Add new bill analyses for UT, SC, and RI

scripts/compute_impacts.py

@@ -22,6 +22,7 @@
     from policyengine_us import Microsimulation
     from policyengine_core.reforms import Reform
     from policyengine_core.periods import instant
+    from microdf import MicroSeries
     import numpy as np
     HAS_POLICYENGINE = True
 except ImportError:
@@ -46,6 +47,15 @@
         3: "Congressional District 3",
         4: "Congressional District 4",
     },
+    "SC": {
+        1: "Congressional District 1",
+        2: "Congressional District 2",
+        3: "Congressional District 3",
+        4: "Congressional District 4",
+        5: "Congressional District 5",
+        6: "Congressional District 6",
+        7: "Congressional District 7",
+    },
     "OK": {
         1: "Congressional District 1",
         2: "Congressional District 2",
@@ -67,6 +77,59 @@
             }
         }
     },
+    {
+        "id": "ut-hb210-marriage-penalty-removal",
+        "state": "ut",
+        "label": "Utah HB210 Marriage Penalty Removal",
+        "reform": {
+            "gov.contrib.states.ut.hb210.in_effect": {
+                "2026-01-01.2100-12-31": True
+            },
+            "gov.contrib.states.ut.hb210.taxpayer_credit_add_on.amount.JOINT": {
+                "2026-01-01.2100-12-31": 66.0
+            },
+            "gov.contrib.states.ut.hb210.taxpayer_credit_add_on.amount.SEPARATE": {
+                "2026-01-01.2100-12-31": 33.0
+            },
+            "gov.contrib.states.ut.hb210.taxpayer_credit_add_on.amount.SURVIVING_SPOUSE": {
+                "2026-01-01.2100-12-31": 66.0
+            },
+            "gov.states.ut.tax.income.credits.ctc.reduction.start.HEAD_OF_HOUSEHOLD": {
+                "2026-01-01.2100-12-31": 27000.0
+            },
+            "gov.states.ut.tax.income.credits.ctc.reduction.start.SINGLE": {
+                "2026-01-01.2100-12-31": 27000.0
+            },
+            "gov.states.ut.tax.income.credits.earned_income.rate": {
+                "2026-01-01.2100-12-31": 0.0
+            },
+            "gov.states.ut.tax.income.credits.retirement.phase_out.threshold.HEAD_OF_HOUSEHOLD": {
+                "2026-01-01.2100-12-31": 16000.0
+            },
+            "gov.states.ut.tax.income.credits.retirement.phase_out.threshold.SINGLE": {
+                "2026-01-01.2100-12-31": 16000.0
+            },
+            "gov.states.ut.tax.income.credits.taxpayer.phase_out.threshold.HEAD_OF_HOUSEHOLD": {
+                "2026-01-01.2100-12-31": 18625.8
+            },
+            "gov.states.ut.tax.income.credits.ss_benefits.phase_out.threshold.HEAD_OF_HOUSEHOLD": {
+                "2026-01-01.2100-12-31": 45000.0
+            },
+            "gov.states.ut.tax.income.credits.ss_benefits.phase_out.threshold.SINGLE": {
+                "2026-01-01.2100-12-31": 45000.0
+            }
+        }
+    },
+    {
+        "id": "sc-h3492-refundable-eitc",
+        "state": "sc",
+        "label": "SC H.3492 Partially Refundable EITC",
+        "reform": {
+            "gov.contrib.states.sc.h3492.in_effect": {
+                "2026-01-01.2100-12-31": True
+            }
+        }
+    },
     {
         "id": "ok-hb2229-eitc",
         "state": "ok",
@@ -244,7 +307,7 @@ def compute_district_impacts(state: str, reform_dict: dict, year: int = 2026) ->
 
     # Get state FIPS code for filtering
     STATE_FIPS = {
-        "UT": 49, "CA": 6, "NY": 36, "TX": 48, "FL": 12, "OK": 40,
+        "UT": 49, "CA": 6, "NY": 36, "TX": 48, "FL": 12, "SC": 45, "OK": 40,
         # Add more as needed
     }
 
@@ -304,6 +367,8 @@ def apply(self):
         income_change = reform_income - baseline_income
 
         household_weight = baseline.calculate("household_weight", year).values
+        household_count_people = baseline.calculate("household_count_people", year).values
+        household_income_decile = baseline.calculate("household_income_decile", year).values
         state_code = baseline.calculate("state_code_str", year).values
         cd_geoid = baseline.calculate("congressional_district_geoid", year).values
 
@@ -344,9 +409,43 @@ def apply(self):
             total_benefit = float(np.sum(district_income_change * district_weights))
             avg_benefit = total_benefit / total_households if total_households > 0 else 0
 
-            # Winners share (households with positive income change)
-            winners = district_income_change > 1  # More than $1 gain
-            winners_share = float(np.sum(district_weights[winners]) / total_households) if total_households > 0 else 0
+            # Winners share - match API's intra_decile_impact calculation exactly
+            # API methodology:
+            # 1. Calculate relative income change using capped values
+            # 2. Use MicroSeries with weights for proper weighted sums
+            # 3. Calculate proportion of winners per decile
+            # 4. Average across 10 deciles
+            district_baseline = baseline_income[in_district]
+            district_reform = reform_income[in_district]
+            absolute_change = district_reform - district_baseline
+            capped_baseline = np.maximum(district_baseline, 1)
+            capped_reform = np.maximum(district_reform, 1) + absolute_change
+            relative_change = (capped_reform - capped_baseline) / capped_baseline
+
+            # Create MicroSeries with weights (matching API pattern)
+            district_people = MicroSeries(
+                household_count_people[in_district],
+                weights=district_weights
+            )
+            district_decile = household_income_decile[in_district]
+
+            # API threshold: > 0.001 (0.1%) = winner
+            is_winner = relative_change > 0.001
+
+            # Calculate proportion of winners per decile, then average
+            decile_proportions = []
+            for decile in range(1, 11):
+                in_decile = district_decile == decile
+                if not np.any(in_decile):
+                    decile_proportions.append(0.0)
+                    continue
+                people_in_decile = float(district_people[in_decile].sum())
+                winners_in_decile = float(district_people[in_decile & is_winner].sum())
+                proportion = winners_in_decile / people_in_decile if people_in_decile > 0 else 0.0
+                decile_proportions.append(proportion)
+
+            # Average across deciles (matching API's sum / 10)
+            winners_share = sum(decile_proportions) / 10
 
             district_impacts[f"{state_upper}-{district_num}"] = {
                 "districtName": district_name,

src/components/reform/DistrictMap.jsx

@@ -87,6 +87,15 @@ const STATE_DISTRICTS = {
     { id: "3", name: "District 3", region: "Central & Eastern Utah" },
     { id: "4", name: "District 4", region: "Salt Lake Suburbs" },
   ],
+  SC: [
+    { id: "1", name: "District 1", region: "Charleston & Coast" },
+    { id: "2", name: "District 2", region: "Columbia & Midlands" },
+    { id: "3", name: "District 3", region: "Upstate West" },
+    { id: "4", name: "District 4", region: "Greenville-Spartanburg" },
+    { id: "5", name: "District 5", region: "Rock Hill & North Central" },
+    { id: "6", name: "District 6", region: "Pee Dee & Rural East" },
+    { id: "7", name: "District 7", region: "Myrtle Beach & Northeast" },
+  ],
   OK: [
     { id: "1", name: "District 1", region: "Tulsa Area" },
     { id: "2", name: "District 2", region: "Eastern Oklahoma" },