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Abolish SALT notebook #79

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3ba836c
Abolish SALT notebook
DTrim99 Sep 17, 2025
bd714a8
Fix memory issues in abolish_salt notebook and add debug script
MaxGhenis Sep 17, 2025
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244 changes: 244 additions & 0 deletions us/deductions/abolish_salt.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "762a9c7e",
"metadata": {},
"outputs": [],
"source": [
"from policyengine_us import Microsimulation\n",
"from policyengine_core.reforms import Reform\n",
"\n",
"reform = Reform.from_dict({\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.JOINT\": {\n",
" \"2025-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SINGLE\": {\n",
" \"2025-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SEPARATE\": {\n",
" \"2025-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SURVIVING_SPOUSE\": {\n",
" \"2025-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.HEAD_OF_HOUSEHOLD\": {\n",
" \"2025-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.phase_out.floor.applies\": {\n",
" \"2025-01-01.2029-12-31\": False\n",
" }\n",
"}, country_id=\"us\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "m36duawjesh",
"metadata": {},
"outputs": [],
"source": [
"# Congressional District Analysis - Setup\n",
"from policyengine_us import Microsimulation\n",
"import pandas as pd\n",
"\n",
"print(\"Setting up congressional district microsimulations...\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bgcpg1k1guu",
"metadata": {},
"outputs": [],
"source": [
"# Create baseline microsimulation with congressional district data\n",
"cd_baseline = Microsimulation(dataset=\"hf://policyengine/test/sparse_cd_stacked_2023.h5\")\n",
"\n",
"# Get congressional district geoids and correct state_fips\n",
"cd_geoids = cd_baseline.calculate(\"congressional_district_geoid\").values\n",
"correct_state_fips = cd_geoids // 100\n",
"cd_baseline.set_input(\"state_fips\", 2023, correct_state_fips)\n",
"\n",
"print(\"Baseline microsimulation ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99exvffovks",
"metadata": {},
"outputs": [],
"source": [
"# Create reform microsimulation \n",
"cd_reformed = Microsimulation(dataset=\"hf://policyengine/test/sparse_cd_stacked_2023.h5\", reform=reform)\n",
"cd_reformed.set_input(\"state_fips\", 2023, correct_state_fips)\n",
"\n",
"print(\"Reform microsimulation ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "whqowthn9rc",
"metadata": {},
"outputs": [],
"source": [
"# Calculate baseline data - individual variables to save memory\n",
"print(\"Calculating baseline variables individually...\")\n",
"household_ids = cd_baseline.calculate(\"household_id\")\n",
"state_fips = cd_baseline.calculate(\"state_fips\") \n",
"cd_geoids = cd_baseline.calculate(\"congressional_district_geoid\")\n",
"baseline_income = cd_baseline.calculate(\"household_net_income\", period=2025)\n",
"baseline_tax = cd_baseline.calculate(\"income_tax\", period=2025)\n",
"weights = cd_baseline.calculate(\"household_weight\", period=2025)\n",
"\n",
"print(f\"Baseline variables calculated for {len(household_ids)} households\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "wg4ow67ppok",
"metadata": {},
"outputs": [],
"source": [
"# Calculate reform data - individual variables\n",
"print(\"Calculating reform variables...\")\n",
"reform_income = cd_reformed.calculate(\"household_net_income\", period=2025)\n",
"reform_tax = cd_reformed.calculate(\"income_tax\", period=2025)\n",
"\n",
"print(f\"Reform variables calculated\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "tvag721fho",
"metadata": {},
"outputs": [],
"source": [
"# Create DataFrame with calculated impacts\n",
"print(\"Creating impact dataframe...\")\n",
"import pandas as pd\n",
"\n",
"cd_data = pd.DataFrame({\n",
" 'household_id': household_ids,\n",
" 'state_fips': state_fips,\n",
" 'congressional_district_geoid': cd_geoids,\n",
" 'baseline_income': baseline_income,\n",
" 'reform_income': reform_income,\n",
" 'baseline_tax': baseline_tax,\n",
" 'reform_tax': reform_tax,\n",
" 'household_weight': weights\n",
"})\n",
"\n",
"# Calculate impacts\n",
"cd_data['reform_impact'] = cd_data['reform_income'] - cd_data['baseline_income']\n",
"cd_data['tax_change'] = cd_data['reform_tax'] - cd_data['baseline_tax']\n",
"cd_data['district_number'] = cd_data['congressional_district_geoid'] % 100\n",
"\n",
"print(f\"Impact dataframe created with {len(cd_data)} households\")\n",
"\n",
"# Free memory\n",
"del household_ids, state_fips, cd_geoids, baseline_income, reform_income, baseline_tax, reform_tax, weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "axc8cqndhyk",
"metadata": {},
"outputs": [],
"source": [
"# Group by congressional district and calculate weighted averages\n",
"print(\"Aggregating by congressional district...\")\n",
"\n",
"cd_summary = cd_data.groupby(['state_fips', 'congressional_district_geoid', 'district_number']).apply(\n",
" lambda x: pd.Series({\n",
" 'avg_income_impact': (x['reform_impact'] * x['household_weight']).sum() / x['household_weight'].sum(),\n",
" 'avg_tax_change': (x['tax_change'] * x['household_weight']).sum() / x['household_weight'].sum(),\n",
" 'total_households': x['household_weight'].sum()\n",
" })\n",
").reset_index()\n",
"\n",
"cd_summary = cd_summary.sort_values('avg_income_impact', ascending=False)\n",
"\n",
"print(f\"Summary created for {len(cd_summary)} congressional districts\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41r7sfheye8",
"metadata": {},
"outputs": [],
"source": [
"# Show top 10 districts\n",
"print(\"Top 10 Congressional Districts by Average Household Income Impact from Abolishing SALT Cap:\")\n",
"print(cd_summary.head(10)[['state_fips', 'district_number', 'avg_income_impact', 'avg_tax_change', 'total_households']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "saawvdbwkra",
"metadata": {},
"outputs": [],
"source": [
"# Show bottom 10 districts \n",
"print(\"Bottom 10 Congressional Districts (least benefit or most harm):\")\n",
"print(cd_summary.tail(10)[['state_fips', 'district_number', 'avg_income_impact', 'avg_tax_change', 'total_households']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7uf8bvtd96o",
"metadata": {},
"outputs": [],
"source": [
"# Overall statistics\n",
"print(f\"Overall Statistics:\")\n",
"print(f\"Total Congressional Districts: {len(cd_summary)}\")\n",
"print(f\"Districts with positive income impact: {len(cd_summary[cd_summary['avg_income_impact'] > 0])}\")\n",
"print(f\"Districts with negative income impact: {len(cd_summary[cd_summary['avg_income_impact'] < 0])}\")\n",
"print(f\"Average impact across all districts: ${cd_summary['avg_income_impact'].mean():.2f}\")\n",
"print(f\"Median impact across all districts: ${cd_summary['avg_income_impact'].median():.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4gj9j5j8tp",
"metadata": {},
"outputs": [],
"source": [
"# Display the full summary dataset\n",
"cd_summary"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pe",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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