tests with new parameter definitions

Author: jdebacker

ogcore/pensions.py

@@ -189,7 +189,8 @@ def DB_amount(w, e, n, j, p):
     Calculate public pension from a defined benefits system.
     """
     L_inc_avg = np.zeros(0)
-    L_inc_avg_s = np.zeros(p.avg_earn_num_years)
+    equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1
+    L_inc_avg_s = np.zeros(equiv_periods)
 
     if n.shape[0] < p.S:
         per_rmn = n.shape[0]
@@ -212,7 +213,7 @@ def DB_amount(w, e, n, j, p):
             L_inc_avg_s,
             L_inc_avg,
             DB,
-            p.avg_earn_num_years,
+            equiv_periods,
             p.alpha_db,
             p.yr_contr,
         )
@@ -232,15 +233,15 @@ def DB_amount(w, e, n, j, p):
                 L_inc_avg_s,
                 L_inc_avg,
                 DB,
-                p.avg_earn_num_years,
+                equiv_periods,
                 p.alpha_db,
                 p.yr_contr,
             )
 
         elif np.ndim(n) == 2:
             DB_sj = np.zeros((p.retire, p.J))
             DB = np.zeros((p.S, p.J))
-            L_inc_avg_sj = np.zeros((p.avg_earn_num_years, p.J))
+            L_inc_avg_sj = np.zeros((equiv_periods, p.J))
             DB = DB_2dim_loop(
                 w,
                 e,
@@ -251,7 +252,7 @@ def DB_amount(w, e, n, j, p):
                 L_inc_avg_sj,
                 L_inc_avg,
                 DB,
-                p.avg_earn_num_years,
+                equiv_periods,
                 p.alpha_db,
                 p.yr_contr,
             )
@@ -443,7 +444,7 @@ def deriv_DB(w, e, per_rmn, p):
     """
     Change in DB pension benefits for another unit of labor supply
     """
-
+    equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1
     if per_rmn < (p.S - p.retire + 1):
         d_theta = np.zeros(p.S)
     else:
@@ -453,7 +454,7 @@ def deriv_DB(w, e, per_rmn, p):
             p.S,
             p.retire,
             per_rmn,
-            p.avg_earn_num_years,
+            equiv_periods,
             p.alpha_db,
             p.yr_contr,
         )

tests/test_pensions.py

@@ -73,26 +73,24 @@ def test_replacement_rate_vals(n, w, factor, j, p_in, expected):
 
 
 p = Specifications()
-# p.update_specifications({
-#     "S": 7,
-#     "rep_rate_py":  0.2
-# })
 p.S = 7
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 p.retire = 4
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = 4
 p.g_y = np.ones(p.T) * 0.03
 j = 1
 w = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
 e = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
 n = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
+equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1
 L_inc_avg = np.zeros(0)
-L_inc_avg_s = np.zeros(p.last_career_yrs)
+L_inc_avg_s = np.zeros(equiv_periods)
 DB = np.zeros(p.S)
 DB_loop_expected1 = np.array(
     [0, 0, 0, 0, 0.337864778, 0.327879365, 0.318189065]
 )
+
 args1 = (
     w,
     e,
@@ -103,8 +101,8 @@ def test_replacement_rate_vals(n, w, factor, j, p_in, expected):
     L_inc_avg_s,
     L_inc_avg,
     DB,
-    p.last_career_yrs,
-    p.rep_rate_py,
+    equiv_periods,
+    p.alpha_db,
     p.yr_contr,
 )
 
@@ -154,24 +152,25 @@ def test_DB_1dim_loop(args, DB_loop_expected):
 p.S = 7
 p.retire = 4
 per_rmn = p.S
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = p.retire
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 p.g_y = np.ones(p.T) * 0.03
 w = np.array([1.2, 1.1, 1.21, 1, 1.01, 0.99, 0.8])
 e = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
 deriv_DB_loop_expected = np.array(
     [0.352, 0.3256, 0.2904, 0.232, 0.0, 0.0, 0.0]
 )
 d_theta_empty = np.zeros_like(w)
+equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1
 args3 = (
     w,
     e,
     p.S,
     p.retire,
     per_rmn,
-    p.last_career_yrs,
-    p.rep_rate_py,
+    equiv_periods,
+    p.alpha_db,
     p.yr_contr,
 )
 
@@ -228,9 +227,9 @@ def test_deriv_PS_loop(args, deriv_PS_loop_expected):
 p = Specifications()
 p.S = 7
 p.retire = 4
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = p.retire
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 p.g_y = np.ones(p.T) * 0.03
 n_ddb1 = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
 w_ddb1 = np.array([1.2, 1.1, 1.21, 1, 1.01, 0.99, 0.8])
@@ -246,7 +245,7 @@ def test_deriv_PS_loop(args, deriv_PS_loop_expected):
 p2.retire = 5
 p2.last_career_yrs = 2
 p2.yr_contr = p2.retire
-p2.rep_rate_py = 0.2
+p2.alpha_db = 0.2
 p2.g_y = 0.03
 n_ddb2 = np.array([0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
 w_ddb1 = np.array([1.1, 1.21, 1, 1.01, 0.99, 0.8])
@@ -387,9 +386,9 @@ def test_deriv_NDC(args, d_NDC_expected):
 p.pension_system = "Defined Benefits"
 p.S = 7
 p.retire = 4
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = p.retire
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 p.g_y = np.ones(p.T) * 0.03
 w_db = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
 e_db = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
@@ -449,14 +448,16 @@ def test_deriv_NDC(args, d_NDC_expected):
 p4 = Specifications()
 p4.pension_system = "US-Style Social Security"
 p4.S = 7
-p4.retire = 4
+p4.retire = (np.ones(p4.T) * 4).astype(int)
 w_ss = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
 e_ss = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
 n_ss = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
 omegas = (1 / p4.S) * np.ones(p4.S)
-theta = 0.4
-p.replacement_rate_adjust = np.ones(p4.T)
-pension_expected_ss = [0, 0, 0, 0, 0.004164689, 0.004041603, 0.003922156]
+theta = np.array([0.4, 0.4])
+p4.replacement_rate_adjust = np.ones(p4.T)
+pension_expected_ss = [0, 0, 0, 0, 0.404, 0.396, 0.32]
+method = "TPI"
+shift = False
 args_ss = (r, w_ss, n_ss, Y, theta, t, j, shift, method, e_ss, factor, p4)
 
 
@@ -592,9 +593,9 @@ def test_delta_ret_loop(args, dir_delta_ret_expected):
 p = Specifications()
 p.S = 7
 p.retire = 4
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = p.retire
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 p.g_y = np.ones(p.T) * 0.03
 j = 1
 w = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
@@ -607,9 +608,9 @@ def test_delta_ret_loop(args, dir_delta_ret_expected):
 p2 = Specifications()
 p2.S = 7
 p2.retire = 4
-p2.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p2.yr_contr = p2.retire
-p2.rep_rate_py = 0.2
+p2.alpha_db = 0.2
 p2.g_y = np.ones(p2.T) * 0.03
 j = 1
 w2 = np.array([1.21, 1.0, 1.01, 0.99, 0.8])
@@ -663,9 +664,9 @@ def test_DB(args, DB_expected):
 p.S = 7
 p.retire = 4
 per_rmn = p.S
-p.last_career_yrs = 3
+p.avg_earn_num_years = 50
 p.yr_contr = p.retire
-p.rep_rate_py = 0.2
+p.alpha_db = 0.2
 w_ddb = np.array([1.2, 1.1, 1.21, 1, 1.01, 0.99, 0.8])
 e_ddb = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
 p.g_y = np.ones(p.T) * 0.03