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2025 aggregate impact on current weights versus merged :\n\n- mean: (, )\n- recipient mean: (, )\n- recipient rate: unchanged at \n- mean: unchanged at \n- mean: unchanged at \n- relative poverty AHC: unchanged at \n- relative poverty BHC: unchanged at \n\nLocal validation on the final rebased head:\n- WARNING:policyengine_core.scripts:Several country packages detected : policyengine_uk/tests/policy/baseline/gov/dfe/maintenance_loans/maintenance_loan.yaml ..................... ============================== 21 passed in 0.20s ==============================\n- ..... [100%] -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html |
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Dataset-backed aggregate check on
The broader household aggregates I checked were unchanged to displayed precision:
So the follow-up is tightening the maintenance-loan proxy and raising award amounts for the same modeled recipient pool, without moving the broader tax/poverty aggregates on current weights. |
Summary
Validation
uv run policyengine-core test policyengine_uk/tests/policy/baseline/gov/dfe/maintenance_loans/maintenance_loan.yamluv run pytest -q policyengine_uk/tests/test_maintenance_loan_proxies.pyenhanced_frs_2023_24.h5: candidate benunit income0.02s, sponsor detection0.05s, sponsor income0.01s, household income0.01s, award0.02sAggregate Impact
Measured on
enhanced_frs_2023_24.h5at2025, comparing merged#1586against this branch on current weights.maintenance_loanmean:143.9555 -> 162.5093(+18.5539,+12.9%)maintenance_loanrecipient mean:8057.7370 -> 9096.2669(+1038.5298,+12.9%)maintenance_loanrecipient rate: unchanged at1.78655%household_taxmean: unchanged at24904.1915hbai_household_net_incomemean: unchanged at44999.992821.4478%17.2909%Caveat
maintenance_loan_in_england_systemstill proxies Student Finance England membership from current household country, so cross-border domicile cases remain approximate.