Update SINCO to ISCO process

Support/B - Country Survey Details/MEX/ENOE/Correspondence_occup_ISCO.md

@@ -36,12 +36,14 @@ The most difficult case is the case in the red box. Here SINCO code `1524` descr
 
 ### Direct SINCO to ISCO mapping
 
-In the easiest case, we use the SINCO to ISCO correspondence and create a map for every four-digit SINCO code, following the logic outlined above. Note that not all SINCO codes appear to have a correspondence. The case of SINCO code `2421` of biomedical engineers (red box in the image below) is described by INEGI as having no correspondence in ISCO. This code is therefore not mapped.
+In the easiest case, we use the SINCO to ISCO correspondence and create a map for every four-digit SINCO code, following the logic outlined above. Note that not all SINCO codes appear to have a correspondence. The case of SINCO code `2421` of biomedical engineers (red box in the image below) is described by INEGI as having no correspondence in ISCO. 
 
 <br></br>
 ![](utilities/sinco_isco_ing_biomed.png)
 <br></br>
 
+For these codes (38 instances) there is a manual mapping (in the [correspondence R code](utilities/sinco_to_isco_correspondance.R)) whenever feasible (29 instances).
+
 ### Indirect CMO to ISCO mapping
 
 The more difficult case is for surveys between 2005 and 2012. Here we first use the mapping logic to map CMO codes to SINCO codes. This includes reducing the SINCO accuracy to achieve more certain matches. For example, in the image below, CMO code `6160` cannot be matched unique to a four-digit SINCO code so the mapping occurs to code `1710`.

Support/B - Country Survey Details/MEX/ENOE/utilities/sinco_to_isco_correspondance.R

@@ -51,6 +51,20 @@ df <- df[!(df$...1 < 1000 & !is.na(df$...1)),]
 
 # Fill ISCO description down, Drop when SINCO has no correspondence with ISCO
 df <- df %>% fill(...4, .direction = "down") 
+
+# There are odd cases that require manual treatment, set them aside
+
+# First when there is a note instead of a correspondence code
+odd_nota <- df[(df$...4 %in% c("Nota: clasifica a los supervisores junto a los trabajadores que supervisa")),] %>%
+  # Keep only four digit codes, three digit ones should go
+  filter(...1 > 999)
+
+# Then for cases defined as "without correspondence"
+odd_no_correspond <- df[(df$...4 %in% c("No tiene correspondencia")),] %>%
+  # Keep only four digit codes
+  filter(...1 > 999)
+
+# Once saved, focus df only on what has correspondence
 df <- df[!(df$...4 %in% c("No tiene correspondencia", 
                           "Nota: clasifica a los supervisores junto a los trabajadores que supervisa")),]
 
@@ -176,4 +190,65 @@ concord <- bind_rows(match_1, match_2, match_3, match_4) %>%
          isco  = str_pad(isco, 4, pad = "0", side = "right"))
 
 
+#=========================================================================#
+# Step 7 - Add manual matches ---------------------------------------------
+#=========================================================================#
+
+# We manually assign codes to the codes present in SINCO but for which the
+# correspondence table gives us no information. This is based on SINCO 
+# descriptions (1) and ISCO 08 ones (2)
+
+# Links below as of 2024/11/06
+# (1) http://internet.contenidos.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/metodologias/est/sinco_2011.pdf
+# (2) https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@dcomm/@publ/documents/publication/wcms_172572.pdf
+
+orphan_sinco_codes <- as.character(c(odd_nota$...1, odd_no_correspond$...1))
+orphan_isco_codes <- c("5152", # Supervisors of housework, same as workers
+                       "5410", # Supervisors of protective service workers (psw) as psw
+                       "6100", # Agro/fish supervisor as market ag workers
+                       "6200", # Fish/aquaculture supervisor as market forest, fish
+                       "7200", # Metalwork supervisors as metalworkers
+                       "",     # Don't have a good one, should be few people
+                       "7510", # Food process (fp) supervisors as fp workers
+                       "7310", # Handicraft supervisors as handicraft
+                       "",     # Don't have a good one, should be few people
+                       "1100", # Other presidents, CEO as Chie Executives
+                       "1300", # Public utilities Managers as Production Managers
+                       "1300", # Coordinator public utilities as Production Managers
+                       "1300", # Other coordinators as Production Managers
+                       "1300", # Other coordinators in ITC PM
+                       "",     # Other directors, no code, should be few
+                       "2654", # Set designers
+                       "2300", # Alphabetisers as teachers
+                       "2300", # Bilingual (indigenous) teacher as teacher
+                       "2300", # Other teachers n.e.c. as teacher
+                       "2200", # Biomedical engineers as health professionals
+                       "",     # Don't have a good one, should be few people
+                       "3130", # Electrical technician supervisors as process control techs
+                       "7400", # Electrical tecs n.e.c.as Eletrical Workers
+                       "5312", # Auxiliary teaching as teachers' aides
+                       "",     # Too vague
+                       "",     # Too vague
+                       "5249", # Mobile goods rental as sales n.e.c. (example is rental salesperson)
+                       "5200", # Other sales workers as generic sales workers
+                       "6100", # Other ag workers as market ag workers
+                       "6100", # Other ag workers as market ag workers
+                       "6100", # Other ag workers as market ag workers
+                       "6200", # Other fish sector workers as market fishery
+                       "6200", # Other fishery, water workers as market fishery
+                       "",     # Too vague
+                       "8300", # Other drivers as drivers, mobile plant operators
+                       "9100", # Car handler on tips as cleaners and helpers
+                       "",     # Unclear
+                       "")     # Too vague
+
+orphan_df <- data.frame(sinco = orphan_sinco_codes, isco = orphan_isco_codes, match = 999) %>%
+  filter(isco != "")
+
+#=========================================================================#
+# Step 8 - Combine manual, save -------------------------------------------
+#=========================================================================#
+
+concord <- bind_rows(concord, orphan_df)
+
 write_dta(concord, path_out)