Add 2025 ACA and Medicaid target coverage for calibration

.gitignore

@@ -13,10 +13,12 @@ node_modules
 ## old (not clean) targets
 !healthcare_spending.csv
 !medicaid_enrollment_2024.csv
+!medicaid_enrollment_2025.csv
 !eitc.csv
 !spm_threshold_agi.csv
 !population_by_state.csv
 !aca_spending_and_enrollment_2024.csv
+!aca_spending_and_enrollment_2025.csv
 !real_estate_taxes_by_state_acs.csv
 !snap_state.csv
 !age_state.csv
@@ -29,6 +31,7 @@ docs/.ipynb_checkpoints/
 
 ## ACA PTC state-level uprating factors
 !policyengine_us_data/storage/aca_ptc_multipliers_2022_2024.csv
+!policyengine_us_data/storage/aca_ptc_multipliers_2022_2025.csv
 
 ## Calibration run outputs (weights, diagnostics, packages, config)
 policyengine_us_data/storage/calibration/

changelog.d/ecps-2025-target-coverage.changed.md

@@ -0,0 +1 @@
+Add 2025 ACA and Medicaid calibration target artifacts, plus year-aware ACA target loading and state uprating factors for 2025 builds.

policyengine_us_data/calibration/unified_matrix_builder.py

@@ -21,7 +21,7 @@
 
 from policyengine_us_data.db.create_database_tables import create_or_replace_views
 from policyengine_us_data.storage import STORAGE_FOLDER
-from policyengine_us_data.utils.census import STATE_NAME_TO_FIPS
+from policyengine_us_data.utils.census import STATE_ABBREV_TO_FIPS, STATE_NAME_TO_FIPS
 from policyengine_us_data.calibration.calibration_utils import (
     get_calculated_variables,
     apply_op,
@@ -1685,11 +1685,22 @@ def _get_uprating_info(
     def _load_aca_ptc_factors(
         self,
     ) -> Dict[int, Dict[str, float]]:
-        csv_path = STORAGE_FOLDER / "aca_ptc_multipliers_2022_2024.csv"
+        csv_candidates = {}
+        for path in STORAGE_FOLDER.glob("aca_ptc_multipliers_2022_*.csv"):
+            suffix = path.stem.removeprefix("aca_ptc_multipliers_2022_")
+            if suffix.isdigit():
+                csv_candidates[int(suffix)] = path
+
+        eligible_years = [
+            year for year in csv_candidates if year <= self.time_period
+        ] or sorted(csv_candidates)
+        csv_path = csv_candidates[max(eligible_years)]
         df = pd.read_csv(csv_path)
         result = {}
         for _, row in df.iterrows():
             fips_str = STATE_NAME_TO_FIPS.get(row["state"])
+            if fips_str is None:
+                fips_str = STATE_ABBREV_TO_FIPS.get(row["state"])
             if fips_str is None:
                 continue
             fips_int = int(fips_str)

policyengine_us_data/storage/README.md

@@ -5,11 +5,22 @@
   • Date: 2024  
   • Location: https://www.cms.gov/files/document/health-insurance-exchanges-2024-open-enrollment-report-final.pdf
 
+- **aca_spending_and_enrollment_2025.csv**  
+  • Source: CMS “Effectuated Enrollment: Early 2025 Snapshot and Full Year 2024 Average”, Table 2 and Table 3  
+  • Date: March 15, 2025 snapshot  
+  • Location: https://www.cms.gov/files/document/effectuated-enrollment-early-snapshot-2025-and-full-year-2024-average.pdf  
+  • Notes: `enrollment` is APTC enrollment by state; `spending` is monthly APTC enrollment multiplied by average monthly APTC for APTC recipients
+
 - **medicaid_enrollment_2024.csv**  
   • Source: MACPAC Enrollment Tables, FFY 2024  
   • Date: 2024  
   • Location: https://www.medicaid.gov/resources-for-states/downloads/eligib-oper-and-enrol-snap-december2024.pdf#page=26
 
+- **medicaid_enrollment_2025.csv**  
+  • Source: Medicaid.gov performance indicator dataset, latest final-report month available in the March 2026 release  
+  • Date: November 2025 final reports  
+  • Location: https://data.medicaid.gov/dataset/State-Medicaid-and-CHIP-Applications-Eligibility-Deter/pi-dataset-march-2026release
+
 - **district_mapping.csv**
   • Source: created by the script `policyengine_us/storage/calibration_targets/make_district_mapping.py`
   • Notes: this script is not part of `make data` because of the length of time it takes to run and the

policyengine_us_data/storage/aca_ptc_multipliers_2022_2025.csv

@@ -0,0 +1,52 @@
+state,enroll_2022,aptc_2022,enroll_2025,aptc_2025,vol_mult,val_mult
+AK,18576,695.4,24249,1008.66,1.3053940568475453,1.450474547023296
+AL,200963,713.19,446956,611.48,2.224071097664744,0.8573872320139093
+AR,73925,451.83,143734,508.4,1.9443219479201894,1.1252019564880598
+AZ,159471,448.69,370817,458.26,2.32529425412771,1.021328757048296
+CA,1586076,476.02,1751717,559.49,1.1044344659398415,1.1753497752195285
+CO,132564,373.95,202916,465.8,1.5307021514136567,1.2456210723358738
+CT,78832,657.22,128440,783.8,1.629287598944591,1.1925991296673868
+DC,2232,343.0,3458,489.13,1.549283154121864,1.4260349854227405
+DE,27345,607.2,45885,588.14,1.678003291278113,0.9686100131752304
+FL,2544476,552.78,4472539,591.39,1.7577446201103881,1.069846955389124
+GA,612951,466.22,1408787,573.3,2.2983680587844706,1.229676976534683
+HI,17724,576.23,19676,577.9,1.1101331527871812,1.0028981483088348
+IA,62355,598.02,117890,471.05,1.8906262529067437,0.787682686197786
+ID,57637,453.64,100605,406.51,1.7454933462879747,0.8961070452341063
+IL,266563,508.89,395850,542.1,1.485014799503307,1.0652596828391205
+IN,121953,459.49,289683,427.8,2.375365919657573,0.9310322313869726
+KS,93793,531.71,182303,579.53,1.9436738349343767,1.0899362434409734
+KY,56614,484.54,77424,526.97,1.367576924435652,1.0875675898790607
+LA,89229,658.46,276294,593.53,3.0964596711831356,0.9013911247456184
+MA,179791,377.37,294241,409.73,1.6365724646951183,1.0857513845827702
+MD,134906,374.94,181274,405.13,1.3437059878730375,1.0805195497946338
+ME,52938,470.92,53808,607.03,1.016434319392497,1.2890299838613777
+MI,248623,392.06,470840,449.38,1.8937910008325858,1.1462021119216446
+MN,69218,342.85,91520,363.43,1.3221994278944784,1.0600262505468863
+MO,216351,548.21,383029,564.46,1.7704054984723898,1.029641925539483
+MS,129385,569.78,320193,575.98,2.474730455616957,1.0108813928182807
+MT,43431,501.35,67185,544.77,1.546936519997237,1.0866061633589308
+NC,608078,580.69,888059,573.88,1.460435996697792,0.9882725722846957
+ND,26577,452.14,38239,485.74,1.4388004665688376,1.0743132658026275
+NE,90106,614.95,127135,602.33,1.4109493263489667,0.979478006341979
+NH,35528,336.67,47900,340.5,1.3482323800945732,1.0113761249888615
+NJ,272035,490.07,457128,546.69,1.6804014189350636,1.1155345154773808
+NM,27522,472.35,60742,591.28,2.2070343725019983,1.251783635016407
+NV,86938,434.76,94596,464.67,1.088085762267363,1.068796577422026
+NY,118805,361.6,118526,433.79,0.9976516139893102,1.1996404867256636
+OH,196185,438.57,513517,500.44,2.6175140810969237,1.1410721207560937
+OK,173176,558.99,289763,559.31,1.673228391924978,1.0005724610458147
+OR,109031,491.48,106167,531.17,0.9737322412891747,1.0807560836656627
+PA,325333,499.67,421996,538.87,1.2971201814755957,1.0784517781735945
+RI,27386,401.71,36438,471.54,1.3305338494121084,1.1738318687610467
+SC,271359,513.06,587715,523.46,2.165820923573569,1.0202705336607807
+SD,38076,641.7,50340,599.92,1.3220926567916798,0.9348916939379771
+TN,238632,544.86,592946,592.36,2.4847715310603773,1.0871783577432734
+TX,1656384,503.86,3713574,540.82,2.2419764982033152,1.0733537093637122
+UT,235188,370.03,395357,462.92,1.6810253924519958,1.251033699970273
+VA,264517,508.29,326268,405.46,1.23344813376834,0.7976942296720375
+VT,22625,566.36,30280,946.45,1.338342541436464,1.6711102478988629
+WA,147712,420.54,226813,445.26,1.5355082863951472,1.0587815665572835
+WI,181848,531.3,271589,585.19,1.4934945668910298,1.1014304536043669
+WV,20466,1007.49,63036,1103.76,3.0800351802990327,1.0955542983056903
+WY,32278,813.47,43457,915.98,1.3463349649916352,1.1260157104748791

policyengine_us_data/storage/calibration_targets/aca_spending_and_enrollment_2025.csv

@@ -0,0 +1,52 @@
+state,enrollment,spending
+AK,24249,24458996.34
+AL,446956,273304654.88
+AR,143734,73074365.6
+AZ,370817,169930598.42
+CA,1751717,980068144.33
+CO,202916,94518272.8
+CT,128440,100671272.0
+DC,3458,1691411.54
+DE,45885,26986803.9
+FL,4472539,2645014839.21
+GA,1408787,807657587.1
+HI,19676,11370760.4
+IA,117890,55532084.5
+ID,100605,40896938.55
+IL,395850,214590285.0
+IN,289683,123926387.4
+KS,182303,105650057.59
+KY,77424,40800125.28
+LA,276294,163988777.82
+MA,294241,120559364.93
+MD,181274,73439535.62
+ME,53808,32663070.24
+MI,470840,211586079.2
+MN,91520,33261113.6
+MO,383029,216204549.34
+MS,320193,184424764.14
+MT,67185,36600372.45
+NC,888059,509639298.92
+ND,38239,18574211.86
+NE,127135,76577224.55
+NH,47900,16309950.0
+NJ,457128,249907306.32
+NM,60742,35915529.76
+NV,94596,43955923.32
+NY,118526,51415393.54
+OH,513517,256984447.48
+OK,289763,162067343.53
+OR,106167,56392725.39
+PA,421996,227400984.52
+RI,36438,17181974.52
+SC,587715,307645293.9
+SD,50340,30199972.8
+TN,592946,351237492.56
+TX,3713574,2008375090.68
+UT,395357,183018662.44
+VA,326268,132288623.28
+VT,30280,28658506.0
+WA,226813,100990756.38
+WI,271589,158931166.91
+WV,63036,69576615.36
+WY,43457,39805742.86

policyengine_us_data/storage/calibration_targets/medicaid_enrollment_2025.csv

@@ -0,0 +1,52 @@
+state,enrollment
+AK,199460
+AL,752535
+AR,723536
+AZ,1579905
+CA,11554412
+CO,1046707
+CT,882013
+DC,242274
+DE,227234
+FL,3447907
+GA,1698374
+HI,364787
+IA,585282
+ID,293777
+IL,2719408
+IN,1431868
+KS,333620
+KY,1200890
+LA,1274536
+MA,1405793
+MD,1209670
+ME,314237
+MI,2131207
+MN,1158706
+MO,1133140
+MS,509631
+MT,192806
+NC,2527040
+ND,100854
+NE,299026
+NH,159398
+NJ,1514284
+NM,640061
+NV,679632
+NY,5894332
+OH,2508352
+OK,913130
+OR,1124039
+PA,2732233
+RI,263719
+SC,875777
+SD,122031
+TN,1233717
+TX,3776984
+UT,298758
+VA,1503054
+VT,146362
+WA,1722640
+WI,1030040
+WV,451904
+WY,54143

policyengine_us_data/tests/test_calibration/test_loss_targets.py

@@ -0,0 +1,45 @@
+import pytest
+
+from policyengine_us_data.utils.loss import (
+    _get_aca_national_targets,
+    _get_medicaid_national_targets,
+    _load_aca_spending_and_enrollment_targets,
+    _load_medicaid_enrollment_targets,
+)
+
+
+def test_aca_targets_roll_forward_to_2025():
+    targets, data_year = _load_aca_spending_and_enrollment_targets(2025)
+
+    assert data_year == 2025
+    assert len(targets) == 51
+    assert int(targets["enrollment"].sum()) == 21_822_894
+
+
+def test_aca_targets_use_latest_available_year():
+    _, data_year = _load_aca_spending_and_enrollment_targets(2026)
+    assert data_year == 2025
+
+
+def test_aca_national_targets_annualize_2025_state_file():
+    spending, enrollment, data_year = _get_aca_national_targets(2025)
+
+    assert data_year == 2025
+    assert enrollment == 21_822_894
+    assert spending == pytest.approx(143_951_057_388.72)
+
+
+def test_medicaid_targets_roll_forward_to_2025():
+    targets, data_year = _load_medicaid_enrollment_targets(2025)
+
+    assert data_year == 2025
+    assert len(targets) == 51
+    assert int(targets["enrollment"].sum()) == 69_185_225
+
+
+def test_medicaid_national_targets_use_2025_values():
+    spending, enrollment, data_year = _get_medicaid_national_targets(2025)
+
+    assert data_year == 2025
+    assert enrollment == 69_185_225
+    assert spending == pytest.approx(1_000_645_800_000.0001)

policyengine_us_data/tests/test_calibration/test_unified_matrix_builder.py

@@ -275,9 +275,10 @@ def test_inactive_targets_are_excluded(self):
         self.assertEqual(float(baseline_rows.iloc[0]["value"]), 10000.0)
 
     def test_legacy_target_overview_without_reform_id(self):
+        b = self._make_builder()
         _create_legacy_target_overview(self.engine)
         try:
-            b = self._make_builder()
+            b._target_overview_columns = None
             df = b._query_targets({"domain_variables": ["aca_ptc"]})
             self.assertGreater(len(df), 0)
             self.assertIn("reform_id", df.columns)
@@ -439,6 +440,13 @@ def test_aca_ptc_uses_csv_factors(self):
         self.assertIn(6, result)
         self.assertIn(37, result)
 
+    def test_aca_ptc_2025_uses_2025_csv_factors(self):
+        b = UnifiedMatrixBuilder(db_uri=self.db_uri, time_period=2025)
+        result = b._load_aca_ptc_factors()
+
+        self.assertAlmostEqual(result[1]["tax_unit_count"], 2.224071097664744)
+        self.assertAlmostEqual(result[1]["aca_ptc"], 1.906890162228912)
+
     def test_non_aca_uses_national_factors(self):
         b = UnifiedMatrixBuilder(db_uri=self.db_uri, time_period=2024)
         df = pd.DataFrame(