diff --git a/us/irs/income/credits/ctc/harris/harris_ctc_budget.ipynb b/us/irs/income/credits/ctc/harris/harris_ctc_budget.ipynb
new file mode 100644
index 0000000..b5257df
--- /dev/null
+++ b/us/irs/income/credits/ctc/harris/harris_ctc_budget.ipynb
@@ -0,0 +1,158 @@
+{
+ "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}
Impact: $%{y:,.0f}
($%{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
+}
diff --git a/us/irs/income/credits/ctc/harris/harris_ctc_household.ipynb b/us/irs/income/credits/ctc/harris/harris_ctc_household.ipynb
new file mode 100644
index 0000000..444c2aa
--- /dev/null
+++ b/us/irs/income/credits/ctc/harris/harris_ctc_household.ipynb
@@ -0,0 +1,208 @@
+{
+ "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\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
+}
diff --git a/us/irs/income/credits/ctc/harris_ctc.ipynb b/us/irs/income/credits/ctc/harris_ctc.ipynb
index 1a039a2..56337a8 100644
--- a/us/irs/income/credits/ctc/harris_ctc.ipynb
+++ b/us/irs/income/credits/ctc/harris_ctc.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
@@ -35,27 +35,30 @@
" },\n",
" \"gov.irs.credits.ctc.refundable.fully_refundable\": {\n",
" \"2023-01-01.2028-12-31\": True\n",
+ " },\n",
+ " \"gov.irs.credits.ctc.amount.base[1].threshold\": {\n",
+ " \"2024-01-01.2100-12-31\": 18\n",
" }\n",
"}, country_id=\"us\")"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
- "YEAR = \"2024\"\n",
- "MAX_INCOME = 250000"
+ "YEAR = \"2025\"\n",
+ "MAX_INCOME = 450000"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
- "def create_situation(filing_status, child_age):\n",
+ "def create_situation(is_married, child_age):\n",
" situation = {\n",
" \"people\": {\n",
" \"adult\": {\n",
@@ -76,24 +79,23 @@
" \"name\": \"employment_income\",\n",
" \"min\": 0,\n",
" \"max\": MAX_INCOME,\n",
- " \"count\": 201,\n",
+ " \"count\": 200,\n",
" \"period\": YEAR,\n",
" }\n",
" ]]\n",
" }\n",
" \n",
- " if filing_status == \"married\":\n",
+ " if is_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"
+ " return situation\n"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
@@ -104,32 +106,232 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_ctc_reform_dataframe():\n",
+ " data = []\n",
+ "\n",
+ " for is_married in [True, False]:\n",
+ " for child_age in [0, 5, 16, 17]:\n",
+ " situation = create_situation(is_married, child_age)\n",
+ " baseline_sim = Simulation(situation=situation)\n",
+ " reform_sim = Simulation(situation=situation, reform=reform)\n",
+ " \n",
+ " earnings = baseline_sim.calc(\"tax_unit_earned_income\", YEAR)\n",
+ " baseline = baseline_sim.calc(\"household_net_income\", YEAR)\n",
+ " reform_result = reform_sim.calc(\"household_net_income\", YEAR)\n",
+ " \n",
+ " data.append(pd.DataFrame({\n",
+ " \"is_married\": is_married,\n",
+ " \"child_age\": child_age,\n",
+ " \"earnings\": earnings,\n",
+ " \"net_impact\": reform_result - baseline\n",
+ " }))\n",
+ "\n",
+ " df = pd.concat(data, ignore_index=True)\n",
+ " df[\"child_age_label\"] = df.child_age.map({\n",
+ " 0: \"Newborn\",\n",
+ " 5: \"Child Age 1-5\",\n",
+ " 16: \"Child Age 6-16\",\n",
+ " 17: \"Child Age 17\"\n",
+ " })\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " marital_status | \n",
+ " child_age | \n",
+ " earnings | \n",
+ " net_impact | \n",
+ " child_age_label | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Married | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ " 6000.000000 | \n",
+ " Newborn | \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " Married | \n",
+ " 0 | \n",
+ " 2261.306641 | \n",
+ " 6000.000000 | \n",
+ " Newborn | \n",
+ " \n",
+ " \n",
+ " 2 | \n",
+ " Married | \n",
+ " 0 | \n",
+ " 4522.613281 | \n",
+ " 5696.609375 | \n",
+ " Newborn | \n",
+ " \n",
+ " \n",
+ " 3 | \n",
+ " Married | \n",
+ " 0 | \n",
+ " 6783.919434 | \n",
+ " 5357.412109 | \n",
+ " Newborn | \n",
+ " \n",
+ " \n",
+ " 4 | \n",
+ " Married | \n",
+ " 0 | \n",
+ " 9045.226562 | \n",
+ " 5018.216797 | \n",
+ " Newborn | \n",
+ " \n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " \n",
+ " \n",
+ " 1595 | \n",
+ " Single | \n",
+ " 17 | \n",
+ " 440954.781250 | \n",
+ " 0.000000 | \n",
+ " Child Age 17 | \n",
+ " \n",
+ " \n",
+ " 1596 | \n",
+ " Single | \n",
+ " 17 | \n",
+ " 443216.093750 | \n",
+ " 0.000000 | \n",
+ " Child Age 17 | \n",
+ " \n",
+ " \n",
+ " 1597 | \n",
+ " Single | \n",
+ " 17 | \n",
+ " 445477.375000 | \n",
+ " 0.000000 | \n",
+ " Child Age 17 | \n",
+ " \n",
+ " \n",
+ " 1598 | \n",
+ " Single | \n",
+ " 17 | \n",
+ " 447738.687500 | \n",
+ " 0.000000 | \n",
+ " Child Age 17 | \n",
+ " \n",
+ " \n",
+ " 1599 | \n",
+ " Single | \n",
+ " 17 | \n",
+ " 450000.000000 | \n",
+ " 0.000000 | \n",
+ " Child Age 17 | \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1600 rows × 5 columns \n",
+ " "
+ ],
+ "text/plain": [
+ " marital_status child_age earnings net_impact child_age_label\n",
+ "0 Married 0 0.000000 6000.000000 Newborn\n",
+ "1 Married 0 2261.306641 6000.000000 Newborn\n",
+ "2 Married 0 4522.613281 5696.609375 Newborn\n",
+ "3 Married 0 6783.919434 5357.412109 Newborn\n",
+ "4 Married 0 9045.226562 5018.216797 Newborn\n",
+ "... ... ... ... ... ...\n",
+ "1595 Single 17 440954.781250 0.000000 Child Age 17\n",
+ "1596 Single 17 443216.093750 0.000000 Child Age 17\n",
+ "1597 Single 17 445477.375000 0.000000 Child Age 17\n",
+ "1598 Single 17 447738.687500 0.000000 Child Age 17\n",
+ "1599 Single 17 450000.000000 0.000000 Child Age 17\n",
+ "\n",
+ "[1600 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create the DataFrame\n",
+ "df = create_ctc_reform_dataframe()\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
"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",
+ " \"married_0\": \"#0066cc\", # Dark blue\n",
+ " \"married_5\": \"#4d94ff\", # Medium blue\n",
+ " \"married_16\": \"#99c2ff\", # Light blue\n",
+ " \"married_17\": \"#cce0ff\", # Very light blue\n",
+ " \"single_0\": \"#333333\", # Dark grey\n",
+ " \"single_5\": \"#666666\", # Medium grey\n",
+ " \"single_16\": \"#999999\", # Light grey\n",
+ " \"single_17\": \"#cccccc\", # Very light grey\n",
" }\n",
"\n",
- " x = np.linspace(0, MAX_INCOME, 201)\n",
+ " x = np.linspace(0, MAX_INCOME, 200)\n",
" fig = go.Figure()\n",
"\n",
- " for filing_status in [\"married\", \"hoh\"]:\n",
- " for child_age in [0, 5, 16]:\n",
- " baseline = calculate_income(create_situation(filing_status, child_age))\n",
- " reform_result = calculate_income(create_situation(filing_status, child_age), reform)\n",
+ " for marital_status in [\"married\", \"single\"]:\n",
+ " for child_age in [0, 5, 17]:\n",
+ " baseline = calculate_income(create_situation(marital_status, child_age))\n",
+ " reform_result = calculate_income(create_situation(marital_status, child_age), reform)\n",
+ " \n",
+ " if child_age == 0:\n",
+ " child_label = \"Newborn\"\n",
+ " elif child_age == 5:\n",
+ " child_label = \"Child Age 1-5\"\n",
+ " else:\n",
+ " child_label = \"Child Age 17\"\n",
" \n",
- " label = f\"{'Married' if filing_status == 'married' else 'Single'}, Child Age {child_age}\"\n",
- " color = colors[f\"{filing_status}_{child_age}\"]\n",
+ " label = f\"{marital_status.capitalize()}, {child_label}\"\n",
+ " color = colors[f\"{marital_status}_{child_age}\"]\n",
" \n",
- " line_style = 'solid' if filing_status == \"married\" else 'dot'\n",
+ " line_style = 'solid' if marital_status == \"married\" else 'dot'\n",
" \n",
" fig.add_trace(go.Scatter(\n",
" x=x, \n",
@@ -140,7 +342,7 @@
" ))\n",
"\n",
" fig.update_layout(\n",
- " title='Impact of the Harris CTC Reform by Filing Status and Child Age',\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",
@@ -160,7 +362,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 94,
"metadata": {},
"outputs": [
{
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