diff --git a/us/healthcare/healthcare_hack.ipynb b/us/healthcare/healthcare_hack.ipynb new file mode 100644 index 0000000..ad45a09 --- /dev/null +++ b/us/healthcare/healthcare_hack.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pavelmakarchuk/anaconda3/envs/pe/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from policyengine_us import Microsimulation\n", + "from policyengine_core.reforms import Reform\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Set random seed for reproducibility\n", + "np.random.seed(42)\n", + "\n", + "# Get baseline values for each program\n", + "baseline_snap = baseline.calculate(\"snap\", map_to=\"household\", period=2026)\n", + "baseline_medicaid = baseline.calculate(\"medicaid\", map_to=\"household\", period=2026)\n", + "baseline_aca_ptc = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "\n", + "# Create copies to modify\n", + "baseline_snap_modified = baseline_snap.copy()\n", + "baseline_medicaid_modified = baseline_medicaid.copy()\n", + "baseline_aca_ptc_modified = baseline_aca_ptc.copy()\n", + "\n", + "# For SNAP: randomly set 5% of positive values to 0\n", + "snap_recipients = baseline_snap_modified > 0\n", + "snap_indices = np.where(snap_recipients)[0]\n", + "snap_to_zero = np.random.choice(snap_indices, size=int(0.05 * len(snap_indices)), replace=False)\n", + "baseline_snap_modified[snap_to_zero] = 0\n", + "\n", + "# For Medicaid: randomly set 5% of positive values to 0\n", + "medicaid_recipients = baseline_medicaid_modified > 0\n", + "medicaid_indices = np.where(medicaid_recipients)[0]\n", + "medicaid_to_zero = np.random.choice(medicaid_indices, size=int(0.05 * len(medicaid_indices)), replace=False)\n", + "baseline_medicaid_modified[medicaid_to_zero] = 0\n", + "\n", + "# For ACA PTC: randomly set 5% of positive values to 0\n", + "aca_recipients = baseline_aca_ptc_modified > 0\n", + "aca_indices = np.where(aca_recipients)[0]\n", + "aca_to_zero = np.random.choice(aca_indices, size=int(0.05 * len(aca_indices)), replace=False)\n", + "baseline_aca_ptc_modified[aca_to_zero] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "household_data = pd.DataFrame({\n", + " 'Baseline SNAP': baseline_snap,\n", + " 'Reformed SNAP': baseline_snap_modified,\n", + " 'Baseline Medicaid': baseline_medicaid,\n", + " 'Reformed Medicaid': baseline_medicaid_modified,\n", + " 'Baseline ACA PTC': baseline_aca_ptc,\n", + " 'Reformed ACA PTC': baseline_aca_ptc_modified\n", + "})\n", + "# Add summary columns\n", + "household_data['Looses SNAP'] = (household_data['Baseline SNAP'] > 0) & (household_data['Reformed SNAP'] == 0)\n", + "household_data['Looses Medicaid'] = (household_data['Baseline Medicaid'] > 0) & (household_data['Reformed Medicaid'] == 0)\n", + "household_data['Looses ACA PTC'] = (household_data['Baseline ACA PTC'] > 0) & (household_data['Reformed ACA PTC'] == 0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Save to CSV\n", + "household_data.to_csv('household_benefits_reform.csv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data saved to 'household_benefits_reform.csv'\n", + "\n", + "Total households: 41,310\n", + "\n", + "Summary of changes:\n", + "SNAP benefits removed: 571 households\n", + "Medicaid benefits removed: 548 households\n", + "ACA PTC benefits removed: 242 households\n", + "\n", + "First 10 rows of the data:\n", + " household_id Baseline SNAP Reformed SNAP Baseline Medicaid \\\n", + "0 12 0.000000 0.000000 0.000000 \n", + "1 21 0.000000 0.000000 0.000000 \n", + "2 22 3896.510529 3896.510529 11924.721680 \n", + "3 30 288.630402 288.630402 11924.721680 \n", + "4 36 0.000000 0.000000 11924.721680 \n", + "5 41 0.000000 0.000000 0.000000 \n", + "6 44 1524.380127 1524.380127 23849.443359 \n", + "7 68 731.480103 731.480103 11924.721680 \n", + "8 73 0.000000 0.000000 0.000000 \n", + "9 78 0.000000 0.000000 0.000000 \n", + "\n", + " Reformed Medicaid Baseline ACA PTC Reformed ACA PTC Looses SNAP \\\n", + "0 0.000000 0.0 0.0 False \n", + "1 0.000000 0.0 0.0 False \n", + "2 11924.721680 0.0 0.0 False \n", + "3 11924.721680 0.0 0.0 False \n", + "4 11924.721680 0.0 0.0 False \n", + "5 0.000000 0.0 0.0 False \n", + "6 23849.443359 0.0 0.0 False \n", + "7 11924.721680 0.0 0.0 False \n", + "8 0.000000 0.0 0.0 False \n", + "9 0.000000 0.0 0.0 False \n", + "\n", + " Looses Medicaid Looses ACA PTC \n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "5 False False \n", + "6 False False \n", + "7 False False \n", + "8 False False \n", + "9 False False \n" + ] + } + ], + "source": [ + "# Print summary statistics\n", + "print(\"Data saved to 'household_benefits_reform.csv'\")\n", + "print(f\"\\nTotal households: {len(household_data):,}\")\n", + "print(\"\\nSummary of changes:\")\n", + "print(f\"SNAP benefits removed: {household_data['Looses SNAP'].sum():,} households\")\n", + "print(f\"Medicaid benefits removed: {household_data['Looses Medicaid'].sum():,} households\")\n", + "print(f\"ACA PTC benefits removed: {household_data['Looses ACA PTC'].sum():,} households\")\n", + "\n", + "# Display first few rows\n", + "print(\"\\nFirst 10 rows of the data:\")\n", + "print(household_data.head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/healthcare/healthcare_impacts.ipynb b/us/healthcare/healthcare_impacts.ipynb new file mode 100644 index 0000000..4153af6 --- /dev/null +++ b/us/healthcare/healthcare_impacts.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pavelmakarchuk/anaconda3/envs/pe/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from policyengine_us import Microsimulation\n", + "from policyengine_core.reforms import Reform\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define multiple takeup rates for each program\n", + "takeup_scenarios = {\n", + " \"snap\": [0.7, 0.85, 1.0],\n", + " \"medicaid\": [0.6, 0.8, 0.95],\n", + " \"aca_ptc\": [0.3, 0.5, 0.7]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create baseline simulation\n", + "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Store results\n", + "results = []\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "SNAP - Testing takeup rates: [0.7, 0.85, 1.0]\n", + "============================================================\n", + "Baseline recipients: 76.81 million\n", + "\n", + "Takeup rate 70%:\n", + " Reformed recipients: 76.81 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 85%:\n", + " Reformed recipients: 76.81 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 100%:\n", + " Reformed recipients: 76.81 million\n", + " Net change: +0.00 million\n", + "\n", + "============================================================\n", + "MEDICAID - Testing takeup rates: [0.6, 0.8, 0.95]\n", + "============================================================\n", + "Baseline recipients: 93.64 million\n", + "\n", + "Takeup rate 60%:\n", + " Reformed recipients: 93.64 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 80%:\n", + " Reformed recipients: 93.64 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 95%:\n", + " Reformed recipients: 93.64 million\n", + " Net change: +0.00 million\n", + "\n", + "============================================================\n", + "ACA_PTC - Testing takeup rates: [0.3, 0.5, 0.7]\n", + "============================================================\n", + "Baseline recipients: 52.15 million\n", + "\n", + "Takeup rate 30%:\n", + " Reformed recipients: 52.15 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 50%:\n", + " Reformed recipients: 52.15 million\n", + " Net change: +0.00 million\n", + "\n", + "Takeup rate 70%:\n", + " Reformed recipients: 52.15 million\n", + " Net change: +0.00 million\n" + ] + } + ], + "source": [ + "# Test each program with different takeup rates\n", + "for program, rates in takeup_scenarios.items():\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"{program.upper()} - Testing takeup rates: {rates}\")\n", + " print(f\"{'='*60}\")\n", + " \n", + " # Map program names to reform parameter names\n", + " param_map = {\n", + " \"snap\": \"gov.usda.snap.takeup_rate\",\n", + " \"medicaid\": \"gov.hhs.medicaid.takeup_rate\",\n", + " \"aca_ptc\": \"gov.aca.takeup_rate\"\n", + " }\n", + " \n", + " # Calculate baseline\n", + " baseline_values = baseline.calculate(program, map_to=\"person\", period=2026)\n", + " baseline_recipients = (baseline_values > 0).sum() / 1e6\n", + " print(f\"Baseline recipients: {baseline_recipients:.2f} million\")\n", + " \n", + " # Test each takeup rate\n", + " for rate in rates:\n", + " # Create reform for this rate\n", + " reform = Reform.from_dict(\n", + " {\n", + " param_map[program]: {\n", + " \"2026-01-01.2100-12-31\": rate\n", + " }\n", + " },\n", + " country_id=\"us\"\n", + " )\n", + " \n", + " # Run simulation\n", + " reformed_sim = Microsimulation(\n", + " dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\", \n", + " reform=reform\n", + " )\n", + " reformed_values = reformed_sim.calculate(program, map_to=\"person\", period=2026)\n", + " \n", + " # Calculate statistics\n", + " reformed_recipients = (reformed_values > 0).sum() / 1e6\n", + " people_gained = ((baseline_values == 0) & (reformed_values > 0)).sum() / 1e6\n", + " people_lost = ((baseline_values > 0) & (reformed_values == 0)).sum() / 1e6\n", + " net_change = reformed_recipients - baseline_recipients\n", + " \n", + " # Store and print results\n", + " results.append({\n", + " \"program\": program,\n", + " \"takeup_rate\": rate,\n", + " \"baseline_millions\": baseline_recipients,\n", + " \"reformed_millions\": reformed_recipients,\n", + " \"gained_millions\": people_gained,\n", + " \"lost_millions\": people_lost,\n", + " \"net_change_millions\": net_change\n", + " })\n", + " \n", + " print(f\"\\nTakeup rate {rate:.0%}:\")\n", + " print(f\" Reformed recipients: {reformed_recipients:.2f} million\")\n", + " print(f\" Net change: {net_change:+.2f} million\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "SUMMARY TABLE:\n", + " program takeup_rate baseline_millions reformed_millions \\\n", + "0 snap 0.70 76.81 76.81 \n", + "1 snap 0.85 76.81 76.81 \n", + "2 snap 1.00 76.81 76.81 \n", + "3 medicaid 0.60 93.64 93.64 \n", + "4 medicaid 0.80 93.64 93.64 \n", + "5 medicaid 0.95 93.64 93.64 \n", + "6 aca_ptc 0.30 52.15 52.15 \n", + "7 aca_ptc 0.50 52.15 52.15 \n", + "8 aca_ptc 0.70 52.15 52.15 \n", + "\n", + " gained_millions lost_millions net_change_millions \n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "\n", + "\n", + "NET CHANGE BY TAKEUP RATE (millions):\n", + "program aca_ptc medicaid snap\n", + "takeup_rate \n", + "0.30 0.0 NaN NaN\n", + "0.50 0.0 NaN NaN\n", + "0.60 NaN 0.0 NaN\n", + "0.70 0.0 NaN 0.0\n", + "0.80 NaN 0.0 NaN\n", + "0.85 NaN NaN 0.0\n", + "0.95 NaN 0.0 NaN\n", + "1.00 NaN NaN 0.0\n" + ] + } + ], + "source": [ + "# Create summary DataFrame\n", + "summary_df = pd.DataFrame(results)\n", + "print(\"\\n\\nSUMMARY TABLE:\")\n", + "print(summary_df.round(2))\n", + "\n", + "# Pivot table for easier comparison\n", + "pivot_df = summary_df.pivot(index='takeup_rate', columns='program', values='net_change_millions')\n", + "print(\"\\n\\nNET CHANGE BY TAKEUP RATE (millions):\")\n", + "print(pivot_df.round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from policyengine_us import Microsimulation\n", + "from policyengine_core.reforms import Reform\n", + "\n", + "reform = Reform.from_dict({\n", + " \"gov.usda.snap.takeup_rate\": {\n", + " \"2026-01-01.2100-12-31\": 0.7\n", + " }\n", + "}, country_id=\"us\")\n", + "\n", + "\n", + "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n", + "reformed = Microsimulation(reform=reform, dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n", + "baseline_income = baseline.calculate(\"household_net_income\", period=2026)\n", + "reformed_income = reformed.calculate(\"household_net_income\", period=2026)\n", + "difference_income = reformed_income - baseline_income" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "difference_income.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/healthcare/household_benefits_reform.csv b/us/healthcare/household_benefits_reform.csv new file mode 100644 index 0000000..b309ca6 --- /dev/null +++ b/us/healthcare/household_benefits_reform.csv @@ -0,0 +1,41311 @@ +household_id,Baseline SNAP,Reformed SNAP,Baseline Medicaid,Reformed Medicaid,Baseline ACA PTC,Reformed ACA PTC,Looses SNAP,Looses Medicaid,Looses ACA PTC +12,0.0,0.0,0.0,0.0,0.0,0.0,False,False,False +21,0.0,0.0,0.0,0.0,0.0,0.0,False,False,False +22,3896.510528564453,3896.510528564453,11924.7216796875,11924.7216796875,0.0,0.0,False,False,False +30,288.6304016113281,288.6304016113281,11924.7216796875,11924.7216796875,0.0,0.0,False,False,False 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