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Adjust the harris CTC household notebook #16

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Adjust the harris CTC household notebook #16

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d8abf49
Adjust the harris CTC housheold notebook
PavelMakarchuk Aug 16, 2024
20c7649
adjust graph with 16 year olds
PavelMakarchuk Aug 16, 2024
d627cca
graph
PavelMakarchuk Aug 16, 2024
20ee1aa
adj
PavelMakarchuk Aug 16, 2024
1a2b604
minor but still need the chart
PavelMakarchuk Aug 16, 2024
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158 changes: 158 additions & 0 deletions us/irs/income/credits/ctc/harris/harris_ctc_budget.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from policyengine_us import Microsimulation\n",
"from policyengine_core.reforms import Reform\n",
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"from multiprocessing import Pool, cpu_count\n",
"from functools import lru_cache"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"reform = Reform.from_dict({\n",
" \"gov.contrib.congress.delauro.american_family_act.baby_bonus\": {\n",
" \"2024-01-01.2100-12-31\": 2400\n",
" },\n",
" \"gov.irs.credits.ctc.amount.arpa[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 3600\n",
" },\n",
" \"gov.irs.credits.ctc.amount.arpa[1].amount\": {\n",
" \"2023-01-01.2028-12-31\": 3000\n",
" },\n",
" \"gov.irs.credits.ctc.phase_out.arpa.in_effect\": {\n",
" \"2023-01-01.2028-12-31\": True\n",
" },\n",
" \"gov.irs.credits.ctc.refundable.fully_refundable\": {\n",
" \"2023-01-01.2028-12-31\": True\n",
" }\n",
"}, country_id=\"us\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Initialize simulations\n",
"baseline = Microsimulation(dataset=\"enhanced_cps_2022\")\n",
"reformed = Microsimulation(reform=reform, dataset=\"enhanced_cps_2022\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"@lru_cache(maxsize=None)\n",
"def calculate_difference(year):\n",
" try:\n",
" baseline_person = baseline.calculate(\"household_net_income\", period=year)\n",
" reformed_person = reformed.calculate(\"household_net_income\", period=year)\n",
" difference = (reformed_person - baseline_person).sum()\n",
" return year, difference\n",
" except Exception as e:\n",
" print(f\"Error calculating for year {year}: {str(e)}\")\n",
" return year, None"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" years = list(range(2025, 2035))\n",
" \n",
" # Calculate differences sequentially\n",
" results = [calculate_difference(year) for year in years]\n",
" \n",
" # Filter out any None results from errors\n",
" valid_results = [(year, diff) for year, diff in results if diff is not None]\n",
" valid_years, differences = zip(*valid_results)\n",
"\n",
" # Create Plotly bar chart\n",
" fig = go.Figure(data=[\n",
" go.Bar(name='Impact', x=valid_years, y=differences)\n",
" ])\n",
"\n",
" # Update layout\n",
" fig.update_layout(\n",
" title='Impact of CTC Reform on Household Net Income (2025-2034)',\n",
" xaxis_title='Year',\n",
" yaxis_title='Total Difference in Household Net Income',\n",
" yaxis_tickformat='$,.0f',\n",
" )\n",
"\n",
" # Add hover template to show values in billions\n",
" fig.update_traces(\n",
" hovertemplate='Year: %{x}<br>Impact: $%{y:,.0f}<br>($%{y:,.1f} billion)',\n",
" text=[f'${d/1e9:.1f}B' for d in differences],\n",
" textposition='outside'\n",
" )\n",
"\n",
" # Show the plot\n",
" fig.show()\n",
"\n",
" # Print numerical results\n",
" for year, difference in zip(valid_years, differences):\n",
" print(f\"{year}: ${difference/1e9:.2f} billion\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"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
}
208 changes: 208 additions & 0 deletions us/irs/income/credits/ctc/harris/harris_ctc_household.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import plotly.graph_objects as go\n",
"from policyengine_us import Simulation\n",
"from policyengine_core.reforms import Reform\n",
"from policyengine_core.charts import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Define the reform\n",
"reform = Reform.from_dict({\n",
" \"gov.contrib.congress.delauro.american_family_act.baby_bonus\": {\n",
" \"2024-01-01.2100-12-31\": 2400\n",
" },\n",
" \"gov.irs.credits.ctc.amount.arpa[0].amount\": {\n",
" \"2023-01-01.2028-12-31\": 3600\n",
" },\n",
" \"gov.irs.credits.ctc.amount.arpa[1].amount\": {\n",
" \"2023-01-01.2028-12-31\": 3000\n",
" },\n",
" \"gov.irs.credits.ctc.phase_out.arpa.in_effect\": {\n",
" \"2023-01-01.2028-12-31\": True\n",
" },\n",
" \"gov.irs.credits.ctc.refundable.fully_refundable\": {\n",
" \"2023-01-01.2028-12-31\": True\n",
" }\n",
"}, country_id=\"us\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"YEAR = \"2025\"\n",
"MAX_INCOME = 250000"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def create_situation(filing_status, child_age):\n",
" situation = {\n",
" \"people\": {\n",
" \"adult\": {\n",
" \"age\": {YEAR: 40},\n",
" },\n",
" \"child\": {\n",
" \"age\": {YEAR: child_age},\n",
" }\n",
" },\n",
" \"families\": {\"family\": {\"members\": [\"adult\", \"child\"]}},\n",
" \"marital_units\": {\"marital_unit\": {\"members\": [\"adult\"]}},\n",
" \"tax_units\": {\"tax_unit\": {\"members\": [\"adult\", \"child\"]}},\n",
" \"households\": {\n",
" \"household\": {\"members\": [\"adult\", \"child\"], \"state_name\": {YEAR: \"TX\"}}\n",
" },\n",
" \"axes\": [[\n",
" {\n",
" \"name\": \"employment_income\",\n",
" \"min\": 0,\n",
" \"max\": MAX_INCOME,\n",
" \"count\": 201,\n",
" \"period\": YEAR,\n",
" }\n",
" ]]\n",
" }\n",
" \n",
" if filing_status == \"married\":\n",
" situation[\"people\"][\"spouse\"] = {\"age\": {YEAR: 40}}\n",
" for unit in [\"families\", \"marital_units\", \"tax_units\", \"households\"]:\n",
" situation[unit][list(situation[unit].keys())[0]][\"members\"].append(\"spouse\")\n",
" \n",
" return situation\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def calculate_income(situation, reform=None):\n",
" simulation = Simulation(situation=situation, reform=reform)\n",
" return simulation.calculate(\"household_net_income\", YEAR)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def create_ctc_reform_comparison_graph():\n",
" colors = {\n",
" \"married_0\": \"#0066cc\", # Dark blue\n",
" \"married_5\": \"#4d94ff\", # Medium blue\n",
" \"married_16\": \"#99c2ff\", # Light blue\n",
" \"hoh_0\": \"#333333\", # Dark grey\n",
" \"hoh_5\": \"#666666\", # Medium grey\n",
" \"hoh_16\": \"#999999\", # Light grey\n",
" }\n",
"\n",
" x = np.linspace(0, MAX_INCOME, 201)\n",
" fig = go.Figure()\n",
"\n",
" for filing_status in [\"married\", \"hoh\"]:\n",
" for child_age in [0, 5, 17]:\n",
" baseline = calculate_income(create_situation(filing_status, child_age))\n",
" reform_result = calculate_income(create_situation(filing_status, child_age), reform)\n",
" \n",
" label = f\"{'Married' if filing_status == 'married' else 'Single'}, Child Age {child_age}\"\n",
" color = colors[f\"{filing_status}_{child_age}\"]\n",
" \n",
" line_style = 'solid' if filing_status == \"married\" else 'dot'\n",
" \n",
" fig.add_trace(go.Scatter(\n",
" x=x, \n",
" y=reform_result - baseline, \n",
" mode='lines', \n",
" name=label, \n",
" line=dict(color=color, dash=line_style)\n",
" ))\n",
"\n",
" fig.update_layout(\n",
" title='Impact of the Harris CTC Reform by Marital Status and Child Age',\n",
" xaxis_title=\"Earnings\",\n",
" yaxis_title=\"Net Impact\",\n",
" xaxis=dict(tickformat='$,.0f', range=[0, MAX_INCOME]),\n",
" yaxis=dict(tickformat='$,.0f'),\n",
" legend=dict(\n",
" yanchor=\"top\",\n",
" y=0.99,\n",
" xanchor=\"left\",\n",
" x=1.01\n",
" ),\n",
" height=600,\n",
" width=800,\n",
" )\n",
"\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'married_17'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/t5/1q_n2l7d1ndddzm52gy9n93w0000gn/T/ipykernel_29607/698317625.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Create and display the chart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_ctc_reform_comparison_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat_fig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/var/folders/t5/1q_n2l7d1ndddzm52gy9n93w0000gn/T/ipykernel_29607/1298352449.py\u001b[0m in \u001b[0;36mcreate_ctc_reform_comparison_graph\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{'Married' if filing_status == 'married' else 'Single'}, Child Age {child_age}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{filing_status}_{child_age}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mline_style\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'solid'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfiling_status\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"married\"\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'dot'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'married_17'"
]
}
],
"source": [
"# Create and display the chart\n",
"fig = create_ctc_reform_comparison_graph()\n",
"fig = format_fig(fig)\n",
"fig.show()"
]
}
],
"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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