Read US scenario inputs at the benchmark year (TAX_YEAR)

policybench/scenarios.py

@@ -590,6 +590,13 @@ def load_enhanced_cps_person_frame() -> tuple[pd.DataFrame, int]:
     from policybench.policyengine_runtime import make_us_microsimulation
 
     sim = make_us_microsimulation()
+    # Keep the dataset's native period for provenance, but read promptable
+    # inputs at the benchmark year (TAX_YEAR) so households are represented at
+    # the year they are scored. PolicyEngine uprates monetary inputs across
+    # periods, so reading at the dataset period (e.g. 2024) leaves incomes
+    # several percent below their TAX_YEAR level and out of step with the
+    # population weights, which are computed at TAX_YEAR. The UK loader already
+    # reads at TAX_YEAR; this keeps the US path consistent.
     dataset_year = sim.default_input_period
     input_specs = get_promptable_input_specs()
 
@@ -598,7 +605,7 @@ def load_enhanced_cps_person_frame() -> tuple[pd.DataFrame, int]:
         values[output_name] = np.asarray(
             sim.calculate(
                 variable_name,
-                dataset_year,
+                TAX_YEAR,
                 map_to="person",
                 use_weights=False,
             )
@@ -608,7 +615,7 @@ def load_enhanced_cps_person_frame() -> tuple[pd.DataFrame, int]:
         values[spec.output_name] = np.asarray(
             sim.calculate(
                 spec.source_name,
-                dataset_year,
+                TAX_YEAR,
                 map_to="person",
                 use_weights=False,
             )

tests/test_scenarios.py

@@ -1073,3 +1073,29 @@ def test_sample_household_ids_requires_enough_positive_weight():
     )
     with pytest.raises(ValueError, match="positive sampling weight"):
         _sample_household_ids(eligible, n=2, seed=0)
+
+
+def test_load_enhanced_cps_person_frame_reads_inputs_at_tax_year(monkeypatch):
+    from unittest.mock import MagicMock
+
+    import numpy as np
+
+    import policybench.policyengine_runtime as runtime
+    from policybench.config import TAX_YEAR
+    from policybench.scenarios import load_enhanced_cps_person_frame
+
+    sim = MagicMock()
+    # Dataset's native input period differs from the benchmark year.
+    sim.default_input_period = 2024
+    sim.calculate.return_value = np.array([1.0])
+    monkeypatch.setattr(runtime, "make_us_microsimulation", lambda: sim)
+
+    _frame, dataset_year = load_enhanced_cps_person_frame()
+
+    # Provenance keeps the dataset's native period ...
+    assert dataset_year == 2024
+    # ... but every promptable input is read at the benchmark year (TAX_YEAR),
+    # so households are represented at the year they are scored, not 2024.
+    assert sim.calculate.call_count > 0
+    periods = {call.args[1] for call in sim.calculate.call_args_list}
+    assert periods == {TAX_YEAR}