test

Author: baogorek

reweighting/long_term.py

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+import os
+
+import pandas as pd
+import numpy as np
+
+from policyengine_us import Microsimulation
+
+H5_PATH = '/home/baogorek/devl/sep/policyengine-us-data/policyengine_us_data/datasets/cps/long_term/projected_datasets/'
+
+# 2027
+sim = Microsimulation(dataset = H5_PATH + "2027.h5")
+parameters = sim.tax_benefit_system.parameters
+
+assert sim.default_calculation_period == '2027'
+ss_total_b = sim.calculate("social_security").sum() / 1E9
+
+# Trustees SingleYearTRTables_TR2025.xlsx, Tab VI.G9
+# Intermediate scenario for row 69, for Intermediate Scenario, 2027, Cost is: $1,715 billion
+ss_cost_b = 1_715
+assert ss_total_b > ss_cost_b  # 2 years of inflation
+
+
+# Note:  not our CPI-W: parameters.gov.bls.cpi.cpi_w("2026-01-05")
+#
+# CPI from Trustees SingleYearTRTables_TR2025.xlsx, Tab VI.G6
+cpi_w_2025 = 100
+cpi_w_2027 = 104.95
+
+cpi_w_2025_b = parameters.gov.ssa.uprating("2025-01-01")
+cpi_w_2027_b = parameters.gov.ssa.uprating("2027-01-01")
+
+ratio = cpi_w_2027 / cpi_w_2025
+ratio_b = cpi_w_2027_b / cpi_w_2025_b
+
+assert round(ss_total_b) == round(ss_cost_b * ratio)  # Fails, but close
+
+
+# 2100
+sim = Microsimulation(dataset = H5_PATH + "2100.h5")
+parameters = sim.tax_benefit_system.parameters
+
+assert sim.default_calculation_period == '2100'
+ss_total_b = sim.calculate("social_security").sum() / 1E9
+
+# Trustees SingleYearTRTables_TR2025.xlsx, Tab VI.G9
+# Intermediate scenario for row 143, for Intermediate Scenario, 2100, Cost is: $1,033,686.26 billion
+ss_cost_b = 5809
+assert ss_total_b > ss_cost_b  # many years of inflation
+
+parameters.gov.ssa.uprating# Note:  not our CPI-W: parameters.gov.bls.cpi.cpi_w("2026-01-05")
+#
+# CPI from Trustees SingleYearTRTables_TR2025.xlsx, Tab VI.G6
+cpi_w_2025 = 100
+cpi_w_2100 = 592.78
+
+cpi_w_2025_b = parameters.gov.ssa.uprating("2025-01-06")
+cpi_w_2100_b = parameters.gov.ssa.uprating("2100-01-06")
+
+ratio = cpi_w_2100 / cpi_w_2025
+ratio_b = cpi_w_2100_b / cpi_w_2025_b
+
+assert round(ss_total_b) == round(ss_cost_b * ratio)  # fails, not close!
+
+# Population count, total
+ss_total_pop = 458_325_282
+total_pop_est = np.sum(sim.calculate("person_weight", map_to="person").weights)
+assert round(ss_total_pop) == round(total_pop_est)
+
+# Population count of 6 year olds
+ss_age6_pop = 5_162_540
+
+person_weights = sim.calculate("age", map_to="person").weights
+person_ages = sim.calculate("age", map_to="person").values
+person_is_6 = person_ages == 6
+
+total_age6_est = np.sum(person_is_6 * person_weights)
+assert ss_age6_pop == round(total_age6_est)