Add CTC phase-out threshold reduction analysis for 2026-2035

us/irs/ctc/phase_out_75k_150k/ctc_reform_2026_2035.py

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+#!/usr/bin/env python
+"""
+CTC Reform Analysis for 2026-2035
+Calculates changes in net income and income tax for each year
+"""
+
+from policyengine_us import Microsimulation
+from policyengine_core.reforms import Reform
+import pandas as pd
+import gc
+import os
+
+# Define the CTC reform
+reform = Reform.from_dict(
+    {
+        "gov.irs.credits.ctc.phase_out.threshold.JOINT": {
+            "2025-01-01.2100-12-31": 150000
+        },
+        "gov.irs.credits.ctc.phase_out.threshold.SINGLE": {
+            "2025-01-01.2100-12-31": 75000
+        },
+        "gov.irs.credits.ctc.phase_out.threshold.SEPARATE": {
+            "2025-01-01.2100-12-31": 75000
+        },
+        "gov.irs.credits.ctc.phase_out.threshold.SURVIVING_SPOUSE": {
+            "2025-01-01.2100-12-31": 150000
+        },
+        "gov.irs.credits.ctc.phase_out.threshold.HEAD_OF_HOUSEHOLD": {
+            "2025-01-01.2100-12-31": 75000
+        },
+    },
+    country_id="us",
+)
+
+print("Running CTC Reform Analysis for 2026-2035...")
+print("=" * 50)
+
+# CSV filename
+csv_filename = "us/ctc_reform_2026_2035_results.csv"
+
+# Remove existing file if it exists
+if os.path.exists(csv_filename):
+    os.remove(csv_filename)
+    print(f"Removed existing {csv_filename}")
+
+# Store results
+results = []
+
+# Analyze each year
+for year in range(2026, 2036):
+    print(f"\nAnalyzing year {year}...")
+
+    # Create simulations fresh for each year to avoid memory issues
+    baseline = Microsimulation()
+    reformed = Microsimulation(reform=reform)
+
+    # Calculate net income
+    baseline_income = baseline.calculate("household_net_income", period=year)
+    reformed_income = reformed.calculate("household_net_income", period=year)
+    income_change = (reformed_income - baseline_income).sum()
+
+    # Calculate income tax (aggregated to household level)
+    baseline_tax = baseline.calculate("income_tax", period=year, map_to="household")
+    reformed_tax = reformed.calculate("income_tax", period=year, map_to="household")
+    tax_change = (reformed_tax - baseline_tax).sum()
+
+    result = {
+        "year": year,
+        "net_income_change": income_change,
+        "income_tax_change": tax_change
+    }
+    results.append(result)
+
+    print(f"  Net income change: ${income_change:,.0f}")
+    print(f"  Income tax change: ${tax_change:,.0f}")
+
+    # Write to CSV incrementally
+    df_temp = pd.DataFrame(results)
+    df_temp.to_csv(csv_filename, index=False)
+    print(f"  Saved progress to {csv_filename}")
+
+    # Clean up memory
+    del baseline
+    del reformed
+    del baseline_income
+    del reformed_income
+    del baseline_tax
+    del reformed_tax
+    gc.collect()
+
+# Create final DataFrame with total
+df = pd.DataFrame(results)
+
+# Add total row
+total_row = pd.DataFrame([{
+    "year": "Total",
+    "net_income_change": df["net_income_change"].sum(),
+    "income_tax_change": df["income_tax_change"].sum()
+}])
+
+df = pd.concat([df, total_row], ignore_index=True)
+
+# Save final CSV with total
+df.to_csv(csv_filename, index=False)
+
+print("\n" + "=" * 50)
+print("SUMMARY RESULTS")
+print("=" * 50)
+print(df.to_string(index=False))
+
+print(f"\nResults saved to: {csv_filename}")
+print("Done!")

us/irs/ctc/phase_out_75k_150k/ctc_reform_2026_2035_results.csv

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+year,net_income_change,income_tax_change
+2026,-21319920026.623302,21327176345.411194
+2027,-23334467358.78863,23342990910.804153
+2028,-25413250650.04707,25019280284.84611
+2029,-27982635607.501617,27542251556.544712
+2030,-29864186181.356224,29397985066.77014
+2031,-32238075553.132988,31731941956.156883
+2032,-33806888859.782974,33277892781.083076
+2033,-36206939820.67604,35636937981.324974
+2034,-37942321527.10655,37340956600.95574
+2035,-40479871308.47856,39837039465.4972
+Total,-308588556893.494,304454452949.3942