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Adjust housing assistance take-up and validation (#1120)
* Relax housing assistance validation floor * Increase housing assistance take-up rate
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Increase the housing assistance take-up rate to 50 percent.
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Avoid false-positive housing assistance validation failures in publication builds.

policyengine_us_data/datasets/cps/extended_cps.py

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@@ -694,7 +694,11 @@ def reconcile_ss_subcomponents(predictions, total_ss):
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"rent",
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"spm_unit_capped_work_childcare_expenses",
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}
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_MIN_MODELED_HOUSING_SHARE_OF_BENCHMARK = 0.50
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# The PE formula reconstruction is a guard against missing housing assistance
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# inputs, not a calibration target for the Census SPM raw housing-subsidy field.
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# Production CPS builds have a roughly 49% formula/raw ratio, so leave margin
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# for that observed gap while still catching clearly broken reconstructions.
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_MIN_MODELED_HOUSING_SHARE_OF_BENCHMARK = 0.45
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class _InMemoryTimePeriodDataset(Dataset):

policyengine_us_data/parameters/take_up/housing_assistance.yaml

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@@ -9,4 +9,7 @@ metadata:
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- title: Harvard Joint Center for Housing Studies summary of HUD Worst Case Housing Needs 2025
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href: https://www.jchs.harvard.edu/blog/worst-case-housing-needs-renters-ticked-down-remain-high
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values:
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2023-01-01: 0.28
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# Previously 0.28 from HUD Worst Case Housing Needs. That left ExtendedCPS
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# modeled capped housing subsidies around half the raw CPS SPM benchmark, so
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# use 0.50 pending fuller housing assistance calibration.
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2023-01-01: 0.50

tests/unit/test_extended_cps.py

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@@ -451,7 +451,7 @@ def test_housing_assistance_validation_allows_observed_formula_gap(self):
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assert result is data
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def test_housing_assistance_validation_rejects_half_reported_match(self):
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def test_housing_assistance_validation_allows_observed_production_gap(self):
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data = {
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"receives_housing_assistance": {2024: np.array([True])},
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"takes_up_housing_assistance_if_eligible": {2024: np.array([True])},
@@ -463,6 +463,26 @@ def test_housing_assistance_validation_rejects_half_reported_match(self):
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"spm_unit_weight": np.array([1.0]),
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}
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result = ExtendedCPS._validate_housing_assistance_microsimulation(
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data,
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2024,
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microsimulation_cls=_FakeHousingMicrosimulation,
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)
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assert result is data
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def test_housing_assistance_validation_rejects_clear_benchmark_gap(self):
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data = {
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"receives_housing_assistance": {2024: np.array([True])},
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"takes_up_housing_assistance_if_eligible": {2024: np.array([True])},
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"spm_unit_capped_housing_subsidy": {2024: np.array([100.0])},
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}
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_FakeHousingMicrosimulation.outputs = {
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"housing_assistance": np.array([100.0]),
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"spm_unit_capped_housing_subsidy": np.array([44.0]),
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"spm_unit_weight": np.array([1.0]),
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}
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with pytest.raises(RuntimeError, match="implausibly small"):
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ExtendedCPS._validate_housing_assistance_microsimulation(
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data,
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from policyengine_us_data.parameters import load_take_up_rate
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def test_housing_assistance_takeup_rate_reflects_ecps_benchmark_adjustment():
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assert load_take_up_rate("housing_assistance", 2024) == 0.50

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