Add state income tax calibration targets from Census STC

.beads/issues.jsonl

@@ -0,0 +1,4 @@
+{"id":"policyengine-us-data-apq","title":"Add age and demographics to pre-tax contribution QRF imputation","description":"The QRF in puf.py that imputes pre_tax_contributions from CPS to PUF uses only employment_income as a predictor. Age, filing status, and number of dependents are strong predictors of 401(k) participation and contribution rates. Adding these should improve the distributional accuracy.","status":"closed","priority":2,"issue_type":"feature","created_at":"2026-01-31T08:01:22.72749-05:00","updated_at":"2026-01-31T08:08:02.675063-05:00","closed_at":"2026-01-31T08:08:02.675063-05:00"}
+{"id":"policyengine-us-data-jhh","title":"Parameterize retirement contribution limits by year","description":"The contribution waterfall in cps.py hardcodes 2022 limits ($20,500 401k, $6,500 catch-up, $6,000 IRA, $1,000 IRA catch-up). These should be pulled from PolicyEngine parameters or a year-indexed lookup so the dataset builds correctly for any year.","status":"closed","priority":2,"issue_type":"bug","created_at":"2026-01-31T08:01:18.941246-05:00","updated_at":"2026-01-31T08:08:02.614396-05:00","closed_at":"2026-01-31T08:08:02.614396-05:00"}
+{"id":"policyengine-us-data-mnw","title":"Use SS_SC source code for Social Security retirement/disability split","description":"Currently cps.py uses a hard age-62 cutoff to split SS into retirement vs disability. The CPS ASEC has SS_SC (Social Security source codes) that distinguish retirement, disability, and survivor benefits. Use these codes instead of the age heuristic.","status":"closed","priority":2,"issue_type":"bug","created_at":"2026-01-31T08:01:21.01419-05:00","updated_at":"2026-01-31T08:08:02.644611-05:00","closed_at":"2026-01-31T08:08:02.644611-05:00"}
+{"id":"policyengine-us-data-x4q","title":"Calibrate taxable pension fraction from SOI data","description":"imputation_parameters.yaml sets taxable_pension_fraction to 1.0 with the comment 'no SOI data, so arbitrary assumption.' But the SOI targets CSV includes both total_pension_income and taxable_pension_income by AGI bracket. Use the ratio of these to set a data-driven fraction instead of assuming 100% taxable.","status":"closed","priority":2,"issue_type":"bug","created_at":"2026-01-31T08:01:24.590331-05:00","updated_at":"2026-01-31T08:08:02.70425-05:00","closed_at":"2026-01-31T08:08:02.70425-05:00"}

Makefile

@@ -61,6 +61,7 @@ database:
 	python policyengine_us_data/db/etl_age.py
 	python policyengine_us_data/db/etl_medicaid.py
 	python policyengine_us_data/db/etl_snap.py
+	python policyengine_us_data/db/etl_state_income_tax.py
 	python policyengine_us_data/db/etl_irs_soi.py
 	python policyengine_us_data/db/validate_database.py
 

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - Add state income tax calibration targets from Census STC FY2023 data

policyengine_us_data/datasets/cps/local_area_calibration/fit_calibration_weights.py

@@ -105,10 +105,11 @@
 targets_df, X_sparse, household_id_mapping = builder.build_matrix(
     sim,
     target_filter={
-        "stratum_group_ids": [4],
+        "stratum_group_ids": [4, 7],  # 4=SNAP households, 7=state income tax
         "variables": [
             "health_insurance_premiums_without_medicare_part_b",
             "snap",
+            "state_income_tax",  # Census STC state income tax collections
         ],
     },
 )

policyengine_us_data/db/DATABASE_GUIDE.md

@@ -30,8 +30,9 @@ make promote-database   # Copy DB + raw inputs to HuggingFace clone
 | 4 | `etl_age.py` | Census ACS 1-year | Age distribution: 18 bins x 488 geographies |
 | 5 | `etl_medicaid.py` | Census ACS + CMS | Medicaid enrollment (admin state-level, survey district-level) |
 | 6 | `etl_snap.py` | USDA FNS + Census ACS | SNAP participation (admin state-level, survey district-level) |
-| 7 | `etl_irs_soi.py` | IRS | Tax variables, EITC by child count, AGI brackets, conditional strata |
-| 8 | `validate_database.py` | No | Checks all target variables exist in policyengine-us |
+| 7 | `etl_state_income_tax.py` | No | State income tax collections (Census STC FY2023, hardcoded) |
+| 8 | `etl_irs_soi.py` | IRS | Tax variables, EITC by child count, AGI brackets, conditional strata |
+| 9 | `validate_database.py` | No | Checks all target variables exist in policyengine-us |
 
 ### Raw Input Caching
 
@@ -108,6 +109,7 @@ The `stratum_group_id` field categorizes strata:
 | 4 | SNAP | SNAP recipient strata |
 | 5 | Medicaid | Medicaid enrollment strata |
 | 6 | EITC | EITC recipients by qualifying children |
+| 7 | State Income Tax | State-level income tax collections (Census STC) |
 | 100-118 | IRS Conditional | Each IRS variable paired with conditional count constraints |
 
 ### Conditional Strata (IRS SOI)
@@ -216,6 +218,7 @@ SELECT
         WHEN 4 THEN 'SNAP'
         WHEN 5 THEN 'Medicaid'
         WHEN 6 THEN 'EITC'
+        WHEN 7 THEN 'State Income Tax'
     END AS group_name,
     COUNT(*) AS stratum_count
 FROM strata