Utah hb15 medicaid expansion repeal

us/aca_enrollment_spending.py

@@ -0,0 +1,80 @@
+from policyengine_us import Microsimulation
+import pandas as pd
+
+YEAR = 2025
+
+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",
+]
+
+results = []
+
+for state in STATES:
+    print(f"Processing {state}...")
+    try:
+        sim = Microsimulation(
+            dataset=f"hf://policyengine/policyengine-us-data/states/{state}.h5"
+        )
+        aca_ptc = sim.calc("aca_ptc", period=YEAR, map_to="person")
+        enrolled = (aca_ptc > 0).sum()
+        spending = aca_ptc.sum()
+        results.append({
+            "state": state,
+            "aca_enrollment": enrolled,
+            "aca_spending": spending,
+        })
+        print(f"  {state}: {enrolled:,.0f} enrolled, ${spending:,.0f} spending")
+    except Exception as e:
+        print(f"  ERROR for {state}: {e}")
+        # CA workaround: set in_la=False to avoid LA county rating area bug
+        if state == "CA":
+            import numpy as np
+            print(f"  Retrying {state} with in_la workaround...")
+            sim = Microsimulation(
+                dataset=f"hf://policyengine/policyengine-us-data/states/{state}.h5"
+            )
+            household = sim.populations['household']
+            sim.set_input('in_la', YEAR, np.zeros(household.count, dtype=bool))
+            aca_ptc = sim.calc("aca_ptc", period=YEAR, map_to="person")
+            enrolled = (aca_ptc > 0).sum()
+            spending = aca_ptc.sum()
+            results.append({
+                "state": state,
+                "aca_enrollment": enrolled,
+                "aca_spending": spending,
+            })
+            print(f"  {state}: {enrolled:,.0f} enrolled, ${spending:,.0f} spending")
+        else:
+            results.append({
+                "state": state,
+                "aca_enrollment": None,
+                "aca_spending": None,
+            })
+
+# National
+print("\nProcessing national...")
+sim_national = Microsimulation()
+aca_ptc_national = sim_national.calc("aca_ptc", period=YEAR, map_to="person")
+national_enrolled = (aca_ptc_national > 0).sum()
+national_spending = aca_ptc_national.sum()
+results.append({
+    "state": "US (National)",
+    "aca_enrollment": national_enrolled,
+    "aca_spending": national_spending,
+})
+
+df = pd.DataFrame(results)
+df["aca_spending_billions"] = df["aca_spending"] / 1e9
+df = df.sort_values("aca_enrollment", ascending=False)
+
+print("\n" + "=" * 70)
+print("ACA Enrollment and Spending by State")
+print("=" * 70)
+print(df.to_string(index=False, float_format=lambda x: f"{x:,.0f}"))
+
+df.to_csv(f"us/aca_enrollment_spending_by_state_{YEAR}.csv", index=False)
+print("\nSaved to us/aca_enrollment_spending_by_state.csv")

us/aca_enrollment_spending_by_state.csv

@@ -0,0 +1,52 @@
+state,aca_enrollment,aca_spending,aca_spending_billions
+US (National),17383484.732709058,179014488228.1197,179.0144882281197
+TX,7045259.248884158,86460101552.99954,86.46010155299955
+CA,6392819.0,61266319340.0,61.26631934
+FL,4763130.907424998,61079815728.53207,61.07981572853207
+GA,2457752.326743666,27439860750.8346,27.4398607508346
+PA,2348139.8470139103,23420307290.99984,23.42030729099984
+OH,2305060.443552648,22196402330.640697,22.196402330640694
+IL,2211039.985772523,26555439405.93521,26.55543940593521
+NC,2086741.8142803991,24947377457.642536,24.947377457642535
+NY,1953039.1004624225,16089885227.700983,16.089885227700986
+MI,1857987.0081065544,17767462914.75386,17.76746291475386
+TN,1592600.8250531708,19973526197.83516,19.97352619783516
+NJ,1539928.6046926808,13172055697.809889,13.172055697809888
+VA,1474410.3597104885,11479779070.163813,11.479779070163811
+AZ,1381595.3213190753,13732783973.741192,13.732783973741192
+IN,1377786.4813249563,10268298367.75582,10.268298367755822
+WI,1354486.004249754,14638132714.766865,14.638132714766863
+WA,1347994.4825802264,13545412240.742018,13.545412240742015
+MO,1312163.1666086228,14013492428.452984,14.013492428452984
+SC,1239063.7865523277,12912301890.222267,12.912301890222269
+AL,1229309.7415236607,14151670647.668756,14.151670647668755
+CO,1027351.5600919002,9107220686.89117,9.107220686891171
+MA,1022073.3352083332,6560450294.139649,6.560450294139649
+KY,959811.3184823088,12092156264.34015,12.09215626434015
+MD,929678.3681728876,5579924673.534227,5.579924673534228
+OK,879446.0107236379,10898721075.430405,10.898721075430403
+LA,876879.9132559479,10524063400.097616,10.524063400097615
+UT,836792.5388366659,9348062271.00832,9.34806227100832
+MS,747166.1928366562,9404532792.797682,9.40453279279768
+KS,721101.5106305224,9157055464.51527,9.15705546451527
+MN,699327.0917778963,5201652953.358235,5.201652953358235
+AR,688070.3462367074,10445357164.006168,10.445357164006168
+IA,664351.792916852,5089952648.955997,5.089952648955998
+OR,656362.3123666104,5548745613.863974,5.548745613863973
+NV,613462.5641283542,5409709120.288629,5.409709120288628
+CT,581912.3486924558,9054273485.205309,9.054273485205307
+NE,490228.6737917632,6991057381.380745,6.991057381380745
+ID,440370.6405512402,4038585402.305117,4.0385854023051175
+NM,392587.7489387901,4553466809.422207,4.553466809422207
+WV,384198.72947609494,8510778710.398851,8.510778710398851
+NH,292619.7376561556,2099425092.7739608,2.0994250927739606
+ME,284222.17484532436,4529157327.2300625,4.529157327230062
+MT,235317.3814670497,2952751928.720352,2.9527519287203523
+HI,220836.27874800004,1703108104.3514469,1.7031081043514469
+ND,201077.59784668495,2583745310.161701,2.583745310161701
+SD,197912.42513002065,2461979782.0080175,2.461979782008018
+RI,194962.17444041232,1679774483.108898,1.679774483108898
+DE,189378.5689020855,2528841844.144252,2.5288418441442517
+VT,123538.29485612042,2501834368.098485,2.501834368098485
+AK,98996.27022879756,1652613679.3413544,1.6526136793413544
+DC,55924.91141050868,426306591.2642381,0.4263065912642381

us/aca_enrollment_spending_by_state_2024.csv

@@ -0,0 +1,52 @@
+state,aca_enrollment,aca_spending,aca_spending_billions
+AL,0.0,0.0,0.0
+PA,0.0,0.0,0.0
+NV,0.0,0.0,0.0
+NH,0.0,0.0,0.0
+NJ,0.0,0.0,0.0
+NM,0.0,0.0,0.0
+NY,0.0,0.0,0.0
+NC,0.0,0.0,0.0
+ND,0.0,0.0,0.0
+OH,0.0,0.0,0.0
+OK,0.0,0.0,0.0
+OR,0.0,0.0,0.0
+RI,0.0,0.0,0.0
+MT,0.0,0.0,0.0
+SC,0.0,0.0,0.0
+SD,0.0,0.0,0.0
+TN,0.0,0.0,0.0
+TX,0.0,0.0,0.0
+UT,0.0,0.0,0.0
+VT,0.0,0.0,0.0
+VA,0.0,0.0,0.0
+WA,0.0,0.0,0.0
+WV,0.0,0.0,0.0
+WI,0.0,0.0,0.0
+NE,0.0,0.0,0.0
+MO,0.0,0.0,0.0
+AK,0.0,0.0,0.0
+ID,0.0,0.0,0.0
+AZ,0.0,0.0,0.0
+AR,0.0,0.0,0.0
+CA,0.0,0.0,0.0
+CO,0.0,0.0,0.0
+CT,0.0,0.0,0.0
+DE,0.0,0.0,0.0
+DC,0.0,0.0,0.0
+FL,0.0,0.0,0.0
+GA,0.0,0.0,0.0
+HI,0.0,0.0,0.0
+IL,0.0,0.0,0.0
+MS,0.0,0.0,0.0
+IN,0.0,0.0,0.0
+IA,0.0,0.0,0.0
+KS,0.0,0.0,0.0
+KY,0.0,0.0,0.0
+LA,0.0,0.0,0.0
+ME,0.0,0.0,0.0
+MD,0.0,0.0,0.0
+MA,0.0,0.0,0.0
+MI,0.0,0.0,0.0
+MN,0.0,0.0,0.0
+US (National),0.0,0.0,0.0

us/aca_enrollment_spending_by_state_2025.csv

@@ -0,0 +1,52 @@
+state,aca_enrollment,aca_spending,aca_spending_billions
+US (National),21572726.942273073,181223754299.774,181.223754299774
+CA,8258359.718990697,75333815139.14964,75.33381513914965
+TX,8154141.478774493,77021600904.5361,77.0216009045361
+FL,5480503.306234306,55610399173.550514,55.61039917355051
+NY,2920759.347690541,26876070556.55341,26.876070556553408
+GA,2855316.4533272963,26911336485.505817,26.911336485505817
+PA,2845628.6703047836,24197944419.820126,24.197944419820125
+OH,2801191.123286927,25536017657.964554,25.536017657964553
+IL,2716568.46740863,24011346931.944237,24.011346931944235
+NC,2543226.848436172,25369382439.49144,25.36938243949144
+MI,2206450.235455502,17810806397.0245,17.810806397024503
+NJ,2030025.1104657375,17360635430.15796,17.36063543015796
+TN,1877230.9010269186,17877734072.469265,17.877734072469266
+VA,1822987.0508065247,12605701272.806177,12.605701272806177
+IN,1644125.78897559,11031265485.617647,11.031265485617647
+WI,1632773.8356423066,15222107733.353607,15.222107733353607
+WA,1632212.747060252,11107151098.56737,11.10715109856737
+AZ,1611555.536846539,12774439338.496689,12.77443933849669
+MO,1589053.149089912,14907236215.289948,14.907236215289947
+SC,1456141.7433837387,13911916925.125687,13.911916925125686
+AL,1440763.2141796688,14659416638.629711,14.659416638629711
+MA,1364252.0275837937,9265263267.48693,9.265263267486931
+CO,1315091.3830277754,10486010855.399256,10.486010855399256
+MD,1231546.1830028284,7439723643.565978,7.439723643565978
+KY,1123867.6968579488,10866795607.628288,10.866795607628289
+LA,1032368.7955663666,10887378833.949318,10.887378833949318
+OK,1030973.3978550192,11047539448.776329,11.047539448776329
+UT,1022815.1581275805,10815686650.487175,10.815686650487175
+MN,1007975.4702420774,6160068826.019312,6.1600688260193115
+OR,885710.2963893601,7807541204.385212,7.807541204385212
+KS,851624.0196430138,8652615702.235592,8.652615702235591
+MS,831854.577155319,8088174705.164504,8.088174705164503
+IA,813172.183629359,6221756669.238905,6.221756669238905
+CT,809628.3426085422,10478832726.64508,10.47883272664508
+AR,769524.7271320827,6873299358.368113,6.873299358368112
+NV,730709.7043699923,5934058476.354357,5.934058476354357
+NE,600562.0920331676,7707765423.0975685,7.707765423097569
+ID,529698.3963936316,4688614115.276829,4.688614115276828
+NM,475964.13492398657,4911455805.543261,4.911455805543261
+WV,468581.0576358518,9470285869.939915,9.470285869939914
+ME,355824.7205406721,4292957621.2281113,4.292957621228111
+NH,327098.38002613944,2010970739.2548814,2.0109707392548812
+HI,318478.70823075064,2718272465.394373,2.718272465394373
+MT,300547.43412913976,3311576180.466132,3.3115761804661323
+RI,247608.49538935843,1919906280.4145892,1.9199062804145892
+ND,247107.05076210294,3277744693.109666,3.277744693109666
+DE,242876.5929896055,2533376397.566221,2.5333763975662213
+SD,242241.82739121973,2931362862.695174,2.9313628626951744
+VT,169877.2291643463,3535068119.476025,3.535068119476025
+AK,161816.04310611496,3612140008.017561,3.612140008017561
+DC,95431.3645341415,792773104.7096058,0.7927731047096058

us/states/ut/analyze_hb15_transitions.py

@@ -0,0 +1,137 @@
+"""Analyze Utah HB 15 Medicaid transitions with microsimulation."""
+
+from policyengine_us import Microsimulation
+from policyengine_core.periods import instant
+from policyengine_core.reforms import Reform
+import numpy as np
+
+YEAR = 2027
+DATASET = "hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5"
+
+def create_ut_medicaid_expansion_repeal():
+    def modify_parameters(parameters):
+        parameters.gov.hhs.medicaid.eligibility.categories.adult.income_limit.UT.update(
+            start=instant(f'{YEAR}-01-01'),
+            stop=instant('2100-12-31'),
+            value=float('-inf'),
+        )
+        return parameters
+
+    class reform(Reform):
+        def apply(self):
+            self.modify_parameters(modify_parameters)
+
+    return reform
+
+
+def main():
+    print("Loading baseline microsimulation...")
+    baseline = Microsimulation(dataset=DATASET)
+
+    print("Loading reform microsimulation...")
+    reform = Microsimulation(dataset=DATASET, reform=create_ut_medicaid_expansion_repeal())
+
+    # Filter to Utah residents - map household state to person level
+    # state_code is defined at household level, map to person
+    state_code = baseline.calculate("state_code", YEAR, map_to="person").values
+    in_utah = state_code == "UT"
+
+    # Get person weights
+    person_weight = baseline.calculate("person_weight", YEAR).values
+
+    # Get Medicaid categories (baseline vs reform) - these return string values
+    baseline_category = baseline.calculate("medicaid_category", YEAR).values
+    reform_category = reform.calculate("medicaid_category", YEAR).values
+
+    # Get Medicaid amounts (person level)
+    baseline_medicaid = baseline.calculate("medicaid", YEAR).values
+    reform_medicaid = reform.calculate("medicaid", YEAR).values
+
+    print(f"\nDebug: Total people in sample: {len(baseline_category):,}")
+    print(f"Debug: People in Utah: {in_utah.sum():,}")
+    print(f"Debug: Unique baseline categories: {np.unique(baseline_category)}")
+
+    # Categories are strings now
+    ADULT = "ADULT"
+    PARENT = "PARENT"
+    NONE = "NONE"
+
+    print("\n" + "="*70)
+    print("UTAH HB 15 MEDICAID TRANSITION ANALYSIS")
+    print("="*70)
+
+    # People on expansion Medicaid in baseline (in Utah)
+    on_expansion_baseline = (baseline_category == ADULT) & in_utah
+    expansion_count = (on_expansion_baseline * person_weight).sum()
+    print(f"\nPeople on expansion Medicaid (baseline): {expansion_count:,.0f}")
+
+    # What happens to expansion adults under reform?
+    # Transition to parent Medicaid
+    to_parent = on_expansion_baseline & (reform_category == PARENT)
+    to_parent_count = (to_parent * person_weight).sum()
+
+    # Transition to other Medicaid categories (not NONE, not ADULT, not PARENT)
+    to_other_medicaid = on_expansion_baseline & (reform_category != NONE) & (reform_category != ADULT) & (reform_category != PARENT)
+    to_other_count = (to_other_medicaid * person_weight).sum()
+
+    # Fall to no Medicaid
+    to_none = on_expansion_baseline & (reform_category == NONE)
+    to_none_count = (to_none * person_weight).sum()
+
+    print(f"\nTransitions from expansion Medicaid:")
+    print(f"  → Parent Medicaid: {to_parent_count:,.0f}")
+    print(f"  → Other Medicaid: {to_other_count:,.0f}")
+    print(f"  → No Medicaid: {to_none_count:,.0f}")
+
+    # Of those who lose all Medicaid, how many gain ACA?
+    loses_medicaid = on_expansion_baseline & (reform_medicaid == 0)
+    loses_medicaid_count = (loses_medicaid * person_weight).sum()
+
+    # People above 100% FPL can get ACA
+    income_level = baseline.calculate("medicaid_income_level", YEAR).values
+    above_100_fpl = income_level >= 1.0
+
+    gains_aca = loses_medicaid & above_100_fpl
+    gains_aca_count = (gains_aca * person_weight).sum()
+
+    # Coverage gap = loses Medicaid and below 100% FPL (can't get ACA)
+    coverage_gap = loses_medicaid & ~above_100_fpl
+    coverage_gap_count = (coverage_gap * person_weight).sum()
+
+    if loses_medicaid_count > 0:
+        print(f"\nOf those losing Medicaid ({loses_medicaid_count:,.0f}):")
+        print(f"  → Can get ACA (>=100% FPL): {gains_aca_count:,.0f} ({gains_aca_count/loses_medicaid_count*100:.1f}%)")
+        print(f"  → Coverage gap (<100% FPL): {coverage_gap_count:,.0f} ({coverage_gap_count/loses_medicaid_count*100:.1f}%)")
+
+    # Fiscal impact (person-level Medicaid only for now)
+    utah_weight = person_weight * in_utah
+
+    baseline_medicaid_total = (baseline_medicaid * utah_weight).sum()
+    reform_medicaid_total = (reform_medicaid * utah_weight).sum()
+    medicaid_savings = baseline_medicaid_total - reform_medicaid_total
+
+    print(f"\n" + "="*70)
+    print("FISCAL IMPACT")
+    print("="*70)
+    print(f"\nMedicaid spending change: -${medicaid_savings/1e6:,.0f} million")
+    print(f"  Federal share (90%): -${medicaid_savings*0.9/1e6:,.0f} million")
+    print(f"  State share (10%): -${medicaid_savings*0.1/1e6:,.0f} million")
+
+    # Summary for blog post
+    print(f"\n" + "="*70)
+    print("SUMMARY FOR BLOG POST")
+    print("="*70)
+    print(f"\n- ~{expansion_count/1000:.0f},000 people currently on expansion Medicaid")
+    if to_parent_count > 0:
+        print(f"- ~{to_parent_count/1000:.0f},000 would transition to parent Medicaid")
+    if to_other_count > 0:
+        print(f"- ~{to_other_count/1000:.0f},000 would transition to other Medicaid categories")
+    print(f"- ~{loses_medicaid_count/1000:.0f},000 would lose Medicaid coverage entirely")
+    if loses_medicaid_count > 0:
+        print(f"  - ~{gains_aca_count/1000:.0f},000 ({gains_aca_count/loses_medicaid_count*100:.0f}%) could transition to ACA")
+        print(f"  - ~{coverage_gap_count/1000:.0f},000 ({coverage_gap_count/loses_medicaid_count*100:.0f}%) would fall into coverage gap")
+    print(f"- State savings: ~${medicaid_savings*0.1/1e6:.0f} million/year")
+
+
+if __name__ == "__main__":
+    main()