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New Jersey CTC impact #1

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New Jersey CTC impact
PavelMakarchuk Jun 9, 2024
8711b36
format
PavelMakarchuk Jun 9, 2024
cb05841
NJ
PavelMakarchuk Jun 10, 2024
4601bf4
testing
PavelMakarchuk Jun 10, 2024
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minor
PavelMakarchuk Jul 31, 2024
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209 changes: 209 additions & 0 deletions us/neutralize_state_eitc_and_ctc.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"from policyengine_us import Microsimulation\n",
"from policyengine_core.reforms import Reform\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"REFORMS = {\n",
" \"Baseline\": None,\n",
" \"Expand refundable CTC\": {\n",
" \"gov.contrib.congress.wyden_smith.per_child_actc_phase_in\": {\n",
" \"2024-01-01.2100-12-31\": True\n",
" },\n",
" \"gov.irs.credits.ctc.refundable.individual_max\": {\n",
" \"2024-01-01.2100-12-31\": 2000\n",
" },\n",
" \"gov.irs.credits.ctc.refundable.phase_in.threshold\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" }\n",
" },\n",
" \"Restore ARPA EITC expansion for childless filers\": {\n",
" \"gov.irs.credits.eitc.eligibility.age.max\": {\n",
" \"2023-01-01.2028-12-31\": 100\n",
" },\n",
" \"gov.irs.credits.eitc.eligibility.age.min\": {\n",
" \"2023-01-01.2028-12-31\": 19\n",
" },\n",
" \"gov.irs.credits.eitc.max[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 1502\n",
" },\n",
" \"gov.irs.credits.eitc.phase_in_rate[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 0.153\n",
" },\n",
" \"gov.irs.credits.eitc.phase_out.rate[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 0.153\n",
" },\n",
" \"gov.irs.credits.eitc.phase_out.start[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 11610\n",
" }\n",
" },\n",
" \"SSI Reform\": {\n",
" \"gov.ssa.ssi.income.exclusions.earned_share\": {\n",
" \"2024-01-01.2100-12-31\": 0.7\n",
" }\n",
" },\n",
" \"SNAP Reform\": {\n",
" \"gov.usda.snap.expected_contribution\": {\n",
" \"2024-01-01.2100-12-31\": 0.2\n",
" },\n",
" \"gov.usda.snap.income.deductions.earned_income\": {\n",
" \"2024-01-01.2100-12-31\": 0.3\n",
" },\n",
" \"gov.usda.snap.income.limit.gross\": {\n",
" \"2024-01-01.2100-12-31\": 2\n",
" },\n",
" \"gov.usda.snap.income.limit.net\": {\n",
" \"2024-01-01.2100-12-31\": 10000\n",
" }\n",
" }\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"def get_earners_above_mtr(reform=None, threshold=0.45, adult_index_limit=3):\n",
" if reform is None:\n",
" sim = Microsimulation(dataset=\"enhanced_cps_2022\")\n",
" else:\n",
" sim = Microsimulation(reform=Reform.from_dict(reform, country_id=\"us\"), dataset=\"enhanced_cps_2022\")\n",
" \n",
" mtr = sim.calculate(\"marginal_tax_rate\", map_to=\"person\", period=2024)\n",
"\n",
" adult_index = sim.calculate(\"adult_index\", map_to=\"person\", period=2024)\n",
"\n",
" is_adult = adult_index > 0\n",
" include = is_adult[adult_index <= adult_index_limit]\n",
"\n",
" total = include.sum()\n",
"\n",
" exceeds_mtr = include[mtr > threshold]\n",
" return exceeds_mtr.sum() / total\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Baseline\n",
"Running Expand refundable CTC\n",
"Running Restore ARPA EITC expansion for childless filers\n",
"Running SSI Reform\n",
"Running SNAP Reform\n"
]
}
],
"source": [
"results = []\n",
"\n",
"for reform_name, reform_details in REFORMS.items():\n",
" print(f\"Running {reform_name}\")\n",
" proportion = get_earners_above_mtr(reform=reform_details)\n",
" results.append({\"Reform\": reform_name, \"Proportion Above MTR\": proportion})\n",
"\n",
"# Add one more run with all four reforms applied as a tuple.\n",
"all_reforms = {k: v for reform in REFORMS.values() if reform is not None for k, v in reform.items()}\n",
"results.append({\"Reform\": \"All reforms\", \"Proportion Above MTR\": get_earners_above_mtr(reform=all_reforms)})"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"# Add one more run with all four reforms applied as a tuple.\n",
"all_reforms = {k: v for reform in REFORMS.values() if reform is not None for k, v in reform.items()}\n",
"results.append({\"Reform\": \"All Reforms\", \"Proportion Above MTR\": get_earners_above_mtr(reform=all_reforms)})"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Reform Proportion Above MTR \\\n",
"0 Baseline 0.105700 \n",
"1 Expand refundable CTC 0.064971 \n",
"2 Restore ARPA EITC expansion for childless filers 0.109615 \n",
"3 SSI Reform 0.081134 \n",
"4 SNAP Reform 0.083002 \n",
"5 All reforms 0.079570 \n",
"\n",
" change \n",
"0 0.000000 \n",
"1 -0.385323 \n",
"2 0.037038 \n",
"3 -0.232416 \n",
"4 -0.214738 \n",
"5 -0.247211 \n"
]
}
],
"source": [
"# Create a DataFrame from the results\n",
"df = pd.DataFrame(results)\n",
"\n",
"# Add a column for the reform as a percentage of the baseline\n",
"df[\"change\"] = (df[\"Proportion Above MTR\"] / df.loc[0, \"Proportion Above MTR\"]) - 1\n",
"\n",
"# Display the DataFrame\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "policyengine",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
115 changes: 115 additions & 0 deletions us/states/nj/nj_ctc_impact_neitralize.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from policyengine_us import Microsimulation\n",
"from policyengine_core.reforms import Reform\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"baseline = Microsimulation()\n",
"baseline_net_income = baseline.calculate(\"household_net_income\", period = 2024)\n",
"baseline_in_poverty = baseline.calculate(\"in_poverty\", map_to=\"household\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def calculate_changes_for_program():\n",
" class NeutralizeProgram(Reform):\n",
" def apply(self):\n",
" self.neutralize_variable(\"nj_ctc\")\n",
"\n",
" # Initialize the baseline and reformed simulations\n",
" reformed = Microsimulation(reform = NeutralizeProgram)\n",
" baseline.macro_cache_read = False\n",
" reformed.macro_cache_read = False\n",
"\n",
" # Calculate net income change for the specific state\n",
" state_codes = baseline.calculate(\"state_code\")\n",
" reformed_net_income = reformed.calculate(\"household_net_income\", period = 2024)\n",
" net_income_change = reformed_net_income[state_codes == \"NJ\"].sum() - baseline_net_income[state_codes == \"NJ\"].sum()\n",
"\n",
"\n",
" # Calculate in_poverty mean for persons for the specific state\n",
" reformed_in_poverty = reformed.calculate(\"in_poverty\", map_to=\"household\")\n",
" baseline_in_poverty_mean = baseline_in_poverty[state_codes == \"NJ\"].mean()\n",
" reformed_in_poverty_mean = reformed_in_poverty[state_codes == \"NJ\"].mean()\n",
" in_poverty_mean_change = reformed_in_poverty_mean - baseline_in_poverty_mean\n",
"\n",
" # Calculate Gini index for the specific state\n",
" personal_hh_equiv_income = reformed.calculate(\"equiv_household_net_income\")\n",
" household_count_people = reformed.calculate(\"household_count_people\")\n",
" personal_hh_equiv_income.weights *= household_count_people\n",
" gini_index = personal_hh_equiv_income.gini()\n",
"\n",
" return net_income_change, in_poverty_mean_change, gini_index"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Program State Net Income Change In Poverty Mean Change Gini Index\n",
"0 ca_eitc CA -6.510572e+08 0.0 0.39275\n"
]
}
],
"source": [
"# Create a DataFrame and store the results directly\n",
"results = [{\n",
" 'Program': \"nj_ctc\",\n",
" 'State': \"NJ\",\n",
" 'Net Income Change': calculate_changes_for_program()[0],\n",
" 'In Poverty Mean Change': calculate_changes_for_program()[1],\n",
" 'Gini Index': calculate_changes_for_program()[2]\n",
"}]\n",
"\n",
"# Convert the results to a DataFrame\n",
"results_df = pd.DataFrame(results)\n",
"\n",
"# Display the DataFrame\n",
"print(results_df)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "policyengine",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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