Allow the pension parameter replacement_rate_adjust to vary over T and J

ogcore/TPI.py

@@ -420,7 +420,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
             bq[0, -1, j],
             rm[0, -1, j],
             tr[0, -1, j],
-            theta * p.replacement_rate_adjust[0],
+            theta * p.replacement_rate_adjust[0, j],
             factor,
             ubi[0, -1, j],
             j,
@@ -436,7 +436,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
         ind2 = np.arange(s + 2)
         b_guesses_to_use = np.diag(guesses_b[: p.S, :], p.S - (s + 2))
         n_guesses_to_use = np.diag(guesses_n[: p.S, :], p.S - (s + 2))
-        theta_to_use = theta[j] * p.replacement_rate_adjust[: p.S]
+        theta_to_use = theta[j] * p.replacement_rate_adjust[: p.S, j]
         bq_to_use = np.diag(bq[: p.S, :, j], p.S - (s + 2))
         rm_to_use = np.diag(rm[: p.S, :, j], p.S - (s + 2))
         tr_to_use = np.diag(tr[: p.S, :, j], p.S - (s + 2))
@@ -499,7 +499,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
     for t in range(0, p.T):
         b_guesses_to_use = 0.75 * np.diag(guesses_b[t : t + p.S, :])
         n_guesses_to_use = np.diag(guesses_n[t : t + p.S, :])
-        theta_to_use = theta[j] * p.replacement_rate_adjust[t : t + p.S]
+        theta_to_use = theta[j] * p.replacement_rate_adjust[t : t + p.S, j]
         bq_to_use = np.diag(bq[t : t + p.S, :, j])
         rm_to_use = np.diag(rm[t : t + p.S, :, j])
         tr_to_use = np.diag(tr[t : t + p.S, :, j])

ogcore/default_parameters.json

@@ -4057,16 +4057,16 @@
     },
     "replacement_rate_adjust": {
         "title": "Adjustment to the Social Security retirement benefits replacement rate",
-        "description": "Adjustment to the Social Security retirement benefits replacement rate. Set value for base year, click '+' to add value for next year.  All future years not specified are set to last value entered.",
+        "description": "Adjustment to the Social Security retirement benefits replacement rate for year t and household type j. Set value for base year, click '+' to add value for next year.  All future years not specified are set to last value entered.",
         "section_1": "Fiscal Policy Parameters",
         "section_2": "Government Pension Parameters",
         "notes": "This parameter willy only vary along the time path.  It is assumed to be one in the steady state.  The steady state retirement rate can be adjusted by changing the parameters of the retirement benefits function.",
         "type": "float",
-        "number_dims": 1,
+        "number_dims": 2,
         "value": [
             {
                 "value": [
-                    1.0
+                    [1.0]
                 ]
             }
         ],

ogcore/parameters.py

@@ -170,7 +170,6 @@ def compute_default_params(self):
             "m_wealth",
             "p_wealth",
             "retirement_age",
-            "replacement_rate_adjust",
             "zeta_D",
             "zeta_K",
             "r_gov_scale",
@@ -208,6 +207,7 @@ def compute_default_params(self):
         tp_param_list3 = [
             "labor_income_tax_noncompliance_rate",
             "capital_income_tax_noncompliance_rate",
+            "replacement_rate_adjust",
         ]
         for item in tp_param_list3:
             param_in = getattr(self, item)

ogcore/pensions.py

@@ -168,24 +168,24 @@ def SS_amount(w, n, theta, t, j, shift, method, e, p):
             else:
                 retireTPI = p.retire[t] - 1 - p.S
             pension[retireTPI:] = (
-                theta[j] * p.replacement_rate_adjust[t] * w[retireTPI:]
+                theta[j] * p.replacement_rate_adjust[t, j] * w[retireTPI:]
             )
         elif len(n.shape) == 2:
             for tt in range(pension.shape[0]):
                 pension[tt, retireTPI[tt] :] = (
-                    theta * p.replacement_rate_adjust[t + tt] * w[tt]
+                    theta * p.replacement_rate_adjust[t + tt, j] * w[tt]
                 )
         else:
             for tt in range(pension.shape[0]):
                 pension[tt, retireTPI[tt] :, :] = (
                     theta.reshape(1, p.J)
-                    * p.replacement_rate_adjust[t + tt]
+                    * p.replacement_rate_adjust[t + tt, :].reshape(1, p.J)
                     * w[tt]
                 )
     elif method == "TPI_scalar":
         # The above methods won't work if scalars are used.  This option
         # is only called by the SS_TPI_firstdoughnutring function in TPI.
-        pension = theta * p.replacement_rate_adjust[0] * w
+        pension = theta * p.replacement_rate_adjust[0, j] * w
 
     return pension
 

pyproject.toml

@@ -11,5 +11,5 @@ build-backend = "setuptools.build_meta"
 
 [tool.black]
 line-length = 79
-target-version = ["py311", "py312"]
+target-version = ["py312", "py313"]
 include = '\.pyi?$'

tests/test_aggregates.py

@@ -16,6 +16,7 @@
     "chi_n": np.ones(2),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((40, 2)) / (40 * 2)),
     "lambdas": [0.6, 0.4],
     "omega": np.ones((160, 40)) / 40,
@@ -58,6 +59,7 @@ def test_get_L(n, p, method, expected):
     "e": np.ones((40, 2)),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((40, 2)) / (40 * 2)),
     "lambdas": [0.6, 0.4],
     "omega": np.ones((160, 40)) / 40,
@@ -140,6 +142,7 @@ def test_get_I(b_splus1, K_p1, K, p, method, expected):
     "e": np.ones((40, 2)),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((40, 2)) / (40 * 2)),
     "lambdas": [0.6, 0.4],
     "omega": np.ones((160, 40)) / 40,
@@ -198,6 +201,7 @@ def test_get_B(b, p, method, PreTP, expected):
     "e": np.ones((40, 2)),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((40, 2)) / (40 * 2)),
     "lambdas": [0.6, 0.4],
     "omega": np.ones((160, 40)) / 40,
@@ -1128,6 +1132,7 @@ def test_get_RM(Y, p, method, expected):
     "M": 3,
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((40, 2)) / (40 * 2)),
     "lambdas": [0.6, 0.4],
     "omega": np.ones((160, 40)) / 40,
@@ -1178,6 +1183,7 @@ def test_get_C(c, p, method, expected):
     "e": np.ones((20, 2)),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((20, 2)) / (20 * 2)),
     "lambdas": [0.6, 0.4],
     "tau_bq": [0.17],
@@ -1245,7 +1251,13 @@ def test_get_C(c, p, method, expected):
     "m_wealth": [1.0],
     "cit_rate": [[0.2]],
     "inv_tax_credit": [[0.02]],
-    "replacement_rate_adjust": [1.5, 1.5, 1.5, 1.6, 1.0],
+    "replacement_rate_adjust": [
+        [1.5, 1.5],
+        [1.5, 1.5],
+        [1.5, 1.5],
+        [1.6, 1.6],
+        [1.0, 1.0],
+    ],
     "delta_tau_annual": [
         [float(1 - ((1 - 0.0975) ** (20 / (p3.ending_age - p3.starting_age))))]
     ],
@@ -1272,6 +1284,7 @@ def test_get_C(c, p, method, expected):
     "e": np.ones((20, 2)),
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "eta": (np.ones((20, 2)) / (20 * 2)),
     "lambdas": [0.6, 0.4],
     "tau_bq": [0.17],

tests/test_pensions.py

@@ -14,6 +14,7 @@
     "lambdas": [1.0],
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
     "J": 1,
     "T": 4,
     "chi_n": np.ones(4),
@@ -456,7 +457,7 @@ def test_deriv_NDC(args, d_NDC_expected):
 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 = np.array([0.4, 0.4])
-p4.replacement_rate_adjust = np.ones(p4.T)
+p4.replacement_rate_adjust = np.ones((p4.T, p4.J))
 pension_expected_ss = [0, 0, 0, 0, 0.404, 0.396, 0.32]
 method = "TPI"
 shift = False

tests/test_tax.py

@@ -23,6 +23,7 @@
     "m_wealth": [4],
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
 }
 p1.update_specifications(new_param_values)
 expected1 = np.array([0.14285714, 0.6, 0.93103448])
@@ -41,6 +42,7 @@
     "m_wealth": [3, 4, 3],
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
 }
 p2.update_specifications(new_param_values2)
 expected2 = np.array([0.084615385, 0.278021978, 0.734911243])
@@ -76,6 +78,7 @@ def test_ETR_wealth(b, p, expected):
     "m_wealth": [5],
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
 }
 p1.update_specifications(new_param_values)
 expected1 = np.array([0.81122449, 1.837370242, 2.173849525])
@@ -95,6 +98,7 @@ def test_ETR_wealth(b, p, expected):
     "m_wealth": [3, 4, 3],
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
 }
 p2.update_specifications(new_param_values2)
 expected2 = np.array([0.165976331, 0.522436904, 1.169769966])
@@ -896,6 +900,7 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected):
 p.S = 3
 p.labor_income_tax_noncompliance_rate = np.zeros((p.T, p.S, p.J))
 p.capital_income_tax_noncompliance_rate = np.zeros((p.T, p.S, p.J))
+p.replacement_rate_adjust = np.ones((p.T, p.J))
 p.lambdas = np.array([1.0])
 p.e = np.array([0.5, 0.45, 0.3]).reshape(3, 1)
 p.h_wealth = np.ones(p.T + p.S) * 1
@@ -910,13 +915,15 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected):
 p3.T = 3
 p3.labor_income_tax_noncompliance_rate = np.zeros((p3.T, p3.S, p3.J))
 p3.capital_income_tax_noncompliance_rate = np.zeros((p3.T, p3.S, p3.J))
+p3.replacement_rate_adjust = np.ones((p3.T, p3.J))
 p4 = copy.deepcopy(p)
 p5 = copy.deepcopy(p)
 p5.e = np.array([[0.3, 0.2], [0.5, 0.4], [0.45, 0.3]])
 p5.J = 2
 p5.T = 3
 p5.labor_income_tax_noncompliance_rate = np.zeros((p5.T, p5.S, p5.J))
 p5.capital_income_tax_noncompliance_rate = np.zeros((p5.T, p5.S, p5.J))
+p5.replacement_rate_adjust = np.ones((p5.T, p5.J))
 p5.lambdas = np.array([0.65, 0.35])
 # set variables and other parameters for each case
 r1 = 0.04
@@ -1139,7 +1146,7 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected):
 p7.retire = (np.array([1, 2, 2])).astype(int)
 
 p8 = copy.deepcopy(p6)
-p8.replacement_rate_adjust = [1.5, 0.6, 1.0]
+p8.replacement_rate_adjust = np.array([[1.5, 1.5], [0.6, 0.6], [1.0, 1.0]])
 
 factor = 105000
 ubi1 = np.zeros((p1.T, p1.S, p1.J))
@@ -1163,6 +1170,7 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected):
     "ubi_nom_65p": 500,
     "labor_income_tax_noncompliance_rate": [[0.0]],
     "capital_income_tax_noncompliance_rate": [[0.0]],
+    "replacement_rate_adjust": [[1.0]],
 }
 p_u.update_specifications(new_param_values_ubi)
 p_u.tax_func_type = "DEP"
@@ -1298,13 +1306,15 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected):
 p12.capital_income_tax_noncompliance_rate = (
     np.ones((p12.T + p12.S, p12.J)) * 0.05
 )
+p12.replacement_rate_adjust = np.ones((p12.T + p12.S, p12.J)) * 1.0
 p13 = copy.deepcopy(p5)
 p13.labor_income_tax_noncompliance_rate = (
     np.ones((p13.T + p13.S, p13.J)) * 0.05
 )
 p13.capital_income_tax_noncompliance_rate = (
     np.ones((p13.T + p13.S, p13.J)) * 0.05
 )
+p13.replacement_rate_adjust = np.ones((p13.T + p13.S, p13.J)) * 1.0
 
 expected1 = np.array([0.47374766, -0.09027663, 0.03871394])
 expected2 = np.array([0.20374766, -0.09027663, 0.03871394])

tests/testing_params.json

@@ -16,6 +16,7 @@
 "chi_b": [80, 80],
 "labor_income_tax_noncompliance_rate": [[0.0]],
 "capital_income_tax_noncompliance_rate": [[0.0]],
+"replacement_rate_adjust": [[1.0]],
 "tax_func_type": "linear",
 "age_specific": false,
 "etr_params": [[[0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2], [0.2]],