From 70daf699672f4e34f1ff7e50c12c27e5cce2df6e Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 15:22:35 -0400 Subject: [PATCH 01/14] chore: initialize KFF Medicaid progress log --- PROGRESS.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 PROGRESS.md diff --git a/PROGRESS.md b/PROGRESS.md new file mode 100644 index 0000000..45f1c0e --- /dev/null +++ b/PROGRESS.md @@ -0,0 +1,18 @@ +# KFF Medicaid scorecard lane progress + +## State + +In progress. The worktree is on `us/kff-medicaid`; the pre-staged KFF source directory is present and intentionally unmodified so far. Network access is prohibited for this lane. + +## Done + +- Read the lane brief and established the required descriptive register, certified-bundle constraint, single-period 2024 scope, simulation memory discipline, and sanity anchors. +- Attempted the GitNexus exploration workflow; GitNexus resources and query tools are not exposed in this workspace, so repository files and tests will be used directly. +- Created the implementation and validation plan. + +## Next + +- Read the full KFF provenance and inspect the existing Yale/TPC moments adapters, campaign/populace reform staging, schemas, and tests. +- Add the KFF source adapter and tests. +- Validate baseline and 100%-take-up computation on a uniformly sampled 5,000-household dataset, using one managed simulation per process. +- Run full-file baseline and reform passes separately, stage aggregate diagnostics/results, wire moments, and run offline checks. From 884e8d21cf3b09f526400981c00483f15cfa152f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 15:32:23 -0400 Subject: [PATCH 02/14] Add KFF remaining-uninsured source adapter --- PROGRESS.md | 7 +- data/externals/kff-remaining-uninsured.json | 1204 +++++++++++++++++ sources/kff-remaining-uninsured/PROVENANCE.md | 79 ++ sources/kff-remaining-uninsured/adapter.py | 203 +++ .../kff-remaining-uninsured/annotations.json | 44 + .../raw/state_indicator_2024acs_2025levels.md | 68 + sources/kff-remaining-uninsured/source.json | 21 + 7 files changed, 1623 insertions(+), 3 deletions(-) create mode 100644 data/externals/kff-remaining-uninsured.json create mode 100644 sources/kff-remaining-uninsured/PROVENANCE.md create mode 100644 sources/kff-remaining-uninsured/adapter.py create mode 100644 sources/kff-remaining-uninsured/annotations.json create mode 100644 sources/kff-remaining-uninsured/raw/state_indicator_2024acs_2025levels.md create mode 100644 sources/kff-remaining-uninsured/source.json diff --git a/PROGRESS.md b/PROGRESS.md index 45f1c0e..42e5763 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -2,17 +2,18 @@ ## State -In progress. The worktree is on `us/kff-medicaid`; the pre-staged KFF source directory is present and intentionally unmodified so far. Network access is prohibited for this lane. +In progress. The KFF source population is adapted into 57 truthful-vintage moments rows. Network access remains prohibited for this lane. ## Done - Read the lane brief and established the required descriptive register, certified-bundle constraint, single-period 2024 scope, simulation memory discipline, and sanity anchors. - Attempted the GitNexus exploration workflow; GitNexus resources and query tools are not exposed in this workspace, so repository files and tests will be used directly. - Created the implementation and validation plan. +- Read the complete KFF provenance and staged table, plus the existing moments and campaign contracts. +- Added the KFF source registry, descriptive construct annotations, and adapter: 52 state-indicator percentage rows (US + 50 states + DC) and five 2022 flagship-brief rows. The selected rendered column has no NSD cells; the adapter retains any NSD selected cell as an explicitly suppressed row. ## Next -- Read the full KFF provenance and inspect the existing Yale/TPC moments adapters, campaign/populace reform staging, schemas, and tests. -- Add the KFF source adapter and tests. +- Extend the moments builder for multiple PE payloads, deterministic primary/alternative variants, truthful-period references, row metadata, and null-safe suppression handling; add tests. - Validate baseline and 100%-take-up computation on a uniformly sampled 5,000-household dataset, using one managed simulation per process. - Run full-file baseline and reform passes separately, stage aggregate diagnostics/results, wire moments, and run offline checks. diff --git a/data/externals/kff-remaining-uninsured.json b/data/externals/kff-remaining-uninsured.json new file mode 100644 index 0000000..d317efd --- /dev/null +++ b/data/externals/kff-remaining-uninsured.json @@ -0,0 +1,1204 @@ +[ + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "US", + "unit_concept": "percent", + "period": "2024", + "value": 24.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "AL", + "unit_concept": "percent", + "period": "2024", + "value": 9.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "AK", + "unit_concept": "percent", + "period": "2024", + "value": 40.8, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "AZ", + "unit_concept": "percent", + "period": "2024", + "value": 32.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "AR", + "unit_concept": "percent", + "period": "2024", + "value": 39.6, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "CA", + "unit_concept": "percent", + "period": "2024", + "value": 32.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "CO", + "unit_concept": "percent", + "period": "2024", + "value": 28.2, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "CT", + "unit_concept": "percent", + "period": "2024", + "value": 20.8, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "DE", + "unit_concept": "percent", + "period": "2024", + "value": 27.8, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "DC", + "unit_concept": "percent", + "period": "2024", + "value": 50.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "FL", + "unit_concept": "percent", + "period": "2024", + "value": 8.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "GA", + "unit_concept": "percent", + "period": "2024", + "value": 27.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "HI", + "unit_concept": "percent", + "period": "2024", + "value": 39.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "ID", + "unit_concept": "percent", + "period": "2024", + "value": 31.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "IL", + "unit_concept": "percent", + "period": "2024", + "value": 32.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "IN", + "unit_concept": "percent", + "period": "2024", + "value": 36.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "IA", + "unit_concept": "percent", + "period": "2024", + "value": 37.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "KS", + "unit_concept": "percent", + "period": "2024", + "value": 11.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "KY", + "unit_concept": "percent", + "period": "2024", + "value": 37.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "LA", + "unit_concept": "percent", + "period": "2024", + "value": 35.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "ME", + "unit_concept": "percent", + "period": "2024", + "value": 34.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MD", + "unit_concept": "percent", + "period": "2024", + "value": 23.3, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MA", + "unit_concept": "percent", + "period": "2024", + "value": 27.3, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MI", + "unit_concept": "percent", + "period": "2024", + "value": 36.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MN", + "unit_concept": "percent", + "period": "2024", + "value": 31.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MS", + "unit_concept": "percent", + "period": "2024", + "value": 12.2, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MO", + "unit_concept": "percent", + "period": "2024", + "value": 44.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "MT", + "unit_concept": "percent", + "period": "2024", + "value": 40.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NE", + "unit_concept": "percent", + "period": "2024", + "value": 33.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NV", + "unit_concept": "percent", + "period": "2024", + "value": 29.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NH", + "unit_concept": "percent", + "period": "2024", + "value": 27.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NJ", + "unit_concept": "percent", + "period": "2024", + "value": 28.2, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NM", + "unit_concept": "percent", + "period": "2024", + "value": 33.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NY", + "unit_concept": "percent", + "period": "2024", + "value": 33.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "NC", + "unit_concept": "percent", + "period": "2024", + "value": 31.6, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "ND", + "unit_concept": "percent", + "period": "2024", + "value": 31.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "OH", + "unit_concept": "percent", + "period": "2024", + "value": 34.2, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "OK", + "unit_concept": "percent", + "period": "2024", + "value": 39.2, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "OR", + "unit_concept": "percent", + "period": "2024", + "value": 34.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "PA", + "unit_concept": "percent", + "period": "2024", + "value": 38.4, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "RI", + "unit_concept": "percent", + "period": "2024", + "value": 31.4, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "SC", + "unit_concept": "percent", + "period": "2024", + "value": 11.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "SD", + "unit_concept": "percent", + "period": "2024", + "value": 49.4, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "TN", + "unit_concept": "percent", + "period": "2024", + "value": 13.5, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "TX", + "unit_concept": "percent", + "period": "2024", + "value": 12.9, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "UT", + "unit_concept": "percent", + "period": "2024", + "value": 30.3, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "VT", + "unit_concept": "percent", + "period": "2024", + "value": 28.6, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "VA", + "unit_concept": "percent", + "period": "2024", + "value": 27.7, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "WA", + "unit_concept": "percent", + "period": "2024", + "value": 27.1, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "WV", + "unit_concept": "percent", + "period": "2024", + "value": 40.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "WI", + "unit_concept": "percent", + "period": "2024", + "value": 30.4, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2025_rules", + "geography": "WY", + "unit_concept": "percent", + "period": "2024", + "value": 14.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "Medicaid/Other Public (%)", + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_uninsured_count", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "persons", + "period": "2022", + "value": 6400000, + "value_kind": "count", + "status": "ok", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_uninsured_count", + "subgroup": "adults", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "persons", + "period": "2022", + "value": 4200000, + "value_kind": "count", + "status": "ok", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_uninsured_count", + "subgroup": "children", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "persons", + "period": "2022", + "value": 2200000, + "value_kind": "count", + "status": "ok", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_uninsured_count", + "subgroup": "expansion_states", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "persons", + "period": "2022", + "value": 5200000, + "value_kind": "count", + "status": "ok", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "percent", + "period": "2022", + "value": 25.0, + "value_kind": "share", + "status": "ok", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024" + } +] diff --git a/sources/kff-remaining-uninsured/PROVENANCE.md b/sources/kff-remaining-uninsured/PROVENANCE.md new file mode 100644 index 0000000..83b3cdf --- /dev/null +++ b/sources/kff-remaining-uninsured/PROVENANCE.md @@ -0,0 +1,79 @@ +# KFF: eligibility for coverage among the remaining uninsured + +Source adapter for KFF's eligibility-among-the-uninsured estimates (the KFF/Damico +eligibility simulation). Two populations, different vintages: + +## 1. Flagship brief (national counts) + +- **Title**: A Closer Look at the Remaining Uninsured Population Eligible for + Medicaid and CHIP +- **URL**: https://www.kff.org/uninsured/a-closer-look-at-the-remaining-uninsured-population-eligible-for-medicaid-and-chip/ +- **Published**: 2024-03-15. **Accessed**: 2026-08-18 (WebFetch, main session). +- **Vintage**: 2022 survey data (ACS), 2023 Medicaid eligibility levels. + Pre-unwinding (continuous enrollment ended 2023-03-31); the brief itself notes + unwinding disenrollment is expected to increase the uninsured. +- **Headline numbers** (nonelderly, uninsured AND eligible for Medicaid/CHIP): + - Total: 6.4M = 25% of the 25.6M nonelderly uninsured (2022) + - Adults 4.2M; children 2.2M (children's leg includes CHIP) + - In expansion states: 5.2M +- **Universe**: nonelderly (0–64) uninsured; eligibility includes CHIP for + children; MAGI pathways only (see methods below). + +## 2. State Health Facts indicator (state shares, vintage-matched to Populace 2024) + +- **Title**: Distribution of Eligibility for ACA Health Coverage Among the + Remaining Uninsured +- **URL**: https://www.kff.org/health-reform/state-indicator/distribution-of-eligibility-for-aca-coverage-among-the-remaining-uninsured/ +- **Accessed**: 2026-08-18 (WebFetch extraction of the rendered table — NOT the + CSV download; see caution below). +- **Vintage**: 2024 ACS, 2025 Medicaid + premium tax credit eligibility levels + (per the page's methods notes). Post-unwinding on both sides of any join with + populace_us_2024. +- **Raw extraction**: `raw/state_indicator_2024acs_2025levels.md`. +- **⚠️ Column-label caution**: the extraction labeled the first column + "Medicaid/Other Public (%)" — verify category semantics against KFF's own + column headers before finalizing claim metric names (KFF's category set for + this indicator historically: Medicaid eligible / Tax-credit eligible / + in coverage gap / ineligible due to income or ESI offer / ineligible due to + immigration status). NSD = below statistical-reliability minimum; NA (coverage + gap) = expansion state. Shares are of each state's remaining uninsured + (nonelderly universe). + +## KFF methodology (verified from KFF's own methods boxes, 2026-08-18) + +Static eligibility microsimulation (analyst: Anthony Damico). Current vintage: +ACS microdata (moved from CPS ASEC with the 2017 estimates); separate +Medicaid-vs-Marketplace health insurance units built per person (tax-unit +reconstruction ~99% match to IPUMS); ACA-definition MAGI vs state-reported +eligibility levels (KFF/Georgetown CCF 50-state survey); immigration status +imputed (current methods box: regression trained on the 2023 KFF/LA Times +Survey of Immigrants, 5 implicates, benchmarked to Pew state estimates; earlier +vintages: SIPP-based SHADAC/Van Hook model benchmarked to DHS); ESI offers +imputed from a CPS-ASEC regression. MAGI pathway only (no disability/non-MAGI +pathways; they argue SSI auto-linkage makes the residual small). Annual income +snapshot (they note monthly churn means annual flow exceeds the point-in-time +stock). No stated correction for survey Medicaid underreporting. + +Methods sources: +- https://www.kff.org/medicaid/how-many-uninsured-are-in-the-coverage-gap-and-how-many-could-be-eligible-if-all-states-adopted-the-medicaid-expansion/ (current ACS-era methods box) +- https://files.kff.org/attachment/data-note-new-estimates-of-eligibility-for-aca-coverage-among-the-uninsured (2016 data note, CPS-era Methods box) + +## Named construct differences vs PolicyEngine (annotate on every join; both directions, descriptive) + +1. **Pathway breadth**: KFF = MAGI-only; PE `is_medicaid_eligible` includes + SSI-linked, medically needy, optional senior/disabled categories → PE + eligibility construct is broader. +2. **CHIP**: KFF's counts include CHIP children; Microcosm deliberately does not + assign standalone CHIP take-up (M-CHIP concept split pending, microcosm#321) + → condition PE counterparts on Medicaid-only or annotate. +3. **Coverage measurement**: KFF's uninsured retains survey Medicaid + misreporters (no undercount correction stated); PE enrollment is + anchor-and-fill calibrated to CMS Dec-2024 state counts with reported + coverage as a floor. +4. **Data/imputations**: ACS vs CPS-ASEC-based Populace; immigration status — + KFF regression + Pew benchmarks (5 implicates) vs Microcosm + derive_immigration_status (CPS citizenship/entry-year/nativity + Pew anchors); + ESI offer imputed by KFF, reported at interview in Populace. +5. **Vintage** (brief population only): 2022 ACS + 2023 levels vs single-period + 2024 artifact — join as vintage-mismatched; the state indicator population is + the vintage-matched join. diff --git a/sources/kff-remaining-uninsured/adapter.py b/sources/kff-remaining-uninsured/adapter.py new file mode 100644 index 0000000..1017d81 --- /dev/null +++ b/sources/kff-remaining-uninsured/adapter.py @@ -0,0 +1,203 @@ +"""Adapt KFF eligibility-among-the-uninsured estimates to moments rows. + +The state indicator contributes the rendered table's first percentage +column only. The source labels that column ``Medicaid/Other Public``; +``source_column`` preserves that wording because the KFF CSV headers still +need an offline re-check. Percentages stay on KFF's published 0--100 scale. + +The flagship brief contributes five national 2022-vintage claims. Those +rows remain on their own period so the moments join reports ``not_computed`` +against the single-period 2024 certified artifact and can show the 2024 PE +value as a dated reference instead. + +KFF uses ``NSD`` for statistically suppressed cells. A suppressed selected +cell is emitted with ``value=None``, ``status=suppressed``, and an explicit +``suppression_flag``; it is never dropped. There happen to be no NSD values +in the selected first column of the staged table. +""" + +from __future__ import annotations + +import json +import re +from pathlib import Path + +HERE = Path(__file__).resolve().parent +OUT = HERE.parent.parent / "data" / "externals" / "kff-remaining-uninsured.json" + +SOURCE = "kff-remaining-uninsured" +PROGRAM = "medicaid" +STATE_VARIANT = "kff_reported_uninsured_magi_medicaid_chip_2025_rules" +BRIEF_VARIANT = "kff_reported_uninsured_magi_medicaid_chip_2023_rules" +STATE_SOURCE_COLUMN = "Medicaid/Other Public (%)" + +STATE_CODES = { + "Alabama": "AL", + "Alaska": "AK", + "Arizona": "AZ", + "Arkansas": "AR", + "California": "CA", + "Colorado": "CO", + "Connecticut": "CT", + "Delaware": "DE", + "District of Columbia": "DC", + "Florida": "FL", + "Georgia": "GA", + "Hawaii": "HI", + "Idaho": "ID", + "Illinois": "IL", + "Indiana": "IN", + "Iowa": "IA", + "Kansas": "KS", + "Kentucky": "KY", + "Louisiana": "LA", + "Maine": "ME", + "Maryland": "MD", + "Massachusetts": "MA", + "Michigan": "MI", + "Minnesota": "MN", + "Mississippi": "MS", + "Missouri": "MO", + "Montana": "MT", + "Nebraska": "NE", + "Nevada": "NV", + "New Hampshire": "NH", + "New Jersey": "NJ", + "New Mexico": "NM", + "New York": "NY", + "North Carolina": "NC", + "North Dakota": "ND", + "Ohio": "OH", + "Oklahoma": "OK", + "Oregon": "OR", + "Pennsylvania": "PA", + "Rhode Island": "RI", + "South Carolina": "SC", + "South Dakota": "SD", + "Tennessee": "TN", + "Texas": "TX", + "Utah": "UT", + "Vermont": "VT", + "Virginia": "VA", + "Washington": "WA", + "West Virginia": "WV", + "Wisconsin": "WI", + "Wyoming": "WY", +} + +STATE_LINE = re.compile(r"^([^:]+):\s*(.+)$") + + +def parse_percent(raw: str) -> tuple[float | None, str, str | None]: + """Return value, row status, and suppression flag for one KFF cell.""" + token = raw.strip() + if token == "NSD": + return None, "suppressed", "NSD" + if not token.endswith("%"): + raise ValueError(f"expected a percentage or NSD, got {token!r}") + return float(token.removesuffix("%")), "ok", None + + +def _base_row() -> dict: + return { + "source": SOURCE, + "program": PROGRAM, + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + } + + +def state_rows(raw_path: Path | None = None) -> list[dict]: + raw_path = raw_path or HERE / "raw" / "state_indicator_2024acs_2025levels.md" + rows = [] + seen = set() + for line in raw_path.read_text().splitlines(): + match = STATE_LINE.match(line) + if match is None: + continue + name, rendered = match.groups() + if name == "United States": + geography = "US" + elif name in STATE_CODES: + geography = STATE_CODES[name] + else: + continue + first_cell = rendered.split("|")[0].strip() + value, status, suppression_flag = parse_percent(first_cell) + rows.append( + { + **_base_row(), + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": STATE_VARIANT, + "geography": geography, + "unit_concept": "percent", + "period": "2024", + "value": value, + "value_kind": "share", + "status": status, + "suppression_flag": suppression_flag, + "source_column": STATE_SOURCE_COLUMN, + "publication_population": "state_indicator", + "data_vintage": "2024 ACS", + "rules_vintage": "2025 eligibility levels", + } + ) + seen.add(geography) + expected = {"US", *STATE_CODES.values()} + if seen != expected: + raise ValueError( + "state table geography mismatch: " + f"missing={sorted(expected - seen)}, extra={sorted(seen - expected)}" + ) + return rows + + +def brief_rows() -> list[dict]: + claims = ( + ("eligible_uninsured_count", "total", "persons", 6_400_000, "count"), + ("eligible_uninsured_count", "adults", "persons", 4_200_000, "count"), + ("eligible_uninsured_count", "children", "persons", 2_200_000, "count"), + ( + "eligible_uninsured_count", + "expansion_states", + "persons", + 5_200_000, + "count", + ), + ("eligible_share_among_uninsured", "total", "percent", 25.0, "share"), + ) + return [ + { + **_base_row(), + "metric": metric, + "subgroup": subgroup, + "variant": BRIEF_VARIANT, + "geography": "US", + "unit_concept": unit, + "period": "2022", + "value": value, + "value_kind": value_kind, + "status": "ok", + "suppression_flag": None, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024", + } + for metric, subgroup, unit, value, value_kind in claims + ] + + +def main() -> None: + rows = state_rows() + brief_rows() + OUT.parent.mkdir(parents=True, exist_ok=True) + OUT.write_text(json.dumps(rows, indent=1) + "\n") + suppressed = sum(row["status"] == "suppressed" for row in rows) + print(f"{OUT}: {len(rows)} rows ({suppressed} suppressed)") + + +if __name__ == "__main__": + main() diff --git a/sources/kff-remaining-uninsured/annotations.json b/sources/kff-remaining-uninsured/annotations.json new file mode 100644 index 0000000..f8acea6 --- /dev/null +++ b/sources/kff-remaining-uninsured/annotations.json @@ -0,0 +1,44 @@ +[ + { + "id": "kff-medicaid-pathway-breadth", + "applies": {"source": "kff-remaining-uninsured"}, + "text": "KFF applies MAGI pathways only. PolicyEngine's is_medicaid_eligible also includes SSI-linked, medically needy, and optional senior or disabled categories, so the PolicyEngine eligibility construct is broader while KFF's pathway scope is narrower.", + "basis": "PROVENANCE.md construct difference 1" + }, + { + "id": "kff-medicaid-chip-scope", + "applies": {"source": "kff-remaining-uninsured"}, + "text": "KFF includes Medicaid and CHIP for children. The PolicyEngine counterpart is Medicaid-only because the certified artifact deliberately does not assign standalone CHIP take-up; KFF therefore includes a child coverage pathway absent from the PolicyEngine counterpart, while PolicyEngine isolates Medicaid.", + "basis": "PROVENANCE.md construct difference 2; Microcosm concept split tracked in microcosm#321" + }, + { + "id": "kff-medicaid-coverage-measurement", + "applies": {"source": "kff-remaining-uninsured"}, + "text": "KFF's reported-uninsured population retains survey Medicaid underreporting because no correction is stated. PolicyEngine's primary counterpart also starts from reported at-interview coverage, while its modeled-uninsured alternative additionally removes medicaid_enrolled people added by the CMS-calibrated anchor-and-fill process; KFF retains those reported-uninsured people and the alternative does not.", + "basis": "PROVENANCE.md construct difference 3" + }, + { + "id": "kff-medicaid-data-and-imputations", + "applies": {"source": "kff-remaining-uninsured"}, + "text": "KFF uses ACS records, a regression-and-Pew immigration imputation with five implicates, and imputed ESI offers. PolicyEngine uses CPS-ASEC-based Populace records, Microcosm's CPS-and-Pew immigration assignment, and reported at-interview employer coverage; the certified artifact lacks years_since_us_entry, so PolicyEngine's five-year-bar leg uses that variable's default and is not fully equivalent to KFF's status netting.", + "basis": "PROVENANCE.md construct difference 4 and certified-artifact mechanics in the lane brief" + }, + { + "id": "kff-medicaid-rules-vintage", + "applies": {"source": "kff-remaining-uninsured"}, + "text": "KFF applies the eligibility-level vintage named on each source row (2025 levels for the 2024 state indicator; 2023 levels for the 2022 brief). PolicyEngine applies 2024 law to the single-period 2024 artifact; every joined row therefore carries a named rules-vintage difference in both directions.", + "basis": "KFF methods notes and certified-artifact period constraint" + }, + { + "id": "kff-medicaid-brief-vintage", + "applies": {"source": "kff-remaining-uninsured", "period": "2022"}, + "text": "The flagship brief describes 2022 ACS data under 2023 eligibility levels, before the unwinding was complete. The certified PolicyEngine artifact describes 2024 only, so the 2022 claim is not computed on its own period; the joined row shows the separately dated 2024 counterpart as context.", + "basis": "PROVENANCE.md construct difference 5" + }, + { + "id": "kff-medicaid-rendered-column-caution", + "applies": {"source": "kff-remaining-uninsured", "period": "2024"}, + "text": "The staged rendered table labels the selected column Medicaid/Other Public. Its values are preserved, but KFF's downloadable CSV headers still require an offline re-check before the label can be narrowed further.", + "basis": "PROVENANCE.md column-label caution" + } +] diff --git a/sources/kff-remaining-uninsured/raw/state_indicator_2024acs_2025levels.md b/sources/kff-remaining-uninsured/raw/state_indicator_2024acs_2025levels.md new file mode 100644 index 0000000..ffc64e3 --- /dev/null +++ b/sources/kff-remaining-uninsured/raw/state_indicator_2024acs_2025levels.md @@ -0,0 +1,68 @@ +# Raw extraction: KFF State Health Facts — Distribution of Eligibility for ACA Health Coverage Among the Remaining Uninsured + +Accessed 2026-08-18 via WebFetch of +https://www.kff.org/health-reform/state-indicator/distribution-of-eligibility-for-aca-coverage-among-the-remaining-uninsured/ +Data vintage per page: 2024 American Community Survey; 2025 Medicaid and premium +tax credit eligibility levels. + +Format: State | Medicaid/Other Public (%) | Tax Credit (%) | Marketplace Plan +Ineligible (%) | Employer Coverage Ineligible (%) | Citizenship Ineligible (%) | +Coverage Gap (%) + +⚠️ Column labels are the extractor's rendering — verify against KFF's own +headers before naming claim metrics (see PROVENANCE.md). NSD = estimate below +minimum statistical reliability. NA in Coverage Gap = expansion state. + +United States: 24.9% | 28.0% | 9.3% | 13.9% | 19.4% | 4.5% + +Alabama: 9.5% | 38.8% | 6.5% | 13.0% | 10.9% | 21.2% +Alaska: 40.8% | 34.7% | NSD | 21.1% | 3.4% | NA +Arizona: 32.1% | 23.4% | 11.4% | 14.9% | 18.2% | NA +Arkansas: 39.6% | 25.3% | 5.2% | 12.9% | 17.0% | NA +California: 32.5% | 20.7% | 10.7% | 12.3% | 23.8% | NA +Colorado: 28.2% | 24.5% | 12.3% | 16.5% | 18.5% | NA +Connecticut: 20.8% | 21.6% | 8.2% | 15.1% | 34.3% | NA +Delaware: 27.8% | 25.5% | NSD | 17.9% | 28.8% | NA +District of Columbia: 50.7% | NSD | 11.7% | NSD | 37.6% | NA +Florida: 8.5% | 34.5% | 9.2% | 15.2% | 21.6% | 11.0% +Georgia: 27.0% | 34.2% | 8.7% | 13.1% | 17.0% | NA +Hawaii: 39.7% | 22.1% | 13.7% | 14.0% | 10.4% | NA +Idaho: 31.1% | 27.6% | 9.5% | 20.4% | 11.4% | NA +Illinois: 32.1% | 22.3% | 9.3% | 12.4% | 23.9% | NA +Indiana: 36.7% | 26.5% | 10.5% | 14.1% | 12.2% | NA +Iowa: 37.9% | 24.5% | 9.3% | 12.1% | 16.2% | NA +Kansas: 11.7% | 33.4% | 7.8% | 15.8% | 17.7% | 13.6% +Kentucky: 37.0% | 28.1% | 9.0% | 14.2% | 11.7% | NA +Louisiana: 35.1% | 25.3% | 6.9% | 12.7% | 20.0% | NA +Maine: 34.7% | 36.1% | 11.0% | 18.2% | NSD | NA +Maryland: 23.3% | 13.5% | 11.3% | 11.5% | 40.4% | NA +Massachusetts: 27.3% | 19.1% | 14.6% | 17.9% | 21.0% | NA +Michigan: 36.1% | 29.5% | 11.4% | 13.3% | 9.8% | NA +Minnesota: 31.9% | 22.5% | 13.0% | 16.6% | 15.9% | NA +Mississippi: 12.2% | 38.4% | 6.7% | 14.5% | 7.6% | 20.6% +Missouri: 44.9% | 26.9% | 7.1% | 13.3% | 7.8% | NA +Montana: 40.0% | 35.6% | 10.2% | 14.2% | NSD | NA +Nebraska: 33.0% | 26.9% | 6.7% | 14.2% | 19.1% | NA +Nevada: 29.1% | 23.1% | 10.0% | 11.6% | 26.3% | NA +New Hampshire: 27.5% | 28.0% | 24.7% | 19.8% | NSD | NA +New Jersey: 28.2% | 15.9% | 8.1% | 10.9% | 37.0% | NA +New Mexico: 33.5% | 29.7% | 9.0% | 12.7% | 15.1% | NA +New York: 33.9% | 21.4% | 10.2% | 12.4% | 22.2% | NA +North Carolina: 31.6% | 22.7% | 7.8% | 13.5% | 24.3% | NA +North Dakota: 31.9% | 31.2% | 11.8% | 25.0% | NSD | NA +Ohio: 34.2% | 29.9% | 10.0% | 15.6% | 10.3% | NA +Oklahoma: 39.2% | 26.5% | 7.5% | 15.5% | 11.3% | NA +Oregon: 34.5% | 28.4% | 12.5% | 16.6% | 8.0% | NA +Pennsylvania: 38.4% | 24.0% | 11.3% | 13.7% | 12.6% | NA +Rhode Island: 31.4% | 22.5% | 9.1% | 11.6% | 25.4% | NA +South Carolina: 11.5% | 38.4% | 9.4% | 12.8% | 14.5% | 13.4% +South Dakota: 49.4% | 32.0% | NSD | 18.5% | NSD | NA +Tennessee: 13.5% | 36.9% | 8.2% | 12.2% | 15.9% | 13.3% +Texas: 12.9% | 32.5% | 7.9% | 13.6% | 21.1% | 12.0% +Utah: 30.3% | 24.0% | 11.0% | 16.0% | 18.6% | NA +Vermont: 28.6% | 53.6% | 17.9% | NSD | NSD | NA +Virginia: 27.7% | 20.5% | 11.9% | 15.0% | 24.9% | NA +Washington: 27.1% | 22.4% | 11.4% | 15.4% | 23.8% | NA +West Virginia: 40.0% | 31.4% | 8.4% | 18.1% | 2.0% | NA +Wisconsin: 30.4% | 33.6% | 9.4% | 14.8% | 11.8% | NA +Wyoming: 14.0% | 43.3% | 8.5% | 19.2% | NSD | 15.1% diff --git a/sources/kff-remaining-uninsured/source.json b/sources/kff-remaining-uninsured/source.json new file mode 100644 index 0000000..0bfce60 --- /dev/null +++ b/sources/kff-remaining-uninsured/source.json @@ -0,0 +1,21 @@ +{ + "id": "kff-remaining-uninsured", + "name": "KFF — eligibility for coverage among the remaining uninsured", + "organization": "KFF", + "url": "https://www.kff.org/health-reform/state-indicator/distribution-of-eligibility-for-aca-coverage-among-the-remaining-uninsured/", + "data_url": "raw/state_indicator_2024acs_2025levels.md (rendered table extraction) and the national flagship-brief headlines recorded in PROVENANCE.md", + "fetched": "2026-08-18", + "fetch_notes": "The state table uses 2024 ACS data and 2025 eligibility levels. Its rendered first-column label is retained verbatim pending an offline check against KFF's CSV headers. The flagship brief uses 2022 ACS data and 2023 eligibility levels.", + "claim_class": "baseline_moment", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "model": { + "name": "KFF/Damico eligibility simulation", + "open_source": false, + "method": "Static ACS-based eligibility microsimulation for the nonelderly uninsured. KFF reconstructs Medicaid and Marketplace units, applies ACA-definition MAGI and state eligibility levels, and imputes immigration status and employer coverage offers.", + "method_basis": "KFF methods boxes summarized with source links in PROVENANCE.md" + }, + "period": "2022 flagship brief; 2024 State Health Facts indicator", + "geographies": "US plus 50 states and the District of Columbia", + "diagnosis_upstream": "Re-check the downloadable KFF CSV column headers before removing the rendered-label caution." +} From 98b1ca3342c0ab3efde22a19c55bb71532a2928e Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 15:39:29 -0400 Subject: [PATCH 03/14] Support input-override campaign exhibits --- PROGRESS.md | 1 + scorecard_db/ingest_campaign.py | 66 ++++++++++++- tests/test_kff_medicaid_campaign.py | 138 ++++++++++++++++++++++++++++ 3 files changed, 203 insertions(+), 2 deletions(-) create mode 100644 tests/test_kff_medicaid_campaign.py diff --git a/PROGRESS.md b/PROGRESS.md index 42e5763..4a2bf44 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -11,6 +11,7 @@ In progress. The KFF source population is adapted into 57 truthful-vintage momen - Created the implementation and validation plan. - Read the complete KFF provenance and staged table, plus the existing moments and campaign contracts. - Added the KFF source registry, descriptive construct annotations, and adapter: 52 state-indicator percentage rows (US + 50 states + DC) and five 2022 flagship-brief rows. The selected rendered column has no NSD cells; the adapter retains any NSD selected cell as an explicitly suppressed row. +- Extended campaign ingest with a strict `policyengine_us_inputs` ReformRef exhibit route, preserving state geography, baseline values, deltas, and canonical reform identity. Focused plus existing campaign/database tests pass (62 tests). ## Next diff --git a/scorecard_db/ingest_campaign.py b/scorecard_db/ingest_campaign.py index 5552df3..64449bf 100644 --- a/scorecard_db/ingest_campaign.py +++ b/scorecard_db/ingest_campaign.py @@ -33,6 +33,13 @@ exhibit_meta is deferred — tallied and listed in the summary for the campaign to re-emit, never silently dropped, never guessed. +Input-override exhibit rows use the strict alternate ``reform_ref`` route +instead of ``external_claim_match``. The descriptor must be a +``policyengine_us_inputs`` ReformRef; its canonical key/JSON are stored on +the exhibit, while ``exhibit_context`` supplies conditions and the indexed +geography/program fields. This keeps take-up-flag counterfactuals executable +and distinct from the legacy ``policy_ref`` exhibit descriptors above. + Idempotent: this module replaces exactly the run_ids present in the staged files it is ingesting — never a prefix sweep, so re-running the US directory can't erase results another campaign directory (UK) ingested @@ -46,7 +53,7 @@ from pathlib import Path from .db import EXHIBITS_SQL, RESULTS_SQL, ScorecardDB -from .models import ComparisonStatus, PEResult +from .models import ComparisonStatus, PEResult, ReformRef REPO = Path(__file__).resolve().parent.parent STAGED_US = REPO / "sources" / "campaign-20260802" / "us" @@ -166,6 +173,60 @@ def ingest(db_path: Path, staged_dir: Path | None = None) -> dict: # deferred exhibits, so a row that LOSES its exhibit_meta # between runs can't leave its stale pe_exhibits row behind. staged_run_ids.add(row["run_id"]) + if "reform_ref" in row: + if "external_claim_match" in row: + raise ValueError( + f"{path.stem}: reform_ref and external_claim_match " + "are mutually exclusive" + ) + meta = row.get("exhibit_meta") + if not isinstance(meta, dict): + raise ValueError( + f"{path.stem}: reform_ref route requires exhibit_meta" + ) + raw_reform = row["reform_ref"] + if not isinstance(raw_reform, dict): + raise ValueError(f"{path.stem}: reform_ref must be an object") + reform = ReformRef(**raw_reform) + if reform.framework != "policyengine_us_inputs": + raise ValueError( + f"{path.stem}: reform_ref route requires " + "framework='policyengine_us_inputs'" + ) + if "reform_key" in meta and meta["reform_key"] != reform.key(): + raise ValueError( + f"{path.stem}: exhibit_meta reform_key does not match " + "reform_ref" + ) + conditions = row.get("exhibit_context", {}) + if not isinstance(conditions, dict): + raise ValueError(f"{path.stem}: exhibit_context must be an object") + note = " | ".join( + [meta["note"], row["pe_construction"]] + row.get("annotations", []) + ) + exhibits.append( + { + "exhibit": f"campaign:{path.stem}", + "reform_key": reform.key(), + "reform_json": reform.to_json(), + "metric": meta["metric"], + "unit_concept": meta["unit_concept"], + "period": meta["period"], + "time_basis": meta["time_basis"], + "conditions": conditions, + "geography": conditions.get("geography"), + "program": conditions.get("program"), + "value": row["pe_value"], + "baseline_value": row.get("baseline_value"), + "delta": row.get("delta"), + "engine_version": row["engine_version"], + "data_bundle": row["data_bundle"], + "run_id": row["run_id"], + "computed_at": row["computed_at"], + "note": note, + } + ) + continue if row.get("exhibit"): meta = row.get("exhibit_meta") if meta is None: @@ -255,4 +316,5 @@ def ingest(db_path: Path, staged_dir: Path | None = None) -> dict: import sys out = Path(sys.argv[1] if len(sys.argv) > 1 else "data/scorecard.db") - print(json.dumps(ingest(out), indent=1)) + staged = Path(sys.argv[2]) if len(sys.argv) > 2 else None + print(json.dumps(ingest(out, staged_dir=staged), indent=1)) diff --git a/tests/test_kff_medicaid_campaign.py b/tests/test_kff_medicaid_campaign.py new file mode 100644 index 0000000..2f8519e --- /dev/null +++ b/tests/test_kff_medicaid_campaign.py @@ -0,0 +1,138 @@ +"""Input-override campaign exhibits for the KFF Medicaid lane.""" + +import json +import sqlite3 + +import pytest + +from scorecard_db.ingest_campaign import ingest +from scorecard_db.models import ReformRef + + +RUN_ID = "campaign-2026-08-18-kff-medicaid" +BUNDLE = "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z" + + +def _row() -> dict: + return { + "engine_version": "1.764.6", + "data_bundle": BUNDLE, + "pe_value": 77_300_000.0, + "baseline_value": 72_300_000.0, + "delta": 5_000_000.0, + "status": "constructed", + "pe_construction": "eligible minus baseline enrolled", + "computed_at": "2026-08-18T19:00:00+00:00", + "run_id": RUN_ID, + "annotations": [ + "Medicaid enrollment is a point-in-time construct", + "2024 certified Populace bundle", + ], + "reform_ref": { + "framework": "policyengine_us_inputs", + "reform": {"takes_up_medicaid_if_eligible": True}, + }, + "exhibit_context": { + "geography": "CA", + "program": "medicaid", + "component": "total", + }, + "exhibit_meta": { + "metric": "enrollment", + "unit_concept": "persons", + "period": 2024, + "time_basis": "point_in_time", + "note": "Take-up forced to 100%", + }, + } + + +def _stage(tmp_path, row: dict): + staged = tmp_path / "staged" + staged.mkdir() + (staged / "kff_medicaid.jsonl").write_text(json.dumps(row) + "\n") + return staged + + +def test_input_reform_exhibit_roundtrip_and_idempotency(tmp_path): + staged = _stage(tmp_path, _row()) + db_path = tmp_path / "scorecard.db" + + first = ingest(db_path, staged_dir=staged) + second = ingest(db_path, staged_dir=staged) + assert ( + first + == second + == { + "attached": 0, + "exhibits": 1, + "families": {}, + "exhibits_deferred": [], + } + ) + + conn = sqlite3.connect(db_path) + conn.row_factory = sqlite3.Row + rows = conn.execute( + "SELECT * FROM pe_exhibits WHERE run_id = ?", (RUN_ID,) + ).fetchall() + conn.close() + assert len(rows) == 1 + result = rows[0] + + reform = ReformRef( + framework="policyengine_us_inputs", + reform={"takes_up_medicaid_if_eligible": True}, + ) + assert reform.key() == "849973669b6526d6" + assert reform.to_json() == ( + '{"baseline":null,"framework":"policyengine_us_inputs",' + '"reform":{"takes_up_medicaid_if_eligible":true},' + '"rulespec_ref":null}' + ) + assert result["reform_key"] == reform.key() + assert result["reform_json"] == reform.to_json() + assert result["exhibit"] == "campaign:kff_medicaid" + assert result["metric"] == "enrollment" + assert result["unit_concept"] == "persons" + assert result["period"] == 2024 + assert result["time_basis"] == "point_in_time" + assert json.loads(result["conditions"]) == { + "component": "total", + "geography": "CA", + "program": "medicaid", + } + assert result["geography"] == "CA" + assert result["program"] == "medicaid" + assert result["value"] == 77_300_000.0 + assert result["baseline_value"] == 72_300_000.0 + assert result["delta"] == 5_000_000.0 + assert result["engine_version"] == "1.764.6" + assert result["data_bundle"] == BUNDLE + assert result["computed_at"] == "2026-08-18T19:00:00+00:00" + assert result["note"] == ( + "Take-up forced to 100% | eligible minus baseline enrolled | " + "Medicaid enrollment is a point-in-time construct | " + "2024 certified Populace bundle" + ) + + +def test_mixed_reform_and_external_match_route_rejected(tmp_path): + row = _row() + row["external_claim_match"] = {"claim_id": "not-used"} + staged = _stage(tmp_path, row) + + with pytest.raises(ValueError, match="mutually exclusive"): + ingest(tmp_path / "scorecard.db", staged_dir=staged) + + +def test_reform_route_requires_policyengine_us_inputs(tmp_path): + row = _row() + row["reform_ref"] = { + "framework": "policyengine_us", + "reform": {"gov.example.parameter": {"2024": True}}, + } + staged = _stage(tmp_path, row) + + with pytest.raises(ValueError, match="requires framework"): + ingest(tmp_path / "scorecard.db", staged_dir=staged) From e1974ef939af6d6b55703d0b1d7f5c1e22f1e5bd Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 15:45:16 -0400 Subject: [PATCH 04/14] Add Medicaid take-up computation pipeline --- PROGRESS.md | 7 +- pipeline/build_kff_medicaid_outputs.py | 430 +++++++++++++++++++++++ pipeline/build_kff_medicaid_sample.py | 134 +++++++ pipeline/compute_kff_medicaid_extract.py | 300 ++++++++++++++++ 4 files changed, 869 insertions(+), 2 deletions(-) create mode 100644 pipeline/build_kff_medicaid_outputs.py create mode 100644 pipeline/build_kff_medicaid_sample.py create mode 100644 pipeline/compute_kff_medicaid_extract.py diff --git a/PROGRESS.md b/PROGRESS.md index 4a2bf44..5c69663 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -2,7 +2,7 @@ ## State -In progress. The KFF source population is adapted into 57 truthful-vintage moments rows. Network access remains prohibited for this lane. +In progress. The KFF source population and campaign schema are implemented, and the full baseline/reform computation path has been validated on the required deterministic 5,000-household sample. Network access remains prohibited for this lane. ## Done @@ -12,9 +12,12 @@ In progress. The KFF source population is adapted into 57 truthful-vintage momen - Read the complete KFF provenance and staged table, plus the existing moments and campaign contracts. - Added the KFF source registry, descriptive construct annotations, and adapter: 52 state-indicator percentage rows (US + 50 states + DC) and five 2022 flagship-brief rows. The selected rendered column has no NSD cells; the adapter retains any NSD selected cell as an explicitly suppressed row. - Extended campaign ingest with a strict `policyengine_us_inputs` ReformRef exhibit route, preserving state geography, baseline values, deltas, and canonical reform identity. Focused plus existing campaign/database tests pass (62 tests). +- Added memory-disciplined sample, one-simulation extraction, and aggregate-output pipelines. The deterministic sample selects 5,000 of 57,240 households (seed 20260818), rescales `household_weight` by 11.448, retains 14,254 people and every referenced group entity, and runs baseline/reform in separate processes. +- Sample validation: 75.282M eligible, 69.039M enrolled, +6.243M enrollment; eligible-minus-enrolled identity gap 0.000000004 persons. The bridge is 3.552M reported-uninsured and 2.691M other-coverage. The fixed-baseline-denominator spending construction produces +$115.558B. These are sample checks, not published estimates. +- Confirmed the fiscal mechanic: a standalone take-up override re-normalizes fixed state Medicaid spending. The reform extractor supplies the baseline state-allocation denominator, reproducing policyengine-us's intended baseline-branch cost semantics without retaining two simulations. This execution choice is explicit in every staged reform row. +- Confirmed installed policyengine-us 1.764.6 has no module `__version__` attribute; the pipeline uses `importlib.metadata.version("policyengine-us")`, asserts it equals `sim.policyengine_bundle["model_version"]`, and records bundle `us-5.0.2` plus the full certified Build P id. ## Next - Extend the moments builder for multiple PE payloads, deterministic primary/alternative variants, truthful-period references, row metadata, and null-safe suppression handling; add tests. -- Validate baseline and 100%-take-up computation on a uniformly sampled 5,000-household dataset, using one managed simulation per process. - Run full-file baseline and reform passes separately, stage aggregate diagnostics/results, wire moments, and run offline checks. diff --git a/pipeline/build_kff_medicaid_outputs.py b/pipeline/build_kff_medicaid_outputs.py new file mode 100644 index 0000000..d8708db --- /dev/null +++ b/pipeline/build_kff_medicaid_outputs.py @@ -0,0 +1,430 @@ +"""Aggregate Medicaid baseline/reform extracts into scorecard staging files.""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pandas as pd + +YEAR = 2024 +RUN_ID = "campaign-2026-08-18-kff-medicaid" +DENOMINATOR = "medicaid_slcsp_state_denominator" +NON_EXPANSION_2024 = {"AL", "FL", "GA", "KS", "MS", "SC", "TN", "TX", "WI", "WY"} +REPORTED_VARIANT = "pe_reported_uninsured_all_medicaid_pathways_2024_rules" +MODELED_VARIANT = "pe_modeled_uninsured_all_medicaid_pathways_2024_rules" +REFORM_REF = { + "framework": "policyengine_us_inputs", + "reform": {"takes_up_medicaid_if_eligible": True}, +} + + +def load_meta(path: Path) -> dict: + return json.loads(Path(f"{path}.meta.json").read_text()) + + +def weighted_count(frame: pd.DataFrame, mask: np.ndarray) -> float: + return float(frame.loc[mask, "person_weight"].sum()) + + +def weighted_dollars(frame: pd.DataFrame, mask: np.ndarray | None = None) -> float: + if mask is None: + mask = np.ones(len(frame), dtype=bool) + selected = frame.loc[mask] + return float((selected["person_weight"] * selected["medicaid"]).sum()) + + +def aggregate( + baseline_path: Path, + reform_path: Path, + diagnostics_dir: Path, + counterpart_path: Path, + campaign_path: Path, + full: bool, +) -> dict: + baseline = pd.read_csv(baseline_path) + reform = pd.read_csv(reform_path) + baseline_meta = load_meta(baseline_path) + reform_meta = load_meta(reform_path) + if len(baseline) != len(reform): + raise ValueError("baseline and reform row counts differ") + if not np.array_equal(baseline["person_id"], reform["person_id"]): + raise ValueError("baseline and reform people differ") + if not np.array_equal(baseline["state"], reform["state"]): + raise ValueError("baseline and reform states differ") + if not np.allclose(baseline["person_weight"], reform["person_weight"]): + raise ValueError("baseline and reform weights differ") + if not np.array_equal( + baseline["is_medicaid_eligible"], reform["is_medicaid_eligible"] + ): + raise ValueError("eligibility changed under the take-up input") + if not np.array_equal(baseline["reported_uninsured"], reform["reported_uninsured"]): + raise ValueError("reported coverage changed under the take-up input") + if not np.allclose(baseline[DENOMINATOR], reform[DENOMINATOR], equal_nan=True): + raise ValueError("reform did not retain the baseline Medicaid denominator") + eligible = baseline["is_medicaid_eligible"].to_numpy(dtype=bool) + enrolled = baseline["medicaid_enrolled"].to_numpy(dtype=bool) + reform_enrolled = reform["medicaid_enrolled"].to_numpy(dtype=bool) + if not np.array_equal(reform_enrolled, eligible): + raise AssertionError("reform enrollment is not identical to eligibility") + if np.any(enrolled & ~reform_enrolled): + raise AssertionError("the reform removes baseline enrollees") + marginal = reform_enrolled & ~enrolled + reported = baseline["reported_uninsured"].to_numpy(dtype=bool) + modeled = baseline["modeled_uninsured"].to_numpy(dtype=bool) + under65 = baseline["age"].to_numpy() < 65 + child = baseline["age"].to_numpy() < 19 + adult = under65 & ~child + states = sorted(set(baseline["state"])) + geographies = ["US", *states] + expansion_states = set(states) - NON_EXPANSION_2024 + computed_at = datetime.now(timezone.utc).isoformat() + + engine_version = baseline_meta["engine_version"] + data_bundle = baseline_meta["data_bundle"] + bundle_id = baseline_meta["bundle_id"] + if (engine_version, data_bundle, bundle_id) != ( + reform_meta["engine_version"], + reform_meta["data_bundle"], + reform_meta["bundle_id"], + ): + raise ValueError("baseline and reform provenance differs") + + national_eligible = weighted_count(baseline, eligible) + national_enrolled = weighted_count(baseline, enrolled) + national_delta = weighted_count(baseline, marginal) + identity_gap = national_delta - (national_eligible - national_enrolled) + if abs(identity_gap) > 1e-6 * max(national_delta, 1): + raise AssertionError(f"enrollment identity gap {identity_gap}") + if full: + anchors = { + "eligible": (national_eligible, 77_300_000), + "enrolled": (national_enrolled, 72_300_000), + } + for name, (actual, expected) in anchors.items(): + relative_error = abs(actual / expected - 1) + if relative_error > 0.02: + raise SystemExit( + f"STOP: {name} anchor {actual:,.0f} differs from " + f"{expected:,.0f} by {relative_error:.2%}" + ) + + common = { + "engine_version": engine_version, + "data_bundle": data_bundle, + "bundle_id": bundle_id, + "computed_at": computed_at, + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": baseline_meta["coverage_inputs"], + "medicare_handling": baseline_meta["medicare_handling"], + } + moment_diagnostics = [] + counterpart_rows = [] + variants = ( + ("reported_uninsured", REPORTED_VARIANT, reported), + ("modeled_uninsured", MODELED_VARIANT, modeled), + ) + for geography in geographies: + geo_mask = ( + np.ones(len(baseline), dtype=bool) + if geography == "US" + else baseline["state"].to_numpy() == geography + ) + for coverage_variant, variant, uninsured in variants: + universe = geo_mask & under65 & uninsured + numerator_mask = universe & eligible + denominator = weighted_count(baseline, universe) + numerator = weighted_count(baseline, numerator_mask) + share = 100 * numerator / denominator if denominator else None + moment_diagnostics.append( + { + "geography": geography, + "coverage_variant": coverage_variant, + "uninsured_count": denominator, + "eligible_uninsured_count": numerator, + "eligible_share_percent": share, + "age_universe": "age_under_65", + "engine_version": engine_version, + "data_bundle": data_bundle, + "bundle_id": bundle_id, + "rules_vintage": "PolicyEngine 2024 law", + "computed_at": computed_at, + } + ) + for metric, unit, value in ( + ("eligible_share_among_uninsured", "percent", share), + ("eligible_uninsured_count", "persons", numerator), + ): + counterpart_rows.append( + { + "source": "pe", + "program": "medicaid", + "metric": metric, + "subgroup": "total", + "variant": variant, + "coverage_variant": coverage_variant, + "geography": geography, + "unit_concept": unit, + "period": "2024", + "value": value, + "numerator": numerator, + "denominator": denominator, + "age_universe": "age_under_65", + **common, + } + ) + + subgroup_masks = { + "adults": adult, + "children": child, + "expansion_states": under65 + & baseline["state"].isin(expansion_states).to_numpy(), + } + for coverage_variant, variant, uninsured in variants: + for subgroup, subgroup_mask in subgroup_masks.items(): + mask = subgroup_mask & uninsured & eligible + value = weighted_count(baseline, mask) + counterpart_rows.append( + { + "source": "pe", + "program": "medicaid", + "metric": "eligible_uninsured_count", + "subgroup": subgroup, + "variant": variant, + "coverage_variant": coverage_variant, + "geography": "US", + "unit_concept": "persons", + "period": "2024", + "value": value, + "numerator": value, + "denominator": None, + "age_universe": ( + "age_19_to_64" + if subgroup == "adults" + else "age_under_19" + if subgroup == "children" + else "age_under_65_in_2024_expansion_states" + ), + "expansion_classification": ( + "2024 expansion set; non-expansion AL FL GA KS MS SC TN TX WI WY" + if subgroup == "expansion_states" + else None + ), + **common, + } + ) + + takeup_rows = [] + campaign_rows = [] + denominator_note = ( + "Reform Medicaid spending holds the baseline state-allocation denominator " + "fixed, matching policyengine-us's baseline-branch reform semantics." + ) + for geography in geographies: + geo_mask = ( + np.ones(len(baseline), dtype=bool) + if geography == "US" + else baseline["state"].to_numpy() == geography + ) + base_eligible = weighted_count(baseline, geo_mask & eligible) + base_enrolled = weighted_count(baseline, geo_mask & enrolled) + reform_enrollment = weighted_count(baseline, geo_mask & reform_enrolled) + delta_enrollment = weighted_count(baseline, geo_mask & marginal) + baseline_spending = weighted_dollars(baseline, geo_mask) + reform_spending = weighted_dollars(reform, geo_mask) + delta_spending = reform_spending - baseline_spending + bridge_reported = weighted_count(baseline, geo_mask & marginal & reported) + bridge_other = weighted_count(baseline, geo_mask & marginal & ~reported) + if abs(delta_enrollment - bridge_reported - bridge_other) > 1e-6 * max( + delta_enrollment, 1 + ): + raise AssertionError(f"bridge identity differs in {geography}") + takeup_rows.append( + { + "geography": geography, + "baseline_eligible": base_eligible, + "baseline_enrolled": base_enrolled, + "baseline_medicaid_usd": baseline_spending, + "reform_enrolled": reform_enrollment, + "reform_medicaid_usd": reform_spending, + "delta_enrollment": delta_enrollment, + "delta_medicaid_usd": delta_spending, + "marginal_reported_uninsured": bridge_reported, + "marginal_other_coverage": bridge_other, + "enrollment_identity_gap": delta_enrollment + - (base_eligible - base_enrolled), + "bridge_identity_gap": delta_enrollment + - bridge_reported + - bridge_other, + "engine_version": engine_version, + "data_bundle": data_bundle, + "bundle_id": bundle_id, + "computed_at": computed_at, + "cost_construction": denominator_note, + } + ) + result_specs = ( + ( + "total", + "enrollment", + "persons", + "point_in_time", + reform_enrollment, + base_enrolled, + delta_enrollment, + ), + ( + "total_spending", + "benefit_cost", + "usd", + "annual", + reform_spending, + baseline_spending, + delta_spending, + ), + ( + "marginal_reported_uninsured", + "enrollment", + "persons", + "point_in_time", + bridge_reported, + 0.0, + bridge_reported, + ), + ( + "marginal_other_coverage", + "enrollment", + "persons", + "point_in_time", + bridge_other, + 0.0, + bridge_other, + ), + ) + for ( + component, + metric, + unit, + time_basis, + value, + baseline_value, + delta, + ) in result_specs: + annotations = [ + "Take-up is forced with the annual takes_up_medicaid_if_eligible input.", + "Enrollment is point-in-time; Medicaid spending is annual.", + denominator_note, + ( + "reported_uninsured means none of the nine reported coverage flags " + "and not modeled medicare_enrolled; other coverage is its complement" + ), + ] + campaign_rows.append( + { + "engine_version": engine_version, + "data_bundle": data_bundle, + "bundle_id": bundle_id, + "policyengine_bundle": reform_meta["policyengine_bundle"], + "pe_value": value, + "baseline_value": baseline_value, + "delta": delta, + "status": "constructed", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "pe_construction": ( + f"2024 {component}; one managed reform simulation; annual " + "Medicaid take-up input True; baseline allocation denominator retained" + ), + "computed_at": reform_meta["computed_at"], + "run_id": RUN_ID, + "annotations": annotations, + "reform_ref": REFORM_REF, + "exhibit_context": { + "geography": geography, + "program": "medicaid", + "component": component, + }, + "exhibit_meta": { + "metric": metric, + "unit_concept": unit, + "period": YEAR, + "time_basis": time_basis, + "note": "Medicaid take-up increased to 100%", + }, + } + ) + + diagnostics_dir.mkdir(parents=True, exist_ok=True) + pd.DataFrame(moment_diagnostics).to_csv( + diagnostics_dir / "kff_medicaid_moments_2024.csv", index=False + ) + pd.DataFrame(takeup_rows).to_csv( + diagnostics_dir / "kff_medicaid_takeup_2024.csv", index=False + ) + counterpart_path.parent.mkdir(parents=True, exist_ok=True) + counterpart_payload = { + "provenance": { + **common, + "policyengine_bundle": baseline_meta["policyengine_bundle"], + "baseline_computed_at": baseline_meta["computed_at"], + "reform_computed_at": reform_meta["computed_at"], + "expansion_classification": ( + "2024 expansion set; non-expansion AL FL GA KS MS SC TN TX WI WY" + ), + "adult_child_split": "children age under 19; adults age 19 to 64", + }, + "rows": counterpart_rows, + } + counterpart_path.write_text(json.dumps(counterpart_payload, indent=1) + "\n") + campaign_path.parent.mkdir(parents=True, exist_ok=True) + campaign_path.write_text( + "".join(json.dumps(row, sort_keys=True) + "\n" for row in campaign_rows) + ) + summary = { + "sample": not full, + "people": len(baseline), + "engine_version": engine_version, + "data_bundle": data_bundle, + "bundle_id": bundle_id, + "eligible": national_eligible, + "enrolled": national_enrolled, + "delta_enrollment": national_delta, + "identity_gap": identity_gap, + "baseline_medicaid_usd": weighted_dollars(baseline), + "reform_medicaid_usd": weighted_dollars(reform), + "delta_medicaid_usd": weighted_dollars(reform) - weighted_dollars(baseline), + "bridge_reported_uninsured": weighted_count(baseline, marginal & reported), + "bridge_other_coverage": weighted_count(baseline, marginal & ~reported), + "counterpart_rows": len(counterpart_rows), + "campaign_rows": len(campaign_rows), + } + print(json.dumps(summary, indent=2)) + return summary + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--baseline", type=Path, required=True) + parser.add_argument("--reform", type=Path, required=True) + parser.add_argument("--diagnostics-dir", type=Path, required=True) + parser.add_argument("--counterparts", type=Path, required=True) + parser.add_argument("--campaign", type=Path, required=True) + parser.add_argument("--full", action="store_true") + args = parser.parse_args() + aggregate( + args.baseline, + args.reform, + args.diagnostics_dir, + args.counterparts, + args.campaign, + args.full, + ) + + +if __name__ == "__main__": + main() diff --git a/pipeline/build_kff_medicaid_sample.py b/pipeline/build_kff_medicaid_sample.py new file mode 100644 index 0000000..0eafe57 --- /dev/null +++ b/pipeline/build_kff_medicaid_sample.py @@ -0,0 +1,134 @@ +"""Build the deterministic 5,000-household Medicaid validation sample. + +The source is the certified managed dataset resolved by policyengine.py. The +output is a local, unmanaged derivative used only to exercise the complete +baseline/reform path before the full-file runs; it is never staged as a +certified result. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import pandas as pd +from policyengine.provenance.dataset_sources import materialize_dataset_source +from policyengine.provenance.manifest import ( + resolve_local_managed_dataset_source, + resolve_managed_dataset_reference, +) + +HOUSEHOLDS = 5_000 +SEED = 20_260_818 +CHUNK_SIZE = 25_000 +GROUPS = ("tax_unit", "spm_unit", "family", "marital_unit") + + +def filtered_chunks( + store: pd.HDFStore, key: str, column: str, ids: set +) -> pd.DataFrame: + pieces = [] + for chunk in store.select(key, chunksize=CHUNK_SIZE): + selected = chunk[chunk[column].isin(ids)] + if not selected.empty: + pieces.append(selected) + if not pieces: + raise ValueError(f"no {key} rows selected on {column}") + return pd.concat(pieces, ignore_index=True) + + +def build(source: Path, output: Path) -> dict: + output.parent.mkdir(parents=True, exist_ok=True) + if output.exists(): + output.unlink() + + with pd.HDFStore(source, mode="r") as src: + household_ids = src.select("household", columns=["household_id"])[ + "household_id" + ].to_numpy() + total_households = len(household_ids) + if total_households < HOUSEHOLDS: + raise ValueError(f"source has only {total_households} households") + rng = np.random.default_rng(SEED) + selected_ids = set( + rng.choice(household_ids, HOUSEHOLDS, replace=False).tolist() + ) + households = filtered_chunks(src, "household", "household_id", selected_ids) + people = filtered_chunks(src, "person", "person_household_id", selected_ids) + membership_ids = { + group: set(people[f"person_{group}_id"].tolist()) for group in GROUPS + } + group_frames = { + group: filtered_chunks(src, group, f"{group}_id", membership_ids[group]) + for group in GROUPS + } + time_period = src.get("_time_period") + + scale = total_households / HOUSEHOLDS + households["household_weight"] *= scale + + assert len(households) == HOUSEHOLDS + assert households["household_id"].is_unique + assert set(households["household_id"]) == selected_ids + assert set(people["person_household_id"]) == selected_ids + for group, frame in group_frames.items(): + id_column = f"{group}_id" + assert frame[id_column].is_unique + assert set(frame[id_column]) == membership_ids[group] + + with pd.HDFStore(output, mode="w") as dst: + dst.put("_time_period", time_period, format="table", data_columns=True) + dst.put("household", households, format="table", data_columns=True) + dst.put("person", people, format="table", data_columns=True) + for group, frame in group_frames.items(): + dst.put(group, frame, format="table", data_columns=True) + + metadata = { + "source": str(source), + "output": str(output), + "seed": SEED, + "households": HOUSEHOLDS, + "source_households": total_households, + "household_weight_scale": scale, + "people": len(people), + "entities": { + "household": len(households), + "person": len(people), + **{group: len(frame) for group, frame in group_frames.items()}, + }, + "period": 2024, + "certification_status": "unmanaged_sample_of_certified_artifact", + } + Path(f"{output}.metadata.json").write_text(json.dumps(metadata, indent=2) + "\n") + return metadata + + +def managed_source() -> tuple[str, Path]: + uri = resolve_managed_dataset_reference("us") + local = resolve_local_managed_dataset_source("us", uri) + return uri, Path(materialize_dataset_source(local)) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source", type=Path) + parser.add_argument( + "--output", + type=Path, + default=Path("/private/tmp/kff_medicaid_sample_5000.h5"), + ) + args = parser.parse_args() + uri, resolved = managed_source() + source = args.source or resolved + metadata = build(source, args.output) + metadata["managed_dataset_uri"] = uri + Path(f"{args.output}.metadata.json").write_text( + json.dumps(metadata, indent=2) + "\n" + ) + print(json.dumps(metadata, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/pipeline/compute_kff_medicaid_extract.py b/pipeline/compute_kff_medicaid_extract.py new file mode 100644 index 0000000..a24d0e1 --- /dev/null +++ b/pipeline/compute_kff_medicaid_extract.py @@ -0,0 +1,300 @@ +"""Run one managed Medicaid simulation and write a person extract. + +Run this program once for baseline and again in a separate OS process for the +100%-take-up world. The reform process sets the annual Medicaid take-up input +before any calculation. It also receives the baseline allocation denominator +as an execution input, reproducing policyengine-us's intended baseline-branch +cost behavior without retaining two multi-GB simulations in memory. +""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime, timezone +from importlib.metadata import version +from pathlib import Path + +import numpy as np +import pandas as pd + +YEAR = 2024 +TAKEUP = "takes_up_medicaid_if_eligible" +DENOMINATOR = "medicaid_slcsp_state_denominator" +EXPECTED_DATASET_URI = ( + "hf://policyengine/populace-us/populace_us_2024.h5@" + "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z" +) +EXPECTED_BUILD = "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z" +COVERAGE_INPUTS = ( + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview", +) +MEDICARE_PROXY = "medicare_enrolled" + + +def serializable(value): + if isinstance(value, dict): + return {str(key): serializable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [serializable(item) for item in value] + if value is None or isinstance(value, (str, int, float, bool)): + return value + return str(value) + + +def values(sim, variable: str, *, map_to: str | None = None) -> np.ndarray: + kwargs = {"map_to": map_to} if map_to else {} + return np.asarray(sim.calculate(variable, YEAR, **kwargs).values) + + +def variable_metadata(sim, variable: str) -> dict: + definition = sim.tax_benefit_system.variables[variable] + return { + "label": definition.label, + "entity": definition.entity.key, + "definition_period": str(definition.definition_period), + "unit": str(getattr(definition, "unit", None)), + "documentation": getattr(definition, "documentation", None), + } + + +def run( + mode: str, + output: Path, + dataset: Path | None, + baseline_extract: Path | None, +) -> dict: + import policyengine as pe + import policyengine_us + from policyengine_core.periods import YEAR as YEAR_PERIOD + + started = datetime.now(timezone.utc).isoformat() + if dataset is None: + sim = pe.us.managed_microsimulation() + else: + sim = pe.us.managed_microsimulation( + dataset=str(dataset), + allow_unmanaged=True, + ) + + variables = sim.tax_benefit_system.variables + assert variables[TAKEUP].definition_period == YEAR_PERIOD + n_people = sim.populations["person"].count + baseline_ids = None + denominator_preserved = False + if mode == "reform": + if baseline_extract is None: + raise ValueError("reform mode requires --baseline-extract") + baseline = pd.read_csv( + baseline_extract, + usecols=["person_id", DENOMINATOR], + ) + if len(baseline) != n_people: + raise ValueError( + f"baseline has {len(baseline)} people; reform has {n_people}" + ) + baseline_ids = baseline["person_id"].to_numpy() + baseline_denominator = baseline[DENOMINATOR].to_numpy(dtype=float) + sim.set_input(TAKEUP, YEAR, np.ones(n_people, dtype=bool)) + sim.set_input(DENOMINATOR, YEAR, baseline_denominator) + denominator_preserved = True + + person_id = values(sim, "person_id").astype(np.int64) + if baseline_ids is not None and not np.array_equal(person_id, baseline_ids): + raise ValueError("baseline and reform person ordering differs") + + age = values(sim, "age").astype(float) + state = values(sim, "state_code", map_to="person").astype(str) + weight = values(sim, "person_weight").astype(float) + coverage = {name: values(sim, name).astype(bool) for name in COVERAGE_INPUTS} + medicare = values(sim, MEDICARE_PROXY).astype(bool) + has_coverage = medicare.copy() + for covered in coverage.values(): + has_coverage |= covered + reported_uninsured = ~has_coverage + + eligible = values(sim, "is_medicaid_eligible").astype(bool) + enrolled = values(sim, "medicaid_enrolled").astype(bool) + medicaid = values(sim, "medicaid").astype(float) + denominator = values(sim, DENOMINATOR).astype(float) + modeled_uninsured = reported_uninsured & ~enrolled + + if mode == "reform" and not np.array_equal(enrolled, eligible): + raise AssertionError("100%-take-up enrollment does not equal eligibility") + expected_states = { + "AL", + "AK", + "AZ", + "AR", + "CA", + "CO", + "CT", + "DE", + "DC", + "FL", + "GA", + "HI", + "ID", + "IL", + "IN", + "IA", + "KS", + "KY", + "LA", + "ME", + "MD", + "MA", + "MI", + "MN", + "MS", + "MO", + "MT", + "NE", + "NV", + "NH", + "NJ", + "NM", + "NY", + "NC", + "ND", + "OH", + "OK", + "OR", + "PA", + "RI", + "SC", + "SD", + "TN", + "TX", + "UT", + "VT", + "VA", + "WA", + "WV", + "WI", + "WY", + } + observed_states = set(state) + if dataset is None and observed_states != expected_states: + raise AssertionError( + f"full artifact state set differs: {sorted(observed_states)}" + ) + if dataset is not None and not observed_states <= expected_states: + raise AssertionError(f"sample has unexpected states: {sorted(observed_states)}") + + frame = pd.DataFrame( + { + "person_id": person_id, + "state": state, + "age": age, + "person_weight": weight, + "is_medicaid_eligible": eligible, + "medicaid_enrolled": enrolled, + "medicaid": medicaid, + DENOMINATOR: denominator, + "reported_uninsured": reported_uninsured, + "modeled_uninsured": modeled_uninsured, + MEDICARE_PROXY: medicare, + } + ) + output.parent.mkdir(parents=True, exist_ok=True) + frame.to_csv(output, index=False, compression="gzip") + + bundle = serializable(dict(sim.policyengine_bundle)) + engine_version = getattr(policyengine_us, "__version__", None) or version( + "policyengine-us" + ) + if bundle.get("model_version") != engine_version: + raise AssertionError( + f"engine {engine_version} != bundle model {bundle.get('model_version')}" + ) + if dataset is None: + if bundle.get("default_dataset_uri") != EXPECTED_DATASET_URI: + raise AssertionError("managed dataset URI differs from certified pin") + if bundle.get("certified_data_build_id") != EXPECTED_BUILD: + raise AssertionError("managed data build differs from certified pin") + + calculated = ( + "person_id", + "state_code", + "age", + "person_weight", + *COVERAGE_INPUTS, + MEDICARE_PROXY, + "is_medicaid_eligible", + "medicaid_enrolled", + "medicaid", + DENOMINATOR, + ) + assert "chip" not in calculated and "early_head_start" not in calculated + metadata = { + "mode": mode, + "period": YEAR, + "sample": dataset is not None, + "dataset": str(dataset) if dataset is not None else None, + "engine_version": engine_version, + "engine_version_source": ( + "policyengine_us.__version__" + if getattr(policyengine_us, "__version__", None) + else "importlib.metadata (module has no __version__ attribute)" + ), + "data_bundle": bundle.get("certified_data_build_id"), + "bundle_id": bundle.get("bundle_id"), + "policyengine_bundle": bundle, + "computed_at": datetime.now(timezone.utc).isoformat(), + "started_at": started, + "people": n_people, + "calculated_variables": list(calculated), + "coverage_inputs": list(COVERAGE_INPUTS), + "medicare_handling": ( + "medicare_enrolled is the modeled under-65 Medicare proxy; all other " + "coverage inputs are reported at interview" + ), + "reported_uninsured_definition": ( + "none of coverage_inputs and not medicare_enrolled; age<65 is " + "applied during moment aggregation" + ), + "modeled_uninsured_definition": ( + "reported_uninsured and not medicaid_enrolled" + ), + "takeup_definition_period": str(variables[TAKEUP].definition_period), + "takeup_forced_true": mode == "reform", + "baseline_denominator_preserved": denominator_preserved, + "denominator_note": ( + "The baseline Medicaid state-allocation denominator is supplied in " + "the reform process to reproduce the engine's baseline-branch cost " + "semantics without holding two simulations in memory." + if denominator_preserved + else "Baseline engine value extracted for the reform process." + ), + "variables": {name: variable_metadata(sim, name) for name in calculated}, + } + Path(f"{output}.meta.json").write_text(json.dumps(metadata, indent=2) + "\n") + print( + json.dumps( + {key: metadata[key] for key in metadata if key != "variables"}, indent=2 + ) + ) + return metadata + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("mode", choices=("baseline", "reform")) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--dataset", type=Path) + parser.add_argument("--baseline-extract", type=Path) + args = parser.parse_args() + run(args.mode, args.output, args.dataset, args.baseline_extract) + + +if __name__ == "__main__": + main() From 90af3c236faf45486b5629643a944d6a130c5579 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:15:02 -0400 Subject: [PATCH 05/14] Bound Medicaid full-file simulation memory --- PROGRESS.md | 12 +- pipeline/compute_kff_medicaid_extract.py | 152 ++++++++++++++++++++++- 2 files changed, 158 insertions(+), 6 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index 5c69663..f945a4b 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -2,7 +2,7 @@ ## State -In progress. The KFF source population and campaign schema are implemented, and the full baseline/reform computation path has been validated on the required deterministic 5,000-household sample. Network access remains prohibited for this lane. +In progress. Full certified 2024 baseline and take-up-reform extracts have been computed in separate processes, passed the national anchors and identities, and have been aggregated into diagnostics, moments counterparts, and campaign staging. Network access remains prohibited for this lane. ## Done @@ -16,8 +16,14 @@ In progress. The KFF source population and campaign schema are implemented, and - Sample validation: 75.282M eligible, 69.039M enrolled, +6.243M enrollment; eligible-minus-enrolled identity gap 0.000000004 persons. The bridge is 3.552M reported-uninsured and 2.691M other-coverage. The fixed-baseline-denominator spending construction produces +$115.558B. These are sample checks, not published estimates. - Confirmed the fiscal mechanic: a standalone take-up override re-normalizes fixed state Medicaid spending. The reform extractor supplies the baseline state-allocation denominator, reproducing policyengine-us's intended baseline-branch cost semantics without retaining two simulations. This execution choice is explicit in every staged reform row. - Confirmed installed policyengine-us 1.764.6 has no module `__version__` attribute; the pipeline uses `importlib.metadata.version("policyengine-us")`, asserts it equals `sim.policyengine_bundle["model_version"]`, and records bundle `us-5.0.2` plus the full certified Build P id. +- Added a bounded-memory eligibility execution: each configured native Medicaid category pathway is materialized separately and its dependency cache is cleared before the final native `is_medicaid_eligible` formula runs. On the required sample, all 14,254 person rows are exactly identical to the ordinary native calculation across every extracted column. +- Completed the full certified baseline: 77.325M eligible, 72.336M enrolled, and $880.019B in Medicaid spending. Both population anchors differ from the lane anchors by less than 0.1%. +- Completed the separate full reform process with the annual take-up input forced true. Policy-invariant baseline eligibility and the baseline state-allocation denominator are supplied from the baseline extract; enrollment and Medicaid spending are recomputed independently. +- Full reform results: +4.989M enrollment and +$71.527B Medicaid spending. The marginal-enrollment bridge is 2.792M reported-uninsured and 2.196M other-coverage; the enrollment and bridge identities hold to floating-point precision. +- Built 214 PE moment counterparts, 208 campaign rows, and US/state diagnostic CSVs. The 2024 reported-uninsured counterpart is 34.921M eligible among 80.932M uninsured (43.15%); the modeled-uninsured alternative is 2.785M among 48.796M (5.71%). ## Next -- Extend the moments builder for multiple PE payloads, deterministic primary/alternative variants, truthful-period references, row metadata, and null-safe suppression handling; add tests. -- Run full-file baseline and reform passes separately, stage aggregate diagnostics/results, wire moments, and run offline checks. +- Review and commit the aggregate-output and moments-builder changes, including sample/full provenance gates and their tests. +- Validate campaign ingest against a temporary database, decide whether the repository database can be updated cleanly offline, and document the ready-to-run command. +- Run the complete offline test, formatting, and application-data checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. diff --git a/pipeline/compute_kff_medicaid_extract.py b/pipeline/compute_kff_medicaid_extract.py index a24d0e1..642ac9a 100644 --- a/pipeline/compute_kff_medicaid_extract.py +++ b/pipeline/compute_kff_medicaid_extract.py @@ -10,7 +10,9 @@ from __future__ import annotations import argparse +import gc import json +import sys from datetime import datetime, timezone from importlib.metadata import version from pathlib import Path @@ -38,6 +40,97 @@ "has_indian_health_service_coverage_at_interview", ) MEDICARE_PROXY = "medicare_enrolled" +CATEGORY_PATHWAYS = ( + # This order mirrors medicaid_category.formula in policyengine-us 1.764.6. + "is_ssi_recipient_for_medicaid", + "is_infant_for_medicaid", + "is_young_child_for_medicaid", + "is_older_child_for_medicaid", + "is_pregnant_for_medicaid", + "is_parent_for_medicaid", + "is_young_adult_for_medicaid", + "is_adult_for_medicaid", + "is_optional_senior_or_disabled_for_medicaid", + "is_medically_needy_for_medicaid", + "is_working_disabled_buy_in_for_medicaid", + "is_medicaid_1115_mec_adult", +) +MISSISSIPPI_WAIVER_PATHWAY = "ms_hmw_eligible" + + +def log(message: str) -> None: + print(message, file=sys.stderr, flush=True) + + +def release_loaded_dataset_frames(sim) -> int: + """Release HDF DataFrames after core has copied inputs into holders. + + policyengine-us's entity-HDF adapter retains every source DataFrame on + ``sim.dataset`` after ``build_from_dataset`` has populated the simulation. + Formula evaluation reads holders, not those frames. Replacing the retained + frames with empty frames avoids keeping a second multi-GB copy while + preserving the managed dataset object's name, period, and cache metadata. + """ + released = 0 + datasets = getattr(sim.dataset, "datasets", {}) + for single_year in datasets.values(): + empty_tables = [] + for name in single_year.table_names: + frame = getattr(single_year, name) + released += int(frame.memory_usage(index=True, deep=True).sum()) + empty = pd.DataFrame() + setattr(single_year, name, empty) + empty_tables.append(empty) + single_year.tables = tuple(empty_tables) + gc.collect() + return released + + +def clear_formula_caches(sim, keep: set[str] | None = None) -> int: + """Drop calculated holders while retaining dataset and explicit inputs.""" + keep = set(keep or ()) + keep.update(sim.input_variables) + keep.update(key[0] for key in getattr(sim, "_user_input_keys", set())) + cleared = 0 + for population in sim.populations.values(): + for name, holder in population._holders.items(): + if name in keep: + continue + usage = holder.get_memory_usage()["total_nb_bytes"] + if holder.get_known_periods(): + holder.delete_arrays() + cleared += int(usage) + sim._fast_cache.clear() + gc.collect() + return cleared + + +def calculate_medicaid_eligibility(sim) -> tuple[np.ndarray, list[str]]: + """Calculate native Medicaid eligibility with bounded formula-cache use. + + ``medicaid_category`` selects from several independent eligibility + pathways. Evaluating it in one expression retains every pathway's full + dependency graph at once, which exceeds the memory available for the + certified file. Materializing each configured pathway as a temporary + execution input lets PolicyEngine clear that pathway's dependencies before + moving to the next one. The final value still comes from the native + ``is_medicaid_eligible`` formula, including immigration, state-coverage, + and any active work-requirement legs. + """ + parameters = sim.tax_benefit_system.parameters(YEAR) + covered = set(parameters.gov.hhs.medicaid.eligibility.categories.covered) + pathways = [name for name in CATEGORY_PATHWAYS if name in covered] + pathways.append(MISSISSIPPI_WAIVER_PATHWAY) + + for pathway in pathways: + log(f"calculating Medicaid category pathway: {pathway}") + result = values(sim, pathway).astype(bool) + sim.set_input(pathway, YEAR, result) + cleared = clear_formula_caches(sim, {pathway}) + log(f"materialized {pathway}; cleared {cleared / 1e9:.2f} GB") + + log("calculating native is_medicaid_eligible from materialized pathways") + return values(sim, "is_medicaid_eligible").astype(bool), pathways def serializable(value): @@ -77,6 +170,7 @@ def run( from policyengine_core.periods import YEAR as YEAR_PERIOD started = datetime.now(timezone.utc).isoformat() + log(f"constructing {mode} managed simulation") if dataset is None: sim = pe.us.managed_microsimulation() else: @@ -84,29 +178,45 @@ def run( dataset=str(dataset), allow_unmanaged=True, ) + bundle = serializable(dict(sim.policyengine_bundle)) + released_dataset_bytes = release_loaded_dataset_frames(sim) + log( + f"managed simulation ready; released {released_dataset_bytes / 1e9:.2f} GB " + "of retained source frames" + ) variables = sim.tax_benefit_system.variables assert variables[TAKEUP].definition_period == YEAR_PERIOD n_people = sim.populations["person"].count baseline_ids = None + baseline_eligibility = None denominator_preserved = False + eligibility_preserved = False if mode == "reform": if baseline_extract is None: raise ValueError("reform mode requires --baseline-extract") baseline = pd.read_csv( baseline_extract, - usecols=["person_id", DENOMINATOR], + usecols=["person_id", "is_medicaid_eligible", DENOMINATOR], ) if len(baseline) != n_people: raise ValueError( f"baseline has {len(baseline)} people; reform has {n_people}" ) baseline_ids = baseline["person_id"].to_numpy() + baseline_eligibility = baseline["is_medicaid_eligible"].to_numpy(dtype=bool) baseline_denominator = baseline[DENOMINATOR].to_numpy(dtype=float) sim.set_input(TAKEUP, YEAR, np.ones(n_people, dtype=bool)) + sim.set_input("is_medicaid_eligible", YEAR, baseline_eligibility) sim.set_input(DENOMINATOR, YEAR, baseline_denominator) denominator_preserved = True + eligibility_preserved = True + log( + "annual take-up input set; policy-invariant baseline eligibility " + "and cost denominator supplied" + ) + log("calculating identifiers, demographics, and coverage") person_id = values(sim, "person_id").astype(np.int64) if baseline_ids is not None and not np.array_equal(person_id, baseline_ids): raise ValueError("baseline and reform person ordering differs") @@ -120,11 +230,43 @@ def run( for covered in coverage.values(): has_coverage |= covered reported_uninsured = ~has_coverage + cleared = clear_formula_caches(sim) + log(f"cleared {cleared / 1e9:.2f} GB of preliminary formula caches") - eligible = values(sim, "is_medicaid_eligible").astype(bool) + log("calculating Medicaid eligibility") + if baseline_eligibility is None: + eligible, materialized_pathways = calculate_medicaid_eligibility(sim) + log("Medicaid eligibility complete; materializing it as an execution input") + sim.set_input("is_medicaid_eligible", YEAR, eligible) + eligibility_execution = ( + "Native is_medicaid_eligible formula after sequentially " + "materializing its configured medicaid_category pathways as " + "execution inputs to bound cache memory." + ) + else: + eligible = values(sim, "is_medicaid_eligible").astype(bool) + materialized_pathways = [] + eligibility_execution = ( + "Policy-invariant 2024 eligibility loaded from the separate " + "certified baseline extract; the take-up input does not enter the " + "eligibility formula." + ) + cleared = clear_formula_caches(sim, {"is_medicaid_eligible"}) + log(f"cleared {cleared / 1e9:.2f} GB of eligibility dependencies") + + log("calculating Medicaid enrollment") enrolled = values(sim, "medicaid_enrolled").astype(bool) + sim.set_input("medicaid_enrolled", YEAR, enrolled) + cleared = clear_formula_caches( + sim, + {"is_medicaid_eligible", "medicaid_enrolled"}, + ) + log(f"cleared {cleared / 1e9:.2f} GB of enrollment dependencies") + + log("calculating Medicaid spending") medicaid = values(sim, "medicaid").astype(float) denominator = values(sim, DENOMINATOR).astype(float) + log("Medicaid spending complete") modeled_uninsured = reported_uninsured & ~enrolled if mode == "reform" and not np.array_equal(enrolled, eligible): @@ -207,8 +349,8 @@ def run( ) output.parent.mkdir(parents=True, exist_ok=True) frame.to_csv(output, index=False, compression="gzip") + log(f"wrote {len(frame)} person rows to {output}") - bundle = serializable(dict(sim.policyengine_bundle)) engine_version = getattr(policyengine_us, "__version__", None) or version( "policyengine-us" ) @@ -252,6 +394,9 @@ def run( "computed_at": datetime.now(timezone.utc).isoformat(), "started_at": started, "people": n_people, + "released_dataset_frame_bytes": released_dataset_bytes, + "eligibility_execution": eligibility_execution, + "materialized_medicaid_category_pathways": materialized_pathways, "calculated_variables": list(calculated), "coverage_inputs": list(COVERAGE_INPUTS), "medicare_handling": ( @@ -267,6 +412,7 @@ def run( ), "takeup_definition_period": str(variables[TAKEUP].definition_period), "takeup_forced_true": mode == "reform", + "baseline_eligibility_preserved": eligibility_preserved, "baseline_denominator_preserved": denominator_preserved, "denominator_note": ( "The baseline Medicaid state-allocation denominator is supplied in " From 57a9b18e03d3fb3c54e0bfd378a41e3561bdd217 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:16:07 -0400 Subject: [PATCH 06/14] Stage Medicaid take-up reform results --- PROGRESS.md | 5 +- .../diagnostics/kff_medicaid_moments_2024.csv | 105 + .../diagnostics/kff_medicaid_takeup_2024.csv | 53 + data/pe/kff_medicaid_counterparts.json | 7548 +++++++++++++++++ data/scorecard.db | Bin 41172992 -> 41594880 bytes pipeline/build_kff_medicaid_outputs.py | 57 +- .../campaign-20260818/us/kff_medicaid.jsonl | 208 + tests/test_kff_medicaid_outputs.py | 189 + 8 files changed, 8162 insertions(+), 3 deletions(-) create mode 100644 data/pe/diagnostics/kff_medicaid_moments_2024.csv create mode 100644 data/pe/diagnostics/kff_medicaid_takeup_2024.csv create mode 100644 data/pe/kff_medicaid_counterparts.json create mode 100644 sources/campaign-20260818/us/kff_medicaid.jsonl create mode 100644 tests/test_kff_medicaid_outputs.py diff --git a/PROGRESS.md b/PROGRESS.md index f945a4b..8353893 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -21,9 +21,10 @@ In progress. Full certified 2024 baseline and take-up-reform extracts have been - Completed the separate full reform process with the annual take-up input forced true. Policy-invariant baseline eligibility and the baseline state-allocation denominator are supplied from the baseline extract; enrollment and Medicaid spending are recomputed independently. - Full reform results: +4.989M enrollment and +$71.527B Medicaid spending. The marginal-enrollment bridge is 2.792M reported-uninsured and 2.196M other-coverage; the enrollment and bridge identities hold to floating-point precision. - Built 214 PE moment counterparts, 208 campaign rows, and US/state diagnostic CSVs. The 2024 reported-uninsured counterpart is 34.921M eligible among 80.932M uninsured (43.15%); the modeled-uninsured alternative is 2.785M among 48.796M (5.71%). +- Added strict sample/full provenance gates and explicit bridge-scope annotations to the aggregate builder. The real builder-to-JSONL-to-database test covers reform identity, state shape, identities, provenance, and idempotency. +- Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. ## Next -- Review and commit the aggregate-output and moments-builder changes, including sample/full provenance gates and their tests. -- Validate campaign ingest against a temporary database, decide whether the repository database can be updated cleanly offline, and document the ready-to-run command. +- Review and commit the moments-builder changes and regenerated `moments.json`. - Run the complete offline test, formatting, and application-data checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. diff --git a/data/pe/diagnostics/kff_medicaid_moments_2024.csv b/data/pe/diagnostics/kff_medicaid_moments_2024.csv new file mode 100644 index 0000000..c8aa2c0 --- /dev/null +++ b/data/pe/diagnostics/kff_medicaid_moments_2024.csv @@ -0,0 +1,105 @@ +geography,coverage_variant,uninsured_count,eligible_uninsured_count,eligible_share_percent,age_universe,engine_version,data_bundle,bundle_id,sample,rules_vintage,computed_at +US,reported_uninsured,80931506.73897925,34920781.90222003,43.148562666511,age_under_65,1.764.6,populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z,us-5.0.2,False,PolicyEngine 2024 law,2026-08-18T20:13:52.420977+00:00 +US,modeled_uninsured,48795621.72433898,2784896.887579762,5.707267966196793,age_under_65,1.764.6,populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z,us-5.0.2,False,PolicyEngine 2024 law,2026-08-18T20:13:52.420977+00:00 +AK,reported_uninsured,299693.5855944903,146388.87054294435,48.846180759110524,age_under_65,1.764.6,populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z,us-5.0.2,False,PolicyEngine 2024 law,2026-08-18T20:13:52.420977+00:00 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"expansion_states", + "variant": "pe_reported_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "reported_uninsured", + "geography": "US", + "unit_concept": "persons", + "period": "2024", + "value": 28734323.225533415, + "numerator": 28734323.225533415, + "denominator": null, + "age_universe": "age_under_65_in_2024_expansion_states", + "expansion_classification": "2024 expansion set; non-expansion AL FL GA KS MS SC TN TX WI WY", + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + }, + { + "source": "pe", + "program": "medicaid", + "metric": "eligible_uninsured_count", + "subgroup": "adults", + "variant": "pe_modeled_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "modeled_uninsured", + "geography": "US", + "unit_concept": "persons", + "period": "2024", + "value": 1528446.9030052093, + "numerator": 1528446.9030052093, + "denominator": null, + "age_universe": "age_19_to_64", + "expansion_classification": null, + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + }, + { + "source": "pe", + "program": "medicaid", + "metric": "eligible_uninsured_count", + "subgroup": "children", + "variant": "pe_modeled_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "modeled_uninsured", + "geography": "US", + "unit_concept": "persons", + "period": "2024", + "value": 1256449.9845745526, + "numerator": 1256449.9845745526, + "denominator": null, + "age_universe": "age_under_19", + "expansion_classification": null, + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + }, + { + "source": "pe", + "program": "medicaid", + "metric": "eligible_uninsured_count", + "subgroup": "expansion_states", + "variant": "pe_modeled_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "modeled_uninsured", + "geography": "US", + "unit_concept": "persons", + "period": "2024", + "value": 2602018.5599436834, + "numerator": 2602018.5599436834, + "denominator": null, + "age_universe": "age_under_65_in_2024_expansion_states", + "expansion_classification": "2024 expansion set; non-expansion AL FL GA KS MS SC TN TX WI WY", + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + } + ] +} diff --git a/data/scorecard.db b/data/scorecard.db index b0395e238aa6726e9c82cf54f8b104f19a254388..d6452375c2901ea5d3fee4fda2cb86fbe9c694ba 100644 GIT binary patch delta 353308 zcmeHw349Y}`hSwr(mMspS&pHcD#db@qbQkTxsQSc71TCOChbU?#2l0=b%@94s#H9n 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-0400 Subject: [PATCH 07/14] Wire KFF Medicaid moments into scorecard --- PROGRESS.md | 2 +- app/public/data/moments.json | 11383 +++++++++++++++- pipeline/build_moments.py | 324 +- sources/kff-remaining-uninsured/PROVENANCE.md | 2 +- tests/test_tariff_moments.py | 241 +- 5 files changed, 11315 insertions(+), 637 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index 8353893..e0e88d0 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -23,8 +23,8 @@ In progress. Full certified 2024 baseline and take-up-reform extracts have been - Built 214 PE moment counterparts, 208 campaign rows, and US/state diagnostic CSVs. The 2024 reported-uninsured counterpart is 34.921M eligible among 80.932M uninsured (43.15%); the modeled-uninsured alternative is 2.785M among 48.796M (5.71%). - Added strict sample/full provenance gates and explicit bridge-scope annotations to the aggregate builder. The real builder-to-JSONL-to-database test covers reform identity, state shape, identities, provenance, and idempotency. - Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. +- Extended the additive moments pipeline for multiple counterpart payloads, deterministic primary/alternative variants, null-safe suppression, period-specific annotations, and dated reference counterparts. The regenerated 603-row payload contains 52 KFF `concept_mismatch` rows for 2024 and five truthful `not_computed` 2022 rows with separately dated 2024 PE references. ## Next -- Review and commit the moments-builder changes and regenerated `moments.json`. - Run the complete offline test, formatting, and application-data checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. diff --git a/app/public/data/moments.json b/app/public/data/moments.json index e52f26a..7c6825f 100644 --- a/app/public/data/moments.json +++ b/app/public/data/moments.json @@ -1,21 +1,133 @@ { - "built": "2026-08-09", + "built": "2026-08-18", "claim_class": "baseline_moment", "pe_provenance": { - "margins_source": "microcosm #620 (merge d4b0855157af996bd367146bc200a2f72ee7d15d)", - "construct": "sum(cal_dut_mo)/sum(con_val_mo) monthly, all lines x countries", - "reconciliation": "exact-integer vs publisher control totals, 3 axes x 18 months, 0 failures", - "computed_from": "/Users/maxghenis/PolicyEngine/policyengine-scorecard/data/pe/tariff_expost_monthly.csv" + "tariff": { + "margins_source": "microcosm #620 (merge d4b0855157af996bd367146bc200a2f72ee7d15d)", + "construct": "sum(cal_dut_mo)/sum(con_val_mo) monthly, all lines x countries", + "reconciliation": "exact-integer vs publisher control totals, 3 axes x 18 months, 0 failures", + "computed_from": "/Users/maxghenis/PolicyEngine/policyengine-scorecard/data/pe/tariff_expost_monthly.csv" + }, + "kff_medicaid": { + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview", + "policyengine_bundle": { + "bundle_id": "us-5.0.2", + "country_id": "us", + "policyengine_version": "5.0.2", + "model_package": "policyengine-us", + "model_version": "1.764.6", + "data_package": "populace-data", + "data_version": "0.1.0", + "default_dataset": "populace_us_2024", + "default_dataset_uri": "hf://policyengine/populace-us/populace_us_2024.h5@populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "certified_data_build_id": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "certified_data_artifact_sha256": "48b9d479fb4fd1c3537f9383ce4697d130b6f618658409d74f6233c43b994c7e", + "data_build_model_version": "1.764.6", + "data_build_model_git_sha": null, + "data_build_fingerprint": null, + "compatibility_basis": "built_with_model_package", + "certified_by": "policyengine.py bundle certification", + "runtime_dataset": "populace_us_2024", + "runtime_dataset_uri": "hf://policyengine/populace-us/populace_us_2024.h5@populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "runtime_dataset_source": "/Users/maxghenis/.cache/huggingface/hub/datasets--policyengine--populace-us/snapshots/26dcad66867687f15735dc4926523e3741920836/populace_us_2024.h5", + "managed_by": "policyengine.py" + }, + "baseline_computed_at": "2026-08-18T20:11:52.766355+00:00", + "reform_computed_at": "2026-08-18T20:13:23.659367+00:00", + "expansion_classification": "2024 expansion set; non-expansion AL FL GA KS MS SC TN TX WI WY", + "adult_child_split": "children age under 19; adults age 19 to 64" + } + }, + "source_meta": { + "yale-tariff-tracker": { + "id": "yale-tariff-tracker", + "name": "Yale Budget Lab \u2014 tariff-rate-tracker daily effective statutory rates", + "organization": "The Budget Lab at Yale", + "url": "https://github.com/Budget-Lab-Yale/tariff-rate-tracker", + "data_url": "release/data/daily_overall_2026-06-09.csv, recovered via `git show 39d394d` (the 2026-06-09 publication was untracked from the working tree 2026-07-21 as stale; 39d394d is the publish commit)", + "fetched": "2026-08-08", + "fetch_notes": "Rates are decimals (0.029 = 2.9%). Vintage covers data through 2026 revision 10 and, per the repo's own deviations registry at that vintage, does not yet include the Brazil \u00a7301 or \u00a7338 actions.", + "claim_class": "baseline_moment", + "model": { + "name": "tariff-rate-tracker (open source, MIT)", + "open_source": true, + "method": "Interval-encoded statutory-rate panel at HTS-10 \u00d7 country parsed from USITC HTS revision archives plus curated policy parameters; daily overall series = stored total_rate (post mutual-exclusion stacking and USMCA/FTA/exemption reductions, i.e. an EFFECTIVE statutory rate) weighted by FIXED 2024 Census consumption-import values re-keyed to each revision's codes; weighted_etr = sum(rate x weight) / sum(all weights).", + "method_basis": "docs/methodology.md, docs/weights.md, src/pipeline/09_daily_series.R, src/io/build_import_weights.R at the pinned repo" + }, + "period": "2025-01-01 .. 2026-06-09 daily (adapted here to monthly means)", + "geographies": "US total", + "diagnosis_upstream": "Construct questions go to the tracker repo's issues; our six standing filings (#23-#28) are tracked in the tariff campaign memory. Divergence claims follow the disposition standard: derivation-backed and author-attributed until maintainer-acknowledged." + }, + "tpc-tariffs": { + "id": "tpc-tariffs", + "name": "Tax Policy Center \u2014 Tracking Trump Tariffs (average statutory tariff rate)", + "organization": "Urban-Brookings Tax Policy Center", + "url": "https://taxpolicycenter.org/features/tracking-trump-tariffs", + "data_url": "Datawrapper chart datasets, version-pinned: aO4iG v44 (daily average statutory rate incl. All Goods), MC81F v43 (daily by authority type). Old version paths serve frozen vintages.", + "fetched": "2026-08-08", + "fetch_notes": "Values are PERCENT (9.34 = 9.34%); adapter converts to fractions. Latest publish event 2026-07-24 (the \u00a7122-expiry step); All Goods 9.34% from 2026-07-24 at v44.", + "claim_class": "baseline_moment", + "model": { + "name": "TPC Tariff Rules Engine v2.1", + "open_source": false, + "method": "Average STATUTORY tariff rate: sum of tariff rates on covered products from all countries on each day, weighted by FIXED 2025 import volumes; excludes AD/CVD; MFN base rates inside the 'Other' type. Closed source \u2014 agreement is testable only at published chart granularity (all-goods, by-type, by-country).", + "method_basis": "Chart notes on the Datawrapper datasets (TPC-authored) and the feature page methodology text, fetched 2026-08-08" + }, + "period": "2024-10-01 onward daily (adapted here to monthly means)", + "geographies": "US total", + "diagnosis_upstream": "No public code or issue tracker; construct questions can only be resolved against published chart text. Divergences remain author-attributed with our derivation recorded." + }, + "kff-remaining-uninsured": { + "id": "kff-remaining-uninsured", + "name": "KFF \u2014 eligibility for coverage among the remaining uninsured", + "organization": "KFF", + "url": "https://www.kff.org/health-reform/state-indicator/distribution-of-eligibility-for-aca-coverage-among-the-remaining-uninsured/", + "data_url": "raw/state_indicator_2024acs_2025levels.md (rendered table extraction) and the national flagship-brief headlines recorded in PROVENANCE.md", + "fetched": "2026-08-18", + "fetch_notes": "The state table uses 2024 ACS data and 2025 eligibility levels. Its rendered first-column label is retained verbatim pending an offline check against KFF's CSV headers. The flagship brief uses 2022 ACS data and 2023 eligibility levels.", + "claim_class": "baseline_moment", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "model": { + "name": "KFF/Damico eligibility simulation", + "open_source": false, + "method": "Static ACS-based eligibility microsimulation for the nonelderly uninsured. KFF reconstructs Medicaid and Marketplace units, applies ACA-definition MAGI and state eligibility levels, and imputes immigration status and employer coverage offers.", + "method_basis": "KFF methods boxes summarized with source links in PROVENANCE.md" + }, + "period": "2022 flagship brief; 2024 State Health Facts indicator", + "geographies": "US plus 50 states and the District of Columbia", + "diagnosis_upstream": "Re-check the downloadable KFF CSV column headers before removing the rendered-label caution." + } }, "sources": [ "yale-tariff-tracker", - "tpc-tariffs" + "tpc-tariffs", + "kff-remaining-uninsured" ], "summary": { - "rows": 546, + "rows": 603, "by_status": { - "not_computed": 510, - "concept_mismatch": 36 + "not_computed": 515, + "concept_mismatch": 88 } }, "annotations": [ @@ -82,6 +194,64 @@ }, "text": "The spring-2026 divergence between the two fixed-base statutory series is the weight year, quantified by replication: running Yale's own daily-series stage and own rate panel (c4307e51, built locally) with the weight base rebuilt via Yale's own weights recipe for 2025 instead of 2024 moves the 2026-04..06 mean from 10.72% to 9.38%, within 0.08 points of TPC's published 9.46% (weight-year effect -1.34p; construct residual ~0). Author-attributed; leg outputs committed at data/pe/diagnostics/daily_overall_weights{2024,2025}.csv. The validation leg (2024 weights) tracks Yale's published series within 0.2-0.6p in stable months, with departures explained by vintage (their 2026-06-09 publication predates their Brazil-301/338 encodings) and by single-pass vs per-revision weight keying.", "basis": "P5 stage-1 runs 2026-08-08/09 (~/PolicyEngine/_tariff-p5/stage1/); weight bases built via tracker src/io/build_import_weights.R --no-gtap over sha-manifested Census archives (2024 $3,119.3B; 2025 $3,242.5B)" + }, + { + "id": "kff-medicaid-pathway-breadth", + "applies": { + "source": "kff-remaining-uninsured" + }, + "text": "KFF applies MAGI pathways only. PolicyEngine's is_medicaid_eligible also includes SSI-linked, medically needy, and optional senior or disabled categories, so the PolicyEngine eligibility construct is broader while KFF's pathway scope is narrower.", + "basis": "PROVENANCE.md construct difference 1" + }, + { + "id": "kff-medicaid-chip-scope", + "applies": { + "source": "kff-remaining-uninsured" + }, + "text": "KFF includes Medicaid and CHIP for children. The PolicyEngine counterpart is Medicaid-only because the certified artifact deliberately does not assign standalone CHIP take-up; KFF therefore includes a child coverage pathway absent from the PolicyEngine counterpart, while PolicyEngine isolates Medicaid.", + "basis": "PROVENANCE.md construct difference 2; Microcosm concept split tracked in microcosm#321" + }, + { + "id": "kff-medicaid-coverage-measurement", + "applies": { + "source": "kff-remaining-uninsured" + }, + "text": "KFF's reported-uninsured population retains survey Medicaid underreporting because no correction is stated. PolicyEngine's primary counterpart also starts from reported at-interview coverage, while its modeled-uninsured alternative additionally removes medicaid_enrolled people added by the CMS-calibrated anchor-and-fill process; KFF retains those reported-uninsured people and the alternative does not.", + "basis": "PROVENANCE.md construct difference 3" + }, + { + "id": "kff-medicaid-data-and-imputations", + "applies": { + "source": "kff-remaining-uninsured" + }, + "text": "KFF uses ACS records, a regression-and-Pew immigration imputation with five implicates, and imputed ESI offers. PolicyEngine uses CPS-ASEC-based Populace records, Microcosm's CPS-and-Pew immigration assignment, and reported at-interview employer coverage; the certified artifact lacks years_since_us_entry, so PolicyEngine's five-year-bar leg uses that variable's default and is not fully equivalent to KFF's status netting.", + "basis": "PROVENANCE.md construct difference 4 and certified-artifact mechanics in the lane brief" + }, + { + "id": "kff-medicaid-rules-vintage", + "applies": { + "source": "kff-remaining-uninsured" + }, + "text": "KFF applies the eligibility-level vintage named on each source row (2025 levels for the 2024 state indicator; 2023 levels for the 2022 brief). PolicyEngine applies 2024 law to the single-period 2024 artifact; every joined row therefore carries a named rules-vintage difference in both directions.", + "basis": "KFF methods notes and certified-artifact period constraint" + }, + { + "id": "kff-medicaid-brief-vintage", + "applies": { + "source": "kff-remaining-uninsured", + "period": "2022" + }, + "text": "The flagship brief describes 2022 ACS data under 2023 eligibility levels, before the unwinding was complete. The certified PolicyEngine artifact describes 2024 only, so the 2022 claim is not computed on its own period; the joined row shows the separately dated 2024 counterpart as context.", + "basis": "PROVENANCE.md construct difference 5" + }, + { + "id": "kff-medicaid-rendered-column-caution", + "applies": { + "source": "kff-remaining-uninsured", + "period": "2024" + }, + "text": "The staged rendered table labels the selected column Medicaid/Other Public. Its values are preserved, but KFF's downloadable CSV headers still require an offline re-check before the label can be narrowed further.", + "basis": "PROVENANCE.md column-label caution" } ], "rows": [ @@ -94,10 +264,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-01", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.016865575856809815, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -116,10 +292,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-01", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.02909405387654363, + "claim_class": "baseline_moment", "pe_value": 0.02216065751505904, "pe_variant": "expost_collections_contemporaneous", + "pe_counterpart": { + "source": "pe", + "program": "tariff", + "metric": "average_tariff_rate", + "subgroup": "total", + "variant": "expost_collections_contemporaneous", + "geography": "us", + "unit_concept": "fraction_of_customs_value_contemporaneous", + "period": "2025-01", + "value": 0.02216065751505904, + "numerator_usd": 7133693804, + "denominator_usd": 321908039017 + }, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": -0.00693339636148459, "status": "concept_mismatch", "annotation_ids": [ @@ -138,10 +332,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-02", - "claim_class": "baseline_moment", + "n_days": 28, + "external_status": "ok", "external_value": 0.0287987298792057, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -160,10 +360,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-02", - "claim_class": "baseline_moment", + "n_days": 28, + "external_status": "ok", "external_value": 0.041029271589660896, + "claim_class": "baseline_moment", "pe_value": 0.02330996035393699, "pe_variant": "expost_collections_contemporaneous", + "pe_counterpart": { + "source": "pe", + "program": "tariff", + "metric": "average_tariff_rate", + "subgroup": "total", + "variant": "expost_collections_contemporaneous", + "geography": "us", + "unit_concept": "fraction_of_customs_value_contemporaneous", + "period": "2025-02", + "value": 0.02330996035393699, + "numerator_usd": 6807216123, + "denominator_usd": 292030360397 + }, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": -0.017719311235723907, "status": "concept_mismatch", "annotation_ids": [ @@ -182,10 +400,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-03", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.05223720095094168, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -204,10 +428,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-03", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.06445050854264818, + "claim_class": "baseline_moment", "pe_value": 0.03718383945400941, "pe_variant": "expost_collections_contemporaneous", + "pe_counterpart": { + "source": "pe", + "program": "tariff", + "metric": "average_tariff_rate", + "subgroup": "total", + "variant": "expost_collections_contemporaneous", + "geography": "us", + "unit_concept": "fraction_of_customs_value_contemporaneous", + "period": "2025-03", + "value": 0.03718383945400941, + "numerator_usd": 12667399660, + "denominator_usd": 340669490994 + }, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": -0.02726666908863877, "status": "concept_mismatch", "annotation_ids": [ @@ -226,10 +468,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 30, + "external_status": "ok", "external_value": 0.15961033872745098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -248,10 +496,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 30, + "external_status": "ok", "external_value": 0.17179467654273337, + "claim_class": "baseline_moment", "pe_value": 0.07182631915480352, "pe_variant": "expost_collections_contemporaneous", + "pe_counterpart": { + "source": "pe", + "program": "tariff", + "metric": "average_tariff_rate", + "subgroup": "total", + "variant": "expost_collections_contemporaneous", + "geography": "us", + "unit_concept": "fraction_of_customs_value_contemporaneous", + "period": "2025-04", + "value": 0.07182631915480352, + "numerator_usd": 19639110004, + "denominator_usd": 273424981749 + }, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": -0.09996835738792985, "status": "concept_mismatch", "annotation_ids": [ @@ -270,10 +536,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-05", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.14190667908975077, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -292,10 +564,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-05", - "claim_class": "baseline_moment", + "n_days": 31, + "external_status": "ok", "external_value": 0.154132473654054, + "claim_class": "baseline_moment", "pe_value": 0.09059812045604639, "pe_variant": "expost_collections_contemporaneous", + "pe_counterpart": { + "source": "pe", + "program": "tariff", + "metric": "average_tariff_rate", + "subgroup": "total", + "variant": "expost_collections_contemporaneous", + "geography": "us", + "unit_concept": "fraction_of_customs_value_contemporaneous", + "period": "2025-05", + "value": 0.09059812045604639, + "numerator_usd": 24709786463, + "denominator_usd": 272740608068 + }, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": -0.06353435319800761, "status": "concept_mismatch", "annotation_ids": [ @@ -314,10 +604,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-06", - "claim_class": "baseline_moment", + "n_days": 30, + "external_status": "ok", "external_value": 0.11839517387313345, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -336,10 +632,28 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2024_weights", "period": "2025-06", - "claim_class": "baseline_moment", + "n_days": 30, + "external_status": "ok", "external_value": 0.13057693104741253, + 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"delta": null, "status": "not_computed", "annotation_ids": [ @@ -2712,10 +4028,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.011971836707606646, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2734,10 +4056,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.00708203708277127, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2756,10 +4084,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.009217665110625186, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2778,10 +4112,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.09608959644200168, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2800,10 +4140,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2822,10 +4168,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.008756954524916613, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2844,10 +4196,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-04", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2866,10 +4224,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-05", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.011971836707606646, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -2888,10 +4252,16 @@ "geography": 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"fraction_of_customs_value_fixed2025_weights", "period": "2025-08", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.011971836707606646, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -3350,10 +4840,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2025-08", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.025855591807566608, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -3372,10 +4868,16 @@ "geography": "us", "unit_concept": 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+ "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -5902,10 +8088,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2026-12", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.03569521666617444, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -5924,10 +8116,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2026-12", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + 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"annotation_ids": [ @@ -6122,10 +8368,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6144,10 +8396,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.030136241880578105, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6166,10 +8424,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6188,10 +8452,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6210,10 +8480,16 @@ "geography": "us", 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"period": "2027-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03574364357274011, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6540,10 +8900,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6562,10 +8928,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-05", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6584,10 +8956,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-05", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6606,10 +8984,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-05", - "claim_class": 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"external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6672,10 +9068,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-05", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03574364357274011, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6694,10 +9096,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-05", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", 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"pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6848,10 +9292,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-06", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6870,10 +9320,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6892,10 +9348,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6914,10 +9376,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.030136241880578105, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6936,10 +9404,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6958,10 +9432,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -6980,10 +9460,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03574364357274011, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7002,10 +9488,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7024,10 +9516,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7046,10 +9544,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7068,10 +9572,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.030136241880578108, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7090,10 +9600,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7112,10 +9628,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7134,10 +9656,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.03574364357274011, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7156,10 +9684,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-08", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7178,10 +9712,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-09", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", 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-7244,10 +9796,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-09", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7266,10 +9824,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2027-09", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -7288,10 +9852,16 @@ "geography": "us", 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"pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8058,10 +10832,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-02", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8080,10 +10860,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-02", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": 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"not_computed", "annotation_ids": [ @@ -8234,10 +11056,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-03", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8256,10 +11084,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8278,10 +11112,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8300,10 +11140,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03233566977731832, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8322,10 +11168,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8344,10 +11196,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-04", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8366,10 +11224,16 @@ "geography": "us", 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"fraction_of_customs_value_fixed2025_weights", "period": "2028-05", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8432,10 +11308,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-05", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8454,10 +11336,16 @@ "geography": "us", "unit_concept": 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"period": "2028-06", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8696,10 +11644,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-06", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8718,10 +11672,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8740,10 +11700,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8762,10 +11728,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-07", - 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"n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8828,10 +11812,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -8850,10 +11840,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-07", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": 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"pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9136,10 +12204,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-09", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9158,10 +12232,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-09", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": 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"not_computed", "annotation_ids": [ @@ -9312,10 +12428,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-10", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0023564554074805098, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9334,10 +12456,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-11", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9356,10 +12484,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-11", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9378,10 +12512,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-11", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03233566977731832, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9400,10 +12540,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-11", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9422,10 +12568,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2028-11", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -9444,10 +12596,16 @@ "geography": "us", 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+14136,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2029-07", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -10676,10 +14164,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2029-07", - "claim_class": "baseline_moment", + "n_days": 5, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -10698,10 +14192,16 @@ "geography": "us", 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"not_computed", "annotation_ids": [ @@ -11644,10 +15396,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.01176609410542242, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11666,10 +15424,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0207376391693876, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11688,10 +15452,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.033593607559253745, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11710,10 +15480,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11732,10 +15508,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.0, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11754,10 +15536,16 @@ "geography": "us", "unit_concept": "fraction_of_customs_value_fixed2025_weights", "period": "2030-02", - "claim_class": "baseline_moment", + "n_days": 4, + "external_status": "ok", "external_value": 0.03577813783320739, + "claim_class": "baseline_moment", "pe_value": null, "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": null, + "pe_reference_alternatives": [], "delta": null, "status": "not_computed", "annotation_ids": [ @@ -11776,10 +15564,16 @@ "geography": "us", 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"kff-medicaid-chip-scope", + "kff-medicaid-coverage-measurement", + "kff-medicaid-data-and-imputations", + "kff-medicaid-rules-vintage", + "kff-medicaid-brief-vintage" + ], + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "computed_at": "2026-08-18T20:13:52.420977+00:00" + }, + { + "source": "kff-remaining-uninsured", + "program": "medicaid", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "coverage_variant": "reported_uninsured", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "kff_reported_uninsured_magi_medicaid_chip_2023_rules", + "geography": "US", + "unit_concept": "percent", + "period": "2022", + "value_kind": "share", + "status": "not_computed", + "suppression_flag": null, + "source_column": "flagship brief headline", + "publication_population": "flagship_brief", + "data_vintage": "2022 ACS", + "rules_vintage": "2023 eligibility levels", + "reference_period": "2024", + "external_status": "ok", + "external_value": 25.0, + "claim_class": "baseline_moment", + "pe_value": null, + "pe_variant": null, + "pe_counterpart": null, + "pe_alternatives": [], + "pe_reference": { + "source": "pe", + "program": "medicaid", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "pe_reported_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "reported_uninsured", + "geography": "US", + "unit_concept": "percent", + "period": "2024", + "value": 43.148562666511, + "numerator": 34920781.90222003, + "denominator": 80931506.73897925, + "age_universe": "age_under_65", + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + }, + "pe_reference_alternatives": [ + { + "source": "pe", + "program": "medicaid", + "metric": "eligible_share_among_uninsured", + "subgroup": "total", + "variant": "pe_modeled_uninsured_all_medicaid_pathways_2024_rules", + "coverage_variant": "modeled_uninsured", + "geography": "US", + "unit_concept": "percent", + "period": "2024", + "value": 5.707267966196793, + "numerator": 2784896.887579762, + "denominator": 48795621.72433898, + "age_universe": "age_under_65", + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "sample": false, + "computed_at": "2026-08-18T20:13:52.420977+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + "rules_vintage": "PolicyEngine 2024 law", + "coverage_inputs": [ + "has_esi", + "has_marketplace_health_coverage_at_interview", + "has_non_marketplace_direct_purchase_health_coverage_at_interview", + "has_medicaid_health_coverage_at_interview", + "has_tricare_health_coverage_at_interview", + "has_va_health_coverage_at_interview", + "has_champva_health_coverage_at_interview", + "has_other_means_tested_health_coverage_at_interview", + "has_indian_health_service_coverage_at_interview" + ], + "medicare_handling": "medicare_enrolled is the modeled under-65 Medicare proxy; all other coverage inputs are reported at interview" + } + ], + "delta": null, + "annotation_ids": [ + "kff-medicaid-pathway-breadth", + "kff-medicaid-chip-scope", + "kff-medicaid-coverage-measurement", + "kff-medicaid-data-and-imputations", + "kff-medicaid-rules-vintage", + "kff-medicaid-brief-vintage" + ], + "engine_version": "1.764.6", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", + "bundle_id": "us-5.0.2", + "computed_at": "2026-08-18T20:13:52.420977+00:00" } ] } diff --git a/pipeline/build_moments.py b/pipeline/build_moments.py index 8851dc6..2499ba6 100644 --- a/pipeline/build_moments.py +++ b/pipeline/build_moments.py @@ -1,113 +1,285 @@ -"""Baseline moments: join external tracker claims with our counterparts. - -Additive sibling of build_comparison.py for the claim class Max named -baseline moments: current-law statistics published by external modelers -(average tariff rates today; average tax rate tables tomorrow), each -compared against our computation with the construct difference stated. - -Join key: (program, metric, subgroup, geography, period). Status logic: - comparable external and PE rows share the same variant - concept_mismatch a PE row exists for the period but under a different - construct variant (the delta is still shown, annotated) - not_computed no PE row for the period -Every external row appears; the misses stay on the page. -Output: app/public/data/moments.json. +"""Join external baseline moments to PolicyEngine counterparts. + +The join key is ``(program, metric, subgroup, geography, period)``. Variant +is a construct axis rather than part of the key: + +* ``comparable``: the selected external and PE rows share a variant; +* ``concept_mismatch``: a same-period PE row exists under another variant; +* ``not_computed``: no PE value exists for the external row's period; and +* ``suppressed``: the external source withheld the value. + +Every external row remains in the output. A row may also name a +``reference_period``. That dated PE value is attached as context without +changing the status or period of the external claim. + +Output: ``app/public/data/moments.json``. """ +from __future__ import annotations + import json +from collections import defaultdict from datetime import date from pathlib import Path ROOT = Path(__file__).resolve().parent.parent EXTERNALS = ROOT / "data" / "externals" -SOURCES = ("yale-tariff-tracker", "tpc-tariffs") +SOURCES = ( + "yale-tariff-tracker", + "tpc-tariffs", + "kff-remaining-uninsured", +) +COUNTERPART_FILES = { + "tariff": ROOT / "data" / "pe" / "tariff_counterparts.json", + "kff_medicaid": ROOT / "data" / "pe" / "kff_medicaid_counterparts.json", +} + +JOIN_FIELDS = ("program", "metric", "subgroup", "geography", "period") +PROVENANCE_FIELDS = ( + "engine_version", + "data_bundle", + "bundle_id", + "computed_at", +) -def load_annotations(): +def load_annotations(sources: tuple[str, ...] = SOURCES) -> list[dict]: entries = [] - for source in SOURCES: + for source in sources: path = ROOT / "sources" / source / "annotations.json" entries.extend(json.loads(path.read_text())) return entries -def main(): - pe_payload = json.loads( - (ROOT / "data" / "pe" / "tariff_counterparts.json").read_text() - ) - pe_rows = { - (r["program"], r["metric"], r["subgroup"], r["geography"], r["period"]): r - for r in pe_payload["rows"] +def load_counterpart_payloads() -> dict[str, dict]: + """Load available declared counterpart files. + + A missing file means those rows have not been computed yet; it is not a + reason to discard their external claims. Once a counterpart file is + staged, the same builder attaches it without another wiring change. + """ + return { + name: json.loads(path.read_text()) + for name, path in COUNTERPART_FILES.items() + if path.exists() } - annotations = load_annotations() - source_annotation_ids = {} - for entry in annotations: - source_annotation_ids.setdefault(entry["applies"]["source"], []).append( - entry["id"] + + +def join_key(row: dict, period: str | None = None) -> tuple: + return tuple( + period if field == "period" and period is not None else row[field] + for field in JOIN_FIELDS + ) + + +def index_counterparts(rows: list[dict]) -> dict[tuple, list[dict]]: + """Index all variants without silently overwriting alternatives.""" + indexed = defaultdict(list) + identities = defaultdict(set) + for row in rows: + key = join_key(row) + identity = (row.get("variant"), row.get("coverage_variant")) + if identity in identities[key]: + raise ValueError(f"duplicate PE counterpart {key!r} {identity!r}") + identities[key].add(identity) + indexed[key].append(row) + return dict(indexed) + + +def _select_counterpart( + external: dict, candidates: list[dict] +) -> tuple[dict | None, list[dict]]: + """Choose the primary construct and return every other variant. + + Exact full-variant matches take priority. KFF's source and PE variant + names deliberately differ because their eligibility constructs differ, + so ``coverage_variant`` selects the closest reported-uninsured PE row. + """ + if not candidates: + return None, [] + + exact = [row for row in candidates if row.get("variant") == external.get("variant")] + if len(exact) > 1: + raise ValueError(f"ambiguous exact PE variant for {join_key(external)!r}") + if exact: + primary = exact[0] + else: + coverage_variant = external.get("coverage_variant") + coverage_matches = [ + row + for row in candidates + if coverage_variant is not None + and row.get("coverage_variant") == coverage_variant + ] + if len(coverage_matches) > 1: + raise ValueError( + f"ambiguous PE coverage variant for {join_key(external)!r}" + ) + if coverage_matches: + primary = coverage_matches[0] + elif len(candidates) == 1: + primary = candidates[0] + else: + raise ValueError(f"no primary PE variant for {join_key(external)!r}") + + alternatives = [row for row in candidates if row is not primary] + alternatives.sort( + key=lambda row: (row.get("coverage_variant") or "", row.get("variant") or "") + ) + return primary, alternatives + + +def _annotation_applies(annotation: dict, row: dict) -> bool: + for field, expected in annotation.get("applies", {}).items(): + if expected is None: + continue + actual = row.get(field) + if isinstance(expected, list): + if actual not in expected: + return False + elif actual != expected: + return False + return True + + +def annotation_ids_for(annotations: list[dict], row: dict) -> list[str]: + return [ + annotation["id"] + for annotation in annotations + if _annotation_applies(annotation, row) + ] + + +def _copy_counterpart(row: dict | None) -> dict | None: + return None if row is None else dict(row) + + +def join_external_row( + external: dict, + counterparts: dict[tuple, list[dict]], + annotations: list[dict], + claim_class: str = "baseline_moment", +) -> dict: + """Join one external claim while preserving its source metadata.""" + external_status = external.get("status", "ok") + external_value = external.get("value") + candidates = counterparts.get(join_key(external), []) + + if external_status == "suppressed": + status = "suppressed" + primary = None + alternatives = [] + else: + primary, alternatives = _select_counterpart(external, candidates) + if primary is None or primary.get("value") is None: + status = "not_computed" + elif primary.get("variant") == external.get("variant"): + status = "comparable" + else: + status = "concept_mismatch" + + reference = None + reference_alternatives = [] + reference_period = external.get("reference_period") + if external_status != "suppressed" and reference_period is not None: + reference, reference_alternatives = _select_counterpart( + external, + counterparts.get(join_key(external, period=reference_period), []), ) + pe_value = primary.get("value") if primary is not None else None + delta = ( + pe_value - external_value + if pe_value is not None and external_value is not None + else None + ) + row = dict(external) + row.pop("value", None) + row["external_status"] = external_status + row["external_value"] = external_value + row["claim_class"] = claim_class + row["pe_value"] = pe_value + row["pe_variant"] = primary.get("variant") if primary is not None else None + row["pe_counterpart"] = _copy_counterpart(primary) + row["pe_alternatives"] = [_copy_counterpart(item) for item in alternatives] + row["pe_reference"] = _copy_counterpart(reference) + row["pe_reference_alternatives"] = [ + _copy_counterpart(item) for item in reference_alternatives + ] + row["delta"] = delta + row["status"] = status + row["annotation_ids"] = annotation_ids_for(annotations, row) + + provenance_row = primary or reference + if provenance_row is not None: + for field in PROVENANCE_FIELDS: + if field in provenance_row: + row[field] = provenance_row[field] + return row + + +def build_payload( + external_rows: dict[str, list[dict]], + source_meta: dict[str, dict], + counterpart_payloads: dict[str, dict], + annotations: list[dict], + built: str | None = None, +) -> dict: + pe_rows = [ + row + for payload in counterpart_payloads.values() + for row in payload.get("rows", []) + ] + counterpart_index = index_counterparts(pe_rows) rows = [] for source in SOURCES: - meta = json.loads((ROOT / "sources" / source / "source.json").read_text()) - for ext in json.loads((EXTERNALS / f"{source}.json").read_text()): - key = ( - ext["program"], - ext["metric"], - ext["subgroup"], - ext["geography"], - ext["period"], - ) - pe = pe_rows.get(key) - if pe is None: - status, pe_value, pe_variant = "not_computed", None, None - elif pe["variant"] == ext["variant"]: - status, pe_value, pe_variant = "comparable", pe["value"], pe["variant"] - else: - status, pe_value, pe_variant = ( - "concept_mismatch", - pe["value"], - pe["variant"], - ) - rows.append( - { - **{ - k: ext[k] - for k in ( - "source", - "program", - "metric", - "subgroup", - "variant", - "geography", - "unit_concept", - "period", - ) - }, - "claim_class": meta.get("claim_class", "baseline_moment"), - "external_value": ext["value"], - "pe_value": pe_value, - "pe_variant": pe_variant, - "delta": None if pe_value is None else pe_value - ext["value"], - "status": status, - "annotation_ids": source_annotation_ids.get(source, []), - } + meta = source_meta[source] + rows.extend( + join_external_row( + external, + counterpart_index, + annotations, + claim_class=meta.get("claim_class", "baseline_moment"), ) + for external in external_rows[source] + ) statuses = {} for row in rows: statuses[row["status"]] = statuses.get(row["status"], 0) + 1 - payload = { - "built": date.today().isoformat(), + return { + "built": built or date.today().isoformat(), "claim_class": "baseline_moment", - "pe_provenance": pe_payload["provenance"], + "pe_provenance": { + name: payload.get("provenance", {}) + for name, payload in counterpart_payloads.items() + }, + "source_meta": source_meta, "sources": SOURCES, "summary": {"rows": len(rows), "by_status": statuses}, "annotations": annotations, "rows": rows, } + + +def main() -> None: + external_rows = { + source: json.loads((EXTERNALS / f"{source}.json").read_text()) + for source in SOURCES + } + source_meta = { + source: json.loads((ROOT / "sources" / source / "source.json").read_text()) + for source in SOURCES + } + payload = build_payload( + external_rows, + source_meta, + load_counterpart_payloads(), + load_annotations(), + ) out = ROOT / "app" / "public" / "data" / "moments.json" out.write_text(json.dumps(payload, indent=1) + "\n") - print(f"{out}: {len(rows)} rows {statuses}") + print(f"{out}: {payload['summary']['rows']} rows {payload['summary']['by_status']}") if __name__ == "__main__": diff --git a/sources/kff-remaining-uninsured/PROVENANCE.md b/sources/kff-remaining-uninsured/PROVENANCE.md index 83b3cdf..61f202c 100644 --- a/sources/kff-remaining-uninsured/PROVENANCE.md +++ b/sources/kff-remaining-uninsured/PROVENANCE.md @@ -30,7 +30,7 @@ eligibility simulation). Two populations, different vintages: (per the page's methods notes). Post-unwinding on both sides of any join with populace_us_2024. - **Raw extraction**: `raw/state_indicator_2024acs_2025levels.md`. -- **⚠️ Column-label caution**: the extraction labeled the first column +- **Column-label caution**: the extraction labeled the first column "Medicaid/Other Public (%)" — verify category semantics against KFF's own column headers before finalizing claim metric names (KFF's category set for this indicator historically: Medicaid eligible / Tax-credit eligible / diff --git a/tests/test_tariff_moments.py b/tests/test_tariff_moments.py index b33c6e7..27df64c 100644 --- a/tests/test_tariff_moments.py +++ b/tests/test_tariff_moments.py @@ -2,8 +2,14 @@ import csv import json +import runpy +from collections import Counter from pathlib import Path +import pytest + +from pipeline import build_moments + ROOT = Path(__file__).resolve().parent.parent @@ -50,13 +56,17 @@ def test_counterparts_match_committed_extract(): def test_moments_join_statuses_and_deltas(): payload = load("app/public/data/moments.json") - rows = payload["rows"] - assert payload["summary"]["by_status"] == { + rows = [ + row + for row in payload["rows"] + if row["source"] in {"yale-tariff-tracker", "tpc-tariffs"} + ] + assert Counter(row["status"] for row in rows) == { "not_computed": 510, "concept_mismatch": 36, } - # Every external row appears; misses stay on the page. - assert payload["summary"]["rows"] == len(rows) == 546 + # The additive KFF source must not change the existing tariff population. + assert len(rows) == 546 mismatch = [r for r in rows if r["status"] == "concept_mismatch"] # Mismatch rows are exactly the total-series months our margins cover. assert all(r["subgroup"] == "total" for r in mismatch) @@ -75,6 +85,229 @@ def test_moments_join_statuses_and_deltas(): assert all(set(r["annotation_ids"]) <= annotation_ids for r in rows) +def _kff_external(period, metric, subgroup="total"): + return next( + row + for row in load("data/externals/kff-remaining-uninsured.json") + if row["period"] == period + and row["metric"] == metric + and row["subgroup"] == subgroup + and row["geography"] == "US" + ) + + +def test_kff_adapter_population_percent_units_and_suppression_parser(): + rows = load("data/externals/kff-remaining-uninsured.json") + adapter = runpy.run_path( + ROOT / "sources" / "kff-remaining-uninsured" / "adapter.py" + ) + state_rows = [ + row for row in rows if row["publication_population"] == "state_indicator" + ] + brief_rows = [ + row for row in rows if row["publication_population"] == "flagship_brief" + ] + + assert len(rows) == 57 + assert len(state_rows) == 52 + assert len(brief_rows) == 5 + assert {row["geography"] for row in state_rows} == { + "US", + *adapter["STATE_CODES"].values(), + } + assert all(row["unit_concept"] == "percent" for row in state_rows) + assert all(0 <= row["value"] <= 100 for row in state_rows) + assert _kff_external("2024", "eligible_share_among_uninsured")["value"] == 24.9 + + assert adapter["parse_percent"]("NSD") == (None, "suppressed", "NSD") + assert adapter["parse_percent"]("24.9%") == (24.9, "ok", None) + + +def _kff_pe_row( + coverage_variant, + value, + *, + metric="eligible_share_among_uninsured", + subgroup="total", +): + return { + "source": "pe", + "program": "medicaid", + "metric": metric, + "subgroup": subgroup, + "variant": (f"pe_{coverage_variant}_all_medicaid_pathways_2024_rules"), + "coverage_variant": coverage_variant, + "geography": "US", + "unit_concept": ( + "percent" if metric == "eligible_share_among_uninsured" else "persons" + ), + "period": "2024", + "value": value, + "engine_version": "1.764.6", + "data_bundle": ( + "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z" + ), + "bundle_id": "us-5.0.2", + "computed_at": "2026-08-18T20:00:00+00:00", + "benchmark_class": "different_model", + "calibration_relationship": "held_out", + } + + +def test_kff_primary_variant_is_descriptive_concept_mismatch(): + external = _kff_external("2024", "eligible_share_among_uninsured") + reported = _kff_pe_row("reported_uninsured", 31.2) + modeled = _kff_pe_row("modeled_uninsured", 28.1) + counterparts = build_moments.index_counterparts([modeled, reported]) + annotations = load("sources/kff-remaining-uninsured/annotations.json") + + row = build_moments.join_external_row(external, counterparts, annotations) + + assert row["status"] == "concept_mismatch" + assert row["pe_value"] == 31.2 + assert row["pe_variant"] == reported["variant"] + assert row["delta"] == pytest.approx(31.2 - 24.9) + assert row["pe_counterpart"] == reported + assert row["pe_alternatives"] == [modeled] + assert row["benchmark_class"] == "different_model" + assert row["calibration_relationship"] == "held_out" + assert row["engine_version"] == "1.764.6" + assert row["data_bundle"] == reported["data_bundle"] + assert { + "kff-medicaid-pathway-breadth", + "kff-medicaid-chip-scope", + "kff-medicaid-coverage-measurement", + "kff-medicaid-data-and-imputations", + "kff-medicaid-rules-vintage", + } <= set(row["annotation_ids"]) + assert "kff-medicaid-rendered-column-caution" in row["annotation_ids"] + assert "kff-medicaid-brief-vintage" not in row["annotation_ids"] + + +def test_kff_2022_claim_keeps_2024_reference_out_of_main_comparison(): + external = _kff_external("2022", "eligible_uninsured_count", "children") + reported = _kff_pe_row( + "reported_uninsured", + 2_700_000, + metric="eligible_uninsured_count", + subgroup="children", + ) + modeled = _kff_pe_row( + "modeled_uninsured", + 2_100_000, + metric="eligible_uninsured_count", + subgroup="children", + ) + counterparts = build_moments.index_counterparts([reported, modeled]) + annotations = load("sources/kff-remaining-uninsured/annotations.json") + + row = build_moments.join_external_row(external, counterparts, annotations) + + assert row["period"] == "2022" + assert row["status"] == "not_computed" + assert row["pe_value"] is None + assert row["pe_variant"] is None + assert row["pe_counterpart"] is None + assert row["pe_alternatives"] == [] + assert row["delta"] is None + assert row["pe_reference"] == reported + assert row["pe_reference_alternatives"] == [modeled] + assert row["engine_version"] == "1.764.6" + assert "kff-medicaid-brief-vintage" in row["annotation_ids"] + assert "kff-medicaid-rendered-column-caution" not in row["annotation_ids"] + + +def test_kff_suppressed_value_is_retained_without_a_delta(): + external = dict(_kff_external("2024", "eligible_share_among_uninsured")) + external.update(value=None, status="suppressed", suppression_flag="NSD") + reported = _kff_pe_row("reported_uninsured", 31.2) + counterparts = build_moments.index_counterparts([reported]) + + row = build_moments.join_external_row(external, counterparts, []) + + assert row["external_status"] == "suppressed" + assert row["external_value"] is None + assert row["suppression_flag"] == "NSD" + assert row["status"] == "suppressed" + assert row["pe_value"] is None + assert row["pe_counterpart"] is None + assert row["delta"] is None + + +def test_kff_moments_wiring_carries_source_and_pe_provenance(): + external = _kff_external("2024", "eligible_share_among_uninsured") + reported = _kff_pe_row("reported_uninsured", 31.2) + modeled = _kff_pe_row("modeled_uninsured", 28.1) + external_rows = {source: [] for source in build_moments.SOURCES} + external_rows["kff-remaining-uninsured"] = [external] + source_meta = { + source: {"id": source, "claim_class": "baseline_moment"} + for source in build_moments.SOURCES + } + source_meta["kff-remaining-uninsured"] = load( + "sources/kff-remaining-uninsured/source.json" + ) + counterpart_payloads = { + "kff_medicaid": { + "provenance": { + "engine_version": reported["engine_version"], + "data_bundle": reported["data_bundle"], + }, + "rows": [reported, modeled], + } + } + + payload = build_moments.build_payload( + external_rows, + source_meta, + counterpart_payloads, + load("sources/kff-remaining-uninsured/annotations.json"), + built="2026-08-18", + ) + + assert "kff-remaining-uninsured" in payload["sources"] + assert payload["source_meta"]["kff-remaining-uninsured"]["benchmark_class"] == ( + "different_model" + ) + assert ( + payload["pe_provenance"]["kff_medicaid"] + == (counterpart_payloads["kff_medicaid"]["provenance"]) + ) + assert payload["summary"] == { + "rows": 1, + "by_status": {"concept_mismatch": 1}, + } + + +def test_committed_kff_moments_have_expected_statuses_and_variants(): + payload = load("app/public/data/moments.json") + rows = [ + row for row in payload["rows"] if row["source"] == "kff-remaining-uninsured" + ] + state_rows = [row for row in rows if row["period"] == "2024"] + brief_rows = [row for row in rows if row["period"] == "2022"] + + assert len(rows) == 57 + assert Counter(row["status"] for row in rows) == { + "concept_mismatch": 52, + "not_computed": 5, + } + assert all(row["unit_concept"] == "percent" for row in state_rows) + assert all( + row["pe_counterpart"]["coverage_variant"] == "reported_uninsured" + for row in state_rows + ) + assert all( + [alternative["coverage_variant"] for alternative in row["pe_alternatives"]] + == ["modeled_uninsured"] + for row in state_rows + ) + assert all(row["pe_value"] is None for row in brief_rows) + assert all(row["pe_reference"]["period"] == "2024" for row in brief_rows) + assert all(row["benchmark_class"] == "different_model" for row in rows) + assert all(row["calibration_relationship"] == "held_out" for row in rows) + + def test_divergence_decomposition_annotation_present(): payload = load("app/public/data/moments.json") ids = {a["id"] for a in payload["annotations"]} From af9c170939de2bd7ec5116dbd07ad57b7d881e2b Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:19:54 -0400 Subject: [PATCH 08/14] Scope campaign idempotency checks by run --- PROGRESS.md | 3 ++- tests/test_campaign_ingest.py | 35 ++++++++++++++++++++++++-------- tests/test_export_populations.py | 2 -- 3 files changed, 28 insertions(+), 12 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index e0e88d0..6872cea 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -24,7 +24,8 @@ In progress. Full certified 2024 baseline and take-up-reform extracts have been - Added strict sample/full provenance gates and explicit bridge-scope annotations to the aggregate builder. The real builder-to-JSONL-to-database test covers reform identity, state shape, identities, provenance, and idempotency. - Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. - Extended the additive moments pipeline for multiple counterpart payloads, deterministic primary/alternative variants, null-safe suppression, period-specific annotations, and dated reference counterparts. The regenerated 603-row payload contains 52 KFF `concept_mismatch` rows for 2024 and five truthful `not_computed` 2022 rows with separately dated 2024 PE references. +- Completed the full offline Python suite through `uv run`: 179 tests pass. Ruff lint passes repository-wide, and the CI-scoped `scorecard_db`, `tests`, and `pipeline` trees are fully formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. ## Next -- Run the complete offline test, formatting, and application-data checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. +- Run the application lint/build checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. diff --git a/tests/test_campaign_ingest.py b/tests/test_campaign_ingest.py index 6ceea9b..5d53686 100644 --- a/tests/test_campaign_ingest.py +++ b/tests/test_campaign_ingest.py @@ -20,6 +20,30 @@ def db_copy(tmp_path): return path +def _staged_run_ids(staged_dir): + return sorted( + { + json.loads(line)["run_id"] + for path in staged_dir.glob("*.jsonl") + for line in path.read_text().splitlines() + } + ) + + +def _campaign_counts(conn, staged_dir): + run_ids = _staged_run_ids(staged_dir) + placeholders = ",".join("?" for _ in run_ids) + results = conn.execute( + f"SELECT COUNT(*) FROM pe_results WHERE run_id IN ({placeholders})", + run_ids, + ).fetchone()[0] + exhibits = conn.execute( + f"SELECT COUNT(*) FROM pe_exhibits WHERE run_id IN ({placeholders})", + run_ids, + ).fetchone()[0] + return results, exhibits + + def test_normalizers(): cond, reform = _normalize( "cpsp", @@ -185,12 +209,7 @@ def test_reingest_idempotent(db_copy): again = ingest(db_copy) assert again == first conn = sqlite3.connect(db_copy) - n = conn.execute( - "SELECT COUNT(*) FROM pe_results WHERE run_id LIKE 'campaign-%'" - ).fetchone()[0] - n_ex = conn.execute( - "SELECT COUNT(*) FROM pe_exhibits WHERE run_id LIKE 'campaign-%'" - ).fetchone()[0] + n, n_ex = _campaign_counts(conn, STAGED_US) conn.close() assert n == first["attached"] assert n_ex == first["exhibits"] @@ -214,9 +233,7 @@ def test_metaless_exhibit_defers(db_copy, tmp_path): assert len(summary["exhibits_deferred"]) == 1 assert "marginal increment" in summary["exhibits_deferred"][0] conn = sqlite3.connect(db_copy) - n = conn.execute( - "SELECT COUNT(*) FROM pe_exhibits WHERE run_id LIKE 'campaign-%'" - ).fetchone()[0] + _, n = _campaign_counts(conn, staged) conn.close() assert n == 3 # the stripped row's prior exhibit did not linger diff --git a/tests/test_export_populations.py b/tests/test_export_populations.py index 64ba296..5975dea 100644 --- a/tests/test_export_populations.py +++ b/tests/test_export_populations.py @@ -2,8 +2,6 @@ guards, lane sync, and an integration pass over the committed DB.""" import json -from pathlib import Path - import pytest from scorecard_db import ScorecardDB From 5d50311a6266a09a9e25c911705204336521ee18 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:22:07 -0400 Subject: [PATCH 09/14] Finalize KFF Medicaid progress record --- PROGRESS.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index 6872cea..8b4f4c5 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -2,7 +2,7 @@ ## State -In progress. Full certified 2024 baseline and take-up-reform extracts have been computed in separate processes, passed the national anchors and identities, and have been aggregated into diagnostics, moments counterparts, and campaign staging. Network access remains prohibited for this lane. +Complete and ready for review. Full certified results are committed, the moments and campaign databases are wired, all available offline Python and Ruff checks pass, and no network operation was attempted. ## Done @@ -25,7 +25,10 @@ In progress. Full certified 2024 baseline and take-up-reform extracts have been - Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. - Extended the additive moments pipeline for multiple counterpart payloads, deterministic primary/alternative variants, null-safe suppression, period-specific annotations, and dated reference counterparts. The regenerated 603-row payload contains 52 KFF `concept_mismatch` rows for 2024 and five truthful `not_computed` 2022 rows with separately dated 2024 PE references. - Completed the full offline Python suite through `uv run`: 179 tests pass. Ruff lint passes repository-wide, and the CI-scoped `scorecard_db`, `tests`, and `pipeline` trees are fully formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. +- Wrote the required uncommitted `PR_BODY.md`, including the results table, construct annotations, staged/ingested state, exact offline ingest command, and required follow-ups. +- The app lint/build remains unrun because this worktree has no `app/node_modules`. Network access is prohibited, so dependencies were not installed; Python and data-pipeline validation is complete. ## Next -- Run the application lint/build checks; write the uncommitted PR body and final report with the three largest state differences and exact commit list. +- Main session: review the committed diff and uncommitted `PR_BODY.md`/`FINAL_REPORT.md`, then push and open the PR if approved. +- Run the app lint/build in an environment with the frozen Bun dependencies already installed or with authorized network access. From 4d9d6a5f41666a328b17bd913e625dbe3b56d383 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:22:34 -0400 Subject: [PATCH 10/14] Use descriptive validation language --- PROGRESS.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index 8b4f4c5..fb1b2c4 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -2,7 +2,7 @@ ## State -Complete and ready for review. Full certified results are committed, the moments and campaign databases are wired, all available offline Python and Ruff checks pass, and no network operation was attempted. +Complete and ready for review. Full certified results are committed, the moments and campaign databases are wired, all available offline Python and Ruff checks completed successfully, and no network operation was attempted. ## Done @@ -11,7 +11,7 @@ Complete and ready for review. Full certified results are committed, the moments - Created the implementation and validation plan. - Read the complete KFF provenance and staged table, plus the existing moments and campaign contracts. - Added the KFF source registry, descriptive construct annotations, and adapter: 52 state-indicator percentage rows (US + 50 states + DC) and five 2022 flagship-brief rows. The selected rendered column has no NSD cells; the adapter retains any NSD selected cell as an explicitly suppressed row. -- Extended campaign ingest with a strict `policyengine_us_inputs` ReformRef exhibit route, preserving state geography, baseline values, deltas, and canonical reform identity. Focused plus existing campaign/database tests pass (62 tests). +- Extended campaign ingest with a strict `policyengine_us_inputs` ReformRef exhibit route, preserving state geography, baseline values, deltas, and canonical reform identity. The 62 focused and existing campaign/database tests completed successfully. - Added memory-disciplined sample, one-simulation extraction, and aggregate-output pipelines. The deterministic sample selects 5,000 of 57,240 households (seed 20260818), rescales `household_weight` by 11.448, retains 14,254 people and every referenced group entity, and runs baseline/reform in separate processes. - Sample validation: 75.282M eligible, 69.039M enrolled, +6.243M enrollment; eligible-minus-enrolled identity gap 0.000000004 persons. The bridge is 3.552M reported-uninsured and 2.691M other-coverage. The fixed-baseline-denominator spending construction produces +$115.558B. These are sample checks, not published estimates. - Confirmed the fiscal mechanic: a standalone take-up override re-normalizes fixed state Medicaid spending. The reform extractor supplies the baseline state-allocation denominator, reproducing policyengine-us's intended baseline-branch cost semantics without retaining two simulations. This execution choice is explicit in every staged reform row. @@ -24,7 +24,7 @@ Complete and ready for review. Full certified results are committed, the moments - Added strict sample/full provenance gates and explicit bridge-scope annotations to the aggregate builder. The real builder-to-JSONL-to-database test covers reform identity, state shape, identities, provenance, and idempotency. - Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. - Extended the additive moments pipeline for multiple counterpart payloads, deterministic primary/alternative variants, null-safe suppression, period-specific annotations, and dated reference counterparts. The regenerated 603-row payload contains 52 KFF `concept_mismatch` rows for 2024 and five truthful `not_computed` 2022 rows with separately dated 2024 PE references. -- Completed the full offline Python suite through `uv run`: 179 tests pass. Ruff lint passes repository-wide, and the CI-scoped `scorecard_db`, `tests`, and `pipeline` trees are fully formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. +- Completed the full offline Python suite through `uv run`: 179 successful tests. Ruff lint completed successfully repository-wide, and the CI-scoped `scorecard_db`, `tests`, and `pipeline` trees are fully formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. - Wrote the required uncommitted `PR_BODY.md`, including the results table, construct annotations, staged/ingested state, exact offline ingest command, and required follow-ups. - The app lint/build remains unrun because this worktree has no `app/node_modules`. Network access is prohibited, so dependencies were not installed; Python and data-pipeline validation is complete. From aa162afcd149a162235ab7b2c5bdd7f9bbc8a2b8 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:27:46 -0400 Subject: [PATCH 11/14] Apply repository-wide Ruff formatting --- PROGRESS.md | 2 +- .../tpc/reparse_t26_0009.py | 34 +++++++++---------- sources/urban-sotsn/adapter.py | 27 +++++++++------ 3 files changed, 33 insertions(+), 30 deletions(-) diff --git a/PROGRESS.md b/PROGRESS.md index fb1b2c4..424bfb5 100644 --- a/PROGRESS.md +++ b/PROGRESS.md @@ -24,7 +24,7 @@ Complete and ready for review. Full certified results are committed, the moments - Added strict sample/full provenance gates and explicit bridge-scope annotations to the aggregate builder. The real builder-to-JSONL-to-database test covers reform identity, state shape, identities, provenance, and idempotency. - Ingested the full staging file into `data/scorecard.db` offline: 208 exhibits, 52 geographies, no deferred rows, all under canonical ReformRef key `849973669b6526d6`. A second temporary-database ingest returned the same 208-row result. - Extended the additive moments pipeline for multiple counterpart payloads, deterministic primary/alternative variants, null-safe suppression, period-specific annotations, and dated reference counterparts. The regenerated 603-row payload contains 52 KFF `concept_mismatch` rows for 2024 and five truthful `not_computed` 2022 rows with separately dated 2024 PE references. -- Completed the full offline Python suite through `uv run`: 179 successful tests. Ruff lint completed successfully repository-wide, and the CI-scoped `scorecard_db`, `tests`, and `pipeline` trees are fully formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. +- Completed the full offline Python suite through `uv run`: 179 successful tests. Ruff lint completed successfully repository-wide, and all 51 Python files are Ruff-formatted. Campaign idempotency tests now count only the staged run IDs under test, so separately committed campaigns correctly survive scoped re-ingest. - Wrote the required uncommitted `PR_BODY.md`, including the results table, construct annotations, staged/ingested state, exact offline ingest command, and required follow-ups. - The app lint/build remains unrun because this worktree has no `app/node_modules`. Network access is prohibited, so dependencies were not installed; Python and data-pipeline validation is complete. diff --git a/sources/harvest-2026-08-02/tpc/reparse_t26_0009.py b/sources/harvest-2026-08-02/tpc/reparse_t26_0009.py index 2a24246..1f618aa 100644 --- a/sources/harvest-2026-08-02/tpc/reparse_t26_0009.py +++ b/sources/harvest-2026-08-02/tpc/reparse_t26_0009.py @@ -65,7 +65,7 @@ "A4": "Tax Provisions in Title VII of H.R.1", "A5": "Baseline: Law Prior to the 2025 Budget Reconciliation Act", "A6": "Distribution of Federal Tax Change by Expanded Cash Income" - " Percentile, 2026 ¹", + " Percentile, 2026 ¹", } # Tax-change panel header anchors (identical on all five sheets). @@ -104,16 +104,21 @@ # harvest's lowercased "main / sub / sub" header style) COLUMNS = { "C": ( - "share_with_tax_cut", "percent_of_tax_units", False, - "tax units with tax increase or cut 4 / with tax cut /" - " pct of tax units", + "share_with_tax_cut", + "percent_of_tax_units", + False, + "tax units with tax increase or cut 4 / with tax cut / pct of tax units", ), "K": ( - "pct_change_after_tax_income", "percent", False, + "pct_change_after_tax_income", + "percent", + False, "percent change in after-tax income 5", ), "O": ( - "avg_tax_change_usd", "usd_per_tax_unit", True, + "avg_tax_change_usd", + "usd_per_tax_unit", + True, "average federal tax change ($)", ), } @@ -130,18 +135,14 @@ def manifest_entry() -> dict: def check_anchor(ws, coord: str, expected: str) -> None: got = ws[coord].value if got != expected: - raise ValueError( - f"{ws.title}!{coord}: expected {expected!r}, found {got!r}" - ) + raise ValueError(f"{ws.title}!{coord}: expected {expected!r}, found {got!r}") def parse(workbook: Path) -> list[dict]: manifest = manifest_entry() sha = hashlib.sha256(workbook.read_bytes()).hexdigest() if sha != manifest["sha256"]: - raise ValueError( - f"workbook sha256 {sha} != manifested {manifest['sha256']}" - ) + raise ValueError(f"workbook sha256 {sha} != manifested {manifest['sha256']}") wb = openpyxl.load_workbook(workbook, data_only=True) if wb.sheetnames != list(SHEETS): @@ -203,8 +204,7 @@ def parse(workbook: Path) -> list[dict]: "title": manifest["title"], "url": manifest["url"], "date": manifest["date"], - "vintage": - "TPC model estimates T-series (PRELIMINARY)", + "vintage": "TPC model estimates T-series (PRELIMINARY)", "file": manifest["local_path"], "sha256": manifest["sha256"], "sheet": sheet, @@ -255,8 +255,7 @@ def verify(staged_path: Path, workbook: Path) -> int: cell_value = wb[pub["sheet"]][pub["cell"]].value if float(cell_value) != row["value"]: raise ValueError( - f"{pub['sheet']}!{pub['cell']} = {cell_value}," - f" staged {row['value']}" + f"{pub['sheet']}!{pub['cell']} = {cell_value}, staged {row['value']}" ) if row["proposed_unit"] in ("percent", "percent_of_tax_units"): if not -10 <= row["value"] <= 100: @@ -280,8 +279,7 @@ def verify(staged_path: Path, workbook: Path) -> int: } if seen != expected: raise ValueError( - f"coverage mismatch: missing {expected - seen}," - f" extra {seen - expected}" + f"coverage mismatch: missing {expected - seen}, extra {seen - expected}" ) return len(staged) diff --git a/sources/urban-sotsn/adapter.py b/sources/urban-sotsn/adapter.py index c3851d6..0dee079 100644 --- a/sources/urban-sotsn/adapter.py +++ b/sources/urban-sotsn/adapter.py @@ -70,16 +70,20 @@ "fullpart_spm_pov_rate_100_pop_total": ("poverty_rate_fullpart", "total"), "fullpart_spm_pov_rate_100_pop_child": ("poverty_rate_fullpart", "child"), "fullpart_change_spm_pov_rate_100_pop_total": ( - "poverty_rate_relative_change_fullpart", "total", + "poverty_rate_relative_change_fullpart", + "total", ), "fullpart_change_spm_pov_rate_100_pop_child": ( - "poverty_rate_relative_change_fullpart", "child", + "poverty_rate_relative_change_fullpart", + "child", ), "fullpart_change_spm_pov_num_100_pop_total": ( - "poverty_count_change_fullpart", "total", + "poverty_count_change_fullpart", + "total", ), "fullpart_change_spm_pov_num_100_pop_child": ( - "poverty_count_change_fullpart", "child", + "poverty_count_change_fullpart", + "child", ), } @@ -111,15 +115,18 @@ def parse_column(col): program = next((p for p in PROGRAMS if name.startswith(p + "_base_")), None) if program is None: raise ValueError(f"unparsed column: {col}") - rest = name[len(program) + len("_base_"):] + rest = name[len(program) + len("_base_") :] metric_slug = next( - (m for m in ("elig_rate", "elig", "part_rate", "part_gap") - if rest == m or rest.startswith(m + "_")), + ( + m + for m in ("elig_rate", "elig", "part_rate", "part_gap") + if rest == m or rest.startswith(m + "_") + ), None, ) if metric_slug is None: raise ValueError(f"unparsed metric in: {col}") - rest = rest[len(metric_slug):].lstrip("_") + rest = rest[len(metric_slug) :].lstrip("_") variant = None for v in VARIANTS: @@ -180,9 +187,7 @@ def run(): if t["program"] == "spm_poverty" else "2023 average month" ), - "value": ( - None if suppressed else float(val) * t["scale"] - ), + "value": (None if suppressed else float(val) * t["scale"]), "status": "suppressed" if suppressed else "ok", "source_column": col, } From c22885597dda427a504b66591906541f597b1527 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:32:28 -0400 Subject: [PATCH 12/14] Annotate reported-uninsured denominator context from certified-artifact probe The reported Medicaid-at-interview input sums to 24.6M under-65 on the sparse Build P artifact versus roughly 60M survey-reported Medicaid, leaving an 80.9M reported-uninsured denominator against roughly 27M ACS-reported nonelderly uninsured. Named on the joined rows so the 43.15% primary share reads with that context; modeled-uninsured and eligible-not-enrolled rows flagged as the informative counterparts. Co-Authored-By: Claude Fable 5 --- app/public/data/moments.json | 61 +++++++++++++++++++ .../kff-remaining-uninsured/annotations.json | 6 ++ 2 files changed, 67 insertions(+) diff --git a/app/public/data/moments.json b/app/public/data/moments.json index 7c6825f..ed20983 100644 --- a/app/public/data/moments.json +++ b/app/public/data/moments.json @@ -244,6 +244,15 @@ "text": "The flagship brief describes 2022 ACS data under 2023 eligibility levels, before the unwinding was complete. The certified PolicyEngine artifact describes 2024 only, so the 2022 claim is not computed on its own period; the joined row shows the separately dated 2024 counterpart as context.", "basis": "PROVENANCE.md construct difference 5" }, + { + "id": "kff-medicaid-reported-denominator-sanity", + "applies": { + "source": "kff-remaining-uninsured", + "period": "2024" + }, + "text": "Denominator context measured on the certified artifact: the reported-uninsured construct counts 80.9M people under 65 (of 278.9M), versus roughly 27M ACS-reported nonelderly uninsured in 2024. The dominant driver is the artifact's reported Medicaid-at-interview input, which sums to 24.6M people under 65 against roughly 60M survey-reported Medicaid, while employer coverage (146.9M) and Marketplace (21.4M) inputs sit near survey levels; modeled enrollment is separately CMS-calibrated and does not backfill the reported flag. The reported-uninsured share is therefore not a survey-faithful uninsured measure on this artifact; the modeled-uninsured variant and the eligible-and-not-enrolled counts are the informative PolicyEngine rows for this join, pending an artifact-side review of thinned reported-coverage inputs.", + "basis": "Main-session review probe of certified Build P person inputs, 2026-08-18" + }, { "id": "kff-medicaid-rendered-column-caution", "applies": { @@ -16080,6 +16089,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16192,6 +16202,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16304,6 +16315,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16416,6 +16428,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16528,6 +16541,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16640,6 +16654,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16752,6 +16767,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16864,6 +16880,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -16976,6 +16993,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17088,6 +17106,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17200,6 +17219,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17312,6 +17332,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17424,6 +17445,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17536,6 +17558,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17648,6 +17671,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17760,6 +17784,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17872,6 +17897,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -17984,6 +18010,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18096,6 +18123,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18208,6 +18236,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18320,6 +18349,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18432,6 +18462,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18544,6 +18575,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18656,6 +18688,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18768,6 +18801,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18880,6 +18914,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -18992,6 +19027,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19104,6 +19140,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19216,6 +19253,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19328,6 +19366,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19440,6 +19479,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19552,6 +19592,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19664,6 +19705,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19776,6 +19818,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -19888,6 +19931,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20000,6 +20044,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20112,6 +20157,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20224,6 +20270,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20336,6 +20383,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20448,6 +20496,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20560,6 +20609,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20672,6 +20722,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20784,6 +20835,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -20896,6 +20948,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21008,6 +21061,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21120,6 +21174,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21232,6 +21287,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21344,6 +21400,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21456,6 +21513,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21568,6 +21626,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21680,6 +21739,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", @@ -21792,6 +21852,7 @@ "kff-medicaid-coverage-measurement", "kff-medicaid-data-and-imputations", "kff-medicaid-rules-vintage", + "kff-medicaid-reported-denominator-sanity", "kff-medicaid-rendered-column-caution" ], "engine_version": "1.764.6", diff --git a/sources/kff-remaining-uninsured/annotations.json b/sources/kff-remaining-uninsured/annotations.json index f8acea6..0d6839a 100644 --- a/sources/kff-remaining-uninsured/annotations.json +++ b/sources/kff-remaining-uninsured/annotations.json @@ -35,6 +35,12 @@ "text": "The flagship brief describes 2022 ACS data under 2023 eligibility levels, before the unwinding was complete. The certified PolicyEngine artifact describes 2024 only, so the 2022 claim is not computed on its own period; the joined row shows the separately dated 2024 counterpart as context.", "basis": "PROVENANCE.md construct difference 5" }, + { + "id": "kff-medicaid-reported-denominator-sanity", + "applies": {"source": "kff-remaining-uninsured", "period": "2024"}, + "text": "Denominator context measured on the certified artifact: the reported-uninsured construct counts 80.9M people under 65 (of 278.9M), versus roughly 27M ACS-reported nonelderly uninsured in 2024. The dominant driver is the artifact's reported Medicaid-at-interview input, which sums to 24.6M people under 65 against roughly 60M survey-reported Medicaid, while employer coverage (146.9M) and Marketplace (21.4M) inputs sit near survey levels; modeled enrollment is separately CMS-calibrated and does not backfill the reported flag. The reported-uninsured share is therefore not a survey-faithful uninsured measure on this artifact; the modeled-uninsured variant and the eligible-and-not-enrolled counts are the informative PolicyEngine rows for this join, pending an artifact-side review of thinned reported-coverage inputs.", + "basis": "Main-session review probe of certified Build P person inputs, 2026-08-18" + }, { "id": "kff-medicaid-rendered-column-caution", "applies": {"source": "kff-remaining-uninsured", "period": "2024"}, From 8d18357603e9125ecb44b4a10384a1afc8a239c9 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 16:38:10 -0400 Subject: [PATCH 13/14] Make KFF Medicaid builder stdlib-only for the pandas-free CI suite CI runs pytest without pandas; the builder now reads extracts with csv/gzip and computes masks as boolean lists, matching the other pipeline modules. Rerunning it on the full-file extracts reproduces all 208 committed staged rows (worst relative value difference 1.7e-9, every non-numeric field identical). Co-Authored-By: Claude Fable 5 --- pipeline/build_kff_medicaid_outputs.py | 194 +++++++++++++++++-------- tests/test_kff_medicaid_outputs.py | 50 ++++--- 2 files changed, 166 insertions(+), 78 deletions(-) diff --git a/pipeline/build_kff_medicaid_outputs.py b/pipeline/build_kff_medicaid_outputs.py index 9a502a8..6d57fa8 100644 --- a/pipeline/build_kff_medicaid_outputs.py +++ b/pipeline/build_kff_medicaid_outputs.py @@ -1,15 +1,20 @@ -"""Aggregate Medicaid baseline/reform extracts into scorecard staging files.""" +"""Aggregate Medicaid baseline/reform extracts into scorecard staging files. + +Standard-library only, like the other pipeline modules: CI runs the test +suite without pandas, so extracts are read as typed columns and masks are +plain boolean lists. +""" from __future__ import annotations import argparse +import csv +import gzip import json +import math from datetime import datetime, timezone from pathlib import Path -import numpy as np -import pandas as pd - YEAR = 2024 RUN_ID = "campaign-2026-08-18-kff-medicaid" DENOMINATOR = "medicaid_slcsp_state_denominator" @@ -20,21 +25,83 @@ "framework": "policyengine_us_inputs", "reform": {"takes_up_medicaid_if_eligible": True}, } +BOOL_COLUMNS = ( + "is_medicaid_eligible", + "medicaid_enrolled", + "reported_uninsured", + "modeled_uninsured", +) +FLOAT_COLUMNS = ("age", "person_weight", "medicaid", DENOMINATOR) def load_meta(path: Path) -> dict: return json.loads(Path(f"{path}.meta.json").read_text()) -def weighted_count(frame: pd.DataFrame, mask: np.ndarray) -> float: - return float(frame.loc[mask, "person_weight"].sum()) +def _parse_bool(text: str) -> bool: + if text in ("True", "true", "1"): + return True + if text in ("False", "false", "0"): + return False + raise ValueError(f"not a boolean: {text!r}") + + +def _parse_float(text: str) -> float: + return math.nan if text == "" else float(text) + + +def load_extract(path: Path) -> dict[str, list]: + opener = gzip.open if path.suffix == ".gz" else open + with opener(path, "rt", newline="") as handle: + reader = csv.DictReader(handle) + fieldnames = reader.fieldnames or [] + columns: dict[str, list] = {name: [] for name in fieldnames} + for row in reader: + for name in fieldnames: + columns[name].append(row[name]) + for name in BOOL_COLUMNS: + columns[name] = [_parse_bool(text) for text in columns[name]] + for name in FLOAT_COLUMNS: + columns[name] = [_parse_float(text) for text in columns[name]] + return columns + + +def _close(actual: float, expected: float) -> bool: + if math.isnan(actual) and math.isnan(expected): + return True + return abs(actual - expected) <= 1e-8 + 1e-5 * abs(expected) + + +def _and(*masks: list[bool]) -> list[bool]: + return [all(values) for values in zip(*masks)] + + +def _not(mask: list[bool]) -> list[bool]: + return [not value for value in mask] -def weighted_dollars(frame: pd.DataFrame, mask: np.ndarray | None = None) -> float: +def weighted_count(columns: dict[str, list], mask: list[bool]) -> float: + return float( + sum( + weight + for weight, selected in zip(columns["person_weight"], mask) + if selected + ) + ) + + +def weighted_dollars(columns: dict[str, list], mask: list[bool] | None = None) -> float: if mask is None: - mask = np.ones(len(frame), dtype=bool) - selected = frame.loc[mask] - return float((selected["person_weight"] * selected["medicaid"]).sum()) + mask = [True] * len(columns["person_weight"]) + return float( + sum( + weight * dollars + for weight, dollars, selected in zip( + columns["person_weight"], columns["medicaid"], mask + ) + if selected + ) + ) def aggregate( @@ -45,39 +112,38 @@ def aggregate( campaign_path: Path, full: bool, ) -> dict: - baseline = pd.read_csv(baseline_path) - reform = pd.read_csv(reform_path) + baseline = load_extract(baseline_path) + reform = load_extract(reform_path) baseline_meta = load_meta(baseline_path) reform_meta = load_meta(reform_path) - if len(baseline) != len(reform): + people = len(baseline["person_weight"]) + if people != len(reform["person_weight"]): raise ValueError("baseline and reform row counts differ") - if not np.array_equal(baseline["person_id"], reform["person_id"]): + if baseline["person_id"] != reform["person_id"]: raise ValueError("baseline and reform people differ") - if not np.array_equal(baseline["state"], reform["state"]): + if baseline["state"] != reform["state"]: raise ValueError("baseline and reform states differ") - if not np.allclose(baseline["person_weight"], reform["person_weight"]): + if not all(map(_close, baseline["person_weight"], reform["person_weight"])): raise ValueError("baseline and reform weights differ") - if not np.array_equal( - baseline["is_medicaid_eligible"], reform["is_medicaid_eligible"] - ): + if baseline["is_medicaid_eligible"] != reform["is_medicaid_eligible"]: raise ValueError("eligibility changed under the take-up input") - if not np.array_equal(baseline["reported_uninsured"], reform["reported_uninsured"]): + if baseline["reported_uninsured"] != reform["reported_uninsured"]: raise ValueError("reported coverage changed under the take-up input") - if not np.allclose(baseline[DENOMINATOR], reform[DENOMINATOR], equal_nan=True): + if not all(map(_close, baseline[DENOMINATOR], reform[DENOMINATOR])): raise ValueError("reform did not retain the baseline Medicaid denominator") - eligible = baseline["is_medicaid_eligible"].to_numpy(dtype=bool) - enrolled = baseline["medicaid_enrolled"].to_numpy(dtype=bool) - reform_enrolled = reform["medicaid_enrolled"].to_numpy(dtype=bool) - if not np.array_equal(reform_enrolled, eligible): + eligible = baseline["is_medicaid_eligible"] + enrolled = baseline["medicaid_enrolled"] + reform_enrolled = reform["medicaid_enrolled"] + if reform_enrolled != eligible: raise AssertionError("reform enrollment is not identical to eligibility") - if np.any(enrolled & ~reform_enrolled): + if any(_and(enrolled, _not(reform_enrolled))): raise AssertionError("the reform removes baseline enrollees") - marginal = reform_enrolled & ~enrolled - reported = baseline["reported_uninsured"].to_numpy(dtype=bool) - modeled = baseline["modeled_uninsured"].to_numpy(dtype=bool) - under65 = baseline["age"].to_numpy() < 65 - child = baseline["age"].to_numpy() < 19 - adult = under65 & ~child + marginal = _and(reform_enrolled, _not(enrolled)) + reported = baseline["reported_uninsured"] + modeled = baseline["modeled_uninsured"] + under65 = [age < 65 for age in baseline["age"]] + child = [age < 19 for age in baseline["age"]] + adult = _and(under65, _not(child)) states = sorted(set(baseline["state"])) geographies = ["US", *states] expansion_states = set(states) - NON_EXPANSION_2024 @@ -158,13 +224,13 @@ def aggregate( ) for geography in geographies: geo_mask = ( - np.ones(len(baseline), dtype=bool) + [True] * people if geography == "US" - else baseline["state"].to_numpy() == geography + else [state == geography for state in baseline["state"]] ) for coverage_variant, variant, uninsured in variants: - universe = geo_mask & under65 & uninsured - numerator_mask = universe & eligible + universe = _and(geo_mask, under65, uninsured) + numerator_mask = _and(universe, eligible) denominator = weighted_count(baseline, universe) numerator = weighted_count(baseline, numerator_mask) share = 100 * numerator / denominator if denominator else None @@ -210,12 +276,14 @@ def aggregate( subgroup_masks = { "adults": adult, "children": child, - "expansion_states": under65 - & baseline["state"].isin(expansion_states).to_numpy(), + "expansion_states": _and( + under65, + [state in expansion_states for state in baseline["state"]], + ), } for coverage_variant, variant, uninsured in variants: for subgroup, subgroup_mask in subgroup_masks.items(): - mask = subgroup_mask & uninsured & eligible + mask = _and(subgroup_mask, uninsured, eligible) value = weighted_count(baseline, mask) counterpart_rows.append( { @@ -276,19 +344,21 @@ def aggregate( ) for geography in geographies: geo_mask = ( - np.ones(len(baseline), dtype=bool) + [True] * people if geography == "US" - else baseline["state"].to_numpy() == geography + else [state == geography for state in baseline["state"]] ) - base_eligible = weighted_count(baseline, geo_mask & eligible) - base_enrolled = weighted_count(baseline, geo_mask & enrolled) - reform_enrollment = weighted_count(baseline, geo_mask & reform_enrolled) - delta_enrollment = weighted_count(baseline, geo_mask & marginal) + base_eligible = weighted_count(baseline, _and(geo_mask, eligible)) + base_enrolled = weighted_count(baseline, _and(geo_mask, enrolled)) + reform_enrollment = weighted_count(baseline, _and(geo_mask, reform_enrolled)) + delta_enrollment = weighted_count(baseline, _and(geo_mask, marginal)) baseline_spending = weighted_dollars(baseline, geo_mask) reform_spending = weighted_dollars(reform, geo_mask) delta_spending = reform_spending - baseline_spending - bridge_reported = weighted_count(baseline, geo_mask & marginal & reported) - bridge_other = weighted_count(baseline, geo_mask & marginal & ~reported) + bridge_reported = weighted_count(baseline, _and(geo_mask, marginal, reported)) + bridge_other = weighted_count( + baseline, _and(geo_mask, marginal, _not(reported)) + ) if abs(delta_enrollment - bridge_reported - bridge_other) > 1e-6 * max( delta_enrollment, 1 ): @@ -415,12 +485,14 @@ def aggregate( ) diagnostics_dir.mkdir(parents=True, exist_ok=True) - pd.DataFrame(moment_diagnostics).to_csv( - diagnostics_dir / "kff_medicaid_moments_2024.csv", index=False - ) - pd.DataFrame(takeup_rows).to_csv( - diagnostics_dir / "kff_medicaid_takeup_2024.csv", index=False - ) + for filename, rows in ( + ("kff_medicaid_moments_2024.csv", moment_diagnostics), + ("kff_medicaid_takeup_2024.csv", takeup_rows), + ): + with (diagnostics_dir / filename).open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) counterpart_path.parent.mkdir(parents=True, exist_ok=True) counterpart_payload = { "provenance": { @@ -440,9 +512,11 @@ def aggregate( campaign_path.write_text( "".join(json.dumps(row, sort_keys=True) + "\n" for row in campaign_rows) ) + baseline_dollars = weighted_dollars(baseline) + reform_dollars = weighted_dollars(reform) summary = { "sample": baseline_sample, - "people": len(baseline), + "people": people, "engine_version": engine_version, "data_bundle": data_bundle, "bundle_id": bundle_id, @@ -450,11 +524,13 @@ def aggregate( "enrolled": national_enrolled, "delta_enrollment": national_delta, "identity_gap": identity_gap, - "baseline_medicaid_usd": weighted_dollars(baseline), - "reform_medicaid_usd": weighted_dollars(reform), - "delta_medicaid_usd": weighted_dollars(reform) - weighted_dollars(baseline), - "bridge_reported_uninsured": weighted_count(baseline, marginal & reported), - "bridge_other_coverage": weighted_count(baseline, marginal & ~reported), + "baseline_medicaid_usd": baseline_dollars, + "reform_medicaid_usd": reform_dollars, + "delta_medicaid_usd": reform_dollars - baseline_dollars, + "bridge_reported_uninsured": weighted_count(baseline, _and(marginal, reported)), + "bridge_other_coverage": weighted_count( + baseline, _and(marginal, _not(reported)) + ), "counterpart_rows": len(counterpart_rows), "campaign_rows": len(campaign_rows), } diff --git a/tests/test_kff_medicaid_outputs.py b/tests/test_kff_medicaid_outputs.py index 2263461..b86dc34 100644 --- a/tests/test_kff_medicaid_outputs.py +++ b/tests/test_kff_medicaid_outputs.py @@ -1,9 +1,9 @@ """Aggregate-output coverage for the KFF Medicaid campaign lane.""" +import csv import json import sqlite3 -import pandas as pd import pytest from pipeline.build_kff_medicaid_outputs import REFORM_REF, RUN_ID, aggregate @@ -16,28 +16,37 @@ BUNDLE_ID = "us-5.0.2" +def _write_csv(path, columns): + rows = [ + dict(zip(columns, values, strict=True)) + for values in zip(*columns.values(), strict=True) + ] + with path.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(columns)) + writer.writeheader() + writer.writerows(rows) + + def _write_extracts(tmp_path, *, sample: bool = True): - baseline = pd.DataFrame( - { - "person_id": [1, 2, 3, 4, 5, 6], - "state": ["CA", "CA", "CA", "TX", "TX", "TX"], - "age": [30, 10, 70, 40, 17, 25], - "person_weight": [2.0, 3.0, 5.0, 7.0, 11.0, 13.0], - "is_medicaid_eligible": [True, True, True, True, False, True], - "medicaid_enrolled": [True, False, False, False, False, True], - "medicaid": [1_000.0, 0.0, 0.0, 0.0, 0.0, 2_000.0], - "medicaid_slcsp_state_denominator": [10.0] * 3 + [20.0] * 3, - "reported_uninsured": [False, True, False, False, True, True], - "modeled_uninsured": [False, True, False, False, True, False], - } - ) - reform = baseline.copy() + baseline = { + "person_id": [1, 2, 3, 4, 5, 6], + "state": ["CA", "CA", "CA", "TX", "TX", "TX"], + "age": [30, 10, 70, 40, 17, 25], + "person_weight": [2.0, 3.0, 5.0, 7.0, 11.0, 13.0], + "is_medicaid_eligible": [True, True, True, True, False, True], + "medicaid_enrolled": [True, False, False, False, False, True], + "medicaid": [1_000.0, 0.0, 0.0, 0.0, 0.0, 2_000.0], + "medicaid_slcsp_state_denominator": [10.0] * 3 + [20.0] * 3, + "reported_uninsured": [False, True, False, False, True, True], + "modeled_uninsured": [False, True, False, False, True, False], + } + reform = dict(baseline) reform["medicaid_enrolled"] = [True, True, True, True, False, True] reform["medicaid"] = [1_100.0, 1_200.0, 1_300.0, 1_400.0, 0.0, 2_100.0] baseline_path = tmp_path / "baseline.csv" reform_path = tmp_path / "reform.csv" - baseline.to_csv(baseline_path, index=False) - reform.to_csv(reform_path, index=False) + _write_csv(baseline_path, baseline) + _write_csv(reform_path, reform) bundle = { "model_version": ENGINE, @@ -146,7 +155,10 @@ def test_builder_emits_and_ingests_complete_reform_shape(tmp_path): "kff_medicaid_moments_2024.csv", "kff_medicaid_takeup_2024.csv", ): - assert pd.read_csv(diagnostics / filename)["sample"].all() + with (diagnostics / filename).open(newline="") as handle: + diagnostic_rows = list(csv.DictReader(handle)) + assert diagnostic_rows + assert all(row["sample"] == "True" for row in diagnostic_rows) db_path = tmp_path / "scorecard.db" first = ingest(db_path, staged_dir=staged.parent) From bf8c9cfd63282570664beb600b5c1f2f840dd41a Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 18 Aug 2026 22:20:20 -0400 Subject: [PATCH 14/14] Fail closed on non-finite inputs and hard-pin full-file provenance Resolves the three blockers from the merge audit: - Blank or non-finite values in age/person_weight/medicaid now raise at parse (the denominator column keeps NaN, used only in the paired retention check); identity/anchor/bridge gates use NaN-proof inverted comparisons; JSON writes set allow_nan=False. - The full path requires the exact certified provenance triple via a shared require_certified_provenance (engine 1.764.6 / us-5.0.2 / Build P id) instead of trusting any self-consistent sidecar; the extractor calls the same pin. - The extractor validates provenance before any computation or write, and publishes CSV + metadata sidecar together via temp-and-rename, so a rejected rerun can no longer leave a fresh CSV beside a stale certified sidecar. New tests cover blank-cell rejection, denominator NaN tolerance, the anchor-passing full path, and rejection of a wrong-but-self-consistent triple (179 -> 183). Committed artifacts are unaffected: they are all finite, and under the previous lenient parser any blank in a strict column would have propagated NaN into them. Co-Authored-By: Claude Fable 5 --- pipeline/build_kff_medicaid_outputs.py | 65 ++++++++++++++++--- pipeline/compute_kff_medicaid_extract.py | 52 ++++++++++----- tests/test_kff_medicaid_outputs.py | 82 +++++++++++++++++++++--- 3 files changed, 165 insertions(+), 34 deletions(-) diff --git a/pipeline/build_kff_medicaid_outputs.py b/pipeline/build_kff_medicaid_outputs.py index 6d57fa8..b7ce9dd 100644 --- a/pipeline/build_kff_medicaid_outputs.py +++ b/pipeline/build_kff_medicaid_outputs.py @@ -31,13 +31,40 @@ "reported_uninsured", "modeled_uninsured", ) -FLOAT_COLUMNS = ("age", "person_weight", "medicaid", DENOMINATOR) +FINITE_FLOAT_COLUMNS = ("age", "person_weight", "medicaid") +NAN_ALLOWED_FLOAT_COLUMNS = (DENOMINATOR,) +CERTIFIED_PROVENANCE = { + "engine_version": "1.764.6", + "bundle_id": "us-5.0.2", + "data_bundle": "populace-us-2024-buildp-sparse-rmloss100-cae8640-20260728T011454Z", +} def load_meta(path: Path) -> dict: return json.loads(Path(f"{path}.meta.json").read_text()) +def require_certified_provenance( + engine_version: str, bundle_id: str, data_bundle: str +) -> None: + """Hard-pin full-file provenance to the certified triple. + + Self-reported sidecar provenance is not trusted on the full path: a + wrong-but-self-consistent triple must fail. Update CERTIFIED_PROVENANCE + deliberately when re-running on a newer certified bundle. + """ + actual = { + "engine_version": engine_version, + "bundle_id": bundle_id, + "data_bundle": data_bundle, + } + if actual != CERTIFIED_PROVENANCE: + raise ValueError( + f"full-file provenance {actual} does not match the certified pin " + f"{CERTIFIED_PROVENANCE}" + ) + + def _parse_bool(text: str) -> bool: if text in ("True", "true", "1"): return True @@ -46,10 +73,19 @@ def _parse_bool(text: str) -> bool: raise ValueError(f"not a boolean: {text!r}") -def _parse_float(text: str) -> float: +def _parse_lenient_float(text: str) -> float: return math.nan if text == "" else float(text) +def _parse_finite_float(text: str, column: str) -> float: + if text == "": + raise ValueError(f"blank value in required numeric column {column}") + value = float(text) + if not math.isfinite(value): + raise ValueError(f"non-finite value {text!r} in column {column}") + return value + + def load_extract(path: Path) -> dict[str, list]: opener = gzip.open if path.suffix == ".gz" else open with opener(path, "rt", newline="") as handle: @@ -61,8 +97,10 @@ def load_extract(path: Path) -> dict[str, list]: columns[name].append(row[name]) for name in BOOL_COLUMNS: columns[name] = [_parse_bool(text) for text in columns[name]] - for name in FLOAT_COLUMNS: - columns[name] = [_parse_float(text) for text in columns[name]] + for name in FINITE_FLOAT_COLUMNS: + columns[name] = [_parse_finite_float(text, name) for text in columns[name]] + for name in NAN_ALLOWED_FLOAT_COLUMNS: + columns[name] = [_parse_lenient_float(text) for text in columns[name]] return columns @@ -184,12 +222,14 @@ def aggregate( raise ValueError("--full cannot be used with validation-sample extracts") if not full and not baseline_sample: raise ValueError("full-file extracts require --full and its anchor gates") + if full: + require_certified_provenance(engine_version, bundle_id, data_bundle) national_eligible = weighted_count(baseline, eligible) national_enrolled = weighted_count(baseline, enrolled) national_delta = weighted_count(baseline, marginal) identity_gap = national_delta - (national_eligible - national_enrolled) - if abs(identity_gap) > 1e-6 * max(national_delta, 1): + if not abs(identity_gap) <= 1e-6 * max(national_delta, 1): raise AssertionError(f"enrollment identity gap {identity_gap}") if full: anchors = { @@ -198,7 +238,7 @@ def aggregate( } for name, (actual, expected) in anchors.items(): relative_error = abs(actual / expected - 1) - if relative_error > 0.02: + if not relative_error <= 0.02: raise SystemExit( f"STOP: {name} anchor {actual:,.0f} differs from " f"{expected:,.0f} by {relative_error:.2%}" @@ -359,7 +399,7 @@ def aggregate( bridge_other = weighted_count( baseline, _and(geo_mask, marginal, _not(reported)) ) - if abs(delta_enrollment - bridge_reported - bridge_other) > 1e-6 * max( + if not abs(delta_enrollment - bridge_reported - bridge_other) <= 1e-6 * max( delta_enrollment, 1 ): raise AssertionError(f"bridge identity differs in {geography}") @@ -507,10 +547,15 @@ def aggregate( }, "rows": counterpart_rows, } - counterpart_path.write_text(json.dumps(counterpart_payload, indent=1) + "\n") + counterpart_path.write_text( + json.dumps(counterpart_payload, indent=1, allow_nan=False) + "\n" + ) campaign_path.parent.mkdir(parents=True, exist_ok=True) campaign_path.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in campaign_rows) + "".join( + json.dumps(row, sort_keys=True, allow_nan=False) + "\n" + for row in campaign_rows + ) ) baseline_dollars = weighted_dollars(baseline) reform_dollars = weighted_dollars(reform) @@ -534,7 +579,7 @@ def aggregate( "counterpart_rows": len(counterpart_rows), "campaign_rows": len(campaign_rows), } - print(json.dumps(summary, indent=2)) + print(json.dumps(summary, indent=2, allow_nan=False)) return summary diff --git a/pipeline/compute_kff_medicaid_extract.py b/pipeline/compute_kff_medicaid_extract.py index 642ac9a..11fd9d1 100644 --- a/pipeline/compute_kff_medicaid_extract.py +++ b/pipeline/compute_kff_medicaid_extract.py @@ -12,6 +12,7 @@ import argparse import gc import json +import os import sys from datetime import datetime, timezone from importlib.metadata import version @@ -20,6 +21,11 @@ import numpy as np import pandas as pd +try: + from pipeline.build_kff_medicaid_outputs import require_certified_provenance +except ImportError: # executed as a script from inside pipeline/ + from build_kff_medicaid_outputs import require_certified_provenance + YEAR = 2024 TAKEUP = "takes_up_medicaid_if_eligible" DENOMINATOR = "medicaid_slcsp_state_denominator" @@ -179,6 +185,25 @@ def run( allow_unmanaged=True, ) bundle = serializable(dict(sim.policyengine_bundle)) + engine_version = getattr(policyengine_us, "__version__", None) or version( + "policyengine-us" + ) + # Validate provenance BEFORE any computation or output write, so a + # rejected rerun can never replace an existing certified CSV. + if bundle.get("model_version") != engine_version: + raise AssertionError( + f"engine {engine_version} != bundle model {bundle.get('model_version')}" + ) + if dataset is None: + if bundle.get("default_dataset_uri") != EXPECTED_DATASET_URI: + raise AssertionError("managed dataset URI differs from certified pin") + if bundle.get("certified_data_build_id") != EXPECTED_BUILD: + raise AssertionError("managed data build differs from certified pin") + require_certified_provenance( + engine_version, + bundle.get("bundle_id"), + bundle.get("certified_data_build_id"), + ) released_dataset_bytes = release_loaded_dataset_frames(sim) log( f"managed simulation ready; released {released_dataset_bytes / 1e9:.2f} GB " @@ -348,21 +373,8 @@ def run( } ) output.parent.mkdir(parents=True, exist_ok=True) - frame.to_csv(output, index=False, compression="gzip") - log(f"wrote {len(frame)} person rows to {output}") - - engine_version = getattr(policyengine_us, "__version__", None) or version( - "policyengine-us" - ) - if bundle.get("model_version") != engine_version: - raise AssertionError( - f"engine {engine_version} != bundle model {bundle.get('model_version')}" - ) - if dataset is None: - if bundle.get("default_dataset_uri") != EXPECTED_DATASET_URI: - raise AssertionError("managed dataset URI differs from certified pin") - if bundle.get("certified_data_build_id") != EXPECTED_BUILD: - raise AssertionError("managed data build differs from certified pin") + csv_tmp = output.with_name(output.name + ".tmp") + frame.to_csv(csv_tmp, index=False, compression="gzip") calculated = ( "person_id", @@ -423,7 +435,15 @@ def run( ), "variables": {name: variable_metadata(sim, name) for name in calculated}, } - Path(f"{output}.meta.json").write_text(json.dumps(metadata, indent=2) + "\n") + # Publish the CSV and its metadata sidecar together at the end, via + # temp-and-rename, so no earlier failure can leave a fresh CSV beside a + # stale certified sidecar. (A hard crash between the two renames is the + # remaining narrow window.) + meta_tmp = Path(f"{output}.meta.json.tmp") + meta_tmp.write_text(json.dumps(metadata, indent=2) + "\n") + os.replace(csv_tmp, output) + os.replace(meta_tmp, Path(f"{output}.meta.json")) + log(f"wrote {len(frame)} person rows to {output}") print( json.dumps( {key: metadata[key] for key in metadata if key != "variables"}, indent=2 diff --git a/tests/test_kff_medicaid_outputs.py b/tests/test_kff_medicaid_outputs.py index b86dc34..40fc1bd 100644 --- a/tests/test_kff_medicaid_outputs.py +++ b/tests/test_kff_medicaid_outputs.py @@ -27,12 +27,20 @@ def _write_csv(path, columns): writer.writerows(rows) -def _write_extracts(tmp_path, *, sample: bool = True): +def _write_extracts( + tmp_path, + *, + sample: bool = True, + weights: list | None = None, + engine: str = ENGINE, + data_bundle: str = DATA_BUNDLE, + bundle_id: str = BUNDLE_ID, +): baseline = { "person_id": [1, 2, 3, 4, 5, 6], "state": ["CA", "CA", "CA", "TX", "TX", "TX"], "age": [30, 10, 70, 40, 17, 25], - "person_weight": [2.0, 3.0, 5.0, 7.0, 11.0, 13.0], + "person_weight": weights or [2.0, 3.0, 5.0, 7.0, 11.0, 13.0], "is_medicaid_eligible": [True, True, True, True, False, True], "medicaid_enrolled": [True, False, False, False, False, True], "medicaid": [1_000.0, 0.0, 0.0, 0.0, 0.0, 2_000.0], @@ -49,15 +57,15 @@ def _write_extracts(tmp_path, *, sample: bool = True): _write_csv(reform_path, reform) bundle = { - "model_version": ENGINE, - "certified_data_build_id": DATA_BUNDLE, - "bundle_id": BUNDLE_ID, + "model_version": engine, + "certified_data_build_id": data_bundle, + "bundle_id": bundle_id, } common = { "sample": sample, - "engine_version": ENGINE, - "data_bundle": DATA_BUNDLE, - "bundle_id": BUNDLE_ID, + "engine_version": engine, + "data_bundle": data_bundle, + "bundle_id": bundle_id, "policyengine_bundle": bundle, "coverage_inputs": ["has_esi"], "medicare_handling": "medicare_enrolled modeled proxy", @@ -199,3 +207,61 @@ def test_builder_requires_sample_full_provenance_agreement( baseline, reform = _write_extracts(tmp_path, sample=sample) with pytest.raises(ValueError, match=message): _aggregate(tmp_path, baseline, reform, full=full) + + +def _blank_cell(path, column, row_index=1): + lines = path.read_text().splitlines() + header = lines[0].split(",") + cells = lines[row_index].split(",") + cells[header.index(column)] = "" + lines[row_index] = ",".join(cells) + path.write_text("\n".join(lines) + "\n") + + +def test_blank_required_numeric_cell_is_rejected(tmp_path): + baseline, reform = _write_extracts(tmp_path) + _blank_cell(baseline, "person_weight") + with pytest.raises(ValueError, match="person_weight"): + _aggregate(tmp_path, baseline, reform) + + +def test_blank_denominator_cell_is_allowed(tmp_path): + baseline, reform = _write_extracts(tmp_path) + for path in (baseline, reform): + _blank_cell(path, "medicaid_slcsp_state_denominator") + summary, staged, _, _ = _aggregate(tmp_path, baseline, reform) + assert summary["campaign_rows"] == 12 + for line in staged.read_text().splitlines(): + json.loads(line) + + +# Anchor-passing full-file weights: eligible sums to 77.325M, enrolled to +# 72.300M, both within the 2% anchor gates. +FULL_WEIGHTS = [ + 40_000_000.0, + 2_000_000.0, + 1_500_000.0, + 1_525_000.0, + 1_000_000.0, + 32_300_000.0, +] + + +def test_full_path_passes_anchors_with_certified_provenance(tmp_path): + baseline, reform = _write_extracts(tmp_path, sample=False, weights=FULL_WEIGHTS) + summary, staged, _, _ = _aggregate(tmp_path, baseline, reform, full=True) + assert summary["sample"] is False + assert summary["eligible"] == pytest.approx(77_325_000.0) + assert summary["enrolled"] == pytest.approx(72_300_000.0) + assert summary["delta_enrollment"] == pytest.approx(5_025_000.0) + rows = [json.loads(line) for line in staged.read_text().splitlines()] + assert len(rows) == 12 + assert all(row["sample"] is False for row in rows) + + +def test_full_path_rejects_self_consistent_wrong_provenance(tmp_path): + baseline, reform = _write_extracts( + tmp_path, sample=False, weights=FULL_WEIGHTS, engine="1.999.0" + ) + with pytest.raises(ValueError, match="certified pin"): + _aggregate(tmp_path, baseline, reform, full=True)