroups "all fe ma" + local possible_aggregations "global region adminregion incomelevel lendingtype" + foreach aggregation of local possible_aggregations { + foreach subgroup of local possible_subgroups { + bys `aggregation': egen `aggregation'_lpov_`subgroup' = wtmean(adj_nonprof_`subgroup'), weight(`aggregation'_weight) + bys `aggregation': egen `aggregation'_bmp_`subgroup' = wtmean(nonprof_`subgroup'), weight(`aggregation'_weight) + bys `aggregation': egen `aggregation'_enr_`subgroup' = wtmean(enrollment_`subgroup'), weight(`aggregation'_weight) + * From Enrollment to Out-of-School + gen `aggregation'_oos_`subgroup' = 100 - `aggregation'_enr_`subgroup' + } + } + + * Keep only variables that matter + keep part2 `possible_aggregations' *_lpov_* *_bmp_* *_oos_* + + * Drop duplicates (started at country level but only want aggregates)* + duplicates drop + + * Reshape dataset long in gender + reshape long part2_lpov part2_bmp part2_oos global_lpov global_bmp global_oos /// + region_lpov region_bmp region_oos adminregion_lpov adminregion_bmp adminregion_oos /// + incomelevel_lpov incomelevel_bmp incomelevel_oos /// + lendingtype_lpov lendingtype_bmp lendingtype_oos, /// + i(part2 global region adminregion incomelevel lendingtype) j(subgroup) string + + * Reshape dataset long in aggregation + rename (part2 global region adminregion incomelevel lendingtype) /// + (part2_code global_code region_code adminregion_code incomelevel_code lendingtype_code) + + preserve + clear + save "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta", emptyok replace + restore + + foreach aggregation in part2 global region adminregion incomelevel lendingtype { + preserve + keep subgroup `aggregation'_code `aggregation'_bmp `aggregation'_oos `aggregation'_lpov + rename `aggregation'_* * + append using "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta" + save "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta", replace + restore + } + + use "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta", clear + duplicates drop + drop if missing(lpov) | missing(code) + + rename (lpov bmp oos) (value_lpov value_bmp value_oos) + reshape long value, i(code subgroup) j(aux) string + gen str indicator = "SE.LPV.PRIM" + replace indicator = indicator + ".OOS" if aux=="_oos" + replace indicator = indicator + ".BMP" if aux=="_bmp" + replace indicator = indicator + ".FE" if subgroup=="_fe" + replace indicator = indicator + ".MA" if subgroup=="_ma" + + gen str value_metadata = "" + replace value_metadata = "Aggregated Learning Poverty; reporting window 2011-2019" if aux=="_lpov" + replace value_metadata = "Aggregated OOS; reporting window 2011-2019" if aux=="_oos" + replace value_metadata = "Aggregated BMP in reading per GAML MPL; reporting window 2011-2019" if aux=="_bmp" + + * Final touches + gen year = 2015 + rename code countrycode + keep countrycode year indicator value value_metadata + gen str cty_or_agg = "agg" + + save "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta", replace + + noi disp as txt _newline "{phang}Generated aggregated Learning Poverty indicators for WDI{p_end}" + + + *-------------------- + * Country Level (mask) + *-------------------- + + * Starts from chosen preference to build a mask of preferred + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", clear + + * Excludes any countries that we want to avoid reporting + * NOTE: they are being dropped both from the aggregate and cty level + foreach country of local countries_not_reported { + drop if countrycode == "`country'" + } + + * Only keep countries with LP data and variables that matter + drop if missing(nonprof_all) + keep countrycode test nla_code subject idgrade + + describe + noi disp as txt "{phang}Generating Learning Poverty indicators for `r(N)' countries for WDI{p_end}" + + * Saves a mask of country-assessment-subject that will go into WDI + save "${clone}/03_export_tables/033_outputs/WDI_TEMP_country_mask.dta", replace + + + *-------------------- + * Country Level (data) + *-------------------- + + * Starts from rawfull, to have multiple years of LPOV data whenever possible + use "${clone}/01_data/013_outputs/rawfull.dta", clear + + * Only keep country-assessment-subject in the mask + merge m:1 countrycode test nla_code subject idgrade using "${clone}/03_export_tables/033_outputs/WDI_TEMP_country_mask.dta", nogen keep(match) + + * Drop and rename variables + * - drop most metadata and population variables, which are to the right of enrollment + keep countrycode-surveyid countryname regionname + * - drop standard errors of non proficiency, which are in the middle + drop se_* + * - only keep enrollment validated variables, which is the one used in rawlatest + drop enrollment_interpolated* + rename enrollment_validated_* enrollment_* + * - drop and rename other enrollment variables that could cause trouble in reshape + drop enrollment_flag + rename enrollment_definition definition_enrollment + rename enrollment_source source_enrollment + + * Adjusts non-proficiency by out-of school (only rawlatest has LPov, not in rawfull) + foreach subgroup in all fe ma { + gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) + } + + * This is the data we have to export, now we need to reshape it + reshape long adj_nonprof enrollment nonprof, i(countrycode idgrade test nla_code subject year_assessment min_proficiency_threshold source_assessment definition_enrollment source_enrollment) j(subgroup) string + + * Will only generate series if has learning poverty data for the subgroup + drop if missing(adj_nonprof) + + + *----------------------------------------------------------------------------* + * Manual corrections that need to be done to rawlatest wrt "exceptions" + * that were disguised as NLAs (Mali Madagascar) and with recent year (Congo) + replace year_assessment = 2010 if countrycode == "COD" + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + *----------------------------------------------------------------------------* + + + * Creates fields that will be exported to WDI + * Indicator follows grammar in: https://datahelpdesk.worldbank.org/knowledgebase/articles/201175-how-does-the-world-bank-code-its-indicators + * with topic = SE (social: education); general subject = LPV (learning poverty); specific subject (primary) + * this will be followed by specific subject = nothing (LP) / BMP / OOS + extension nothing (all) / FE / MA + gen str indicator = "SE.LPV.PRIM" + gen float value = . + gen str value_metadata = "" + + * Auxiliary variables for metadata (cannot concatenate numeric variables) + gen str_grade = string(int(idgrade),"%01.0f") + gen str_year = string(int(year_assessment),"%04.0f") + replace subject = "reading" if subject == "read" + + * Separate the indicators that are wide into long + * by repeatedly expanding the dataset + expand 2, gen(expanded) + * - Learning Poverty + replace value = adj_nonprof if expanded == 0 + replace value_metadata = "OOS: " + definition_enrollment + "; BMP: " + test + " " + str_year + " for grade " + str_grade + " using MPL " + min_proficiency_threshold + " for " + subject if expanded == 0 + * - Out-of-School + replace value = 100 - enrollment if expanded == 1 + replace indicator = indicator + ".OOS" if expanded == 1 + replace value_metadata = definition_enrollment if expanded == 1 + * - Below Minimum proficiency + expand 2 if expanded == 0, gen(expanded_again) + replace value = nonprof if expanded_again == 1 + replace indicator = indicator + ".BMP" if expanded_again == 1 + replace value_metadata = test + " " + str_year + " for grade " + str_grade + " using MPL " + min_proficiency_threshold + " for " + subject if expanded_again == 1 + + * Add sufix for gender in the indicator (subgroup == "_all" has no sufix ) + replace indicator = indicator + ".FE" if subgroup == "_fe" + replace indicator = indicator + ".MA" if subgroup == "_ma" + + * Final touches + rename year_assessment year + keep countrycode countryname regionname year indicator value value_metadata + gen str cty_or_agg = "cty" + + * Save dataset for WDI at the country level + compress + save "${clone}/03_export_tables/033_outputs/WDI_TEMP_country_level.dta", replace + + noi disp as txt "{phang}Generated country level Learning Poverty indicators for WDI{p_end}" + + *-------------------- + * Append both + *-------------------- + append using "${clone}/03_export_tables/033_outputs/WDI_TEMP_aggregates.dta" + + * Final touches + order countrycode cty_or_agg year indicator value value_metadata + sort countrycode year indicator + * Check that it is uniquely identified + isid countrycode year indicator + + save "${clone}/03_export_tables/033_outputs/WDI_indicators.dta", replace + export delimited "${clone}/03_export_tables/033_outputs/WDI_indicators.csv", replace + + * Quality assurance display + noi disp as txt _newline "{phang}QA: observation breakdown by first/single year vs other years {p_end}" + bys countrycode indicator: gen tag_first_obs = (_n == 1) + noi tab cty_or_agg tag_first_obs if indicator == "SE.LPV.PRIM" + + /******************************************************** + Create an Excel file that will not be used in any way + by the script, nor tracked in the repo, but is meant + to be used as a convinent way to share all the data + produced to someone that prefers Excel versus csv + ********************************************************/ + * List of csv files to be added to the excel file + local csvfiles "lpv_metadata WDI_indicators" + + * Loop over each csv file and add it to the Excel sheet + noi di "" + foreach csvfile of local csvfiles { + noi di as text "{phang}Exporting `csvfile' to lpv_edstats.xls{p_end}" + if ("lpv_metadata" == "`csvfile'") { + import delimited using "${clone}/03_export_tables/031_rawdata/`csvfile'.csv", clear varnames(1) case(preserve) encoding("utf-8") + export excel using "${clone}/03_export_tables/033_outputs/lpv_edstats.xls", sheet("`csvfile'", replace) firstrow(variables) + } + else { + import delimited using "${clone}/03_export_tables/033_outputs/`csvfile'.csv", clear varnames(1) case(preserve) + export excel using "${clone}/03_export_tables/033_outputs/lpv_edstats.xls", sheet("`csvfile'", replace) firstrow(variables) + } + } + + foreach wdi_temp in WDI_TEMP_aggregates WDI_TEMP_country_level WDI_TEMP_country_mask { + cap erase "${clone}/03_export_tables/033_outputs/`wdi_temp'.dta" + } + + noi disp as res _newline "{phang}Exported all Learning Poverty indicators for WDI{p_end}" + +} diff --git a/03_export_tables/032_programs/032_run.do b/03_export_tables/032_programs/032_run.do index f4c1f02..6c58587 100644 --- a/03_export_tables/032_programs/032_run.do +++ b/03_export_tables/032_programs/032_run.do @@ -23,13 +23,18 @@ global chosen_preference 1005 // Chosen preference created in 01 rawlatest *----------------------------------------------------------------------------- * Subroutines for this task *----------------------------------------------------------------------------- -* Run script to produce tables for paper (Part 1) -do "${clone}/03_export_tables/032_programs/0321_put_to_excel.do" +* Run script to produce SPELLS summary statistics tables for paper +do "${clone}/03_export_tables/032_programs/0320_spells_statistics.do" -* Run script to produce tables for paper (Part 2) -do "${clone}/03_export_tables/032_programs/0322_gender_tables.do" +* Run script to produce CURRENT SITUATION tables for paper +do "${clone}/03_export_tables/032_programs/0321_create_outlines.do" + +* Produce decomposition of Learning Poverty levels and change +do "${clone}/03_export_tables/032_programs/0322_decomposition.do" * Produce numbers for learning poverty for the country annex table do "${clone}/03_export_tables/032_programs/0323_country_annex.do" +* Export indicators to WDI +do "${clone}/03_export_tables/032_programs/0324_export_WDI.do" *----------------------------------------------------------------------------- diff --git a/03_export_tables/032_programs/03211_preferred_list_tables_ci.do b/03_export_tables/032_programs/old_version/03211_preferred_list_tables_ci.do similarity index 97% rename from 03_export_tables/032_programs/03211_preferred_list_tables_ci.do rename to 03_export_tables/032_programs/old_version/03211_preferred_list_tables_ci.do index 44c2394..e6fda2f 100644 --- a/03_export_tables/032_programs/03211_preferred_list_tables_ci.do +++ b/03_export_tables/032_programs/old_version/03211_preferred_list_tables_ci.do @@ -1,808 +1,810 @@ -*==============================================================================* -* PROGRAMS: CREATES TABLES WITH CONFIDENCE INTERVAL FOR TECHNICAL PAPER -*==============================================================================* - -* 0.3.1 fixed typo on output line -* 0.3 add anchor year; and select variable closest to the anchore year -* 0.2 add the counstruction of Global weights + Global Weight Window + Global Weights Filter -* 0.1 run quietly with a NOIsily option JPA - -*Author: Brian Stacy; Hongxi Zhao; Diana Goldberg; Kristopher Bjarkefur; Joao Pedro Azevedo - - -/*This an ado file: -1)Develops database for preferred list -The user must specify a number of options. -(1) nla() - which dictates that the countries in the list are to use the National Learning Assessment. This option takes countrycodes. -(2) threshold() - which dictates which threshold to use for the proficiency levels. Current options are 0, I, II, IIB. -(3) droplist() - which dictate which countries to not calculate proficiency levels. This option takes country codes -(4) dropassess() - which dictate which assessments to not calculate proficiency levels. This option takes assessment names -(5) runname() - which dictates the name of the run. It also identifies the run in the rawlatest file (i.e. preference="912") -(6) TIMSS()-dictates either math or science for TIMSS. either enter string "math" or "science" -(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated -(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. -As an example: _preferred_list, nla(BGD CHN IND PAK) threshold(IIB) droplist(NGA PAK) dropassess(SACMEQ) runname(912) timss_subject(science) -Specifies that Bangladesh, China, India, and Pakistan use National Learning Assessments. Threshold IIB is applied for all assessments. -*/ -cap program drop _preferred_list_tables_ci -program define _preferred_list_tables_ci, rclass - version 14 - syntax [, /// - PREFERENCE(string) /// - POPULATION(string) /// - NOIsily /// - GLOBALWEIGHT(string) /// - GLOBALWEIGHTWINDOW(string) /// - GLOBALWEIGHTFILTER(string) /// - ANCHORYEAR(string) /// - REPETITIONS(string) /// - RUNNAME(string) /// - ] - - * Apply checks - - if "`noisily'" != "" { - local noi "noi" - } - else { - local noi "qui" - } - - * Rawlatest with merged non-proficiency split by gender from CLO - use "${clone}/01_data/013_outputs/preference`preference'.dta", clear - - * Generate dummy on whether assessment is inside TIMEWINDOW() - cap drop include_assessment - if "`globalweightwindow'" == "" { - * If not specified, all observations are included by default - gen byte include_assessment = 1 - } - else { - * If specified, apply the condition to create a dummy - cap gen byte include_assessment = (`globalweightwindow') & !missing(year_assessment) - if _rc != 0 { - noi di as err `"The option TIMEWINDOW() is incorrectly specified. Good example: timewindow(year_assessment>=2011)"' - break - } - } - - - * Generate dummy on whether country is inside COUNTRYFILTER() - cap drop include_country - if "`globalweightfilter'" == "" { - * If not specified, all observations are included - gen byte include_country = 1 - } - else { - * If specified, apply the condition to create a dummy - cap gen byte include_country = (`globalweightfilter') - if _rc != 0 { - noi di as err `"The option COUNTRYFILTER() is incorrectly specified. Good example: countryfilter(incomelevel!="HIC" & lendingtype!="LNX")"' - break - } - } - - // * Whether or not the confidence intervals will be calculated - // if "`ci_repetitions'" != "" { - // local CI = 1 - // } - // else { - // local CI = 0 - // } - - - *----------------------------* - * Calculation of pop weights * - *----------------------------* - - * Tentatively drop pre-existing auxiliary and final variables to avoid errors: - foreach var in wgt_included wgt_population_total wgt_population_assessed wgt_scaling_factor weight_global_number { - cap drop `var' - } - - * The main variable we're set to build is wgt_pop, which is integer so we can use as frequency weights - gen long weight_global_number = . - label var weight_global_number "Population scaled as weights for global/regional aggregations" - - * A country learning poverty number is POTENTIALLY included only if it satisfies both the TIMEWINDOWN() and COUNTRYFILTER() - gen byte wgt_included = include_country * include_assessment - label var wgt_included "Observation is considered in global/regional aggregations" - * Total 2015 population 10-14 years old that matters for the global number - egen wgt_population_total = total(anchor_population * include_country), by(`globalweight') - * Total population for which some assessment data will be used in the calculation - egen wgt_population_assessed = total(anchor_population_w_assessment * wgt_included), by(`globalweight') - * Scaling factor (would be 1 if all kids were assessed, but it's more than 1) - gen wgt_scaling_factor = wgt_population_total / wgt_population_assessed - label var wgt_scaling_factor "Scaling factor of population in global/regional aggregations" - - * The weight to use is the scaling up of those numbers: - replace weight_global_number = round(anchor_population_w_assessment * wgt_included * wgt_scaling_factor) //if preference == "`preference'" - * Except when there is no learning_poverty (prof) - replace weight_global_number = . if adj_nonprof_all == . - - * Calculate population coverage - gen pop_cov=100*wgt_population_assessed/wgt_population_total - - - if "`globalweightfilter'" == "" { - * If not specified, all observations are included - gen ctry_selected = 1 - gen ctry_assessment = 1 if (adj_nonprof_all != .) & ctry_selected == 1 - gen ctry_assessment_prefered = ctry_assessment if (`globalweightwindow') & ctry_selected == 1 - egen countries=total(ctry_assessment_prefered), by(preference `globalweight') - } - else { - * Countries - gen ctry_selected = 1 if (`globalweightfilter') - gen ctry_assessment = 1 if (adj_nonprof_all != .) & ctry_selected == 1 - gen ctry_assessment_prefered = ctry_assessment if (`globalweightwindow') & ctry_selected == 1 - egen countries=total(ctry_assessment_prefered), by(preference `globalweight') - } - - ************************************************************************** - *Calculate Std Errors - ************************************************************************** - - * Bootstrap approach for std errors - - if "`repetitions'"!="" { - forv i=1/`repetitions' { - - preserve - - * Form new adjprof based on random draw in boostrap sim - gen adj_nonprof_bs=100 * ( 1 - (enrollment_all/100) * (1 - rnormal(nonprof_all, se_nonprof_all)/100)) - collapse adj_nonprof_all adj_nonprof_bs [fw=weight_global_number], by(`globalweight') - gen rep=`i' - tempfile temp`i' - save "`temp`i''" - - restore - - } - - * Produce global total in similar way - forv i=1/`repetitions' { - - preserve - - * Form new adjprof based on random draw in boostrap sim - gen adj_nonprof_bs=100 * ( 1 - (enrollment_all/100) * (1 - rnormal(nonprof_all, se_nonprof_all)/100)) - collapse adj_nonprof_all adj_nonprof_bs [fw=weight_global_number], - gen rep=`i' - gen `globalweight' = "Global" - tempfile temp`i'_total - save "`temp`i'_total'" - - restore - - } - - } - - ***************************************** - ** Results - ***************************************** - - if ("`globalweight'" != "") { - - noi di "" - noi di in g "runame: " in y "`runname'" - noi di in g "weigths: " in y "weight_global_number by `globalweight'" - noi di in g "filters: " in y `"`globalweightfilter'"' - noi di in g "window: " in y "`globalweightwindow'" - - * Formatting for wgt_population_assessed wgt_population_total - replace wgt_population_assessed=wgt_population_assessed/1000000 - replace wgt_population_total=wgt_population_total/1000000 - - putexcel set "${clone}/03_export_tables/033_outputs/tabs_5_6_`runname'.xlsx", modify - putexcel A1 = ("Tables 5 & 6 Combined") - putexcel A2 = ("Country Groups") - putexcel B2 = ("Learning Poverty") - putexcel C2 = ("Population Coverage") - putexcel D2 = ("Population w/ Assessment") - putexcel E2 = ("Regional Population") - putexcel F2 = ("Countries") - putexcel G2 = ("Enrollment") - - putexcel H1 = ("Learning Poverty") - putexcel H2 = ("Min") - putexcel I2 = ("Max") - putexcel J2 = ("S.E.") - - if "`globalweight'"=="region" { - mat drop _all - noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') missing format(%20.1fc) save - - * Because # of regions may change based on settings, rename matrices after tabstat - forval i=1/8 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - - return list - // Mean - putexcel A3 = ("By Region") - putexcel A4 = (" EAP") B4 = matrix(M_EAS) - putexcel A5 = (" ECA") B5 = matrix(M_ECS) - putexcel A6 = (" LAC") B6 = matrix(M_LCN) - putexcel A7 = (" MNA") B7 = matrix(M_MEA) - cap putexcel A8 = (" NAC") B8 = matrix(M_NAC) - putexcel A9 = (" SAR") B9 = matrix(M_SAS) - putexcel A10 = (" SSF") B10 = matrix(M_SSF) - - // Min - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save missing stat(min) - - *Because # of regions may change based on settings, rename matrices after tabstat - forval i=1/8 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - - putexcel A4 = (" EAP") H4 = matrix(M_EAS) - putexcel A5 = (" ECA") H5 = matrix(M_ECS) - putexcel A6 = (" LAC") H6 = matrix(M_LCN) - putexcel A7 = (" MNA") H7 = matrix(M_MEA) - cap putexcel A8 = (" NAC") H8 = matrix(M_NAC) - putexcel A9 = (" SAR") H9 = matrix(M_SAS) - putexcel A10 = (" SSF") H10 = matrix(M_SSF) - - // Max - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save missing stat(max) - - * Because # of regions may change based on settings, rename matrices after tabstat - forval i=1/8 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - putexcel A4 = (" EAP") I4 = matrix(M_EAS) - putexcel A5 = (" ECA") I5 = matrix(M_ECS) - putexcel A6 = (" LAC") I6 = matrix(M_LCN) - putexcel A7 = (" MNA") I7 = matrix(M_MEA) - cap putexcel A8 = (" NAC") I8 = matrix(M_NAC) - putexcel A9 = (" SAR") I9 = matrix(M_SAS) - putexcel A10 = (" SSF") I10 = matrix(M_SSF) - - // S.E. - if "`repetitions'"!="" { - - preserve - //Code on appending files together to produce S.E. - clear - use "`temp1'" - append using "`temp1_total'" - forv i=2/`repetitions' { - append using "`temp`i''" - append using "`temp`i'_total'" - } - save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace - mat drop _all - tabstat adj_nonprof_bs, by(`globalweight') stat(sd) missing save - - *Because # of regions may change based on settings, rename matrices after tabstat - forval i=1/8 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - - noi return list - restore - - putexcel A4 = (" EAP") J4 = matrix(M_EAS) - putexcel A5 = (" ECA") J5 = matrix(M_ECS) - putexcel A6 = (" LAC") J6 = matrix(M_LCN) - putexcel A7 = (" MNA") J7 = matrix(M_MEA) - cap putexcel A8 = (" NAC") J8 = matrix(M_NAC) - putexcel A9 = (" SAR") J9 = matrix(M_SAS) - putexcel A10 = (" SSF") J10 = matrix(M_SSF) - putexcel A21 = ("Total") J21=matrix(M_Global) - - } - - } - - if "`globalweight'"=="lendingtype" { - mat drop _all - noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save - return list - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - - putexcel A11 = ("By Lending Type") - putexcel A12 = (" IBRD") B12 = matrix(M_IBD) - putexcel A13 = (" Blend" ) B13 = matrix(M_IDB) - putexcel A14 = (" IDA") B14 = matrix(M_IDX) - cap putexcel A15 = (" Not classified") B15 = matrix(M_LNX) - - // Min - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(min) - - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - putexcel A12 = (" IBRD") H12 = matrix(M_IBD) - putexcel A13 = (" Blend" ) H13 = matrix(M_IDB) - putexcel A14 = (" IDA") H14 = matrix(M_IDX) - cap putexcel A15 = (" Not classified") H15 = matrix(M_LNX) - - // Max - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(max) - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - putexcel A12 = (" IBRD") I12 = matrix(M_IBD) - putexcel A13 = (" Blend" ) I13 = matrix(M_IDB) - putexcel A14 = (" IDA") I14 = matrix(M_IDX) - cap putexcel A15 = (" Not classified") I15 = matrix(M_LNX) - - // S.E. - if "`repetitions'"!="" { - - preserve - - * Code on appending files together to produce S.E. - clear - use "`temp1'" - forv i=2/`repetitions' { - append using "`temp`i''" - } - save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace - tabstat adj_nonprof_bs, by(`globalweight') stat(sd) save - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - noi return list - - restore - - putexcel A12 = (" IBRD") J12 = matrix(M_IBD) - putexcel A13 = (" Blend" ) J13 = matrix(M_IDB) - putexcel A14 = (" IDA") J14 = matrix(M_IDX) - cap putexcel A15 = (" Not classified") J15 = matrix(M_LNX) - - } - - } - - if "`globalweight'"=="incomelevel" { - - mat drop _all - noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - return list - putexcel A16 = ("By Income Level") - cap putexcel A17 = (" High Income") B17 = matrix(M_HIC) - putexcel A18 = (" Upper middle income") B18 = matrix(M_UMC) - putexcel A19 = (" Lower middle income") B19 = matrix(M_LMC) - putexcel A20 = (" Low income") B20 = matrix(M_LIC) - - // Min - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(min) - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - return list - putexcel A16 = ("By Income Level") - cap putexcel A17 = (" High Income") H17 = matrix(M_HIC) - putexcel A18 = (" Upper middle income") H18 = matrix(M_UMC) - putexcel A19 = (" Lower middle income") H19 = matrix(M_LMC) - putexcel A20 = (" Low income") H20 = matrix(M_LIC) - - // Max - mat drop _all - noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(max) - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - return list - putexcel A16 = ("By Income Level") - cap putexcel A17 = (" High Income") I17 = matrix(M_HIC) - putexcel A18 = (" Upper middle income") I18 = matrix(M_UMC) - putexcel A19 = (" Lower middle income") I19 = matrix(M_LMC) - putexcel A20 = (" Low income") I20 = matrix(M_LIC) - - // S.E. - if "`repetitions'"!="" { - - preserve - - //Code on appending files together to produce S.E. - clear - use "`temp1'" - forv i=2/`repetitions' { - append using "`temp`i''" - } - save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace - mat drop _all - tabstat adj_nonprof_bs, by(`globalweight') stat(sd) save - * Because # of groups may change based on settings, rename matrices after tabstat - forval i=1/5 { - di "`r(name`i')'" - local matname M_`r(name`i')' - matrix `matname' = r(Stat`i') - } - noi return list - - restore - - cap putexcel A17 = (" High Income") J17 = matrix(M_HIC) - putexcel A18 = (" Upper middle income") J18 = matrix(M_UMC) - putexcel A19 = (" Lower middle income") J19 = matrix(M_LMC) - putexcel A20 = (" Low income") J20 = matrix(M_LIC) - - } - - } - - - if "`globalweight'"=="region" { - mat drop _all - noi tabstat adj_nonprof_all pop_cov [fw=weight_global_number], format(%20.1fc) save - return list - putexcel A21 = ("Total") B21=matrix(r(StatTotal)) - } - } - - else { - noi di "" - noi di in g "runame: " in y "`runname'" - noi di in g "weigths: " in y "population_2015_all" - noi di in g "filters: " in y "none" - noi di in g "window: " in y "none" - noi tabstat adj_nonprof_all population_2015_all [fw=population_2015_all], by(region) format(%20.1fc) - } - - ***************************************** - - ***************************************** - - - ***************************************** - ** Results for log decomposition - ***************************************** - if ("`globalweight'" != "") { - noi di "" - noi di in g "runame: " in y "`runname'" - noi di in g "weigths: " in y "weight_global_number by `globalweight'" - noi di in g "filters: " in y `"`globalweightfilter'"' - noi di in g "window: " in y "`globalweightwindow'" - - gen adj_pct_reading_low=100-adj_nonprof_all - gen pct_reading_low_target=1-nonprof_all/100 - - foreach var in adj_pct_reading_low pct_reading_low_target enrollment_all { - gen log_`var'=log(`var') - } - - gen frac_enrollment=100*log_enrollment_all/(log_enrollment_all+log_pct_reading_low_target) - gen frac_proficiency=100*log_pct_reading_low_target/(log_enrollment_all+log_pct_reading_low_target) - - if "`globalweight'"=="region" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8_`runname'_country.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="lendingtype" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_`runname'_country.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="incomelevel" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_`runname'_country.xlsx", firstrow(varl) replace - } - - tempfile temp - preserve - collapse adj_pct_reading_low pct_reading_low_target enrollment_all pop_cov wgt_population_assessed wgt_population_total countries [fw=weight_global_number] - gen `globalweight'="Overall" - save `temp' - restore - - mat drop _all - - collapse adj_pct_reading_low pct_reading_low_target enrollment_all pop_cov wgt_population_assessed wgt_population_total countries [fw=weight_global_number], by(`globalweight') - - append using `temp' - replace adj_pct_reading_low=adj_pct_reading_low/100 - replace enrollment_all=enrollment_all/100 - - * Generate log learning poverty measures - foreach var in adj_pct_reading_low pct_reading_low_target enrollment_all { - gen log_`var'=log(`var') - } - - gen frac_enrollment=100*log_enrollment_all/(log_enrollment_all+log_pct_reading_low_target) - gen frac_proficiency=100*log_pct_reading_low_target/(log_enrollment_all+log_pct_reading_low_target) - - label var `globalweight' "Group" - label var adj_pct_reading_low "Adjusted Proficiency" - label var pct_reading_low_target "Proficiency" - label var enrollment_all "Enrollment" - label var pop_cov "Percent of Population Covered" - label var wgt_population_assessed "Total population for which assessment will be included" - label var wgt_population_total "Total population 10-14 years old" - label var countries "Number of Countries" - label var log_adj_pct_reading_low "Log Adjusted Proficiency" - label var log_pct_reading_low_target "Log Proficiency" - label var log_enrollment_all "Log Enrollment" - label var frac_enrollment "Percentage of Log Adj Proficiency Explained by Enrollment" - label var frac_proficiency "Percentage of Log Adj Proficiency Explained by Proficiency" - - if "`globalweight'"=="region" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8_`runname'.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="lendingtype" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_`runname'.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="incomelevel" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_`runname'.xlsx", firstrow(varl) replace - } - - loc preference `preference' - loc enrollment validated - loc inputfolder clone - - use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear - gen year=year_assessment - *nla_code should be distributed over s rather than being available in each . - - *temporarily rename test to assessment and idgrade to grade, change back after merge. - rename test assessment - rename idgrade grade - - *------------------------------* - * Learning poverty calculation - *------------------------------* - * Adjusts non-proficiency by out-of school - foreach subgroup in all fe ma { - gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_validated_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) - label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" - } - - gen adj_pct_reading_low_rawfull= 100-adj_nonprof_all - - merge m:1 countrycode using "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", keepusing(test idgrade incomelevel lendingtype nonprof_all) - gen adj_pct_reading_low= 100-adj_nonprof_all - - * Change name in rawlatest of assessment to test_rawlatest, and revert back to test from assessment - rename adj_pct_reading_low adj_pct_reading_low_rawlatest - rename adj_pct_reading_low_rawfull adj_pct_reading_low - - gen initial_poverty_level_temp=100-adj_pct_reading_low_rawlatest - cap gen initial_poverty_level="0-25% Learning Poverty" - cap replace initial_poverty_level="25-50% Learning Poverty" if initial_poverty_level_temp>=25 - cap replace initial_poverty_level="50-75% Learning Poverty" if initial_poverty_level_temp>=50 - cap replace initial_poverty_level="75-100% Learning Poverty" if initial_poverty_level_temp>=75 - - rename test test_rawlatest - rename assessment test - - *Same for grade - rename idgrade idgrade_rawlatest - rename grade idgrade - - drop if _merge==1 - - gen enrollment=enrollment_validated_all - drop if subject!="science" & test=="TIMSS" & countrycode!="JOR" - - sort countrycode nla_code idgrade test subject - count - - cap gen adj_pct_reading_low=100-adj_nonprof_all - cap gen pct_reading_low_target=100-nonprof_all - - * Cleaning the data file - keep region regionname countrycode countryname incomelevel incomelevelname lendingtype /// - lendingtypename year_population year_assessment idgrade test source_assessment /// - enrollment /// - adj_pct_reading_low* pct_reading_low_target enrollment* subject nla_code initial_poverty_level - - * Generating all possible combinations of forward spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen spell_c1 = string(year_assessment[_n-1]) + "-" + string(year_assessment) - - bysort countrycode nla_code idgrade test subject : gen spell_c2 = string(year_assessment[_n-2]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c3 = string(year_assessment[_n-3]) + "-" + string(year_assessment) - bysort countrycode nla_code idgrade test subject : gen spell_c4 = string(year_assessment[_n-4]) + "-" + string(year_assessment) - - reshape long spell_c, i(countrycode nla_code idgrade test subject year_assessment subject) j(lag) - ren spell_c spell - - * Tag if actual spell: - gen spell_exists=(length(spell) == 9 ) - - ********************************************** - *Preparing the data for simulations: - ********************************************** - *The data should be restructured for unique identifiers: - sort countrycode nla_code idgrade test subject year_assessment spell lag - - *Rules for cleaning the spell data: - *Bringing in the list of countries and spells for which the data is not comparable: - - merge m:1 countrycode idgrade test year_assessment spell using "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", assert(master match using) keep(master match) keepusing(comparable) nogen - drop if comparable == 0 - - *Generating preferred consecutive spells: - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : egen lag_min = min(lag) - *Keeping the comparable consecutive spells - keep if lag == lag_min - - *All comparable spells for TIMSS/PIRLS - assert comparable == 1 if !missing(comparable) - - *Annual change in enrollment, adjusted proficiency and proficiency - sort countrycode nla_code idgrade test subject year_assessment - bysort countrycode nla_code idgrade test subject : gen delta_adj_pct = ((adj_pct_reading_low-adj_pct_reading_low[_n-1])/(year_assessment-year_assessment[_n-1])) - bysort countrycode nla_code idgrade test subject : gen initial_adj_pct = adj_pct_reading_low[_n-1] - bysort countrycode nla_code idgrade test subject : gen final_adj_pct = adj_pct_reading_low - - *Annualized log change for decomposition - bysort countrycode nla_code idgrade test subject : gen delta_log_adj_pct = ((log(adj_pct_reading_low)-log(adj_pct_reading_low[_n-1]))/(year_assessment-year_assessment[_n-1])) - bysort countrycode nla_code idgrade test subject : gen delta_log_prof = ((log(pct_reading_low_target)-log(pct_reading_low_target[_n-1]))/(year_assessment-year_assessment[_n-1])) - bysort countrycode nla_code idgrade test subject : gen delta_log_enrollment = ((log(enrollment)-log(enrollment[_n-1]))/(year_assessment-year_assessment[_n-1])) - - bysort countrycode nla_code idgrade test subject : gen delta_prof = (pct_reading_low_target-pct_reading_low_target[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen delta_enrollment_all = (enrollment_interpolated_all-enrollment_interpolated_all[_n-1])/(year_assessment-year_assessment[_n-1]) - - *drop observatoins specified by [if] [in]. - if `"`ifspell'"'!="" { - di `"`ifspell'"' - keep `ifspell' - } - - - /* weights */ - - if ("`weight'" == "") { - cap tempname wtg - cap gen `wtg' = 1 - local weight2 "" - loc weight "fw" - loc exp "=`wtg'" - } - - - * Generating deltas in terms of reduction of gap to the frontier - gen gap_to_frontier = 100-adj_pct_reading_low - bysort countrycode nla_code idgrade test subject : gen red_gap_frontier = -1*(gap_to_frontier-gap_to_frontier[_n-1])/(year_assessment-year_assessment[_n-1]) - bysort countrycode nla_code idgrade test subject : gen pct_red_gap = (red_gap_frontier/gap_to_frontier[_n-1]) - gen pct_red_gap_100 = pct_red_gap*100 - - - *Following threshold IIB specification as the baseline file to be used will be threshold IIB. - *Not using spell data for SACMEQ 2007 - SACMEQ 2013: - *replace delta_adj_pct = . if test == "SACMEQ" & year == 2013 - *replace pct_red_gap_100 = . if test == "SACMEQ" & year == 2013 - - * Generating categories of countries - gen catinitial = . - foreach var in 25 50 75 100 { - replace catinitial= `var' if initial_adj_pct <= `var' & catinitial== . - } - - gen initial_learning_poverty = 100-initial_adj_pct - - - **************************************************************** - * Identify only selected spells (n=71) - - keep if test != "no assessment" & test != "EGRA" & delta_adj_pct != . & delta_adj_pct > -2 & delta_adj_pct < 4 & test != "PASEC" & year_assessment>2000 & lendingtype!="LNX" & test!="NLA" - bysort countrycode : gen tot = _N - gen wtg = 1/tot - - preserve - - keep countrycode countryname region incomelevel idgrade test year_assessment spell adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment - - keep if !missing(delta_adj_pct) - - gen frac_proficiency=100*delta_log_prof/(delta_log_prof+delta_log_enrollment) - gen frac_enrollment_all=100*delta_log_enrollment/(delta_log_prof+delta_log_enrollment) - - label var adj_pct_reading_low "Adjusted Proficiency" - label var pct_reading_low_target "Proficiency" - label var enrollment_interpolated_all "Enrollment" - label var delta_adj_pct "Annualized Change in Adjusted Proficiency" - label var delta_prof "Annualized Change in Proficiency" - label var delta_enrollment_all "Annualized Change in Enrollment" - - label var delta_log_adj_pct "Log Annualized Change in Adjusted Proficiency" - label var delta_log_prof "Log Annualized Change in Proficiency" - label var delta_log_enrollment "Log Annualized Change in Enrollment" - label var frac_enrollment_all "Percentage of Log Annualized Change in Adjusted Proficiency due to Enrollment" - label var frac_proficiency "Percentage of Log Annualized Change in Adjusted Proficiency due to Proficiency" - - if "`globalweight'"=="region" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8_spells_country_`runname'.xlsx", firstrow(varl) replace - } - - restore - - tempfile temp2 - - preserve - collapse adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment [aw=wtg] - gen `globalweight'="Overall" - save `temp2' - restore - - collapse adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment [aw=wtg], by(`globalweight') - - append using `temp2' - - replace pct_reading_low_target=pct_reading_low_target - replace delta_prof=delta_prof - - keep if !missing(delta_adj_pct) - - - gen frac_proficiency=100*delta_log_prof/(delta_log_prof+delta_log_enrollment) - gen frac_enrollment_all=100*delta_log_enrollment/(delta_log_prof+delta_log_enrollment) - - label var `globalweight' "Group" - label var adj_pct_reading_low "Adjusted Proficiency" - label var pct_reading_low_target "Proficiency" - label var enrollment_interpolated_all "Enrollment" - label var delta_adj_pct "Annualized Change in Adjusted Proficiency" - label var delta_prof "Annualized Change in Proficiency" - label var delta_enrollment_all "Annualized Change in Enrollment" - - label var delta_log_adj_pct "Log Annualized Change in Adjusted Proficiency" - label var delta_log_prof "Log Annualized Change in Proficiency" - label var delta_log_enrollment "Log Annualized Change in Enrollment" - label var frac_enrollment_all "Percentage of Log Annualized Change in Adjusted Proficiency due to Enrollment" - label var frac_proficiency "Percentage of Log Annualized Change in Adjusted Proficiency due to Proficiency" - - if "`globalweight'"=="region" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8_spells_`runname'.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="lendingtype" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_spells_`runname'.xlsx", firstrow(varl) replace - } - - if "`globalweight'"=="incomelevel" { - export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_spells_`runname'.xlsx", firstrow(varl) replace - } - - } - -end +*==============================================================================* +* PROGRAMS: CREATES TABLES WITH CONFIDENCE INTERVAL FOR TECHNICAL PAPER +*==============================================================================* + +* 0.3.1 fixed typo on output line +* 0.3 add anchor year; and select variable closest to the anchore year +* 0.2 add the counstruction of Global weights + Global Weight Window + Global Weights Filter +* 0.1 run quietly with a NOIsily option JPA + +*Author: Brian Stacy; Hongxi Zhao; Diana Goldberg; Kristopher Bjarkefur; Joao Pedro Azevedo + + +/*This an ado file: +1)Develops database for preferred list +The user must specify a number of options. +(1) nla() - which dictates that the countries in the list are to use the National Learning Assessment. This option takes countrycodes. +(2) threshold() - which dictates which threshold to use for the proficiency levels. Current options are 0, I, II, IIB. +(3) droplist() - which dictate which countries to not calculate proficiency levels. This option takes country codes +(4) dropassess() - which dictate which assessments to not calculate proficiency levels. This option takes assessment names +(5) runname() - which dictates the name of the run. It also identifies the run in the rawlatest file (i.e. preference="912") +(6) TIMSS()-dictates either math or science for TIMSS. either enter string "math" or "science" +(7) enrollment() -dictates which enrollment to use. original enrollment, validated, or interpolated +(8) EGRADROP() -drop specific EGRAs, 3rd grade, 4th grade, non-nationally representative. +As an example: _preferred_list, nla(BGD CHN IND PAK) threshold(IIB) droplist(NGA PAK) dropassess(SACMEQ) runname(912) timss_subject(science) +Specifies that Bangladesh, China, India, and Pakistan use National Learning Assessments. Threshold IIB is applied for all assessments. +*/ +cap program drop _preferred_list_tables_ci +program define _preferred_list_tables_ci, rclass + version 14 + syntax [, /// + PREFERENCE(string) /// + POPULATION(string) /// + NOIsily /// + GLOBALWEIGHT(string) /// + GLOBALWEIGHTWINDOW(string) /// + GLOBALWEIGHTFILTER(string) /// + ANCHORYEAR(string) /// + REPETITIONS(string) /// + RUNNAME(string) /// + ] + + * Apply checks + + if "`noisily'" != "" { + local noi "noi" + } + else { + local noi "qui" + } + + * Rawlatest with merged non-proficiency split by gender from CLO + use "${clone}/01_data/013_outputs/preference`preference'.dta", clear + + * Generate dummy on whether assessment is inside TIMEWINDOW() + cap drop include_assessment + if "`globalweightwindow'" == "" { + * If not specified, all observations are included by default + gen byte include_assessment = 1 + } + else { + * If specified, apply the condition to create a dummy + cap gen byte include_assessment = (`globalweightwindow') & !missing(year_assessment) + if _rc != 0 { + noi di as err `"The option TIMEWINDOW() is incorrectly specified. Good example: timewindow(year_assessment>=2011)"' + break + } + } + + + * Generate dummy on whether country is inside COUNTRYFILTER() + cap drop include_country + if "`globalweightfilter'" == "" { + * If not specified, all observations are included + gen byte include_country = 1 + } + else { + * If specified, apply the condition to create a dummy + cap gen byte include_country = (`globalweightfilter') + if _rc != 0 { + noi di as err `"The option COUNTRYFILTER() is incorrectly specified. Good example: countryfilter(incomelevel!="HIC" & lendingtype!="LNX")"' + break + } + } + + // * Whether or not the confidence intervals will be calculated + // if "`ci_repetitions'" != "" { + // local CI = 1 + // } + // else { + // local CI = 0 + // } + + + *----------------------------* + * Calculation of pop weights * + *----------------------------* + + * Tentatively drop pre-existing auxiliary and final variables to avoid errors: + foreach var in wgt_included wgt_population_total wgt_population_assessed wgt_scaling_factor weight_global_number { + cap drop `var' + } + + * The main variable we're set to build is wgt_pop, which is integer so we can use as frequency weights + gen long weight_global_number = . + label var weight_global_number "Population scaled as weights for global/regional aggregations" + + * A country learning poverty number is POTENTIALLY included only if it satisfies both the TIMEWINDOWN() and COUNTRYFILTER() + gen byte wgt_included = include_country * include_assessment + label var wgt_included "Observation is considered in global/regional aggregations" + * Total 2015 population 10-14 years old that matters for the global number + egen wgt_population_total = total(anchor_population * include_country), by(`globalweight') + * Total population for which some assessment data will be used in the calculation + egen wgt_population_assessed = total(anchor_population_w_assessment * wgt_included), by(`globalweight') + * Scaling factor (would be 1 if all kids were assessed, but it's more than 1) + gen wgt_scaling_factor = wgt_population_total / wgt_population_assessed + label var wgt_scaling_factor "Scaling factor of population in global/regional aggregations" + + * The weight to use is the scaling up of those numbers: + replace weight_global_number = round(anchor_population_w_assessment * wgt_included * wgt_scaling_factor) //if preference == "`preference'" + * Except when there is no learning_poverty (prof) + replace weight_global_number = . if adj_nonprof_all == . + + * Calculate population coverage + gen pop_cov=100*wgt_population_assessed/wgt_population_total + + + if "`globalweightfilter'" == "" { + * If not specified, all observations are included + gen ctry_selected = 1 + gen ctry_assessment = 1 if (adj_nonprof_all != .) & ctry_selected == 1 + gen ctry_assessment_prefered = ctry_assessment if (`globalweightwindow') & ctry_selected == 1 + egen countries=total(ctry_assessment_prefered), by(preference `globalweight') + } + else { + * Countries + gen ctry_selected = 1 if (`globalweightfilter') + gen ctry_assessment = 1 if (adj_nonprof_all != .) & ctry_selected == 1 + gen ctry_assessment_prefered = ctry_assessment if (`globalweightwindow') & ctry_selected == 1 + egen countries=total(ctry_assessment_prefered), by(preference `globalweight') + } + + ************************************************************************** + *Calculate Std Errors + ************************************************************************** + + * Bootstrap approach for std errors + + if "`repetitions'"!="" { + forv i=1/`repetitions' { + + preserve + + * Form new adjprof based on random draw in boostrap sim + gen adj_nonprof_bs=100 * ( 1 - (enrollment_all/100) * (1 - rnormal(nonprof_all, se_nonprof_all)/100)) + collapse adj_nonprof_all adj_nonprof_bs [fw=weight_global_number], by(`globalweight') + gen rep=`i' + tempfile temp`i' + save "`temp`i''" + + restore + + } + + * Produce global total in similar way + forv i=1/`repetitions' { + + preserve + + * Form new adjprof based on random draw in boostrap sim + gen adj_nonprof_bs=100 * ( 1 - (enrollment_all/100) * (1 - rnormal(nonprof_all, se_nonprof_all)/100)) + collapse adj_nonprof_all adj_nonprof_bs [fw=weight_global_number], + gen rep=`i' + gen `globalweight' = "Global" + tempfile temp`i'_total + save "`temp`i'_total'" + + restore + + } + + } + + ***************************************** + ** Results + ***************************************** + + if ("`globalweight'" != "") { + + noi di "" + noi di in g "runame: " in y "`runname'" + noi di in g "weigths: " in y "weight_global_number by `globalweight'" + noi di in g "filters: " in y `"`globalweightfilter'"' + noi di in g "window: " in y "`globalweightwindow'" + + * Formatting for wgt_population_assessed wgt_population_total + replace wgt_population_assessed=wgt_population_assessed/1000000 + replace wgt_population_total=wgt_population_total/1000000 + + putexcel set "${clone}/03_export_tables/033_outputs/tabs_5_6_`runname'.xlsx", modify + putexcel A1 = ("Tables 5 & 6 Combined") + putexcel A2 = ("Country Groups") + putexcel B2 = ("Learning Poverty") + putexcel C2 = ("Population Coverage") + putexcel D2 = ("Population w/ Assessment") + putexcel E2 = ("Regional Population") + putexcel F2 = ("Countries") + putexcel G2 = ("Enrollment") + + putexcel H1 = ("Learning Poverty") + putexcel H2 = ("Min") + putexcel I2 = ("Max") + putexcel J2 = ("S.E.") + + if "`globalweight'"=="region" { + mat drop _all + noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') missing format(%20.1fc) save + + * Because # of regions may change based on settings, rename matrices after tabstat + forval i=1/8 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + + return list + // Mean + putexcel A3 = ("By Region") + putexcel A4 = (" EAP") B4 = matrix(M_EAS) + putexcel A5 = (" ECA") B5 = matrix(M_ECS) + putexcel A6 = (" LAC") B6 = matrix(M_LCN) + putexcel A7 = (" MNA") B7 = matrix(M_MEA) + cap putexcel A8 = (" NAC") B8 = matrix(M_NAC) + putexcel A9 = (" SAR") B9 = matrix(M_SAS) + putexcel A10 = (" SSF") B10 = matrix(M_SSF) + + // Min + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save missing stat(min) + + *Because # of regions may change based on settings, rename matrices after tabstat + forval i=1/8 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + + putexcel A4 = (" EAP") H4 = matrix(M_EAS) + putexcel A5 = (" ECA") H5 = matrix(M_ECS) + putexcel A6 = (" LAC") H6 = matrix(M_LCN) + putexcel A7 = (" MNA") H7 = matrix(M_MEA) + cap putexcel A8 = (" NAC") H8 = matrix(M_NAC) + putexcel A9 = (" SAR") H9 = matrix(M_SAS) + putexcel A10 = (" SSF") H10 = matrix(M_SSF) + + // Max + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save missing stat(max) + + * Because # of regions may change based on settings, rename matrices after tabstat + forval i=1/8 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + putexcel A4 = (" EAP") I4 = matrix(M_EAS) + putexcel A5 = (" ECA") I5 = matrix(M_ECS) + putexcel A6 = (" LAC") I6 = matrix(M_LCN) + putexcel A7 = (" MNA") I7 = matrix(M_MEA) + cap putexcel A8 = (" NAC") I8 = matrix(M_NAC) + putexcel A9 = (" SAR") I9 = matrix(M_SAS) + putexcel A10 = (" SSF") I10 = matrix(M_SSF) + + // S.E. + if "`repetitions'"!="" { + + preserve + //Code on appending files together to produce S.E. + clear + use "`temp1'" + append using "`temp1_total'" + forv i=2/`repetitions' { + append using "`temp`i''" + append using "`temp`i'_total'" + } + save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace + mat drop _all + tabstat adj_nonprof_bs, by(`globalweight') stat(sd) missing save + + *Because # of regions may change based on settings, rename matrices after tabstat + forval i=1/8 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + + noi return list + restore + + putexcel A4 = (" EAP") J4 = matrix(M_EAS) + putexcel A5 = (" ECA") J5 = matrix(M_ECS) + putexcel A6 = (" LAC") J6 = matrix(M_LCN) + putexcel A7 = (" MNA") J7 = matrix(M_MEA) + cap putexcel A8 = (" NAC") J8 = matrix(M_NAC) + putexcel A9 = (" SAR") J9 = matrix(M_SAS) + putexcel A10 = (" SSF") J10 = matrix(M_SSF) + putexcel A21 = ("Total") J21=matrix(M_Global) + + } + + } + + if "`globalweight'"=="lendingtype" { + mat drop _all + noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save + return list + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + + putexcel A11 = ("By Lending Type") + putexcel A12 = (" IBRD") B12 = matrix(M_IBD) + putexcel A13 = (" Blend" ) B13 = matrix(M_IDB) + putexcel A14 = (" IDA") B14 = matrix(M_IDX) + cap putexcel A15 = (" Not classified") B15 = matrix(M_LNX) + + // Min + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(min) + + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + putexcel A12 = (" IBRD") H12 = matrix(M_IBD) + putexcel A13 = (" Blend" ) H13 = matrix(M_IDB) + putexcel A14 = (" IDA") H14 = matrix(M_IDX) + cap putexcel A15 = (" Not classified") H15 = matrix(M_LNX) + + // Max + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(max) + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + putexcel A12 = (" IBRD") I12 = matrix(M_IBD) + putexcel A13 = (" Blend" ) I13 = matrix(M_IDB) + putexcel A14 = (" IDA") I14 = matrix(M_IDX) + cap putexcel A15 = (" Not classified") I15 = matrix(M_LNX) + + // S.E. + if "`repetitions'"!="" { + + preserve + + * Code on appending files together to produce S.E. + clear + use "`temp1'" + forv i=2/`repetitions' { + append using "`temp`i''" + } + save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace + tabstat adj_nonprof_bs, by(`globalweight') stat(sd) save + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + noi return list + + restore + + putexcel A12 = (" IBRD") J12 = matrix(M_IBD) + putexcel A13 = (" Blend" ) J13 = matrix(M_IDB) + putexcel A14 = (" IDA") J14 = matrix(M_IDX) + cap putexcel A15 = (" Not classified") J15 = matrix(M_LNX) + + } + + } + + if "`globalweight'"=="incomelevel" { + + mat drop _all + noi tabstat adj_nonprof_all pop_cov wgt_population_assessed wgt_population_total countries enrollment_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + return list + putexcel A16 = ("By Income Level") + cap putexcel A17 = (" High Income") B17 = matrix(M_HIC) + putexcel A18 = (" Upper middle income") B18 = matrix(M_UMC) + putexcel A19 = (" Lower middle income") B19 = matrix(M_LMC) + putexcel A20 = (" Low income") B20 = matrix(M_LIC) + + // Min + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(min) + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + return list + putexcel A16 = ("By Income Level") + cap putexcel A17 = (" High Income") H17 = matrix(M_HIC) + putexcel A18 = (" Upper middle income") H18 = matrix(M_UMC) + putexcel A19 = (" Lower middle income") H19 = matrix(M_LMC) + putexcel A20 = (" Low income") H20 = matrix(M_LIC) + + // Max + mat drop _all + noi tabstat adj_nonprof_all [fw=weight_global_number], by(`globalweight') format(%20.1fc) save stat(max) + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + return list + putexcel A16 = ("By Income Level") + cap putexcel A17 = (" High Income") I17 = matrix(M_HIC) + putexcel A18 = (" Upper middle income") I18 = matrix(M_UMC) + putexcel A19 = (" Lower middle income") I19 = matrix(M_LMC) + putexcel A20 = (" Low income") I20 = matrix(M_LIC) + + // S.E. + if "`repetitions'"!="" { + + preserve + + //Code on appending files together to produce S.E. + clear + use "`temp1'" + forv i=2/`repetitions' { + append using "`temp`i''" + } + save "${clone}/03_export_tables/033_outputs/bs_reps_`globalweight'.dta", replace + mat drop _all + tabstat adj_nonprof_bs, by(`globalweight') stat(sd) save + * Because # of groups may change based on settings, rename matrices after tabstat + forval i=1/5 { + di "`r(name`i')'" + local matname M_`r(name`i')' + matrix `matname' = r(Stat`i') + } + noi return list + + restore + + cap putexcel A17 = (" High Income") J17 = matrix(M_HIC) + putexcel A18 = (" Upper middle income") J18 = matrix(M_UMC) + putexcel A19 = (" Lower middle income") J19 = matrix(M_LMC) + putexcel A20 = (" Low income") J20 = matrix(M_LIC) + + } + + } + + + if "`globalweight'"=="region" { + mat drop _all + noi tabstat adj_nonprof_all pop_cov [fw=weight_global_number], format(%20.1fc) save + return list + putexcel A21 = ("Total") B21=matrix(r(StatTotal)) + } + } + + else { + noi di "" + noi di in g "runame: " in y "`runname'" + noi di in g "weigths: " in y "population_2015_all" + noi di in g "filters: " in y "none" + noi di in g "window: " in y "none" + noi tabstat adj_nonprof_all population_2015_all [fw=population_2015_all], by(region) format(%20.1fc) + } + + ***************************************** + + ***************************************** + + + ***************************************** + ** Results for log decomposition + ***************************************** + if ("`globalweight'" != "") { + noi di "" + noi di in g "runame: " in y "`runname'" + noi di in g "weigths: " in y "weight_global_number by `globalweight'" + noi di in g "filters: " in y `"`globalweightfilter'"' + noi di in g "window: " in y "`globalweightwindow'" + + gen adj_pct_reading_low=100-adj_nonprof_all + gen pct_reading_low_target=1-nonprof_all/100 + + foreach var in adj_pct_reading_low pct_reading_low_target enrollment_all { + gen log_`var'=log(`var') + } + + gen frac_enrollment=100*log_enrollment_all/(log_enrollment_all+log_pct_reading_low_target) + gen frac_proficiency=100*log_pct_reading_low_target/(log_enrollment_all+log_pct_reading_low_target) + + if "`globalweight'"=="region" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8_`runname'_country.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="lendingtype" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_`runname'_country.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="incomelevel" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_`runname'_country.xlsx", firstrow(varl) replace + } + + tempfile temp + preserve + collapse adj_pct_reading_low pct_reading_low_target enrollment_all pop_cov wgt_population_assessed wgt_population_total countries [fw=weight_global_number] + gen `globalweight'="Overall" + save `temp' + restore + + mat drop _all + + collapse adj_pct_reading_low pct_reading_low_target enrollment_all pop_cov wgt_population_assessed wgt_population_total countries [fw=weight_global_number], by(`globalweight') + + append using `temp' + replace adj_pct_reading_low=adj_pct_reading_low/100 + replace enrollment_all=enrollment_all/100 + + * Generate log learning poverty measures + foreach var in adj_pct_reading_low pct_reading_low_target enrollment_all { + gen log_`var'=log(`var') + } + + gen frac_enrollment=100*log_enrollment_all/(log_enrollment_all+log_pct_reading_low_target) + gen frac_proficiency=100*log_pct_reading_low_target/(log_enrollment_all+log_pct_reading_low_target) + + label var `globalweight' "Group" + label var adj_pct_reading_low "Adjusted Proficiency" + label var pct_reading_low_target "Proficiency" + label var enrollment_all "Enrollment" + label var pop_cov "Percent of Population Covered" + label var wgt_population_assessed "Total population for which assessment will be included" + label var wgt_population_total "Total population 10-14 years old" + label var countries "Number of Countries" + label var log_adj_pct_reading_low "Log Adjusted Proficiency" + label var log_pct_reading_low_target "Log Proficiency" + label var log_enrollment_all "Log Enrollment" + label var frac_enrollment "Percentage of Log Adj Proficiency Explained by Enrollment" + label var frac_proficiency "Percentage of Log Adj Proficiency Explained by Proficiency" + + if "`globalweight'"=="region" { + save "${clone}/03_export_tables/033_outputs/tabs_8_`runname'.dta", replace + export excel using "${clone}/03_export_tables/033_outputs/tabs_8_`runname'.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="lendingtype" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_`runname'.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="incomelevel" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_`runname'.xlsx", firstrow(varl) replace + } + + loc preference `preference' + loc enrollment validated + loc inputfolder clone + + use "${`inputfolder'}/01_data/013_outputs/rawfull.dta", clear + gen year=year_assessment + *nla_code should be distributed over s rather than being available in each . + + *temporarily rename test to assessment and idgrade to grade, change back after merge. + rename test assessment + rename idgrade grade + + *------------------------------* + * Learning poverty calculation + *------------------------------* + * Adjusts non-proficiency by out-of school + foreach subgroup in all fe ma { + gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_validated_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) + label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" + } + + gen adj_pct_reading_low_rawfull= 100-adj_nonprof_all + + merge m:1 countrycode using "${`inputfolder'}/01_data/013_outputs/preference`preference'.dta", keepusing(test idgrade incomelevel lendingtype nonprof_all) + gen adj_pct_reading_low= 100-adj_nonprof_all + + * Change name in rawlatest of assessment to test_rawlatest, and revert back to test from assessment + rename adj_pct_reading_low adj_pct_reading_low_rawlatest + rename adj_pct_reading_low_rawfull adj_pct_reading_low + + gen initial_poverty_level_temp=100-adj_pct_reading_low_rawlatest + cap gen initial_poverty_level="0-25% Learning Poverty" + cap replace initial_poverty_level="25-50% Learning Poverty" if initial_poverty_level_temp>=25 + cap replace initial_poverty_level="50-75% Learning Poverty" if initial_poverty_level_temp>=50 + cap replace initial_poverty_level="75-100% Learning Poverty" if initial_poverty_level_temp>=75 + + rename test test_rawlatest + rename assessment test + + *Same for grade + rename idgrade idgrade_rawlatest + rename grade idgrade + + drop if _merge==1 + + gen enrollment=enrollment_validated_all + drop if subject!="science" & test=="TIMSS" & countrycode!="JOR" + + sort countrycode nla_code idgrade test subject + count + + cap gen adj_pct_reading_low=100-adj_nonprof_all + cap gen pct_reading_low_target=100-nonprof_all + + * Cleaning the data file + keep region regionname countrycode countryname incomelevel incomelevelname lendingtype /// + lendingtypename year_population year_assessment idgrade test source_assessment /// + enrollment /// + adj_pct_reading_low* pct_reading_low_target enrollment* subject nla_code initial_poverty_level + + * Generating all possible combinations of forward spells: + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : gen spell_c1 = string(year_assessment[_n-1]) + "-" + string(year_assessment) + + bysort countrycode nla_code idgrade test subject : gen spell_c2 = string(year_assessment[_n-2]) + "-" + string(year_assessment) + bysort countrycode nla_code idgrade test subject : gen spell_c3 = string(year_assessment[_n-3]) + "-" + string(year_assessment) + bysort countrycode nla_code idgrade test subject : gen spell_c4 = string(year_assessment[_n-4]) + "-" + string(year_assessment) + + reshape long spell_c, i(countrycode nla_code idgrade test subject year_assessment subject) j(lag) + ren spell_c spell + + * Tag if actual spell: + gen spell_exists=(length(spell) == 9 ) + + ********************************************** + *Preparing the data for simulations: + ********************************************** + *The data should be restructured for unique identifiers: + sort countrycode nla_code idgrade test subject year_assessment spell lag + + *Rules for cleaning the spell data: + *Bringing in the list of countries and spells for which the data is not comparable: + + merge m:1 countrycode idgrade test year_assessment spell using "${clone}\02_simulation\021_rawdata\comparability_TIMSS_PIRLS_yr.dta", assert(master match using) keep(master match) keepusing(comparable) nogen + drop if comparable == 0 + + *Generating preferred consecutive spells: + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : egen lag_min = min(lag) + *Keeping the comparable consecutive spells + keep if lag == lag_min + + *All comparable spells for TIMSS/PIRLS + assert comparable == 1 if !missing(comparable) + + *Annual change in enrollment, adjusted proficiency and proficiency + sort countrycode nla_code idgrade test subject year_assessment + bysort countrycode nla_code idgrade test subject : gen delta_adj_pct = ((adj_pct_reading_low-adj_pct_reading_low[_n-1])/(year_assessment-year_assessment[_n-1])) + bysort countrycode nla_code idgrade test subject : gen initial_adj_pct = adj_pct_reading_low[_n-1] + bysort countrycode nla_code idgrade test subject : gen final_adj_pct = adj_pct_reading_low + + *Annualized log change for decomposition + bysort countrycode nla_code idgrade test subject : gen delta_log_adj_pct = ((log(adj_pct_reading_low)-log(adj_pct_reading_low[_n-1]))/(year_assessment-year_assessment[_n-1])) + bysort countrycode nla_code idgrade test subject : gen delta_log_prof = ((log(pct_reading_low_target)-log(pct_reading_low_target[_n-1]))/(year_assessment-year_assessment[_n-1])) + bysort countrycode nla_code idgrade test subject : gen delta_log_enrollment = ((log(enrollment)-log(enrollment[_n-1]))/(year_assessment-year_assessment[_n-1])) + + bysort countrycode nla_code idgrade test subject : gen delta_prof = (pct_reading_low_target-pct_reading_low_target[_n-1])/(year_assessment-year_assessment[_n-1]) + bysort countrycode nla_code idgrade test subject : gen delta_enrollment_all = (enrollment_interpolated_all-enrollment_interpolated_all[_n-1])/(year_assessment-year_assessment[_n-1]) + + *drop observatoins specified by [if] [in]. + if `"`ifspell'"'!="" { + di `"`ifspell'"' + keep `ifspell' + } + + + /* weights */ + + if ("`weight'" == "") { + cap tempname wtg + cap gen `wtg' = 1 + local weight2 "" + loc weight "fw" + loc exp "=`wtg'" + } + + + * Generating deltas in terms of reduction of gap to the frontier + gen gap_to_frontier = 100-adj_pct_reading_low + bysort countrycode nla_code idgrade test subject : gen red_gap_frontier = -1*(gap_to_frontier-gap_to_frontier[_n-1])/(year_assessment-year_assessment[_n-1]) + bysort countrycode nla_code idgrade test subject : gen pct_red_gap = (red_gap_frontier/gap_to_frontier[_n-1]) + gen pct_red_gap_100 = pct_red_gap*100 + + + *Following threshold IIB specification as the baseline file to be used will be threshold IIB. + *Not using spell data for SACMEQ 2007 - SACMEQ 2013: + *replace delta_adj_pct = . if test == "SACMEQ" & year == 2013 + *replace pct_red_gap_100 = . if test == "SACMEQ" & year == 2013 + + * Generating categories of countries + gen catinitial = . + foreach var in 25 50 75 100 { + replace catinitial= `var' if initial_adj_pct <= `var' & catinitial== . + } + + gen initial_learning_poverty = 100-initial_adj_pct + + + **************************************************************** + * Identify only selected spells (n=71) + + keep if test != "no assessment" & test != "EGRA" & delta_adj_pct != . & delta_adj_pct > -2 & delta_adj_pct < 4 & test != "PASEC" & year_assessment>2000 & lendingtype!="LNX" & test!="NLA" + bysort countrycode : gen tot = _N + gen wtg = 1/tot + + preserve + + keep countrycode countryname region incomelevel idgrade test year_assessment spell adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment + + keep if !missing(delta_adj_pct) + + gen frac_proficiency=100*delta_log_prof/(delta_log_prof+delta_log_enrollment) + gen frac_enrollment_all=100*delta_log_enrollment/(delta_log_prof+delta_log_enrollment) + + label var adj_pct_reading_low "Adjusted Proficiency" + label var pct_reading_low_target "Proficiency" + label var enrollment_interpolated_all "Enrollment" + label var delta_adj_pct "Annualized Change in Adjusted Proficiency" + label var delta_prof "Annualized Change in Proficiency" + label var delta_enrollment_all "Annualized Change in Enrollment" + + label var delta_log_adj_pct "Log Annualized Change in Adjusted Proficiency" + label var delta_log_prof "Log Annualized Change in Proficiency" + label var delta_log_enrollment "Log Annualized Change in Enrollment" + label var frac_enrollment_all "Percentage of Log Annualized Change in Adjusted Proficiency due to Enrollment" + label var frac_proficiency "Percentage of Log Annualized Change in Adjusted Proficiency due to Proficiency" + + if "`globalweight'"=="region" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8_spells_country_`runname'.xlsx", firstrow(varl) replace + } + + restore + + tempfile temp2 + + preserve + collapse adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment [aw=wtg] + gen `globalweight'="Overall" + save `temp2' + restore + + collapse adj_pct_reading_low pct_reading_low_target enrollment_interpolated_all delta_adj_pct delta_prof delta_enrollment_all delta_log_adj_pct delta_log_prof delta_log_enrollment [aw=wtg], by(`globalweight') + + append using `temp2' + + replace pct_reading_low_target=pct_reading_low_target + replace delta_prof=delta_prof + + keep if !missing(delta_adj_pct) + + + gen frac_proficiency=100*delta_log_prof/(delta_log_prof+delta_log_enrollment) + gen frac_enrollment_all=100*delta_log_enrollment/(delta_log_prof+delta_log_enrollment) + + label var `globalweight' "Group" + label var adj_pct_reading_low "Adjusted Proficiency" + label var pct_reading_low_target "Proficiency" + label var enrollment_interpolated_all "Enrollment" + label var delta_adj_pct "Annualized Change in Adjusted Proficiency" + label var delta_prof "Annualized Change in Proficiency" + label var delta_enrollment_all "Annualized Change in Enrollment" + + label var delta_log_adj_pct "Log Annualized Change in Adjusted Proficiency" + label var delta_log_prof "Log Annualized Change in Proficiency" + label var delta_log_enrollment "Log Annualized Change in Enrollment" + label var frac_enrollment_all "Percentage of Log Annualized Change in Adjusted Proficiency due to Enrollment" + label var frac_proficiency "Percentage of Log Annualized Change in Adjusted Proficiency due to Proficiency" + + if "`globalweight'"=="region" { + save "${clone}/03_export_tables/033_outputs/tabs_8_spells_`runname'.dta", replace + export excel using "${clone}/03_export_tables/033_outputs/tabs_8_spells_`runname'.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="lendingtype" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8b_spells_`runname'.xlsx", firstrow(varl) replace + } + + if "`globalweight'"=="incomelevel" { + export excel using "${clone}/03_export_tables/033_outputs/tabs_8c_spells_`runname'.xlsx", firstrow(varl) replace + } + + } + +end diff --git a/03_export_tables/032_programs/old_version/0325_spending_and_other_figures_annex.do b/03_export_tables/032_programs/old_version/0325_spending_and_other_figures_annex.do new file mode 100644 index 0000000..cb914a5 --- /dev/null +++ b/03_export_tables/032_programs/old_version/0325_spending_and_other_figures_annex.do @@ -0,0 +1,278 @@ +*==============================================================================* +* 0325 SUBTASK: SPENDING AND OTHER FIGURES FOR ANNEX +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + **************************************************** + **** Table on Expenditure and Learning Poverty + **************************************************** + + import delimited "${clone}/08_2pager/081_data/cleaned_expenditure_data.csv", varnames(1) clear + + sort countrycode year + bysort countrycode : keep if _N==_n + + sort countrycode + + merge countrycode using "${clone}/01_data/013_outputs/preference`chosen_preference'.dta" + + tab _merge if adj_nonprof_all != . + + + + **************************************************** + **** Table on Expenditure and Learning Poverty + **************************************************** + + tempfile tmp1 tmp2 tmp3 tmp4 + + *** spending + + use "${clone}/01_data/011_rawdata/primary_expenditure.dta", clear + + drop if exp_pri_perchild_total == . + sort countrycode year + bysort countrycode : keep if _N==_n + + rename year year_spending + + sort countrycode + + save `tmp1', replace + + + *** gdp + + use "${clone}/01_data/011_rawdata/poverty_gdp_indicators.dta", clear + + drop if ny_gdp_pcap_cd == . + sort countrycode year + bysort countrycode : keep if _N==_n + + rename year year_gdp + + keep ny_gdp_pcap_cd countrycode year_gdp + + sort countrycode + + save `tmp2', replace + + ** poverty + + use "${clone}/01_data/011_rawdata/poverty_gdp_indicators.dta", clear + + drop if si_pov_dday == . + sort countrycode year + bysort countrycode : keep if _N==_n + + rename year year_poverty + + keep si_pov_dday si_pov_lmic si_pov_umic countrycode year_poverty + + sort countrycode + + save `tmp3', replace + + ** HCI + + use "${clone}/01_data/011_rawdata/hci_indicators.dta", clear + + sort countrycode + + save `tmp4', replace + + + ** Preference + + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", clear + sort countrycode + + merge countrycode using `tmp1' + rename _merge merge_spending + tab merge_spending if adj_nonprof_all != . + sort countrycode + + merge countrycode using `tmp2' + rename _merge merge_gdp + tab merge_gdp if adj_nonprof_all != . + sort countrycode + + merge countrycode using `tmp3' + rename _merge merge_poverty + tab merge_poverty if adj_nonprof_all != . + sort countrycode + + merge countrycode using `tmp4' + rename _merge merge_hci + tab merge_hci if adj_nonprof_all != . + + **************************************************************** + + gen lp = adj_nonprof_all + gen exp = ny_gdp_pcap_cd + gen gdp = exp_pri_perchild_total + gen dday = si_pov_dday + gen lays = LAYS_mf + gen hci = HCI_mf + + gen ln_lp = ln( adj_nonprof_all) + gen ln_gdp = ln(ny_gdp_pcap_cd) + gen ln_exp = ln( exp_pri_perchild_total) + gen ln_dday = ln(si_pov_dday) + gen ln_lays = ln(LAYS_mf) + gen ln_hci = ln(HCI_mf) + + tab region, gen(region) + + label var ln_lp "Log(Learning Poverty)" + label var ln_exp "log(Primary Spending per Child)" + label var ln_gdp "log(GDP per capita PPP 2011)" + label var ln_dday "log(Poverty [DDay])" + label var ln_lays "log(LAYS)" + label var ln_hci "log(HCI)" + + label var lp "Learning Poverty" + label var exp "Primary Spending per Child" + label var gdp "GDP per capita PPP 2011" + label var dday "Poverty [DDay]" + label var lays "LAYS" + label var hci "HCI" + + **************************************************************** + + foreach var in ln_gdp ln_exp ln_dday ln_lays ln_hci { + regress `var' ln_lp + est store `var' + } + + estout ln*, cells(b(star fmt(%9.3f)) se(par)) /// + stats(r2_a N, fmt(%9.3f %9.0g) labels(R-squared)) /// + legend label collabels(none) varlabels(_cons Constant) + + + foreach var in gdp exp dday lays hci { + regress `var' ln_lp + est store o_`var' + } + + estout o_*, cells(b(star fmt(%9.3f)) se(par)) /// + stats(r2_a N, fmt(%9.3f %9.0g) labels(R-squared)) /// + legend label collabels(none) varlabels(_cons Constant) + + **************************************************************** + + + export delimited using "${clone}/03_export_tables/033_outputs/viz_latest.csv", replace + + + ********************* NEED TO MOVE THIS TO ANALYSIS FOR THE PAPER ********************************** + + use "${clone}/01_data/013_outputs/rawfull.dta" , clear + + gen year = year_assessment + + sort countrycode year + + merge countrycode year using "${clone}/01_data/011_rawdata/primary_expenditure.dta" + + local enrollment "validated" + + * Check ENROLLMENT() option + * Must be one of enrollment methods supported + * Assume "validated" as default if not specified + if "`enrollment'" == "validated" | "`enrollment'" == "" { + drop enrollment_interpolated* + rename enrollment_validated_* enrollment_* + } + else if "`enrollment'" == "interpolated" { + drop enrollment_validated* + rename enrollment_interpolated_* enrollment_* + } + else { + noi dis as error `"ENROLLMENT must be either "interpolated" or "validated". Try again."' + break + } + + * Adjusts non-proficiency by out-of school + foreach subgroup in all fe ma { + gen adj_nonprof_`subgroup' = 100 * ( 1 - (enrollment_`subgroup'/100) * (1 - nonprof_`subgroup'/100)) + label var adj_nonprof_`subgroup' "Learning Poverty (adjusted non-proficiency, `subgroup')" + } + + tab _merge if adj_nonprof_all != . + + **************************************************************** + gen lp = adj_nonprof_all + gen c1_lp = 100-lp + gen ln_1_lp = ln(c1_lp) + gen ln_lp = ln( adj_nonprof_all) + gen ln_exp = ln( exp_pri_perchild_total) + tab region, gen(region) + tab incomelevel, gen(income) + tab lendingtype, gen(lendingtype) + + label var ln_1_lp "Log(100-Learning Poverty)" + label var ln_lp "Log(Learning Poverty)" + label var ln_exp "log(Primary Spending per Child)" + + **************************************************************** + + regress ln_lp ln_exp + + keep if e(sample) + + bysort countrycode: gen wtg = (1/_N)*10 + + qreg ln_lp ln_exp [fw=int(wtg)], vce(robust) + grqreg [fw=int(wtg)], ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_1_lp ln_exp [fw=int(wtg)], vce(robust) + grqreg [fw=int(wtg)], ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + // to JP: I commented out those lines that were breaking (pw not allowed in grqreg) + //qreg ln_1_lp ln_exp [pw=wtg], vce(robust) + //grqreg [pw=wtg], ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_1_lp ln_exp, vce(robust) + grqreg , ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_1_lp ln_exp, vce(robust) + grqreg, ci ols olsci qmin(.2) qmax(.8) qstep(.05) + + regress ln_lp ln_exp region2 region4 region6 region7 region9 + + qreg ln_lp ln_exp, vce(robust) + grqreg, ci ols olsci qmin(.2) qmax(.8) qstep(.05) + + qreg ln_lp ln_exp region2 region4 region6 region7 region9 , vce(robust) + grqreg ln_exp , ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_lp ln_exp income1 income2 income3, vce(robust) + grqreg ln_exp , ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_lp ln_exp lendingtype1 lendingtype2 lendingtype3, vce(robust) + grqreg ln_exp , ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + + qreg ln_lp ln_exp region2 region4 region6 region7 region9 [fw=int(wtg)], vce(robust) + grqreg ln_exp [fw=int(wtg)], ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + + qreg adj_nonprof_all exp_pri_perchild_total, vce(robust) + grqreg, ci ols olsci qmin(.2) qmax(.8) qstep(.1) mfx(eyex ) + + // to JP: I commented out those lines that were breaking (pw not allowed in grqreg) + //qreg ln_lp ln_exp [pw=wtg], vce(robust) + //grqreg [pweight=wtg], ci ols olsci qmin(.05) qmax(.95) qstep(.05) + + qreg ln_lp ln_exp, vce(robust) + grqreg, ci ols olsci qmin(.2) qmax(.8) qstep(.05) + + noi disp as res _newline "Finished exporting Spending and other figures for annex." + +} diff --git a/03_export_tables/032_programs/old_version/0326_datagaps_annex.do b/03_export_tables/032_programs/old_version/0326_datagaps_annex.do new file mode 100644 index 0000000..577a174 --- /dev/null +++ b/03_export_tables/032_programs/old_version/0326_datagaps_annex.do @@ -0,0 +1,153 @@ +*==============================================================================* +* 0326 SUBTASK: DATAGAPS ANNEX +*==============================================================================* +qui { + + * Change here only if wanting to use a different preference + * than what is being passed in the global in 032_run + * But don't commit any change here (only commit in global 032_run) + local chosen_preference = $chosen_preference + + use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace + + drop if countryname == "CUB" + + gen byte checkme = year_assessment>=2011 & !missing( adj_nonprof_all ) & lendingtype!="LNX" + tab checkme + + tostring year_assessment, gen(year_str) + gen flag = adj_nonprof_all !=. + gen client = lendingtype != "LNX" + gen year = year_str == "" + gen str = string(flag)+string(client)+string(year) + tab str + tab str, m + tab checkme + tab region checkme + + gen checkme2 = . + replace checkme2 = 1 if checkme2 == . & checkme == 1 + replace checkme2 = 2 if checkme2 == . & checkme == 0 & !missing( adj_nonprof_all ) & year_assessment>=2011 + replace checkme2 = 3 if checkme2 == . & checkme == 0 & !missing( adj_nonprof_all ) + replace checkme2 = 4 if checkme2 == . & checkme == 0 + + label define checkme2 1 "Part2" 2 "Part1" 3 "OldData" 4 "NoData" + label values checkme2 checkme2 + + tab region checkme2 + + sum + + *browse countryname adj_nonprof_all enrollment_all nonprof_all test year_assessment if adj_nonprof_all != . & year_assessment >= 2011 + + kdensity year_assessment if year_str != "", title("") ytitle("Distribution of End of Primary Assessments") + + //mdensity year_assessment if year_str != "", title("") ytitle("Distribution of End of Primary Assessments") by(checkme) sort + + tab year_assessment if year_str != "" + + tab year_assessment checkme if year_str != "", col nof + di 38/54 + di (8+12+2)/62 + + tab year_str checkme [aw = population_2015_all ], col nof m + + tab year_assessment if year_str != "" + + tab idgrade if idgrade != -999 + tab idgrade checkme if idgrade != -999 + tab idgrade checkme if idgrade != -999 & year_assessment >= 2011 + + tab idgrade checkme if idgrade != -999 & year_assessment >= 2011 [aw = population_2015_all ], col nof + + + tab region checkme + + tab region checkme2 + tab region checkme2 [aw = population_2015_all ], cell nof + tab region checkme2 [aw = population_2015_all ], row nof + tab region checkme2 [aw = population_2015_all ], col nof + + gen region2 = region if checkme2 != 4 + tab region2 incomelevel , m col nof + tab region2 incomelevel [aw = population_2015_all ], m col nof + + tab lendingtypename checkme2 [aw = population_2015_all ], m col nof + tab lendingtypename checkme2 [aw = population_2015_all ], m row nof + + tab lendingtypename checkme2 [aw = population_2015_all ] if lendingtype != "LNX", m row nof + + noi disp as res _newline "Finished exporting datagaps for annex." + +} + +******************************************* + +clear + +cd "${clone}\08_2pager\081_data\" + + +local chosen_preference = $chosen_preference +use "${clone}/01_data/013_outputs/preference`chosen_preference'.dta", replace +keep countrycode *pop* +sort countrycode +sum +save tmpk, replace + +import delimited "${clone}\08_2pager\081_data\cleaned_assessment_metadata.csv", clear +sort countrycode +save tmp1, replace + +import delimited "${clone}\08_2pager\081_data\cleaned_learning_poverty_gender_split.csv", clear +sort countrycode +save tmp2, replace + +merge countrycode using tmp1 + +gen datastatus = . +replace datastatus = 1 if !missing(learning_poverty_rawlatest) & year >= 2011 & missing(datastatus) +replace datastatus = 2 if countrycode == "COD" +replace datastatus = 3 if !missing(learning_poverty_rawlatest) & year < 2011 & missing(datastatus) +replace datastatus = 4 if missing(learning_poverty_rawlatest) & uis_412b_equal_1 == 1 & missing(datastatus) +replace datastatus = 3 if countrycode == "PHL" +replace datastatus = 5 if countrycode == "CUB" +replace datastatus = 5 if countrycode == "GRL" +replace datastatus = 6 if missing(datastatus) + +label define datastatus 1 "LP>=2011" 2 "Exception" 3 "LP<2011" 4 "NLA below bar" 5 "Out" 6 "NoData" +label values datastatus datastatus + +gen datastatus2 = datastatus +recode datastatus2 2=3 +recode datastatus2 5=. + +label values datastatus2 datastatus + +_countrymetadata, match(countrycode) + +drop _merge +sort countrycode +merge countrycode using tmpk +drop _merge + + +tab datastatus2 +tab datastatus2 [fw=anchor_population] +tab datastatus2 region [fw=anchor_population], col nof + +export delimited "${clone}/03_export_tables/033_outputs/data_coverage_learning_poverty.csv", replace + +******************************************* + +tab datastatus + +tab region datastatus + +tab datastatus if lendingtype != "LNX" +tab region datastatus if lendingtype != "LNX" + + +order region countrycode countryname incomelevelname lendingtypename lendingtypename idgrade year test datastatus uis_412b_equal_1 +browse region countrycode countryname incomelevelname lendingtypename lendingtypename idgrade year test datastatus uis_412b_equal_1 + \ No newline at end of file diff --git a/03_export_tables/032_programs/0321_put_to_excel.do b/03_export_tables/032_programs/old_version/032x_put_to_excel.do similarity index 100% rename from 03_export_tables/032_programs/0321_put_to_excel.do rename to 03_export_tables/032_programs/old_version/032x_put_to_excel.do diff --git a/03_export_tables/033_outputs/placeholderfile.md b/03_export_tables/033_outputs/individual_tables/placeholderfile.md similarity index 100% rename from 03_export_tables/033_outputs/placeholderfile.md rename to 03_export_tables/033_outputs/individual_tables/placeholderfile.md diff --git a/04_repo_update/041_rawdata/national_assessment_proficiency.md b/04_repo_update/041_rawdata/national_assessment_proficiency.md index 44b4a8f..e918a3e 100644 --- a/04_repo_update/041_rawdata/national_assessment_proficiency.md +++ b/04_repo_update/041_rawdata/national_assessment_proficiency.md @@ -1,24 +1,24 @@ -|wbcode| year | idgrade | pct_reading_low_target_wb_v | cutoff | source | justification| status| -|---|---|---|---|---|---|---|---| -|AFG_2|2013|5|13|Level 10|2013 Grade 6 National Assessment (MTEG)| UIS (Tuesday, September 10, 2019 12:00 PM)| Accepted| -|CHN|2016|4|81.8|Moderate level|Chinese National Compulsory Education Quality Assessment; Grade 4|UIS (National Learning Assessment (NLA): Chinese National Compulsory Education Quality Assessment; Grade 4 (+1); Minimum proficiency level: Moderate). | Accepted| -|BGD_3|2015|5|45|Proficient|National Student Assessment (NSA); Grade 5 Bangla|National Learning Assessment (NLA): National Student Assessment (NSA); Grade 5; Minimum proficiency level: Proficient; Domain: Language; UIS (Thursday, May 30, 2019 4:28 PM)| Accepted| -|BGD_3|2017|5|44|Proficient level|National Student Assessment (NSA); Grade 5 Bangla|National Learning Assessment (NLA): National Student Assessment (NSA); Grade 5; Minimum proficiency level: Proficient; Domain: Language; UIS (Thursday, May 30, 2019 4:28 PM)| Accepted| -|IND_4|2017|5|46.3|Intermediate level| 2017 NAS grade 5 language; Class V|National Learning Assessment (NLA): National Achievement Survey - Class V; Grade 5; Minimum proficiency level: Intermediate; UIS (Wednesday, May 29, 2019 10:53 AM)| Accepted| -|UGA_1|2014|6|38.3|Score|National Assessment of Progress in Education (NAPE), Grade 6 English test |email from Marguerite: The National Assessment of Progress in Education (NAPE) is Uganda’s main national assessment program. In 2014, it assessed the English and Math achievement levels of a nationally representative sample of students in Grade 3 and 6, covering both government and private schools. Achievement was measured in relation to the objectives of the national curriculum. The Grade 6 English test covers reading, writing and grammar. 40% of the test is devoted to reading, 40% to writing, and 20% to grammar. | Rejected| -|UGA_2|2014|6|18.9|Advanced level||UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|PAK_3|2014|4|35|Proficient level|2014 Grade 4 National Achievement Test (English)| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|LKA_3|2015|4|86| Score Above 40 mark|2015 National Grade 4 Language Test| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|VNM_1|2011|5|98.92|Acceptable Level|National Learning Assessment|National Learning Assessment (NLA): National Assessment; Grade 5; Minimum proficiency level: Acceptable| Accepted| -|ETH_1|2015|4|57.32|Basic Level| Grade 4 National Assessment|Marguerite: fairly comfortable taking students scoring Basic or above as corresponding to our global definition of “minimum proficiency”, which would mean that 57.32% of Grade 4 students in Ethiopia reach the “minimum proficiency” level in reading. If we instead took Proficient or above as the cut off, then only 11.28% of students would reach the required level, which seems too harsh. If we don’t like either of those numbers, then the other option would be to go with the % of students reaching 50% or more correct on the test, which would give us 44.9% of students reaching “minimum proficiency”. | Rejected| -|ETH_2|2015|4|44.9|Score| Grade 4 National Assessment|Marguerite: fairly comfortable taking students scoring Basic or above as corresponding to our global definition of “minimum proficiency”, which would mean that 57.32% of Grade 4 students in Ethiopia reach the “minimum proficiency” level in reading. If we instead took Proficient or above as the cut off, then only 11.28% of students would reach the required level, which seems too harsh. If we don’t like either of those numbers, then the other option would be to go with the % of students reaching 50% or more correct on the test, which would give us 44.9% of students reaching “minimum proficiency”. | rejected| -|ETH_3|2015|4|11.28|Proficient level| Grade 4 National Assessment| UIS (Tuesday, September 10, 2019 12:00 PM) |Accepted| -|COD|2011|5|37.981|Level 4|PASEC 2010 - level 4|| Accepted| -|KHM_1|2013|6|50.2|Proficient level| National Learning Assessment (NLA): National Assessment; Grade 6; Minimum proficiency level: Level 3: Proficient | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|MYS_1|2017|6|88.3|Level D| National Learning Assessment (NLA): Mid year exam 2017; Grade 6; Minimum proficiency level: D | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|ALB_1|2016|5|95.1|Level 1 (6-11 points)| Learning Assessment (NLA): National Assessment; Grade 5; Minimum proficiency level: Level 1 (6-11 points)| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|KGZ_1|2014|4|36.2|Basic level|National Learning Assessment (NLA): National Sample-Based Assessment (NSBA); Grade 4; Minimum proficiency level: Basic level; Domain: Language| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|GHA_1|2016|6|72|Minimum Competency| National Learning Assessment (NLA): National Education Assessment (NEA); Grade 6; Minimum proficiency level: Minimum Competency| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|MDG_1|2015|5|4.2|Level 4| PASEC 2015 - level 4 | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| -|MLI_1|2012|5|13.4|Level 4| PASEC 2012 - level 4 | National Report sent by Country Team | Accepted| -|HND_1|2013|6|30.6|Level III (SERCE scale)| LLECE 2013 - level III | GLAD from CLO | Accepted|| +| wbcode | year | idgrade | pct_reading_low_target_wb_v | pct_read_low_fe | pct_read_low_ma | cutoff | source | justification | status | +|---|---|---|---|---|---|---|---|---|---| +|AFG_2|2013|5|13|||Level 10|2013 Grade 6 National Assessment (MTEG)| UIS (Tuesday, September 10, 2019 12:00 PM)| Accepted| +|CHN|2016|4|81.8|||Moderate level|Chinese National Compulsory Education Quality Assessment; Grade 4|UIS (National Learning Assessment (NLA): Chinese National Compulsory Education Quality Assessment; Grade 4 (+1); Minimum proficiency level: Moderate). | Accepted| +|BGD_3|2015|5|45|||Proficient|National Student Assessment (NSA); Grade 5 Bangla|National Learning Assessment (NLA): National Student Assessment (NSA); Grade 5; Minimum proficiency level: Proficient; Domain: Language; UIS (Thursday, May 30, 2019 4:28 PM)| Accepted| +|BGD_3|2017|5|44|||Proficient level|National Student Assessment (NSA); Grade 5 Bangla|National Learning Assessment (NLA): National Student Assessment (NSA); Grade 5; Minimum proficiency level: Proficient; Domain: Language; UIS (Thursday, May 30, 2019 4:28 PM)| Accepted| +|IND_4|2017|5|46.3|47|45|Intermediate level| 2017 NAS grade 5 language; Class V|National Learning Assessment (NLA): National Achievement Survey - Class V; Grade 5; Minimum proficiency level: Intermediate; UIS (Wednesday, May 29, 2019 10:53 AM)| Accepted| +|UGA_1|2014|6|38.3|||Score|National Assessment of Progress in Education (NAPE), Grade 6 English test |email from Marguerite: The National Assessment of Progress in Education (NAPE) is Uganda’s main national assessment program. In 2014, it assessed the English and Math achievement levels of a nationally representative sample of students in Grade 3 and 6, covering both government and private schools. Achievement was measured in relation to the objectives of the national curriculum. The Grade 6 English test covers reading, writing and grammar. 40% of the test is devoted to reading, 40% to writing, and 20% to grammar. | Rejected| +|UGA_2|2014|6|18.9|||Advanced level||UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|PAK_3|2014|4|35|||Proficient level|2014 Grade 4 National Achievement Test (English)| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|LKA_3|2015|4|86||| Score Above 40 mark|2015 National Grade 4 Language Test| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|VNM_1|2011|5|98.92|||Acceptable Level|National Learning Assessment|National Learning Assessment (NLA): National Assessment; Grade 5; Minimum proficiency level: Acceptable| Accepted| +|ETH_1|2015|4|57.32|||Basic Level| Grade 4 National Assessment|Marguerite: fairly comfortable taking students scoring Basic or above as corresponding to our global definition of “minimum proficiency”, which would mean that 57.32% of Grade 4 students in Ethiopia reach the “minimum proficiency” level in reading. If we instead took Proficient or above as the cut off, then only 11.28% of students would reach the required level, which seems too harsh. If we don’t like either of those numbers, then the other option would be to go with the % of students reaching 50% or more correct on the test, which would give us 44.9% of students reaching “minimum proficiency”. | Rejected| +|ETH_2|2015|4|44.9|||Score| Grade 4 National Assessment|Marguerite: fairly comfortable taking students scoring Basic or above as corresponding to our global definition of “minimum proficiency”, which would mean that 57.32% of Grade 4 students in Ethiopia reach the “minimum proficiency” level in reading. If we instead took Proficient or above as the cut off, then only 11.28% of students would reach the required level, which seems too harsh. If we don’t like either of those numbers, then the other option would be to go with the % of students reaching 50% or more correct on the test, which would give us 44.9% of students reaching “minimum proficiency”. | rejected| +|ETH_3|2015|4|11.28|||Proficient level| Grade 4 National Assessment| UIS (Tuesday, September 10, 2019 12:00 PM) |Accepted| +|COD|2011|5|37.981|||Level 4|PASEC 2010 - level 4|| Accepted| +|KHM_1|2013|6|50.2|||Proficient level| National Learning Assessment (NLA): National Assessment; Grade 6; Minimum proficiency level: Level 3: Proficient | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|MYS_1|2017|6|88.3|||Level D| National Learning Assessment (NLA): Mid year exam 2017; Grade 6; Minimum proficiency level: D | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|ALB_1|2016|5|95.1|||Level 1 (6-11 points)| Learning Assessment (NLA): National Assessment; Grade 5; Minimum proficiency level: Level 1 (6-11 points)| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|KGZ_1|2014|4|36.2|||Basic level|National Learning Assessment (NLA): National Sample-Based Assessment (NSBA); Grade 4; Minimum proficiency level: Basic level; Domain: Language| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|GHA_1|2016|6|72|||Minimum Competency| National Learning Assessment (NLA): National Education Assessment (NEA); Grade 6; Minimum proficiency level: Minimum Competency| UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|MDG_1|2015|5|4.2|||Level 4| PASEC 2015 - level 4 | UIS (Tuesday, September 10, 2019 12:00 PM) | Accepted| +|MLI_1|2012|5|14.26|15.45|13.14|Level 4| PASEC 2012 - level 4 | National Report sent by Country Team | Accepted| +|HND_1|2013|6|30.6|||Level III (SERCE scale)| LLECE 2013 - level III | GLAD from CLO | Accepted|| diff --git a/04_repo_update/042_programs/0421_country_metadata_csv.do b/04_repo_update/042_programs/0421_country_metadata_csv.do index d4fc221..629aafb 100644 --- a/04_repo_update/042_programs/0421_country_metadata_csv.do +++ b/04_repo_update/042_programs/0421_country_metadata_csv.do @@ -1,101 +1,111 @@ -*==============================================================================* -* 0421 SUBTASK: PREPARE METADATA CSV TO UPDATE THE 011_RAWDATA IN REPO FOLDER -*==============================================================================* -qui { - - * Locals to point to User-Specific locations - * - where the CMU excel is found - local cmu_csv "${clone}/04_repo_update/041_rawdata/cmu_ctry.csv" - * - where to save the resulting CSV with/without labels - local csv_with_labels "${clone}/04_repo_update/043_outputs/country_metadata.csv" - - - /***************************************** - CMU and Regional Focal Point info - from Excel file in ONE DRIVE - *****************************************/ - noi di _newline "{phang}Getting CMU information from csv{p_end}" - - * Import the Excel file with the CMU information - import delimited "`cmu_csv'", varnames(1) clear - - * Keep only the metadata that is not available in wbopendata - keep countrycode cmu - - * Beautify: variable names and variable labels - label var cmu "WB Country Management Unit" - - * Store it as a tempfile - tempfile cmu_dta - save `cmu_dta' - - - /***************************************** - Region, IncomeLevel, other metadata - from WB Opendata - *****************************************/ - noi di "{phang}Getting other metadata from wbopendata{p_end}" - - * Get metadata from wbopendata - doesn't matter the indicator or year - wbopendata, indicator(SP.POP.TOTL) year(2000) clear long nometadata full - - * Drop all aggregates (non-countries) - drop if region == "NA" - - * Keep only the metadata (drop indicator and year) - keep country* *region* incomelevel* lendingtype* - - - - /********* Combine both sources ***********/ - - * With the wbopendata one in memory, bring the cmu data - merge 1:1 countrycode using `cmu_dta', keep(match) nogen - - sort countrycode - - * Double check we have exactly the 217 countries - assert `c(N)' == 217 - - * Could already export as csv the country metadata (but the variable labels would be loss) - - - /********* PRESERVING THE VARIBLE LABELS ***********/ - - * Variable labels are saved in the last observation before exporting to csv - * Note that this is a good solution because all vars are strings anyway - - * Create extra observation in the end, to store the labels - local label_obs = _N +1 - set obs `label_obs' - - * Loop through all vars, storing their label in the last observation - foreach thisvar of varlist _all { - replace `thisvar' ="`: variable label `thisvar''" if _n == `label_obs' - } - - * Export csv that contains the labels - export delimited using "`csv_with_labels'", replace - - noi di "{phang}Saved `csv_with_labels'{p_end}" - -} - -exit - - -/* EXAMPLE OF HOW TO REVERT BACK THE VARIABLE LABELS WHEN IMPORTING THE CSV - -* Open the csv with both data and labels (in the last observation) -import delimited using "`csv_with_labels'", varnames(1) case(preserve) clear - -* Loop through all variables, labelling them per last observation -foreach thisvar of varlist _all { - label var `thisvar' "`= `thisvar'[_N]'" -} - -* Drop last observation, which only had the var labels -drop if _n == _N - -* Compress (remember that the string vars got larger because of labels) -compress +*==============================================================================* +* 0421 SUBTASK: PREPARE METADATA CSV TO UPDATE THE 011_RAWDATA IN REPO FOLDER +*==============================================================================* +qui { + + * Locals to point to User-Specific locations + * - where the CMU excel is found + local cmu_csv "${clone}/04_repo_update/041_rawdata/cmu_ctry.csv" + * - where to save the resulting CSV with/without labels + local csv_with_labels "${clone}/04_repo_update/043_outputs/country_metadata.csv" + + + /***************************************** + CMU and Regional Focal Point info + from Excel file in ONE DRIVE + *****************************************/ + noi di _newline "{phang}Getting CMU information from csv{p_end}" + + * Import the Excel file with the CMU information + import delimited "`cmu_csv'", varnames(1) clear + + * Keep only the metadata that is not available in wbopendata + keep countrycode cmu + + * Beautify: variable names and variable labels + label var cmu "WB Country Management Unit" + + * Store it as a tempfile + tempfile cmu_dta + save `cmu_dta' + + + /***************************************** + Region, IncomeLevel, other metadata + from WB Opendata + *****************************************/ + noi di "{phang}Getting other metadata from wbopendata{p_end}" + + * Ensures the metadata is up to date + cap wbopendata, update all + + * Get metadata from wbopendata - doesn't matter the indicator or year + wbopendata, indicator(SP.POP.TOTL) latest clear long nometadata full + + preserve + * Keep only the aggregates (non-countries) + keep if region == "NA" + keep countrycode countryname + export delimited using "${clone}/04_repo_update/043_outputs/region_metadata.csv", replace + restore + + * Drop all aggregates (non-countries) + drop if region == "NA" + + * Keep only the metadata (drop indicator and year) + keep country* *region* incomelevel* lendingtype* + + + + /********* Combine both sources ***********/ + + * With the wbopendata one in memory, bring the cmu data + merge 1:1 countrycode using `cmu_dta', keep(match) nogen + + sort countrycode + + * Double check we have exactly the 217 countries + assert `c(N)' == 217 + + * Could already export as csv the country metadata (but the variable labels would be loss) + + + /********* PRESERVING THE VARIBLE LABELS ***********/ + + * Variable labels are saved in the last observation before exporting to csv + * Note that this is a good solution because all vars are strings anyway + + * Create extra observation in the end, to store the labels + local label_obs = _N +1 + set obs `label_obs' + + * Loop through all vars, storing their label in the last observation + foreach thisvar of varlist _all { + replace `thisvar' ="`: variable label `thisvar''" if _n == `label_obs' + } + + * Export csv that contains the labels + export delimited using "`csv_with_labels'", replace + + noi di "{phang}Saved `csv_with_labels'{p_end}" + +} + +exit + + +/* EXAMPLE OF HOW TO REVERT BACK THE VARIABLE LABELS WHEN IMPORTING THE CSV + +* Open the csv with both data and labels (in the last observation) +import delimited using "`csv_with_labels'", varnames(1) case(preserve) clear + +* Loop through all variables, labelling them per last observation +foreach thisvar of varlist _all { + label var `thisvar' "`= `thisvar'[_N]'" +} + +* Drop last observation, which only had the var labels +drop if _n == _N + +* Compress (remember that the string vars got larger because of labels) +compress diff --git a/04_repo_update/042_programs/0424_proficiency_data.do b/04_repo_update/042_programs/0424_proficiency_data.do index 45c61f8..df81b9c 100644 --- a/04_repo_update/042_programs/0424_proficiency_data.do +++ b/04_repo_update/042_programs/0424_proficiency_data.do @@ -1,187 +1,185 @@ -*==============================================================================* -* 0424 SUBTASK: PREPARE PROFICIENCY CSVS TO UPDATE THE 011_RAWDATA IN REPO -*==============================================================================* -qui { - - /*********************************** - Proficiency from NLA md - ***********************************/ - noi di _newline "{phang}Proficiency from NLA markdown {p_end}" - - * Prepare local to create file - local clonefile "${clone}/04_repo_update/043_outputs/proficiency_from_NLA_md.csv" - - * Import the rawdata in the repo - import delimited "${clone}/04_repo_update/041_rawdata/national_assessment_proficiency.md", delimiter("|") varnames(1) clear - - * Corrections/problmes that come with the md importing - keep wbcode-status - drop if _n==1 - destring year, replace - destring idgrade, replace - destring pct_reading_low_target_wb_v, replace - - * Parsing out countrycode from NLA table (ie: BGD_1 or BGD_2 into BGD) - gen nla_code = wbcode - split wbcode, p("_") - gen countrycode = wbcode1 - rename source nla_source - replace nla_source = nla_source + ";" + cutoff - - * From percentage of proficient students to below proficiency - gen nonprof_all = 100 - pct_reading_low_target_wb_v - - * Only relevant variables kept - keep countrycode year idgrade nonprof_all nla_code - - * Copy the file from network to csv folder - export delimited using "`clonefile'", replace - noi di "{phang}Saved `clonefile'{p_end}" - - - /*********************************** - Proficiency from L4A RAWFULL - ***********************************/ - noi di _newline "{phang}Proficiency from L4A rawfull (quick fix){p_end}" - - * Prepare local to create file - local clonefile "${clone}/04_repo_update/043_outputs/proficiency_no_microdata.csv" - - * This section: on-the-fly attempt to enrich rawfull in LP based on rawfull in L4A - * by appending any missing proficiency data points - * TODO: revise, make it unnecessary, less crappy!!! PLACEHOLDER!!! - - * Start with rawfull in MY L4A clone **** THIS IS WHERE THE DANGER LIVES - cap use "C:\Users\WB552057\Documents\Github\Learning4all\01_data\013_outputs\rawfull.dta", clear - - * If L4A could be opened, extract info from it - if _rc == 0 { - - * Keep only proficiency-related variables - local vars_to_keep "countrycode idgrade test nlacode subject threshold pct_reading_low pct_reading_low_doubl pct_reading_low_target nonprof assessment_cutoff year_assessment source_assessment" - keep `vars_to_keep' - - * Keep threshold III (only one supported in LP) and non-missing proficiency obs - keep if threshold == "III" - drop threshold - drop if test == "no assessment" - - * Drop duplicate obs, due to the L4A rawlatest being LONG on enrollment (the LP is WIDE on enrollment) - duplicates drop - - * The pct variables are very confusing... - * It seems that the only one in use is pct_reading_low_target, which is 1-nonprof - drop pct_reading* - - * In LP, we use read for reading - replace subject = "read" if subject == "reading" - - * Plus other attempts to reconcile LP and L4A - rename nlacode nla_code - replace nla_code = "N.A." if nla_code=="-99" - rename nonprof nonprof_all - rename assessment_cutoff min_proficiency_threshold - rename year_assessment year - replace source_assessment = "HAD (Harmonized Assessment Database)" - - //* Saving to compare_files in proficiency, not needed in this task - //save "${clone}/04_repo_update/043_outputs/proficiency_in_L4A_rawfull.dta", replace - - * Only keep what we don't have in GLAD/CLOs: - * - SACMEQ from 2013 (we have Excel only, no microdata) - * - PASEC before 2014 (only the 2014 was harmonized in GLAD) - * - EGRA's didnt make it to GLAD yet - keep if (test=="PASEC" & year < 2014) | (test=="SACMEQ" & year == 2013) | (test=="EGRA") - - * Export csv - export delimited using "`clonefile'", replace - noi di "{phang}Saved `clonefile'{p_end}" - } - - else { - noi di as error "{phang}Could not update `clonefile' (L4A rawfull not available){p_end}" - } - - - /*********************************** - Proficiency from GLAD_CLO - ***********************************/ - - * TODO / PLACHOLDER: only attempts to run this if which datalibweb not error - - noi di _newline "{phang}Proficiency from Country Level Outcomes of GLAD{p_end}" - - * Prepare local to create file - local clonefile "${clone}/04_repo_update/043_outputs/proficiency_from_GLAD.csv" - - * List of needed CLO for Learning4All (only those for which we have microdata) - local l4a_clos "LAC_2006_LLECE LAC_2013_LLECE SSA_2000_SACMEQ SSA_2007_SACMEQ SSA_2014_PASEC WLD_2001_PIRLS WLD_2006_PIRLS WLD_2011_PIRLS WLD_2016_PIRLS WLD_2003_TIMSS WLD_2007_TIMSS WLD_2011_TIMSS WLD_2015_TIMSS" - - * Creates an empty file where all CLOs will be appended - touch "${clone}/04_repo_update/043_outputs/l4a_country_level_outcomes.dta", replace - - * Loop through each CLO file, read metadata and append and save - foreach survey of local l4a_clos { - - * Parsing region year and assessment to query this CLO in datalibweb - gettoken region aux_token : survey, parse("_") - gettoken trash aux_token : aux_token, parse("_") - gettoken year aux_token : aux_token, parse("_") - gettoken trash test : aux_token, parse("_") - - local ending = "v01_M_wrk_A_GLAD_CLO.dta" - local surveyid = "`survey'_v01_M" - local clo_file = "`survey'_`ending'" - - * Query datalibweb - datalibweb, country(`region') year(`year') type(GLAD) surveyid(`surveyid') filename(`clo_file') - - * Append this CLO info in the destination file - append using "${clone}/04_repo_update/043_outputs/l4a_country_level_outcomes.dta" - save "${clone}/04_repo_update/043_outputs/l4a_country_level_outcomes.dta", replace - - } - - - * Simplifies and prepare-wide to be ready for L4A - *----------------------------------------------------------------------------- - - * Only valuevar needed in L4A is harmonized proficiency (hpro) - local idvars "countrycode year test idgrade subgroup" - keep `idvars' *hpro* - - * Though urban breakdown is calculated in clo, not relevant for L4A - keep if inlist(subgroup,"all", "male=0","male=1") - - * Use the same subgroup naming convention as wbopendata - replace subgroup = "_all" if subgroup == "all" - replace subgroup = "_fe" if subgroup == "male=0" - replace subgroup = "_ma" if subgroup == "male=1" - - * From harmonized_ proficiency to non-proficiency and from share to percentage - unab subjects : m_hpro_* - local subjects = subinstr("`subjects'" , "m_hpro_" , "" , .) - foreach subject of local subjects { - gen nonprof`subject' = 100 * (1 - m_hpro_`subject') - gen se_nonprof`subject' = 100 * (se_hpro_`subject') - drop m_hpro_`subject' se_hpro_`subject' n_hpro_`subject' - } - - * Prepare for bringing into LearningPoverty rawlatest: - * Reshape long on subject (read, math, science) - reshape long nonprof se_nonprof, i(countrycode test year idgrade subgroup) j(subject) string - * Reshape wide on subgroups - reshape wide nonprof se_nonprof, i(countrycode test year idgrade subject) j(subgroup) string - - * Drop observations with all nonprof values (_all, _fe, _ma) missing - missings dropobs nonprof_*, force - - * Beautify: format, order and label - order countrycode year test idgrade subject *nonprof_all *nonprof_ma *nonprof_fe - sort countrycode year test idgrade - - * Export CSV with proficiency - export delimited using "`clonefile'", replace - noi di "{phang}Saved `clonefile'{p_end}" - -} +*==============================================================================* +* 0424 SUBTASK: PREPARE PROFICIENCY CSVS TO UPDATE THE 011_RAWDATA IN REPO +*==============================================================================* +qui { + + /*********************************** + Proficiency from NLA md + ***********************************/ + noi di _newline "{phang}Proficiency from NLA markdown {p_end}" + + * Prepare local to create file + local clonefile "${clone}/04_repo_update/043_outputs/proficiency_from_NLA_md.csv" + + * Import the rawdata in the repo + import delimited "${clone}/04_repo_update/041_rawdata/national_assessment_proficiency.md", delimiter("|") varnames(1) clear + + * Corrections/problmes that come with the md importing + keep wbcode-status + drop if _n==1 + destring year, replace + destring idgrade, replace + destring pct_reading_low_target_wb_v pct_read_low_fe pct_read_low_ma, replace + + * Parsing out countrycode from NLA table (ie: BGD_1 or BGD_2 into BGD) + gen nla_code = wbcode + split wbcode, p("_") + gen countrycode = wbcode1 + rename source nla_source + replace nla_source = nla_source + ";" + cutoff + + * From percentage of proficient students to below proficiency + gen nonprof_all = 100 - pct_reading_low_target_wb_v + gen nonprof_fe = 100 - pct_read_low_fe + gen nonprof_ma = 100 - pct_read_low_ma + + * Only relevant variables kept + keep countrycode year idgrade nonprof_* nla_code + + * Copy the file from network to csv folder + export delimited using "`clonefile'", replace + noi di "{phang}Saved `clonefile'{p_end}" + + + /*********************************** + Proficiency from L4A RAWFULL + ***********************************/ + noi di _newline "{phang}Proficiency from L4A rawfull (quick fix){p_end}" + + * Prepare local to create file + local clonefile "${clone}/04_repo_update/043_outputs/proficiency_no_microdata.csv" + + * This section: on-the-fly attempt to enrich rawfull in LP based on rawfull in L4A + * by appending any missing proficiency data points + * TODO: revise, make it unnecessary, less crappy!!! PLACEHOLDER!!! + + * Start with rawfull in MY L4A clone **** THIS IS WHERE THE DANGER LIVES + cap use "C:\Users\WB552057\Documents\Github\Learning4all\01_data\013_outputs\rawfull.dta", clear + + * If L4A could be opened, extract info from it + if _rc == 0 { + + * Keep only proficiency-related variables + local vars_to_keep "countrycode idgrade test nlacode subject threshold pct_reading_low pct_reading_low_doubl pct_reading_low_target nonprof assessment_cutoff year_assessment source_assessment" + keep `vars_to_keep' + + * Keep threshold III (only one supported in LP) and non-missing proficiency obs + keep if threshold == "III" + drop threshold + drop if test == "no assessment" + + * Drop duplicate obs, due to the L4A rawlatest being LONG on enrollment (the LP is WIDE on enrollment) + duplicates drop + + * The pct variables are very confusing... + * It seems that the only one in use is pct_reading_low_target, which is 1-nonprof + drop pct_reading* + + * In LP, we use read for reading + replace subject = "read" if subject == "reading" + + * Plus other attempts to reconcile LP and L4A + rename nlacode nla_code + replace nla_code = "N.A." if nla_code=="-99" + rename nonprof nonprof_all + rename assessment_cutoff min_proficiency_threshold + rename year_assessment year + replace source_assessment = "HAD (Harmonized Assessment Database)" + + //* Saving to compare_files in proficiency, not needed in this task + //save "${clone}/04_repo_update/043_outputs/proficiency_in_L4A_rawfull.dta", replace + + * Only keep what we don't have in GLAD/CLOs: + * - SACMEQ from 2013 (we have Excel only, no microdata) + * - PASEC before 2014 (only the 2014 was harmonized in GLAD) + * - EGRA's didnt make it to GLAD yet + keep if (test=="PASEC" & year < 2014) | (test=="SACMEQ" & year == 2013) | (test=="EGRA") + + * Export csv + export delimited using "`clonefile'", replace + noi di "{phang}Saved `clonefile'{p_end}" + } + + else { + noi di as error "{phang}Could not update `clonefile' (L4A rawfull not available){p_end}" + } + + + /*********************************** + Proficiency from GLAD_CLO + ***********************************/ + +if $network_is_available { + + noi di _newline "{phang}Proficiency from Country Level Outcomes of GLAD{p_end}" + + * Prepare local to create file + local clonefile "${clone}/04_repo_update/043_outputs/proficiency_from_GLAD.csv" + + * If getting the files from the network, the path is + local networkfile "${network}/GDB/Projects/WLD_2020_FGT-CLO/clo_fgt_learning.dta" + + * Open the file + use "`networkfile'", clear + + * List of needed CLO for Learning Poverty (only those for which we have microdata) + local lp_clos "LAC_2006_LLECE LAC_2013_LLECE SSA_2000_SACMEQ SSA_2007_SACMEQ SSA_2014_PASEC WLD_2001_PIRLS WLD_2006_PIRLS WLD_2011_PIRLS WLD_2016_PIRLS WLD_2003_TIMSS WLD_2007_TIMSS WLD_2011_TIMSS WLD_2015_TIMSS" + + * Check that it contains all the CLO of all surveys required for LP + levelsof survey, local(clos_in_networkfile) + local missing_clos : list lp_clos - clos_in_networkfile + if "`missing_clos'" != "" noi disp as error _n "Could not find some CLO needed for Learning Poverty in the network file (`missing_clos')" + + * Simplifies and prepare-wide to be ready for L4A + *----------------------------------------------------------------------------- + + * Name that is worse but we had used already + rename assessment test + + * Only valuevar needed in L4A is harmonized proficiency (hpro) + local idvars "countrycode year test idgrade subgroup" + keep `idvars' *hpro* + + * Though urban breakdown is calculated in clo, not relevant for L4A + keep if inlist(subgroup,"all", "male=0","male=1") + + * Use the same subgroup naming convention as wbopendata + replace subgroup = "_all" if subgroup == "all" + replace subgroup = "_fe" if subgroup == "male=0" + replace subgroup = "_ma" if subgroup == "male=1" + + * From harmonized_ proficiency to non-proficiency and from share to percentage + unab subjects : m_hpro_* + local subjects = subinstr("`subjects'" , "m_hpro_" , "" , .) + foreach subject of local subjects { + gen nonprof`subject' = 100 * (1 - m_hpro_`subject') + gen se_nonprof`subject' = 100 * (se_hpro_`subject') + clonevar fgt1`subject' = m_fgt1_hpro_`subject' + clonevar fgt2`subject' = m_fgt2_hpro_`subject' + drop m_*_`subject' se_*_`subject' n_*_`subject' + } + + * Prepare for bringing into LearningPoverty rawlatest: + * Reshape long on subject (read, math, science) + reshape long nonprof se_nonprof fgt1 fgt2, i(countrycode test year idgrade subgroup) j(subject) string + * Reshape wide on subgroups + reshape wide nonprof se_nonprof fgt1 fgt2, i(countrycode test year idgrade subject) j(subgroup) string + + * Drop observations with all nonprof values (_all, _fe, _ma) missing + missings dropobs nonprof_*, force + + * Beautify: format, order and label + order countrycode year test idgrade subject *nonprof_all *nonprof_ma *nonprof_fe fgt1* fgt2* + sort countrycode year test idgrade subject + + * Export CSV with proficiency + export delimited using "`clonefile'", replace + noi di "{phang}Saved `clonefile'{p_end}" + +} + +else { + noi di as error "{phang}Could not update `clonefile' (network not available){p_end}" +} + +} diff --git a/04_repo_update/042_programs/042_run.do b/04_repo_update/042_programs/042_run.do index 812fcfe..0cdb748 100644 --- a/04_repo_update/042_programs/042_run.do +++ b/04_repo_update/042_programs/042_run.do @@ -31,9 +31,7 @@ do "${clone}/04_repo_update/042_programs/0422_population_data_from_api.do" do "${clone}/04_repo_update/042_programs/0423_enrollment_data_from_api.do" * Update PROFICIENCY DATA from multiple sources to store as csv in 011_rawdata -if $datalibweb_is_available { - do "${clone}/04_repo_update/042_programs/0424_proficiency_data.do" -} +do "${clone}/04_repo_update/042_programs/0424_proficiency_data.do" * Update Other WB API data to store as csv in 011_rawdata do "${clone}/04_repo_update/042_programs/0425_otherdata_from_api.do" diff --git a/05_working_paper/051_rawdata/LPV_Tables_Figures_Template.xlsx b/05_working_paper/051_rawdata/LPV_Tables_Figures_Template.xlsx new file mode 100644 index 0000000..c173616 Binary files /dev/null and b/05_working_paper/051_rawdata/LPV_Tables_Figures_Template.xlsx differ diff --git a/05_working_paper/051_rawdata/bin_scores.csv b/05_working_paper/051_rawdata/bin_scores.csv new file mode 100644 index 0000000..5458f69 --- /dev/null +++ b/05_working_paper/051_rawdata/bin_scores.csv @@ -0,0 +1,35713 @@ +survey,assessment,year,countrycode,idgrade,outcome,subject,bin,pdf,m_score_bin,se_score_bin,n_score_bin +LAC_2006_LLECE,LLECE,2006,ARG,3,score_llece_math,math,0,1,506.34995,4.211216,6272 +LAC_2006_LLECE,LLECE,2006,ARG,3,score_llece_math,math,1,.06666667,325.8819,4.470439,367 +LAC_2006_LLECE,LLECE,2006,ARG,3,score_llece_math,math,2,.06666667,403.2377,.55033839,356 +LAC_2006_LLECE,LLECE,2006,ARG,3,score_llece_math,math,3,.06666667,430.00012,.42723814,356 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+countrycode,countryname,region,regionname,adminregion,adminregionname,incomelevel,incomelevelname,lendingtype,lendingtypename,year,ny_gdp_pcap_pp_kd,lo_pisa_mat,lo_pisa_mat_0,lo_pisa_mat_1,lo_pisa_mat_2,lo_pisa_mat_3,lo_pisa_mat_4,lo_pisa_mat_5,lo_pisa_mat_6,lo_pisa_rea,lo_pisa_rea_0_b1c,lo_pisa_rea_1c,lo_pisa_rea_1b,lo_pisa_rea_1a,lo_pisa_rea_2,lo_pisa_rea_3,lo_pisa_rea_4,lo_pisa_rea_5,lo_pisa_rea_6,lo_pisa_sci,lo_pisa_sci_0,lo_pisa_sci_1a,lo_pisa_sci_1b,lo_pisa_sci_2,lo_pisa_sci_3,lo_pisa_sci_4,lo_pisa_sci_5,lo_pisa_sci_6,math,read,sci,check,latest,tot,wtg,reg,inc +ALB,Albania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,6040.936,381,,,,,,,,348.84952,6,14,23,27,21,8,1,0,0,376.45322,,,,,,,,,,100,,0,0,5,.2,3,4 +ALB,Albania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,10572.6,377.46106,40.486889,27.2477,20.151577,9.058156,2.6495128,.39443076,0,384.81659,3,8,19,27,26,14,3,0,0,390.69574,7,30.984585,20,27.657343,12.911489,2.0481675,.092819169,0,99.988274,100,100.6944,0,0,5,.2,3,4 +ALB,Albania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,11509.592,394.32935,32.525291,28.139683,22.909758,11.992331,3.635767,.76693875,.030233147,393.96426,5,7,16,24,25,16,6,1,0,397.3732,8,29.572119,15,28.53463,14.392144,3.5625608,.39002359,.02106715,100,100,99.472542,0,0,5,.2,3,4 +ALB,Albania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,12176.4,413.15701,26.321711,26.961262,25.420101,14.802178,5.396152,1.0107844,.087811105,405.25879,1,6,16,27,27,16,5,1,0,427.22501,1.5717773,29.828928,10.296627,34.518223,18.935745,4.4961109,.3422457,.010343606,100.00001,99,100.00001,0,0,5,.2,3,4 +ALB,Albania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,13670.103,437.22198,16.914099,25.45875,28.562374,19.272074,7.496542,1.9734969,.32266486,405.42935,0,3,16,33,30,14,3,0,0,416.72626,1.5359147,33.741611,11.73962,34.827457,15.083992,2.8667212,.20089194,.0037930519,100,99,100,0,1,5,.2,3,4 +ARE,United Arab Emirates,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2009,58249.195,,25.019302,26.2794,24.282612,14.823593,6.7294307,2.303612,.56205165,431.41611,,,,,,,,,,437.80643,,25.376892,,29.871403,20.46382,8.2751732,1.9418269,.25315395,100,,86.182266,1,0,4,.25,5,2 +ARE,United Arab Emirates,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2012,57267.438,,20.53158,25.751884,24.857927,16.896063,8.4874535,2.9261067,.54898411,441.70374,0,3,10,22,29,24,10,2,0,448.3703,2,23.818289,10,29.900505,22.341232,10.069763,2.2744939,.25738737,99.999992,100,100.66167,1,0,4,.25,5,2 +ARE,United Arab Emirates,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2015,65078.699,427.4827,24.353371,24.376616,23.15115,15.905272,8.4998083,3.1228781,.59090692,433.5423,1,5,13,22,25,21,11,3,0,436.73111,2.6015716,26.145945,13.023475,26.940458,19.030348,9.4684687,2.5404074,.24932702,100,101,100,0,0,4,.25,5,2 +ARE,United Arab Emirates,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2018,65943.281,434.94727,24.243759,21.265926,21.501562,17.190657,10.354585,4.2085919,1.2349198,431.78177,1,6,15,22,23,18,11,4,1,433.63681,3.7293706,24.663185,14.398299,25.569553,19.237829,9.5458689,2.5863469,.26954597,100,101,100.00001,0,1,4,.25,5,2 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,18625.451,388,,,,,,,,418.2507,2,6,14,21,25,20,9,2,0,396.16675,,,,,,,,,,99,,0,0,6,.16666667,4,4 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,20777.834,381.25391,39.418175,24.725304,20.439981,10.607917,3.7665229,.90906858,.13303027,373.72379,8,11,17,22,22,14,5,1,0,391.2439,,27.918783,,25.597542,13.624886,4.0755119,.43715996,.011133579,100,100,71.665009,0,0,6,.16666667,4,4 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,21520.082,388.06812,37.176197,26.37838,20.776962,10.885816,3.9236064,.79343069,.065610014,398.26089,4,7,16,25,25,16,6,1,0,400.83853,,27.213091,,26.700846,15.408216,4.8283868,.61679322,.034701351,100.00002,100,74.802025,0,0,6,.16666667,4,4 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,24119.078,388.4317,34.879421,31.605509,22.190271,9.2492161,1.8026978,.26647618,.0064076385,395.97913,2,6,18,28,27,15,4,0,0,405.63202,,31.039219,,31.101967,14.789367,3.0049949,.23869206,.0029285098,100,100,80.17717,0,0,6,.16666667,4,4 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,23934.096,409.03329,26.644358,29.410028,25.976023,12.964993,4.1852751,.74723923,.072086871,425.3031,0,3,13,26,31,20,6,1,0,432.2262,1.3815459,28.216349,10.136407,34.171131,20.109499,5.2765541,.68155116,.026965382,100.00001,100,100,0,0,6,.16666667,4,4 +ARG,Argentina,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,22745.904,379.45383,40.473343,28.493776,19.618681,8.8199873,2.2565641,.32754305,.010105846,401.50311,1,7,17,27,26,16,5,1,0,404.07144,4.8869758,30.388742,18.214432,26.971825,14.990992,4.0562687,.47661331,.01415235,100,100,100,0,1,6,.16666667,4,4 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2000,38652.016,533,,,,,,,,528.2785,0,1,3,9,19,26,25,14,4,527.50208,,,,,,,,,,101,,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,40623.664,524.26599,4.3226061,10.019993,18.594345,24.008856,23.279072,14.008043,5.767087,525.427,0,1,3,8,18,28,27,12,2,525.0545,,9.7558613,,20.100817,27.980324,24.246412,11.369908,2.8890636,100,99,96.342384,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,43106.043,519.90778,3.2832291,9.6839657,20.532356,26.925793,23.155378,12.116504,4.3027749,512.89331,0,1,3,10,21,30,25,9,1,526.87958,1,9.8227053,3,20.221897,27.728764,24.576611,11.773028,2.8448479,99.999992,100,100.96785,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,45132.309,514.34045,5.1099997,10.763554,20.254169,25.768986,21.677555,11.937175,4.4885621,514.90063,0,1,3,10,20,28,24,11,2,527.27051,1,9.1566248,3,19.978199,28.428719,24.502514,11.450163,3.0881526,100,99,100.60437,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,46801.57,504.15076,6.1324825,13.534663,21.930593,24.568626,19.023912,10.515042,4.2946796,511.80399,0,1,3,10,22,29,23,10,2,521.49475,1,10.214972,3,21.492785,28.530212,22.774616,10.912487,2.6441066,100,100,100.56918,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,48020.254,493.89621,7.578043,14.39643,22.629673,25.388554,18.666082,8.6377935,2.7034254,502.9006,0,1,5,12,21,28,22,9,2,509.9939,.56471801,12.826826,4.2538152,21.567724,27.296412,22.303234,9.1510077,2.0362639,100,100,100,0,0,7,.14285715,2,2 +AUS,Australia,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,49575.98,491.36002,7.6186714,14.814451,23.376646,25.550001,18.177124,8.0039968,2.45911,502.63171,0,1,6,13,21,25,21,10,3,502.96457,.64464271,13.748146,4.4672318,23.00219,27.493664,21.162924,7.9244561,1.5567465,100,100,100,0,1,7,.14285715,2,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,46725.566,502.5,,,,,,,,507,0,1,5,12,24,28,22,7,1,504.69684,,,,,,,,,,100,,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,47895.219,505.61099,5.5998483,13.169991,21.58342,24.881693,20.482555,10.533938,3.7485526,490.69324,0,2,5,13,23,27,21,7,1,490.98459,,15.106015,,24.839695,28.299566,19.278618,5.941267,.6392417,100,99,94.104408,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,51120.449,505.48361,7.4722767,12.527715,19.51943,23.349304,21.342924,12.297503,3.4908471,490.19397,1,2,6,13,22,26,21,8,1,510.83716,1,12.038821,4,21.783802,28.254055,23.649609,8.7854595,1.1856422,100,100,100.69739,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,51311.527,495.90866,7.7987099,15.441186,21.205605,23.026203,19.578491,9.9109278,3.038877,470.28363,0,2,8,18,24,26,17,5,0,494.32767,1,14.267354,6,23.842272,26.607845,20.581354,7.0503726,.97675526,100,100,100.32594,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,53590.719,505.54074,5.7027674,12.952162,21.926388,24.167416,20.962996,11.021935,3.2663364,489.60934,0,1,5,14,24,30,21,5,0,505.78125,0,12.168763,3,24.337187,30.112761,21.912075,7.0114765,.84245461,100,100,99.38472,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,53164.801,496.74231,7.8105383,13.940846,21.251076,24.646397,19.88818,9.7260981,2.7368653,484.8656,0,2,7,14,23,27,20,6,1,495.03751,.4956798,15.829793,4.501265,23.892263,28.06164,19.499182,6.8122082,.90797013,99.999992,100,100,0,0,7,.14285715,3,2 +AUT,Austria,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,55687.188,498.94232,7.2719517,13.806926,20.827326,24.906693,20.628733,10.033267,2.5251055,484.39258,0,1,6,16,23,26,19,7,1,489.78043,.55763251,16.452095,4.8414812,24.975882,27.63983,19.237713,5.8407159,.45465156,100,99,100,0,1,7,.14285715,3,2 +AZE,Azerbaijan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,9647.0967,475.99521,.16841747,10.367085,47.585857,34.366142,6.633657,.64413452,.23470716,352.89185,,,,38.258541,16.463499,3.4274468,.56638491,0,0,382.33392,,53.131439,,22.405432,4.6795044,.40426388,.0038384427,0,100.00001,58.71587,80.624481,0,0,2,.5,3,4 +AZE,Azerbaijan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,13882.586,430.97531,11.474954,33.79985,35.312893,14.775855,3.5568886,.85341322,.22614478,361.51532,,,26.061281,36.993912,21.490366,5.2518182,.45669866,.012567362,0,373.18359,,38.474121,,22.432083,6.706902,.81461811,.026811523,0,100,90.266632,68.454536,0,1,2,.5,3,4 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,43058.453,520,,,,,,,,507.12589,0,2,6,11,17,26,26,11,1,495.73502,,,,,,,,,,100,,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,44196.121,529.28644,7.1654058,9.3320675,15.934052,20.091875,21.033075,17.477482,8.9660416,506.98734,1,2,5,10,18,26,25,11,2,508.82898,,12.123144,,20.088478,25.544617,24.258739,10.057912,1.4419367,99.999992,100,93.514839,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,47250.215,520.349,7.1267982,10.217092,16.987606,21.448189,21.876684,15.953127,6.3905048,500.9006,1,2,6,11,19,26,24,10,1,510.36349,1,12.208096,4,20.771568,27.649279,24.456221,9.1285534,.95207894,100,100,100.1658,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,47102.332,515.27228,7.7128024,11.338373,17.475443,21.80295,21.300049,14.550292,5.8200917,505.9458,0,1,5,12,20,26,25,10,1,506.57553,1,11.681432,5,20.655567,27.192141,24.017157,9.0414085,1.051367,100,100,99.639076,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,48249.375,514.52924,6.9587722,12.004417,18.425102,22.44516,20.622038,13.421458,6.1230531,508.61789,0,1,4,10,20,27,24,10,1,504.86765,1,11.827158,5,21.514214,28.72789,22.986591,8.1161385,.94176203,100,97,100.11375,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,49491.625,506.98441,7.2093258,12.853335,18.842976,23.401913,21.841776,12.293633,3.557044,498.5242,0,1,5,13,21,27,23,8,1,501.99969,.49153647,14.369028,4.9225092,21.949154,26.794041,22.485662,8.0324049,.95566273,100,99,100,0,0,7,.14285715,3,2 +BEL,Belgium,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,51250.324,508.07031,6.8631034,12.798822,18.616673,23.76589,22.223698,12.499154,3.2326591,492.86444,0,1,6,14,22,26,20,8,1,498.77313,.55065089,14.150575,5.305871,22.224533,28.402781,21.332323,7.3175001,.7157675,100,98,99.999992,0,1,7,.14285715,3,2 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,10056.202,430,,,,,,,,430.40344,1,5,12,22,27,21,9,2,0,448.2847,,,,,,,,,,99,,0,0,6,.16666667,3,4 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,15226.318,413.44916,29.360214,23.91733,22.036596,14.926375,6.6772914,2.4796364,.60255688,401.93497,3,9,17,22,22,16,8,2,0,434.07977,4,24.349646,14,25.16708,18.839163,10.292212,2.6157258,.44015163,100,99,99.703979,0,0,6,.16666667,3,4 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,17058.826,428.06613,24.451691,22.669083,23.429062,17.47131,8.1979828,3.027555,.75331706,429.08109,2,6,13,20,23,22,11,3,0,439.29416,4,22.271221,12,26.62047,20.997334,10.916878,2.4417236,.20767383,100.00001,100,99.455299,0,0,6,.16666667,3,4 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,17955.322,438.73825,19.985205,23.771372,24.377792,17.888807,9.9147272,3.3752475,.68684679,436.12573,2,6,13,19,22,21,13,4,1,446.46872,3,22.497522,11,26.317516,22.496567,11.20118,2.8095276,.27550033,99.999992,101,99.597801,0,0,6,.16666667,3,4 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,19352.201,441.18991,20.834276,21.239183,23.674911,19.272785,10.55848,3.6126053,.80775803,431.7175,1,6,14,20,22,21,12,3,0,445.772,2.654701,22.777809,12.426745,25.178267,22.640562,11.436754,2.6671166,.21804635,99.999985,99,100,0,0,6,.16666667,3,4 +BGR,Bulgaria,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,21992.648,436.04056,21.920588,22.492254,23.724764,18.203794,9.444932,3.333149,.88051903,419.84399,0,5,17,25,25,17,8,2,0,424.07358,2.9501984,28.274544,15.281874,26.697868,17.879181,7.3976698,1.4383254,.080340996,99.999992,99,99.999992,0,1,6,.16666667,3,4 +BIH,Bosnia and Herzegovina,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,14463.407,406.3844,28.708622,28.85479,24.192822,13.137134,4.3143573,.7380684,.054209549,402.97781,0,3,18,33,29,14,3,0,0,398.49765,2.9258935,35.637032,18.215797,29.438889,11.708111,1.9398403,.13443677,0,99.999992,100,100,0,1,1,1,3,4 +BLR,Belarus,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,18885.236,471.86514,11.373434,17.989151,24.707071,23.408531,15.224264,6.0567722,1.2407771,473.78845,0,1,6,17,29,28,16,4,0,471.26367,.51825756,18.664238,5.0363317,31.269333,28.78133,13.125067,2.4730964,.13234527,100,101,100,0,1,1,1,3,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,11597.144,334,,,,,,,,396.02908,1,5,17,33,28,13,3,1,0,375.16895,,,,,,,,,,101,,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,11782.468,356.01578,53.26395,21.904646,14.126677,6.8167787,2.6985183,.91249073,.27694029,402.7963,3,8,16,23,25,17,6,2,0,389.61963,,30.830328,,23.438299,11.649748,4.2729263,.76790923,0,100,100,70.959206,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,12917.917,369.51505,46.570564,25.949366,16.561323,7.086566,2.8142025,.8359884,.18199082,392.8851,3,7,18,28,25,13,5,1,0,390.33157,6,33.093842,22,23.779308,11.250546,3.4022558,.51763058,.035811633,100,100,100.0794,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,13957.679,385.81412,38.125664,30.993505,18.963324,8.1353378,2.9832652,.72886825,.07003817,411.75491,1,4,16,29,27,16,6,1,0,405.40399,3,34.471279,17,28.783587,12.616811,3.8515313,.55339783,0,100.00001,100,100.27661,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,15473.712,391.4599,35.224049,31.865042,20.405207,8.8927059,2.8598847,.70923108,.043882485,410.12186,1,4,16,30,29,15,4,0,0,404.71054,3,35.096657,16,30.726803,12.493133,2.7695868,.29555196,.0026040259,100,99,100.38433,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,15059.234,377.06949,43.744038,26.507332,17.182701,8.5816498,3.0900421,.76534474,.12889381,407.3486,1,6,17,27,25,16,6,1,0,400.6821,4.3750162,32.373219,19.850357,25.361366,13.147515,4.2197561,.64872539,.024046412,100,99,100.00001,0,0,7,.14285715,4,4 +BRA,Brazil,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,14596.246,383.56912,41.025276,27.06859,18.236675,9.2843781,3.4458179,.80972719,.12953556,412.87332,0,5,18,27,25,16,7,2,0,403.62094,4.0250077,31.443823,19.918602,25.300734,13.89587,4.6199107,.7596789,.036373883,99.999992,100,100,0,1,7,.14285715,4,4 +BRN,Brunei Darussalam,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,60388.902,430.11298,22.148388,25.719864,24.03742,16.168699,8.8935795,2.6767154,.35533407,408.06995,0,5,19,27,24,15,7,1,0,430.98438,1.8618369,29.676222,14.196197,25.548643,17.433638,9.0155029,2.1279867,.13997273,99.999992,98,100,0,1,1,1,2,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2000,37345.543,533,,,,,,,,534.31274,0,0,2,7,18,28,28,14,3,529.36023,,,,,,,,,,100,,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2003,39427.785,532.48676,2.4235225,7.6967869,18.277178,26.207087,25.096676,14.846433,5.4523187,527.91364,0,0,2,7,18,31,29,11,2,518.74518,,10.357668,,21.839825,28.593033,23.386173,10.00336,2.2237546,100,100,96.403809,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2006,43534.375,527.0072,2.7669327,8.0196762,18.618822,27.5261,25.134729,13.550141,4.3835979,527.01129,0,1,3,8,18,29,27,12,2,534.46979,0,7.8237705,2,19.05792,28.848581,27.65867,12.026756,2.3937094,100,100,99.809402,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2009,44182.434,526.80518,3.144397,8.3319359,18.765757,26.503256,24.974516,13.892099,4.3880401,524.24182,0,0,2,8,20,30,27,11,2,528.70496,0,7.5041647,2,20.888765,31.228628,26.228243,10.516174,1.5874897,100,100,99.953461,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2012,46313.984,518.07037,3.6356461,10.192295,21.025915,26.355955,22.39312,12.075287,4.321784,523.1167,0,0,2,8,19,31,26,11,2,525.461,0,7.9999938,2,20.963573,31.974079,25.334625,9.4668255,1.8446018,100,99,99.583702,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2015,47729.605,515.6474,3.8136616,10.549056,20.439907,27.113134,23.024691,11.393705,3.6658435,526.66779,0,0,2,8,19,30,27,12,2,527.70471,.14526461,9.128232,1.8128742,20.198582,30.281706,26.071527,10.36808,1.9937335,99.999992,100,100,0,0,7,.14285715,6,2 +CAN,Canada,NAC,North America,,,HIC,High income,LNX,Not classified,2018,48786.762,512.01697,4.9857807,11.275408,20.811211,25.876745,21.721628,11.311397,4.0178318,520.08551,0,1,3,10,20,27,24,12,3,517.99768,.39944822,10.461167,2.5753,22.380404,29.349304,23.50288,9.5079727,1.8235234,99.999992,100,100,0,1,7,.14285715,6,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,58603.609,529,,,,,,,,494.37161,0,1,5,13,21,28,21,8,1,495.66724,,,,,,,,,,98,,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,58238.258,526.55316,4.9368062,9.5956888,17.519201,24.250547,22.484056,14.185511,7.0281882,499.1214,0,1,4,11,23,31,22,7,1,512.97552,,11.917788,,20.467295,26.661379,22.260178,10.382324,2.6517236,100,100,94.340691,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,62938.629,529.65613,4.5595012,8.9665194,17.422396,23.201178,23.211107,15.888751,6.7505465,499.27777,0,1,4,11,23,30,23,7,1,511.5239,1,11.556684,4,21.7764,28.180399,23.517345,9.0592375,1.4034605,100,100,100.49352,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,63254.344,533.96063,4.4758825,8.9947395,15.919739,23.00316,23.485456,16.272766,7.8482571,500.50021,0,1,4,12,23,30,23,7,1,516.5675,0,10.558478,3,21.314802,29.793985,24.114243,9.2088308,1.5351408,100.00001,101,99.525482,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,64805.703,530.93103,3.5655422,8.8772383,17.766453,24.528049,23.894529,14.611974,6.7562151,509.04025,0,0,3,10,22,31,24,8,1,515.29755,0,9.829258,3,22.839674,31.317707,23.714626,8.3058233,1.0002265,100.00001,99,100.00732,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,66161.836,521.25061,4.9209161,10.859171,18.097668,23.615355,23.260204,13.951728,5.2949595,492.19821,0,1,5,14,23,28,21,7,1,505.5058,.51675808,13.928608,4.009069,22.839859,26.25613,22.677315,8.6410646,1.1311976,100,100,100.00001,0,0,7,.14285715,3,2 +CHE,Switzerland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,68488.32,515.3147,4.7789245,12.049098,19.537378,24.387625,22.288404,12.078122,4.8804469,483.92941,0,1,7,15,23,26,19,7,1,495.27628,.42267847,15.218466,4.5838714,24.923101,27.772966,19.273592,6.9455142,.85981047,100.00001,99,99.999992,0,1,7,.14285715,3,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2000,15152.57,384,,,,,,,,409.55679,1,5,14,28,30,17,5,0,0,414.85309,,,,,,,,,,100,,0,0,6,.16666667,4,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2006,18995.172,411.35034,28.19931,26.931578,23.930237,13.858628,5.6155658,1.3355422,.12913941,442.09134,1,3,11,21,28,21,11,3,0,438.17746,2,26.654106,11,29.868267,20.10257,8.4026432,1.7848589,.11883402,100,99,99.931274,0,0,6,.16666667,4,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2009,19668.16,421.06049,21.659872,29.365217,27.280794,14.765493,5.6060367,1.2087476,.11383952,449.3696,0,1,7,22,33,26,9,1,0,447.46735,1,23.919884,7,35.165432,23.614565,7.8572412,1.0571514,.031098688,100,99,99.645363,0,0,6,.16666667,4,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2012,22577.176,422.63235,21.996868,29.54549,25.323416,15.402895,6.1504793,1.4516425,.12921138,441.39816,0,1,8,24,35,24,7,1,0,444.93393,1,26.346025,8,34.584076,22.374079,7.5499845,.96273583,.0414153,100.00001,100,100.85831,0,0,6,.16666667,4,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2015,23681.75,422.67139,23.046009,26.306961,25.474583,17.363699,6.4126716,1.2718877,.1241886,458.57089,0,1,7,20,30,27,12,2,0,446.95609,.98270237,24.990139,8.8519363,31.012192,23.819752,9.1038246,1.1992728,.040182941,100.00001,99,100,0,0,6,.16666667,4,2 +CHL,Chile,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2018,24337.701,417.40656,24.677317,27.231241,25.528044,15.62227,5.7148662,1.0979947,.12826666,452.27255,0,2,9,21,29,24,12,2,0,443.58255,.97172755,25.544428,8.8273449,33.124783,22.561535,7.9401197,.99574149,.034319114,99.999992,99,99.999992,0,1,6,.16666667,4,2 +CHN,China,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,8002.1523,600.07623,1.4299238,3.440248,8.7376709,15.172355,20.791441,23.784292,26.64407,555.82813,,,,,,,,,,574.61737,,,,10.51583,25.967321,36.05896,20.36677,3.9236987,100,,96.832581,0,0,4,.25,2,4 +CHN,China,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,10310.609,612.67554,.84690601,2.9467375,7.5098157,13.101391,20.171799,24.595238,30.828114,569.58838,,,,,,,,,,580.11786,,,,10.020867,24.564011,35.453876,22.985487,4.2400899,100,,97.264336,0,0,4,.25,2,4 +CHN,China,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,12555.207,531.29608,5.7864904,10.033609,16.278378,20.512764,21.760796,16.634602,8.9933605,493.94119,0,2,6,14,21,25,21,9,2,517.7793,.57905453,11.826515,3.8133042,20.667269,25.766785,23.777668,11.496316,2.0730906,100,100,100,0,0,4,.25,2,4 +CHN,China,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,15010.557,591.39362,.4973956,1.9149555,6.8878684,17.500925,28.897453,27.842529,16.458874,555.23627,0,0,1,4,14,28,31,18,4,590.45264,.038968828,1.7583109,.25380152,8.3700686,23.4492,34.623745,24.258698,7.2472086,100,100,100,0,1,4,.25,2,4 +COL,Colombia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,10684.747,369.97852,44.589897,27.306299,18.165646,7.5938458,1.9370008,.37125465,.036057428,385.30804,4,9,17,25,25,14,4,1,0,388.03827,7,33.989986,20,27.203568,10.55998,1.9028014,.15937196,.0025756995,100.00001,99,100.81829,0,0,5,.2,4,4 +COL,Colombia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,11517.441,380.84821,38.837193,31.610067,20.320023,7.5145626,1.6024407,.11554152,0,413.18149,1,4,14,29,31,17,5,1,0,401.75012,4,33.691437,17,30.183769,13.111648,2.4817443,.13513385,0,99.999825,102,100.60373,0,0,5,.2,4,4 +COL,Colombia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,13020.574,376.48859,41.6465,32.170517,17.829817,6.4366851,1.614979,.27690527,.024597131,403.40253,1,4,15,31,31,15,3,0,0,398.67862,3,36.335308,17,30.792473,11.019544,1.8987161,.1371731,.00090763147,100.00001,100,100.18413,0,0,5,.2,4,4 +COL,Colombia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,14234.428,389.6438,35.394146,30.89259,21.525223,9.5205116,2.3537083,.30043027,.013391731,424.90521,0,3,14,26,29,20,7,1,0,415.72879,1.6963737,32.826214,14.487845,30.608337,15.925455,4.1043692,.34514648,.0062584509,100,100,100,0,0,5,.2,4,4 +COL,Colombia,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,14458.837,390.93228,35.500748,29.886045,21.088514,9.9501581,3.0586965,.48672444,.029111061,412.29507,0,4,16,30,28,16,6,1,0,413.323,2.0667214,32.989773,15.310341,29.641056,15.366948,4.1840677,.42860848,.012483045,100,101,100,0,1,5,.2,4,4 +CRI,Costa Rica,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,15344.641,409.38538,23.572542,33.121078,27.834715,12.151048,3.0050838,.30994794,0,442.58093,,,,,,,,,,430.48129,,29.382364,,37.876534,18.540411,4.2098951,.34015307,0,99.994408,,90.349358,0,0,4,.25,4,4 +CRI,Costa Rica,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,16975.838,406.99988,23.639826,36.233376,26.822634,10.144996,2.5950193,.5007748,.063373826,440.54782,0,1,7,24,38,23,6,1,0,429.35098,1,30.718615,8,39.196976,17.780556,3.4481854,.22728878,.011112924,100,100,100.38274,0,0,4,.25,4,4 +CRI,Costa Rica,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,18010.068,400.25339,27.391819,35.104984,25.826397,9.4086246,2.002867,.25426173,.011047524,427.48749,0,2,10,28,35,19,5,1,0,419.608,.69560653,35.573494,10.104484,35.499077,15.237201,2.7423027,.14623119,.001604845,100,100,100,0,0,4,.25,4,4 +CRI,Costa Rica,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,19319.604,402.33057,27.818007,32.18008,25.645273,11.193971,2.8419211,.31307644,.0076710721,426.49823,0,2,11,29,32,19,6,1,0,415.62247,1.3372542,34.461716,12.044916,34.378937,14.900669,2.7576611,.11884809,0,99.999992,100,100,0,1,4,.25,4,4 +CYP,Cyprus,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,31678.896,,19.034046,22.99547,25.49806,19.16186,9.6092386,3.0601857,.64114076,448.95297,2,4,10,17,25,25,13,3,1,437.67645,,23.656029,,30.323473,21.268923,8.4123526,1.7809601,.17925826,100.00001,100,85.620995,1,0,3,.33333334,3,2 +CYP,Cyprus,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,30608.436,437.14429,20.233114,22.361441,25.794331,18.922331,9.4583158,2.8379064,.39256236,442.8443,1,4,11,20,27,23,11,3,0,432.59641,2.3228571,26.878647,12.93692,28.618916,19.616402,8.0550251,1.4776683,.093565211,100,100,100,0,0,3,.33333334,3,2 +CYP,Cyprus,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,34569.211,450.68298,17.201899,19.653454,24.695005,21.975586,12.092378,3.7260509,.65562695,424.35825,0,4,15,24,27,19,8,2,0,439.00824,1.9846956,25.026983,11.940108,28.917561,21.429295,9.055604,1.5304806,.11527309,100,99,100,0,1,3,.33333334,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,25045.227,498,,,,,,,,491.57703,0,2,4,11,25,31,20,6,1,511.41452,,,,,,,,,,100,,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,27306.211,516.45502,4.9825292,11.584485,20.089733,24.271418,20.795565,12.926894,5.3493738,488.5423,0,1,5,13,25,30,19,6,1,523.25372,,10.489999,,21.691948,27.334774,23.618534,10.983408,2.9258106,99.999992,100,97.044472,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,32466.096,509.85934,7.2129292,11.948074,20.49147,22.958138,19.100933,12.297169,5.9912863,482.71515,1,2,7,15,22,24,19,8,2,512.86072,0,12.053073,3,23.404184,27.769312,21.707224,9.7720966,1.815378,100,100,99.521263,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,32856.016,492.81409,7.0333605,15.299485,24.220743,24.383564,17.425697,8.4633846,3.1737664,478.18671,0,1,5,17,27,27,17,5,0,500.49707,1,12.647349,4,25.58173,28.834284,19.913931,7.1567521,1.201309,100,99,100.33536,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,33711.051,498.95789,6.8043108,14.158798,21.692301,24.802769,19.664898,9.6432247,3.2336972,492.88974,0,1,4,13,26,31,19,5,1,508.29907,1,10.457984,3,24.698467,31.670916,22.2262,6.7276239,.8958227,100,100,100.67702,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,36166.992,492.32541,7.3778081,14.313223,23.328939,26.178501,18.437819,8.1258831,2.2378261,487.25009,0,1,6,15,23,28,19,7,1,492.82999,.29056132,16.066242,4.3017378,25.90045,27.727715,18.416727,6.34869,.94787854,100,100,100,0,0,7,.14285715,3,2 +CZE,Czech Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,39495.629,499.46765,6.6257873,13.759293,22.105959,25.194517,19.646933,9.5267067,3.1408045,490.21881,0,1,5,15,25,27,19,7,1,496.79132,.38147813,14.485357,3.8978667,25.925215,28.712048,19.063635,6.583992,.95040941,100,100,100,0,1,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,42858.02,490,,,,,,,,483.99081,1,3,6,13,22,27,19,8,1,487.10583,,,,,,,,,,100,,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,43019.906,502.98553,9.1891394,12.415189,18.997541,22.565973,20.612858,12.167978,4.0513206,491.358,1,2,7,13,20,26,22,8,1,502.33649,,13.12582,,20.088137,25.714851,21.72715,9.57092,1.869741,100,100,92.096626,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,45605.742,503.79086,7.3466511,12.522523,21.210875,24.032791,19.447432,10.987115,4.4526138,494.94443,1,2,5,12,20,27,23,8,2,515.64911,1,11.309144,3,21.359011,27.869667,23.568907,9.9904842,1.8342574,100,100,99.93148,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,44975.961,512.77765,6.4227414,12.195354,18.754534,23.080814,21.698727,13.222422,4.6254091,497.30505,0,1,4,13,22,29,23,7,1,520.40533,1,10.70171,3,20.128328,27.33795,24.971519,10.858289,1.9319366,100,100,99.929741,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,49796.156,513.52509,5.5451298,12.193542,19.404186,23.735384,21.666233,12.773757,4.6817675,507.67654,0,0,3,11,22,30,25,8,1,524.12079,0,9.3121843,2,20.530964,28.922001,26.167757,10.574335,1.5906348,100.00001,100,99.097878,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,51209.086,505.97131,5.1386275,12.052344,21.839888,26.822926,21.202845,10.092738,2.8506317,509.1041,0,1,4,11,21,28,24,10,2,509.14059,.39321336,12.766854,3.8357131,22.746208,27.668896,22.003994,8.8003483,1.784773,100,101,100.00001,0,0,7,.14285715,3,2 +DEU,Germany,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,53659.988,500.04379,7.5620818,13.534534,20.740656,24.026693,20.816189,10.509824,2.8100207,498.27927,0,1,6,14,21,25,21,10,2,502.98889,.7624315,13.807421,5.0309062,22.031406,26.918715,21.455326,8.5121975,1.4815978,100.00001,100,100.00001,0,1,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,49023.781,514,,,,,,,,496.87097,0,1,5,12,23,29,22,7,1,481.00534,,,,,,,,,,100,,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,49380.266,514.28766,4.7093201,10.697779,20.560492,26.219635,21.895412,11.802981,4.1143808,492.32336,0,1,4,12,25,33,20,5,0,475.22266,,16.974218,,27.243603,25.559835,16.006058,4.7319655,.77179813,100,100,91.287476,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,53449.602,513.02594,3.5903959,10.043575,21.417082,28.766657,22.466862,10.882026,2.8334036,494.48297,0,1,4,11,26,32,21,5,1,495.89432,0,14.086845,4,25.956675,29.317795,19.510061,6.080492,.70885533,100.00001,101,99.660721,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,50233.703,503.27814,4.9434843,12.110109,23.031424,27.378075,20.971657,9.0738802,2.4913707,494.91617,0,0,3,12,26,33,21,4,0,499.33698,1,12.455479,4,26.017399,30.63776,20.059052,5.8641319,.86141092,100,99,100.89524,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,51338.594,500.02676,4.3503776,12.490259,24.388533,29.003532,19.794205,8.3161345,1.6569574,496.13089,0,1,3,11,26,34,21,5,0,498.47421,1,11.986575,4,25.651054,31.270218,19.63238,6.0699263,.68559736,100,101,100.29575,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,53018.934,511.08759,3.1210427,10.45092,21.914829,29.479879,23.371084,9.7767859,1.8854594,499.81461,0,0,3,11,24,32,22,6,1,501.93689,.34303495,12.524967,3.000066,25.850796,31.091644,20.17642,6.1444602,.86861253,100.00001,99,100.00001,0,0,7,.14285715,3,2 +DNK,Denmark,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,56102.82,509.39838,3.7152443,10.855948,21.970503,28.819544,23.010397,9.5073185,2.1210444,501.12994,0,1,4,12,24,30,22,7,1,492.63702,.65742069,13.850992,4.1473808,26.568378,30.114506,19.133841,5.0002122,.52726978,100,101,100.00001,0,1,7,.14285715,3,2 +DOM,Dominican Republic,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,15328.353,327.702,68.305351,22.242823,7.7442985,1.5019259,.19437186,.011228976,0,357.7377,1,12,28,31,20,7,1,0,0,331.63879,15.759032,30.390095,39.600151,11.30515,2.5962181,.3388496,.010504605,0,100,100,100.00001,0,0,2,.5,4,4 +DOM,Dominican Republic,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,17711.766,325.10056,69.280037,21.277874,7.290884,1.8438839,.27932498,.02799979,0,341.62561,1,16,33,29,15,5,1,0,0,335.62994,13.622566,31.605406,39.607018,12.270323,2.5899169,.29658136,.0081878686,0,100.00001,100,100.00001,0,1,2,.5,4,4 +DZA,Algeria,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,11510.815,359.6062,50.563637,30.393129,14.16299,4.0324521,.79212773,.053019095,.0026474979,349.85931,1,10,31,37,17,4,0,0,0,375.74509,3.9285936,42.765503,24.063812,22.699244,5.5685587,.94317019,.031118974,0,100,100,99.999992,0,1,1,1,5,4 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,34826.289,476,,,,,,,,492.55298,0,1,3,12,26,33,21,4,0,490.939,,,,,,,,,,100,,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,36823.055,485.10806,8.0851774,14.881382,24.729071,26.672386,17.692137,6.5199771,1.4198693,480.53799,0,2,5,14,26,30,18,5,0,487.09256,,14.831912,,25.605116,28.101244,18.063435,5.6551261,.79286993,100,100,93.049698,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,38935.246,479.95752,8.5537453,16.138447,25.157442,26.152853,16.767195,6.0595703,1.1707481,460.83011,0,2,6,17,30,30,13,2,0,488.42453,1,14.905524,4,27.361008,30.222963,17.887239,4.5478277,.34004843,100,100,100.26461,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,37504.633,483.49313,9.1222105,14.609268,23.948917,26.613844,17.655781,6.7342649,1.3157153,481.04233,0,1,5,14,27,33,18,3,0,488.25464,1,13.624341,4,27.923862,32.328457,17.550085,3.7463598,.22187035,100.00001,101,100.39498,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,35840.133,484.31931,7.7663856,15.839019,24.864697,25.958134,17.571228,6.7253394,1.2751988,487.93918,0,1,4,13,26,31,19,5,1,496.44583,1,12.014455,3,27.296408,32.795776,19.401844,4.4654112,.34212714,100.00001,100,100.31602,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,37450.918,485.8432,7.1895866,15.005779,24.919996,27.510975,18.129883,6.2518296,.99195033,495.57639,0,1,3,12,24,32,22,5,0,492.7861,.31280023,14.308837,3.6661386,26.512217,31.324957,18.89584,4.6728244,.30638561,100.00001,99,100.00001,0,0,7,.14285715,3,2 +ESP,Spain,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,40329.984,481.39261,8.7006369,15.994834,24.44693,26.032564,17.544928,6.181251,1.098856,,,,,,,,,,,483.25204,.55124754,16.197617,4.5258789,28.369465,29.401594,16.769962,3.8711553,.31307852,100,,100,1,1,7,.14285715,3,2 +EST,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,28847.385,514.57544,2.7097752,9.3689871,21.930799,30.178675,23.272001,9.9585428,2.5812194,500.74976,0,1,3,10,24,34,22,6,0,531.39178,0,6.7001452,1,21.004223,33.685459,26.162464,10.088503,1.4060616,100,100,100.04685,0,0,5,.2,3,2 +EST,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,25433.941,512.10419,3.0039451,9.6337519,22.724041,29.901497,22.659161,9.8451157,2.2324874,500.96185,0,0,2,11,26,34,21,5,1,527.83173,0,7.0497651,1,21.313681,34.29422,25.682491,8.9540625,1.4377985,100,100,99.732018,0,0,5,.2,3,2 +EST,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,29197.852,520.54553,1.9759312,8.568552,22.000683,29.425373,23.430872,11.0073,3.5912888,516.29419,0,0,1,8,23,35,25,7,1,541.40479,0,4.4954171,1,18.950655,34.452282,28.74608,11.105368,1.7081518,100,100,100.45795,0,0,5,.2,3,2 +EST,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,31209.098,519.52911,2.2186472,8.9990082,21.480339,28.878536,24.230268,11.293112,2.9000888,519.14288,0,0,2,8,22,31,25,10,1,534.19373,.026899824,7.5164185,1.2396103,20.079048,30.670242,26.928495,11.605965,1.9333209,100,99,100,0,0,5,.2,3,2 +EST,Estonia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,35308.086,523.41455,2.0866728,8.1297436,20.790874,28.953316,24.553715,11.820762,3.6649175,523.01703,0,0,2,9,21,30,24,11,3,530.10803,.09542723,7.5327177,1.1294515,21.512144,32.106342,25.424818,10.246242,1.95286,100,100,100,0,1,5,.2,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,39930.172,536,,,,,,,,546.46912,0,0,1,5,14,29,32,15,3,537.74127,,,,,,,,,,99,,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,42206.555,544.28918,1.4526452,5.3065262,16.035633,27.688402,26.131218,16.730415,6.6551619,543.46252,0,0,1,5,15,32,33,13,2,548.22498,,5.5290413,,16.630629,29.397694,29.547045,14.376124,3.0981884,100,101,98.57872,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,46453.898,548.3584,1.1498414,4.8023853,14.372865,27.171837,28.074432,18.118597,6.3100414,546.86829,0,0,1,4,16,31,32,14,2,563.32281,0,3.5577023,1,13.610695,29.138659,32.246914,16.961861,3.9496531,100.00001,100,100.46548,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,44702.012,540.50433,1.7066358,6.1393995,15.563934,27.122093,27.811596,16.730545,4.925796,535.87799,0,0,2,6,17,30,31,13,2,554.07953,0,4.9152107,1,15.262603,28.779081,31.243191,15.3854,3.3160543,100,101,99.901543,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,45993.32,518.75031,3.3447759,8.9231071,20.490648,28.817902,23.171461,11.708006,3.5440998,524.02167,0,1,2,8,19,29,27,11,2,545.44196,0,5.9128265,2,16.792522,29.607981,28.845587,13.851821,3.2090864,100,99,100.21983,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,45120.313,511.0769,3.5923498,9.9716311,21.816629,29.288158,23.653933,9.4836845,2.1936138,526.42468,0,1,3,8,18,30,28,12,2,530.66119,.25352195,8.895731,2.3053145,19.050755,29.19174,25.980465,11.888173,2.4343002,99.999992,102,100,0,0,7,.14285715,3,2 +FIN,Finland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,48199.98,507.30136,3.8451536,11.130382,22.295645,28.936811,22.666552,9.3159485,1.8095094,520.07874,0,1,3,9,19,28,25,12,2,521.88458,.43770182,9.6518583,2.7973964,21.075241,28.886576,24.884846,10.477664,1.7887193,100,99,99.999992,0,1,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,39896.895,517,,,,,,,,504.7424,0,0,4,11,22,31,24,8,1,500.48633,,,,,,,,,,101,,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,40601.004,510.79947,5.5975204,11.033499,20.222181,25.904472,22.128048,11.62775,3.4865284,496.18878,0,1,5,11,23,30,23,7,1,511.22546,,12.288906,,20.832676,24.705448,21.938835,11.179278,2.6490552,100,101,93.5942,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,42543.098,495.53833,8.3517857,13.903738,21.388851,24.228262,19.628023,9.9455719,2.553766,487.70624,1,2,6,13,21,28,22,7,1,495.21985,1,14.545394,5,22.796371,27.16497,20.857225,7.1981153,.80633706,99.999992,101,99.368408,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,41718.617,496.78232,9.4525814,13.071737,19.853283,23.825066,20.117449,10.424681,3.2552028,495.61655,0,2,6,12,21,27,22,8,1,498.2269,2,12.165111,6,22.072805,28.849945,21.672064,7.3092461,.82438636,100,99,100.89355,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,42967.922,494.98468,8.7316494,13.622705,22.144638,23.758976,18.850636,9.7657213,3.1256745,505.48148,0,2,5,12,19,26,23,11,2,498.97089,1,12.633288,5,22.879562,29.166855,21.312651,6.9453826,.95509422,99.999992,100,99.89283,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,43525.371,492.92041,8.7743044,14.703637,20.65309,23.775105,20.649748,9.5002298,1.9438882,499.30609,0,2,6,13,19,24,23,10,2,494.9776,.87508422,15.341327,5.844182,22.030466,26.475691,21.44746,7.1808271,.80496317,100,99,99.999992,0,0,7,.14285715,3,2 +FRA,France,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,45479.785,495.40759,8.0465288,13.209104,21.101688,25.575411,21.042753,9.2315063,1.7930096,492.60648,0,1,6,14,23,27,20,8,1,492.97708,.60209328,14.892057,5.0054359,24.573936,28.345535,20.030018,5.9132752,.6376496,100,100,100.00001,0,1,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,38131.375,529,,,,,,,,523,0,1,3,9,20,27,24,12,3,532,,,,,,,,,,99,,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,40973.297,508.25809,5.2250352,12.538111,21.206686,25.571764,20.601439,11.002912,3.854054,507.01276,0,1,3,11,22,30,23,9,1,518.40338,,,,,,,,,100.00001,100,,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,43609.734,495.44415,5.9113894,13.842266,24.718496,26.331841,18.076696,8.6692505,2.4500616,495.0835,0,1,5,12,23,29,21,8,1,514.77356,1,11.921187,4,21.79344,25.921564,21.807177,10.858463,2.888694,100,100,100.19053,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,41672.941,492.41409,6.2328486,13.951121,24.879034,27.201679,17.885534,8.0912437,1.7585393,494.18201,0,1,4,13,25,29,20,7,1,513.71027,1,11.187955,3,22.665012,28.764641,22.176403,9.49259,1.8831565,99.999992,100,100.16975,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,42799.105,493.93423,7.8263001,13.986483,23.204124,24.772707,18.379225,8.9573898,2.8737707,499.32312,0,1,4,11,23,30,21,8,1,514.12933,1,10.716015,3,22.438025,28.424416,22.978418,9.3482399,1.8417121,100,99,99.746819,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,44912.824,492.47849,7.7386146,14.128443,22.670296,26.0098,18.813076,8.2912617,2.3485086,497.97189,0,1,4,13,24,28,20,8,1,509.2215,.38187823,13.615228,3.4082067,22.568617,27.527958,21.625671,9.0775337,1.7949077,100,99,100,0,0,7,.14285715,3,2 +GBR,United Kingdom,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,46330.344,501.7699,6.4113803,12.817977,22.024263,25.456585,20.427418,9.7514143,3.1109605,503.9281,0,1,4,12,23,27,21,9,2,504.66748,.6424793,12.918324,3.8743231,24.000195,28.10075,20.784624,8.2271738,1.4521308,99.999992,99,100,0,1,7,.14285715,3,2 +GEO,Georgia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,9105.6777,379.49097,40.292744,28.421276,20.129528,8.1433401,2.416424,.47808018,.1186098,374.25989,,,,,,,,,,372.65295,,32.112309,,23.589157,8.826705,1.7436452,.21735922,0,100.00001,,66.489174,0,0,3,.33333334,3,4 +GEO,Georgia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,12611.084,403.83319,31.195099,25.900949,22.807428,13.378386,5.1606636,1.3604522,.19701985,401.28809,2,7,16,26,25,16,6,1,0,411.1315,4.2197213,30.539209,16.033438,28.206724,15.219701,4.9104433,.81004518,.060718138,99.999992,99,100,0,0,3,.33333334,3,4 +GEO,Georgia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,14247.609,397.58636,33.731197,27.32329,21.554277,11.946678,4.4209304,.91455203,.10907391,379.75253,0,7,24,33,23,10,2,0,0,382.6636,5.8289447,35.663933,22.884649,24.320602,9.5297518,1.6693797,.10273828,0,100,99,100,0,1,3,.33333334,3,4 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,29369.719,447,,,,,,,,473.79886,0,2,7,16,26,28,17,5,0,460.55386,,,,,,,,,,101,,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,33248.383,444.91187,17.772301,21.170622,26.30547,20.163179,10.594088,3.3729801,.62136203,472.26688,1,2,7,15,25,27,17,5,1,481.02228,,16.696054,,26.222895,26.507153,16.699903,5.3120904,.87504631,100,100,92.313141,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,36815.73,459.202,13.29117,19.048525,26.777803,23.221386,12.612306,4.1845593,.86425221,459.71118,1,3,8,16,27,28,14,3,0,473.37796,1,16.869888,6,28.941854,29.426504,14.169762,3.1901214,.2322779,100,100,99.830414,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,35980.922,466.09637,11.253779,19.06465,26.369781,23.972149,13.611256,4.9090228,.81936175,482.77625,0,1,6,14,26,29,18,5,1,470.11575,1,18.123489,6,29.798079,27.87471,13.98679,2.7845604,.27582258,99.999985,100,99.843452,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,28808.307,452.97342,14.453807,21.235399,27.15637,22.074385,11.177814,3.2563417,.64588529,477.19717,0,2,6,14,25,30,17,5,1,466.72202,1,18.081486,6,31.020416,28.79698,12.182794,2.2891018,.19409643,99.999992,100,99.564873,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,28536.846,453.62991,15.089936,20.666594,25.999086,22.052008,12.267329,3.4480889,.47695798,467.03949,0,2,8,17,25,27,16,4,0,454.8288,1.1796755,22.418957,9.1025057,28.350224,25.24567,11.570947,2.0068054,.12521581,100,99,99.999992,0,0,7,.14285715,3,2 +GRC,Greece,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,29715.123,451.37033,15.316803,20.522116,26.827139,22.492989,11.096952,3.2055752,.53842664,457.4144,0,2,9,19,27,25,13,3,0,451.63275,1.1919132,22.41457,8.1297026,31.644114,26.042542,9.2660151,1.2628608,.048283935,100.00001,98,100.00001,0,1,7,.14285715,3,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2000,36341.352,560,,,,,,,,525.45593,0,0,2,6,17,33,31,9,1,540.80914,,,,,,,,,,99,,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,37911.727,550.38324,3.8956988,6.5086689,13.9242,19.963249,25.022846,20.164078,10.521257,509.53516,0,1,3,9,20,35,27,5,0,539.4975,,7.3311248,,16.978416,28.012255,29.804144,13.623425,2.0286267,100,100,97.777985,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,46494.605,547.46069,2.9362795,6.5901742,14.404121,22.731279,25.623995,18.715897,8.9982538,536.06555,0,0,1,6,17,32,32,12,1,542.21136,0,7.0163159,2,16.934441,28.690758,29.712835,13.851688,2.0663545,100,101,100.27239,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,48492.313,554.52753,2.5862026,6.1779499,13.242517,21.948132,25.389841,19.899052,10.756304,533.15125,0,0,1,7,16,31,32,11,1,549.02869,0,5.1948562,1,15.104218,29.377108,32.706974,14.164847,2.0252004,100,99,99.573204,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,53821,561.24109,2.5745699,5.9424086,12.015584,19.693817,26.067913,21.447756,12.257952,544.6001,0,0,1,5,14,29,33,15,2,554.93744,0,4.3762822,1,13.02711,29.83107,34.898888,14.870544,1.8101569,100,99,99.814049,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,57254.082,547.93103,2.5371561,6.4489532,13.587751,23.448528,27.43528,18.811043,7.7312903,526.67529,0,0,2,7,18,32,29,10,1,523.2774,.085514672,7.7723866,1.555439,19.727003,36.093529,27.404205,6.9201736,.44174916,100.00001,99,100,0,0,7,.14285715,2,2 +HKG,"Hong Kong SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,61234.969,551.1543,2.7737134,6.3963423,13.512732,22.093365,26.26096,19.462141,9.5007477,524.28314,0,1,4,8,18,28,27,12,2,516.68616,.18248542,8.9314241,2.4492397,21.727488,33.842014,25.04171,7.1206794,.7049607,100,100,100.00001,0,1,7,.14285715,2,2 +HRV,Croatia,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2006,24685.586,467.2467,9.3252172,19.255444,28.866779,24.279224,13.565648,3.9558642,.75182199,477.36423,0,1,5,15,28,31,17,3,0,493.20441,0,13.956388,3,29.281916,31.001436,17.667078,4.5622711,.53115863,100.00001,100,100.00024,0,0,5,.2,3,2 +HRV,Croatia,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2009,24530.803,459.9389,12.363979,20.789446,26.749537,22.666515,12.507598,4.3016438,.62128103,475.7489,0,1,5,16,27,31,16,3,0,486.36343,1,14.895791,3,29.978376,31.114084,16.730053,3.4928346,.20280154,100,99,100.41395,0,0,5,.2,3,2 +HRV,Croatia,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2012,23757.467,471.13147,9.4996405,20.369806,26.705791,22.948326,13.492134,5.3565593,1.6277424,484.56503,0,1,4,14,28,31,18,4,0,491.3566,0,14.043896,3,29.085281,31.440954,17.603151,4.2630405,.31411237,99.999992,100,99.750427,0,0,5,.2,3,2 +HRV,Croatia,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2015,24545.684,464.0401,11.537001,20.501989,26.3013,23.023359,13.075377,4.5961375,.96483797,486.86319,0,1,5,15,27,29,19,5,1,475.3912,.43002108,19.162237,5.0514431,29.45936,27.518141,14.436423,3.5837104,.35866413,100,102,100,0,0,5,.2,3,2 +HRV,Croatia,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2018,27648.738,464.20428,10.980081,20.180658,27.379068,23.298992,13.020559,4.3151088,.82553244,478.98917,0,1,5,16,28,29,16,4,0,472.36261,.62029243,19.142715,5.6051192,29.979168,26.86504,14.155555,3.3200874,.31202358,100.00001,99,100.00002,0,1,5,.2,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,19547.197,488,,,,,,,,479.96677,0,1,6,16,25,29,18,5,0,496.07632,,,,,,,,,,100,,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,22356.715,490.01239,7.7963314,15.203786,23.793736,24.306166,18.22061,8.192729,2.4866428,481.86969,0,1,5,14,27,30,18,4,0,503.27887,,12.587653,,24.406813,28.526077,21.023142,7.6738467,1.1750778,100,99,95.392601,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,25561.008,490.93738,6.6924095,14.480462,25.10504,26.488726,16.893524,7.7017975,2.6380413,482.37451,0,1,5,14,25,31,19,4,0,503.93173,0,12.3466,2,26.030884,31.09156,20.962862,6.2385092,.63901573,99.999992,99,99.309425,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,24276.727,490.17001,8.099309,14.209017,23.226357,26.012102,18.359596,8.1368885,1.9567308,494.17874,0,0,5,12,24,31,22,6,0,502.64276,0,10.350632,3,25.509178,33.175953,21.780706,5.086421,.29895088,100,100,99.201843,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,24769.109,477.04446,9.9005232,18.161167,25.267139,22.969351,14.43764,7.138134,2.1260426,488.46133,0,1,5,14,24,30,20,5,0,494.30234,1,13.972914,3,26.415379,30.899494,18.716047,5.4503064,.47532693,99.999992,99,99.929466,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,27541.453,476.8309,11.329762,16.627502,23.095913,24.498394,16.305849,6.6517525,1.490826,469.52328,0,1,8,18,24,27,17,4,0,476.7475,.80625755,18.378441,6.823112,25.451853,27.317329,16.622768,4.2617221,.33851752,100,99,100,0,0,7,.14285715,3,2 +HUN,Hungary,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,31072.904,481.08264,9.5891361,16.053806,23.631191,25.234003,17.524723,6.538518,1.4286228,475.98666,0,1,7,17,25,26,18,5,1,480.91168,.64651984,17.806255,5.6512322,26.109877,28.07543,17.01532,4.2898016,.40556368,99.999992,100,100,0,1,7,.14285715,3,2 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2000,5688.2949,367,,,,,,,,370.61465,1,6,24,38,25,6,0,0,0,393.32993,,,,,,,,,,100,,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2003,6197.6021,360.15884,50.496468,27.640192,14.767817,5.4821901,1.3701562,.22997816,.013196679,381.59036,1,5,20,37,27,8,1,0,0,395.04239,,41.899166,,29.987846,8.5854692,1.1570474,0,0,100,99,81.629532,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2006,6973.2627,391.00681,35.238914,30.525366,20.405079,10.594347,2.823761,.39142609,.02110653,392.93283,0,4,18,37,29,11,2,0,0,393.48102,2,41.283234,18,27.536436,9.4534273,1.3789178,.025245432,0,99.999992,101,99.677269,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2009,7904.9204,371.30093,43.528938,33.140736,16.902657,5.4364319,.91606086,.075176187,0,401.7052,0,2,14,38,34,11,1,0,0,382.56946,4,41.026382,21,26.952915,6.9176936,.52895826,0,0,100,100,100.42595,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2012,9078.5,375.11444,42.311695,33.382244,16.849243,5.6933579,1.4877759,.2713615,.0043196911,396.12009,0,4,16,35,32,11,2,0,0,381.9115,4,41.924335,21,26.281883,6.5159011,.59595698,.0043196911,0,100,100,100.32239,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2015,10147.882,386.10959,37.90102,30.748756,19.551922,8.3792849,2.7491128,.61093944,.058962867,397.25949,0,4,17,35,31,12,2,0,0,403.0997,1.175639,40.357864,14.420122,31.747078,10.622545,1.5760789,.10067525,0,100.00001,101,100.00001,0,0,7,.14285715,2,3 +IDN,Indonesia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2018,11369.515,378.66812,40.567684,31.296017,18.568064,6.803627,2.2718675,.44960868,.043131419,370.96539,0,6,27,37,22,7,1,0,0,396.07007,1.8211068,41.352123,16.830933,29.170784,9.2138929,1.5507468,.05970788,.00070360588,99.999992,100,99.999985,0,1,7,.14285715,2,3 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,48017.461,503,,,,,,,,526.66864,0,1,3,8,18,30,27,12,2,513.36768,,,,,,,,,,101,,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,52520.598,502.83731,4.6740193,12.135763,23.602232,27.998707,20.226929,9.1196795,2.2426705,515.4801,0,1,2,8,21,32,26,9,1,505.39084,,11.614124,,25.05275,29.624315,21.731241,7.3812046,.83725566,100,100,96.240891,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,58214.066,501.47177,4.0909147,12.324611,24.140223,28.555092,20.645454,8.6155472,1.6281588,517.31323,0,0,3,9,21,30,25,10,2,508.32892,0,11.964489,3,24.007183,29.719952,21.350037,8.2785463,1.1340408,100,100,99.454247,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,52382.961,487.13638,7.2604098,13.552673,24.49017,28.601246,19.438141,5.7878933,.86946779,495.6391,0,1,4,12,23,31,22,6,1,507.98416,1,10.742621,3,23.293999,29.927799,22.893728,7.52672,1.183291,100,100,99.568153,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,52886.887,501.49747,4.8184261,12.07827,23.937637,28.230652,20.281929,8.489851,2.1632335,523.17322,0,0,2,7,20,33,26,10,1,522.00397,0,8.5283613,2,22.043526,31.145275,24.981127,9.2624388,1.4772439,100,99,99.437965,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,71243.422,503.72198,3.5160668,11.461202,24.077053,29.976191,21.15609,8.3114223,1.5019761,520.81482,0,0,2,8,21,32,26,9,1,502.5751,.28091335,12.3715,2.6788077,26.416861,31.114399,20.085314,6.2847085,.76749653,100,99,99.999992,0,0,7,.14285715,3,2 +IRL,Ireland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,83470.742,499.6329,3.8316078,11.855329,24.720942,30.529312,20.83382,7.2009439,1.0280455,518.07843,0,0,2,9,22,30,24,10,2,496.11356,.31887463,13.435051,3.2725518,26.87344,31.309359,18.950788,5.3651237,.47481066,100,99,100,0,1,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,40594.824,514,,,,,,,,506.93045,0,1,3,10,22,31,24,8,1,495.91422,,,,,,,,,,100,,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,42180.605,515.10864,4.5118999,10.489957,20.239491,26.098145,23.194593,11.740514,3.7254028,491.74695,0,2,5,12,24,30,21,6,1,494.74524,,13.164248,,25.963745,29.489048,19.227612,6.0903153,.73003638,100.00001,101,94.665001,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,48606.836,505.54483,5.1071939,11.711109,22.276329,26.552275,21.701834,10.138444,2.5128157,484.44528,0,2,5,13,25,30,19,5,1,490.79376,1,14.738692,5,25.877254,28.250237,18.984394,5.5975447,.72636366,100,100,100.17448,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,48207.992,506.66916,5.6799178,11.273824,21.261953,27.295189,20.900957,10.487277,3.1008825,500.28333,0,1,4,12,22,31,22,8,1,495.59851,1,12.474539,4,25.820456,30.448431,18.830706,6.1257353,.83254755,99.999992,101,99.53241,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,47710.316,492.79568,7.4547749,14.021418,23.608232,25.697708,18.066055,8.8854351,2.2663763,482.52243,0,2,5,13,25,30,19,5,1,478.1546,2,16.016666,6,27.465513,27.15457,16.174494,4.564796,.64989352,100,100,100.02593,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,51364.641,488.0332,8.3803244,15.234745,23.70668,24.841448,17.519474,8.1022673,2.21506,481.52551,0,1,6,14,26,27,18,6,1,473.2301,.78930062,18.745583,5.7935324,29.032227,27.285416,14.591989,3.4868977,.27505511,100,99,100,0,0,7,.14285715,3,2 +ISL,Iceland,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,56591.715,495.18738,7.3575006,13.331695,21.996548,26.741877,20.186798,8.473053,1.9125301,473.9743,0,2,8,16,25,25,17,6,1,475.02411,.49660581,18.558887,5.9284163,28.321609,27.698835,15.168225,3.6005838,.22683577,100,100,100.00001,0,1,7,.14285715,3,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2000,31568.043,433,,,,,,,,452.17239,1,4,10,18,24,24,15,4,0,434.13623,,,,,,,,,,100,,0,0,6,.16666667,5,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2006,32512.377,441.85873,22.168829,19.806404,21.764456,18.403666,11.794964,4.7856526,1.2760298,438.67227,2,5,13,19,22,21,13,4,1,453.90372,3,21.234493,12,24.029222,20.808075,13.775517,4.4343438,.80590326,100.00001,100,100.08755,0,0,6,.16666667,5,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2009,33684.793,446.86377,20.519913,18.933332,22.549061,20.081333,12.046257,4.700356,1.1697484,473.98996,1,3,8,15,22,25,18,6,1,454.85095,4,19.228428,10,26.012566,24.125494,12.825928,3.4815381,.45385012,100,99,100.1278,0,0,6,.16666667,5,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2012,36067.172,466.48145,15.871855,17.63032,21.57453,20.956867,14.599312,7.1866169,2.1804986,485.80322,1,3,7,13,21,25,21,8,2,470.07266,3,17.66255,8,24.817677,24.373241,16.095705,5.2112436,.61851579,99.999992,101,99.778931,0,0,6,.16666667,5,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2015,37633.445,469.66949,15.044943,17.082254,21.133186,21.660358,16.148304,7.054111,1.8768431,478.9606,1,3,8,15,22,24,19,8,1,466.5528,2.0520654,19.876856,9.4910345,24.419626,23.297277,15.02353,5.1095247,.730088,100,101,100.00001,0,0,6,.16666667,5,2 +ISR,Israel,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2018,39543.219,463.03455,17.727816,16.367407,20.694931,21.049429,15.353712,6.9948325,1.8118727,470.41519,1,5,10,15,19,22,18,8,2,462.19662,3.2304215,19.155376,10.714435,23.124826,22.851049,15.106155,5.1649079,.65282732,100.00001,100,100,0,1,6,.16666667,5,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,43250.211,457,,,,,,,,487.46964,0,1,4,14,26,31,19,5,0,477.60333,,,,,,,,,,100,,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,43980.813,465.66422,13.236614,18.697779,24.671843,22.943541,13.423689,5.4773521,1.5491827,475.66159,1,2,6,15,25,28,18,5,1,486.45422,,15.005019,,23.840633,26.261251,18.272779,6.6452608,1.3139057,100,101,91.338852,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,45122.945,461.68872,13.509309,19.313932,25.479645,22.126755,13.347914,4.9617515,1.2606933,468.5231,1,3,7,15,25,26,18,5,1,475.39722,1,17.996468,6,27.608709,27.389071,15.134616,4.1998801,.40844247,100.00001,101,99.737183,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,42266.734,482.90848,9.056447,15.873826,24.164743,24.610886,17.332895,7.3614841,1.5997176,486.05109,0,1,5,14,24,29,20,5,0,488.83136,1,14.514288,5,25.467648,29.462198,18.643353,5.3092575,.48776758,99.999992,98,99.884506,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,41690.91,485.32117,8.5315933,16.137339,24.071817,24.587748,16.748344,7.7630262,2.1601326,489.75439,0,1,5,13,24,30,20,6,1,493.54147,1,13.816661,4,26.04241,30.119511,19.063616,5.5004358,.57839996,100,100,100.12103,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,40431.313,489.7287,8.3038883,14.947735,23.287601,24.664371,18.280134,8.0700779,2.446193,484.758,0,1,5,15,25,29,19,5,1,480.54681,.55488908,17.220222,5.4447317,27.124233,28.554375,17.01984,3.8386431,.24306436,100,100,100,0,0,7,.14285715,3,2 +ITA,Italy,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,42188.508,486.58984,9.059824,14.763472,22.913054,25.606464,18.118011,7.5270109,2.0121655,476.28467,0,2,7,15,26,28,17,5,0,468.01172,1.0848368,18.243595,6.550724,30.184149,27.82165,13.380795,2.5593731,.1748769,100.00001,100,99.999992,0,1,7,.14285715,3,2 +JOR,Jordan,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,10776.752,384.03604,36.909378,29.442141,21.922531,9.3156633,2.1734183,.23471187,.0021549233,400.58124,2,6,15,27,31,16,3,0,0,421.97183,4,28.166191,13,30.75374,18.716728,5.5802832,.61044931,.016601034,99.999992,100,100.84399,0,0,5,.2,5,4 +JOR,Jordan,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,11460.764,386.66382,35.417881,29.851175,22.874577,9.4625416,2.1269543,.26641074,0,405.00931,2,5,14,28,32,16,3,0,0,415.38779,4,27.596991,14,32.203159,17.572151,4.1200824,.45963576,.011333527,99.999535,100,99.963356,0,0,5,.2,5,4 +JOR,Jordan,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,10520.157,385.59555,36.47599,32.086197,20.960251,8.0725269,1.8424542,.48672178,.075859718,399.03476,2,5,15,28,31,15,3,0,0,409.36722,4,31.394403,14,32.221046,14.972404,3.0041895,.24213299,.0042551565,100,99,99.838432,0,0,5,.2,5,4 +JOR,Jordan,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,9970.5898,380.259,38.863674,28.66556,20.882318,9.2120056,2.1261656,.23668295,.01359318,408.1022,2,6,14,25,31,19,4,0,0,408.6691,4.1690831,30.394341,15.199325,30.864824,16.136948,3.0680184,.16746131,0,100.00001,101,99.999992,0,0,5,.2,5,4 +JOR,Jordan,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,9853.9268,399.7619,30.747797,28.577892,24.029755,12.410707,3.5705945,.5989598,.064293317,419.06366,1,4,11,25,34,20,4,0,0,429.25208,3.1750546,26.166487,10.965521,32.369179,20.676701,5.9772105,.64153379,.028313015,100,99,99.999992,0,1,5,.2,5,4 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2000,35520.273,557,,,,,,,,522.23492,0,0,2,7,18,33,29,9,1,550.40375,,,,,,,,,,99,,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,36003.996,534.13647,4.7496614,8.5693064,16.338732,22.416336,23.638857,16.069933,8.2171745,498.10519,1,2,5,12,21,27,23,8,1,547.63824,,7.836412,,16.20542,23.615894,26.805386,16.458706,5.7451477,100,100,96.666962,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,37900.457,523.10254,3.9314935,9.1134539,18.890602,26.055288,23.711023,13.491959,4.8061805,497.95706,0,1,5,12,22,29,22,8,1,531.38849,0,8.8534317,3,18.457106,27.481833,26.97821,12.429682,2.6210163,100,100,99.821281,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,35987.781,528.9931,3.9514942,8.5267477,17.421066,25.716946,23.503233,14.70642,6.1740942,519.85773,0,1,3,9,18,28,27,11,2,539.43097,1,7.4683485,2,16.269836,26.600876,29.514671,14.383359,2.5518985,100,99,99.788986,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,38137.469,536.40692,3.1572189,7.9050827,16.915525,24.661324,23.690239,16.040352,7.6302576,538.05151,0,1,2,7,17,27,28,15,4,546.7356,0,6.4374194,2,16.319935,27.53396,29.45372,14.829043,3.4070027,100,101,99.981079,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,39675.293,532.43988,2.9395714,7.7525835,17.226036,25.819216,25.917492,15.04852,5.2965808,515.9585,0,1,3,9,20,30,26,10,1,538.39478,.15642095,7.7371502,1.7190814,18.098286,28.193018,28.781279,12.899546,2.4152195,99.999992,100,100,0,0,7,.14285715,2,2 +JPN,Japan,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,41205.965,526.97327,2.8580499,8.5968256,18.719959,26.411606,25.072817,14.028059,4.3126841,503.85605,0,1,4,12,22,29,22,9,2,529.13544,.15484975,8.8708649,1.8175188,19.899757,29.694963,26.508734,11.407347,1.645964,100,101,100.00001,0,1,7,.14285715,2,2 +KAZ,Kazakhstan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,19614.734,404.8847,29.578945,29.559052,23.464342,12.039897,4.1997337,.90005332,.25797608,390.41006,1,7,20,31,24,13,4,0,0,400.37613,5,32.963985,18,27.855614,12.794716,3.6114769,.31165659,0,99.999992,100,100.53744,0,0,4,.25,3,4 +KAZ,Kazakhstan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,22702.58,431.7984,14.535092,30.708494,31.537432,16.856476,5.4269176,.85664415,.078942701,392.73624,0,4,17,36,31,10,1,0,0,424.70752,1,30.656466,10,36.790218,17.801331,3.2662528,.19585556,0,100,99,99.710121,0,0,4,.25,3,4 +KAZ,Kazakhstan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,24290.418,459.81601,10.223238,21.92827,29.75898,22.838629,11.018885,3.4630008,.76899827,427.14099,0,2,11,29,33,19,6,1,0,456.48361,.22012223,23.817398,4.0524349,38.201061,23.85915,8.0534554,1.6740389,.12233612,99.999992,101,99.999992,0,0,4,.25,3,4 +KAZ,Kazakhstan,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,25549.967,423.14612,22.311947,26.788076,26.642677,16.02532,6.2820787,1.6240506,.32584929,386.90927,0,4,22,38,24,9,3,0,0,397.10483,2.215915,40.29528,17.819334,26.910177,9.873538,2.4978199,.36872947,.019204143,100.00001,100,99.999992,0,1,4,.25,3,4 +KGZ,Kyrgyz Republic,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDX,IDA,2006,3595.2124,310.58383,72.923866,16.487812,7.0551767,2.7736278,.70152634,.049254596,.0087326765,284.7066,,,,17.764179,8.1137543,2.9683833,.59596896,0,0,322.03159,,28.177519,,10.044746,2.8908563,.70556951,.022716854,0,100,29.442286,41.841412,0,0,2,.5,3,3 +KGZ,Kyrgyz Republic,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDX,IDA,2009,4218.9849,331.15552,64.8451,21.785925,9.326704,3.2953622,.70775372,.039152209,0,314.0199,,,29.673943,23.776819,11.526088,4.1760559,.96810889,.10836992,0,329.54562,,29.043869,,13.25416,4.0098076,.73559076,.042622771,0,100.00001,70.229378,47.086048,0,1,2,.5,3,3 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2000,22277.201,547,,,,,,,,524.75427,0,0,1,5,19,39,31,6,0,552.11853,,,,,,,,,,101,,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,25274.342,542.22736,2.4562232,7.0632496,16.629015,24.112846,24.966669,16.692459,8.0795383,534.09125,0,0,1,5,17,33,31,11,1,538.42511,,,,,,,,,100,99,,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,28652.574,547.4585,2.3209896,6.5340719,15.150874,23.469187,25.465399,17.990786,9.0686941,556.02191,0,0,1,4,13,27,33,18,4,522.14813,0,8.7255077,2,21.181372,31.755272,25.492487,9.2337275,1.0981059,100,100,99.486473,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,30740.908,546.22852,1.9150565,6.2103062,15.622705,24.390347,26.293535,17.718842,7.8492084,539.26746,0,0,1,5,15,33,33,12,1,537.98621,0,5.2395086,1,18.502388,33.144428,30.396925,10.544921,1.0768396,99.999992,100,99.905014,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,34104.535,553.76666,2.7168691,6.4162378,14.660503,21.413902,23.891085,18.773512,12.127892,535.79047,0,0,2,5,16,31,31,13,2,537.7876,0,5.4533143,1,18.040548,33.564568,30.080025,10.568552,1.1157874,100,100,99.822792,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,36681.145,524.1062,5.4402928,10.022593,17.232201,23.706394,22.699905,14.315256,6.5833578,517.43671,0,1,3,10,19,29,25,11,2,515.80988,.39429092,11.082346,2.9055052,21.738943,29.234745,24.042387,9.2171946,1.3845885,99.999992,100,100.00001,0,0,7,.14285715,2,2 +KOR,"Korea, Rep",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,39492.27,525.93298,5.3895187,9.6090078,17.327257,23.370108,22.921551,14.439338,6.9432201,514.05231,0,1,4,10,20,28,25,11,2,519.00726,.47535843,10.622878,3.0686922,20.972063,28.583811,24.483881,9.9943581,1.7989608,100.00001,101,99.999992,0,1,7,.14285715,2,2 +LBN,Lebanon,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,16413.229,396.24969,36.643757,23.599888,19.509567,12.333344,5.9303074,1.688853,.29428443,346.54901,8,16,25,22,16,9,4,1,0,386.48541,6.822506,32.251621,23.557678,22.038118,11.614216,3.2754414,.39619356,.044225845,99.999992,101,100,0,0,2,.5,5,4 +LBN,Lebanon,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,16026.494,393.45004,38.033871,21.751162,19.095964,13.057387,6.0282187,1.7402666,.29313028,353.35532,6,17,23,22,17,10,4,1,0,383.71698,8.9467821,29.684492,23.647991,21.820042,11.806648,3.588567,.481047,.024430009,100,100,100,0,1,2,.5,5,4 +LIE,Liechtenstein,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,,514,,,,,,,,482.58691,,,,14.52032,23.231764,30.091303,19.487957,4.6861024,0,476.09985,,,,,,,,,,92.017448,,0,0,5,.2,3,2 +LIE,Liechtenstein,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,,535.79541,4.8164473,7.4545979,17.348867,21.579464,23.173504,18.324457,7.3026624,525.07501,,,,7.9069085,18.654388,30.336519,27.640255,11.239933,1.7396902,525.17542,,10.311472,,20.497452,27.824854,22.093473,12.151737,3.4154747,100,97.517693,96.294456,0,0,5,.2,3,2 +LIE,Liechtenstein,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,,524.96741,3.9515707,9.2247639,18.214214,26.396624,23.728897,12.648489,5.8354411,510.43777,,,,9.4155741,19.956652,31.259699,24.637075,8.206687,1.6397069,522.16101,,10.288755,,20.959919,28.691021,25.209681,9.9866772,2.2307818,100,95.115402,97.366837,0,0,5,.2,3,2 +LIE,Liechtenstein,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,,536.04846,2.9535706,6.5351553,14.977721,26.244762,31.234697,13.032354,5.0217385,499.32205,,,2.8438566,12.837288,24.010429,31.053169,24.623131,4.236269,0,519.91418,,9.9143429,,23.796291,29.833834,25.357996,8.9767418,.72974467,100,99.604141,98.60894,0,0,5,.2,3,2 +LIE,Liechtenstein,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,,534.96509,3.4879839,10.589945,15.186559,22.688475,23.20285,17.445295,7.398891,515.52167,,,1.9147562,10.460793,22.378597,28.630648,25.674307,10.352959,.58794212,524.69501,,9.6230545,,21.989265,30.762047,26.714605,9.1043158,1.0289035,100,100.00001,99.222191,0,1,5,.2,3,2 +LTU,Lithuania,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,23058.605,486.42133,7.7984099,15.154905,25.05341,25.088696,17.818037,7.304491,1.7820519,470.07077,0,1,7,17,27,27,16,4,0,487.9567,0,16.00425,4,27.401468,29.813011,17.465004,4.5399981,.43744051,100,99,99.661171,0,0,5,.2,3,2 +LTU,Lithuania,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,23151.424,476.60229,9.0344658,17.298145,26.07057,25.270227,15.350763,5.6748333,1.3009948,468.44275,0,1,6,18,30,29,14,3,0,491.40979,0,13.519807,3,28.929045,32.447491,17.005676,4.2584147,.36225185,100,101,99.52269,0,0,5,.2,3,2 +LTU,Lithuania,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,27381.381,478.82327,8.7442312,17.274075,25.936125,24.599829,15.384643,6.6244755,1.4366214,477.30661,0,1,5,16,28,31,16,3,0,495.70007,0,12.726722,3,27.615557,32.859852,18.343828,4.7486949,.35521224,100,100,99.649864,0,0,5,.2,3,2 +LTU,Lithuania,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,30801.422,478.38339,8.4741592,16.934874,26.379385,25.361895,15.920749,5.7872863,1.1416528,472.40659,0,1,7,17,27,27,17,4,0,475.40891,.48019749,18.871077,5.3777084,29.659445,26.293301,15.143629,3.9127562,.26188612,100,100,100.00001,0,0,5,.2,3,2 +LTU,Lithuania,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,35389.977,481.19119,9.275773,16.360666,24.185829,25.20109,16.544514,6.7519422,1.6801848,475.87347,0,1,6,17,26,28,17,5,0,482.06702,.47828773,16.976641,4.7297049,28.362761,28.741846,16.270416,3.9752104,.46513489,100,100,100,0,1,5,.2,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,96340.297,446,,,,,,,,441,1,3,10,21,27,25,11,2,0,443.07059,,,,,,,,,,100,,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,100685.62,493.20856,7.4444914,14.268516,22.93857,25.854809,18.658028,8.4793072,2.3562815,479.42258,1,2,6,14,24,29,19,5,0,482.76401,,16.038952,,25.125975,26.3491,17.642412,5.9010592,.78060269,100.00001,100,91.838097,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,108174.26,490.00183,8.3250027,14.46687,23.193586,25.239536,18.190752,8.2475939,2.336658,479.36655,0,2,6,14,25,28,19,5,0,486.32437,1,15.576683,6,25.354271,28.574331,18.08831,5.3503513,.53254473,99.999992,99,100.47649,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,105077.95,489.06726,9.5655327,14.356631,22.677958,23.079264,18.967209,9.0087442,2.3446605,472.1734,1,3,7,16,24,27,17,5,0,483.92844,2,15.259681,6,24.308836,27.123594,18.153572,6.0165329,.69729662,99.999992,100,99.559517,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,105557.27,489.84509,8.7952309,15.536146,22.320669,23.62303,18.494238,8.5874071,2.6432812,487.80704,0,2,6,14,23,26,20,7,1,491.21518,1,15.059579,6,24.20196,26.215321,19.184015,7.0116768,1.1682074,100.00001,99,99.840752,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,110951.99,485.7706,8.8384514,16.981003,22.52441,23.62438,18.042641,7.8253078,2.1638079,481.43909,0,2,8,16,22,25,19,7,1,482.8064,.52458262,18.91386,6.4182978,24.772196,25.143354,17.314999,6.0398917,.87281817,100.00001,100,100.00001,0,0,7,.14285715,3,2 +LUX,Luxembourg,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,114109.97,483.42148,10.860588,16.357006,21.692081,22.608536,17.669796,8.5564337,2.2555585,469.98538,0,2,9,18,24,23,16,6,1,476.76941,.81938601,19.164406,6.7818432,25.679592,25.554785,16.551041,4.9338789,.51506722,100,99,100,0,1,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,12854.619,463,,,,,,,,458.07108,1,3,9,18,26,25,14,4,0,460.06082,,,,,,,,,,100,,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,16426.736,483.37494,7.6271563,16.102331,25.469601,26.279854,16.550297,6.3317671,1.6389931,490.56253,0,1,4,13,26,31,20,5,1,489.12491,,15.013004,,27.697271,29.130886,17.282202,5.2499232,.7493571,99.999992,101,95.122643,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,22743.813,486.16608,6.4319777,14.26807,26.321245,29.019453,17.378536,5.4747629,1.1059556,479.49182,0,1,5,15,28,30,17,4,0,489.54379,0,13.840988,3,28.985571,32.908504,16.584215,3.8135827,.2741172,100,100,99.406975,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,21482.766,481.95383,5.8198719,16.739674,27.206161,28.155357,16.376123,5.0838413,.618972,483.96014,0,0,3,14,29,33,17,3,0,493.87894,0,12.460801,2,29.068003,35.530006,17.580715,2.9815848,.11872627,100,99,99.739838,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,23911.875,490.57101,4.822741,15.120096,26.628929,27.821363,17.619457,6.4651875,1.5222255,488.69443,0,1,4,13,27,33,19,4,0,502.18619,0,10.525097,2,28.175367,35.120773,19.969074,4.0465178,.32813463,100,101,100.16496,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,26489.758,482.30511,5.6729093,15.76002,28.267849,28.812508,16.329929,4.549088,.60769719,487.75809,0,0,4,13,27,32,19,4,0,490.22501,.12671544,14.540861,2.5810289,29.807117,31.707373,17.428089,3.4917953,.31701902,100,99,100.00001,0,0,7,.14285715,3,2 +LVA,Latvia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,30038.465,496.12634,4.3925452,12.924186,25.805563,29.371767,19.018381,7.0546613,1.4328952,478.69867,0,1,5,17,27,29,17,4,0,487.25058,.30000576,14.823495,3.3667843,29.496672,31.469873,16.819626,3.4679155,.25562978,99.999992,100,100.00001,0,1,7,.14285715,3,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,58976.977,527.27026,2.3432145,8.8342218,19.643179,26.814993,23.712265,13.825516,4.826611,497.64008,0,0,1,9,28,41,19,2,0,524.68372,,8.4157419,,22.163191,31.500256,25.759911,9.0858717,1.4268157,100,100,98.351784,0,0,6,.16666667,2,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,85302.156,525.0025,2.6173568,8.3212004,19.95854,27.293318,24.397093,13.643044,3.7694457,492.28781,0,0,2,10,29,37,19,3,0,510.83972,0,8.8764505,1,25.983704,35.667351,22.767733,5.0376616,.25584918,100,100,99.588745,0,0,6,.16666667,2,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,95939.344,525.28302,2.7993436,8.1632833,19.558641,27.802107,24.536835,12.846477,4.293313,486.63535,0,0,3,12,31,35,17,3,0,511.06961,0,8.1266966,1,25.165604,37.772472,22.652767,4.5410967,.23434845,100,101,99.492996,0,0,6,.16666667,2,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,149070.67,538.13452,3.2173238,7.5640965,16.434202,24.011765,24.435362,16.772276,7.5649753,508.94925,0,0,2,9,23,34,24,6,1,520.57086,0,7.3937201,1,22.229784,36.188103,26.156519,6.2367396,.43875107,100,99,99.643616,0,0,6,.16666667,2,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,120294.9,543.8078,1.3175837,5.328537,15.105973,27.307684,29.079172,16.898695,4.9623556,508.69049,0,0,2,9,23,34,24,6,0,528.54962,.058099657,6.8966947,1.1012678,20.604372,34.189194,27.975351,8.2564697,.91855031,100,98,100,0,0,6,.16666667,2,2 +MAC,"Macao SAR, China",EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,131908.16,557.67023,1.0347962,3.9664845,12.254188,24.825134,30.269512,19.952251,7.6976337,525.11615,0,0,2,8,19,30,26,12,2,543.58575,.097394049,5.1221008,.78023922,17.222063,32.296162,30.844641,11.933397,1.7040026,100,99,100,0,1,6,.16666667,2,2 +MAR,Morocco,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2018,7641.3735,367.72736,47.133404,28.498535,16.868999,6.2013397,1.163839,.13073313,.0031504151,359.38788,0,9,31,33,21,6,1,0,0,376.59689,2.7348633,40.664223,26.050821,24.049383,6.0643368,.42721224,.0091621941,0,100.00001,101,100.00001,0,1,1,1,5,3 +MDA,Moldova,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDB,Blend,2009,7975.3843,397.4921,32.077435,28.586065,23.269215,11.821878,3.5727019,.61954123,.053162016,388.07776,,,,,,,,,,412.771,,28.730799,,31.775518,16.944569,3.7164407,.21431164,0,99.999992,,81.38163,0,0,3,.33333334,3,3 +MDA,Moldova,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDB,Blend,2015,10360.829,419.66351,24.794275,25.467154,25.004787,16.251949,6.7365932,1.5087693,.23647243,416.22931,1,5,15,25,28,19,7,1,0,427.9978,2.3013458,28.15943,11.782149,31.463945,19.662573,5.8913379,.70423794,.0349802,100.00001,101,100,0,0,3,.33333334,3,3 +MDA,Moldova,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IDB,Blend,2018,12266.098,420.604,26.141937,24.191204,23.456518,16.491232,7.2832589,2.0466819,.38916773,423.99298,0,4,13,25,28,21,7,1,0,428.48526,2.426291,27.441402,12.736398,29.710337,20.249771,6.5645509,.84694916,.024300996,100,99,100,0,1,3,.33333334,3,3 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,17783.125,387,,,,,,,,421.96063,0,2,14,28,30,19,6,1,0,421.53735,,,,,,,,,,100,,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,17231.721,385.21835,38.053055,27.86964,20.844303,10.108844,2.7468369,.36100715,.016311757,399.72192,1,7,17,27,28,16,4,0,0,404.89618,,32.244324,,28.812538,13.982971,3.6973014,0,0,99.999992,100,78.737137,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,18346.879,405.65463,28.354324,28.14526,25.211363,13.149332,4.3044949,.77561152,.059613518,410.49646,2,5,14,26,29,18,5,1,0,409.65195,3,32.77634,16,30.750601,14.83084,3.1959667,.27604362,.0016428033,100,100,100.83144,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,17194.656,418.50909,21.921179,28.875315,28.271816,15.571801,4.6523314,.65948737,.04807049,425.26529,1,3,11,26,33,21,5,0,0,415.90866,2,32.841278,12,33.570057,15.783021,3.0879951,.19581807,0,100,100,99.478165,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,18622.707,413.28146,22.827961,31.884239,27.824249,13.128089,3.7079079,.59244734,.035107497,423.55377,0,2,11,27,34,20,4,0,0,414.92014,1,34.423698,11,36.964005,13.774352,2.1073134,.13794655,.00016789581,100.00001,98,99.407486,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,19288.246,408.0235,25.533512,31.107864,26.941463,12.887855,3.1983168,.31482953,.016159367,423.27649,0,2,11,28,34,20,4,0,0,415.7099,1.1364003,34.964878,11.666792,34.684643,15.096684,2.3304625,.12014027,0,100,99,100,0,0,7,.14285715,4,4 +MEX,Mexico,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,19992.23,408.80148,25.961695,30.28475,26.412415,13.119197,3.7142062,.46894267,.038795047,420.4689,0,2,13,29,32,17,5,1,0,419.20471,1.021098,34.180679,11.605914,33.949593,15.453605,3.5363235,.2527875,0,99.999992,99,100,0,1,7,.14285715,4,4 +MKD,North Macedonia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,10203.305,381,,,,,,,,373,3,10,22,28,24,11,2,0,0,400.71545,,,,,,,,,,100,,0,0,3,.33333334,3,4 +MKD,North Macedonia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,15104.32,371.3114,45.106506,25.119955,17.256561,8.5707855,3.1088712,.68400943,.15330961,351.74149,5,14,24,28,19,8,2,0,0,383.6824,6.8059611,33.803612,22.304306,24.598927,10.268183,2.0270863,.1834964,.0084257741,100,100,99.999985,0,0,3,.33333334,3,4 +MKD,North Macedonia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,15958.99,394.44684,35.240742,25.795332,21.337811,12.053776,4.4752374,.95679259,.14030908,392.66776,2,7,18,28,27,14,4,0,0,413.04468,4.5067406,29.412603,15.536661,28.153852,16.41703,5.1617589,.76903951,.04231317,100,100,100,0,1,3,.33333334,3,4 +MLT,Malta,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2009,30937.451,462.87619,16.280703,17.432037,21.863852,21.28207,15.417232,6.3551259,1.3689818,442.05249,,,,,,,,,,461.33942,,17.991188,,23.301579,22.740248,15.480838,5.1850934,.79512155,99.999992,,85.494064,0,0,3,.33333334,5,2 +MLT,Malta,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2015,38961.051,478.64481,14.694831,14.442183,19.9872,21.558964,17.472727,8.8681812,2.9759171,446.66611,2,5,11,17,22,22,14,5,1,464.78189,3.9066925,17.977354,10.636734,23.354326,21.701567,14.783672,6.0810065,1.5586493,100,99,100.00001,0,0,3,.33333334,5,2 +MLT,Malta,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2018,43085.324,471.72443,14.338905,15.908561,21.53331,23.175333,16.551075,6.7099013,1.7829134,448.23477,1,5,12,19,24,22,13,4,1,456.59033,3.3870249,19.377232,10.758266,24.913397,23.666653,13.48722,3.923502,.48670664,100,101,100,0,1,3,.33333334,5,2 +MNE,Montenegro,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,15250.37,399.30768,31.648211,28.40209,23.306881,11.835412,3.9647555,.79174358,.050907809,391.97543,1,6,19,30,27,13,3,0,0,411.78842,2,32.956898,15,30.996094,14.878644,3.6141593,.27725008,.0029639846,99.999992,99,99.726006,0,0,5,.2,3,4 +MNE,Montenegro,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,16366.321,402.51535,29.644381,28.771839,24.600533,12.212433,3.8105934,.90772909,.052492566,407.54776,1,5,16,28,28,17,5,1,0,401.27747,5,31.390493,17,29.445093,13.604249,3.1375523,.24712159,0,100,101,99.824509,0,0,5,.2,3,4 +MNE,Montenegro,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,16821.141,409.62662,27.50157,29.145092,24.224796,13.146145,4.9406495,.92934948,.11239801,422.11136,1,4,13,26,29,20,7,1,0,410.0976,3,32.008247,15,29.673716,15.428646,3.8239083,.3833372,0,100.00001,101,99.317856,0,0,5,.2,3,4 +MNE,Montenegro,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,18283.988,417.93411,25.008369,26.88599,24.859823,15.664522,6.0595188,1.3502697,.17150727,426.88449,0,4,13,25,29,20,8,1,0,411.3136,3.1383982,32.083015,15.808372,28.974281,15.131021,4.4019566,.45527691,.0076798722,100,100,100,0,0,5,.2,3,4 +MNE,Montenegro,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,20709.672,429.61154,19.890614,26.270071,27.25666,17.899097,6.9099402,1.6226286,.15098561,421.05759,0,3,14,28,30,18,6,1,0,415.17224,2.233495,31.43856,14.552796,31.460997,15.937053,4.0456862,.32998687,.0014256751,99.999992,100,99.999992,0,1,5,.2,3,4 +MUS,Mauritius,SSF,Sub-Saharan Africa,SSA,Sub-Saharan Africa (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,16131.707,419.99716,25.144682,25.018806,24.947872,16.683783,6.5854416,1.4934213,.12599392,406.57184,,,13.554042,24.307894,29.936855,18.170135,4.8487549,.35637441,0,417.20374,,26.984337,,28.907911,17.808767,5.7653737,.66366339,0,100,91.174057,80.130058,0,1,1,1,7,4 +MYS,Malaysia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,19446.645,404.25812,27.028696,32.299324,26.290239,11.249114,2.7574718,.36318332,0,413.81204,,,,,,,,,,422.23163,,30.330927,,36.206348,17.260263,3.3035421,.20161566,0,99.988022,,87.302696,0,0,4,.25,2,4 +MYS,Malaysia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,22136.406,420.51297,22.961172,28.793169,26.022684,14.882091,5.997448,1.2350345,.1084033,398.19604,1,5,16,30,31,14,3,0,0,419.50266,2,31.039207,13,33.894257,16.534456,3.7370105,.32329285,.0050551179,99.999992,100,100.53328,0,0,4,.25,2,4 +MYS,Malaysia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,24792.488,446.1098,13.7696,23.749685,29.466679,21.919703,9.0657225,1.788911,.23969933,430.57819,0,2,10,25,34,23,5,0,0,442.94751,.46230564,25.948523,7.3108153,36.379555,23.569754,5.7646194,.55341899,.011009334,99.999992,99,100,0,0,4,.25,2,4 +MYS,Malaysia,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,27536.918,440.20755,16.059504,25.43828,28.264959,19.272839,8.503314,2.1703811,.29072195,414.97952,0,4,14,28,31,18,4,0,0,437.61807,.7367872,27.570713,8.2711782,35.858471,21.542131,5.4279542,.58766097,.0051020603,100,99,100,0,1,4,.25,2,4 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,48031.891,537.8233,2.5553362,8.3508959,18.035662,22.992775,22.562672,18.211668,7.2909913,513.11896,0,0,2,9,23,31,26,8,1,524.36871,,11.090648,,22.546778,25.692835,23.464699,12.677883,2.3100295,100.00001,100,97.782867,0,0,6,.16666667,3,2 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,51337.855,530.65405,2.4487557,9.0793333,18.871727,24.323502,24.135395,15.772856,5.3684311,506.74698,0,1,4,10,21,29,26,8,1,524.86151,0,10.6793,2,21.118109,26.941189,25.843365,11.483154,1.6519535,100.00001,100,99.717072,0,0,6,.16666667,3,2 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,51850.5,525.83569,2.8102765,10.605908,18.951334,23.860653,23.916731,15.434964,4.4201312,508.40372,0,0,2,12,25,28,24,9,1,522.21814,0,10.615113,2,21.825565,26.914835,25.342293,11.239275,1.499769,100,101,99.436852,0,0,6,.16666667,3,2 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,52104.141,522.97174,3.8282924,10.965255,17.926992,24.202389,23.826284,14.888803,4.3619847,511.22998,0,1,3,10,21,29,26,9,1,522.05585,0,10.054554,3,20.134583,29.133408,25.80224,10.468499,1.336897,100,100,99.930183,0,0,6,.16666667,3,2 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,53223.441,512.25281,5.2282071,11.503762,19.821228,24.949986,22.960321,12.334138,3.2023594,502.95911,0,1,4,13,22,27,23,9,1,508.5748,.28757626,14.285086,3.9722662,21.845549,26.146469,22.362469,9.4947309,1.6058549,100.00001,100,100,0,0,6,.16666667,3,2 +NLD,Netherlands,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,56454.926,519.23096,4.5183229,11.232308,19.027088,23.217449,23.584234,14.153676,4.2669196,484.78372,0,1,7,16,24,24,19,8,1,503.38382,.89756769,14.376084,4.764792,22.437346,24.859776,22.05448,9.1004934,1.5094614,100,100,100.00001,0,1,6,.16666667,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,56137.23,499,,,,,,,,505.28098,0,1,5,11,20,28,24,10,2,500.33899,,,,,,,,,,101,,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,57710.262,495.18539,6.9311557,13.9099,23.666187,25.160339,18.943335,8.6917648,2.6973186,499.74023,0,1,5,12,21,29,21,8,2,484.18124,,16.056108,,25.148623,26.550079,17.264418,6.0431027,1.1993411,100,99,92.261673,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,61757.656,489.84634,7.3217859,14.915947,24.287802,25.629177,17.423264,8.2859373,2.1360888,484.29257,1,2,6,14,23,28,19,7,1,486.52795,1,15.211354,4,27.281773,28.474291,17.09613,5.4685745,.61338347,99.999992,101,99.1455,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,60620.336,497.95569,5.5178761,12.659899,24.348688,27.528746,19.7276,8.4036036,1.8135866,503.23001,0,0,3,11,24,31,22,8,1,499.8757,1,11.932382,3,26.64591,31.114916,20.061623,5.9005675,.52147084,99.999992,100,100.17686,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,60916.25,489.37308,7.2416854,15.067351,24.28681,25.682507,18.322159,7.3091092,2.0903783,503.93668,0,1,4,11,22,29,22,9,2,494.52393,1,13.642971,5,24.83429,28.947248,19.029263,6.4176736,1.127903,100,100,99.999344,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,61895.797,501.7298,4.8170018,12.259523,23.597713,27.712126,20.971706,8.7255821,1.916347,513.19122,0,1,4,11,20,29,24,10,2,498.48111,.58346534,14.025289,4.0931149,24.625011,29.134298,19.557892,6.9104953,1.0704325,100.00001,101,100,0,0,7,.14285715,3,2 +NOR,Norway,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,63332.801,500.96378,6.4933357,12.372594,21.821472,26.495075,20.634209,9.8276787,2.3556342,499.45096,0,2,6,12,21,26,22,10,2,490.41315,1.0578908,14.102113,5.6853313,25.045246,28.581247,18.73914,6.0541911,.73484111,100,101,100,0,1,7,.14285715,3,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2000,31903.164,537,,,,,,,,528.79974,0,1,4,9,17,25,26,14,4,527.68665,,,,,,,,,,100,,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2003,34611.016,523.48657,4.9239807,10.14503,19.168083,23.215866,21.866722,14.071655,6.6086626,521.55151,0,1,4,10,19,26,24,13,3,520.9043,,11.822383,,20.45418,26.687389,22.793959,11.515653,2.9698472,100.00001,100,96.243416,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2006,36822.02,521.98883,4.0422626,9.992856,19.50444,25.459229,22.077559,13.243859,5.6797948,521.03265,0,1,4,10,19,26,24,13,3,530.38434,1,9.6827936,4,19.704016,25.083836,23.907955,13.608504,3.9931204,100,100,100.98022,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,36440.988,519.30084,5.2571502,10.152072,19.06559,24.39459,22.243385,13.62868,5.2585325,520.88,0,1,3,10,19,26,25,13,3,532.0069,1,9.3750954,3,18.052559,25.829988,25.131176,13.979898,3.6490114,99.999992,100,100.01773,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,37782.063,499.74991,7.5194831,15.124602,21.589144,22.699736,18.066223,10.46982,4.5309911,512.18677,0,1,4,11,21,26,23,11,3,515.63629,1,11.594717,4,21.712948,26.366993,22.305309,10.68836,2.6643066,100,100,100.33263,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,39936.316,495.2233,7.0570769,14.575724,22.646528,25.323019,19.007267,8.6053324,2.7850525,509.27069,0,1,5,11,21,26,22,11,3,513.30353,.40038085,13.038135,4.0107398,21.63121,26.308199,21.771515,10.102289,2.7375319,100,100,100,0,0,7,.14285715,2,2 +NZL,New Zealand,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,41648.207,494.48969,7.5725713,14.206271,22.76026,25.027122,18.882774,8.8466072,2.7043951,505.72729,0,1,5,13,21,25,23,11,2,508.49072,.61092097,13.118638,4.3122382,21.968576,26.830015,21.848177,9.539341,1.7720916,100,101,100,0,1,7,.14285715,2,2 +OED,OECD members,NA,Aggregates,,,NA,Aggregates,,,2018,45185.777,489.28665,9.1308231,14.845527,22.207756,24.359676,18.52824,8.5356569,2.3923202,487.12601,,,,,,,,,,488.66238,.71143705,16.030645,5.2319846,25.758991,27.401834,18.112442,5.9369526,.81571227,100,,100,0,1,1,1,1,1 +PAN,Panama,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2009,20602.518,359.74817,51.494717,27.261755,13.868928,5.5924101,1.4038033,.35487518,0,370.72729,,,,,,,,,,375.89557,,32.354191,,23.172071,9.3098488,2.1758583,.2102298,0,99.976479,,67.222191,0,0,2,.5,4,2 +PAN,Panama,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2018,31076.994,352.84625,53.662582,27.518003,13.499965,4.3079467,.93899304,.071824521,.00068715971,376.97131,1,8,23,32,23,10,3,0,0,364.62378,10.464488,33.511303,27.309813,19.726049,7.3605199,1.5172818,.11054602,0,100,100,100,0,1,2,.5,4,2 +PER,Peru,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,6422.4805,292,,,,,,,,327.08484,7,19,28,25,14,5,1,0,0,333.34097,,,,,,,,,,99,,0,0,5,.2,4,4 +PER,Peru,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,9368.0479,365.10727,47.644062,25.887417,16.900654,6.8479075,2.0945716,.53308171,.092305325,369.69577,3,11,22,29,22,10,3,0,0,369.35178,10,33.024342,25,21.700016,7.9944615,1.7954357,.18232767,0,100,100,99.696571,0,0,5,.2,4,4 +PER,Peru,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,11176.088,368.10254,46.967964,27.61343,16.130531,6.6573091,2.0580244,.54603404,.026704425,384.15121,2,8,21,29,25,11,3,0,0,373.11343,7,37.018539,24,23.48328,7.0459981,.95863986,.037311628,0,99.999992,99,99.54377,0,0,5,.2,4,4 +PER,Peru,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,12110.269,386.56061,37.730816,28.429697,20.993366,9.7692471,2.689285,.37128803,.016300568,397.54141,1,6,19,28,27,15,4,0,0,396.68359,2.781225,36.670555,19.010008,27.876936,11.496093,2.0439694,.12121228,0,100.00001,100,100,0,0,5,.2,4,4 +PER,Peru,LCN,Latin America and Caribbean,LAC,Latin America and Caribbean (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,12782.376,399.83768,32.041481,28.294996,23.062582,11.621729,4.1187358,.78959644,.070878975,400.5137,0,5,20,29,26,14,5,1,0,404.21561,2.6890664,34.541126,17.255064,29.01487,13.160735,3.1205978,.21396586,.0045744767,100,100,100.00001,0,1,5,.2,4,4 +PHL,Philippines,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2018,8123.3359,352.56708,54.443832,26.276726,13.619831,4.6695805,.91092312,.076051302,.0030539243,339.69159,1,15,38,27,13,5,1,0,0,356.93332,7.4768219,35.205082,35.288124,15.357398,5.6087241,1.0078152,.056031812,0,100,100,100,0,1,1,1,2,3 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2000,16220.029,470,,,,,,,,479.12167,0,2,6,15,24,28,19,5,0,483.1221,,,,,,,,,,99,,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2003,17379.277,490.23877,6.8019505,15.244697,24.842562,25.342863,17.697029,7.7629261,2.3079724,496.60526,0,1,4,11,24,30,21,7,1,497.78012,,15.059936,,25.230055,25.907228,19.189177,7.5014739,1.9117122,100,99,94.799583,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2006,20112.074,495.4285,5.6700358,14.174303,24.692856,26.235279,18.605648,8.6379967,1.9838831,507.63953,0,1,4,11,21,28,23,10,2,497.80649,0,13.765972,3,27.511314,29.427456,19.30205,6.0759044,.69518036,100.00002,100,99.777878,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2009,23068.398,494.80292,6.1246266,14.362768,23.991413,26.138275,18.994493,8.2287512,2.159672,500.47849,0,1,3,11,24,31,22,7,1,508.0683,0,10.861928,2,26.069658,32.07711,21.15834,6.774241,.77201182,100,100,99.713287,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2012,25562.436,517.5011,3.2828705,11.103734,22.080841,25.461914,21.335043,11.739674,4.9959226,518.18689,0,0,2,8,21,32,26,9,1,525.81647,0,7.7088356,1,22.544035,33.146564,24.469976,9.1259766,1.7028493,100,99,99.698242,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2015,27862.543,504.4693,4.4533062,12.740675,22.891783,27.097836,20.623383,9.2710924,2.9219267,505.69711,0,0,3,11,22,31,23,7,1,501.4353,.32408902,13.325583,2.6005116,26.602749,29.92128,19.893301,6.2972708,1.0352153,100,98,100.00001,0,0,7,.14285715,3,2 +POL,Poland,ECS,Europe and Central Asia,,,HIC,High income,IBD,IBRD,2018,31695.176,515.64789,4.2099757,10.509695,20.652895,26.543983,22.29982,11.702285,4.081347,511.85568,0,1,3,11,22,28,23,10,2,511.03555,.20872831,11.129304,2.5042396,24.915375,29.987284,21.975275,8.112607,1.1671869,100,100,100,0,1,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,30421.186,454,,,,,,,,470.15463,0,2,8,17,25,27,17,4,0,458.99579,,,,,,,,,,100,,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,30460.699,466.01669,11.304598,18.763174,27.11274,24.006283,13.438801,4.5587125,.8156907,477.56863,0,1,6,14,26,30,18,4,0,467.73511,,18.129049,,29.465975,27.052868,13.612648,3.1462922,0,100,99,91.40683,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,31564.227,466.16098,11.973497,18.742731,25.113615,24.033476,14.443513,4.8883619,.8048045,472.30429,0,2,7,16,25,28,17,4,0,474.30588,1,18.696453,5,28.821098,28.833382,14.746062,3.0410712,.086689025,99.999992,99,100.22476,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,31308.566,486.88831,8.3851042,15.331967,23.929808,25.029949,17.691254,7.7175326,1.9143869,489.33493,0,1,4,13,26,32,20,5,0,492.94998,0,13.481903,3,28.913126,32.345642,18.063137,3.9069998,.26709518,100.00001,101,99.977898,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,30194.465,487.06317,8.8928194,16.01326,22.777922,24.021765,17.660412,8.5148411,2.1189828,487.75769,0,1,5,12,25,30,20,5,0,489.27472,1,14.265461,4,27.274868,31.35939,17.83979,4.2260633,.31019455,100,98,100.27577,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,31157.76,491.62701,8.67274,15.091981,21.649376,23.943224,19.215923,8.8975525,2.5292039,498.12891,0,1,4,13,23,30,22,7,1,501.1001,.2055089,14.004359,3.18137,25.390141,28.753981,21.032993,6.7447276,.68691915,100,101,100.00001,0,0,7,.14285715,3,2 +PRT,Portugal,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,33948.125,492.4874,9.286499,13.994621,20.897272,24.485855,19.72706,9.1441135,2.4645786,491.80078,0,1,5,14,23,28,21,6,1,491.67734,.44833177,14.742639,4.3698282,26.242321,29.3876,19.184719,5.1423821,.48218077,99.999992,99,100,0,1,7,.14285715,3,2 +QAT,Qatar,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2006,98326.117,317.95566,71.695961,15.486359,7.4863625,3.32388,1.3849767,.5337801,.088680826,312.21347,12,23,27,20,11,5,2,0,0,349.31314,12,31.496426,36,13.887436,5.0439854,1.6415505,.30939242,.019549917,100.00002,100,100.39834,0,0,5,.2,5,2 +QAT,Qatar,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2009,94440.367,368.11249,51.077003,22.68609,13.068694,7.2078609,4.1833215,1.4897693,.28726214,371.71603,4,14,22,23,18,11,5,2,0,379.44696,10,28.780392,26,18.751591,9.8077812,4.8108501,1.287899,.13833016,100,99,99.576851,0,0,5,.2,5,2 +QAT,Qatar,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2012,101020.78,376.44839,46.98867,22.566338,15.181094,8.7872105,4.4569025,1.7133594,.30642316,387.50412,4,10,19,25,22,13,6,1,0,383.64255,11,27.968763,24,19.648003,11.220679,5.0548282,1.3138176,.14684458,99.999985,100,100.35294,0,0,5,.2,5,2 +QAT,Qatar,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2015,97305.922,402.4007,34.702484,23.959551,19.872684,12.843234,6.3931475,1.8872362,.34166425,401.88739,2,9,18,23,23,17,7,1,0,417.61121,3.9118202,27.958588,17.928513,24.643509,16.356447,7.5034695,1.5624679,.13518569,100,100,100,0,0,5,.2,5,2 +QAT,Qatar,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2018,94502.688,414.22873,29.676611,23.984377,21.898251,14.614921,6.8807616,2.3825643,.56251448,407.0918,1,8,18,24,23,16,7,2,0,419.12717,5.2370906,26.480373,16.646305,24.91522,16.971319,7.5182567,1.9989765,.23246028,99.999992,99,100.00001,0,1,5,.2,5,2 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,12119.877,426,,,,,,,,427.93152,1,4,14,22,27,20,9,2,0,441,,,,,,,,,,99,,0,0,6,.16666667,3,4 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,18240.322,414.79596,24.706306,28.03747,26.511564,14.111747,5.3628025,1.1283786,.14173244,395.93173,1,7,18,28,28,15,3,0,0,418.38638,2,30.937716,14,31.827234,16.562073,4.2448845,.45694566,.0073351013,100,100,100.03618,0,0,6,.16666667,3,4 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,21020.184,427.07916,19.543379,27.475555,28.554602,17.266371,5.9046712,1.1795168,.075906284,424.45834,1,3,13,24,32,21,6,1,0,428.17889,2,29.465906,10,34.120647,19.703106,4.3855543,.38465443,0,100.00001,101,100.05986,0,0,6,.16666667,3,4 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,21358.1,444.55423,13.978345,26.845955,28.32218,19.23147,8.4445448,2.5858498,.59165597,437.59949,0,2,10,24,31,22,9,2,0,438.76807,1,28.652962,8,34.5625,21.006729,6.1799016,.86871266,.048497383,100,100,100.31931,0,0,6,.16666667,3,4 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,24038.494,443.95432,16.236731,23.7097,27.430151,20.058575,9.3130512,2.8111682,.4406254,433.61661,1,3,12,23,30,21,8,2,0,434.88489,.9126935,28.365225,9.2664747,35.032745,19.872589,5.8762751,.66827512,.0057223849,99.999992,100,99.999992,0,0,6,.16666667,3,4 +ROU,Romania,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,28554.578,429.91684,22.605076,23.946205,24.495394,17.283667,8.5193176,2.731585,.41875428,427.70316,1,4,13,23,28,21,9,1,0,425.76123,2.9051902,27.990053,13.051368,29.761509,18.911518,6.4014864,.92922199,.049652986,100,100,99.999992,0,1,6,.16666667,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,14759.221,478,,,,,,,,461.76202,0,1,7,18,29,27,13,3,0,460.31393,,,,,,,,,,98,,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,17661.318,468.40665,11.396613,18.826488,26.432522,23.106251,13.205755,5.388402,1.6439677,442.19684,1,3,9,21,30,25,9,2,0,489.28613,,14.807841,,27.06683,26.987917,17.509367,6.0733271,1.2486423,100,100,93.693924,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,22040.096,475.67908,9.0520735,17.577257,27.011034,24.193766,14.741137,5.7383437,1.6863898,439.85574,0,3,10,22,30,24,9,2,0,479.47043,1,16.962938,5,30.203617,28.34428,15.066166,3.672044,.50819665,99.999992,100,100.75723,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,23237.664,467.81219,9.5492201,19.025087,28.489491,25.025978,12.674493,4.2555375,.98019373,459.39594,0,1,7,19,32,27,11,3,0,478.29752,1,16.50362,5,30.665852,29.035988,13.942074,3.9125302,.44658738,100,100,100.50665,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,26188.326,482.1694,7.4991064,16.453138,26.587482,25.971033,15.699028,6.298315,1.4918979,475.14938,0,1,5,16,29,28,15,4,0,486.2955,0,15.106375,3,30.130951,31.184917,15.678158,3.9173086,.33464453,100,98,99.352356,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,25652.777,494.06,5.0672584,13.864071,25.507429,27.458637,19.306446,7.289752,1.5064057,494.62781,0,0,3,13,27,31,19,6,1,486.63101,.10387184,15.196329,2.8570738,31.208265,30.865396,16.024212,3.518888,.22596487,100,100,100,0,0,7,.14285715,3,4 +RUS,Russian Federation,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,26669.223,487.78653,6.7536292,14.889202,24.994057,27.479069,17.83078,6.568645,1.4846191,478.50192,0,1,6,15,28,28,16,5,1,477.71768,.3859624,16.706627,4.1201158,31.696781,30.025007,13.98309,2.8735797,.20883645,99.999992,100,100.00001,0,1,7,.14285715,3,4 +SAU,Saudi Arabia,MEA,Middle East and North Africa,,,HIC,High income,LNX,Not classified,2018,47596.727,373.2388,42.836479,29.887337,18.815865,6.7521915,1.5320767,.15891969,.017130986,399.15167,1,5,17,29,30,15,3,0,0,386.24615,4.9401793,35.586685,21.72887,26.559103,9.6184292,1.481194,.085540503,0,99.999992,100,100,0,1,1,1,5,2 +SGP,Singapore,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2009,69537.461,562.01874,3.0122361,6.8074956,13.129434,18.662117,22.776203,19.985096,15.627419,525.89655,0,0,3,9,19,28,26,13,3,541.6955,0,8.7111387,2,17.499695,25.382387,25.714426,15.316067,4.592741,99.999992,101,99.216461,0,0,4,.25,2,2 +SGP,Singapore,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2012,82985.414,573.46832,2.1971009,6.0574841,12.207456,17.545973,21.952677,21.015955,19.023354,542.21582,0,0,2,7,17,25,27,16,5,551.49316,0,7.4037299,2,16.740898,23.95982,26.993959,16.919413,5.794198,99.999992,99,99.812012,0,0,4,.25,2,2 +SGP,Singapore,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2015,89248.938,564.1897,2.0494823,5.5087252,12.443746,20.029476,25.133453,21.746099,13.089018,535.10022,0,0,2,8,17,26,27,15,4,555.57471,.15276094,7.4924455,1.9536793,15.091437,23.408598,27.719841,18.559908,5.6213293,100.00001,99,100,0,0,4,.25,2,2 +SGP,Singapore,EAS,East Asia and Pacific,,,HIC,High income,LNX,Not classified,2018,96477.219,569.00775,1.8180043,5.3167005,11.106865,19.050613,25.762108,23.167135,13.778573,549.46472,0,0,3,8,14,22,26,19,7,550.93689,.16361748,7.1053452,1.7683588,15.147429,25.404181,29.6656,16.973293,3.7721763,100,99,100.00001,0,1,4,.25,2,2 +SRB,Serbia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,10167.894,436.87128,17.625174,24.462286,28.646931,18.85335,8.1174383,2.070508,.22431456,411.73743,,,,,,,,,,436.37369,,,,,,,,,100,,,0,0,5,.2,3,4 +SRB,Serbia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,12957.013,435.37534,19.609146,22.990858,26.835686,18.652765,9.0850887,2.4105296,.41592467,401.0275,,,,,,,,,,435.63635,,26.640963,,32.262718,21.830685,6.5731401,.78771853,.016854683,100,,88.112076,0,0,5,.2,3,4 +SRB,Serbia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,14349.03,442.3819,17.640095,22.94265,26.456861,19.935701,9.4869204,2.911613,.62616056,442.01669,,,,,,,,,,442.7858,,24.251368,,33.872688,23.616108,7.0828972,1.0178487,0,100,,89.840904,0,0,5,.2,3,4 +SRB,Serbia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,14895.414,448.85913,15.499061,23.410839,26.530382,19.512064,10.476753,3.5156808,1.0552204,446.13019,0,2,9,21,31,23,11,2,0,444.80411,,24.680508,,32.368599,22.844046,8.1088667,1.5994302,.09389282,100,99,89.695343,0,0,5,.2,3,4 +SRB,Serbia,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,17423.385,448.28305,18.113091,21.636637,24.081959,19.208073,11.732733,4.2109675,1.0165422,439.46634,0,3,12,23,28,22,10,2,0,439.8739,1.9404634,25.273487,11.110297,29.892883,21.1369,9.0533228,1.497133,.0955121,100,100,100,0,1,5,.2,3,4 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,18365.76,498.18344,6.7438588,13.20327,23.48801,24.943695,18.935848,9.766655,2.9186614,469.15903,0,1,6,17,28,28,15,3,0,494.8584,,14.092649,,25.951527,27.243874,18.763107,7.2652249,1.2562057,100,98,94.572586,0,0,6,.16666667,3,2 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,22368.209,492.10623,8.0969324,12.771916,24.050617,25.296473,18.816151,8.5927172,2.3751945,466.34976,1,3,8,17,25,26,16,5,0,488.43341,1,15.016746,4,28.034729,28.093796,17.896448,5.2075148,.5693388,100,101,99.818565,0,0,6,.16666667,3,2 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,24683.99,496.68341,7.0020766,14.024147,23.15848,24.991991,18.124084,9.0894175,3.6098049,477.44336,0,1,6,16,28,29,17,4,0,490.26593,1,14.210105,4,27.596853,29.171377,17.740461,5.5532293,.68043256,100,101,99.952469,0,0,6,.16666667,3,2 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,27245.438,481.64474,11.07652,16.387341,23.122751,22.0858,16.360945,7.8415179,3.1251256,462.76703,1,3,8,16,25,27,16,4,0,471.19318,2,17.619526,7,27.013554,26.218328,15.03931,4.2990642,.57483536,99.999992,100,99.764618,0,0,6,.16666667,3,2 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,29453.768,475.2301,11.630777,16.082777,23.526287,24.275099,16.651871,6.5765176,1.2566704,452.51431,1,4,9,18,26,25,14,3,0,460.7749,2.0706022,19.73156,8.9034634,27.634583,24.758871,13.310369,3.273468,.31708533,99.999992,100,100.00001,0,0,6,.16666667,3,2 +SVK,Slovak Republic,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,32108.055,486.16486,10.732239,14.356174,21.35183,24.235832,18.573927,8.4041367,2.345861,457.98398,0,2,9,20,27,24,14,4,0,464.04758,1.4385954,19.944983,7.9181404,28.52984,25.262262,13.199096,3.3847799,.32230288,100,100,100,0,1,6,.16666667,3,2 +SVN,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,32958.238,504.45596,4.5723734,13.092232,23.508373,25.997231,19.161062,10.29568,3.3730488,494.40903,0,1,4,12,25,32,22,5,0,518.81561,0,11.064054,3,23.142805,27.569487,22.494867,10.738674,2.1715112,100,101,100.1814,0,0,5,.2,3,2 +SVN,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,33198.789,501.47238,6.5362582,13.809403,22.474611,23.933224,19.039804,10.341294,3.8654046,483.08203,0,1,5,15,26,29,19,4,0,511.75946,0,11.697518,3,23.72451,28.680145,22.952496,8.7260942,1.1606996,99.999992,99,99.941467,0,0,5,.2,3,2 +SVN,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,32758.107,501.12741,5.0568929,15.029688,23.580435,23.949429,18.670088,10.282153,3.4313154,481.31628,0,1,5,15,27,28,18,5,0,514.14258,0,10.433581,2,24.51222,30.048103,22.974585,8.3736134,1.2112734,99.999992,99,99.553375,0,0,5,.2,3,2 +SVN,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,33949.602,509.91959,4.3831468,11.730631,21.404881,26.752293,22.25865,10.448539,3.0218618,505.21591,0,0,3,11,23,30,23,8,1,512.86359,.21917161,11.939644,2.843673,23.262796,29.071686,22.063511,9.0922756,1.5072429,100,99,100,0,0,5,.2,3,2 +SVN,Slovenia,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,38022.051,508.89755,4.773591,11.660214,21.597324,26.384674,21.981308,10.50212,3.1007681,495.34561,0,1,4,13,25,30,20,7,1,507.00653,.19613434,11.887167,2.4722068,24.594322,31.752645,21.758713,6.7184491,.62036312,100,101,100,0,1,5,.2,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2000,41052.508,510,,,,,,,,516.33118,0,0,3,9,20,30,26,10,1,512.1283,,,,,,,,,,99,,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2003,43106.992,509.04645,5.5601387,11.714149,21.661257,25.468138,19.813526,11.647288,4.1355014,514.2674,0,1,3,9,21,30,25,10,2,506.12408,,12.685569,,22.538315,26.750166,20.396732,9.6768742,2.2380509,99.999992,101,94.285713,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2006,47752.16,502.35638,5.4115911,12.90131,22.979088,26.013813,20.077345,9.706007,2.9108474,507.31287,0,1,4,10,22,29,23,9,2,503.33401,1,12.573392,3,25.177395,29.484728,21.085064,6.7893705,1.0911301,100.00001,100,100.20107,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2009,46077.246,494.23819,7.5066338,13.568864,23.358347,25.230562,18.952923,8.8850689,2.4976017,497.44943,0,1,4,12,23,30,20,8,1,495.1059,1,13.353375,5,25.629108,28.430815,18.695686,7.0895104,1.0221672,100,99,100.22066,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2012,48941.828,478.26065,9.5465841,17.525331,24.686247,23.929316,16.301603,6.4577622,1.5531569,483.33499,1,2,6,14,24,27,19,7,1,484.79898,2,14.979382,6,26.219118,28.008282,17.197472,5.6065969,.73727483,100,101,100.74813,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2015,51564.977,493.91809,7.0057449,13.831709,23.329683,26.052923,19.382788,8.3507252,2.0464251,500.15561,0,1,5,12,22,27,22,8,1,493.42239,.93320596,15.007597,5.6934056,23.977127,26.835083,19.048674,7.1791162,1.3257918,100,98,100,0,0,7,.14285715,3,2 +SWE,Sweden,ECS,Europe and Central Asia,,,HIC,High income,LNX,Not classified,2018,53243.289,502.3877,5.9770985,12.830845,21.859552,25.701164,21.035437,9.9816084,2.6142941,505.78522,0,1,5,12,21,25,22,11,2,499.4447,.62255341,13.823399,4.5614877,23.990791,27.950422,20.749086,7.3482289,.95403206,100,99,100,0,1,7,.14285715,3,2 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2000,9808.3193,432,,,,,,,,430.68387,0,2,9,27,37,21,5,0,0,436.37854,,,,,,,,,,101,,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,11258.654,416.97797,23.801416,30.162098,25.415213,13.654578,5.3324232,1.4513994,.18287005,419.91483,0,2,11,30,34,17,4,0,0,429.06036,,30.896547,,33.870529,17.072243,5.5766048,.96970952,0,100,98,88.385635,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,12836.195,417.0726,23.30567,29.691666,26.370245,13.997162,5.2993107,1.1478486,.18809848,416.75177,1,3,12,29,33,17,4,0,0,421.01147,1,33.487598,11,33.240505,16.289547,3.9967744,.4014869,.0012738869,100,99,99.417191,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,13456.733,418.58392,22.133062,30.394087,27.298889,14.024471,4.8910322,.97925168,.27920645,421.37442,0,1,10,32,37,17,3,0,0,425.2963,2,30.613935,11,34.697742,17.470076,4.4147439,.58887631,.030041222,99.999992,100,100.81541,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,15422.494,426.73749,19.139973,30.59742,27.340538,14.509502,5.8440738,2.0300729,.53841877,441.21994,0,1,8,24,36,23,7,1,0,443.99994,1,26.610823,6,37.512409,21.573542,6.393681,.86335319,.057148933,100,100,100.01096,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,16283.17,415.46381,24.223623,29.556496,26.1255,13.847926,4.8149233,1.2366956,.19483636,409.1301,0,3,15,32,31,15,4,0,0,421.33731,1.0977741,33.726826,11.921949,32.181511,16.001844,4.6120338,.44835052,.0097107384,100.00001,100,100.00001,0,0,7,.14285715,2,4 +THA,Thailand,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,18042.377,418.55551,25.011515,27.68824,24.640467,14.281944,6.1096416,1.9265496,.34164372,392.88864,0,4,21,35,26,12,3,0,0,425.81345,1.3218651,31.595022,11.566577,31.671171,17.823679,5.2998219,.70559728,.016266193,100,101,99.999992,0,1,7,.14285715,2,4 +TTO,Trinidad and Tobago,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2009,28133.912,414.04495,30.107845,23.078386,21.226011,15.41646,7.7091684,2.1180341,.34409311,416.45233,3,7,14,21,25,19,9,2,0,410.19562,8,24.850494,17,25.156357,15.970393,7.1047492,1.7654394,.099642195,100,100,99.947075,0,0,2,.5,4,2 +TTO,Trinidad and Tobago,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2015,29182.57,417.24341,28.324352,23.928633,22.131947,15.611712,7.5010524,2.150213,.35209271,427.27328,1,5,14,22,26,20,9,2,0,424.59052,2.8781931,27.943283,15.020738,27.128996,18.324589,7.3287296,1.3055166,.069954097,100,99,99.999992,0,1,2,.5,4,2 +TUN,Tunisia,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2003,7879.7036,358.73322,51.067574,26.923113,14.670629,5.6882186,1.4307504,.2114258,.0082903337,374.62241,3,9,21,29,24,11,2,0,0,384.67941,,33.181774,,25.076122,10.457466,2.3673544,0,0,100,99,71.082718,0,0,5,.2,5,3 +TUN,Tunisia,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2006,8888.4854,365.47937,48.47649,23.992985,16.531931,8.0969238,2.399488,.47512174,.027061189,380.33682,3,9,20,28,26,13,3,0,0,385.50671,6,35.08773,22,24.973166,10.221478,1.9373198,.11267412,.0047067977,100,102,100.33707,0,0,5,.2,5,3 +TUN,Tunisia,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2009,9873.6563,371.48349,43.409012,30.157305,18.71884,6.1248031,1.3265221,.24781977,0,403.63266,1,5,15,30,32,15,3,0,0,400.6615,4,32.383865,17,30.906897,13.016707,2.1832204,.15117219,0,99.984299,101,99.641861,0,0,5,.2,5,3 +TUN,Tunisia,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2012,10116.271,387.82462,36.476768,31.273218,21.146194,7.9776368,2.3382914,.68697423,.1009152,404.07846,1,5,15,28,31,16,4,0,0,398.04645,4,33.990707,18,31.052116,11.691275,1.8233472,.10961849,0,100,100,100.66707,0,0,5,.2,5,3 +TUN,Tunisia,MEA,Middle East and North Africa,MNA,Middle East and North Africa (excluding high income),LMC,Lower middle income,IBD,IBRD,2015,10521.463,366.81799,47.43623,27.369467,16.422396,6.4140925,1.8326964,.44645485,.078663684,361.05551,2,9,27,34,21,7,1,0,0,386.40341,1.649116,44.214645,20.020239,26.570313,6.7572818,.74918419,.039221786,0,100,101,100,0,1,5,.2,5,3 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2003,15646.317,423.4194,27.656315,24.570591,22.066532,13.461451,6.762764,3.0988617,2.3834877,440.97098,0,2,10,24,31,21,8,3,1,434.22339,,29.129858,,29.837685,17.179209,6.5588708,2.6387684,.89081073,100,100,86.235199,0,0,6,.16666667,3,4 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2006,19253.469,423.94119,24.008743,28.084974,24.273827,12.791874,6.658247,2.9826777,1.1996567,447.14114,1,2,8,21,31,24,10,2,0,423.83273,1,33.683258,12,31.276941,15.055443,6.1848474,.90066767,.016628504,100.00001,99,100.11778,0,0,6,.16666667,3,4 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2009,18734.758,445.45135,17.685682,24.4627,25.219982,17.376478,9.6144619,4.3777528,1.262942,464.19437,0,1,6,18,32,29,12,2,0,453.90973,1,23.041592,6,34.545807,25.210346,9.1437416,1.0737836,.048445664,100,100,100.06371,0,0,6,.16666667,3,4 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2012,22609.252,447.98441,15.484772,26.495419,25.541416,16.522575,10.086136,4.6705494,1.1991346,475.49146,0,1,4,17,31,29,14,4,0,463.4129,0,21.923571,4,35.443691,25.05801,11.342578,1.763263,.038144704,100,100,99.56926,0,0,6,.16666667,3,4 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2015,26015.488,420.45401,22.93327,28.44924,25.276752,16.338354,5.8746457,1.040738,.087002806,428.33511,0,2,11,27,33,21,6,1,0,425.4895,1.1231874,31.559385,11.773522,31.288994,19.117674,4.8438463,.28857931,.0048111365,100.00001,101,100.00001,0,0,6,.16666667,3,4 +TUR,Turkey,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IBD,IBRD,2018,28298.865,453.50778,13.796247,22.862974,27.306648,20.36624,10.902103,3.877754,.88803434,465.63165,0,1,6,19,30,27,13,3,0,468.29962,.33445933,20.123016,4.6946092,32.772961,27.284809,12.329329,2.3378396,.12297808,99.999992,99,100,0,1,6,.16666667,3,4 +UKR,Ukraine,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),LMC,Lower middle income,IBD,IBRD,2018,8207.2666,453.11749,15.629167,20.291492,26.163576,21.451323,11.476232,4.035008,.95320219,465.95013,0,2,7,17,28,28,14,3,0,468.98773,.99293429,19.173986,6.2669611,30.014395,26.66939,13.388702,3.1665814,.32704866,100.00001,99,100,0,1,1,1,3,3 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2003,11877.373,422.19989,26.261517,21.821301,24.172892,16.77968,8.1558685,2.3285439,.48019943,434.14975,3,6,11,20,24,20,11,4,1,438.37302,,21.558823,,25.753963,20.871313,10.650356,2.8873029,0,100,100,81.721764,0,0,6,.16666667,4,2 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2006,13944.171,426.79904,24.43265,21.681026,24.264416,18.281122,8.1574163,2.6274281,.55594152,412.51672,4,7,14,21,23,18,9,3,0,428.13388,4,25.406136,13,29.78326,19.726538,6.900702,1.3199383,.12192749,100,99,100.2585,0,0,6,.16666667,4,2 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2009,16477.824,426.72379,22.942923,24.62821,25.146149,17.007887,7.8579955,2.0827627,.33407351,425.81335,1,4,12,24,28,20,8,2,0,427.21426,4,25.628885,13,29.264313,19.535715,7.0994129,1.4081347,.10209459,100.00001,99,100.03855,0,0,6,.16666667,4,2 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2012,19173.889,409.29156,29.244524,26.539881,23.019974,14.409044,5.4142017,1.2514038,.1209707,411.34891,1,5,15,26,29,17,6,1,0,415.84293,5,27.239412,15,29.31579,17.076313,5.64502,.99622065,.048523135,100,100,100.32128,0,0,6,.16666667,4,2 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2015,20588.393,417.99191,25.441208,26.987268,24.365934,15.346145,6.1667771,1.5050144,.18765035,436.57211,0,3,12,24,28,21,9,2,0,435.36301,1.1607014,28.409071,11.217633,30.259449,20.282898,7.3973527,1.217327,.055568799,100,99,100.00001,0,0,6,.16666667,4,2 +URY,Uruguay,LCN,Latin America and Caribbean,,,HIC,High income,IBD,IBRD,2018,21590.834,417.66162,24.605202,26.100426,26.45842,15.794128,6.0121899,.97069925,.058935706,427.11761,0,4,14,24,28,20,8,1,0,425.81268,2.0819881,28.604794,13.188362,30.554161,18.736881,6.1194272,.68350446,.030882135,100,99,100,0,1,6,.16666667,4,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2000,50235.824,493,,,,,,,,504.41968,0,1,5,12,21,27,21,10,2,499.46014,,,,,,,,,,99,,0,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2003,51643.766,482.88504,10.203068,15.509384,23.853664,23.799728,16.555761,8.0476713,2.0307231,495.1824,0,1,5,13,23,28,21,8,1,491.26346,,15.415044,,24.604191,26.74794,18.643847,6.9247556,1.1246806,100,100,93.460457,0,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2006,55490.895,474.35214,9.925334,18.210537,26.101732,23.056942,15.085111,6.3541822,1.2661635,,,,,,,,,,,488.90683,1,16.825634,7,24.23797,23.991457,18.295763,7.5468616,1.548736,100,,100.44642,1,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2009,53517.543,487.39651,8.1109419,15.282942,24.417261,25.210062,17.062643,8.035326,1.8808255,499.82681,0,1,4,13,24,28,21,8,2,502.00226,0,13.91098,4,25.033064,27.54567,20.140114,7.8898563,1.2871612,100,101,99.806847,0,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2012,55712.707,481.36679,7.956028,17.88986,26.254629,23.340565,15.787217,6.5805192,2.1911826,497.58173,0,1,4,12,25,30,20,7,1,497.40982,0,13.985997,4,26.653444,28.929022,18.817959,6.3239083,1.1387662,100.00001,100,99.849098,0,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2015,58535.77,469.62851,10.584118,18.794706,26.166813,23.805254,14.743288,5.0148973,.89092457,496.93509,0,1,5,13,23,28,21,8,1,496.2424,.47790068,15.496832,4.3398714,25.499308,26.555939,19.113857,7.2800984,1.2361946,100,100,100,0,0,7,.14285715,6,2 +USA,United States,NAC,North America,,,HIC,High income,LNX,Not classified,2018,61391.371,478.24472,10.169744,16.938702,24.223413,24.059446,16.346691,6.7846465,1.4773577,505.35278,0,1,5,13,21,25,21,11,3,502.38004,.54605025,13.732841,4.3629627,23.639544,27.500971,21.084328,7.8623586,1.2709455,99.999992,100,99.999992,0,1,7,.14285715,6,2 +VNM,Vietnam,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2012,5574.459,511.3382,3.6471384,10.601598,22.793207,28.361914,21.335665,9.7827921,3.4776855,508.21899,0,0,1,8,24,39,23,4,0,528.42328,0,5.827282,1,20.684948,37.530243,26.9832,7.136095,.9789716,99.999992,99,100.14074,0,0,3,.33333334,2,3 +VNM,Vietnam,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2015,6438.2603,494.51831,4.5282235,14.57476,26.387554,27.007324,18.166689,7.1983242,2.1371233,486.7738,0,0,2,12,32,35,16,3,0,524.64447,.0075835916,5.6827478,.23227012,25.31299,36.616901,23.892607,7.0638862,1.1910151,100,100,100.00001,0,0,3,.33333334,2,3 +VNM,Vietnam,EAS,East Asia and Pacific,EAP,East Asia and Pacific (excluding high income),LMC,Lower middle income,IBD,IBRD,2018,7586.3848,495.67542,3.0950077,12.61831,27.30036,31.398842,18.79191,5.6578937,1.1376772,504.50562,,,,,,,,,,543.38489,.011902361,3.5650795,.29223865,18.080891,36.167435,29.78051,10.589309,1.5126352,100,,100,0,1,3,.33333334,2,3 +XKX,Kosovo,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IDX,IDA,2015,9650.1064,361.534,48.674606,29.00255,16.54352,5.0723214,.66424298,.041283127,.0014746992,347.12589,2,12,28,34,19,4,0,0,0,378.4422,4.0494785,39.330776,24.358625,24.393288,7.2069139,.65165287,.0092648324,0,99.999992,99,100,0,0,2,.5,3,4 +XKX,Kosovo,ECS,Europe and Central Asia,ECA,Europe and Central Asia (excluding high income),UMC,Upper middle income,IDX,IDA,2018,10610.308,365.88495,46.970409,29.580887,16.535345,5.3673029,1.3987322,.13523059,.012093375,353.06543,0,9,32,38,17,4,0,0,0,364.88242,4.1524997,43.093616,29.274176,19.221878,3.8976927,.35807604,.0020622276,0,100.00001,100,100,0,1,2,.5,3,4 diff --git a/05_working_paper/052_programs/05211_create_pisa_database.do b/05_working_paper/052_programs/05211_create_pisa_database.do new file mode 100644 index 0000000..ec81132 --- /dev/null +++ b/05_working_paper/052_programs/05211_create_pisa_database.do @@ -0,0 +1,189 @@ +*==============================================================================* +* LEARNING POVERTY (LP) +* Project information at: https://github.com/worldbank/LearningPoverty +* +* TASK 05_WORKINGPAPER: Relationship between Reading and Math Proficiency (PISA) +*==============================================================================* + +cap whereis myados +if _rc == 0 global myados "`r(myados)'" + +global rawdata "${clone}\05_working_paper\051_rawdata" +global output "${clone}\05_working_paper\053_outputs" + +*----------------------------------------------------------------------------- +* Load groupdata ADO + +do "${myados}\groupdata\src\groupdata.ado" + +*----------------------------------------------------------------------------- +* load dataset + +use "\\wbgfscifs01\GEDEDU\GDB\Projects\WLD_2020_LPV-BIN\bin_scores.dta" , clear + +* save files in RAWDATA +cap: save "${rawdata}\bin_scores.dta" , replace +export delimited using "${rawdata}\bin_scores.csv", replace + +* keep PISA +keep if assessment == "PISA" + +*----------------------------------------------------------------------------- +* prepare dataset + +mat drop _all +sort countrycode year outcome idgrade bin +gen tmp = countrycode + "#" + string(year) + "#" + outcome + "#" + string(idgrade) +encode tmp, gen( ctry_year_subject_grade_code) +gen ctry_year_subject_grade = countrycode + "#" + string(year) + "#" + outcome + "#" + string(idgrade) +sort ctry_year_subject_grade_code +bysort ctry_year_subject_grade_code : gen cumulative = pdf if pdf == pdf[1] +bsort ctry_year_subject_grade_code : replace cumulative = pdf + cumulative[_n-1] if pdf != pdf[1] +replace cumulative = cumulative - 1 +replace cumulative = . if bin == 0 +export delimited using "${rawdata}\pisa-reading-distributions.csv", replace +gen bin2 = bin if bin != 0 + +*----------------------------------------------------------------------------- +* Validation of Group data estimation +* loop through all distributions + +levelsof ctry_year_subject_grade + +qui foreach ctry in `r(levels)' { + + noi di "`ctry'" + + list ctry_year_subject_grade in 1 + + * obtain identifiers + levelsof countrycode if ctry_year_subject_grade == "`ctry'" & bin == 0 + local code = `r(levels)' + levelsof assessment if ctry_year_subject_grade == "`ctry'" & bin == 0 + local test = `r(levels)' + levels survey if ctry_year_subject_grade == "`ctry'" & bin == 0 + local surveyid = `r(levels)' + levelsof outcome if ctry_year_subject_grade == "`ctry'" & bin == 0 + local outcome = `r(levels)' + levelsof year if ctry_year_subject_grade == "`ctry'" & bin == 0 + local year = `r(levels)' + levelsof idgrade if ctry_year_subject_grade == "`ctry'" & bin == 0 + local grade = `r(levels)' + + * country code + sum ctry_year_subject_grade_code if ctry_year_subject_grade == "`ctry'" & bin == 0 + local c = r(mean) + + * distribution mean + cap: sum m_score_bin if ctry_year_subject_grade == "`ctry'" & bin == 0 + local m = r(mean) + + * select only PIRLS + if ("`test'" == "PIRLS") { + local cutoff = 400 + } + if ("`test'" == "LLECE") { + local cutoff = 513.66 + } + if ("`test'" == "PASEC") { + local cutoff = 595.1 + } + if ("`test'" == "TIMSS") { + local cutoff = 400 + } + if ("`test'" == "PISA") { + local cutoff = 407.47 + } + if ("`test'" == "SAQMEC") { + local cutoff = 509 + } + + local matname = subinstr("`ctry'","#","_",.) + + if (`m' != .) { + + + * Group data estimate of learnig poverty + groupdata m_score_bin if ctry_year_subject_grade == "`ctry'" & bin != 0 , /// + mean(`m' ) zl(`cutoff') type(5) nofigure binvar(bin2) + + mat `matname' = r(results) + } + +} + +*----------------------------------------------------------------------------- +* append matrixs from memory + +levelsof ctry_year_subject_grade + +qui foreach ctry in `r(levels)' { + + local matname = subinstr("`ctry'","#","_",.) + + * load matrix from memory + clear + cap : svmat double `matname', names(col) + + * create complementary variables + gen name = "`matname'" + order name + + * append loaded matrix to cummulative file + cap: append using "${output}\pisa-groupdata.dta" + save "${output}\pisa-groupdata.dta", replace + +} + +gen countrycode = word(subinstr(name,"_", " ",.),1) +gen year = real(word(subinstr(name,"_", " ",.),2)) +gen test = upper(word(subinstr(name,"_", " ",.),4)) +gen subject = word(subinstr(name,"_", " ",.),5) +gen idgrade = real(word(subinstr(name,"_", " ",.),6)) + +order countrycode year test subject idgrade + +sort countrycode year indicator + + * labels + label define var 1 "FGT(0)" , add modify + label define var 2 "FGT(1)" , add modify + label define var 3 "FGT(2)" , add modify + label define var 4 "Gini" , add modify + + label define var 5 "L(0;pi)=0" , add modify + label define var 6 "L(1;pi)=1" , add modify + label define var 7 "L'(0+;pi)>=0" , add modify + label define var 8 "L''(p;pi)>=0 for p within (0,1)" , add modify + + label define model 0 "Unit Record" , add modify + label define model 1 "QG Lorenz Curve" , add modify + label define model 2 "Beta Lorenz Curve" , add modify + + label define type 1 "Estimated Value" , add modify + label define type 2 "with respect to the Mean" , add modify + label define type 3 "with respect to the Gini" , add modify + label define type 4 "Checking for consistency of lorenz curve estimation", add modify + + label define value -99 "NA" , add modify + label define value 1 "OK" , add modify + label define value 0 "FAIL", add modify + + label values model model + label values type type + label values indicator var + label values value value + + label var model Model + label var type Type + label var indicator Indicator + +sort countrycode year name indicator + +save , replace +export delimited using "${output}\pisa-groupdata.csv", replace +export delimited using "${output}\pisa-groupdata.dta", replace + +*----------------------------------------------------------------------------- + + \ No newline at end of file diff --git a/05_working_paper/052_programs/0521_regression_pisa_gdp.do b/05_working_paper/052_programs/0521_regression_pisa_gdp.do new file mode 100644 index 0000000..146d466 --- /dev/null +++ b/05_working_paper/052_programs/0521_regression_pisa_gdp.do @@ -0,0 +1,232 @@ +*==============================================================================* +* 0521 SUBTASK: Relationship between Reading and Math Proficiency (PISA) +*==============================================================================* +qui { + + *----------------------------------------------------------------------------- + local outputs "${clone}/05_working_paper/053_outputs" + local rawdata "${clone}/05_working_paper/051_rawdata" + local overwrite_csv = 0 // Change here to download new data even if csv already in clone + *----------------------------------------------------------------------------- + + + /*----------------------------------------------------------------------------- + * Download the cross country GDP data + wbopendata , indicators(NY.GDP.PCAP.PP.KD) long clear + keep if ny_gdp_pcap_pp_kd != . + sort countrycode year + tempfile gdp + save `gdp', replace + + * Load PISA results from the GROUPDATA + import delimited "`rawdata'/pisa-groupdata.csv", numericcols(15) clear + + keep if type == 1 + gen flag = . + replace flag = 1 if indicator <= 3 & model == 1 + replace flag = 1 if indicator == 4 & model == 2 + + keep if flag == 1 + drop flag + + drop countrycode year test subject idgrade seqpov seqmean model type + reshape wide value , i(name povline mean sd) j(indicator) + + + gen countrycode = word(subinstr(name,"_", " ",.),1) + gen year = real(word(subinstr(name,"_", " ",.),2)) + gen test = upper(word(subinstr(name,"_", " ",.),4)) + gen subject = word(subinstr(name,"_", " ",.),5) + gen idgrade = real(word(subinstr(name,"_", " ",.),6)) + + drop name + + reshape wide value1 value2 value3 value4 mean sd, i(countrycode year test idgrade povline ) j(subject) string + + *----------------------------------------------------------------------------- + * Merge GDP data + merge m:1 countrycode year using `gdp', keep(master) nogen + + *----------------------------------------------------------------------------- + * Run models using PISA-Groupdata indicators + + regress meanread meanmath , cluster(countrycode) + regress value1read value1math , cluster(countrycode) + regress value2read value2math , cluster(countrycode) + regress value3read value3math , cluster(countrycode) + regress value4read value4math , cluster(countrycode) + + regress value1read meanmath , cluster(countrycode) + regress value2read meanmath , cluster(countrycode) + regress value3read meanmath , cluster(countrycode) + regress value4read meanmath , cluster(countrycode) + + regress meanread meanscience , cluster(countrycode) + regress value1read value1science , cluster(countrycode) + regress value2read value2science , cluster(countrycode) + regress value3read value3science , cluster(countrycode) + regress value4read value4science , cluster(countrycode) + + regress value1read meanscience , cluster(countrycode) + regress value2read meanscience , cluster(countrycode) + regress value3read meanscience , cluster(countrycode) + regress value4read meanscience , cluster(countrycode) + + noi disp as res _n "Finished runing PISA models with groupdata indicators" + + */ + + * In the end, those regressions above are not being used and were just an experiment + * The outputs are not even saved. Rather, we use the edstats indicator restults + + + *----------------------------------------------------------------------------- + * Download EdStats data from WBOPENDATA + *----------------------------------------------------------------------------- + + * Check for a pre-existing frozen version in the clone + cap confirm file "`rawdata'/pisa-wdi.csv" + + * If the frozen version is not found or forced to overwrite with a fresh wbopendata query + if (_rc | `overwrite_csv') { + + + global math " LO.PISA.MAT ; LO.PISA.MAT.0 ; LO.PISA.MAT.1 ; LO.PISA.MAT.2 ; LO.PISA.MAT.3 ; LO.PISA.MAT.4 ; LO.PISA.MAT.5 ; LO.PISA.MAT.6 " + + global read " LO.PISA.REA ; LO.PISA.REA.0.B1C ; LO.PISA.REA.1C ; LO.PISA.REA.1B ; LO.PISA.REA.1A ; LO.PISA.REA.2 ; LO.PISA.REA.3 ; LO.PISA.REA.4 ; LO.PISA.REA.5 ; LO.PISA.REA.6 " + + global sci " LO.PISA.SCI ; LO.PISA.SCI.0 ; LO.PISA.SCI.1A ; LO.PISA.SCI.1B ; LO.PISA.SCI.2 ; LO.PISA.SCI.3 ; LO.PISA.SCI.4 ; LO.PISA.SCI.5 ; LO.PISA.SCI.6 " + + * download the cross country data + wbopendata , indicator(NY.GDP.PCAP.PP.KD ; $math ; $read ; $sci ) long clear + + * generate cummulative scores + + egen math = rowtotal(lo_pisa_mat_*) + egen read = rowtotal(lo_pisa_rea_*) + egen sci = rowtotal(lo_pisa_sci_*) + + recode math 0 = . + recode read 0 = . + recode sci 0 = . + + sum math read sci + + egen check = rowmiss( lo_pisa_mat lo_pisa_rea lo_pisa_sci) + drop if check == 3 + sort countrycode year + bysort countrycode : gen latest = _n == _N + + + * generate weights to control for multiple observations for the same country + bysort countrycode : gen tot = _N + gen wtg = 1/tot + + * encode region and incomelevel + encode regionname, gen(reg) + encode incomelevelname, gen(inc) + + * export microdata + export delimited using "`rawdata'/pisa-wdi.csv", nolabel replace + } + + * If not creating a new csv, simply imports existing csv into a dta + else { + import delimited "`rawdata'/pisa-wdi.csv", numericcols(15) clear + } + + est drop _all + + * regression without ctry weights + regress lo_pisa_rea lo_pisa_mat , cluster(countrycode) + est store m11 + + regress lo_pisa_rea lo_pisa_mat i.reg i.inc , cluster(countrycode) + est store m12 + + regress lo_pisa_rea lo_pisa_mat i.year i.reg i.inc , cluster(countrycode) + est store m13 + + regress lo_pisa_rea lo_pisa_mat ny_gdp_pcap_pp_kd i.year i.reg i.inc , cluster(countrycode) + est store m14 + + regress lo_pisa_rea lo_pisa_sci , cluster(countrycode) + est store m21 + + regress lo_pisa_rea lo_pisa_sci i.reg i.inc , cluster(countrycode) + est store m22 + + regress lo_pisa_rea lo_pisa_sci i.year i.reg i.inc , cluster(countrycode) + est store m23 + + regress lo_pisa_rea lo_pisa_sci ny_gdp_pcap_pp_kd i.year i.reg i.inc , cluster(countrycode) + est store m24 + + + * regression with ctry weights + regress lo_pisa_rea lo_pisa_mat [aw=wtg] , cluster(countrycode) + est store w11 + + regress lo_pisa_rea lo_pisa_mat i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w12 + + regress lo_pisa_rea lo_pisa_mat i.year i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w13 + + regress lo_pisa_rea lo_pisa_mat ny_gdp_pcap_pp_kd i.year i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w14 + + regress lo_pisa_rea lo_pisa_sci [aw=wtg] , cluster(countrycode) + est store w21 + + regress lo_pisa_rea lo_pisa_sci i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w22 + + regress lo_pisa_rea lo_pisa_sci i.year i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w23 + + regress lo_pisa_rea lo_pisa_sci ny_gdp_pcap_pp_kd i.year i.reg i.inc [aw=wtg] , cluster(countrycode) + est store w24 + + + * display output + estout m* using "`outputs'/pisa_1.txt", /// + cells(b(star fmt(%9.3f)) se(par)) /// + stats(r2_a N, fmt(%9.3f %9.0g) labels(R2-Adj)) /// + legend label collabels(none) varlabels(_cons Constant) /// + keep(lo_pisa_mat lo_pisa_sci ) replace + + + + estout w* using "`outputs'/pisa_2.txt", /// + cells(b(star fmt(%9.3f)) se(par)) /// + stats(r2_a N, fmt(%9.3f %9.0g) labels(R2-Adj)) /// + legend label collabels(none) varlabels(_cons Constant) /// + keep(lo_pisa_mat lo_pisa_sci ) replace + + noi disp as res _n "Finished runing PISA models with EdStats indicators" + +} + +exit + +/* +* https://nces.ed.gov/surveys/pisa/2018technotes-6.asp + +In addition to using a range of scale scores as the basic form of measurement, PISA describes student proficiency in terms of levels of proficiency. Higher levels represent the knowledge, skills, and capabilities needed to perform tasks of increasing complexity. PISA results are reported in terms of percentages of the student population at each of the predefined levels. + +To determine the performance levels and cut scores on the literacy scales, IRT techniques were used. With IRT techniques, it is possible to simultaneously estimate the ability of all students taking the PISA assessment, as well as the difficulty of all PISA items. Estimates of student ability and item difficulty can then be mapped on a single continuum. The relative ability of students taking a particular test can be estimated by considering the percentage of test items they get correct. The relative difficulty of items in a test can be estimated by considering the percentage of students getting each item correct. In PISA, all students within a level are expected to answer at least half of the items from that level correctly. Students at the bottom of a level are able to provide the correct answers to about 52 percent of all items from that level, have a 62 percent chance of success on the easiest items from that level, and have a 42 percent chance of success on the most difficult items from that level. Students in the middle of a level have a 62 percent chance of correctly answering items of average difficulty for that level (an overall response probability of 62 percent). Students at the top of a level are able to provide the correct answers to about 70 percent of all items from that level, have a 78 percent chance of success on the easiest items from that level, and have a 62 percent chance of success on the most difficult items from that level. Students just below the top of a level would score less than 50 percent on an assessment at the next higher level. Students at a particular level demonstrate not only the knowledge and skills associated with that level but also the proficiencies defined by lower levels. Patterns of responses for students in the proficiency levels labeled below level 1c for reading literacy, below level 1b for science literacy, and below level 1 for mathematics literacy and financial literacy suggest that these students are unable to answer at least half of the items from those levels correctly. For details about the approach to defining and describing the PISA proficiency levels and establishing the cut scores, see the OECD’s PISA 2018 Technical Report . Table A-2 shows the cut scores for each proficiency level for reading, science, and mathematics literacy. + +Table A-2. Cut scores for proficiency levels for reading, science, and mathematics literacy: 2018 +Proficiency level Reading Science Mathematics Financial literacy +Level 1 (1c) 189.33 to less than 262.04 — 357.77 to less than 420.07 325.57 to less than 400.33 +Level 1 (1b) 262.04 to less than 334.75 260.54 to less than 334.94 +Level 1 (1a) 334.75 to less than 407.47 334.94 to less than 409.54 +Level 2 407.47 to less than 480.18 409.54 to less than 484.14 420.07 to less than 482.38 400.33 to less than 475.10 +Level 3 480.18 to less than 552.89 484.14 to less than 558.73 482.38 to less than 544.68 475.10 to less than 549.86 +Level 4 552.89 to less than 625.61 558.73 to less than 633.33 544.68 to less than 606.99 549.86 to less than 624.63 +Level 5 625.61 to less than 698.32 633.33 to less than 707.93 606.99 to less than 669.30 624.63 to less than 1000 +Level 6 698.32 to less than 1000 707.93 to less than 1000 669.30 to less than 1000 — +— Not applicable. +NOTE: For reading literacy, proficiency level 1 is composed of three levels, 1a, 1b, and 1c. For science literacy, proficiency level 1 is composed of two levels, 1a and 1b. The score range for below level 1 refers to scores below level 1b. For mathematics and financial literacy, there is a single proficiency category at level 1. +SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2018. diff --git a/05_working_paper/052_programs/0522_correlations.do b/05_working_paper/052_programs/0522_correlations.do new file mode 100644 index 0000000..94fd308 --- /dev/null +++ b/05_working_paper/052_programs/0522_correlations.do @@ -0,0 +1,442 @@ +*==============================================================================* +* 0522 SUBTASK: CORRELATIONS BETWEEN ASSESSMENTS AND SUBJECTS +*==============================================================================* +qui { + + * Opens several microdata from assessments to check correlations + + * Since this microdata depends on being able to access datalibweb and + * takes a long time to load, it will by default use the "frozen" csv + * saved in the repository. This code is just a "recipe" of how the csv + * was obtained, and may be used to update it (if the user can access dlw) + + local overwrite_csv = 0 // Change here to download new data even if csv already in clone + + * If overwrite_csv is not specified will just import this very short results csv + if `overwrite_csv' != 1 { + import delimited "${clone}/05_working_paper/053_outputs/assessment_correlations.csv", clear + save "${clone}/05_working_paper/053_outputs/assessment_correlations.dta", replace + exit + } + + * This time-consuming section only runs if overwrite_csv == 1 + * and will save intermediate files to attempt to shortcut further + + *--------------------------------------------------------------------------- + * PIRLS reading and TIMSS math/science. Country level. + *--------------------------------------------------------------------------- + cap confirm file "${clone}/05_working_paper/053_outputs/assessment_correlations_timss.dta" + if _rc != 0 { + + * Open CLO of latest PIRLS + cap dlw, coun(WLD) y(2016) t(GLAD) mod(CLO) verm(01) vera(01) sur(PIRLS) + keep if subgroup == "all" & idgrade == 4 + gen bmp_pirls = 1 - m_hpro_read + keep countrycode m_score_* bmp_pirls + tempfile pirls + save `pirls', replace + + * Open CLO of latest TIMSS + cap dlw, coun(WLD) y(2015) t(GLAD) mod(CLO) verm(01) vera(01) sur(TIMSS) + keep if subgroup == "all" & idgrade == 4 + * From pdf to cdf on the levels + foreach subject in math science { + gen bmp_l1_timss_`subject' = m_d1level_timss_`subject' + gen bmp_l2_timss_`subject' = bmp_l1_timss_`subject' + m_d2level_timss_`subject' + gen bmp_l3_timss_`subject' = bmp_l2_timss_`subject' + m_d3level_timss_`subject' + gen bmp_l4_timss_`subject' = bmp_l3_timss_`subject' + m_d4level_timss_`subject' + } + keep countrycode m_score_* bmp_* + + * Combine both info + merge 1:1 countrycode using `pirls', keep(match) nogen + correl m_score_pirls_read m_score_timss_math + local r_timss_math_country = `r(rho)' + correl m_score_pirls_read m_score_timss_science + local r_timss_science_country = `r(rho)' + + * Info that is not making into the table but we also care about (levels) + noi correl bmp_pirls bmp_l?_timss_* + matrix C = r(C) + clear + svmat C + keep C1 + gen description = "correl_bmp_pirls_low_" + replace description = description + "bmp_pirls_low" if _n == 1 + replace description = description + "bmp_l1_timss_math" if _n == 2 + replace description = description + "bmp_l2_timss_math" if _n == 3 + replace description = description + "bmp_l3_timss_math" if _n == 4 + replace description = description + "bmp_l4_timss_math" if _n == 5 + replace description = description + "bmp_l1_timss_science" if _n == 6 + replace description = description + "bmp_l2_timss_science" if _n == 7 + replace description = description + "bmp_l3_timss_science" if _n == 8 + replace description = description + "bmp_l4_timss_science" if _n == 9 + rename C1 value + save "${clone}/05_working_paper/053_outputs/assessment_correlations_timsslevel.dta", replace + + * Put numbers into the dataset + clear + set obs 2 + generate assessment = "TIMSS 4th grade" + generate subject = "math" if _n == 1 + replace subject = "science" if _n == 2 + generate r_country = `r_timss_math_country' if _n == 1 + replace r_country = `r_timss_science_country' if _n == 2 + + save "${clone}/05_working_paper/053_outputs/assessment_correlations_timss.dta", replace + noi disp as result "Done with TIMSS" + } + + else noi disp as txt "Skipped the TIMSS correlations (already found in clone)" + + + *--------------------------------------------------------------------------- + * LLECE reading and math/science. Country, school and student level. + *--------------------------------------------------------------------------- + cap confirm file "${clone}/05_working_paper/053_outputs/assessment_correlations_llece.dta" + if _rc != 0 { + + * Open CLO of latest LLECE + cap dlw, coun(LAC) y(2013) t(GLAD) mod(CLO) verm(01) vera(01) sur(LLECE) + keep if subgroup == "all" & idgrade == 6 + correl m_score_llece_read m_score_llece_math + local r_llece_math_country = `r(rho)' + correl m_score_llece_read m_score_llece_science + local r_llece_science_country = `r(rho)' + + * Open GLAD of latest LLECE + cap dlw, coun(LAC) y(2013) t(GLAD) mod(ALL) verm(01) vera(01) sur(LLECE) + keep if idgrade == 6 + correl score_llece_read score_llece_math [aw = learner_weight_read] + local r_llece_math_student = `r(rho)' + correl score_llece_read score_llece_science [aw = learner_weight_read] + local r_llece_science_student = `r(rho)' + + * School level correlations + collapse (mean) score_llece_* [aw = learner_weight_quest], by(idschool idgrade) + correl score_llece_read score_llece_math + local r_llece_math_school = `r(rho)' + correl score_llece_read score_llece_science + local r_llece_science_school = `r(rho)' + + * Put numbers into the dataset + clear + set obs 2 + gen assessment = "LLECE 6th grade" + gen subject = "math" if _n == 1 + replace subject = "science" if _n == 2 + gen r_country = `r_llece_math_country' if _n == 1 + replace r_country = `r_llece_science_country' if _n == 2 + gen r_school = `r_llece_math_school' if _n == 1 + replace r_school = `r_llece_science_school' if _n == 2 + gen r_student = `r_llece_math_student' if _n == 1 + replace r_student = `r_llece_science_student' if _n == 2 + + save "${clone}/05_working_paper/053_outputs/assessment_correlations_llece.dta", replace + noi disp as result "Done with LLECE" + } + + else noi disp as txt "Skipped the LLECE correlations (already found in clone)" + + + *--------------------------------------------------------------------------- + * PISA-D reading and math/science. Country, school and student level. + *--------------------------------------------------------------------------- + cap confirm file "${clone}/05_working_paper/053_outputs/assessment_correlations_pisad.dta" + if _rc != 0 { + + * Open EDURAW of PISA-D + cap dlw, country(WLD) year(2017) type(EDURAW) surveyid(WLD_2017_PISA-D_v01_M) filename(CY1MDAI_STU_QQQ.dta) + rename *, lower + + * Student level correlations + repest PISA, estimate(corr pv@read pv@math pv@scie) + matrix b = e(b) + local r_pisad_math_student = b[1,1] + local r_pisad_science_student = b[1,2] + + * Country level aggregations + cap confirm file "${clone}/05_working_paper/053_outputs/pisad_cnt.dta" + if _rc != 0 repest PISA, estimate(means pv@math pv@read pv@scie) by(cnt) outfile("${clone}/05_working_paper/053_outputs/pisad_cnt.dta") + + * School level aggregations + cap confirm file "${clone}/05_working_paper/053_outputs/pisad_cntschid.dta" + if _rc != 0 repest PISA, estimate(means pv@math pv@read pv@scie) by(cntschid) outfile("${clone}/05_working_paper/053_outputs/pisad_cntschid.dta") + + * School level correlations + use "${clone}/05_working_paper/053_outputs/pisad_cntschid.dta", clear + correl pv_math_m_b pv_read_m_b + local r_pisad_math_school = `r(rho)' + correl pv_scie_m_b pv_read_m_b + local r_pisad_science_school = `r(rho)' + + * Country level correlations + use "${clone}/05_working_paper/053_outputs/pisad_cnt", clear + correl pv_math_m_b pv_read_m_b + local r_pisad_math_country = `r(rho)' + correl pv_scie_m_b pv_read_m_b + local r_pisad_science_country = `r(rho)' + + + * Put numbers into the dataset + clear + set obs 2 + gen assessment = "PISA-D 15 years-old" + gen subject = "math" if _n == 1 + replace subject = "science" if _n == 2 + gen r_country = `r_pisad_math_country' if _n == 1 + replace r_country = `r_pisad_science_country' if _n == 2 + gen r_school = `r_pisad_math_school' if _n == 1 + replace r_school = `r_pisad_science_school' if _n == 2 + gen r_student = `r_pisad_math_student' if _n == 1 + replace r_student = `r_pisad_science_student' if _n == 2 + + save "${clone}/05_working_paper/053_outputs/assessment_correlations_pisad.dta", replace + noi disp as result "Done with PISA-D" + } + + else noi disp as txt "Skipped the PISA-D correlations (already found in clone)" + + + *--------------------------------------------------------------------------- + * PISA reading and math/science. Country, school and student level. + *--------------------------------------------------------------------------- + cap confirm file "${clone}/05_working_paper/053_outputs/assessment_correlations_pisa.dta" + if _rc != 0 { + + * Open GLAD of latest PISA + cap dlw, coun(WLD) y(2018) type(GLAD) mod(ALL) verm(01) vera(01) sur(PISA) + + * Rename variables as they are in the RAW data, to be able to use REPEST + * Alternatively, could load EDURAW, but dlw can't handle its size (>1Gb). + rename (countrycode idschool learner_weight) (cnt cntschid w_fstuwt) + forvalues i=1/80 { + rename weight_replicate`i' w_fsturwt`i' + } + foreach subject in read math science { + local subj = substr("`subject'", 1, 4) + forvalues i=1/9 { + rename score_pisa_`subject'_0`i' pv`i'`subj' + } + rename score_pisa_`subject'_10 pv10`subj' + } + + * (Non-restricted) Student level correlations + repest PISA, estimate(corr pv@read pv@math pv@scie) + matrix b = e(b) + local r_pisa_math_student = b[1,1] + local r_pisa_science_student = b[1,2] + + * See alternative analysis of PISA at the very end of this do-file + * restricted only to booklets that had the two subjects being correlated + + * Country level aggregations + cap confirm file "${clone}/05_working_paper/053_outputs/pisa_cnt.dta" + if _rc != 0 repest PISA, estimate(means pv@math pv@read pv@scie) by(cnt) outfile("${clone}/05_working_paper/053_outputs/pisa_cnt.dta") + + * School level aggregations + cap confirm file "${clone}/05_working_paper/053_outputs/pisa_cntschid.dta" + if _rc != 0 repest PISA, estimate(means pv@math pv@read pv@scie) by(cntschid) outfile("${clone}/05_working_paper/053_outputs/pisa_cntschid.dta") + + * School level correlations + use "${clone}/05_working_paper/053_outputs/pisa_cntschid.dta", clear + correl pv_math_m_b pv_read_m_b + local r_pisa_math_school = `r(rho)' + correl pv_scie_m_b pv_read_m_b + local r_pisa_science_school = `r(rho)' + + * Country level correlations + use "${clone}/05_working_paper/053_outputs/pisa_cnt", clear + correl pv_math_m_b pv_read_m_b + local r_pisa_math_country = `r(rho)' + correl pv_scie_m_b pv_read_m_b + local r_pisa_science_country = `r(rho)' + + * Put numbers into the dataset + clear + set obs 2 + gen assessment = "PISA 15 years-old" + gen subject = "math" if _n == 1 + replace subject = "science" if _n == 2 + gen r_country = `r_pisa_math_country' if _n == 1 + replace r_country = `r_pisa_science_country' if _n == 2 + gen r_school = `r_pisa_math_school' if _n == 1 + replace r_school = `r_pisa_science_school' if _n == 2 + gen r_student = `r_pisa_math_student' if _n == 1 + replace r_student = `r_pisa_science_student' if _n == 2 + + save "${clone}/05_working_paper/053_outputs/assessment_correlations_pisa.dta", replace + noi disp as result "Done with PISA" + } + + else noi disp as txt "Skipped the PISA correlations (already found in clone)" + + + *--------------------------------------------------------------------------- + * SAEB reading and math. Municipality, school and student level. + *--------------------------------------------------------------------------- + cap confirm file "${clone}/05_working_paper/053_outputs/assessment_correlations_brazil.dta" + if _rc != 0 { + + * This data is not in datalibweb, rather in another repo + capture whereis github + if _rc == 0 { + + local SAEB_2017_microdata "`r(github)'/LearningPoverty-Brazil/02_rawdata/INEP_SAEB/Downloads/SAEB_ALUNO_2017.dta" + + * Open micro data from Prova Brasil SAEB 2017 + capture use "`SAEB_2017_microdata'", clear + if _rc == 0 { + + * Discard the 0.5% of children out of EMIS that don't make it into official numbers + keep if in_situacao_censo == 1 + + * Student level correlations + correl score_lp score_mt [aw = learner_weight_lp] if idgrade == 5 + local r_saeb5_student = `r(rho)' + correl score_lp score_mt [aw = learner_weight_lp] if idgrade == 9 + local r_saeb9_student = `r(rho)' + + * School level correlations + preserve + collapse (mean) score_lp score_mt [aw = learner_weight_lp], by(idschool idgrade) + correl score_lp score_mt if idgrade == 5 + local r_saeb5_school = `r(rho)' + correl score_lp score_mt if idgrade == 9 + local r_saeb9_school = `r(rho)' + restore + + * County level correlations + collapse (mean) score_lp score_mt [aw = learner_weight_lp], by(idcounty idgrade) + correl score_lp score_mt if idgrade == 5 + local r_saeb5_county = `r(rho)' + correl score_lp score_mt if idgrade == 9 + local r_saeb9_county = `r(rho)' + + * Put numbers into the dataset + clear + set obs 2 + gen subject = "math" + gen assessment = "BRAZIL 5th grade" if _n == 1 + replace assessment = "BRAZIL 9th grade" if _n == 2 + gen r_county = `r_saeb5_county' if _n == 1 + replace r_county = `r_saeb9_county' if _n == 2 + gen r_school = `r_saeb5_school' if _n == 1 + replace r_school = `r_saeb9_school' if _n == 2 + gen r_student = `r_saeb5_student' if _n == 1 + replace r_student = `r_saeb9_student' if _n == 2 + + save "${clone}/05_working_paper/053_outputs/assessment_correlations_brazil.dta", replace + noi disp as result "Done with Brazil" + } + else noi disp as error `"Skipped the Brazil correlations (trouble opening "`SAEB_2017_microdata'")"' + } + else noi disp as error "Skipped the Brazil correlations (requires a clone of LearningPoverty-Brazil and whereis)" + } + else noi disp as txt "Skipped the Brazil correlations (already found in clone)" + + + *--------------------------------------------------------------------------- + * Combine all files into a single one + *--------------------------------------------------------------------------- + * Will append everything in this empty target file + clear + foreach subfile in timss llece pisad pisa brazil { + append using "${clone}/05_working_paper/053_outputs/assessment_correlations_`subfile'.dta" + } + + * Beautify and save + order assessment subject r_country r_county r_school r_student + format %4.3fc r* + save "${clone}/05_working_paper/053_outputs/assessment_correlations.dta", replace + + * Also save as csv, to stay in clone + export delimited "${clone}/05_working_paper/053_outputs/assessment_correlations.csv", replace + +} + +exit + +*------------------------------------------------------------------------------- +* CODE NO LONGER IN USE - Repeats PISA analysis for a restricted sample +*------------------------------------------------------------------------------- + +* The PISA code above worked well for the non-restricted student level correlations +* But since we now will need variables that are not available in GLAD-ALL, +* only in GLAD-BASE or in EDURAW, which are files above >1Gb, will have to +* open the file directly from the network. +use "${network}/GDB/HLO_Database/WLD/WLD_2018_PISA/WLD_2018_PISA_v01_M/Data/Stata/CY07_MSU_STU_QQQ.dta", clear +rename *, lower + + +/* Sample design in PISA 2018 +* Fig 2.4 and 2.5 in PISA documentation pdf linked below +* https://www.oecd.org/pisa/data/pisa2018technicalreport/PISA2018-TecReport-Ch-02-Test-Design-Tab-Fig.pdf + +Paper-based test, subject clusters and share of students: +- forms 01-12 = Reading + Science (46%) +- forms 13-24 = Reading + Math (46%) +- forms 25-30 = Reading + Math + Science (8%) + +Computer-based test, subject clusters and share of students: +- forms 01-12 = Reading + Math (33%) +- forms 13-24 = Reading + Science (33%) +- forms 25-36 = Reading + Math + Science (8%) +- forms 37-48 = Reading + Global competences (22%) +- forms 49-60 = Reading + Science + Global competences (4%) +- forms 61-72 = Reading + Math + Global competences (4%) + +Une-Heure form (99) = Reading + Math + Science +*/ + +* Dummies for whether the test design included questions on a given subject +gen byte form_w_read_math = 0 +replace form_w_read_math = 1 if adminmode == 1 & (bookid >= 13 & bookid <= 30) +replace form_w_read_math = 1 if adminmode == 2 & (bookid <= 12 | (bookid >= 25 & bookid <= 36) | (bookid >= 61 & bookid <= 72)) +replace form_w_read_math = 1 if adminmode == 2 & bookid == 99 +gen byte form_w_read_scie = 0 +replace form_w_read_scie = 1 if adminmode == 1 & (bookid <= 12 | (bookid >= 25 & bookid <= 30)) +replace form_w_read_scie = 1 if adminmode == 2 & ((bookid >= 13 & bookid <= 36) | (bookid >= 49 & bookid <= 60)) +replace form_w_read_scie = 1 if adminmode == 2 & bookid == 99 + +* Booklet structure +noi tab form_w_read_math form_w_read_scie, cell +/* +form_w_rea | form_w_read_scie + d_math | 0 1 | Total +-----------+----------------------+---------- + 0 | 53,983 256,394 | 310,377 + | 8.82 41.89 | 50.71 +-----------+----------------------+---------- + 1 | 256,628 44,999 | 301,627 + | 41.93 7.35 | 49.29 +-----------+----------------------+---------- + Total | 310,611 301,393 | 612,004 + | 50.75 49.25 | 100.00 +*/ + +* (Non-restricted) Student level correlations +repest PISA, estimate(corr pv@read pv@math pv@scie) +matrix b = e(b) +local r_pisa_math_student = b[1,1] +local r_pisa_science_student = b[1,2] +/* +----------------------------------------------------------------------------------- + | Coef. Std. Err. z P>|z| [95% Conf. Interval] +------------------+---------------------------------------------------------------- +c_pv_read_pv_math | .8509131 .0016799 506.52 0.000 .8476205 .8542057 +c_pv_read_pv_scie | .8946202 .0012902 693.38 0.000 .8920914 .897149 +----------------------------------------------------------------------------------- +*/ + +* Restricted student level correlations +repest PISA if form_w_read_math == 1, estimate(corr pv@read pv@math) +repest PISA if form_w_read_scie == 1, estimate(corr pv@read pv@scie) +/* +----------------------------------------------------------------------------------- + | Coef. Std. Err. z P>|z| [95% Conf. Interval] +------------------+---------------------------------------------------------------- +c_pv_read_pv_math | .8548513 .0017775 480.93 0.000 .8513675 .8583351 +c_pv_read_pv_scie | .8970047 .0013781 650.92 0.000 .8943037 .8997056 +----------------------------------------------------------------------------------- +*/ diff --git a/05_working_paper/052_programs/0523_bmp_pisa_validation.do b/05_working_paper/052_programs/0523_bmp_pisa_validation.do new file mode 100644 index 0000000..72aa611 --- /dev/null +++ b/05_working_paper/052_programs/0523_bmp_pisa_validation.do @@ -0,0 +1,377 @@ +*==============================================================================* +* 0523 SUBTASK: Relationship between Learning Poveryt BMP and PISA Level 2 +*==============================================================================* +qui { + + *----------------------------------------------------------------------------- + local outputs "${clone}/05_working_paper/053_outputs" + local rawdata "${clone}/05_working_paper/051_rawdata" + local overwrite_csv = 0 // Change here to download new data even if csv already in clone + *----------------------------------------------------------------------------- + + tempfile learningpoverty learning_poverty_bmp pisa_bmp + + *----------------------------------------------------------------------------- + * create and save Learning Poverty dataset + + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if !missing(adj_nonprof_all) + sort region countryname + local vars2keep "countrycode countryname enrollment_all nonprof_all adj_nonprof_all test year_assessment" + order `vars2keep' + keep `vars2keep' + label var enrollment_all "Enrollment" + label var nonprof_all "BMP" + label var adj_nonprof_all "Learning Poverty" + label var test "Assessment" + label var year_assessment "Assessment Year" + save `learningpoverty', replace + + + *----------------------------------------------------------------------------- + * create and save Learning Poverty BMP database + + import delimited "${clone}/01_data/011_rawdata/hosted_in_repo/proficiency_from_GLAD.csv", encoding(ISO-8859-2) clear + foreach var of varlist fgt* { + replace `var' = `var'*100 + } + bysort countrycode : egen maxyear = max(year) + gen latest_lpbmp = year == maxyear + save `learning_poverty_bmp', replace + + + *----------------------------------------------------------------------------- + * create and save PISA Level 2 database + //use "`outputs'/pisa-groupdata.dta", clear + import delimited "`rawdata'/pisa-groupdata.csv", numericcols(15) clear + * keep estimated values + keep if type == 1 + * keep QG Lorenz Curve + keep if model == 1 + * keep FGTs + keep if indicator <= 3 + gen year_pisa = year + bysort countrycode : egen maxyear = max(year) + gen latest_pisa = year == maxyear + save `pisa_bmp', replace + + *----------------------------------------------------------------------------- + * Join LP-BMP and PISA-BMP databases + + use `learning_poverty_bmp', clear + gen year_lp = year + merge m:1 countrycode using `learningpoverty', nogen + + joinby countrycode using `pisa_bmp', _merge(_merge) + + gen before = year_pisa <= year_lp + gen diff = year_pisa - year_lp + + gen abs_diff = abs(diff) + + ***----------------------------------------------------------------------------- + ** Countrycode + + encode countrycode, gen(ctry) + bysort countrycode : gen tot = _N + gen wtg = 1/(tot/9) + + ***----------------------------------------------------------------------------- + ** PISA subject + + gen subject_pisa = word(subinstr(name,"_"," ",.),-2) + + + ***----------------------------------------------------------------------------- + ** Metadata + + merge m:1 countrycode using "${clone}/01_data/011_rawdata/country_metadata.dta", keep(match) nogen + + ***----------------------------------------------------------------------------- + + preserve + + mat drop _all + + ***----------------------------------------------------------------------------- + * PISA-BMP and LP-BMP + * average values + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" , save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + * correlation + corr value nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + ***----------------------------------------------------------------------------- + + mat final = mean, rho + mat drop mean + mat drop rho + + ***----------------------------------------------------------------------------- + * PISA-BMP and LP + * average values + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + * correlation + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + ***----------------------------------------------------------------------------- + + mat final_lp = mean, rho + mat drop mean + mat drop rho + + ***----------------------------------------------------------------------------- + + mat list final_lp + mat all = final, final_lp + mat list all + + ***----------------------------------------------------------------------------- + + + + ***----------------------------------------------------------------------------- + * PISA-BMP and LP-BMP + * average values + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" , save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" , save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" , save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" , save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + * correlation + corr value nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + ***----------------------------------------------------------------------------- + + mat final = mean, rho + mat drop mean + mat drop rho + + ***----------------------------------------------------------------------------- + * PISA-BMP and LP + * average values + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + tabstat value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX", save stat(mean N) + mat a = r(StatTotal) + mat mean= nullmat(mean)\a + + * correlation + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff >= 0 & diff <= 4 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -3 & diff >= -5 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -6 & diff >= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + corr value adj_nonprof_all [aw=wtg] if indicator == 1 & diff <= -11 & subject == "read" & subject_pisa == "read" & lendingtype == "LNX" + mat b = r(rho) + mat b = b\. + mat rho = nullmat(rho)\b + + ***----------------------------------------------------------------------------- + + mat final_lp = mean, rho + mat drop mean + mat drop rho + + ***----------------------------------------------------------------------------- + + mat list final_lp + mat all_part2 = final, final_lp + mat list all_part2 + + ***----------------------------------------------------------------------------- + * Final + + mat final = all \ all_part2 + mat list final + + ***----------------------------------------------------------------------------- + *** Table XXXX + + drop _all + svmat final + + rename final1 PISA_BMP + rename final2 LP_BMP + rename final3 rho_PISA_LP_BMP + drop final4 + rename final5 LP + rename final6 rho_PISA_LP + + order PISA_BMP LP_BMP LP rho_PISA_LP_BMP rho_PISA_LP + + save "`outputs'/rho-BMP-PISA-LP.dta", replace + + ***----------------------------------------------------------------------------- + + restore + + ***----------------------------------------------------------------------------- + ** Figures 1 + + + keep if indicator == 1 & diff >= 3 & diff <= 5 & subject == "read" & subject_pisa == "read" + + keep countrycode region incomelevel lendingtype test value nonprof_all adj_nonprof_all year_pisa year_lp diff indicator + + bysort countrycode : egen maxyear = max(year_pisa) + gen latest_pisa = year_pisa == maxyear + + gsort -latest_pisa lendingtype countrycode year_lp + + save "`outputs'/pisa-lp-by-country.dta", replace + + + noi disp as res _n "Finished runing PISA BMP validation" + +} diff --git a/05_working_paper/052_programs/0524_bmp_earlygrade_validation.do b/05_working_paper/052_programs/0524_bmp_earlygrade_validation.do new file mode 100644 index 0000000..9cca7aa --- /dev/null +++ b/05_working_paper/052_programs/0524_bmp_earlygrade_validation.do @@ -0,0 +1,214 @@ +*==============================================================================* +* 0524 SUBTASK: Relationship between Learning Poveryt BMP and Early Grade +*==============================================================================* +qui { + + *----------------------------------------------------------------------------- + local outputs "${clone}/05_working_paper/053_outputs" + local rawdata "${clone}/05_working_paper/051_rawdata" + local overwrite_csv = 0 // Change here to download new data even if csv already in clone + *----------------------------------------------------------------------------- + + + *----------------------------------------------------------------------------- + * Check for a pre-existing frozen version in the clone + cap confirm file "`rawdata'/clo_learning_early_end_grades.csv" + + * If the frozen version is not found or forced to overwrite with a fresh query + if (_rc | `overwrite_csv') { + + * pulling data from the HISTORICAL Data folder in NETWORK + use "//wbgfscifs01/GEDEDU/GDB/Projects/WLD_2020_FGT-CLO/clo_fgt_learning.dta", clear + + keep if assessment == "LLECE" | assessment == "PASEC" + + keep survey- se_hpro_read n_total n_male n_urban n_score_pasec_read- se_score_pasec_math + + rename n_total total + rename n_male male + rename n_urban urban + + gen m_fgt0_read = 1 - m_hpro_read + gen n_fgt0_read = n_hpro_read + gen se_fgt0_read = se_hpro_read + + reshape long n_ m_ se_ , i(survey region year assessment countrycode idgrade subgroup total male urban ) j(type) string + + gen level = "early" if idgrade < 6 + replace level = "end" if idgrade == 6 + + drop idgrade + drop if m_ == . + drop survey region assessment + + reshape wide n_ m_ se_ , i(year countrycode subgroup total male urban type) j(level) string + + gen subject = word(subinstr(type,"_"," ",.),-1) + gen indicator = word(subinstr(type,"_"," ",.),1) + + sort year countrycode subgroup subject m_early + + bysort year countrycode subgroup subject : replace n_early = n_early[1] if n_early == . & n_early[1] != . + bysort year countrycode subgroup subject : replace m_early = m_early[1] if m_early == . & m_early[1] != . + bysort year countrycode subgroup subject : replace se_early = se_early[1] if se_early == . & se_early[1] != . + + drop n_* se_* type + + * save CLO dataset + export delimited "`rawdata'/clo_learning_early_end_grades.csv", replace + + } + + * If not creating a new csv, simply imports existing csv into a dta + else { + import delimited "`rawdata'/clo_learning_early_end_grades.csv", clear + } + + + *------------------------------------------------------------------------------- + * use CLO dataset + + ** Metadata + merge m:1 countrycode using "${clone}/01_data/011_rawdata/country_metadata.dta", keep(match) nogen + + corr m_end m_early if subject == "read" & indicator == "hpro" & subgroup == "all" + + bysort region: corr m_end m_early if indicator == "hpro" & subgroup == "all" + bysort region: corr m_end m_early if indicator == "fgt0" & subgroup == "all" + bysort region: corr m_end m_early if indicator == "fgt1" & subgroup == "all" + bysort region: corr m_end m_early if indicator == "fgt2" & subgroup == "all" + bysort region: corr m_end m_early if indicator == "score" & subgroup == "all" + + replace m_end = m_end * 100 if indicator != "score" + + *------------------------------------------------------------------------------- + * set up output matrix + + preserve + + mat drop _all + + *------------------------------------------------------------------------------- + * Latin America (LLECE) + tabstat m_early m_end if indicator != "hpro" & m_end != . & region == "LCN" & subject == "read" & subgroup == "all", /// + by(indicator) stat(mean n) nototal save + + + foreach l in 4 1 2 3 { + local name`l' = r(name`l') + mat lcn = nullmat(lcn)\r(Stat`l') + } + + foreach l in 4 1 2 3 { + corr m_end m_early if indicator == "`name`l''" & m_end != . & region == "LCN" & subject == "read" & subgroup == "all" + mat lcn_rho = nullmat(lcn_rho)\r(rho)\. + } + + mat lcn = lcn, lcn_rho + + *------------------------------------------------------------------------------- + * Africa (PASEC) + tabstat m_early m_end if indicator != "hpro" & m_end != . & region == "SSF" & subject == "read" & subgroup == "all", /// + by(indicator) stat(mean n) nototal save + + + foreach l in 4 1 2 3 { + local name`l' = r(name`l') + mat ssf = nullmat(ssf)\r(Stat`l') + } + + foreach l in 4 1 2 3 { + corr m_end m_early if indicator == "`name`l''" & m_end != . & region == "SSF" & subject == "read" & subgroup == "all" + mat ssf_rho = nullmat(ssf_rho)\r(rho) \. + } + + mat ssf = ssf, ssf_rho + + + *------------------------------------------------------------------------------- + * Africa (PASEC) NO BURUNDI + tabstat m_early m_end if indicator != "hpro" & m_end != . & region == "SSF" /// + & subject == "read" & subgroup == "all" & countryname != "Burundi", /// + by(indicator) stat(mean n) nototal save + + + foreach l in 4 1 2 3 { + local name`l' = r(name`l') + mat ssf2 = nullmat(ssf2)\r(Stat`l') + } + + foreach l in 4 1 2 3 { + corr m_end m_early if indicator == "`name`l''" & m_end != . & region == "SSF" /// + & subject == "read" & subgroup == "all" & countryname != "Burundi" + mat ssf2_rho = nullmat(ssf2_rho)\r(rho) \. + } + + mat ssf2 = ssf2, ssf2_rho + + *------------------------------------------------------------------------------- + + mat final = lcn \ ssf \ ssf2 + + + ***----------------------------------------------------------------------------- + *** Table XXXX + + drop _all + svmat final + + rename final1 Early_Grade + rename final2 End_Primary + rename final3 rho + + save "`outputs'/rho-BMP-early-end.dta", replace + + restore + + ***----------------------------------------------------------------------------- + + preserve + + keep if region == "SSF" + keep if subgroup == "all" + drop if indicator == "hpro" + drop if m_end == . + + reshape wide m_early m_end , i(countryname subject) j(indicator) string + reshape wide m_early* m_end* , i(countryname) j(subject) string + + sort countryname + drop m_earlyfgt0math m_endfgt0math m_earlyfgt1math m_endfgt1math m_earlyfgt2math m_endfgt2math + + order countryname m_earlyscoreread m_endscoreread m_earlyscoremath m_endscoremath m_endfgt0read m_endfgt1read m_endfgt2read + keep countryname m_earlyscoreread m_endscoreread m_earlyscoremath m_endscoremath m_endfgt0read m_endfgt1read m_endfgt2read + + egen score_read_early = rank(m_earlyscoreread*-1 ) + egen score_read_end = rank(m_endscoreread*-1 ) + egen score_math_early = rank(m_earlyscoremath*-1 ) + egen score_math_end = rank(m_endscoremath*-1 ) + egen fgt0_read_end = rank(m_endfgt0read ) + egen fgt1_read_end = rank(m_endfgt1read ) + egen fgt2_read_end = rank(m_endfgt2read) + + save "`outputs'/pasec-rank-early-end.dta", replace + + restore + + + + ***----------------------------------------------------------------------------- + * Figure 2 + + keep if indicator != "hpro" & m_end != . & subject == "read" & subgroup == "all" + + order region countrycode countryname year m_early m_end subject indicator + keep region countrycode countryname year m_early m_end subject indicator + + gen flag = 1 if countryname == "Burundi" + + sort subject indicator region flag countrycode countryname m_early m_end + + save "`outputs'/earlygrade-lp-by-country.dta", replace + + noi disp as res _n "Finished runing early grade validation (LLECE & PASEC)" +} diff --git a/05_working_paper/052_programs/0525_export_excel.do b/05_working_paper/052_programs/0525_export_excel.do new file mode 100644 index 0000000..f9f95d5 --- /dev/null +++ b/05_working_paper/052_programs/0525_export_excel.do @@ -0,0 +1,925 @@ +*==============================================================================* +* 0521 SUBTASK: EXPORT TO EXCEL WORKING PAPER TABLES AND FIGURES +*==============================================================================* +qui { + + * File that will be updated, one worksheet at a time from the template + global template_file "${clone}/05_working_paper/051_rawdata/LPV_Tables_Figures_Template.xlsx" + global excel_file "${clone}/05_working_paper/053_outputs/LPV_Tables_Figures.xlsx" + copy "${template_file}" "${excel_file}", replace + + /* List of input files manipulated to export to Excel + - "${clone}/01_data/013_outputs/preference1005.dta" (T2,T19,T20,T21,F3) + - "${clone}/01_data/013_outputs/rawfull.dta" (T2,F2) + - "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta" (T17) + - "${clone}/02_simulation/021_rawdata/simulation_spells_weighted_region.dta" (T12) + - "${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weigthed_incomelevel.dta" (T12) + - "${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weigthed_initial_poverty_level.dta" (T12) + - "${clone}/02_simulation/023_outputs/simfile_preference_1005_regional_growth_summarytable.dta" (T14) + - "${clone}/02_simulation/023_outputs/simfile_preference_1005_income_level_summarytable.dta" (T14) + - "${clone}/02_simulation/023_outputs/simfile_preference_1005_initial_poverty_level_summarytable.dta" (T14) + - "${clone}/02_simulation/023_outputs/simfile_preference_1005_regional_growth_oldused_summarytable.dta" (T14) + - "${clone}/02_simulation/023_outputs/all_spells.dta" (T4,T5,T13,F6) + - "${clone}/02_simulation/023_outputs/simfile_preference_1005_regional_growth_fulltable.dta" (F7) + - "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_lpv_*.dta" (T9) + - "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_spells.dta" (T11) + - "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_assessment.dta" (T22) + - "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_region.dta" (T23) + - "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_current_lp.dta" (T3,T6,T18) + - "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_gender_lp.dta" (T10) + - "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_SA_lp.dta" (T7,T8) + */ + + *----------------------------------------------------------------------------- + * Auxiliary programs that may be called in any table + *----------------------------------------------------------------------------- + + * Since some people at WB are used with region abbreviations that are not + * the ones used in the system, we change them to the "usual" ones in tables + cap program drop rename_regions + program define rename_regions, nclass + syntax, [regionvar(string) namevar(string)] + if "`regionvar'" == "" local regionvar "region" + replace `regionvar' = "EAP" if `regionvar' == "EAS" + replace `regionvar' = "ECA" if `regionvar' == "ECS" + replace `regionvar' = "LAC" if `regionvar' == "LCN" + replace `regionvar' = "MNA" if `regionvar' == "MEA" + replace `regionvar' = "SAR" if `regionvar' == "SAS" + replace `regionvar' = "SSA" if `regionvar' == "SSF" + if "`namevar'" != "" { + replace `namevar' = "East Asia and Pacific" if `regionvar' == "EAP" + replace `namevar' = "Europe and Central Asia" if `regionvar' == "ECA" + replace `namevar' = "Latin American and Caribbean" if `regionvar' == "LAC" + replace `namevar' = "Middle East and North Africa" if `regionvar' == "MNA" + replace `namevar' = "North America" if `regionvar' == "NAC" + replace `namevar' = "South Asia" if `regionvar' == "SAR" + replace `namevar' = "Sub-Saharan Africa" if `regionvar' == "SSA" + } + end + + * Usual order of groups that is different from alphabetical order + cap program drop order_incomelevel + program define order_incomelevel, nclass + syntax, [incomelevelvar(string) namevar(string)] + if "`incomelevelvar'" == "" local incomelevelvar "incomelevel" + generate order_incomelevel = . + replace order_incomelevel = 1 if `incomelevelvar' == "HIC" + replace order_incomelevel = 2 if `incomelevelvar' == "UMC" + replace order_incomelevel = 3 if `incomelevelvar' == "LMC" + replace order_incomelevel = 4 if `incomelevelvar' == "LIC" + if "`namevar'" != "" { + replace `namevar' = "High income" if `incomelevelvar' == "HIC" + replace `namevar' = "Upper middle income" if `incomelevelvar' == "UMC" + replace `namevar' = "Lower middle income" if `incomelevelvar' == "LMC" + replace `namevar' = "Low income" if `incomelevelvar' == "LIC" + } + end + + * Usual order of groups that is different from alphabetical order + cap program drop order_lendingtype + program define order_lendingtype, nclass + syntax, [lendingtypevar(string) namevar(string)] + if "`lendingtypevar'" == "" local lendingtypevar "lendingtype" + generate order_lendingtype = . + replace order_lendingtype = 1 if `lendingtypevar' == "LNX" + replace order_lendingtype = 2 if `lendingtypevar' == "IBD" + replace order_lendingtype = 3 if `lendingtypevar' == "IDXB" + replace order_lendingtype = 4 if `lendingtypevar' == "IDB" + replace order_lendingtype = 5 if `lendingtypevar' == "IDX" + if "`namevar'" != "" { + replace `namevar' = "Part 1" if `lendingtypevar' == "LNX" + replace `namevar' = "IBRD" if `lendingtypevar' == "IBD" + replace `namevar' = "IDA / Blend" if `lendingtypevar' == "IDXB" + replace `namevar' = "Blend" if `lendingtypevar' == "IDB" + replace `namevar' = "IDA" if `lendingtypevar' == "IDX" + } + end + + * Trick to fill cells in Excel with "N/A" + cap program drop fill_na + program define fill_na, nclass + syntax, ncol(integer) + if `ncol'>1 { + clear + set obs 1 + forvalues i=1/`ncol' { + gen str col`i' = "N/A" + } + } + end + + *----------------------------------------------------------------------------- + * Table 1 Assessment data used in constructing the consolidated global dataset + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/assessment_correlations.dta", clear + gen blank = . + order r_country r_county r_school r_student blank assessment subject + export excel using "${excel_file}", sheet("T1", modify) cell(D7) nolabel keepcellfmt + + noi disp as txt "Table 1 exported" + + *----------------------------------------------------------------------------- + * Table 2 Assessment data used in constructing the consolidated global dataset + *----------------------------------------------------------------------------- + * Only count what is in the Global Number + tempfile in_global_number + use "${clone}/01_data/013_outputs/preference1005.dta", clear + * Those 3 PASEC are "desguised" as NLAs because they belong to an earlier round + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + gen byte included = (year_assessment >= 2011 & lendingtype != "LNX") + keep if !missing(adj_nonprof_all) & included == 1 + collapse (sum) included anchor_population, by(test) + save `in_global_number', replace + + * Now counts everything, including what was used for spells + use "${clone}/01_data/013_outputs/rawfull.dta", clear + * Those 3 PASEC are "desguised" as NLAs because they belong to an earlier round + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + bys countrycode test : keep if _n == 1 + drop if inlist(test, "EGRA", "None") + gen byte exists = 1 + collapse (sum) exists, by(test) + merge 1:1 test using `in_global_number', nogen + + * Same order as in Template + gen byte aux_order = . + replace aux_order = 1 if test == "PIRLS" + replace aux_order = 2 if test == "TIMSS" + replace aux_order = 3 if test == "LLECE" + replace aux_order = 4 if test == "PASEC" + replace aux_order = 5 if test == "SACMEQ" + replace aux_order = 6 if test == "NLA" + sort aux_order + replace anchor_population = anchor_population/1E6 + replace included = 0 if missing(included) + replace anchor_population = 0 if missing(anchor_population) + drop aux_order test + export excel using "${excel_file}", sheet("T2", modify) cell(F7) nolabel keepcellfmt + + * Annex with NLA info + import delimited "${clone}/04_repo_update/041_rawdata/national_assessment_proficiency.md", delimiter("|") varnames(1) clear + keep wbcode year cutoff source + destring year, replace force + tempfile cutoff_nla + save `cutoff_nla', replace + use "${clone}/01_data/013_outputs/preference1005.dta", clear + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + keep if test == "NLA" + keep countryname year_assessment nla_code + rename (year_assessment nla_code) (year wbcode) + merge 1:1 year wbcode using `cutoff_nla', assert(match using) keep(match) nogen + drop wbcode + order countryname year + export excel using "${excel_file}", sheet("T2", modify) cell(B21) nolabel keepcellfmt + + noi disp as txt "Table 2 exported" + + + *----------------------------------------------------------------------------- + * Table 3 Population and country coverage by country groups + * Table 18 Population and country coverage by country groups, latest available learning assessment + *----------------------------------------------------------------------------- + local cell_old_0 "C8" + local cell_old_1 "C29" + foreach i in 0 1 { + use "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_current_lp.dta", clear + keep if inlist(aggregated_by, "incomelevel", "global", "lendingtype", "region") & old == `i' + keep group coverage n_countries total_countries part2_only aggregated_by population_w_data + reshape wide coverage n_countries total_countries population_w_data, i(group) j(part2_only) + rename_regions, regionvar(group) namevar(group) + order_incomelevel, incomelevelvar(group) namevar(group) + order_lendingtype, lendingtypevar(group) namevar(group) + generate order_groups = . + replace order_groups = 1 if aggregated_by == "global" + replace order_groups = 2 if aggregated_by == "region" + replace order_groups = 3 if aggregated_by == "incomelevel" + replace order_groups = 4 if aggregated_by == "lendingtype" + sort order_groups order_incomelevel order_lendingtype group + gen blank = . + order group n_countries0 total_countries0 population_w_data0 coverage0 blank n_countries1 total_countries1 population_w_data1 coverage1 + keep group - coverage1 + replace group = "Overall" if group == "TOTAL" + export excel using "${excel_file}", sheet("T3", modify) cell(`cell_old_`i'') nolabel keepcellfmt + } + + * N/A trick for NAC and Part1 in "Low and Middle Contries" panel + fill_na, ncol(4) + foreach cell in I13 I20 I34 I41 { + export excel using "${excel_file}", sheet("T3", modify) cell(`cell') nolabel keepcellfmt + } + + noi disp as txt "Table 3 & Table 18 exported" + + + *----------------------------------------------------------------------------- + * Table 4 Assessment comparability in terms of grades by region and income level + *----------------------------------------------------------------------------- + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + gen rich_countries = (almostpotential_sim & lendingtype == "LNX") + gen client_countries = (almostused_sim & lendingtype != "LNX") + collapse (max) *_countries, by(countrycode idgrade) + keep if rich_countries == 1 | client_countries == 1 + collapse (sum) *_countries, by(idgrade) + preserve + collapse (sum) *_countries + tempfile rowtotal + save `rowtotal', replace + restore + append using `rowtotal' + egen all_countries = rowtotal(rich_countries client_countries) + sort idgrade + assert inlist(idgrade,4,5,6,.) + tostring idgrade, replace + replace idgrade = "Total" if idgrade == "." + label var idgrade "Grade" + label var rich_countries "High-Income Countries" + label var client_countries "Low- and Middle-Income Countries" + label var all_countries "Total" + export excel using "${excel_file}", sheet("T4", modify) cell(B7) firstrow(varlabels) nolabel keepcellfmt + + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + gen rich_countries = (comparable == 1 & excess_timss == 0 & lendingtype == "LNX") + gen client_countries = (comparable == 1 & excess_timss == 0 & lendingtype != "LNX") + collapse (max) *_countries, by(countrycode idgrade) + keep if rich_countries == 1 | client_countries == 1 + merge m:1 countrycode using "${clone}/01_data/013_outputs/rawlatest.dta", keepusing(population_2015_all) assert(match using) keep(match) nogen + gen population_rich = population_2015_all * rich_countries + gen population_client = population_2015_all * client_countries + collapse (sum) population_rich population_client, by(idgrade) + egen population_all = rowtotal(population_rich population_client) + assert _N == 3 + set obs 4 + foreach segment in rich client all { + sum population_`segment' + replace population_`segment' = 100* population_`segment' / `r(sum)' + replace population_`segment' = 100 if _n == 4 + } + sort idgrade + assert inlist(idgrade,4,5,6,.) + drop idgrade + label var population_rich "High-Income Countries" + label var population_client "Low- and Middle-Income Countries" + label var population_all "Total" + export excel using "${excel_file}", sheet("T4", modify) cell(G7) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 4 exported" + + + *----------------------------------------------------------------------------- + * Table 5 Temporal comparability within assessments + *----------------------------------------------------------------------------- + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + drop if test == "EGRA" + rename (potential_sim used_sim) (world_rangeok part2_rangeok) + rename (almostpotential_sim almostused_sim) (world_anyrange part2_anyrange) + collapse (sum) *_anyrange *_rangeok, by(test) + preserve + collapse (sum) *_anyrange *_rangeok + tempfile rowtotal + save `rowtotal', replace + restore + append using `rowtotal' + replace test = "Total" if _n == _N + label var world_anyrange "All Countries" + label var world_rangeok "All Countries, no outliers" + label var part2_anyrange "Low- and Middle-Income Countries" + label var part2_rangeok "Low- and Middle-Income Countries, no outliers" + gen blank = . + order test world_anyrange world_rangeok blank part2_anyrange part2_rangeok + export excel using "${excel_file}", sheet("T5", modify) cell(B8) nolabel keepcellfmt + + * List of outliers from Part2 + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + rename (potential_sim used_sim) (world_rangeok part2_rangeok) + rename (almostpotential_sim almostused_sim) (world_anyrange part2_anyrange) + keep if part2_rangeok == 0 & part2_anyrange == 1 + keep countrycode-delta_lp + sort test delta_lp + export excel using "${excel_file}", sheet("T5", modify) cell(B19) firstrow(variables) nolabel keepcellfmt + + noi disp as txt "Table 5 exported" + + + *----------------------------------------------------------------------------- + * Table 6 Share of children who are learning-poor by late primary by country groups + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_current_lp.dta", clear + keep if inlist(aggregated_by, "global", "region", "incomelevel", "lendingtype") & old == 0 + keep group mean_lp se_lp min_lp max_lp part2_only aggregated_by + reshape wide mean_lp se_lp min_lp max_lp, i(group) j(part2_only) + rename_regions, regionvar(group) namevar(group) + order_incomelevel, incomelevelvar(group) namevar(group) + order_lendingtype, lendingtypevar(group) namevar(group) + generate order_groups = . + replace order_groups = 1 if aggregated_by == "global" + replace order_groups = 2 if aggregated_by == "region" + replace order_groups = 3 if aggregated_by == "incomelevel" + replace order_groups = 4 if aggregated_by == "lendingtype" + sort order_groups order_incomelevel order_lendingtype group + gen blank = . + order group mean_lp0 se_lp0 min_lp0 max_lp0 blank mean_lp1 se_lp1 min_lp1 max_lp1 + keep group - max_lp1 + replace group = "Overall" if group == "TOTAL" + export excel using "${excel_file}", sheet("T6", modify) cell(C8) nolabel keepcellfmt + + * N/A trick for NAC and Part1 in "Low and Middle Contries" panel + fill_na, ncol(4) + export excel using "${excel_file}", sheet("T6", modify) cell(I13) nolabel keepcellfmt + export excel using "${excel_file}", sheet("T6", modify) cell(I20) nolabel keepcellfmt + + noi disp as txt "Table 6 exported" + + + *----------------------------------------------------------------------------- + * Table 7 Results sensitivity in respect to choice of reporting window + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_SA_lp.dta", clear + keep if aggregated_by == "global" + gen auxfile = substr(file, 15, .) + keep if inlist(auxfile, "2001_part2", "2001_world", "2011_part2", "2011_world", "2013_part2", "2013_world", "2015_part2", "2015_world") + split auxfile, p("_") + rename (auxfile1 auxfile2) (timewindow globaldef) + keep mean_lp se_lp coverage n_countries avg_assess_year timewindow globaldef + reshape wide mean_lp se_lp coverage n_countries avg_assess_year, i(timewindow) j(globaldef) string + foreach globaldef in part2 world { + label var mean_lp`globaldef' "Learning Poverty (%)" + label var se_lp`globaldef' "S.E. L.P. (%)" + label var coverage`globaldef' "Population Coverage (%)" + label var n_countries`globaldef' "N countries" + label var avg_assess_year`globaldef' "Avg. Year" + } + sort timewindow + replace timewindow = "Latest" if timewindow == "2001" + replace timewindow = "8 years" if timewindow == "2011" + replace timewindow = "6 years" if timewindow == "2013" + replace timewindow = "4 years" if timewindow == "2015" + gen blank = . + label var blank " " + label var timewindow "Window" + order timewindow mean_lpworld se_lpworld coverageworld n_countriesworld avg_assess_yearworld /// + blank mean_lppart2 se_lppart2 coveragepart2 n_countriespart2 avg_assess_yearpart2 + export excel using "${excel_file}", sheet("T7", modify) cell(B7) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 7 exported" + + + *----------------------------------------------------------------------------- + * Table 8 Results sensitivity in respect to choice of population of reference + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_SA_lp.dta", clear + keep if aggregated_by == "global" + gen auxfile = substr(file, 20, .) + keep if inlist(auxfile, "1014_part2", "1014_world", "0516_part2", "0516_world") | inlist(auxfile, "10_part2", "10_world", "9plus_part2", "9plus_world", "primary_part2", "primary_world") + split auxfile, p("_") + rename (auxfile1 auxfile2 total_population) (age globaldef population) + keep mean_lp se_lp population coverage learning_poor age globaldef + reshape wide mean_lp se_lp population coverage learning_poor, i(age) j(globaldef) string + foreach globaldef in part2 world { + label var mean_lp`globaldef' "Learning Poverty (%)" + label var se_lp`globaldef' "S.E. L.P. (%)" + label var population`globaldef' "Population (millions)" + label var coverage`globaldef' "Population Coverage (%)" + label var learning_poor`globaldef' "Learning Poor (millions)" + } + replace age = "10-14" if age == "1014" + replace age = "5-16" if age == "0516" + sort age + label var age "Population Definition" + gen blank = . + label var blank " " + order age mean_lpworld se_lpworld populationworld coverageworld learning_poorworld /// + blank mean_lppart2 se_lppart2 populationpart2 coveragepart2 learning_poorpart2 + export excel using "${excel_file}", sheet("T8", modify) cell(B7) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 8 exported" + + + *----------------------------------------------------------------------------- + * Table 9 Decomposition of learning poverty by learning and schooling + *----------------------------------------------------------------------------- + * Pooled genders, All countries and Part 2 + use "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_lpv_all.dta", clear + gen byte part2_only = (filter != "all ctrys") + drop filter + reshape wide total bmp oos shr_bmp shr_oos, i(category panel) j(part2_only) + rename_regions, regionvar(category) namevar(category) + order_incomelevel, incomelevelvar(category) namevar(category) + order_lendingtype, lendingtypevar(category) namevar(category) + replace category = "Overall" if category == "WLD" & panel == "reg" + drop if category == "WLD" + generate order_groups = . + replace order_groups = 1 if category == "Overall" + replace order_groups = 2 if panel == "reg" & category != "Overall" + replace order_groups = 3 if panel == "inc" + replace order_groups = 4 if panel == "len" + gen blank = . + label var blank " " + sort order_groups order_incomelevel order_lendingtype category + order category total0 bmp0 oos0 shr_bmp0 shr_oos0 blank total1 bmp1 oos1 shr_bmp1 shr_oos1 + keep category - shr_oos1 + export excel using "${excel_file}", sheet("T9", modify) cell(C9) nolabel keepcellfmt + + * N/A trick for NAC and Part1 in "Low and Middle Contries" panel + fill_na, ncol(5) + export excel using "${excel_file}", sheet("T9", modify) cell(J14) nolabel keepcellfmt + export excel using "${excel_file}", sheet("T9", modify) cell(J21) nolabel keepcellfmt + + noi disp as txt "Table 9 exported" + + + *----------------------------------------------------------------------------- + * Table 10 Country Ranks on PASEC 2014/2015 + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/pasec-rank-early-end.dta", clear + * Add a blank column and a blank line + gen blank1 = . + gen blank2 = . + label var blank1 " " + label var blank2 " " + + drop m_earlyscoreread m_endscoreread m_earlyscoremath m_endscoremath m_endfgt0read m_endfgt1read m_endfgt2read + drop fgt2_read_end + order countryname score_read_early score_read_end blank1 score_math_early score_math_end score_math_end blank2 + + export excel using "${excel_file}", sheet("T10", modify) cell(B9) nolabel keepcellfmt + noi disp as txt "Table 10 exported" + + + *----------------------------------------------------------------------------- + * Table 11 Learning poverty by boys and girls, and country groups, for a subsample of countries + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/outline_all_gender_lp.dta", clear + keep if inlist(aggregated_by, "global", "region", "incomelevel", "lendingtype") + drop concatenated agg_file file *allcomp* + reshape wide n_countries mean_lp_ma se_lp_ma mean_lp_fe se_lp_fe, i(group aggregated_by) j(part2_only) + rename_regions, regionvar(group) namevar(group) + order_incomelevel, incomelevelvar(group) namevar(group) + order_lendingtype, lendingtypevar(group) namevar(group) + generate order_groups = . + replace order_groups = 1 if aggregated_by == "global" + replace order_groups = 2 if aggregated_by == "region" + replace order_groups = 3 if aggregated_by == "incomelevel" + replace order_groups = 4 if aggregated_by == "lendingtype" + sort order_groups order_incomelevel order_lendingtype group + gen blank = . + order group n_countries0 mean_lp_ma0 se_lp_ma0 mean_lp_fe0 se_lp_fe0 /// + blank n_countries1 mean_lp_ma1 se_lp_ma1 mean_lp_fe1 se_lp_fe1 + keep group - se_lp_fe1 + replace group = "Overall" if group == "TOTAL" + export excel using "${excel_file}", sheet("T11", modify) cell(C9) nolabel keepcellfmt + + * N/A trick for NAC and Part1 in "Low and Middle Contries" panel + fill_na, ncol(5) + export excel using "${excel_file}", sheet("T11", modify) cell(J14) nolabel keepcellfmt + export excel using "${excel_file}", sheet("T11", modify) cell(J21) nolabel keepcellfmt + + * Corresponding notes below the table: + + * - list of Enrollment gender flag countries + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if enrollment_flag & lp_by_gender_is_available + keep countrycode countryname + export excel using "${excel_file}", sheet("T11", modify) cell(B29) nolabel keepcellfmt + + * - share of population with gender disaggregated data + use "${clone}/01_data/013_outputs/preference1005.dta", clear + collapse (sum) population_2015_all, by(lp_by_gender_is_available) + sum population_2015_all + gen share = population_2015_all / `r(sum)' + gen group = "all countries" + export excel using "${excel_file}", sheet("T11", modify) cell(J29) firstrow(variables) nolabel keepcellfmt + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if lendingtype != "LNX" + collapse (sum) population_2015_all, by(lp_by_gender_is_available) + sum population_2015_all + gen share = population_2015_all / `r(sum)' + gen group = "low and middle income countries" + export excel using "${excel_file}", sheet("T11", modify) cell(J34) firstrow(variables) nolabel keepcellfmt + + noi disp as txt "Table 11 exported" + + + *----------------------------------------------------------------------------- + * Table 12 Decomposition of learning poverty by learning and schooling, for boys and girls + *----------------------------------------------------------------------------- + * Gender disaggregated, All countries only + use "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_lpv_ma.dta", clear + gen gender = 0 + append using "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_lpv_fe.dta" + replace gender = 1 if missing(gender) + keep if filter == "all ctrys" + reshape wide total bmp oos shr_bmp shr_oos, i(category panel filter) j(gender) + rename_regions, regionvar(category) namevar(category) + order_incomelevel, incomelevelvar(category) namevar(category) + order_lendingtype, lendingtypevar(category) namevar(category) + replace category = "Overall" if category == "WLD" & panel == "reg" + drop if category == "WLD" + generate order_groups = . + replace order_groups = 1 if category == "Overall" + replace order_groups = 2 if panel == "reg" & category != "Overall" + replace order_groups = 3 if panel == "inc" + replace order_groups = 4 if panel == "len" + gen blank = . + label var blank " " + sort order_groups order_incomelevel order_lendingtype category + order category total0 bmp0 oos0 shr_bmp0 shr_oos0 blank total1 bmp1 oos1 shr_bmp1 shr_oos1 + keep category - shr_oos1 + export excel using "${excel_file}", sheet("T12", modify) cell(C9) nolabel keepcellfmt + + noi disp as txt "Table 12 exported" + + + *----------------------------------------------------------------------------- + * Table 13 Decomposition of the change in learning poverty by learning and schooling + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/decomposition_spells.dta", clear + rename_regions, regionvar(category) namevar(category) + replace category = "Overall" if category == "WLD" + export excel using "${excel_file}", sheet("T13", modify) cell(B6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 13 exported" + + + *----------------------------------------------------------------------------- + * Table 14 Annualized change in learning poverty (in percentage points) by + * country group, 2000-2017 + *----------------------------------------------------------------------------- + **** By Region **** + use "${clone}/02_simulation/021_rawdata/simulation_spells_weighted_region.dta", clear + label var region "Region" + rename_regions, namevar(region) + label var delta_reg_50 "BaU (p50)" + forvalues p=60(10)90 { + label var delta_reg_`p' "r`p'" + } + keep region *50 *60 *70 *80 *90 + * Trick to move overall line to firstrow + gen sortaux = _n + replace sortaux = 0 if region == "Overall" + sort sortaux + drop sortaux + export excel using "${excel_file}", sheet("T14", modify) cell(C7) nolabel keepcellfmt + + **** By Income Level **** + use "${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_incomelevel.dta", clear + label var incomelevel "Income Level" + label var delta_reg_50 "BaU (p50)" + forvalues p=60(10)90 { + label var delta_reg_`p' "r`p'" + } + keep incomelevel *50 *60 *70 *80 *90 + order_incomelevel, namevar(incomelevel) + sort order_incomelevel + drop order_incomelevel + drop if incomelevel == "Overall" + export excel using "${excel_file}", sheet("T14", modify) cell(C14) nolabel keepcellfmt + + **** By Initial Learning Poverty **** + use "${clone}/02_simulation/021_rawdata/sensitivity_checks/simulation_spells_weighted_initial_poverty_level.dta", clear + label var initial_poverty_level "Initial Poverty Level" + label var delta_reg_50 "BaU (p50)" + forvalues p=60(10)90 { + label var delta_reg_`p' "r`p'" + } + keep initial* *50 *60 *70 *80 *90 + drop if initial_poverty_level == "Overall" + export excel using "${excel_file}", sheet("T14", modify) cell(C18) nolabel keepcellfmt + + noi disp as txt "Table 14 exported" + + + *----------------------------------------------------------------------------- + * Table 15 Learning poverty rates in 2030 under two scenarios (simulation using spells by region) + *----------------------------------------------------------------------------- + local table_15_A_file "simfile_preference_1005_regional_growth_summarytable.dta" + local table_15_B_file "simfile_preference_1005_income_level_summarytable.dta" + local table_15_C_file "simfile_preference_1005_initial_poverty_level_summarytable.dta" + local table_15_D_file "simfile_preference_1005_regional_growth_glossy_summarytable.dta" + local table_15_E_file "simfile_preference_1005_regional_growth_min2_summarytable.dta" + local table_15_A_place "B9" + local table_15_B_place "B25" + local table_15_C_place "B37" + local table_15_D_place "B51" + local table_15_E_place "B63" + + * Run for each panel in Table 15 + foreach table in table_15_A table_15_B table_15_C table_15_D table_15_E { + use "${clone}/02_simulation/023_outputs/``table'_file'", clear + forvalues i=1/4 { + gen blank`i' = . + label var blank`i' " " + } + order region pop_2015 pop_2030 blank1 lpv_own_2015 blank2 lpv_own_2030 /// + lpv_r80_2030 blank3 lps_own_2015 blank4 lps_own_2030 lps_r80_2030 + rename_regions, namevar(region) + replace region = "Overall" if region == "_Overall" + export excel using "${excel_file}", sheet("T15", modify) cell(``table'_place') nolabel keepcellfmt + } + + noi disp as txt "Table 15 exported" + + + *----------------------------------------------------------------------------- + * Table 16 Relationship between reading and math proficiency (PISA) + *----------------------------------------------------------------------------- + insheet using "${clone}/05_working_paper/053_outputs/pisa_2.txt", clear + tempfile tmp + save `tmp', replace + + insheet using "${clone}/05_working_paper/053_outputs/pisa_1.txt", clear + append using `tmp' + drop v1 + drop in 9 + drop in 1 + + gen blank1 = . + gen blank2 = . + label var blank1 " " + label var blank2 " " + + order v2 v3 v4 v5 blank1 v6 v7 v8 v9 + drop if _n == _N + export excel using "${excel_file}", sheet("T16", modify) cell(C8) nolabel keepcellfmt + + noi disp as txt "Table 16 exported" + + + *----------------------------------------------------------------------------- + * Table 17 Non-comparable spells in PIRLS and TIMMS + *----------------------------------------------------------------------------- + use "${clone}/02_simulation/021_rawdata/comparability_TIMSS_PIRLS_yr.dta", clear + replace comparable = 0 if countrycode == "ZAF" & test == "PIRLS" + keep if comparable == 0 + keep countrycode country spell test idgrade + sort test countrycode + export excel using "${excel_file}", sheet("T17", modify) cell(G6) firstrow(variables) nolabel keepcellfmt + + noi disp as txt "Table 17 exported" + + + *----------------------------------------------------------------------------- + * Table 19 Source of enrollment data + *----------------------------------------------------------------------------- + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if !missing(adj_nonprof_all) + preserve + collapse (count) freq=adj_nonprof_all, by(enrollment_definition) + qui sum freq + gen percent = freq/`r(sum)' + label var freq "Freq." + label var percent "Percent" + label var enrollment_definition "Type of enrollment indicator" + gsort -freq enrollment_definition + export excel using "${excel_file}", sheet("T19", modify) cell(B6) firstrow(varlabels) nolabel keepcellfmt + restore + + keep countrycode countryname enrollment_definition + order countrycode countryname enrollment_definition + keep if enrollment_definition != "ANER" + export excel using "${excel_file}", sheet("T19", modify) cell(F6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 19 exported" + + + *----------------------------------------------------------------------------- + * Table 20 Population ages 10-14 years old by region and income classifications (Year = 2015) + *----------------------------------------------------------------------------- + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep regionname incomelevel population_2015_all + separate population_2015_all , by(incomelevel) + collapse (sum) population_2015_all?, by(regionname) + label var population_2015_all1 "High income Countries" + label var population_2015_all2 "Low income Countries" + label var population_2015_all3 "Low-middle income" + label var population_2015_all4 "Upper-middle income" + order regionname population_2015_all1 population_2015_all4 population_2015_all3 population_2015_all2 + preserve + collapse (sum) population* + gen regionname = "Global" + tempfile globalpop + save `globalpop', replace + restore + append using `globalpop' + egen population_2015_alltotal = rowtotal(population_2015_all?) + label var population_2015_alltotal "Total" + export excel using "${excel_file}", sheet("T20", modify) cell(B6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 20 exported" + + + *----------------------------------------------------------------------------- + * Table 21 Weighted average and correlation of PISA and Learning Poverty country + * averages according to country groupings and moving windows of PISA data (weighted) + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/rho-BMP-PISA-LP.dta", clear + * add a collumn with N + gen N = LP[_n+1] if rho_PISA_LP != . + drop if N == . + * Add a blank column and a blank line + gen blank1 = . + gen blank2 = . + gen blank3 = . + label var blank1 " " + label var blank2 " " + label var blank3 " " + gen obs_n = _n + order PISA_BMP LP_BMP LP blank1 rho_PISA_LP_BMP rho_PISA_LP blank2 + set obs `=_N +1' + replace obs_n = 5.5 if missing(obs_n) + sort obs_n + export excel using "${excel_file}", sheet("T21", modify) cell(E8) nolabel keepcellfmt + + noi disp as txt "Table 21 exported" + + + *----------------------------------------------------------------------------- + * Table 22 Correlation of early grade and end of primary scores + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/rho-BMP-early-end.dta", clear + * add a collumn with N + gen N = End_Primary[_n+1] if rho != . + drop if N == . + * drop FGT2 + drop in 12 + drop in 8 + drop in 4 + * Add a blank column and a blank line + gen blank1 = . + gen blank2 = . + gen blank3 = . + label var blank1 " " + label var blank2 " " + gen obs_n = _n + set obs `=_N +1' + replace obs_n = 3.5 if missing(obs_n) + set obs `=_N +1' + replace obs_n = 6.5 if missing(obs_n) + sort obs_n + order Early_Grade End_Primary blank1 rho blank2 N + keep Early_Grade-N + export excel using "${excel_file}", sheet("T22", modify) cell(D7) nolabel keepcellfmt + + noi disp as txt "Table 22 exported" + + + *----------------------------------------------------------------------------- + * Table 23 Country Numbers + *----------------------------------------------------------------------------- + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if year_assessment >= 2011 & !missing(adj_nonprof_all) + gen oos_all = 100 - enrollment_all + sort region countryname + rename_regions + * Those 3 PASEC are "desguised" as NLAs because they belong to an earlier round + * and COD also had the actual year of 2010 disguised as 2011 + replace year_assessment = 2010 if countrycode == "COD" + replace test = "PASEC" if inlist(countrycode,"MLI","MDG","COD") + local vars2keep "region countryname oos_all nonprof_all adj_nonprof_all test year_assessment" + order `vars2keep' + keep `vars2keep' + label var oos_all "Out of School (OOS, %)" + label var nonprof_all "Below Minimum Proficiency (BMP, %)" + label var adj_nonprof_all "Learning Poverty (%)" + label var test "Assessment" + label var year_assessment "Assessment Year" + export excel using "${excel_file}", sheet("T23", modify) cell(B6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Table 23 exported" + + + *----------------------------------------------------------------------------- + * Table 24 Summary statistics of the annualized changes in learning poverty + * and initial condition by assessment + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_assessment.dta", clear + * Add a blank column and a blank line + gen blank1 = . + gen blank2 = . + label var blank1 " " + label var blank2 " " + gen obs_n = _n + order test *d blank1 *lp blank2 filter obs_n + set obs `=_N +1' + replace obs_n = 6.5 if missing(obs_n) + sort obs_n + export excel using "${excel_file}", sheet("T24", modify) cell(C8) nolabel keepcellfmt + + noi disp as txt "Table 24 exported" + + + *----------------------------------------------------------------------------- + * Table 25 Summary statistics of the annualized changes in learning poverty and + * initial condition by region, low- and middle-income countries (weighted and unweighted) + *----------------------------------------------------------------------------- + use "${clone}/03_export_tables/033_outputs/individual_tables/spells_stats_by_region.dta", clear + * Add a blank column and a blank line + gen blank1 = . + gen blank2 = . + label var blank1 " " + label var blank2 " " + gen obs_n = _n + order region *d blank1 *lp blank2 weights obs_n + set obs `=_N +1' + replace obs_n = 7.5 if missing(obs_n) + sort obs_n + rename_regions, namevar(region) + export excel using "${excel_file}", sheet("T25", modify) cell(C8) nolabel keepcellfmt + + noi disp as txt "Table 25 exported" + + + *----------------------------------------------------------------------------- + * Figure 1 Rates of non-proficiency in reading: end-primary vs.lower-secondary + * (15-year-olds, PISA) + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/pisa-lp-by-country.dta", clear + keep if latest_pisa == 1 + foreach var in nonprof_all adj_nonprof_all value { + replace `var' = `var'/100 + } + drop indicator diff maxyear latest_pisa + order countrycode region incomelevel lendingtype test nonprof_all year_lp adj_nonprof_all value year_pisa + export excel using "${excel_file}", sheet("F1", modify) cell(O6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Figure 1 exported" + + + *----------------------------------------------------------------------------- + * Figure 2 Proficiency in reading: Early Grade vs End of Primary + *----------------------------------------------------------------------------- + use "${clone}/05_working_paper/053_outputs/earlygrade-lp-by-country.dta", clear + export excel using "${excel_file}", sheet("F2", modify) cell(W6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Figure 2 exported" + + + *----------------------------------------------------------------------------- + * Figure 3 Learning poverty and the average learning gap by country + *----------------------------------------------------------------------------- + use "${clone}/01_data/013_outputs/rawfull.dta", clear + keep if !missing(nonprof_all) & !missing(fgt1_all) & !missing(fgt2_all) + drop if test == "TIMSS" & subject == "math" + order countrycode test idgrade subject year_assessment nonprof_all fgt1_all fgt2_all region incomelevel lendingtype + keep countrycode - lendingtype + sort test + replace nonprof_all = nonprof_all/100 + export excel using "${excel_file}", sheet("F3", modify) cell(O5) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Figure 3 exported" + + + *----------------------------------------------------------------------------- + * Figure 4 Learning poverty gender gap, by country + * Figure 5 Learning poverty gender gap by the level of Learning Poverty + *----------------------------------------------------------------------------- + use "${clone}/01_data/013_outputs/preference1005.dta", clear + keep if lp_by_gender_is_available + keep countrycode adj_* + gen gap = adj_nonprof_ma - adj_nonprof_fe + gen abs_gap = abs(gap) + order countrycode adj_nonprof_all adj_nonprof_fe adj_nonprof_ma gap abs_gap + sort gap + export excel using "${excel_file}", sheet("F4", modify) cell(J6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Figures 4 & 5 exported" + + + *----------------------------------------------------------------------------- + * Figure 6 Learning Poverty by Brazilian Municipality (national definition) + *----------------------------------------------------------------------------- + noi disp as txt "Figure 6 skipped (created in LearningPoverty-Brazil repo only)" + + + *----------------------------------------------------------------------------- + * Figure 7 Distribution of annualized changes in learning poverty for low- and + * middle-income countries, 2000-2017 + *----------------------------------------------------------------------------- + use "${clone}/02_simulation/023_outputs/all_spells.dta", clear + keep if used_sim == 1 + gen str concatenated_spell = countrycode + " " + test + " grade " + strofreal(idgrade) + " " + spell + keep spell_id used_sim delta_lp + order spell_id used_sim delta_lp + sort delta_lp + replace delta_lp = delta_lp * -1 + export excel using "${excel_file}", sheet("F7", modify) cell(J6) firstrow(varlabels) nolabel keepcellfmt + + noi disp as txt "Figure 7 exported" + + + *----------------------------------------------------------------------------- + * Figure 8 Learning poverty under two scenarios, 2015-30 (simulation) + *----------------------------------------------------------------------------- + use "${clone}/02_simulation/023_outputs/simfile_preference_1005_regional_growth_fulltable.dta", clear + keep if year>=2015 & year<=2030 + keep if region == "_Overall" + keep if inlist(benchmark,"_own_","_r80_") + keep year lpv benchmark + reshape wide lpv, i(benchmark) j(year) + export excel using "${excel_file}", sheet("F8", modify) cell(C28) nolabel keepcellfmt + + noi disp as txt "Figure 8 exported" + + + noi disp as res _newline "Finished exporting to excel." + +} diff --git a/05_working_paper/052_programs/052_run.do b/05_working_paper/052_programs/052_run.do new file mode 100644 index 0000000..670f90c --- /dev/null +++ b/05_working_paper/052_programs/052_run.do @@ -0,0 +1,42 @@ +*==============================================================================* +* LEARNING POVERTY (LP) +* Project information at: https://github.com/worldbank/LearningPoverty +* +* TASK 05_WORKINGPAPER: export figures and tables for the Working Paper +*==============================================================================* + + +*------------------------------------------------------------------------------- +* Setup for this task +*------------------------------------------------------------------------------- +* Check that project profile was loaded, otherwise stops code +cap assert ${LP_profile_is_loaded} == 1 +if _rc != 0 { + noi disp as error "Please execute the profile_LearningPoverty initialization do in the root of this project and try again." + exit +} +*------------------------------------------------------------------------------- + + +*------------------------------------------------------------------------------- +* Subroutines for this task +*------------------------------------------------------------------------------- +* Subtasks 0521 through 0524 are validations of LP with other constructs + +* Relationship between Reading and Math Proficiency (PISA) +do "${clone}/05_working_paper/052_programs/0521_regression_pisa_gdp.do" + +* Correlations between assessments and subjects +do "${clone}/05_working_paper/052_programs/0522_correlations.do" + +* Relationship between Learning Poveryt BMP and PISA Level 2 +do "${clone}/05_working_paper/052_programs/0523_bmp_pisa_validation.do" + +* Relationship between Learning Poveryt BMP and Early Grade +do "${clone}/05_working_paper/052_programs/0524_bmp_earlygrade_validation.do" + +* Put every table and figure together to create all numbers in the Working Paper + +* Clones the Excel template and fills it with data +do "${clone}/05_working_paper/052_programs/0525_export_excel.do" +*------------------------------------------------------------------------------- diff --git a/05_working_paper/053_outputs/LPV_Tables_Figures_PAPER.xlsx b/05_working_paper/053_outputs/LPV_Tables_Figures_PAPER.xlsx new file mode 100644 index 0000000..3122397 Binary files /dev/null and b/05_working_paper/053_outputs/LPV_Tables_Figures_PAPER.xlsx differ diff --git a/05_working_paper/053_outputs/assessment_correlations.csv b/05_working_paper/053_outputs/assessment_correlations.csv new file mode 100644 index 0000000..d2c63cc --- /dev/null +++ b/05_working_paper/053_outputs/assessment_correlations.csv @@ -0,0 +1,11 @@ +assessment,subject,r_country,r_county,r_school,r_student +TIMSS 4th grade,math,.92334658,,, +TIMSS 4th grade,science,.96485674,,, +LLECE 6th grade,math,.90384388,,.86728644,.56070709 +LLECE 6th grade,science,.94213599,,.86888194,.58704674 +PISA-D 15 years-old,math,.93854898,,.9359315,.81826109 +PISA-D 15 years-old,science,.97394478,,.95782238,.86789417 +PISA 15 years-old,math,.9489674,,.93901527,.85091311 +PISA 15 years-old,science,.97826397,,.97076571,.89462024 +BRAZIL 5th grade,math,,.96247345,.94291705,.72504085 +BRAZIL 9th grade,math,,.93655533,.90722901,.6715849 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..c838dd6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 EduAnalytics , World Bank Group + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 55b8ab2..0bb767a 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,32 @@ -# Learning Poverty - -This repository contains the analysis presented in the paper _“Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.”_ [1]. - -As a significant contributor to human capital deficits, the learning crisis undermines sustainable growth and poverty reduction. The paper introduces the new concept of _learning poverty_ and provides a synthetic indicator with global coverage to spotlight this crisis. _Learning poverty_ means being unable to read and understand a short, age-appropriate text by age 10. This indicator brings together schooling and learning by adjusting the proportion of kids in school bellow a proficiency threshold by the out-of-school population. - -The new data show that **more than half of all children in World Bank client countries suffer from _learning poverty_** – the majority of them low- and middle-income countries. And progress in reducing _learning poverty_ is far too slow to meet the SDG aspirations: even if countries reduce their _learning poverty_ at the fastest rates we have seen so far in this century, the goal of ending it will not be attained by 2030. - -[1] Azevedo, J.P., and others. 2019. _“Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.”_ World Bank Policy Research Working Paper series. Washington, DC: World Bank. - -## Technical Notes - -Most of the data used to calculate _learning poverty_ comes from the **[Global Learning Assessment Database (GLAD)](https://github.com/worldbank/GLAD)**, a collection of harmonized datasets at the individual and country level. This harmonization has been made possible thanks to the leadership of the UNESCO Institute for Statistics (UIS), the custodian agency for SDG 4, in coordinating a Global Alliance for the Monitoring of Learning (GAML), and in establishing Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning. Working with the UIS, we have built a consolidated global database representing 80% of the population of primary-age children globally. - -See the [Technical Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/001_technical_note/Technical_Note.md) for more contextual information on the _learning poverty_ measure, how it was calculated and which sources were used. - -## Repository Structure - -See the [Repository Structure Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/002_repo_structure/Repo_Structure.md) to understand the sequencing of the tasks, visualizing the data flowcharts in this project and for an overview of the variables in each dataset. - -## Contribution and Replication - -See the [Contribution and Replication Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/003_contribution_and_replication/Contribution_and_Replication.md) for information on how to navigate this repository, how to contribute to the code and how to replicate the numbers. - -## Contact - - -This Repository is maintained by the **EduAnalytics** team at the World Bank Education Global Practice. - -The **EduAnalytics** team aims to provide internal and external clients timely access to high quality data, tools, and analytics that can be used to measure, monitor, and understand the education sector across regions. - -The team can be reached at [eduanalytics@worldbank.org](mailto:eduanalytics@worldbank.org). +# Learning Poverty + +This repository contains the analysis presented in the paper _“Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.”_ [1]. + +As a significant contributor to human capital deficits, the learning crisis undermines sustainable growth and poverty reduction. The paper introduces the new concept of _learning poverty_ and provides a synthetic indicator with global coverage to spotlight this crisis. _Learning poverty_ means being unable to read and understand a short, age-appropriate text by age 10. This indicator brings together schooling and learning by adjusting the proportion of kids in school below a proficiency threshold by the out-of-school population. + +The new data show that **more than half of all children in World Bank client countries suffer from _learning poverty_** – the majority of them low- and middle-income countries. And progress in reducing _learning poverty_ is far too slow to meet the SDG aspirations: even if countries reduce their _learning poverty_ at the fastest rates we have seen so far in this century, the goal of ending it will not be attained by 2030. + +[1] Azevedo, J.P., and others. 2019. _“Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.”_ World Bank Policy Research Working Paper series. Washington, DC: World Bank. + +## Technical Notes + +Most of the data used to calculate _learning poverty_ comes from the **[Global Learning Assessment Database (GLAD)](https://github.com/worldbank/GLAD)**, a collection of harmonized datasets at the individual and country level. This harmonization has been made possible thanks to the leadership of the UNESCO Institute for Statistics (UIS), the custodian agency for SDG 4, in coordinating a Global Alliance for the Monitoring of Learning (GAML), and in establishing Minimum Proficiency Levels (MPLs) that enable countries to benchmark learning. Working with the UIS, we have built a consolidated global database representing 80% of the population of primary-age children globally. + +See the [Technical Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/001_technical_note/Technical_Note.md) for more contextual information on the _learning poverty_ measure, how it was calculated and which sources were used. + +## Repository Structure + +See the [Repository Structure Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/002_repo_structure/Repo_Structure.md) to understand the sequencing of the tasks, visualizing the data flowcharts in this project and for an overview of the variables in each dataset. + +## Contribution and Replication + +See the [Contribution and Replication Note](https://github.com/worldbank/LearningPoverty/blob/master/00_documentation/003_contribution_and_replication/Contribution_and_Replication.md) for information on how to navigate this repository, how to contribute to the code and how to replicate the numbers. + +## Contact + + +This Repository is maintained by the **EduAnalytics** team at the World Bank Education Global Practice. + +The **EduAnalytics** team aims to provide internal and external clients timely access to high quality data, tools, and analytics that can be used to measure, monitor, and understand the education sector across regions. + +The team can be reached at [eduanalytics@worldbank.org](mailto:eduanalytics@worldbank.org). diff --git a/profile_LearningPoverty.do b/profile_LearningPoverty.do index abdcbfa..0e28ee9 100644 --- a/profile_LearningPoverty.do +++ b/profile_LearningPoverty.do @@ -1,68 +1,99 @@ *==============================================================================* -* LEARNING POVERTY (LP) -* Project information at: https://github.com/worldbank/LearningPoverty -* -* This initialization do sets paths, globals and install programs for the Repo +*! LEARNING POVERTY (LP) - PUBLIC VERSION +*! Project information at: https://github.com/worldbank/LearningPoverty +*! EduAnalytics Team, World Bank Group [eduanalytics@worldbank.org] + +*! PROFILE: Required step before running any do-files in this project *==============================================================================* -qui { +quietly { + + /* + Steps in this do-file: + 1) General program setup + 2) Define user-dependant path for local clone repo + 3) Check if can access WB network path and WB datalibweb + 4) Download and install required user written ado's + 5) Flag that profile was successfully loaded + */ *----------------------------------------------------------------------------- - * General program setup + * 1) General program setup *----------------------------------------------------------------------------- clear all capture log close _all set more off set varabbrev off, permanently - set maxvar 10000 + set emptycells drop + set maxvar 2048 + set linesize 135 version 15 *----------------------------------------------------------------------------- *----------------------------------------------------------------------------- - * Define user-dependant global paths + * 2) Define user-dependant path for local clone repo *----------------------------------------------------------------------------- - * User-dependant paths for local repo clone - * Brian - if inlist("`c(username)'","wb469649","WB469649") { - global clone "C:/Users/`c(username)'/Documents/GitHub/LearningPoverty" - } - * Diana - else if inlist("`c(username)'","wb552057","WB552057","diana") { - global clone "C:/Users/`c(username)'/Documents/Github/LearningPoverty" - } - * Joao Pedro I - else if inlist("`c(username)'","wb255520","WB255520") { - global clone "C:/Users/`c(username)'/Documents/mytasks/LearningPoverty" - } - * Joao Pedro II - else if inlist("`c(username)'","azeve") { - global clone "C:/GitHub_mytasks/LearningPoverty" - } - * Kristoffer - else if inlist("`c(username)'","wb462869","WB462869") { - global clone "C:/Users/`c(username)'/Documents/GitHub/LearningPoverty" - } - * If none of above cases, give an error + * Change here only if this repo is renamed + local this_repo "LearningPoverty" + * Change here only if this master run do-file is renamed + local this_run_do "run_LearningPoverty.do" + + * The remaining of this section is standard in EduAnalytics repos + + * One of two options can be used to "know" the clone path for a given user + * A. the user had previously saved their GitHub location with -whereis-, + * so the clone is a subfolder with this Project Name in that location + * B. through a window dialog box where the user manually selects a file + + * Method A - Github location stored in -whereis- + *--------------------------------------------- + capture whereis github + if _rc == 0 global clone "`r(github)'/`this_repo'" + + * Method B - clone selected manually + *--------------------------------------------- else { - noi disp as error _newline "{phang}Your username [`c(username)'] could not be matched with any profile. Please update profile_LearningPoverty do-file accordingly and try again.{p_end}" - error 2222 + * Display an explanation plus warning to force the user to look at the dialog box + noi disp as txt `"{phang}Your GitHub clone local could not be automatically identified by the command {it: whereis}, so you will be prompted to do it manually. To save time, you could install -whereis- with {it: ssc install whereis}, then store your GitHub location, for example {it: whereis github "C:/Users/AdaLovelace/GitHub"}.{p_end}"' + noi disp as error _n `"{phang}Please use the dialog box to manually select the file `this_run_do' in your machine.{p_end}"' + + * Dialog box to select file manually + capture window fopen path_and_run_do "Select the master do-file for this project (`this_run_do'), expected to be inside any path/`this_repo'/" "Do Files (*.do)|*.do|All Files (*.*)|*.*" do + + * If user clicked cancel without selecting a file or chose a file that is not a do, will run into error later + if _rc == 0 { + + * Pretend user chose what was expected in terms of string lenght to parse + local user_chosen_do = substr("$path_and_run_do", - strlen("`this_run_do'"), strlen("`this_run_do'") ) + local user_chosen_path = substr("$path_and_run_do", 1 , strlen("$path_and_run_do") - strlen("`this_run_do'") - 1 ) + + * Replace backward slash with forward slash to avoid possible troubles + local user_chosen_path = subinstr("`user_chosen_path'", "\", "/", .) + + * Check if master do-file chosen by the user is master_run_do as expected + * If yes, attributes the path chosen by user to the clone, if not, exit + if "`user_chosen_do'" == "`this_run_do'" global clone "`user_chosen_path'" + else { + noi disp as error _newline "{phang}You selected $path_and_run_do as the master do file. This does not match what was expected (any path/`this_repo'/`this_run_do'). Code aborted.{p_end}" + error 2222 + } + } } - /* WELCOME!!! ARE YOU NEW TO THIS CODE? - Add yourself by copying the lines above, making sure to adapt your clone */ - - * Checks that files in the clone can be accessed by testing any clone file (like this one) - cap confirm file "${clone}/profile_LearningPoverty.do" + * Regardless of the method above, check clone + *--------------------------------------------- + * Confirm that clone is indeed accessible by testing that master run is there + cap confirm file "${clone}/`this_run_do'" if _rc != 0 { - noi disp as error _newline "{phang}Having issues accessing your local clone of the LearningPoverty repo. Please double check the clone location specified in profile_LearningPoverty do-file and try again.{p_end}" + noi disp as error _n `"{phang}Having issues accessing your local clone of the `this_repo' repo. Please double check the clone location specified in the run do-file and try again.{p_end}"' error 2222 } *----------------------------------------------------------------------------- *----------------------------------------------------------------------------- - * Check if can access WB network path and WB datalibweb + * 3) Check if can access WB network path and WB datalibweb *----------------------------------------------------------------------------- * Network drive is always the same for everyone, but may not be available * if the user is not connected to the World Bank intranet @@ -74,16 +105,20 @@ qui { * Datalibweb is only available in Stata for internal World Bank users * but external users can access it through SOL (TODO add link here) cap which datalibweb - if _rc == 0 global datalibweb_is_available 0 // TODO: change here to 1 after testing + if _rc == 0 global datalibweb_is_available 1 else global datalibweb_is_available 0 + + * Both the network path and datalibweb are only used to update the repo (task 04), + * it is not a problem for users external to WBG attempting to replicate main results *----------------------------------------------------------------------------- *----------------------------------------------------------------------------- - * Download and install required user written ado's + * 4) Download and install required user written ado's *----------------------------------------------------------------------------- * Fill this list will all user-written commands this project requires - local user_commands wbopendata carryforward touch _gwtmean mdensity estout grqreg + * that can be installed automatically from ssc + local user_commands wbopendata carryforward _gwtmean mdensity estout grqreg missings adecomp repest * Loop over all the commands to test if they are already installed, if not, then install foreach command of local user_commands { @@ -110,31 +145,31 @@ qui { noi disp as res _newline "{phang}You have an up-to-date version of the EduAnalytics Toolkit package installed. Thus, automatically generated markdown files will be created to document the most relevant datasets.{p_end}" global use_edukit_save = 1 } - *----------------------------------------------------------------------------- + if $use_edukit_save == 0 noi disp as res "{phang}This will not prevent the replication of the main results, but will skip the creation of markdown documentation.{p_end}" + + + * Load other auxiliary programs, that are found in this Repo - *------------------------------------------------------------------------------------------ - * Load the auxiliary programs in this Repo - *------------------------------------------------------------------------------------------ * Preferred list selects 1 observation per country from rawfull (unique proficiency) * and trims the dataset on the wide sense (keep 1 enrollment, 1 population only) - do "${clone}/01_data/012_programs/01261_preferred_list.do + do "${clone}/01_data/012_programs/01261_preferred_list.do" * Population weights creates frequency weights for aggregations of global numbers - do "${clone}/01_data/012_programs/01262_population_weights.do + do "${clone}/01_data/012_programs/01262_population_weights.do" - * Tables for paper with confidence intervals to csv - do "${clone}/03_export_tables/032_programs/03211_preferred_list_tables_ci.do + * Make sure the ado for running the simulations is loaded + do "${clone}/02_simulation/022_programs/simulate_learning_poverty.ado" * Tables for paper defines programs that export tables to csv - do "${clone}/03_export_tables/032_programs/03221_tables_for_paper.do - *------------------------------------------------------------------------------- + do "${clone}/03_export_tables/032_programs/03211_outline_tables.do" + *----------------------------------------------------------------------------- *----------------------------------------------------------------------------- - * Flag that profile was successfully loaded + * 5) Flag that profile was successfully loaded *----------------------------------------------------------------------------- + noi disp as result _n `"{phang}`this_repo' clone sucessfully set up (${clone}).{p_end}"' global LP_profile_is_loaded = 1 - noi disp as res "{phang}LearningPoverty profile sucessfully loaded.{p_end}" *----------------------------------------------------------------------------- } diff --git a/run_LearningPoverty.do b/run_LearningPoverty.do index 8cd7482..8d26a57 100644 --- a/run_LearningPoverty.do +++ b/run_LearningPoverty.do @@ -1,30 +1,34 @@ *==============================================================================* -* LEARNING POVERTY (LP) -* Project information at: https://github.com/worldbank/LearningPoverty +*! LEARNING POVERTY (LP) - PUBLIC VERSION +*! Project information at: https://github.com/worldbank/LearningPoverty +*! EduAnalytics Team, World Bank Group [eduanalytics@worldbank.org] + +*! MASTER RUN: Executes all tasks sequentially *==============================================================================* -*----------------------------------------------------------------------------- * Check that project profile was loaded, otherwise stops code -*----------------------------------------------------------------------------- cap assert ${LP_profile_is_loaded} == 1 -if _rc != 0 { - noi disp as error "Please execute the profile_LearningPoverty initialization do in the root of this project and try again." - exit +if _rc { + noi disp as error "Please execute the profile initialization do in the root of this project and try again." + exit 601 } - *------------------------------------------------------------------------------- -* Tasks in this project +* Run all tasks in this project *------------------------------------------------------------------------------- * TASK 01_DATA: calculates learning poverty by combining multiple data sources do "${clone}/01_data/012_programs/012_run.do" * TASK 02_SIMULATION: simulates learning poverty from 2015 to 2030 -do "${clone}/02_simulation/022_program/022_run.do" +do "${clone}/02_simulation/022_programs/022_run.do" * TASK 03_EXPORT_TABLES: exports tables for learning poverty technical paper do "${clone}/03_export_tables/032_programs/032_run.do" * TASK 04_REPO_UPDATE: creates csv files that are hosted in repo -do "${clone}/04_repo_update/042_programs/042_run.do" +* do "${clone}/04_repo_update/042_programs/042_run.do" + +* TASK 05_WORKINGPAPER: export figures and tables for the Working Paper +do "${clone}/05_working_paper/052_programs/052_run.do" + *-------------------------------------------------------------------------------