From 42dc183b3a5eaa53f57843ec29711c4fa733e643 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Tue, 27 May 2025 10:23:06 -0400
Subject: [PATCH 01/37] aca stuff
---
us/medicaid/aca_numbers.ipynb | 75 +++
us/medicaid/aca_reform.ipynb | 464 ++++++++++++++++++
.../medicaid_calculation_example.ipynb | 84 +++-
us/medicaid/medicaid_households.ipynb | 56 ++-
4 files changed, 639 insertions(+), 40 deletions(-)
create mode 100644 us/medicaid/aca_numbers.ipynb
create mode 100644 us/medicaid/aca_reform.ipynb
diff --git a/us/medicaid/aca_numbers.ipynb b/us/medicaid/aca_numbers.ipynb
new file mode 100644
index 0000000..63255b3
--- /dev/null
+++ b/us/medicaid/aca_numbers.ipynb
@@ -0,0 +1,75 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/daphnehansell/miniconda3/envs/policyengine/lib/python3.12/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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "Microsimulation.calculate() got an unexpected keyword argument 'year'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m aca \u001b[38;5;241m=\u001b[39m \u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhas_marketplace_health_coverage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myear\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2024\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mTypeError\u001b[0m: Microsimulation.calculate() got an unexpected keyword argument 'year'"
+ ]
+ }
+ ],
+ "source": [
+ "aca = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2025)"
+ ]
+ }
+ ],
+ "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": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/us/medicaid/aca_reform.ipynb b/us/medicaid/aca_reform.ipynb
new file mode 100644
index 0000000..e07fd61
--- /dev/null
+++ b/us/medicaid/aca_reform.ipynb
@@ -0,0 +1,464 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/daphnehansell/miniconda3/envs/policyengine/lib/python3.12/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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "reform = Reform.from_dict({\n",
+ " \"gov.aca.ptc_phase_out_rate[0].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[1].amount\": {\n",
+ " \"2025-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[3].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.02\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[4].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.04\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[5].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.06\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[6].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.085\n",
+ " },\n",
+ " \"gov.aca.ptc_income_eligibility[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": True\n",
+ " }\n",
+ "}, country_id=\"us\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_aca_enrollment = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "reformed_aca_enrollment = reformed.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "difference_aca_enrollment = reformed_aca_enrollment - baseline_aca_enrollment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "33.22788410880193"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "difference_aca_enrollment/1e6\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "38.4306100712353"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_aca_enrollment/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "71.65849418003722"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reformed_aca_enrollment/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_chip_enrollment = baseline.calculate(\"is_chip_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "reformed_chip_enrollment = reformed.calculate(\"is_chip_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "difference_chip_enrollment = reformed_chip_enrollment - baseline_chip_enrollment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "difference_chip_enrollment/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_has_esi = baseline.calculate(\"has_esi\", map_to=\"person\", period=2026).sum()\n",
+ "reformed_has_esi = reformed.calculate(\"has_esi\", map_to=\"person\", period=2026).sum()\n",
+ "difference_has_esi = reformed_has_esi - baseline_has_esi\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "difference_has_esi/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_aca_enrollment24 = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2024).sum()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "70.86273435735636"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_aca_enrollment24/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_aca_enrollment24 = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2024).sum()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "96.20048595727322"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_medicaid_enrollment = baseline.calculate(\"is_medicaid_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "baseline_medicaid_enrollment/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "140.78688548422448"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_has_esi/1e6\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20.269165984168318"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "has_marketplace_health_coverage24 = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2024).sum()\n",
+ "has_marketplace_health_coverage24/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20.60345003861629"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "has_marketplace_health_coverage26 = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2026).sum()\n",
+ "has_marketplace_health_coverage26/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20.720238717416315"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "has_marketplace_health_coverage27 = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2027).sum()\n",
+ "has_marketplace_health_coverage27/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "63.9239444720358"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_medicare = baseline.calculate(\"is_medicare_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "\n",
+ "baseline_medicare/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "347.5581846564728"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "any_coverage = baseline_has_esi + baseline_aca_enrollment + baseline_chip_enrollment + baseline_medicaid_enrollment + baseline_medicare\n",
+ "\n",
+ "any_coverage/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2024 0.05176565423883876\n",
+ "2025 1.0\n",
+ "2026 0.05176565417753795\n"
+ ]
+ }
+ ],
+ "source": [
+ "for y in (2024, 2025, 2026):\n",
+ " cov = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=y)\n",
+ " wt = baseline.calculate(\"person_weight\", map_to=\"person\", period=y)\n",
+ " print(y, (cov * wt).sum() / wt.sum()) # ~0.06 in 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "Variable exchcov25 does not exist.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m exch25 \u001b[38;5;241m=\u001b[39m \u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexchcov25\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mperson\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2025\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(exch25\u001b[38;5;241m.\u001b[39mvalue_counts(dropna\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead())\n",
+ "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.12/site-packages/policyengine_core/simulations/microsimulation.py:54\u001b[0m, in \u001b[0;36mMicrosimulation.calculate\u001b[0;34m(self, variable_name, period, map_to, use_weights, decode_enums)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m period \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_calculation_period \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m period \u001b[38;5;241m=\u001b[39m get_period(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_calculation_period)\n\u001b[0;32m---> 54\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_enums\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_weights:\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m values\n",
+ "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.12/site-packages/policyengine_core/simulations/simulation.py:477\u001b[0m, in \u001b[0;36mSimulation.calculate\u001b[0;34m(self, variable_name, period, map_to, decode_enums)\u001b[0m\n\u001b[1;32m 474\u001b[0m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;28mhash\u001b[39m(variable_name \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(period)) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m1000000\u001b[39m)\n\u001b[1;32m 476\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 477\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_calculate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, EnumArray) \u001b[38;5;129;01mand\u001b[39;00m decode_enums:\n\u001b[1;32m 479\u001b[0m result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mdecode_to_str()\n",
+ "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.12/site-packages/policyengine_core/simulations/simulation.py:602\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 592\u001b[0m \u001b[38;5;124;03mCalculate the variable ``variable_name`` for the period ``period``, using the variable formula if it exists.\u001b[39;00m\n\u001b[1;32m 593\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[38;5;124;03m ArrayLike: The calculated variable.\u001b[39;00m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 601\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m variable_name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtax_benefit_system\u001b[38;5;241m.\u001b[39mvariables:\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVariable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvariable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not exist.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 603\u001b[0m population \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_variable_population(variable_name)\n\u001b[1;32m 604\u001b[0m holder \u001b[38;5;241m=\u001b[39m population\u001b[38;5;241m.\u001b[39mget_holder(variable_name)\n",
+ "\u001b[0;31mValueError\u001b[0m: Variable exchcov25 does not exist."
+ ]
+ }
+ ],
+ "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": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/us/medicaid/medicaid_calculation_example.ipynb b/us/medicaid/medicaid_calculation_example.ipynb
index b90fc03..c8a6841 100644
--- a/us/medicaid/medicaid_calculation_example.ipynb
+++ b/us/medicaid/medicaid_calculation_example.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
@@ -25,7 +25,7 @@
" \"2026\": 40 # Primary earner, age 40 in 2026\n",
" },\n",
" \"employment_income\": {\n",
- " \"2026\": 45000 # Annual employment income of $45,000\n",
+ " \"2026\": 53300 \n",
" }\n",
" },\n",
" \"your partner\": {\n",
@@ -33,7 +33,7 @@
" \"2026\": 40 \n",
" },\n",
" \"employment_income\": {\n",
- " \"2026\": 0 \n",
+ " \"2026\": 53299 #Household income is 1 belo2 400% fpl for 2025 \n",
" }\n",
" },\n",
" \"your first dependent\": {\n",
@@ -103,7 +103,10 @@
" ],\n",
" \"state_name\": {\n",
" \"2026\": \"NY\" # Located in New York state\n",
- " }\n",
+ " },\n",
+ " \"county_fips\": {\n",
+ " \"2026\": \"36061\"\n",
+ " }\n",
" }\n",
" }\n",
"}"
@@ -111,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@@ -123,7 +126,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
@@ -134,21 +137,26 @@
"chip_eligibility = simulation.calculate(\"is_chip_eligible\", period=2026).tolist()\n",
"\n",
"# Calculate ACA Premium Tax Credits for the tax unit and convert to list\n",
- "aca = simulation.calculate(\"aca_ptc\", period=2026).tolist()\n"
+ "aca = simulation.calculate(\"premium_tax_credit\", period=2026).tolist()\n",
+ "\n",
+ "\n",
+ "#MTR with healthcare benefits\n",
+ "mtr = simulation.calculate(\"marginal_tax_rate\", period=2026).tolist()\n",
+ "mtrh = simulation.calculate(\"marginal_tax_rate_including_health_benefits\", period=2026).tolist()"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[True, True, False]"
+ "[False, False, False]"
]
},
- "execution_count": 41,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -160,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
@@ -169,7 +177,7 @@
"[False, False, True]"
]
},
- "execution_count": 42,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -181,16 +189,16 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[0.0]"
+ "[10993.09375]"
]
},
- "execution_count": 43,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
@@ -199,11 +207,51 @@
"# Display the results\n",
"aca"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.281499981880188, 0.281499981880188, 0.0]"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mtr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[11.274593353271484, 11.274593353271484, 0.0]"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mtrh"
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "pe",
+ "display_name": "base",
"language": "python",
"name": "python3"
},
@@ -217,7 +265,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/medicaid_households.ipynb
index 8b27a19..c8b7f46 100644
--- a/us/medicaid/medicaid_households.ipynb
+++ b/us/medicaid/medicaid_households.ipynb
@@ -2,9 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/daphnehansell/miniconda3/lib/python3.12/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 Simulation\n",
"from policyengine_core.reforms import Reform\n",
@@ -16,7 +25,7 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +61,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -103,17 +112,20 @@
" }\n",
" },\n",
" \"households\": {\n",
- " \"your household\": {\n",
- " \"members\": [\n",
- " \"you\",\n",
- " \"your partner\",\n",
- " \"your first dependent\"\n",
- " ],\n",
- " \"state_name\": {\n",
- " \"2026\": \"NY\"\n",
- " }\n",
+ " \"your household\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\", \n",
+ " \"your first dependent\" # All live in the same household\n",
+ " ],\n",
+ " \"state_name\": {\n",
+ " \"2026\": \"NY\" # Located in New York state\n",
+ " },\n",
+ " \"county_fips\": {\n",
+ " \"2026\": \"36061\"\n",
" }\n",
- " },\n",
+ " }\n",
+ " },\n",
" \"marital_units\": {\n",
" \"your marital unit\": {\n",
" \"members\": [\n",
@@ -146,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -225,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -247,7 +259,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -281,7 +293,7 @@
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -293,7 +305,7 @@
},
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -27870,7 +27882,7 @@
}
],
"source": [
- "# Create Vermont graph\n",
+ "# Create NY graph\n",
"fig_vermont = go.Figure()\n",
"\n",
"# Add baseline traces (solid lines)\n",
@@ -28054,7 +28066,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "pe",
+ "display_name": "base",
"language": "python",
"name": "python3"
},
@@ -28068,7 +28080,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.2"
}
},
"nbformat": 4,
From b581f1b8732c1bdd6dff94a56c2feac9419a2c49 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Tue, 27 May 2025 14:32:25 -0400
Subject: [PATCH 02/37] charts
---
.../medicaid_calculation_example.ipynb | 47 +
us/medicaid/medicaid_households.ipynb | 16764 +++++++++++++++-
us/medicaid/mtr.ipynb | 186 +
3 files changed, 16934 insertions(+), 63 deletions(-)
create mode 100644 us/medicaid/mtr.ipynb
diff --git a/us/medicaid/medicaid_calculation_example.ipynb b/us/medicaid/medicaid_calculation_example.ipynb
index c8a6841..fca75f2 100644
--- a/us/medicaid/medicaid_calculation_example.ipynb
+++ b/us/medicaid/medicaid_calculation_example.ipynb
@@ -247,6 +247,53 @@
"source": [
"mtrh"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "# ---------- New York: family of 3 ----------\n",
+ "fig_ny = go.Figure()\n",
+ "\n",
+ "# Baseline (solid)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_ny,\n",
+ " y=baseline_ny_health_net_income,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2) # use your palette constant\n",
+ "))\n",
+ "\n",
+ "# Reform (dotted)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_ny,\n",
+ " y=reform_ny_health_net_income,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Reform)',\n",
+ " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_ny.update_layout(\n",
+ " title='New York Household (Family of 3) – Health-Adjusted Net Income by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Health-Adjusted Net Income',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
+ " yaxis=dict(tickformat='$,.0f'),\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional wrapper if you use one elsewhere\n",
+ "fig_ny = format_fig(fig_ny)\n",
+ "\n",
+ "fig_ny.show()\n"
+ ]
}
],
"metadata": {
diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/medicaid_households.ipynb
index c8b7f46..90129ad 100644
--- a/us/medicaid/medicaid_households.ipynb
+++ b/us/medicaid/medicaid_households.ipynb
@@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/daphnehansell/miniconda3/lib/python3.12/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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from policyengine_us import Simulation\n",
"from policyengine_core.reforms import Reform\n",
@@ -25,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -61,12 +52,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"\n",
- "situation_vermont = {\n",
+ "situation_texas = {\n",
" \"people\": {\n",
" \"you\": {\n",
" \"age\": {\n",
@@ -158,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -237,18 +228,18 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
- "simulation_vermont = Simulation(\n",
- " situation=situation_vermont,\n",
+ "simulation_new_york = Simulation(\n",
+ " situation=situation_new_york,\n",
")\n",
"simulation_texas = Simulation(\n",
" situation=situation_texas,\n",
")\n",
- "reformed_simulation_vermont = Simulation(\n",
- " situation=situation_vermont,\n",
+ "reformed_simulation_new_york = Simulation(\n",
+ " situation=situation_new_york,\n",
" reform=reform,\n",
")\n",
"reformed_simulation_texas = Simulation(\n",
@@ -259,32 +250,43 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
- "household_income_vermont = simulation_vermont.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
- "baseline_vermont_per_capita_chip = simulation_vermont.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
- "baseline_vermont_aca_ptc = simulation_vermont.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "baseline_vermont_medicaid_cost = simulation_vermont.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "household_income_new_york = simulation_new_york.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
+ "baseline_new_york_per_capita_chip = simulation_new_york.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
+ "baseline_new_york_aca_ptc = simulation_new_york.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
+ "baseline_new_york_medicaid_cost = simulation_new_york.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "baseline_new_york_net_income_including_health_benefits = simulation_new_york.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
+ "\n",
+ "\n",
+ "reform_new_york_per_capita_chip = reformed_simulation_new_york.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
+ "reform_new_york_aca_ptc = reformed_simulation_new_york.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
+ "reform_new_york_medicaid_cost = reformed_simulation_new_york.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "reform_new_york_net_income_including_health_benefits = reformed_simulation_new_york.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
+ "\n",
+ "\n",
+ "\n",
"\n",
- "reform_vermont_per_capita_chip = reformed_simulation_vermont.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
- "reform_vermont_aca_ptc = reformed_simulation_vermont.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "reform_vermont_medicaid_cost = reformed_simulation_vermont.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
"\n",
"# Get household-level values for Texas\n",
"household_income_texas = simulation_texas.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
"baseline_texas_per_capita_chip = simulation_texas.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"baseline_texas_aca_ptc = simulation_texas.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
"baseline_texas_medicaid_cost = simulation_texas.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "baseline_texas_net_income_including_health_benefits = simulation_texas.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
+ "\n",
"\n",
"reform_texas_per_capita_chip = reformed_simulation_texas.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"reform_texas_aca_ptc = reformed_simulation_texas.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
"reform_texas_medicaid_cost = reformed_simulation_texas.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "reform_texas_net_income_including_health_benefits = reformed_simulation_texas.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
+ "\n",
"\n",
"# Calculate total benefits for each scenario\n",
- "baseline_vermont_total = [sum(x) for x in zip(baseline_vermont_per_capita_chip, baseline_vermont_aca_ptc, baseline_vermont_medicaid_cost)]\n",
- "reform_vermont_total = [sum(x) for x in zip(reform_vermont_per_capita_chip, reform_vermont_aca_ptc, reform_vermont_medicaid_cost)]\n",
+ "baseline_new_york_total = [sum(x) for x in zip(baseline_new_york_per_capita_chip, baseline_new_york_aca_ptc, baseline_new_york_medicaid_cost)]\n",
+ "reform_new_york_total = [sum(x) for x in zip(reform_new_york_per_capita_chip, reform_new_york_aca_ptc, reform_new_york_medicaid_cost)]\n",
"\n",
"baseline_texas_total = [sum(x) for x in zip(baseline_texas_per_capita_chip, baseline_texas_aca_ptc, baseline_texas_medicaid_cost)]\n",
"reform_texas_total = [sum(x) for x in zip(reform_texas_per_capita_chip, reform_texas_aca_ptc, reform_texas_medicaid_cost)]\n",
@@ -293,7 +295,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -305,7 +307,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -27883,77 +27885,77 @@
],
"source": [
"# Create NY graph\n",
- "fig_vermont = go.Figure()\n",
+ "fig_new_york = go.Figure()\n",
"\n",
"# Add baseline traces (solid lines)\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=baseline_vermont_per_capita_chip, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=baseline_new_york_per_capita_chip, \n",
" mode='lines', \n",
" name='CHIP (Baseline)', \n",
" line=dict(color=GRAY, width=2)\n",
"))\n",
"\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=baseline_vermont_aca_ptc, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=baseline_new_york_aca_ptc, \n",
" mode='lines', \n",
" name='ACA PTC (Baseline)', \n",
" line=dict(color=BLUE_PRIMARY, width=2)\n",
"))\n",
"\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=baseline_vermont_medicaid_cost, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=baseline_new_york_medicaid_cost, \n",
" mode='lines', \n",
" name='Medicaid (Baseline)', \n",
" line=dict(color=TEAL_ACCENT, width=2)\n",
"))\n",
"\n",
"# Add reform traces (dotted lines)\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=reform_vermont_per_capita_chip, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=reform_new_york_per_capita_chip, \n",
" mode='lines', \n",
" name='CHIP (Reform)', \n",
" line=dict(color=GRAY, width=2, dash='dot')\n",
"))\n",
"\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=reform_vermont_aca_ptc, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=reform_new_york_aca_ptc, \n",
" mode='lines', \n",
" name='ACA PTC (Reform)', \n",
" line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n",
"))\n",
"\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=reform_vermont_medicaid_cost, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=reform_new_york_medicaid_cost, \n",
" mode='lines', \n",
" name='Medicaid (Reform)', \n",
" line=dict(color=TEAL_ACCENT, width=2, dash='dot')\n",
"))\n",
"\n",
"# Add total lines\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=baseline_vermont_total, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=baseline_new_york_total, \n",
" mode='lines', \n",
" name='Total Benefits (Baseline)', \n",
" line=dict(color=DARK_GRAY, width=2)\n",
"))\n",
"\n",
- "fig_vermont.add_trace(go.Scatter(\n",
- " x=household_income_vermont, \n",
- " y=reform_vermont_total, \n",
+ "fig_new_york.add_trace(go.Scatter(\n",
+ " x=household_income_new_york, \n",
+ " y=reform_new_york_total, \n",
" mode='lines', \n",
" name='Total Benefits (Reform)', \n",
" line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
"))\n",
"\n",
"# Update layout\n",
- "fig_vermont.update_layout(\n",
+ "fig_new_york.update_layout(\n",
" title='New York Household (Family of 3) - Program Benefits by Income Level',\n",
" xaxis_title='Household Income',\n",
" yaxis_title='Benefit Amount',\n",
@@ -28048,20 +28050,16656 @@
"\n",
"# Apply your format_fig function if it exists\n",
"# If you don't have this function defined, you can remove these lines\n",
- "fig_vermont = format_fig(fig_vermont)\n",
+ "fig_new_york = format_fig(fig_new_york)\n",
"fig_texas = format_fig(fig_texas)\n",
"\n",
"# Display the figures\n",
- "fig_vermont.show()\n",
+ "fig_new_york.show()\n",
"fig_texas.show()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "line": {
+ "color": "#616161",
+ "width": 2
+ },
+ "mode": "lines",
+ "name": "Health Net Income (Baseline)",
+ "type": "scatter",
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+ "width": 800,
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+ "source": [
+ "#House hold net income graphs\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "# ---------- NY fam ----------\n",
+ "fig_ny = go.Figure()\n",
+ "\n",
+ "# Baseline (solid)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=baseline_new_york_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2) # use your palette constant\n",
+ "))\n",
+ "\n",
+ "# Reform (dotted)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=reform_new_york_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Reform)',\n",
+ " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_ny.update_layout(\n",
+ " title='New York Household (Family of 3) – Health-Adjusted Net Income by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Health-Adjusted Net Income',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
+ " yaxis=dict(tickformat='$,.0f'),\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional wrapper if you use one elsewhere\n",
+ "fig_ny = format_fig(fig_ny)\n",
+ "\n",
+ "fig_ny.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "line": {
+ "color": "#616161",
+ "width": 2
+ },
+ "mode": "lines",
+ "name": "Health Net Income (Baseline)",
+ "type": "scatter",
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+ },
+ "width": 800,
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+ "source": [
+ "# ---------- texas couple ----------\n",
+ "fig_tx = go.Figure()\n",
+ "\n",
+ "# Baseline (solid)\n",
+ "fig_tx.add_trace(go.Scatter(\n",
+ " x=household_income_texas,\n",
+ " y=baseline_texas_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2) # use your palette constant\n",
+ "))\n",
+ "\n",
+ "# Reform (dotted)\n",
+ "fig_tx.add_trace(go.Scatter(\n",
+ " x=household_income_texas,\n",
+ " y=reform_texas_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Reform)',\n",
+ " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_tx.update_layout(\n",
+ " title='Texas Household (Married Couple) – Health-Adjusted Net Income by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Health-Adjusted Net Income',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
+ " yaxis=dict(tickformat='$,.0f'),\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional wrapper if you use one elsewhere\n",
+ "fig_tx = format_fig(fig_tx)\n",
+ "\n",
+ "fig_tx.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
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+ "xaxis": {
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+ "title": {
+ "text": "New York Household (Family of 3) – Marginal Tax Rate Including Health Benefits by Household Income"
+ },
+ "width": 800,
+ "xaxis": {
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+ 0,
+ 150000
+ ],
+ "tickformat": "$,.0f",
+ "title": {
+ "text": "Household Income"
+ }
+ },
+ "yaxis": {
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+ "title": {
+ "text": "Marginal Tax Rate (Including Health Benefits)"
+ }
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+ "source": [
+ "# ---------- Pull the inputs ----------\n",
+ "household_income_new_york = simulation_new_york.calculate(\n",
+ " \"employment_income\", map_to=\"household\", period=2026\n",
+ ")\n",
+ "\n",
+ "baseline_new_york_mtr_including_health_benefits = simulation_new_york.calculate(\n",
+ " \"marginal_tax_rate_including_health_benefits\",\n",
+ " map_to=\"household\",\n",
+ " period=2026\n",
+ ")\n",
+ "\n",
+ "reform_new_york_mtr_including_health_benefits = reformed_simulation_new_york.calculate(\n",
+ " \"marginal_tax_rate_including_health_benefits\",\n",
+ " map_to=\"household\",\n",
+ " period=2026\n",
+ ")\n",
+ "\n",
+ "# ---------- Build the graph ----------\n",
+ "fig_new_york_mtr = go.Figure()\n",
+ "\n",
+ "# Baseline trace (solid line)\n",
+ "fig_new_york_mtr.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=baseline_new_york_mtr_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Marginal Tax Rate (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2)\n",
+ "))\n",
+ "\n",
+ "# Reform trace (dotted line)\n",
+ "fig_new_york_mtr.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=reform_new_york_mtr_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Marginal Tax Rate (Reform)',\n",
+ " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_new_york_mtr.update_layout(\n",
+ " title='New York Household (Family of 3) – Marginal Tax Rate Including Health Benefits by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 150_000]),\n",
+ " yaxis=dict(tickformat='.0%'), # assumes MTR is in decimal form (e.g., 0.42 → 42%)\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional formatting helper if you use one elsewhere\n",
+ "fig_new_york_mtr = format_fig(fig_new_york_mtr)\n",
+ "\n",
+ "# Display\n",
+ "fig_new_york_mtr.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
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+ "gridcolor": "#DFE8F3",
+ "gridwidth": 2,
+ "linecolor": "#EBF0F8",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "#EBF0F8"
+ },
+ "zaxis": {
+ "backgroundcolor": "white",
+ "gridcolor": "#DFE8F3",
+ "gridwidth": 2,
+ "linecolor": "#EBF0F8",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "#EBF0F8"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ },
+ "bgcolor": "white",
+ "caxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "#EBF0F8",
+ "linecolor": "#EBF0F8",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "#EBF0F8",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "#EBF0F8",
+ "linecolor": "#EBF0F8",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "#EBF0F8",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Texas Household (Couple) – Marginal Tax Rate Including Health Benefits by Household Income"
+ },
+ "width": 800,
+ "xaxis": {
+ "range": [
+ 0,
+ 400000
+ ],
+ "tickformat": "$,.0f",
+ "title": {
+ "text": "Household Income"
+ }
+ },
+ "yaxis": {
+ "tickformat": ".0%",
+ "title": {
+ "text": "Marginal Tax Rate (Including Health Benefits)"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ---------- Pull the inputs ----------\n",
+ "household_income_texas = simulation_texas.calculate(\n",
+ " \"employment_income\", map_to=\"household\", period=2026\n",
+ ")\n",
+ "\n",
+ "baseline_texas_mtr_including_health_benefits = simulation_texas.calculate(\n",
+ " \"marginal_tax_rate_including_health_benefits\",\n",
+ " map_to=\"household\",\n",
+ " period=2026\n",
+ ")\n",
+ "\n",
+ "reform_texas_mtr_including_health_benefits = reformed_simulation_texas.calculate(\n",
+ " \"marginal_tax_rate_including_health_benefits\",\n",
+ " map_to=\"household\",\n",
+ " period=2026\n",
+ ")\n",
+ "\n",
+ "# ---------- Build the graph ----------\n",
+ "fig_texas_mtr = go.Figure()\n",
+ "\n",
+ "# Baseline trace (solid line)\n",
+ "fig_texas_mtr.add_trace(go.Scatter(\n",
+ " x=household_income_texas,\n",
+ " y=baseline_texas_mtr_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Marginal Tax Rate (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2)\n",
+ "))\n",
+ "\n",
+ "# Reform trace (dotted line)\n",
+ "fig_texas_mtr.add_trace(go.Scatter(\n",
+ " x=household_income_texas,\n",
+ " y=reform_texas_mtr_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Marginal Tax Rate (Reform)',\n",
+ " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_texas_mtr.update_layout(\n",
+ " title='Texas Household (Couple) – Marginal Tax Rate Including Health Benefits by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 150_000]),\n",
+ " yaxis=dict(tickformat='.0%'), # assumes rate is in decimal form (0.42 → 42 %)\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional wrapper if you use one elsewhere\n",
+ "fig_texas_mtr = format_fig(fig_texas_mtr)\n",
+ "\n",
+ "# Display\n",
+ "fig_texas_mtr.show()\n"
+ ]
}
],
"metadata": {
diff --git a/us/medicaid/mtr.ipynb b/us/medicaid/mtr.ipynb
new file mode 100644
index 0000000..b86003c
--- /dev/null
+++ b/us/medicaid/mtr.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/daphnehansell/miniconda3/envs/policyengine/lib/python3.12/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 Simulation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 29898.66 31086.055 32273.451 33560.2 34687.48 35676.727\n",
+ " 36702.16 37731.195 38760.23 39785.664 40771.41 41747.367\n",
+ " 43066.617 43981.58 44722.527 45440.406 46161.883 46848.56\n",
+ " 47526.97 48203.582 48882.895 49562.207 50237.914 50917.227\n",
+ " 51596.54 52275.85 52951.562 53630.87 54310.184 54967.957\n",
+ " 55425.582 55885.914 56273.074 56621.46 56971.11 57320.754\n",
+ " 57669.508 57573.65 57968.11 57506.074 58099.812 58693.555\n",
+ " 59287.293 59880.098 60471.2 61062.305 61653.414 61758.94\n",
+ " 62350.05 62941.152 63532.26 71065.47 71525.13 71979.625\n",
+ " 72430.734 72966.266 73629.625 74289.61 74946.22 75599.445\n",
+ " 76249.31 76870.08 77512.77 78152.09 78788.05 79420.625\n",
+ " 80049.83 80675.65 81298.11 81917.195 82532.9 83145.234\n",
+ " 83754.2 84329.01 84954.2 85600.38 86244.44 86886.41\n",
+ " 87526.25 88163.984 88799.62 89433.15 90064.56 90693.87\n",
+ " 91321.07 91923.76 92546.48 93167.09 93785.59 94401.984\n",
+ " 95016.266 95628.45 96238.52 96846.484 97452.336 98056.086\n",
+ " 98657.72 99231.68 99828.836 100528.234 101227.66 101927.06\n",
+ " 102626.46 109805.92 110498.31 111174.2 111850.086 112525.97\n",
+ " 113201.86 113877.734 114553.625 115213.01 115888.89 116564.78\n",
+ " 117240.67 117916.56 118592.445 119268.32 119944.22 120620.09\n",
+ " 121295.984 121912.06 122482.53 123053.016 123639.984 124226.96\n",
+ " 124813.93 125400.91 125987.875 126574.86 127161.83 127748.805\n",
+ " 128335.78 128922.75 129509.72 130096.695 130683.68 131270.64\n",
+ " 131857.62 132444.6 133031.56 133618.55 134205.52 134792.5\n",
+ " 135379.47 135966.44 136553.42 137140.38 137727.36 138314.34\n",
+ " 138904.42 139498.42 140092.4 140686.4 141280.4 141874.39\n",
+ " 142468.39 143062.38 143656.38 144250.38 144844.36 145438.36\n",
+ " 146032.36 146626.34 147220.34 147814.33 148408.34 149002.33\n",
+ " 149657.8 150317.16 150688.62 151325.7 151962.78 152599.83\n",
+ " 153236.9 153873.95 154511.03 155148.1 155785.16 156422.23\n",
+ " 157059.27 157696.36 158333.42 158970.48 159607.56 160244.61\n",
+ " 160881.69 161518.75 162155.81 162792.89 163429.95 164067.02\n",
+ " 164704.1 165341.12 165978.22 166615.28 167252.34 167889.42\n",
+ " 168526.45 169163.53 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "situation = {\n",
+ " \"people\": {\n",
+ " \"you\": {\n",
+ " \"age\": {\n",
+ " \"2025\": 40\n",
+ " }\n",
+ " },\n",
+ " \"your partner\": {\n",
+ " \"age\": {\n",
+ " \"2025\": 40\n",
+ " }\n",
+ " },\n",
+ " \"your first dependent\": {\n",
+ " \"age\": {\n",
+ " \"2025\": 3\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"families\": {\n",
+ " \"your family\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"marital_units\": {\n",
+ " \"your marital unit\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\"\n",
+ " ]\n",
+ " },\n",
+ " \"your first dependent's marital unit\": {\n",
+ " \"members\": [\n",
+ " \"your first dependent\"\n",
+ " ],\n",
+ " \"marital_unit_id\": {\n",
+ " \"2025\": 1\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"tax_units\": {\n",
+ " \"your tax unit\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"spm_units\": {\n",
+ " \"your household\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"households\": {\n",
+ " \"your household\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ],\n",
+ " \"state_name\": {\n",
+ " \"2025\": \"NY\"\n",
+ " },\n",
+ " \"county_fips\": {\n",
+ " \"2025\": \"36061\"\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"axes\": [\n",
+ " [\n",
+ " {\n",
+ " \"name\": \"employment_income\",\n",
+ " \"count\": 200,\n",
+ " \"min\": 0,\n",
+ " \"max\": 200000\n",
+ " }\n",
+ " ]\n",
+ " ]\n",
+ "}\n",
+ "\n",
+ "simulation = Simulation(\n",
+ " situation=situation,\n",
+ ")\n",
+ "\n",
+ "marginal_tax_rate_including_health_benefits = simulation.calculate(\"marginal_tax_rate_including_health_benefits\", 2025)\n",
+ "print(marginal_tax_rate_including_health_benefits)"
+ ]
+ }
+ ],
+ "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": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
From a031407cf568ce655110684ae1035297ac170682 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Tue, 27 May 2025 14:32:45 -0400
Subject: [PATCH 03/37] even more charts
---
us/medicaid/aca_numbers.ipynb | 30 ++--
.../medicaid_calculation_example.ipynb | 16 ++-
us/medicaid/medicaid_households.ipynb | 4 +-
us/medicaid/mtr.ipynb | 136 +++++++++++++-----
4 files changed, 136 insertions(+), 50 deletions(-)
diff --git a/us/medicaid/aca_numbers.ipynb b/us/medicaid/aca_numbers.ipynb
index 63255b3..6f0944a 100644
--- a/us/medicaid/aca_numbers.ipynb
+++ b/us/medicaid/aca_numbers.ipynb
@@ -31,23 +31,31 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aca = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2025)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
- "ename": "TypeError",
- "evalue": "Microsimulation.calculate() got an unexpected keyword argument 'year'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m aca \u001b[38;5;241m=\u001b[39m \u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhas_marketplace_health_coverage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myear\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2024\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "\u001b[0;31mTypeError\u001b[0m: Microsimulation.calculate() got an unexpected keyword argument 'year'"
- ]
+ "data": {
+ "text/plain": [
+ "20.457362207669945"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "aca = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2025)"
+ "aca.sum()/1e6"
]
}
],
diff --git a/us/medicaid/medicaid_calculation_example.ipynb b/us/medicaid/medicaid_calculation_example.ipynb
index fca75f2..77a8d6d 100644
--- a/us/medicaid/medicaid_calculation_example.ipynb
+++ b/us/medicaid/medicaid_calculation_example.ipynb
@@ -250,9 +250,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 63,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'household_income_ny' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[63], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m fig_ny \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mFigure()\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Baseline (solid)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m fig_ny\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter(\n\u001b[0;32m----> 8\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[43mhousehold_income_ny\u001b[49m,\n\u001b[1;32m 9\u001b[0m y\u001b[38;5;241m=\u001b[39mbaseline_ny_health_net_income,\n\u001b[1;32m 10\u001b[0m mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 11\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHealth Net Income (Baseline)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m line\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(color\u001b[38;5;241m=\u001b[39mDARK_GRAY, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;66;03m# use your palette constant\u001b[39;00m\n\u001b[1;32m 13\u001b[0m ))\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Reform (dotted)\u001b[39;00m\n\u001b[1;32m 16\u001b[0m fig_ny\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter(\n\u001b[1;32m 17\u001b[0m x\u001b[38;5;241m=\u001b[39mhousehold_income_ny,\n\u001b[1;32m 18\u001b[0m y\u001b[38;5;241m=\u001b[39mreform_ny_health_net_income,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 21\u001b[0m line\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(color\u001b[38;5;241m=\u001b[39mDARK_GRAY, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, dash\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 22\u001b[0m ))\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'household_income_ny' is not defined"
+ ]
+ }
+ ],
"source": [
"import plotly.graph_objects as go\n",
"\n",
diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/medicaid_households.ipynb
index 90129ad..8e9ec4f 100644
--- a/us/medicaid/medicaid_households.ipynb
+++ b/us/medicaid/medicaid_households.ipynb
@@ -40534,7 +40534,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -44623,7 +44623,7 @@
"xaxis": {
"range": [
0,
- 400000
+ 150000
],
"tickformat": "$,.0f",
"title": {
diff --git a/us/medicaid/mtr.ipynb b/us/medicaid/mtr.ipynb
index b86003c..7602147 100644
--- a/us/medicaid/mtr.ipynb
+++ b/us/medicaid/mtr.ipynb
@@ -20,47 +20,113 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 29898.66 31086.055 32273.451 33560.2 34687.48 35676.727\n",
- " 36702.16 37731.195 38760.23 39785.664 40771.41 41747.367\n",
- " 43066.617 43981.58 44722.527 45440.406 46161.883 46848.56\n",
- " 47526.97 48203.582 48882.895 49562.207 50237.914 50917.227\n",
- " 51596.54 52275.85 52951.562 53630.87 54310.184 54967.957\n",
- " 55425.582 55885.914 56273.074 56621.46 56971.11 57320.754\n",
- " 57669.508 57573.65 57968.11 57506.074 58099.812 58693.555\n",
- " 59287.293 59880.098 60471.2 61062.305 61653.414 61758.94\n",
- " 62350.05 62941.152 63532.26 71065.47 71525.13 71979.625\n",
- " 72430.734 72966.266 73629.625 74289.61 74946.22 75599.445\n",
- " 76249.31 76870.08 77512.77 78152.09 78788.05 79420.625\n",
- " 80049.83 80675.65 81298.11 81917.195 82532.9 83145.234\n",
- " 83754.2 84329.01 84954.2 85600.38 86244.44 86886.41\n",
- " 87526.25 88163.984 88799.62 89433.15 90064.56 90693.87\n",
- " 91321.07 91923.76 92546.48 93167.09 93785.59 94401.984\n",
- " 95016.266 95628.45 96238.52 96846.484 97452.336 98056.086\n",
- " 98657.72 99231.68 99828.836 100528.234 101227.66 101927.06\n",
- " 102626.46 109805.92 110498.31 111174.2 111850.086 112525.97\n",
- " 113201.86 113877.734 114553.625 115213.01 115888.89 116564.78\n",
- " 117240.67 117916.56 118592.445 119268.32 119944.22 120620.09\n",
- " 121295.984 121912.06 122482.53 123053.016 123639.984 124226.96\n",
- " 124813.93 125400.91 125987.875 126574.86 127161.83 127748.805\n",
- " 128335.78 128922.75 129509.72 130096.695 130683.68 131270.64\n",
- " 131857.62 132444.6 133031.56 133618.55 134205.52 134792.5\n",
- " 135379.47 135966.44 136553.42 137140.38 137727.36 138314.34\n",
- " 138904.42 139498.42 140092.4 140686.4 141280.4 141874.39\n",
- " 142468.39 143062.38 143656.38 144250.38 144844.36 145438.36\n",
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- " 149657.8 150317.16 150688.62 151325.7 151962.78 152599.83\n",
- " 153236.9 153873.95 154511.03 155148.1 155785.16 156422.23\n",
- " 157059.27 157696.36 158333.42 158970.48 159607.56 160244.61\n",
- " 160881.69 161518.75 162155.81 162792.89 163429.95 164067.02\n",
- " 164704.1 165341.12 165978.22 166615.28 167252.34 167889.42\n",
- " 168526.45 169163.53 ]\n"
+ "[-0.12789845 -0.12789845 0. -0.12699997 -0.12699997 0.\n",
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+ " 0.503375 0.503375 0. 0.3754531 0.3754531 0.\n",
+ " 0.37875003 0.37875003 0. 0.38203907 0.38203907 0.\n",
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+ " 0.39189065 0.39189065 0. 0.36945313 0.36945313 0.\n",
+ " 0.3723203 0.3723281 0. 0.40215623 0.40215623 0.\n",
+ " 0.40544534 0.40544534 0. 0.40873438 0.40873438 0.\n",
+ " 0.41201562 0.41201562 0. 0.41530466 0.41530466 0.\n",
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+ " 0.42516404 0.42516404 0. 0.42844534 0.42844534 0.\n",
+ " 0.43173438 0.43173438 0. 0.3925469 0.3925469 0.\n",
+ " 0.37124997 0.37124997 0. 0.39306247 0.39306247 0.\n",
+ " 0.39510936 0.39510936 0. 0.39717185 0.39717185 0.\n",
+ " 0.39921874 0.39921874 0. 0.40127343 0.40127343 0.\n",
+ " 0.40332812 0.40332812 0. 0.4053828 0.4053828 0.\n",
+ " 0.4074375 0.4074375 0. 0.4094922 0.4094922 0.\n",
+ " 0.4115469 0.4115469 0. 0.3912031 0.3912031 0.\n",
+ " 0.3929922 0.3929922 0. 0.41796094 0.41796094 0.\n",
+ " 0.42001563 0.42001563 0. 0.42207032 0.42207032 0.\n",
+ " 0.42412502 0.42412502 0. 0.4261797 0.4261797 0.\n",
+ " 0.4282266 0.4282266 0. 0.43028122 0.43028122 0.\n",
+ " 0.4323359 0.4323359 0. 0.4343906 0.4343906 0.\n",
+ " 0.4364453 0.4364453 0. 0.41293752 0.41293752 0.\n",
+ " 0.3365 0.3365 0. 0.3365 0.3365 0.\n",
+ " 0.3365 0.3365 0. 0.3365 0.3365 0.\n",
+ " -6.143914 -6.143914 0. 0.34315628 0.34315628 0.\n",
+ " 0.35965627 0.35965627 0. 0.3596719 0.3596719 0.\n",
+ " 0.35965627 0.35965627 0. 0.35965627 0.35965627 0.\n",
+ " 0.3596719 0.3596719 0. 0.35965627 0.35965627 0.\n",
+ " 0.35965627 0.35965627 0. 0.35965627 0.35965627 0.\n",
+ " 0.35965627 0.35965627 0. 0.35965627 0.35965627 0.\n",
+ " 0.35965627 0.35965627 0. 0.35966408 0.35966408 0.\n",
+ " 0.35966408 0.35966408 0. 0.35966408 0.35966408 0.\n",
+ " 0.3596719 0.3596719 0. 0.35965627 0.35965627 0.\n",
+ " 0.4140547 0.4140547 0. 0.45966405 0.45966405 0.\n",
+ " 0.45965624 0.45965624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44316405 0.44316405 0. 0.44316405 0.44316405 0.\n",
+ " 0.44315624 0.44315624 0. 0.44317186 0.44317186 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44316405 0.44316405 0. 0.44315624 0.44315624 0.\n",
+ " 0.44316405 0.44316405 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44317186 0.44317186 0.\n",
+ " 0.44315624 0.44315624 0. 0.44315624 0.44315624 0.\n",
+ " 0.44317186 0.44317186 0. 0.44315624 0.44315624 0.\n",
+ " 0.44315624 0.44315624 0. 0.44040626 0.44040626 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.4365 0.4365 0.\n",
+ " 0.4365 0.4365 0. 0.378375 0.4365 0.\n",
+ " 0.37449998 0.4365 0. 0.66121876 0.72321874 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.39565623 0.45765626 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.39565623 0.45765626 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.395625 0.45762497 0. 0.39565623 0.45765626 0.\n",
+ " 0.395625 0.45762497 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.39565623 0.45765626 0. 0.3956406 0.45764065 0.\n",
+ " 0.39565623 0.45765626 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.395625 0.45762497 0.\n",
+ " 0.39565623 0.45765626 0. 0.395625 0.45762497 0.\n",
+ " 0.3956406 0.45764065 0. 0.3956406 0.45764065 0.\n",
+ " 0.3956406 0.45764065 0. 0.395625 0.45762497 0. ]\n"
]
}
],
From 62b2dc5155795e5776570244635235c013848dc7 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Thu, 5 Jun 2025 09:58:37 -0400
Subject: [PATCH 04/37] medicaid
---
us/medicaid/medicaid_households.ipynb | 38 +++++++++++++--------------
1 file changed, 19 insertions(+), 19 deletions(-)
diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/medicaid_households.ipynb
index 8e9ec4f..d8af84e 100644
--- a/us/medicaid/medicaid_households.ipynb
+++ b/us/medicaid/medicaid_households.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@@ -52,12 +52,12 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"\n",
- "situation_texas = {\n",
+ "situation_new_york = {\n",
" \"people\": {\n",
" \"you\": {\n",
" \"age\": {\n",
@@ -149,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
@@ -250,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
@@ -295,7 +295,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
@@ -307,7 +307,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
@@ -28060,7 +28060,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
@@ -32214,7 +32214,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
@@ -36365,7 +36365,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 65,
"metadata": {},
"outputs": [
{
@@ -40454,7 +40454,7 @@
"xaxis": {
"range": [
0,
- 150000
+ 200000
],
"tickformat": "$,.0f",
"title": {
@@ -40519,7 +40519,7 @@
" xaxis_title='Household Income',\n",
" yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
" legend_title='Scenario',\n",
- " xaxis=dict(tickformat='$,.0f', range=[0, 150_000]),\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 200_000]),\n",
" yaxis=dict(tickformat='.0%'), # assumes MTR is in decimal form (e.g., 0.42 → 42%)\n",
" height=600,\n",
" width=1000\n",
@@ -40534,7 +40534,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
@@ -42159,7 +42159,7 @@
},
{
"line": {
- "color": "#616161",
+ "color": "#2C6496",
"dash": "dot",
"width": 2
},
@@ -44623,7 +44623,7 @@
"xaxis": {
"range": [
0,
- 150000
+ 200000
],
"tickformat": "$,.0f",
"title": {
@@ -44679,7 +44679,7 @@
" y=reform_texas_mtr_including_health_benefits,\n",
" mode='lines',\n",
" name='Marginal Tax Rate (Reform)',\n",
- " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n",
"))\n",
"\n",
"# Layout\n",
@@ -44688,7 +44688,7 @@
" xaxis_title='Household Income',\n",
" yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
" legend_title='Scenario',\n",
- " xaxis=dict(tickformat='$,.0f', range=[0, 150_000]),\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 200_000]),\n",
" yaxis=dict(tickformat='.0%'), # assumes rate is in decimal form (0.42 → 42 %)\n",
" height=600,\n",
" width=1000\n",
From 352e4a2d544262c92f4a5a831980ee70fb695ffa Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Mon, 9 Jun 2025 14:49:59 -0400
Subject: [PATCH 05/37] so many notebooks
---
us/medicaid/aca_numbers.ipynb | 68 ++++-
us/medicaid/aca_reform copy.ipynb | 424 ++++++++++++++++++++++++++++++
us/medicaid/aca_reform.ipynb | 90 +++----
us/medicaid/debug_aca.ipynb | 102 +++++++
us/medicaid/mtr.ipynb | 2 +-
us/nyt/ira_ptc.ipynb | 237 +++++++++++++++++
us/nyt/medicaid_work_req.ipynb | 57 ++++
7 files changed, 929 insertions(+), 51 deletions(-)
create mode 100644 us/medicaid/aca_reform copy.ipynb
create mode 100644 us/medicaid/debug_aca.ipynb
create mode 100644 us/nyt/ira_ptc.ipynb
create mode 100644 us/nyt/medicaid_work_req.ipynb
diff --git a/us/medicaid/aca_numbers.ipynb b/us/medicaid/aca_numbers.ipynb
index 6f0944a..f899d23 100644
--- a/us/medicaid/aca_numbers.ipynb
+++ b/us/medicaid/aca_numbers.ipynb
@@ -2,14 +2,14 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/Users/daphnehansell/miniconda3/envs/policyengine/lib/python3.12/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",
+ "/Users/daphnehansell/miniconda3/envs/policyengine/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"
]
}
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -46,7 +46,7 @@
{
"data": {
"text/plain": [
- "20.457362207669945"
+ "20.952726308192194"
]
},
"execution_count": 5,
@@ -57,6 +57,64 @@
"source": [
"aca.sum()/1e6"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "premiums = baseline.calculate(\"health_insurance_premiums_without_medicare_part_b\", map_to=\"household\", period=2025)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "413790460822.6505"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "premiums.sum()/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total premiums paid by ACA marketplace enrollees: $54.73 billion\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Calculate total premiums paid by people with ACA marketplace coverage\n",
+ "aca_household = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"household\", period=2025)\n",
+ "aca_premiums = premiums * (aca_household > 0)\n",
+ "total_aca_premiums = aca_premiums.sum()\n",
+ "\n",
+ "print(f\"Total premiums paid by ACA marketplace enrollees: ${total_aca_premiums/1e9:.2f} billion\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -75,7 +133,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.8"
+ "version": "3.10.16"
}
},
"nbformat": 4,
diff --git a/us/medicaid/aca_reform copy.ipynb b/us/medicaid/aca_reform copy.ipynb
new file mode 100644
index 0000000..ea3dd45
--- /dev/null
+++ b/us/medicaid/aca_reform copy.ipynb
@@ -0,0 +1,424 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from policyengine_us import Microsimulation\n",
+ "from policyengine_core.reforms import Reform\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "reform = Reform.from_dict({\n",
+ " \"gov.aca.ptc_phase_out_rate[0].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[1].amount\": {\n",
+ " \"2025-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[3].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.02\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[4].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.04\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[5].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.06\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[6].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.085\n",
+ " },\n",
+ " \"gov.aca.ptc_income_eligibility[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": True\n",
+ " }\n",
+ "}, country_id=\"us\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "baseline_aca_enrollment = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2026).sum()\n",
+ "\n",
+ "reformed_aca_enrollment = reformed.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2026).sum()\n",
+ "difference_aca_enrollment = reformed_aca_enrollment - baseline_aca_enrollment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6482875.479201315"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_has_coverage = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2026)\n",
+ "baseline_is_eligible = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2026)\n",
+ "\n",
+ "baseline_aca_enrollment = ((baseline_has_coverage & baseline_is_eligible)*baseline_has_coverage.weights).sum()\n",
+ "\n",
+ "baseline_aca_enrollment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "39877715.81533431"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_is_eligible.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20430546.619979884"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_has_coverage.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11822550.349753413"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reform_has_coverage = reformed.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2026)\n",
+ "reform_is_eligible = reformed.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2026)\n",
+ "\n",
+ "reform_aca_enrollment = ((reform_has_coverage & reform_is_eligible)*reform_has_coverage.weights).sum()\n",
+ "\n",
+ "reform_aca_enrollment.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20430546.619979884"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reform_has_coverage.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "75440174.05932575"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reform_is_eligible.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_slcsp = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
+ "baseline_slcsp.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "year=2026\n",
+ "state = baseline.calculate(\"state_code\", map_to=\"household\", period=year)\n",
+ "num_dependents = baseline.calculate(\"tax_unit_dependents\", map_to=\"household\", period=year)\n",
+ "married = baseline.calculate(\"is_married\", map_to=\"household\", period=year)\n",
+ "employment_income = baseline.calculate(\"employment_income\", map_to=\"household\", period=year)\n",
+ "self_employment_income = baseline.calculate(\"self_employment_income\", map_to=\"household\", period=year)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6482875.479201315"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "aca_baseline = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=year)\n",
+ "aca_reform = reformed.calculate(\"aca_ptc\", map_to=\"household\", period=year)\n",
+ "baseline_aca_enrollment.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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diff --git a/us/blog_posts/ira_expire.ipynb b/us/blog_posts/ira_expire.ipynb
new file mode 100644
index 0000000..e774a8c
--- /dev/null
+++ b/us/blog_posts/ira_expire.ipynb
@@ -0,0 +1,1850 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from policyengine_us import Microsimulation\n",
+ "from policyengine_core.reforms import Reform\n",
+ "import pandas as pd\n",
+ "\n",
+ "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reform = Reform.from_dict({\n",
+ " \"gov.aca.ptc_phase_out_rate[0].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[1].amount\": {\n",
+ " \"2025-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[3].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.02\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[4].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.04\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[5].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.06\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[6].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.085\n",
+ " },\n",
+ " \"gov.aca.ptc_income_eligibility[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": True\n",
+ " }\n",
+ "}, country_id=\"us\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "weights = baseline.calculate(\"household_weight\", period=2024)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "40.35911041608722"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_aca_eligible = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"tax_unit\", period=2026).sum()\n",
+ "baseline_aca_eligible/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "227.28695912942868"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_aca_enrollment = baseline.calculate(\"takes_up_aca_if_eligible\", map_to=\"person\", period=2026).sum()\n",
+ "baseline_aca_enrollment/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "184,751,527 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ ]
+ }
+ ],
+ "source": [
+ "period = 2025\n",
+ "sim = baseline\n",
+ "\n",
+ "# ── Tax-unit flags, broadcast to people ──────────────────────────────────────\n",
+ "takes_up = sim.calculate(\"takes_up_aca_if_eligible\",\n",
+ " map_to=\"person\", period=period) # 0/1\n",
+ "aca_ptc = sim.calculate(\"aca_ptc\",\n",
+ " map_to=\"person\", period=period) # $ amount\n",
+ "\n",
+ "# ── PERSON weights (pick any person-level variable) ─────────────────────────\n",
+ "person_wt = sim.calculate(\"age\", map_to=\"person\", period=period).weights\n",
+ "\n",
+ "# ── Build mask & sum weights ────────────────────────────────────────────────\n",
+ "mask = (takes_up == 1) & (aca_ptc > 0)\n",
+ "\n",
+ "people_with_ptc_takeup_wtd = (mask.astype(float) * person_wt).sum()\n",
+ "\n",
+ "print(f\"{people_with_ptc_takeup_wtd:,.0f} weighted people live in tax units \"\n",
+ " \"that take up Marketplace coverage and actually receive a PTC.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "23,752,636 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ ]
+ }
+ ],
+ "source": [
+ "period = 2026\n",
+ "\n",
+ "# ── Tax-unit flags, broadcast to people ──────────────────────────────────────\n",
+ "takes_up_r = reformed.calculate(\"takes_up_aca_if_eligible\",\n",
+ " map_to=\"person\", period=period) # 0/1\n",
+ "aca_ptc_r = reformed.calculate(\"aca_ptc\",\n",
+ " map_to=\"person\", period=period) # $ amount\n",
+ "\n",
+ "# ── PERSON weights (pick any person-level variable) ─────────────────────────\n",
+ "person_wt_r = reformed.calculate(\"age\", map_to=\"person\", period=period).weights\n",
+ "\n",
+ "# ── Build mask & sum weights ────────────────────────────────────────────────\n",
+ "mask = (takes_up_r == 1) & (aca_ptc_r > 0)\n",
+ "\n",
+ "people_with_ptc_takeup_wtd_r = (mask.astype(float) * person_wt_r).sum()\n",
+ "\n",
+ "print(f\"{people_with_ptc_takeup_wtd_r:,.0f} weighted people live in tax units \"\n",
+ " \"that take up Marketplace coverage and actually receive a PTC.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "18,379,449 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ ]
+ }
+ ],
+ "source": [
+ "period = 2026\n",
+ "sim = baseline\n",
+ "\n",
+ "# ── Tax-unit flags, broadcast to people ──────────────────────────────────────\n",
+ "takes_up = sim.calculate(\"takes_up_aca_if_eligible\",\n",
+ " map_to=\"person\", period=period) # 0/1\n",
+ "aca_ptc = sim.calculate(\"aca_ptc\",\n",
+ " map_to=\"person\", period=period) # $ amount\n",
+ "\n",
+ "# ── PERSON weights (pick any person-level variable) ─────────────────────────\n",
+ "person_wt = sim.calculate(\"age\", map_to=\"person\", period=period).weights\n",
+ "\n",
+ "# ── Build mask & sum weights ────────────────────────────────────────────────\n",
+ "mask = (takes_up == 1) & (aca_ptc > 0)\n",
+ "\n",
+ "people_with_ptc_takeup_wtd = (mask.astype(float) * person_wt).sum()\n",
+ "\n",
+ "print(f\"{people_with_ptc_takeup_wtd:,.0f} weighted people live in tax units \"\n",
+ " \"that take up Marketplace coverage and actually receive a PTC.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "year = 2026\n",
+ "state = baseline.calculate(\"state_code\", map_to=\"household\", period=year)\n",
+ "num_dependents = baseline.calculate(\"tax_unit_dependents\", map_to=\"household\", period=year)\n",
+ "married = baseline.calculate(\"is_married\", map_to=\"household\", period=year)\n",
+ "employment_income = baseline.calculate(\"employment_income\", map_to=\"household\", period=year)\n",
+ "self_employment_income = baseline.calculate(\"self_employment_income\", map_to=\"household\", period=year)\n",
+ "aca_baseline = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=year)\n",
+ "rating_area = baseline.calculate(\"slcsp_rating_area\", map_to=\"household\", period=year)\n",
+ "household_id = baseline.calculate(\"household_id\", map_to=\"household\", period=year)\n",
+ "aca_reform = reformed.calculate(\"aca_ptc\", map_to=\"household\", period=year)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ " Married | \n",
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+ " ME | \n",
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+ " 0.0 | \n",
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\n",
+ " \n",
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+ " 284 | \n",
+ " ME | \n",
+ " 30920.968750 | \n",
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+ " 0.0 | \n",
+ "
\n",
+ " \n",
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+ " 315 | \n",
+ " ME | \n",
+ " 13098.019531 | \n",
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+ " 0.0 | \n",
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\n",
+ " \n",
+ " | 41 | \n",
+ " 339 | \n",
+ " ME | \n",
+ " 18794.173828 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 44 | \n",
+ " 356 | \n",
+ " ME | \n",
+ " 36464.535156 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
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+ " \n",
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+ " 380 | \n",
+ " ME | \n",
+ " 43613.914062 | \n",
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+ "
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+ " \n",
+ "
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+ "
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+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Married | \n",
+ " Num_Dependents | \n",
+ " Employment_Income | \n",
+ " Self_Employment_Income | \n",
+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 19620 | \n",
+ " 83988 | \n",
+ " CA | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 160877.21875 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 10092.767578 | \n",
+ " 12765.5 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " income_label | \n",
+ " income_usd | \n",
+ " ptc_baseline | \n",
+ " ptc_ira_reform | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 138 % FPL ($29,187) | \n",
+ " 29187 | \n",
+ " 9128.747070 | \n",
+ " 10090.201172 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 300 % FPL ($63,450) | \n",
+ " 63450 | \n",
+ " 4062.451172 | \n",
+ " 6283.201172 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 400 % FPL ($84,600) | \n",
+ " 84600 | \n",
+ " 0.000000 | \n",
+ " 2899.201172 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " income_label | \n",
+ " income_usd | \n",
+ " ptc_baseline | \n",
+ " ptc_ira_reform | \n",
+ " medicaid_cost | \n",
+ " per_capita_chip | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 154 % FPL ($41,041) | \n",
+ " 41041 | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 16480.696289 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 200 % FPL ($53,300) | \n",
+ " 53300 | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 12930.671875 | \n",
+ " 829.929932 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 405 % FPL ($107,933) | \n",
+ " 107933 | \n",
+ " 0.0 | \n",
+ " 12268.548828 | \n",
+ " 0.000000 | \n",
+ " 829.929932 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
0 | \n",
" 154 % FPL ($41,041) | \n",
" 41041 | \n",
- " 0.0 | \n",
+ " 0.000000 | \n",
" 0.000000 | \n",
" 16480.696289 | \n",
" 0.000000 | \n",
@@ -440,16 +431,25 @@
" 1 | \n",
" 200 % FPL ($53,300) | \n",
" 53300 | \n",
- " 0.0 | \n",
+ " 0.000000 | \n",
" 0.000000 | \n",
" 12930.671875 | \n",
" 829.929932 | \n",
" \n",
" \n",
" | 2 | \n",
+ " 300 % FPL ($79,950) | \n",
+ " 79950 | \n",
+ " 13847.603516 | \n",
+ " 16645.853516 | \n",
+ " 0.000000 | \n",
+ " 829.929932 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
" 405 % FPL ($107,933) | \n",
" 107933 | \n",
- " 0.0 | \n",
+ " 0.000000 | \n",
" 12268.548828 | \n",
" 0.000000 | \n",
" 829.929932 | \n",
@@ -460,17 +460,19 @@
],
"text/plain": [
" income_label income_usd ptc_baseline ptc_ira_reform \\\n",
- "0 154 % FPL ($41,041) 41041 0.0 0.000000 \n",
- "1 200 % FPL ($53,300) 53300 0.0 0.000000 \n",
- "2 405 % FPL ($107,933) 107933 0.0 12268.548828 \n",
+ "0 154 % FPL ($41,041) 41041 0.000000 0.000000 \n",
+ "1 200 % FPL ($53,300) 53300 0.000000 0.000000 \n",
+ "2 300 % FPL ($79,950) 79950 13847.603516 16645.853516 \n",
+ "3 405 % FPL ($107,933) 107933 0.000000 12268.548828 \n",
"\n",
" medicaid_cost per_capita_chip \n",
"0 16480.696289 0.000000 \n",
"1 12930.671875 829.929932 \n",
- "2 0.000000 829.929932 "
+ "2 0.000000 829.929932 \n",
+ "3 0.000000 829.929932 "
]
},
- "execution_count": 18,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -561,22 +563,14 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['you', 'your partner', 'your first dependent']\n"
- ]
- }
- ],
+ "outputs": [],
"source": []
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
@@ -621,7 +615,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -633,7 +627,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -28386,7 +28380,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -32540,7 +32534,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -36691,7 +36685,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -40860,7 +40854,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
From 01abb8db21852408bde157f24867c035602bec27 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Wed, 2 Jul 2025 16:22:57 -0400
Subject: [PATCH 11/37] work req
---
us/nyt/work_req.ipynb | 353 ++++++++++++++++++++++++++++++++++++++++++
1 file changed, 353 insertions(+)
create mode 100644 us/nyt/work_req.ipynb
diff --git a/us/nyt/work_req.ipynb b/us/nyt/work_req.ipynb
new file mode 100644
index 0000000..efbe0f3
--- /dev/null
+++ b/us/nyt/work_req.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/daphnehansell/miniconda3/envs/policyengine/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",
+ "\n",
+ "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "vscode": {
+ "languageId": "javascript"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "reform = Reform.from_dict({\n",
+ " \"gov.contrib.reconciliation.medicaid_work_requirement.senate.in_effect\": {\n",
+ " \"2027-01-01.2100-12-31\": True\n",
+ " },\n",
+ "}, country_id=\"us\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reformed = Microsimulation(reform=reform, dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "year = 2029\n",
+ "state = baseline.calculate(\"state_code\", map_to=\"household\", period=year)\n",
+ "num_dependents = baseline.calculate(\"tax_unit_dependents\", map_to=\"household\", period=year)\n",
+ "married = baseline.calculate(\"is_married\", map_to=\"household\", period=year)\n",
+ "immigration_status = baseline.calculate(\"immigration_status\", map_to=\"person\", period=year)\n",
+ "employment_income = baseline.calculate(\"employment_income\", map_to=\"household\", period=year)\n",
+ "self_employment_income = baseline.calculate(\"self_employment_income\", map_to=\"household\", period=year)\n",
+ "medicaid_baseline = baseline.calculate(\"medicaid_enrolled\", map_to=\"person\", period=year)\n",
+ "household_id = baseline.calculate(\"household_id\", map_to=\"household\", period=year)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "medicaid_reform = reformed.calculate(\"medicaid_enrolled\", map_to=\"person\", period=year)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "112.61243085941472"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "medicaid_baseline.sum()/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "medicaid_reform.sum()/1e6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "medicaid_cost_baseline = baseline.calculate(\"medicaid_cost\", map_to=\"person\", period=year)\n",
+ "medicaid_cost_reform = reformed.calculate(\"medicaid_cost\", map_to=\"person\", period=year)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "823.2432315324515"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "medicaid_cost_baseline.sum()/1e9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "medicaid_cost_reform.sum()/1e9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Married | \n",
+ " employment_income | \n",
+ " Num_Dependents | \n",
+ " Employment_Income | \n",
+ " Self_Employment_Income | \n",
+ " medicaid_baseline | \n",
+ " medicaid_reform | \n",
+ " immigration_status | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 4 | \n",
+ " 36.0 | \n",
+ " ME | \n",
+ " 0.0 | \n",
+ " 41408.097656 | \n",
+ " 0.0 | \n",
+ " 41408.097656 | \n",
+ " 0.0 | \n",
+ " True | \n",
+ " True | \n",
+ " CITIZEN | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 44.0 | \n",
+ " ME | \n",
+ " 0.0 | \n",
+ " 78030.335938 | \n",
+ " 1.0 | \n",
+ " 78030.335938 | \n",
+ " 0.0 | \n",
+ " True | \n",
+ " True | \n",
+ " CITIZEN | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 68.0 | \n",
+ " ME | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 0.0 | \n",
+ " True | \n",
+ " True | \n",
+ " CITIZEN | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 78.0 | \n",
+ " ME | \n",
+ " 0.0 | \n",
+ " 84532.859375 | \n",
+ " 0.0 | \n",
+ " 84532.859375 | \n",
+ " 0.0 | \n",
+ " True | \n",
+ " False | \n",
+ " CITIZEN | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 85.0 | \n",
+ " ME | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 0.0 | \n",
+ " 0.000000 | \n",
+ " 0.0 | \n",
+ " True | \n",
+ " True | \n",
+ " CITIZEN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
PA | \n",
" 1.0 | \n",
" 3.0 | \n",
- " 94992.562805 | \n",
- " 91.854012 | \n",
+ " 122669.62796 | \n",
+ " 644.808105 | \n",
" 0.0 | \n",
- " 39058.191406 | \n",
+ " 38655.492188 | \n",
"
\n",
" \n",
"\n",
@@ -286,13 +295,13 @@
],
"text/plain": [
" household_id State Married Num_Dependents Employment_Income \\\n",
- "23890 103176 PA 1.0 3.0 94992.562805 \n",
+ "23890 103176 PA 1.0 3.0 122669.62796 \n",
"\n",
" Self_Employment_Income aca_baseline aca_reform \n",
- "23890 91.854012 0.0 39058.191406 "
+ "23890 644.808105 0.0 38655.492188 "
]
},
- "execution_count": 72,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -316,7 +325,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -356,101 +365,101 @@
" \n",
" \n",
" \n",
- " | 15170 | \n",
- " 63406 | \n",
- " TX | \n",
- " 28846.123047 | \n",
- " 20097.220703 | \n",
- " 5.797269e+08 | \n",
- "
\n",
- " \n",
" | 11774 | \n",
" 47863 | \n",
" FL | \n",
- " 43584.601562 | \n",
+ " 135658.906250 | \n",
" 17123.369141 | \n",
- " 7.463152e+08 | \n",
+ " 2.322938e+09 | \n",
"
\n",
" \n",
" | 14377 | \n",
" 60850 | \n",
" TX | \n",
- " 46835.007812 | \n",
+ " 23338.992188 | \n",
" 15904.038086 | \n",
- " 7.448657e+08 | \n",
+ " 3.711842e+08 | \n",
"
\n",
" \n",
" | 14628 | \n",
" 61712 | \n",
" TX | \n",
- " 33571.246094 | \n",
+ " 22272.599609 | \n",
" 14010.416992 | \n",
- " 4.703472e+08 | \n",
+ " 3.120484e+08 | \n",
"
\n",
" \n",
" | 6960 | \n",
" 25327 | \n",
" MO | \n",
- " 12508.783203 | \n",
+ " 11227.063477 | \n",
" 13525.498047 | \n",
- " 1.691875e+08 | \n",
+ " 1.518516e+08 | \n",
+ "
\n",
+ " \n",
+ " | 31780 | \n",
+ " 135336 | \n",
+ " FL | \n",
+ " 103136.664062 | \n",
+ " 12612.913086 | \n",
+ " 1.300854e+09 | \n",
"
\n",
" \n",
" | 8987 | \n",
" 38686 | \n",
" NC | \n",
- " 15900.470703 | \n",
+ " 25387.832031 | \n",
" 12249.720703 | \n",
- " 1.947763e+08 | \n",
+ " 3.109939e+08 | \n",
"
\n",
" \n",
- " | 31780 | \n",
- " 135336 | \n",
+ " 8576 | \n",
+ " 36002 | \n",
+ " VA | \n",
+ " 17410.156250 | \n",
+ " 10854.941406 | \n",
+ " 1.889862e+08 | \n",
+ "
\n",
+ " \n",
+ " | 10747 | \n",
+ " 44495 | \n",
" FL | \n",
- " 95542.593750 | \n",
- " 12184.659180 | \n",
- " 1.164154e+09 | \n",
+ " 56612.566406 | \n",
+ " 10672.198242 | \n",
+ " 6.041805e+08 | \n",
"
\n",
" \n",
" | 19620 | \n",
" 83988 | \n",
" CA | \n",
- " 12765.500000 | \n",
+ " 18989.419922 | \n",
" 10092.767578 | \n",
- " 1.288392e+08 | \n",
- "
\n",
- " \n",
- " | 36863 | \n",
- " 159723 | \n",
- " CO | \n",
- " 16615.960938 | \n",
- " 7437.457031 | \n",
- " 1.235805e+08 | \n",
+ " 1.916558e+08 | \n",
"
\n",
" \n",
" | 4013 | \n",
" 16074 | \n",
" OH | \n",
- " 16507.593750 | \n",
+ " 46166.144531 | \n",
" 6990.998047 | \n",
- " 1.154046e+08 | \n",
+ " 3.227474e+08 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State weight net_change wt_change\n",
- "15170 63406 TX 28846.123047 20097.220703 5.797269e+08\n",
- "11774 47863 FL 43584.601562 17123.369141 7.463152e+08\n",
- "14377 60850 TX 46835.007812 15904.038086 7.448657e+08\n",
- "14628 61712 TX 33571.246094 14010.416992 4.703472e+08\n",
- "6960 25327 MO 12508.783203 13525.498047 1.691875e+08\n",
- "8987 38686 NC 15900.470703 12249.720703 1.947763e+08\n",
- "31780 135336 FL 95542.593750 12184.659180 1.164154e+09\n",
- "19620 83988 CA 12765.500000 10092.767578 1.288392e+08\n",
- "36863 159723 CO 16615.960938 7437.457031 1.235805e+08\n",
- "4013 16074 OH 16507.593750 6990.998047 1.154046e+08"
+ " household_id State weight net_change wt_change\n",
+ "11774 47863 FL 135658.906250 17123.369141 2.322938e+09\n",
+ "14377 60850 TX 23338.992188 15904.038086 3.711842e+08\n",
+ "14628 61712 TX 22272.599609 14010.416992 3.120484e+08\n",
+ "6960 25327 MO 11227.063477 13525.498047 1.518516e+08\n",
+ "31780 135336 FL 103136.664062 12612.913086 1.300854e+09\n",
+ "8987 38686 NC 25387.832031 12249.720703 3.109939e+08\n",
+ "8576 36002 VA 17410.156250 10854.941406 1.889862e+08\n",
+ "10747 44495 FL 56612.566406 10672.198242 6.041805e+08\n",
+ "19620 83988 CA 18989.419922 10092.767578 1.916558e+08\n",
+ "4013 16074 OH 46166.144531 6990.998047 3.227474e+08"
]
},
"metadata": {},
@@ -494,34 +503,34 @@
" \n",
" \n",
" \n",
- " | 15 | \n",
- " 99 | \n",
+ " 7 | \n",
+ " 68 | \n",
" ME | \n",
- " 12122.632812 | \n",
+ " 18114.335938 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 24 | \n",
- " 188 | \n",
+ " 21 | \n",
+ " 134 | \n",
" ME | \n",
- " 14602.563477 | \n",
+ " 10089.783203 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 27 | \n",
- " 206 | \n",
+ " 25 | \n",
+ " 194 | \n",
" ME | \n",
- " 13875.028320 | \n",
+ " 15542.672852 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 30 | \n",
- " 261 | \n",
+ " 27 | \n",
+ " 206 | \n",
" ME | \n",
- " 25312.886719 | \n",
+ " 19284.667969 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
@@ -529,47 +538,47 @@
" 31 | \n",
" 275 | \n",
" ME | \n",
- " 19168.126953 | \n",
+ " 11475.574219 | \n",
" 0.0 | \n",
" 0.0 | \n",
" \n",
" \n",
- " | 32 | \n",
- " 284 | \n",
+ " 37 | \n",
+ " 324 | \n",
" ME | \n",
- " 30920.968750 | \n",
+ " 16666.904297 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 35 | \n",
- " 315 | \n",
+ " 41 | \n",
+ " 339 | \n",
" ME | \n",
- " 13098.019531 | \n",
+ " 16523.361328 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 41 | \n",
- " 339 | \n",
+ " 43 | \n",
+ " 354 | \n",
" ME | \n",
- " 18794.173828 | \n",
+ " 11761.126953 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 44 | \n",
- " 356 | \n",
+ " 51 | \n",
+ " 425 | \n",
" ME | \n",
- " 36464.535156 | \n",
+ " 15638.842773 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 47 | \n",
- " 380 | \n",
+ " 53 | \n",
+ " 437 | \n",
" ME | \n",
- " 43613.914062 | \n",
+ " 10633.168945 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
@@ -579,16 +588,16 @@
],
"text/plain": [
" household_id State weight net_change wt_change\n",
- "15 99 ME 12122.632812 0.0 0.0\n",
- "24 188 ME 14602.563477 0.0 0.0\n",
- "27 206 ME 13875.028320 0.0 0.0\n",
- "30 261 ME 25312.886719 0.0 0.0\n",
- "31 275 ME 19168.126953 0.0 0.0\n",
- "32 284 ME 30920.968750 0.0 0.0\n",
- "35 315 ME 13098.019531 0.0 0.0\n",
- "41 339 ME 18794.173828 0.0 0.0\n",
- "44 356 ME 36464.535156 0.0 0.0\n",
- "47 380 ME 43613.914062 0.0 0.0"
+ "7 68 ME 18114.335938 0.0 0.0\n",
+ "21 134 ME 10089.783203 0.0 0.0\n",
+ "25 194 ME 15542.672852 0.0 0.0\n",
+ "27 206 ME 19284.667969 0.0 0.0\n",
+ "31 275 ME 11475.574219 0.0 0.0\n",
+ "37 324 ME 16666.904297 0.0 0.0\n",
+ "41 339 ME 16523.361328 0.0 0.0\n",
+ "43 354 ME 11761.126953 0.0 0.0\n",
+ "51 425 ME 15638.842773 0.0 0.0\n",
+ "53 437 ME 10633.168945 0.0 0.0"
]
},
"metadata": {},
@@ -634,7 +643,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -680,7 +689,7 @@
" 0.0 | \n",
" 0.0 | \n",
" 10092.767578 | \n",
- " 12765.5 | \n",
+ " 18989.419922 | \n",
" \n",
" \n",
"\n",
@@ -690,11 +699,11 @@
" household_id State Married Num_Dependents Employment_Income \\\n",
"19620 83988 CA 1.0 2.0 160877.21875 \n",
"\n",
- " Self_Employment_Income aca_baseline aca_reform weight \n",
- "19620 0.0 0.0 10092.767578 12765.5 "
+ " Self_Employment_Income aca_baseline aca_reform weight \n",
+ "19620 0.0 0.0 10092.767578 18989.419922 "
]
},
- "execution_count": 74,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -705,14 +714,14 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with any change: $2,264.49\n"
+ "Average weighted PTC change among households with any change: $2,442.32\n"
]
}
],
@@ -735,14 +744,14 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with a PTC in both baseline and reform: $1,654.84\n"
+ "Average weighted PTC change among households with a PTC in both baseline and reform: $1,663.64\n"
]
}
],
@@ -772,16 +781,16 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "30.206960672172997"
+ "32.12809192838201"
]
},
- "execution_count": 92,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -811,7 +820,7 @@
},
{
"cell_type": "code",
- "execution_count": 104,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -837,16 +846,16 @@
]
},
"text": [
- "$102",
+ "$127",
"$141",
- "$148",
- "$156",
- "$191",
- "$464",
- "$233",
- "$281",
- "$161",
- "$108"
+ "$120",
+ "$164",
+ "$206",
+ "$256",
+ "$214",
+ "$668",
+ "$185",
+ "$83"
],
"textposition": "inside",
"type": "bar",
@@ -863,16 +872,16 @@
10
],
"y": [
- 102.4264144897461,
- 140.80027770996094,
- 147.8699188232422,
- 156.31858825683594,
- 191.30772399902344,
- 463.7459716796875,
- 232.62808227539062,
- 280.5216979980469,
- 161.26075744628906,
- 107.7364501953125
+ 127.10087585449219,
+ 140.969970703125,
+ 119.75225067138672,
+ 163.75502014160156,
+ 205.8011016845703,
+ 255.56259155273438,
+ 213.85777282714844,
+ 667.6323852539062,
+ 184.54965209960938,
+ 83.39998626708984
]
}
],
@@ -1751,16 +1760,16 @@
]
},
"text": [
- "$102",
+ "$127",
"$141",
- "$148",
- "$156",
- "$191",
- "$464",
- "$233",
- "$281",
- "$161",
- "$108"
+ "$120",
+ "$164",
+ "$206",
+ "$256",
+ "$214",
+ "$668",
+ "$185",
+ "$83"
],
"textposition": "inside",
"type": "bar",
@@ -1777,16 +1786,16 @@
10
],
"y": [
- 102.4264144897461,
- 140.80027770996094,
- 147.8699188232422,
- 156.31858825683594,
- 191.30772399902344,
- 463.7459716796875,
- 232.62808227539062,
- 280.5216979980469,
- 161.26075744628906,
- 107.7364501953125
+ 127.10087585449219,
+ 140.969970703125,
+ 119.75225067138672,
+ 163.75502014160156,
+ 205.8011016845703,
+ 255.56259155273438,
+ 213.85777282714844,
+ 667.6323852539062,
+ 184.54965209960938,
+ 83.39998626708984
]
}
],
diff --git a/us/nyt/work_req.ipynb b/us/nyt/work_req.ipynb
index efbe0f3..1b6715d 100644
--- a/us/nyt/work_req.ipynb
+++ b/us/nyt/work_req.ipynb
@@ -39,6 +39,17 @@
"}, country_id=\"us\")\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "vscode": {
+ "languageId": "javascript"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 3,
@@ -50,42 +61,44 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
- "year = 2029\n",
+ "year = 2028\n",
+ "age = baseline.calculate(\"age\", map_to=\"household\", period=year)\n",
"state = baseline.calculate(\"state_code\", map_to=\"household\", period=year)\n",
"num_dependents = baseline.calculate(\"tax_unit_dependents\", map_to=\"household\", period=year)\n",
"married = baseline.calculate(\"is_married\", map_to=\"household\", period=year)\n",
- "immigration_status = baseline.calculate(\"immigration_status\", map_to=\"person\", period=year)\n",
"employment_income = baseline.calculate(\"employment_income\", map_to=\"household\", period=year)\n",
"self_employment_income = baseline.calculate(\"self_employment_income\", map_to=\"household\", period=year)\n",
- "medicaid_baseline = baseline.calculate(\"medicaid_enrolled\", map_to=\"person\", period=year)\n",
- "household_id = baseline.calculate(\"household_id\", map_to=\"household\", period=year)"
+ "medicaid_baseline = baseline.calculate(\"medicaid_enrolled\", map_to=\"household\", period=year)\n",
+ "household_id = baseline.calculate(\"household_id\", map_to=\"household\", period=year)\n",
+ "disability = baseline.calculate(\"is_disabled\", map_to=\"household\", period=year)\n",
+ "net_income = baseline.calculate(\"household_net_income\", map_to=\"household\", period=year)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
- "medicaid_reform = reformed.calculate(\"medicaid_enrolled\", map_to=\"person\", period=year)"
+ "medicaid_reform = reformed.calculate(\"medicaid_enrolled\", map_to=\"household\", period=year)"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "112.61243085941472"
+ "88.03708112231564"
]
},
- "execution_count": 23,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -96,16 +109,16 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.0"
+ "66.17075737452024"
]
},
- "execution_count": 22,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -116,26 +129,26 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "medicaid_cost_baseline = baseline.calculate(\"medicaid_cost\", map_to=\"person\", period=year)\n",
- "medicaid_cost_reform = reformed.calculate(\"medicaid_cost\", map_to=\"person\", period=year)"
+ "medicaid_cost_baseline = baseline.calculate(\"medicaid_cost\", map_to=\"household\", period=year)\n",
+ "medicaid_cost_reform = reformed.calculate(\"medicaid_cost\", map_to=\"household\", period=year)"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "823.2432315324515"
+ "754.2560939202893"
]
},
- "execution_count": 25,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -146,16 +159,16 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.0"
+ "583.9976766367972"
]
},
- "execution_count": 26,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -191,108 +204,165 @@
" \n",
" | \n",
" household_id | \n",
+ " age | \n",
" State | \n",
" Married | \n",
- " employment_income | \n",
" Num_Dependents | \n",
- " Employment_Income | \n",
- " Self_Employment_Income | \n",
+ " net_income | \n",
" medicaid_baseline | \n",
" medicaid_reform | \n",
- " immigration_status | \n",
+ " disability | \n",
"
\n",
" \n",
" \n",
" \n",
- " | 4 | \n",
- " 36.0 | \n",
- " ME | \n",
+ " 25763 | \n",
+ " 108937 | \n",
+ " 58.0 | \n",
+ " IL | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 76981.750000 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 15052 | \n",
+ " 63037 | \n",
+ " 60.0 | \n",
+ " TX | \n",
+ " 1.0 | \n",
+ " 3.0 | \n",
+ " 66695.476562 | \n",
" 0.0 | \n",
- " 41408.097656 | \n",
" 0.0 | \n",
- " 41408.097656 | \n",
" 0.0 | \n",
- " True | \n",
- " True | \n",
- " CITIZEN | \n",
"
\n",
" \n",
- " | 6 | \n",
- " 44.0 | \n",
- " ME | \n",
+ " 23671 | \n",
+ " 102468 | \n",
+ " 138.0 | \n",
+ " PA | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 36383.925781 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
" 0.0 | \n",
- " 78030.335938 | \n",
+ "
\n",
+ " \n",
+ " | 29397 | \n",
+ " 125991 | \n",
+ " 127.0 | \n",
+ " VA | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 85631.062500 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 35968 | \n",
+ " 153372 | \n",
+ " 133.0 | \n",
+ " TX | \n",
" 1.0 | \n",
- " 78030.335938 | \n",
+ " 1.0 | \n",
+ " 55173.074219 | \n",
+ " 0.0 | \n",
" 0.0 | \n",
- " True | \n",
- " True | \n",
- " CITIZEN | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " | 7 | \n",
- " 68.0 | \n",
- " ME | \n",
+ " 177 | \n",
+ " 1452 | \n",
+ " 168.0 | \n",
+ " NH | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 277564.562500 | \n",
" 0.0 | \n",
- " 0.000000 | \n",
" 0.0 | \n",
- " 0.000000 | \n",
" 0.0 | \n",
- " True | \n",
- " True | \n",
- " CITIZEN | \n",
"
\n",
" \n",
- " | 9 | \n",
- " 78.0 | \n",
- " ME | \n",
+ " 40669 | \n",
+ " 174681 | \n",
+ " 145.0 | \n",
+ " CA | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 108951.515625 | \n",
" 0.0 | \n",
- " 84532.859375 | \n",
" 0.0 | \n",
- " 84532.859375 | \n",
" 0.0 | \n",
- " True | \n",
- " False | \n",
- " CITIZEN | \n",
"
\n",
" \n",
- " | 11 | \n",
- " 85.0 | \n",
- " ME | \n",
+ " 6118 | \n",
+ " 22615 | \n",
+ " 21.0 | \n",
+ " WI | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 60602.281250 | \n",
" 0.0 | \n",
- " 0.000000 | \n",
" 0.0 | \n",
- " 0.000000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 39910 | \n",
+ " 172254 | \n",
+ " 85.0 | \n",
+ " CA | \n",
+ " 0.0 | \n",
" 0.0 | \n",
- " True | \n",
- " True | \n",
- " CITIZEN | \n",
+ " 72438.218750 | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 3931 | \n",
+ " 15787 | \n",
+ " 145.0 | \n",
+ " OH | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 116171.570312 | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State Married employment_income Num_Dependents \\\n",
- "4 36.0 ME 0.0 41408.097656 0.0 \n",
- "6 44.0 ME 0.0 78030.335938 1.0 \n",
- "7 68.0 ME 0.0 0.000000 0.0 \n",
- "9 78.0 ME 0.0 84532.859375 0.0 \n",
- "11 85.0 ME 0.0 0.000000 0.0 \n",
+ " household_id age State Married Num_Dependents net_income \\\n",
+ "25763 108937 58.0 IL 0.0 0.0 76981.750000 \n",
+ "15052 63037 60.0 TX 1.0 3.0 66695.476562 \n",
+ "23671 102468 138.0 PA 0.0 0.0 36383.925781 \n",
+ "29397 125991 127.0 VA 1.0 2.0 85631.062500 \n",
+ "35968 153372 133.0 TX 1.0 1.0 55173.074219 \n",
+ "177 1452 168.0 NH 1.0 0.0 277564.562500 \n",
+ "40669 174681 145.0 CA 1.0 0.0 108951.515625 \n",
+ "6118 22615 21.0 WI 0.0 0.0 60602.281250 \n",
+ "39910 172254 85.0 CA 0.0 0.0 72438.218750 \n",
+ "3931 15787 145.0 OH 1.0 2.0 116171.570312 \n",
"\n",
- " Employment_Income Self_Employment_Income medicaid_baseline \\\n",
- "4 41408.097656 0.0 True \n",
- "6 78030.335938 0.0 True \n",
- "7 0.000000 0.0 True \n",
- "9 84532.859375 0.0 True \n",
- "11 0.000000 0.0 True \n",
- "\n",
- " medicaid_reform immigration_status \n",
- "4 True CITIZEN \n",
- "6 True CITIZEN \n",
- "7 True CITIZEN \n",
- "9 False CITIZEN \n",
- "11 True CITIZEN "
+ " medicaid_baseline medicaid_reform disability \n",
+ "25763 0.0 0.0 1.0 \n",
+ "15052 0.0 0.0 0.0 \n",
+ "23671 3.0 0.0 0.0 \n",
+ "29397 0.0 0.0 0.0 \n",
+ "35968 0.0 0.0 1.0 \n",
+ "177 0.0 0.0 0.0 \n",
+ "40669 0.0 0.0 0.0 \n",
+ "6118 0.0 0.0 1.0 \n",
+ "39910 1.0 1.0 1.0 \n",
+ "3931 1.0 1.0 2.0 "
]
},
"execution_count": 17,
@@ -303,30 +373,218 @@
"source": [
"data = {\n",
" \"household_id\": household_id,\n",
+ " \"age\": age,\n",
" \"State\": state,\n",
" \"Married\": married,\n",
- " \"employment_income\": employment_income,\n",
" \"Num_Dependents\": num_dependents,\n",
- " \"Employment_Income\": employment_income,\n",
- " \"Self_Employment_Income\": self_employment_income,\n",
+ " \"net_income\": net_income,\n",
" \"medicaid_baseline\": medicaid_baseline,\n",
" \"medicaid_reform\": medicaid_reform,\n",
- " \"immigration_status\":immigration_status,\n",
+ " \"disability\": disability,\n",
"}\n",
"\n",
"df_outputs = pd.DataFrame(data)\n",
- "df_outputs.head()\n",
- "\n",
- "# Filter rows where Medicaid is True in the baseline scenario\n",
- "# (works if the column is already Boolean)\n",
- "df_medicaid = df_outputs[df_outputs[\"medicaid_baseline\"]]\n",
- "\n",
- "# ── If the column holds the strings \"true\"/\"false\" instead ──\n",
- "# df_medicaid = df_outputs[df_outputs[\"medicaid_baseline\"].str.lower() == \"true\"]\n",
- "\n",
- "df_medicaid.head()\n",
+ "df_outputs.sample(n=10) \n",
"\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " age | \n",
+ " State | \n",
+ " Married | \n",
+ " Num_Dependents | \n",
+ " net_income | \n",
+ " medicaid_baseline | \n",
+ " medicaid_reform | \n",
+ " disability | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 39714 | \n",
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+ " 0.0 | \n",
+ " 1.0 | \n",
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\n",
+ " \n",
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+ " 2.0 | \n",
+ " 0.0 | \n",
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\n",
+ " \n",
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+ " 21507 | \n",
+ " 87.0 | \n",
+ " MI | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
+ " 126903.640625 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 25970 | \n",
+ " 109575 | \n",
+ " 151.0 | \n",
+ " IL | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 77474.335938 | \n",
+ " 2.0 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 35709 | \n",
+ " 152511 | \n",
+ " 145.0 | \n",
+ " TX | \n",
+ " 1.0 | \n",
+ " 3.0 | \n",
+ " 54013.031250 | \n",
+ " 2.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 21664 | \n",
+ " 96019 | \n",
+ " 147.0 | \n",
+ " CT | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 105085.656250 | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 25351 | \n",
+ " 107589 | \n",
+ " 52.0 | \n",
+ " IL | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 24615.585938 | \n",
+ " 3.0 | \n",
+ " 3.0 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
ptc_ira_reform | \n",
" medicaid_cost | \n",
" per_capita_chip | \n",
+ " SLCSP | \n",
" \n",
" \n",
" \n",
@@ -426,6 +436,7 @@
" 0.000000 | \n",
" 16480.696289 | \n",
" 0.000000 | \n",
+ " 0.000000 | \n",
" \n",
" \n",
" | 1 | \n",
@@ -435,6 +446,7 @@
" 0.000000 | \n",
" 12930.671875 | \n",
" 829.929932 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 2 | \n",
@@ -444,6 +456,7 @@
" 16645.853516 | \n",
" 0.000000 | \n",
" 829.929932 | \n",
+ " 21442.853516 | \n",
"
\n",
" \n",
" | 3 | \n",
@@ -453,6 +466,7 @@
" 12268.548828 | \n",
" 0.000000 | \n",
" 829.929932 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
"\n",
@@ -465,14 +479,14 @@
"2 300 % FPL ($79,950) 79950 13847.603516 16645.853516 \n",
"3 405 % FPL ($107,933) 107933 0.000000 12268.548828 \n",
"\n",
- " medicaid_cost per_capita_chip \n",
- "0 16480.696289 0.000000 \n",
- "1 12930.671875 829.929932 \n",
- "2 0.000000 829.929932 \n",
- "3 0.000000 829.929932 "
+ " medicaid_cost per_capita_chip SLCSP \n",
+ "0 16480.696289 0.000000 0.000000 \n",
+ "1 12930.671875 829.929932 0.000000 \n",
+ "2 0.000000 829.929932 21442.853516 \n",
+ "3 0.000000 829.929932 0.000000 "
]
},
- "execution_count": 26,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -523,9 +537,10 @@
" # ACA PTC\n",
" ptc_base = sim_base.calculate(\"aca_ptc\", map_to=\"household\", period=2026)[0]\n",
" ptc_reform = sim_reform.calculate(\"aca_ptc\", map_to=\"household\", period=2026)[0]\n",
+ " SLCSP = sim_base.calculate(\"slcsp\", map_to=\"household\", period=2026)[0]\n",
"\n",
" # Medicaid benefit (adult or child)\n",
- " medicaid_cost = sim_base.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)[0]\n",
+ " medicaid_cost = sim_base.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)[0]\n",
"\n",
" # CHIP benefit – variable names differ by PE-US version:\n",
" # * If your build has `chip_cost`, use that.\n",
@@ -538,6 +553,7 @@
" ptc_ira_reform = ptc_reform,\n",
" medicaid_cost = medicaid_cost,\n",
" per_capita_chip = per_capita_chip,\n",
+ " SLCSP = SLCSP\n",
" )\n",
"\n",
"# ------------------------------------------------------------------\n",
@@ -570,20 +586,238 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ {
+ "ename": "AttributeError",
+ "evalue": "'NoneType' object has no attribute 'median'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[28], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m baseline_new_york_net_income_including_health_benefits \u001b[38;5;241m=\u001b[39m simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold_net_income_including_health_benefits\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[1;32m 6\u001b[0m baseline_new_york_slcsp \u001b[38;5;241m=\u001b[39m simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslcsp\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbaseline_new_york_slcsp\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\n\u001b[1;32m 9\u001b[0m reform_new_york_per_capita_chip \u001b[38;5;241m=\u001b[39m reformed_simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mper_capita_chip\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[1;32m 10\u001b[0m reform_new_york_aca_ptc \u001b[38;5;241m=\u001b[39m reformed_simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maca_ptc\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'median'"
+ ]
+ }
+ ],
"source": [
"household_income_new_york = simulation_new_york.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
"baseline_new_york_per_capita_chip = simulation_new_york.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"baseline_new_york_aca_ptc = simulation_new_york.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "baseline_new_york_medicaid_cost = simulation_new_york.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "baseline_new_york_medicaid_cost = simulation_new_york.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n",
"baseline_new_york_net_income_including_health_benefits = simulation_new_york.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
- "\n",
+ "baseline_new_york_slcsp = simulation_new_york.calculate(\"slcsp\", map_to=\"household\", period=2026)\n",
"\n",
"reform_new_york_per_capita_chip = reformed_simulation_new_york.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"reform_new_york_aca_ptc = reformed_simulation_new_york.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "reform_new_york_medicaid_cost = reformed_simulation_new_york.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "reform_new_york_medicaid_cost = reformed_simulation_new_york.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n",
"reform_new_york_net_income_including_health_benefits = reformed_simulation_new_york.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
"\n",
"\n",
@@ -594,13 +828,13 @@
"household_income_texas = simulation_texas.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
"baseline_texas_per_capita_chip = simulation_texas.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"baseline_texas_aca_ptc = simulation_texas.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "baseline_texas_medicaid_cost = simulation_texas.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "baseline_texas_medicaid_cost = simulation_texas.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n",
"baseline_texas_net_income_including_health_benefits = simulation_texas.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
- "\n",
+ "baseline_texas_slcsp = simulation_texas.calculate(\"slcsp\", map_to=\"household\", period=2026)\n",
"\n",
"reform_texas_per_capita_chip = reformed_simulation_texas.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
"reform_texas_aca_ptc = reformed_simulation_texas.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n",
- "reform_texas_medicaid_cost = reformed_simulation_texas.calculate(\"medicaid_per_capita_cost\", map_to=\"household\", period=2026)\n",
+ "reform_texas_medicaid_cost = reformed_simulation_texas.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n",
"reform_texas_net_income_including_health_benefits = reformed_simulation_texas.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n",
"\n",
"\n",
@@ -615,7 +849,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -627,7 +861,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -28183,7 +28417,7 @@
"xaxis": {
"range": [
0,
- 400000
+ 200000
],
"tickformat": "$,.0f",
"title": {
@@ -28362,7 +28596,7 @@
" xaxis_title='Household Income',\n",
" yaxis_title='Benefit Amount',\n",
" legend_title='Programs',\n",
- " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n",
" yaxis=dict(tickformat='$,.0f'),\n",
" height=600,\n",
" width=1000\n",
@@ -28380,7 +28614,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 14,
"metadata": {},
"outputs": [
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0.9459999799728394,
0.9459999799728394,
0.9459999799728394,
- 0.9471874833106995,
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+ 0.9459999799728394,
+ 0.9459999799728394,
+ 0.9459999799728394
]
}
],
@@ -40782,6 +41016,10 @@
}
},
"yaxis": {
+ "range": [
+ -1,
+ 1
+ ],
"tickformat": ".0%",
"title": {
"text": "Marginal Tax Rate (Including Health Benefits)"
@@ -40800,61 +41038,59 @@
" \"employment_income\", map_to=\"household\", period=2026\n",
")\n",
"\n",
- "baseline_new_york_mtr_including_health_benefits = simulation_new_york.calculate(\n",
+ "baseline_raw = simulation_new_york.calculate(\n",
" \"marginal_tax_rate_including_health_benefits\",\n",
" map_to=\"household\",\n",
" period=2026\n",
")\n",
"\n",
- "reform_new_york_mtr_including_health_benefits = reformed_simulation_new_york.calculate(\n",
+ "reform_raw = reformed_simulation_new_york.calculate(\n",
" \"marginal_tax_rate_including_health_benefits\",\n",
" map_to=\"household\",\n",
" period=2026\n",
")\n",
"\n",
+ "# ---------- Limit MRT values to ±100 % ----------\n",
+ "baseline_mtr = np.clip(baseline_raw, -1, 1) # -1 ↔ –100 %, 1 ↔ 100 %\n",
+ "reform_mtr = np.clip(reform_raw, -1, 1)\n",
+ "\n",
"# ---------- Build the graph ----------\n",
"fig_new_york_mtr = go.Figure()\n",
"\n",
- "# Baseline trace (solid line)\n",
"fig_new_york_mtr.add_trace(go.Scatter(\n",
" x=household_income_new_york,\n",
- " y=baseline_new_york_mtr_including_health_benefits,\n",
+ " y=baseline_mtr,\n",
" mode='lines',\n",
" name='Marginal Tax Rate (Baseline)',\n",
" line=dict(color=DARK_GRAY, width=2)\n",
"))\n",
"\n",
- "# Reform trace (dotted line)\n",
"fig_new_york_mtr.add_trace(go.Scatter(\n",
" x=household_income_new_york,\n",
- " y=reform_new_york_mtr_including_health_benefits,\n",
+ " y=reform_mtr,\n",
" mode='lines',\n",
" name='Marginal Tax Rate (Reform)',\n",
" line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n",
"))\n",
"\n",
- "# Layout\n",
"fig_new_york_mtr.update_layout(\n",
" title='New York Household (Family of 3) – Marginal Tax Rate Including Health Benefits by Household Income',\n",
" xaxis_title='Household Income',\n",
" yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
" legend_title='Scenario',\n",
" xaxis=dict(tickformat='$,.0f', range=[0, 200_000]),\n",
- " yaxis=dict(tickformat='.0%'), # assumes MTR is in decimal form (e.g., 0.42 → 42%)\n",
+ " yaxis=dict(tickformat='.0%', range=[-1, 1]), # keep the same visual bounds\n",
" height=600,\n",
" width=1000\n",
")\n",
"\n",
- "# Optional formatting helper if you use one elsewhere\n",
"fig_new_york_mtr = format_fig(fig_new_york_mtr)\n",
- "\n",
- "# Display\n",
"fig_new_york_mtr.show()\n"
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -41712,8 +41948,8 @@
0.7865976095199585,
0.7884023189544678,
0.7829999923706055,
- -18.218719482421875,
- -18.184030532836914,
+ -1,
+ -1,
0.8552734851837158,
0.8611522912979126,
0.856624960899353,
@@ -41726,7 +41962,7 @@
0.8067656755447388,
0.8509765863418579,
0.9453905820846558,
- 1.0307109355926514,
+ 1,
0.9737656116485596,
0.8086953163146973,
0.6120624542236328,
@@ -41737,8 +41973,8 @@
0.5586718320846558,
0.5937617421150208,
0.5990468263626099,
- 1.0339374542236328,
- 1.0392265319824219,
+ 1,
+ 1,
0.7274609804153442,
0.765078067779541,
0.6236406564712524,
@@ -41828,8 +42064,8 @@
0.6430000066757202,
0.6430000066757202,
0.6430000066757202,
- 4.670156478881836,
- 4.565812587738037,
+ 1,
+ 1,
0.45299994945526123,
0.45299994945526123,
0.45299994945526123,
@@ -43326,8 +43562,8 @@
0.7865976095199585,
0.7884023189544678,
0.7829999923706055,
- -19.392004013061523,
- -19.397401809692383,
+ -1,
+ -1,
0.7866054773330688,
0.7883983850479126,
0.7829999923706055,
@@ -44951,6 +45187,10 @@
}
},
"yaxis": {
+ "range": [
+ -1,
+ 1
+ ],
"tickformat": ".0%",
"title": {
"text": "Marginal Tax Rate (Including Health Benefits)"
@@ -44964,27 +45204,32 @@
}
],
"source": [
+ "import numpy as np\n",
+ "\n",
"# ---------- Pull the inputs ----------\n",
"household_income_texas = simulation_texas.calculate(\n",
" \"employment_income\", map_to=\"household\", period=2026\n",
")\n",
"\n",
- "baseline_texas_mtr_including_health_benefits = simulation_texas.calculate(\n",
+ "baseline_raw = simulation_texas.calculate(\n",
" \"marginal_tax_rate_including_health_benefits\",\n",
" map_to=\"household\",\n",
" period=2026\n",
")\n",
"\n",
- "reform_texas_mtr_including_health_benefits = reformed_simulation_texas.calculate(\n",
+ "reform_raw = reformed_simulation_texas.calculate(\n",
" \"marginal_tax_rate_including_health_benefits\",\n",
" map_to=\"household\",\n",
" period=2026\n",
")\n",
"\n",
+ "# ---------- Limit MRT values to ±100 % ----------\n",
+ "baseline_texas_mtr_including_health_benefits = np.clip(baseline_raw, -1, 1)\n",
+ "reform_texas_mtr_including_health_benefits = np.clip(reform_raw, -1, 1)\n",
+ "\n",
"# ---------- Build the graph ----------\n",
"fig_texas_mtr = go.Figure()\n",
"\n",
- "# Baseline trace (solid line)\n",
"fig_texas_mtr.add_trace(go.Scatter(\n",
" x=household_income_texas,\n",
" y=baseline_texas_mtr_including_health_benefits,\n",
@@ -44993,7 +45238,6 @@
" line=dict(color=DARK_GRAY, width=2)\n",
"))\n",
"\n",
- "# Reform trace (dotted line)\n",
"fig_texas_mtr.add_trace(go.Scatter(\n",
" x=household_income_texas,\n",
" y=reform_texas_mtr_including_health_benefits,\n",
@@ -45002,22 +45246,18 @@
" line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n",
"))\n",
"\n",
- "# Layout\n",
"fig_texas_mtr.update_layout(\n",
" title='Texas Household (Couple) – Marginal Tax Rate Including Health Benefits by Household Income',\n",
" xaxis_title='Household Income',\n",
" yaxis_title='Marginal Tax Rate (Including Health Benefits)',\n",
" legend_title='Scenario',\n",
" xaxis=dict(tickformat='$,.0f', range=[0, 200_000]),\n",
- " yaxis=dict(tickformat='.0%'), # assumes rate is in decimal form (0.42 → 42 %)\n",
+ " yaxis=dict(tickformat='.0%', range=[-1, 1]), # stays consistent with the clipping\n",
" height=600,\n",
" width=1000\n",
")\n",
"\n",
- "# Optional wrapper if you use one elsewhere\n",
"fig_texas_mtr = format_fig(fig_texas_mtr)\n",
- "\n",
- "# Display\n",
"fig_texas_mtr.show()\n"
]
}
From 7509a8c8bf33c595aa861cb7deb07d2e6dea5048 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Tue, 12 Aug 2025 16:54:19 -0400
Subject: [PATCH 14/37] rename
---
us/blog_posts/ira_expire.ipynb | 400 +-
...olds.ipynb => aca_reform_households.ipynb} | 7335 ++++++++++++++---
2 files changed, 6292 insertions(+), 1443 deletions(-)
rename us/medicaid/{medicaid_households.ipynb => aca_reform_households.ipynb} (90%)
diff --git a/us/blog_posts/ira_expire.ipynb b/us/blog_posts/ira_expire.ipynb
index a891c1d..68c5b94 100644
--- a/us/blog_posts/ira_expire.ipynb
+++ b/us/blog_posts/ira_expire.ipynb
@@ -78,7 +78,7 @@
{
"data": {
"text/plain": [
- "34.53192460674349"
+ "34.4898908313967"
]
},
"execution_count": 4,
@@ -99,7 +99,7 @@
{
"data": {
"text/plain": [
- "227.4910256143858"
+ "228.37424343962823"
]
},
"execution_count": 5,
@@ -121,7 +121,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "179,554,288 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "191,573,928 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -156,7 +156,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "25,364,518 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "24,630,096 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -190,7 +190,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "18,865,437 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "20,074,006 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -272,33 +272,31 @@
" Married | \n",
" Num_Dependents | \n",
" Employment_Income | \n",
- " Self_Employment_Income | \n",
" aca_baseline | \n",
" aca_reform | \n",
" \n",
" \n",
" \n",
" \n",
- " | 23890 | \n",
- " 103176 | \n",
- " PA | \n",
+ " 600 | \n",
+ " 4428 | \n",
+ " MA | \n",
" 1.0 | \n",
- " 3.0 | \n",
- " 122669.62796 | \n",
- " 644.808105 | \n",
+ " 4.0 | \n",
+ " 52859.65625 | \n",
+ " 0.0 | \n",
" 0.0 | \n",
- " 38655.492188 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State Married Num_Dependents Employment_Income \\\n",
- "23890 103176 PA 1.0 3.0 122669.62796 \n",
+ " household_id State Married Num_Dependents Employment_Income \\\n",
+ "600 4428 MA 1.0 4.0 52859.65625 \n",
"\n",
- " Self_Employment_Income aca_baseline aca_reform \n",
- "23890 644.808105 0.0 38655.492188 "
+ " aca_baseline aca_reform \n",
+ "600 0.0 0.0 "
]
},
"execution_count": 10,
@@ -314,13 +312,14 @@
" \"Married\": married,\n",
" \"Num_Dependents\": num_dependents,\n",
" \"Employment_Income\": employment_income,\n",
- " \"Self_Employment_Income\": self_employment_income,\n",
" \"aca_baseline\": aca_baseline,\n",
" \"aca_reform\": aca_reform,\n",
- " }\n",
+ "\n",
+ " }\n",
+ "\n",
"\n",
"df_outputs = pd.DataFrame(data)\n",
- "df_outputs[df_outputs['household_id'] == 103176]\n"
+ "df_outputs[df_outputs['household_id'] == 4428]\n"
]
},
{
@@ -365,101 +364,101 @@
" \n",
" \n",
" \n",
- " | 11774 | \n",
- " 47863 | \n",
+ " 15170 | \n",
+ " 63406 | \n",
+ " TX | \n",
+ " 96633.976562 | \n",
+ " 20097.220703 | \n",
+ " 1.942074e+09 | \n",
+ "
\n",
+ " \n",
+ " | 11278 | \n",
+ " 46357 | \n",
" FL | \n",
- " 135658.906250 | \n",
- " 17123.369141 | \n",
- " 2.322938e+09 | \n",
+ " 63879.199219 | \n",
+ " 15559.541992 | \n",
+ " 9.939311e+08 | \n",
"
\n",
" \n",
- " | 14377 | \n",
- " 60850 | \n",
- " TX | \n",
- " 23338.992188 | \n",
- " 15904.038086 | \n",
- " 3.711842e+08 | \n",
+ " 30957 | \n",
+ " 132558 | \n",
+ " GA | \n",
+ " 34982.687500 | \n",
+ " 14077.200195 | \n",
+ " 4.924583e+08 | \n",
"
\n",
" \n",
" | 14628 | \n",
" 61712 | \n",
" TX | \n",
- " 22272.599609 | \n",
+ " 15532.655273 | \n",
" 14010.416992 | \n",
- " 3.120484e+08 | \n",
+ " 2.176190e+08 | \n",
"
\n",
" \n",
- " | 6960 | \n",
- " 25327 | \n",
- " MO | \n",
- " 11227.063477 | \n",
- " 13525.498047 | \n",
- " 1.518516e+08 | \n",
- "
\n",
- " \n",
- " | 31780 | \n",
- " 135336 | \n",
+ " 11156 | \n",
+ " 46034 | \n",
" FL | \n",
- " 103136.664062 | \n",
- " 12612.913086 | \n",
- " 1.300854e+09 | \n",
+ " 30328.830078 | \n",
+ " 9943.345703 | \n",
+ " 3.015700e+08 | \n",
"
\n",
" \n",
- " | 8987 | \n",
- " 38686 | \n",
- " NC | \n",
- " 25387.832031 | \n",
- " 12249.720703 | \n",
- " 3.109939e+08 | \n",
+ " 31402 | \n",
+ " 133962 | \n",
+ " FL | \n",
+ " 21518.343750 | \n",
+ " 9887.317383 | \n",
+ " 2.127587e+08 | \n",
"
\n",
" \n",
- " | 8576 | \n",
- " 36002 | \n",
- " VA | \n",
- " 17410.156250 | \n",
- " 10854.941406 | \n",
- " 1.889862e+08 | \n",
+ " 4013 | \n",
+ " 16074 | \n",
+ " OH | \n",
+ " 11105.104492 | \n",
+ " 6990.998047 | \n",
+ " 7.763576e+07 | \n",
"
\n",
" \n",
- " | 10747 | \n",
- " 44495 | \n",
+ " 31632 | \n",
+ " 134802 | \n",
" FL | \n",
- " 56612.566406 | \n",
- " 10672.198242 | \n",
- " 6.041805e+08 | \n",
+ " 23929.857422 | \n",
+ " 5788.573242 | \n",
+ " 1.385197e+08 | \n",
"
\n",
" \n",
- " | 19620 | \n",
- " 83988 | \n",
- " CA | \n",
- " 18989.419922 | \n",
- " 10092.767578 | \n",
- " 1.916558e+08 | \n",
+ " 9387 | \n",
+ " 39878 | \n",
+ " NC | \n",
+ " 23245.363281 | \n",
+ " 5382.159180 | \n",
+ " 1.251102e+08 | \n",
"
\n",
" \n",
- " | 4013 | \n",
- " 16074 | \n",
- " OH | \n",
- " 46166.144531 | \n",
- " 6990.998047 | \n",
- " 3.227474e+08 | \n",
+ " 9643 | \n",
+ " 40712 | \n",
+ " SC | \n",
+ " 13224.298828 | \n",
+ " 4779.596680 | \n",
+ " 6.320681e+07 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State weight net_change wt_change\n",
- "11774 47863 FL 135658.906250 17123.369141 2.322938e+09\n",
- "14377 60850 TX 23338.992188 15904.038086 3.711842e+08\n",
- "14628 61712 TX 22272.599609 14010.416992 3.120484e+08\n",
- "6960 25327 MO 11227.063477 13525.498047 1.518516e+08\n",
- "31780 135336 FL 103136.664062 12612.913086 1.300854e+09\n",
- "8987 38686 NC 25387.832031 12249.720703 3.109939e+08\n",
- "8576 36002 VA 17410.156250 10854.941406 1.889862e+08\n",
- "10747 44495 FL 56612.566406 10672.198242 6.041805e+08\n",
- "19620 83988 CA 18989.419922 10092.767578 1.916558e+08\n",
- "4013 16074 OH 46166.144531 6990.998047 3.227474e+08"
+ " household_id State weight net_change wt_change\n",
+ "15170 63406 TX 96633.976562 20097.220703 1.942074e+09\n",
+ "11278 46357 FL 63879.199219 15559.541992 9.939311e+08\n",
+ "30957 132558 GA 34982.687500 14077.200195 4.924583e+08\n",
+ "14628 61712 TX 15532.655273 14010.416992 2.176190e+08\n",
+ "11156 46034 FL 30328.830078 9943.345703 3.015700e+08\n",
+ "31402 133962 FL 21518.343750 9887.317383 2.127587e+08\n",
+ "4013 16074 OH 11105.104492 6990.998047 7.763576e+07\n",
+ "31632 134802 FL 23929.857422 5788.573242 1.385197e+08\n",
+ "9387 39878 NC 23245.363281 5382.159180 1.251102e+08\n",
+ "9643 40712 SC 13224.298828 4779.596680 6.320681e+07"
]
},
"metadata": {},
@@ -503,18 +502,10 @@
" \n",
" \n",
" \n",
- " | 7 | \n",
- " 68 | \n",
- " ME | \n",
- " 18114.335938 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- " | 21 | \n",
- " 134 | \n",
+ " 9 | \n",
+ " 78 | \n",
" ME | \n",
- " 10089.783203 | \n",
+ " 20615.609375 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
@@ -522,47 +513,47 @@
" 25 | \n",
" 194 | \n",
" ME | \n",
- " 15542.672852 | \n",
+ " 14024.491211 | \n",
" 0.0 | \n",
" 0.0 | \n",
" \n",
" \n",
- " | 27 | \n",
- " 206 | \n",
+ " 26 | \n",
+ " 199 | \n",
" ME | \n",
- " 19284.667969 | \n",
+ " 17028.466797 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 31 | \n",
- " 275 | \n",
+ " 38 | \n",
+ " 326 | \n",
" ME | \n",
- " 11475.574219 | \n",
+ " 14531.310547 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 37 | \n",
- " 324 | \n",
+ " 44 | \n",
+ " 356 | \n",
" ME | \n",
- " 16666.904297 | \n",
+ " 15670.582031 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 41 | \n",
- " 339 | \n",
+ " 47 | \n",
+ " 380 | \n",
" ME | \n",
- " 16523.361328 | \n",
+ " 20000.724609 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
- " | 43 | \n",
- " 354 | \n",
+ " 48 | \n",
+ " 407 | \n",
" ME | \n",
- " 11761.126953 | \n",
+ " 10724.878906 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
@@ -570,7 +561,7 @@
" 51 | \n",
" 425 | \n",
" ME | \n",
- " 15638.842773 | \n",
+ " 15117.451172 | \n",
" 0.0 | \n",
" 0.0 | \n",
" \n",
@@ -578,7 +569,15 @@
" 53 | \n",
" 437 | \n",
" ME | \n",
- " 10633.168945 | \n",
+ " 11405.429688 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " \n",
+ " \n",
+ " | 58 | \n",
+ " 475 | \n",
+ " ME | \n",
+ " 12578.248047 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
@@ -588,16 +587,16 @@
],
"text/plain": [
" household_id State weight net_change wt_change\n",
- "7 68 ME 18114.335938 0.0 0.0\n",
- "21 134 ME 10089.783203 0.0 0.0\n",
- "25 194 ME 15542.672852 0.0 0.0\n",
- "27 206 ME 19284.667969 0.0 0.0\n",
- "31 275 ME 11475.574219 0.0 0.0\n",
- "37 324 ME 16666.904297 0.0 0.0\n",
- "41 339 ME 16523.361328 0.0 0.0\n",
- "43 354 ME 11761.126953 0.0 0.0\n",
- "51 425 ME 15638.842773 0.0 0.0\n",
- "53 437 ME 10633.168945 0.0 0.0"
+ "9 78 ME 20615.609375 0.0 0.0\n",
+ "25 194 ME 14024.491211 0.0 0.0\n",
+ "26 199 ME 17028.466797 0.0 0.0\n",
+ "38 326 ME 14531.310547 0.0 0.0\n",
+ "44 356 ME 15670.582031 0.0 0.0\n",
+ "47 380 ME 20000.724609 0.0 0.0\n",
+ "48 407 ME 10724.878906 0.0 0.0\n",
+ "51 425 ME 15117.451172 0.0 0.0\n",
+ "53 437 ME 11405.429688 0.0 0.0\n",
+ "58 475 ME 12578.248047 0.0 0.0"
]
},
"metadata": {},
@@ -672,7 +671,6 @@
" Married | \n",
" Num_Dependents | \n",
" Employment_Income | \n",
- " Self_Employment_Income | \n",
" aca_baseline | \n",
" aca_reform | \n",
" weight | \n",
@@ -680,27 +678,26 @@
" \n",
" \n",
" \n",
- " | 19620 | \n",
- " 83988 | \n",
- " CA | \n",
+ " 600 | \n",
+ " 4428 | \n",
+ " MA | \n",
" 1.0 | \n",
- " 2.0 | \n",
- " 160877.21875 | \n",
+ " 4.0 | \n",
+ " 52859.65625 | \n",
" 0.0 | \n",
" 0.0 | \n",
- " 10092.767578 | \n",
- " 18989.419922 | \n",
+ " 23814.501953 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State Married Num_Dependents Employment_Income \\\n",
- "19620 83988 CA 1.0 2.0 160877.21875 \n",
+ " household_id State Married Num_Dependents Employment_Income \\\n",
+ "600 4428 MA 1.0 4.0 52859.65625 \n",
"\n",
- " Self_Employment_Income aca_baseline aca_reform weight \n",
- "19620 0.0 0.0 10092.767578 18989.419922 "
+ " aca_baseline aca_reform weight \n",
+ "600 0.0 0.0 23814.501953 "
]
},
"execution_count": 12,
@@ -709,7 +706,7 @@
}
],
"source": [
- "df_outputs[df_outputs['household_id'] == 83988]\n"
+ "df_outputs[df_outputs['household_id'] == 4428]\n"
]
},
{
@@ -721,7 +718,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with any change: $2,442.32\n"
+ "Average weighted PTC change among households with any change: $2,239.40\n"
]
}
],
@@ -751,7 +748,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with a PTC in both baseline and reform: $1,663.64\n"
+ "Average weighted PTC change among households with a PTC in both baseline and reform: $1,784.16\n"
]
}
],
@@ -783,14 +780,51 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average weighted PTC change among households that newly receive a PTC under the reform: $4,797.60\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ------------------------------------------------------------------\n",
+ "# 0. Ensure supporting columns exist (already done above)\n",
+ "# ------------------------------------------------------------------\n",
+ "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n",
+ "\n",
+ "# ------------------------------------------------------------------\n",
+ "# 1. Keep only households that *gain* a PTC (reform > 0, baseline == 0)\n",
+ "# ------------------------------------------------------------------\n",
+ "mask_reform_only = (df_outputs[\"aca_baseline\"] == 0) & (df_outputs[\"aca_reform\"] > 0)\n",
+ "df_reform_only = df_outputs[mask_reform_only]\n",
+ "\n",
+ "# ------------------------------------------------------------------\n",
+ "# 2. Weighted average of the net change (household perspective)\n",
+ "# ------------------------------------------------------------------\n",
+ "avg_net_change_reform_only_hh = (\n",
+ " (df_reform_only[\"net_change\"] * df_reform_only[\"weight\"]).sum()\n",
+ " / df_reform_only[\"weight\"].sum()\n",
+ ")\n",
+ "\n",
+ "print(f\"Average weighted PTC change among households that newly receive a PTC \"\n",
+ " f\"under the reform: ${avg_net_change_reform_only_hh:,.2f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "32.12809192838201"
+ "29.856163170330728"
]
},
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -820,7 +854,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -846,16 +880,16 @@
]
},
"text": [
- "$127",
- "$141",
- "$120",
- "$164",
- "$206",
- "$256",
- "$214",
- "$668",
- "$185",
- "$83"
+ "$47",
+ "$126",
+ "$147",
+ "$165",
+ "$209",
+ "$252",
+ "$244",
+ "$281",
+ "$390",
+ "$125"
],
"textposition": "inside",
"type": "bar",
@@ -872,16 +906,16 @@
10
],
"y": [
- 127.10087585449219,
- 140.969970703125,
- 119.75225067138672,
- 163.75502014160156,
- 205.8011016845703,
- 255.56259155273438,
- 213.85777282714844,
- 667.6323852539062,
- 184.54965209960938,
- 83.39998626708984
+ 47.242431640625,
+ 125.9588623046875,
+ 146.8796844482422,
+ 164.8943634033203,
+ 209.1598663330078,
+ 251.72093200683594,
+ 243.78147888183594,
+ 280.51556396484375,
+ 389.531982421875,
+ 124.53172302246094
]
}
],
@@ -1760,16 +1794,16 @@
]
},
"text": [
- "$127",
- "$141",
- "$120",
- "$164",
- "$206",
- "$256",
- "$214",
- "$668",
- "$185",
- "$83"
+ "$47",
+ "$126",
+ "$147",
+ "$165",
+ "$209",
+ "$252",
+ "$244",
+ "$281",
+ "$390",
+ "$125"
],
"textposition": "inside",
"type": "bar",
@@ -1786,16 +1820,16 @@
10
],
"y": [
- 127.10087585449219,
- 140.969970703125,
- 119.75225067138672,
- 163.75502014160156,
- 205.8011016845703,
- 255.56259155273438,
- 213.85777282714844,
- 667.6323852539062,
- 184.54965209960938,
- 83.39998626708984
+ 47.242431640625,
+ 125.9588623046875,
+ 146.8796844482422,
+ 164.8943634033203,
+ 209.1598663330078,
+ 251.72093200683594,
+ 243.78147888183594,
+ 280.51556396484375,
+ 389.531982421875,
+ 124.53172302246094
]
}
],
diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/aca_reform_households.ipynb
similarity index 90%
rename from us/medicaid/medicaid_households.ipynb
rename to us/medicaid/aca_reform_households.ipynb
index b8a630a..e761e03 100644
--- a/us/medicaid/medicaid_households.ipynb
+++ b/us/medicaid/aca_reform_households.ipynb
@@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/daphnehansell/miniconda3/lib/python3.12/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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from policyengine_us import Simulation\n",
"from policyengine_core.reforms import Reform\n",
@@ -25,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -61,7 +52,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -158,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -237,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -259,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -322,7 +313,7 @@
"2 400 % FPL ($84,600) 84600 0.000000 2899.201172"
]
},
- "execution_count": 6,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -394,7 +385,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -486,7 +477,7 @@
"3 0.000000 829.929932 0.000000 "
]
},
- "execution_count": 30,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -586,227 +577,9 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 18,
"metadata": {},
- "outputs": [
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- },
- {
- "ename": "AttributeError",
- "evalue": "'NoneType' object has no attribute 'median'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[28], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m baseline_new_york_net_income_including_health_benefits \u001b[38;5;241m=\u001b[39m simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold_net_income_including_health_benefits\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[1;32m 6\u001b[0m baseline_new_york_slcsp \u001b[38;5;241m=\u001b[39m simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslcsp\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbaseline_new_york_slcsp\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\n\u001b[1;32m 9\u001b[0m reform_new_york_per_capita_chip \u001b[38;5;241m=\u001b[39m reformed_simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mper_capita_chip\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n\u001b[1;32m 10\u001b[0m reform_new_york_aca_ptc \u001b[38;5;241m=\u001b[39m reformed_simulation_new_york\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maca_ptc\u001b[39m\u001b[38;5;124m\"\u001b[39m, map_to\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2026\u001b[39m)\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'median'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"household_income_new_york = simulation_new_york.calculate(\"employment_income\", map_to=\"household\", period=2026)\n",
"baseline_new_york_per_capita_chip = simulation_new_york.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n",
@@ -849,7 +622,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -861,7 +634,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -28567,7 +28340,7 @@
"\n",
"fig_texas.add_trace(go.Scatter(\n",
" x=household_income_texas, \n",
- " y=reform_texas_medicaid_cost, \n",
+ " y=reform_texas_medicaid_cost,\n",
" mode='lines', \n",
" name='Medicaid (Reform)', \n",
" line=dict(color=TEAL_ACCENT, width=2, dash='dot')\n",
@@ -28614,7 +28387,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -32721,56 +32494,7 @@
},
"metadata": {},
"output_type": "display_data"
- }
- ],
- "source": [
- "#House hold net income graphs\n",
- "import plotly.graph_objects as go\n",
- "\n",
- "# ---------- NY fam ----------\n",
- "fig_ny = go.Figure()\n",
- "\n",
- "# Baseline (solid)\n",
- "fig_ny.add_trace(go.Scatter(\n",
- " x=household_income_new_york,\n",
- " y=baseline_new_york_net_income_including_health_benefits,\n",
- " mode='lines',\n",
- " name='Health Net Income (Baseline)',\n",
- " line=dict(color=DARK_GRAY, width=2) # use your palette constant\n",
- "))\n",
- "\n",
- "# Reform (dotted)\n",
- "fig_ny.add_trace(go.Scatter(\n",
- " x=household_income_new_york,\n",
- " y=reform_new_york_net_income_including_health_benefits,\n",
- " mode='lines',\n",
- " name='Health Net Income (Reform)',\n",
- " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
- "))\n",
- "\n",
- "# Layout\n",
- "fig_ny.update_layout(\n",
- " title='New York Household (Family of 3) – Health-Adjusted Net Income by Household Income',\n",
- " xaxis_title='Household Income',\n",
- " yaxis_title='Health-Adjusted Net Income',\n",
- " legend_title='Scenario',\n",
- " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
- " yaxis=dict(tickformat='$,.0f'),\n",
- " height=600,\n",
- " width=1000\n",
- ")\n",
- "\n",
- "# Optional wrapper if you use one elsewhere\n",
- "fig_ny = format_fig(fig_ny)\n",
- "\n",
- "fig_ny.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
+ },
{
"data": {
"application/vnd.plotly.v1+json": {
@@ -32784,7 +32508,7 @@
"width": 2
},
"mode": "lines",
- "name": "Health Net Income (Baseline)",
+ "name": "Δ Net Income (Reform – Baseline)",
"type": "scatter",
"x": [
0,
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+ "bgcolor": "white",
+ "radialaxis": {
+ "gridcolor": "#EBF0F8",
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+ }
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+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "white",
+ "gridcolor": "#DFE8F3",
+ "gridwidth": 2,
+ "linecolor": "#EBF0F8",
+ "showbackground": true,
+ "ticks": "",
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+ "yaxis": {
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+ "showbackground": true,
+ "ticks": "",
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+ "zaxis": {
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+ "gridwidth": 2,
+ "linecolor": "#EBF0F8",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "#EBF0F8"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ },
+ "bgcolor": "white",
+ "caxis": {
+ "gridcolor": "#DFE8F3",
+ "linecolor": "#A2B1C6",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "#EBF0F8",
+ "linecolor": "#EBF0F8",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "#EBF0F8",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "#EBF0F8",
+ "linecolor": "#EBF0F8",
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+ "title": {
+ "standoff": 15
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+ "zerolinecolor": "#EBF0F8",
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+ }
+ },
+ "title": {
+ "text": "New York Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits"
+ },
+ "width": 800,
+ "xaxis": {
+ "range": [
+ 0,
+ 400000
+ ],
+ "tickformat": "$,.0f",
+ "title": {
+ "text": "Household Income"
+ }
+ },
+ "yaxis": {
+ "tickformat": "$,.0f",
+ "title": {
+ "text": "Δ Net Income"
+ },
+ "zeroline": true,
+ "zerolinewidth": 1
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#House hold net income graphs\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "# ---------- NY fam ----------\n",
+ "fig_ny = go.Figure()\n",
+ "\n",
+ "# Baseline (solid)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=baseline_new_york_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2) # use your palette constant\n",
+ "))\n",
+ "\n",
+ "# Reform (dotted)\n",
+ "fig_ny.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=reform_new_york_net_income_including_health_benefits,\n",
+ " mode='lines',\n",
+ " name='Health Net Income (Reform)',\n",
+ " line=dict(color=DARK_GRAY, width=2, dash='dot')\n",
+ "))\n",
+ "\n",
+ "# Layout\n",
+ "fig_ny.update_layout(\n",
+ " title='New York Household (Family of 3) – Health-Adjusted Net Income by Household Income',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Health-Adjusted Net Income',\n",
+ " legend_title='Scenario',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
+ " yaxis=dict(tickformat='$,.0f'),\n",
+ " height=600,\n",
+ " width=1000\n",
+ ")\n",
+ "\n",
+ "# Optional wrapper if you use one elsewhere\n",
+ "fig_ny = format_fig(fig_ny)\n",
+ "\n",
+ "fig_ny.show()\n",
+ "\n",
+ "# --- Δ Health-adjusted net income (Reform – Baseline) ---\n",
+ "delta_ny = (\n",
+ " reform_new_york_net_income_including_health_benefits\n",
+ " - baseline_new_york_net_income_including_health_benefits\n",
+ ")\n",
+ "\n",
+ "fig_delta_ny = go.Figure()\n",
+ "\n",
+ "fig_delta_ny.add_trace(go.Scatter(\n",
+ " x=household_income_new_york,\n",
+ " y=delta_ny,\n",
+ " mode='lines',\n",
+ " name='Δ Net Income (Reform – Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2)\n",
+ "))\n",
+ "\n",
+ "fig_delta_ny.update_layout(\n",
+ " title='New York Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Δ Net Income',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 400_000]),\n",
+ " yaxis=dict(tickformat='$,.0f', zeroline=True, zerolinewidth=1),\n",
+ " height=600,\n",
+ " width=1000,\n",
+ " legend=dict(orientation='h')\n",
+ ")\n",
+ "\n",
+ "fig_delta_ny = format_fig(fig_delta_ny) # if you use the helper elsewhere\n",
+ "fig_delta_ny.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ ]
+ }
+ ],
+ "layout": {
+ "font": {
+ "color": "black",
+ "family": "Roboto Serif"
+ },
+ "height": 600,
+ "images": [
+ {
+ "sizex": 0.15,
+ "sizey": 0.15,
+ "source": "https://raw.githubusercontent.com/PolicyEngine/policyengine-app/master/src/images/logos/policyengine/blue.png",
+ "x": 1.1,
+ "xanchor": "right",
+ "xref": "paper",
+ "y": -0.15,
+ "yanchor": "bottom",
+ "yref": "paper"
+ }
+ ],
+ "legend": {
+ "orientation": "h"
},
"modebar": {
"bgcolor": "rgba(0,0,0,0)",
@@ -36851,7 +41634,7 @@
}
},
"title": {
- "text": "Texas Household (Married Couple) – Health-Adjusted Net Income by Household Income"
+ "text": "Texas Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits"
},
"width": 800,
"xaxis": {
@@ -36867,8 +41650,10 @@
"yaxis": {
"tickformat": "$,.0f",
"title": {
- "text": "Health-Adjusted Net Income"
- }
+ "text": "Δ Net Income"
+ },
+ "zeroline": true,
+ "zerolinewidth": 1
}
}
}
@@ -36914,12 +41699,42 @@
"# Optional wrapper if you use one elsewhere\n",
"fig_tx = format_fig(fig_tx)\n",
"\n",
- "fig_tx.show()\n"
+ "fig_tx.show()\n",
+ "# --- Δ Health-adjusted net income (Reform – Baseline), Texas ---\n",
+ "delta_tx = (\n",
+ " reform_texas_net_income_including_health_benefits\n",
+ " - baseline_texas_net_income_including_health_benefits\n",
+ ")\n",
+ "\n",
+ "fig_delta_tx = go.Figure()\n",
+ "\n",
+ "fig_delta_tx.add_trace(go.Scatter(\n",
+ " x=household_income_texas,\n",
+ " y=delta_tx,\n",
+ " mode='lines',\n",
+ " name='Δ Net Income (Reform – Baseline)',\n",
+ " line=dict(color=DARK_GRAY, width=2)\n",
+ "))\n",
+ "\n",
+ "fig_delta_tx.update_layout(\n",
+ " title='Texas Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits',\n",
+ " xaxis_title='Household Income',\n",
+ " yaxis_title='Δ Net Income',\n",
+ " xaxis=dict(tickformat='$,.0f', range=[0, 200_000]),\n",
+ " yaxis=dict(tickformat='$,.0f', zeroline=True, zerolinewidth=1),\n",
+ " height=600,\n",
+ " width=1000,\n",
+ " legend=dict(orientation='h')\n",
+ ")\n",
+ "\n",
+ "fig_delta_tx = format_fig(fig_delta_tx) # if you’re using that helper\n",
+ "fig_delta_tx.show()\n",
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -41090,7 +45905,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
From 3ac06dc029f2cf9ef9c0af5d93e8dcc4a5681125 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Wed, 27 Aug 2025 14:08:10 -0400
Subject: [PATCH 15/37] notebooks
---
.gitignore | 1 +
us/blog_posts/ira_expire.ipynb | 1801 +-
us/blog_posts/ira_expire_old_data.ipynb | 4242 +++++
us/medicaid/aca_reform.ipynb | 121 +-
us/medicaid/aca_reform_households.ipynb | 19327 +++++++---------------
us/medicaid/analyze_aca_cliff.py | 210 +
us/medicaid/claude_help.md | 48 +
us/medicaid/old_dataset.ipynb | 126 +
us/nyt/ira_ptc.ipynb | 4 +-
9 files changed, 12747 insertions(+), 13133 deletions(-)
create mode 100644 us/blog_posts/ira_expire_old_data.ipynb
create mode 100644 us/medicaid/analyze_aca_cliff.py
create mode 100644 us/medicaid/claude_help.md
create mode 100644 us/medicaid/old_dataset.ipynb
diff --git a/.gitignore b/.gitignore
index 82f9275..81faeef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,4 @@ cython_debug/
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
+us/medicaid/enhanced_cps_2024.h5
diff --git a/us/blog_posts/ira_expire.ipynb b/us/blog_posts/ira_expire.ipynb
index 68c5b94..55f6ec7 100644
--- a/us/blog_posts/ira_expire.ipynb
+++ b/us/blog_posts/ira_expire.ipynb
@@ -78,7 +78,7 @@
{
"data": {
"text/plain": [
- "34.4898908313967"
+ "31.542351631973784"
]
},
"execution_count": 4,
@@ -99,7 +99,7 @@
{
"data": {
"text/plain": [
- "228.37424343962823"
+ "225.39480517300126"
]
},
"execution_count": 5,
@@ -121,7 +121,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "191,573,928 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "205,150,194 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -156,7 +156,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "24,630,096 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "22,890,509 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -190,7 +190,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "20,074,006 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
+ "16,961,632 weighted people live in tax units that take up Marketplace coverage and actually receive a PTC.\n"
]
}
],
@@ -278,7 +278,7 @@
" \n",
" \n",
" \n",
- " | 600 | \n",
+ " 459 | \n",
" 4428 | \n",
" MA | \n",
" 1.0 | \n",
@@ -293,10 +293,10 @@
],
"text/plain": [
" household_id State Married Num_Dependents Employment_Income \\\n",
- "600 4428 MA 1.0 4.0 52859.65625 \n",
+ "459 4428 MA 1.0 4.0 52859.65625 \n",
"\n",
" aca_baseline aca_reform \n",
- "600 0.0 0.0 "
+ "459 0.0 0.0 "
]
},
"execution_count": 10,
@@ -364,101 +364,101 @@
" \n",
"
\n",
" \n",
- " | 15170 | \n",
+ " 3930 | \n",
+ " 22572 | \n",
+ " WI | \n",
+ " 14746.737305 | \n",
+ " 20787.404785 | \n",
+ " 3.065464e+08 | \n",
+ "
\n",
+ " \n",
+ " | 9715 | \n",
" 63406 | \n",
" TX | \n",
- " 96633.976562 | \n",
- " 20097.220703 | \n",
- " 1.942074e+09 | \n",
+ " 313614.062500 | \n",
+ " 20464.279297 | \n",
+ " 6.417886e+09 | \n",
"
\n",
" \n",
- " | 11278 | \n",
- " 46357 | \n",
+ " 7593 | \n",
+ " 47863 | \n",
" FL | \n",
- " 63879.199219 | \n",
- " 15559.541992 | \n",
- " 9.939311e+08 | \n",
+ " 13494.670898 | \n",
+ " 20171.796875 | \n",
+ " 2.722118e+08 | \n",
"
\n",
" \n",
- " | 30957 | \n",
- " 132558 | \n",
- " GA | \n",
- " 34982.687500 | \n",
- " 14077.200195 | \n",
- " 4.924583e+08 | \n",
+ " 12472 | \n",
+ " 83838 | \n",
+ " CA | \n",
+ " 42713.929688 | \n",
+ " 18523.296875 | \n",
+ " 7.912028e+08 | \n",
"
\n",
" \n",
- " | 14628 | \n",
- " 61712 | \n",
- " TX | \n",
- " 15532.655273 | \n",
- " 14010.416992 | \n",
- " 2.176190e+08 | \n",
+ " 11078 | \n",
+ " 74935 | \n",
+ " UT | \n",
+ " 19624.578125 | \n",
+ " 14949.250000 | \n",
+ " 2.933727e+08 | \n",
"
\n",
" \n",
- " | 11156 | \n",
+ " 7287 | \n",
" 46034 | \n",
" FL | \n",
- " 30328.830078 | \n",
- " 9943.345703 | \n",
- " 3.015700e+08 | \n",
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"
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- " 9887.317383 | \n",
- " 2.127587e+08 | \n",
- "
\n",
- " \n",
- " | 4013 | \n",
- " 16074 | \n",
- " OH | \n",
- " 11105.104492 | \n",
- " 6990.998047 | \n",
- " 7.763576e+07 | \n",
+ " 1718 | \n",
+ " 11792 | \n",
+ " NJ | \n",
+ " 22938.029297 | \n",
+ " 9646.639648 | \n",
+ " 2.212749e+08 | \n",
"
\n",
" \n",
- " | 31632 | \n",
- " 134802 | \n",
- " FL | \n",
- " 23929.857422 | \n",
- " 5788.573242 | \n",
- " 1.385197e+08 | \n",
+ " 5058 | \n",
+ " 29932 | \n",
+ " KS | \n",
+ " 10356.357422 | \n",
+ " 9572.796875 | \n",
+ " 9.913931e+07 | \n",
"
\n",
" \n",
- " | 9387 | \n",
- " 39878 | \n",
- " NC | \n",
- " 23245.363281 | \n",
- " 5382.159180 | \n",
- " 1.251102e+08 | \n",
+ " 3335 | \n",
+ " 19714 | \n",
+ " IL | \n",
+ " 12393.625000 | \n",
+ " 7891.378906 | \n",
+ " 9.780279e+07 | \n",
"
\n",
" \n",
- " | 9643 | \n",
- " 40712 | \n",
- " SC | \n",
- " 13224.298828 | \n",
- " 4779.596680 | \n",
- " 6.320681e+07 | \n",
+ " 4053 | \n",
+ " 23036 | \n",
+ " WI | \n",
+ " 20458.789062 | \n",
+ " 7672.568359 | \n",
+ " 1.569715e+08 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " household_id State weight net_change wt_change\n",
- "15170 63406 TX 96633.976562 20097.220703 1.942074e+09\n",
- "11278 46357 FL 63879.199219 15559.541992 9.939311e+08\n",
- "30957 132558 GA 34982.687500 14077.200195 4.924583e+08\n",
- "14628 61712 TX 15532.655273 14010.416992 2.176190e+08\n",
- "11156 46034 FL 30328.830078 9943.345703 3.015700e+08\n",
- "31402 133962 FL 21518.343750 9887.317383 2.127587e+08\n",
- "4013 16074 OH 11105.104492 6990.998047 7.763576e+07\n",
- "31632 134802 FL 23929.857422 5788.573242 1.385197e+08\n",
- "9387 39878 NC 23245.363281 5382.159180 1.251102e+08\n",
- "9643 40712 SC 13224.298828 4779.596680 6.320681e+07"
+ " household_id State weight net_change wt_change\n",
+ "3930 22572 WI 14746.737305 20787.404785 3.065464e+08\n",
+ "9715 63406 TX 313614.062500 20464.279297 6.417886e+09\n",
+ "7593 47863 FL 13494.670898 20171.796875 2.722118e+08\n",
+ "12472 83838 CA 42713.929688 18523.296875 7.912028e+08\n",
+ "11078 74935 UT 19624.578125 14949.250000 2.933727e+08\n",
+ "7287 46034 FL 191966.390625 13774.429688 2.644228e+09\n",
+ "1718 11792 NJ 22938.029297 9646.639648 2.212749e+08\n",
+ "5058 29932 KS 10356.357422 9572.796875 9.913931e+07\n",
+ "3335 19714 IL 12393.625000 7891.378906 9.780279e+07\n",
+ "4053 23036 WI 20458.789062 7672.568359 1.569715e+08"
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"metadata": {},
@@ -678,7 +678,7 @@
" \n",
" \n",
" \n",
- " | 600 | \n",
+ " 459 | \n",
" 4428 | \n",
" MA | \n",
" 1.0 | \n",
@@ -686,7 +686,7 @@
" 52859.65625 | \n",
" 0.0 | \n",
" 0.0 | \n",
- " 23814.501953 | \n",
+ " 4397.432129 | \n",
"
\n",
" \n",
"\n",
@@ -694,10 +694,10 @@
],
"text/plain": [
" household_id State Married Num_Dependents Employment_Income \\\n",
- "600 4428 MA 1.0 4.0 52859.65625 \n",
+ "459 4428 MA 1.0 4.0 52859.65625 \n",
"\n",
- " aca_baseline aca_reform weight \n",
- "600 0.0 0.0 23814.501953 "
+ " aca_baseline aca_reform weight \n",
+ "459 0.0 0.0 4397.432129 "
]
},
"execution_count": 12,
@@ -718,7 +718,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with any change: $2,239.40\n"
+ "Average weighted PTC change among households with any change: $2,730.13\n"
]
}
],
@@ -748,7 +748,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households with a PTC in both baseline and reform: $1,784.16\n"
+ "Average weighted PTC change among households with a PTC in both baseline and reform: $2,239.05\n"
]
}
],
@@ -785,7 +785,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average weighted PTC change among households that newly receive a PTC under the reform: $4,797.60\n"
+ "Average weighted PTC change among households that newly receive a PTC under the reform: $5,302.77\n"
]
}
],
@@ -821,7 +821,7 @@
{
"data": {
"text/plain": [
- "29.856163170330728"
+ "32.76222811087988"
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@@ -880,16 +880,16 @@
]
},
"text": [
- "$47",
- "$126",
- "$147",
+ "$45",
+ "$129",
+ "$173",
+ "$94",
+ "$173",
+ "$154",
"$165",
- "$209",
- "$252",
- "$244",
- "$281",
- "$390",
- "$125"
+ "$283",
+ "$730",
+ "$267"
],
"textposition": "inside",
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+ 266.8133850097656
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],
@@ -1794,16 +1794,16 @@
]
},
"text": [
- "$47",
- "$126",
- "$147",
+ "$45",
+ "$129",
+ "$173",
+ "$94",
+ "$173",
+ "$154",
"$165",
- "$209",
- "$252",
- "$244",
- "$281",
- "$390",
- "$125"
+ "$283",
+ "$730",
+ "$267"
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@@ -1820,16 +1820,16 @@
10
],
"y": [
- 47.242431640625,
- 125.9588623046875,
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],
@@ -2782,6 +2782,1481 @@
"fig.show()\n",
"fig.update_xaxes(dtick=1) # show 1-10 instead of only the evens\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "1jhns1uinylj",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of households gaining PTC under reform: 761\n",
+ "Weighted count: 1,923,488\n",
+ "\n",
+ "Average reform PTC for these households: $5,751.24\n",
+ "Weighted average reform PTC: $5,302.77\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Let's analyze the households affected by the ACA reform\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# First, let's look at households that gain PTC under reform but had none in baseline\n",
+ "gained_ptc = df_outputs[(df_outputs['aca_baseline'] == 0) & (df_outputs['aca_reform'] > 0)]\n",
+ "\n",
+ "print(f\"Number of households gaining PTC under reform: {len(gained_ptc)}\")\n",
+ "print(f\"Weighted count: {gained_ptc['weight'].sum():,.0f}\")\n",
+ "print(f\"\\nAverage reform PTC for these households: ${gained_ptc['aca_reform'].mean():,.2f}\")\n",
+ "print(f\"Weighted average reform PTC: ${(gained_ptc['aca_reform'] * gained_ptc['weight']).sum() / gained_ptc['weight'].sum():,.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "kezjkjwshvl",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'matplotlib'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Let's look at income distribution of households gaining PTC\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Add income deciles to the gained_ptc dataframe\u001b[39;00m\n\u001b[1;32m 5\u001b[0m gained_ptc_with_income \u001b[38;5;241m=\u001b[39m gained_ptc\u001b[38;5;241m.\u001b[39mcopy()\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
+ ]
+ }
+ ],
+ "source": [
+ "# Let's look at income distribution of households gaining PTC\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Add income deciles to the gained_ptc dataframe\n",
+ "gained_ptc_with_income = gained_ptc.copy()\n",
+ "\n",
+ "# Calculate weighted income percentiles for context\n",
+ "income_percentiles = np.percentile(df_outputs['Employment_Income'], [25, 50, 75, 90, 95])\n",
+ "print(\"Income percentiles across all households:\")\n",
+ "for i, pct in enumerate([25, 50, 75, 90, 95]):\n",
+ " print(f\" {pct}th percentile: ${income_percentiles[i]:,.0f}\")\n",
+ "\n",
+ "# Show income distribution of households gaining PTC\n",
+ "print(\"\\nIncome distribution of households GAINING PTC under reform:\")\n",
+ "print(gained_ptc_with_income['Employment_Income'].describe())\n",
+ "\n",
+ "# Show top 10 households by PTC gain amount\n",
+ "print(\"\\nTop 10 households by PTC gain (sorted by reform PTC amount):\")\n",
+ "top_gainers = gained_ptc_with_income.nlargest(10, 'aca_reform')[['household_id', 'State', 'Employment_Income', 'aca_reform', 'Married', 'Num_Dependents', 'weight']]\n",
+ "top_gainers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "qzjyh3eo44",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Income percentiles across all households:\n",
+ " 25th percentile: $8,776\n",
+ " 50th percentile: $59,168\n",
+ " 75th percentile: $121,807\n",
+ " 90th percentile: $205,693\n",
+ " 95th percentile: $287,212\n",
+ "\n",
+ "============================================================\n",
+ "Income distribution of households GAINING PTC under reform:\n",
+ "============================================================\n",
+ "count 761.000000\n",
+ "mean 113532.674681\n",
+ "std 67906.364616\n",
+ "min 0.000000\n",
+ "25% 68947.382812\n",
+ "50% 103907.218750\n",
+ "75% 150131.617188\n",
+ "max 467888.655273\n",
+ "Name: Employment_Income, dtype: float64\n",
+ "\n",
+ "============================================================\n",
+ "Top 10 households by PTC gain (sorted by reform PTC amount):\n",
+ "============================================================\n"
+ ]
+ },
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+ " CA | \n",
+ " 120305.722656 | \n",
+ " 24842.128906 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.003597 | \n",
+ "
\n",
+ " \n",
+ " | 62 | \n",
+ " 548 | \n",
+ " ME | \n",
+ " 0.000000 | \n",
+ " 24623.031250 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1530.439819 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " aca_reform | \n",
+ " Married | \n",
+ " Num_Dependents | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 10241 | \n",
+ " 69304 | \n",
+ " WY | \n",
+ " 172368.441406 | \n",
+ " 27582.343750 | \n",
+ " 1.0 | \n",
+ " 3.0 | \n",
+ " 6615.325195 | \n",
+ "
\n",
+ " \n",
+ " | 4600 | \n",
+ " 25635 | \n",
+ " MO | \n",
+ " 37921.058594 | \n",
+ " 27411.421875 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 4003.946045 | \n",
+ "
\n",
+ " \n",
+ " | 17827 | \n",
+ " 136329 | \n",
+ " FL | \n",
+ " 96147.054688 | \n",
+ " 27191.490234 | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 0.052937 | \n",
+ "
\n",
+ " \n",
+ " | 9197 | \n",
+ " 59697 | \n",
+ " OK | \n",
+ " 68947.382812 | \n",
+ " 26790.708984 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1484.846436 | \n",
+ "
\n",
+ " \n",
+ " | 4610 | \n",
+ " 25669 | \n",
+ " MO | \n",
+ " 0.000000 | \n",
+ " 26257.669922 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1353.809692 | \n",
+ "
\n",
+ " \n",
+ " | 4565 | \n",
+ " 25484 | \n",
+ " MO | \n",
+ " 120657.902344 | \n",
+ " 25160.201172 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 4692.614746 | \n",
+ "
\n",
+ " \n",
+ " | 20880 | \n",
+ " 170145 | \n",
+ " CA | \n",
+ " 97803.962158 | \n",
+ " 25064.998047 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1.485237 | \n",
+ "
\n",
+ " \n",
+ " | 21249 | \n",
+ " 173580 | \n",
+ " CA | \n",
+ " 107563.710938 | \n",
+ " 24998.007812 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.000211 | \n",
+ "
\n",
+ " \n",
+ " | 21284 | \n",
+ " 173817 | \n",
+ " CA | \n",
+ " 120305.722656 | \n",
+ " 24842.128906 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.003597 | \n",
+ "
\n",
+ " \n",
+ " | 62 | \n",
+ " 548 | \n",
+ " ME | \n",
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+ " 24623.031250 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1530.439819 | \n",
+ "
\n",
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\n",
+ "
\n",
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\n",
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\n",
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\n",
+ "
\n",
+ "\n",
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\n",
+ " \n",
+ " \n",
+ " | \n",
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+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " net_change | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
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+ " 31882.274902 | \n",
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+ " 0.000197 | \n",
+ "
\n",
+ " \n",
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+ " 131771.823242 | \n",
+ " 6317.419434 | \n",
+ " 28958.584473 | \n",
+ " 22641.165039 | \n",
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+ "
\n",
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+ " 22409.271729 | \n",
+ " 1788.657227 | \n",
+ "
\n",
+ " \n",
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+ " 14746.737305 | \n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " aca_reform | \n",
+ " Married | \n",
+ " Num_Dependents | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 21655 | \n",
+ " 95958 | \n",
+ " CT | \n",
+ " 586290.419922 | \n",
+ " 29020.246094 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.000351 | \n",
+ "
\n",
+ " \n",
+ " | 20926 | \n",
+ " 92239 | \n",
+ " VT | \n",
+ " 169552.753906 | \n",
+ " 28546.548828 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 2193.554443 | \n",
+ "
\n",
+ " \n",
+ " | 16135 | \n",
+ " 69304 | \n",
+ " WY | \n",
+ " 172368.441406 | \n",
+ " 27582.343750 | \n",
+ " 1.0 | \n",
+ " 3.0 | \n",
+ " 1964.266357 | \n",
+ "
\n",
+ " \n",
+ " | 7046 | \n",
+ " 25635 | \n",
+ " MO | \n",
+ " 37921.058594 | \n",
+ " 27411.421875 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1342.842773 | \n",
+ "
\n",
+ " \n",
+ " | 27701 | \n",
+ " 115102 | \n",
+ " MO | \n",
+ " 41373.524902 | \n",
+ " 27333.187500 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.002778 | \n",
+ "
\n",
+ " \n",
+ " | 14065 | \n",
+ " 59697 | \n",
+ " OK | \n",
+ " 68947.382812 | \n",
+ " 26790.708984 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 3511.932373 | \n",
+ "
\n",
+ " \n",
+ " | 7058 | \n",
+ " 25669 | \n",
+ " MO | \n",
+ " 0.000000 | \n",
+ " 26257.669922 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 2463.500000 | \n",
+ "
\n",
+ " \n",
+ " | 34720 | \n",
+ " 149164 | \n",
+ " OK | \n",
+ " 76835.164062 | \n",
+ " 26118.769531 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.004654 | \n",
+ "
\n",
+ " \n",
+ " | 19738 | \n",
+ " 84350 | \n",
+ " CA | \n",
+ " 88482.460938 | \n",
+ " 25993.980469 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 334.342865 | \n",
+ "
\n",
+ " \n",
+ " | 40316 | \n",
+ " 173580 | \n",
+ " CA | \n",
+ " 102149.837891 | \n",
+ " 25538.492188 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.002264 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " aca_reform | \n",
+ " Married | \n",
+ " Num_Dependents | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 21655 | \n",
+ " 95958 | \n",
+ " CT | \n",
+ " 586290.419922 | \n",
+ " 29020.246094 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.000351 | \n",
+ "
\n",
+ " \n",
+ " | 20926 | \n",
+ " 92239 | \n",
+ " VT | \n",
+ " 169552.753906 | \n",
+ " 28546.548828 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 2193.554443 | \n",
+ "
\n",
+ " \n",
+ " | 16135 | \n",
+ " 69304 | \n",
+ " WY | \n",
+ " 172368.441406 | \n",
+ " 27582.343750 | \n",
+ " 1.0 | \n",
+ " 3.0 | \n",
+ " 1964.266357 | \n",
+ "
\n",
+ " \n",
+ " | 7046 | \n",
+ " 25635 | \n",
+ " MO | \n",
+ " 37921.058594 | \n",
+ " 27411.421875 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1342.842773 | \n",
+ "
\n",
+ " \n",
+ " | 27701 | \n",
+ " 115102 | \n",
+ " MO | \n",
+ " 41373.524902 | \n",
+ " 27333.187500 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.002778 | \n",
+ "
\n",
+ " \n",
+ " | 14065 | \n",
+ " 59697 | \n",
+ " OK | \n",
+ " 68947.382812 | \n",
+ " 26790.708984 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 3511.932373 | \n",
+ "
\n",
+ " \n",
+ " | 7058 | \n",
+ " 25669 | \n",
+ " MO | \n",
+ " 0.000000 | \n",
+ " 26257.669922 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 2463.500000 | \n",
+ "
\n",
+ " \n",
+ " | 34720 | \n",
+ " 149164 | \n",
+ " OK | \n",
+ " 76835.164062 | \n",
+ " 26118.769531 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.004654 | \n",
+ "
\n",
+ " \n",
+ " | 19738 | \n",
+ " 84350 | \n",
+ " CA | \n",
+ " 88482.460938 | \n",
+ " 25993.980469 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 334.342865 | \n",
+ "
\n",
+ " \n",
+ " | 40316 | \n",
+ " 173580 | \n",
+ " CA | \n",
+ " 102149.837891 | \n",
+ " 25538.492188 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 0.002264 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " net_change | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " net_change | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 25895 | \n",
+ " 109280 | \n",
+ " IL | \n",
+ " 135262.814827 | \n",
+ " 2166.071045 | \n",
+ " 26361.749023 | \n",
+ " 24195.677979 | \n",
+ " 0.005656 | \n",
+ "
\n",
+ " \n",
+ " | 21501 | \n",
+ " 95388 | \n",
+ " CT | \n",
+ " 229315.603271 | \n",
+ " 2461.534180 | \n",
+ " 25936.906494 | \n",
+ " 23475.372314 | \n",
+ " 0.000415 | \n",
+ "
\n",
+ " \n",
+ " | 20625 | \n",
+ " 88926 | \n",
+ " HI | \n",
+ " 119508.789062 | \n",
+ " 3365.458740 | \n",
+ " 25774.730469 | \n",
+ " 22409.271729 | \n",
+ " 729.324707 | \n",
+ "
\n",
+ " \n",
+ " | 6106 | \n",
+ " 22572 | \n",
+ " WI | \n",
+ " 91929.835449 | \n",
+ " 5390.636230 | \n",
+ " 26178.041016 | \n",
+ " 20787.404785 | \n",
+ " 6035.339844 | \n",
+ "
\n",
+ " \n",
+ " | 15170 | \n",
+ " 63406 | \n",
+ " TX | \n",
+ " 56307.025391 | \n",
+ " 3461.937988 | \n",
+ " 23926.217285 | \n",
+ " 20464.279297 | \n",
+ " 36817.671875 | \n",
+ "
\n",
+ " \n",
+ " | 32728 | \n",
+ " 138331 | \n",
+ " KY | \n",
+ " 143819.542969 | \n",
+ " 2214.557861 | \n",
+ " 20885.525879 | \n",
+ " 18670.968018 | \n",
+ " 0.046630 | \n",
+ "
\n",
+ " \n",
+ " | 16147 | \n",
+ " 69708 | \n",
+ " WY | \n",
+ " 40219.304688 | \n",
+ " 7641.085938 | \n",
+ " 23462.459961 | \n",
+ " 15821.374023 | \n",
+ " 1604.936035 | \n",
+ "
\n",
+ " \n",
+ " | 36802 | \n",
+ " 159175 | \n",
+ " WY | \n",
+ " 322659.273438 | \n",
+ " 6483.185059 | \n",
+ " 22133.864258 | \n",
+ " 15650.679199 | \n",
+ " 1445.953613 | \n",
+ "
\n",
+ " \n",
+ " | 20237 | \n",
+ " 85992 | \n",
+ " CA | \n",
+ " 264716.572266 | \n",
+ " 2762.978271 | \n",
+ " 17937.049805 | \n",
+ " 15174.071533 | \n",
+ " 5193.151367 | \n",
+ "
\n",
+ " \n",
+ " | 39405 | \n",
+ " 170489 | \n",
+ " CA | \n",
+ " 200459.942383 | \n",
+ " 3582.745850 | \n",
+ " 17706.862793 | \n",
+ " 14124.116943 | \n",
+ " 0.002236 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " household_id | \n",
+ " State | \n",
+ " Employment_Income | \n",
+ " fpl_ratio | \n",
+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " net_change | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 12 | \n",
+ " 91 | \n",
+ " ME | \n",
+ " 85344.212891 | \n",
+ " 403.900676 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 4.466252 | \n",
+ "
\n",
+ " \n",
+ " | 61 | \n",
+ " 495 | \n",
+ " ME | \n",
+ " 105351.593262 | \n",
+ " 395.315547 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 17.447075 | \n",
+ "
\n",
+ " \n",
+ " | 281 | \n",
+ " 2971 | \n",
+ " VT | \n",
+ " 85035.097656 | \n",
+ " 402.437755 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 332.319000 | \n",
+ "
\n",
+ " \n",
+ " | 476 | \n",
+ " 3945 | \n",
+ " MA | \n",
+ " 106868.429688 | \n",
+ " 401.007241 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 57237.523438 | \n",
+ "
\n",
+ " \n",
+ " | 661 | \n",
+ " 4662 | \n",
+ " MA | \n",
+ " 62052.640625 | \n",
+ " 398.539760 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 7.987278 | \n",
+ "
\n",
+ " \n",
+ " | 806 | \n",
+ " 5462 | \n",
+ " RI | \n",
+ " 106868.433594 | \n",
+ " 401.007256 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 9386.629883 | \n",
+ "
\n",
+ " \n",
+ " | 907 | \n",
+ " 6168 | \n",
+ " CT | \n",
+ " 62052.640625 | \n",
+ " 398.539760 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 24166.259766 | \n",
+ "
\n",
+ " \n",
+ " | 1081 | \n",
+ " 6785 | \n",
+ " NY | \n",
+ " 84344.468750 | \n",
+ " 399.169279 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 4.944809 | \n",
+ "
\n",
+ " \n",
+ " | 1104 | \n",
+ " 6872 | \n",
+ " NY | \n",
+ " 85035.101562 | \n",
+ " 402.437774 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 10.947828 | \n",
+ "
\n",
+ " \n",
+ " | 1106 | \n",
+ " 6874 | \n",
+ " NY | \n",
+ " 62052.640625 | \n",
+ " 398.539760 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 4.106772 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Employment_Income | \n",
+ " fpl_ratio | \n",
+ " aca_baseline | \n",
+ " aca_reform | \n",
+ " net_change | \n",
+ " weight | \n",
+ "
\n",
+ " \n",
+ " | \n",
+ " min | \n",
+ " max | \n",
+ " mean | \n",
+ " mean | \n",
+ " mean | \n",
+ " mean | \n",
+ " mean | \n",
+ " sum | \n",
+ "
\n",
+ " \n",
+ " | decile_num | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 0.00 | \n",
+ " 22082.70 | \n",
+ " 3081.23 | \n",
+ " 16.50 | \n",
+ " 481.01 | \n",
+ " 618.01 | \n",
+ " 136.99 | \n",
+ " 56060974.39 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 22096.56 | \n",
+ " 40219.30 | \n",
+ " 31811.30 | \n",
+ " 167.52 | \n",
+ " 1638.77 | \n",
+ " 1989.05 | \n",
+ " 350.27 | \n",
+ " 18267145.22 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 40220.45 | \n",
+ " 59168.34 | \n",
+ " 50102.24 | \n",
+ " 254.53 | \n",
+ " 1575.92 | \n",
+ " 2062.44 | \n",
+ " 486.52 | \n",
+ " 13547056.92 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 59179.83 | \n",
+ " 80438.61 | \n",
+ " 69904.57 | \n",
+ " 343.95 | \n",
+ " 1437.25 | \n",
+ " 2029.91 | \n",
+ " 592.66 | \n",
+ " 12499143.93 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 80438.61 | \n",
+ " 106574.53 | \n",
+ " 93253.97 | \n",
+ " 434.80 | \n",
+ " 1303.70 | \n",
+ " 2060.30 | \n",
+ " 756.60 | \n",
+ " 11057517.34 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 106597.34 | \n",
+ " 142491.25 | \n",
+ " 122908.07 | \n",
+ " 554.42 | \n",
+ " 816.63 | \n",
+ " 1518.36 | \n",
+ " 701.72 | \n",
+ " 13853349.38 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 142491.25 | \n",
+ " 205693.01 | \n",
+ " 169652.75 | \n",
+ " 727.25 | \n",
+ " 489.02 | \n",
+ " 1177.16 | \n",
+ " 688.14 | \n",
+ " 10689611.79 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 205693.02 | \n",
+ " 3305428.97 | \n",
+ " 382158.51 | \n",
+ " 1548.38 | \n",
+ " 210.36 | \n",
+ " 482.04 | \n",
+ " 271.67 | \n",
+ " 11524346.53 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " State | \n",
- " Married | \n",
- " Num_Dependents | \n",
- " Employment_Income | \n",
- " Self_Employment_Income | \n",
- " ACA_Baseline | \n",
- " ACA_Reform | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " ME | \n",
- " 1.0 | \n",
- " 0.0 | \n",
- " 4412.632324 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " ME | \n",
- " 1.0 | \n",
- " 0.0 | \n",
- " 101122.818359 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " ME | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.000000 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " ME | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 91929.835938 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " ME | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 36588.075684 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " household_id | \n",
- " State | \n",
- " Married | \n",
- " Num_Dependents | \n",
- " Employment_Income | \n",
- " Self_Employment_Income | \n",
- " aca_baseline | \n",
- " rating_area | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 23890 | \n",
- " 103176 | \n",
- " PA | \n",
- " 1.0 | \n",
- " 3.0 | \n",
- " 84008.713715 | \n",
- " 0.0 | \n",
- " 0.0 | \n",
- " 9 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
" | 0 | \n",
- " 138 % FPL ($29,187) | \n",
- " 29187 | \n",
- " 9128.747070 | \n",
+ " 138 % FPL ($29,897.10) | \n",
+ " 29897.10 | \n",
+ " 9052.595703 | \n",
" 10090.201172 | \n",
"
\n",
" \n",
" | 1 | \n",
- " 300 % FPL ($63,450) | \n",
- " 63450 | \n",
- " 4062.451172 | \n",
- " 6283.201172 | \n",
+ " 300 % FPL ($64,993.70) | \n",
+ " 64993.70 | \n",
+ " 3915.799805 | \n",
+ " 6076.840332 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 400 % FPL ($84,600) | \n",
- " 84600 | \n",
+ " 400 % FPL ($86,658.27) | \n",
+ " 86658.27 | \n",
" 0.000000 | \n",
- " 2899.201172 | \n",
+ " 2724.248047 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " income_label income_usd ptc_baseline ptc_ira_reform\n",
- "0 138 % FPL ($29,187) 29187 9128.747070 10090.201172\n",
- "1 300 % FPL ($63,450) 63450 4062.451172 6283.201172\n",
- "2 400 % FPL ($84,600) 84600 0.000000 2899.201172"
+ " income_label income_usd ptc_baseline ptc_ira_reform\n",
+ "0 138 % FPL ($29,897.10) 29897.10 9052.595703 10090.201172\n",
+ "1 300 % FPL ($64,993.70) 64993.70 3915.799805 6076.840332\n",
+ "2 400 % FPL ($86,658.27) 86658.27 0.000000 2724.248047"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -332,61 +323,62 @@
"import pandas as pd\n",
"from policyengine_us import Simulation\n",
"\n",
+ "PERIOD = 2026\n",
+ "\n",
"# ------------------------------------------------------------------\n",
- "# 1. Convenience: build & run a one-income Texas couple simulation\n",
+ "# Helper: get the tax unit's 2026 FPG from the situation\n",
"# ------------------------------------------------------------------\n",
- "def calc_ptc_for_income(base_situation: dict, income: float, *, use_reform=False):\n",
- " \"\"\"\n",
- " Return ACA PTC (household-level) for the given income.\n",
+ "def get_tax_unit_fpg(base_situation: dict, period=PERIOD) -> float:\n",
+ " \"\"\"Return the tax unit FPG for the given situation/year (first tax unit).\"\"\"\n",
+ " sit = copy.deepcopy(base_situation)\n",
+ " sit.pop(\"axes\", None)\n",
"\n",
- " Parameters\n",
- " ----------\n",
- " base_situation : dict\n",
- " The original `situation_texas` dict from the notebook.\n",
- " income : float\n",
- " Total household employment income to test (USD, annual).\n",
- " use_reform : bool, default False\n",
- " If True, applies the IRA-style `reform` object.\n",
+ " # Ensure income isn't interfering (FPG shouldn't depend on income, but be safe)\n",
+ " for person in sit[\"people\"].values():\n",
+ " person.setdefault(\"employment_income\", {})\n",
+ " person[\"employment_income\"][str(period)] = 0\n",
"\n",
- " Returns\n",
- " -------\n",
- " float\n",
- " PTC in dollars for period 2026.\n",
+ " sim = Simulation(situation=sit)\n",
+ " fpg = sim.calculate(\"tax_unit_fpg\", map_to=\"tax_unit\", period=period)[0]\n",
+ " return float(fpg)\n",
+ "\n",
+ "# ------------------------------------------------------------------\n",
+ "# 1) Convenience: run a one-income Texas couple simulation (unchanged)\n",
+ "# ------------------------------------------------------------------\n",
+ "def calc_ptc_for_income(base_situation: dict, income: float, *, use_reform=False, period=PERIOD):\n",
+ " \"\"\"\n",
+ " Return ACA PTC (household-level) for the given income and year.\n",
" \"\"\"\n",
- " # Deep-copy so we don’t mutate the original dict\n",
" sit = copy.deepcopy(base_situation)\n",
- "\n",
- " # Remove axes so we run a single-point simulation\n",
" sit.pop(\"axes\", None)\n",
"\n",
" # Split income 50/50 between the two adults\n",
" for person in [\"you\", \"your partner\"]:\n",
- " sit[\"people\"][person][\"employment_income\"] = {\"2026\": income / 2}\n",
+ " sit[\"people\"][person][\"employment_income\"] = {str(period): income / 2}\n",
"\n",
- " # Run the simulation (baseline or reform)\n",
" sim = Simulation(situation=sit, reform=reform if use_reform else None)\n",
- " # Household-level PTC for 2026 comes back as a 1-element array\n",
- " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=2026)[0]\n",
+ " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n",
"\n",
"# ------------------------------------------------------------------\n",
- "# 2. Pull the three requested incomes\n",
+ "# 2) Build targets from model-derived FPG and compute PTCs\n",
"# ------------------------------------------------------------------\n",
- "targets = {\n",
- " \"138 % FPL ($29,187)\": 29_187,\n",
- " \"300 % FPL ($63,450)\": 63_450,\n",
- " \"400 % FPL ($84,600)\": 84_600,\n",
+ "fpg_2026 = get_tax_unit_fpg(situation_texas, period=PERIOD)\n",
+ "\n",
+ "percent_targets = {\n",
+ " \"138 % FPL\": 1.38,\n",
+ " \"300 % FPL\": 3.00,\n",
+ " \"400 % FPL\": 4.00,\n",
"}\n",
"\n",
"rows = []\n",
- "for label, inc in targets.items():\n",
- " rows.append(\n",
- " {\n",
- " \"income_label\": label,\n",
- " \"income_usd\": inc,\n",
- " \"ptc_baseline\": calc_ptc_for_income(situation_texas, inc, use_reform=False),\n",
- " \"ptc_ira_reform\": calc_ptc_for_income(situation_texas, inc, use_reform=True),\n",
- " }\n",
- " )\n",
+ "for label, mult in percent_targets.items():\n",
+ " inc = round(fpg_2026 * mult, 2)\n",
+ " rows.append({\n",
+ " \"income_label\": f\"{label} (${inc:,.2f})\",\n",
+ " \"income_usd\": inc,\n",
+ " \"ptc_baseline\": calc_ptc_for_income(situation_texas, inc, use_reform=False, period=PERIOD),\n",
+ " \"ptc_ira_reform\": calc_ptc_for_income(situation_texas, inc, use_reform=True, period=PERIOD),\n",
+ " })\n",
"\n",
"ptc_df = pd.DataFrame(rows)\n",
"ptc_df\n"
@@ -394,7 +386,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -486,7 +478,7 @@
"3 0.000000 829.929932 0.000000 "
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -586,7 +578,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -631,7 +623,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -643,7 +635,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -21914,7 +21906,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -28589,7 +28581,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -35261,7 +35253,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -39432,7 +39424,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
diff --git a/us/medicaid/aca_reform_ny_only.ipynb b/us/medicaid/aca_reform_ny_only.ipynb
new file mode 100644
index 0000000..13133e8
--- /dev/null
+++ b/us/medicaid/aca_reform_ny_only.ipynb
@@ -0,0 +1,25162 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from policyengine_us import Simulation\n",
+ "from policyengine_core.reforms import Reform\n",
+ "import numpy as np\n",
+ "import plotly.graph_objects as go\n",
+ "from plotly.subplots import make_subplots\n",
+ "from policyengine_core.charts import format_fig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reform = Reform.from_dict({\n",
+ " \"gov.aca.ptc_phase_out_rate[0].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[1].amount\": {\n",
+ " \"2025-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[3].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.02\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[4].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.04\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[5].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.06\n",
+ " },\n",
+ " \"gov.aca.ptc_phase_out_rate[6].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": 0.085\n",
+ " },\n",
+ " \"gov.aca.ptc_income_eligibility[2].amount\": {\n",
+ " \"2026-01-01.2100-12-31\": True\n",
+ " }\n",
+ "}, country_id=\"us\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "situation_new_york = {\n",
+ " \"people\": {\n",
+ " \"you\": {\n",
+ " \"age\": {\n",
+ " \"2026\": 30\n",
+ " }\n",
+ " },\n",
+ " \"your partner\": {\n",
+ " \"age\": {\n",
+ " \"2026\": 30\n",
+ " }\n",
+ " },\n",
+ " \"your first dependent\": {\n",
+ " \"age\": {\n",
+ " \"2026\": 3\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"families\": {\n",
+ " \"your family\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"spm_units\": {\n",
+ " \"your household\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"tax_units\": {\n",
+ " \"your tax unit\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\",\n",
+ " \"your first dependent\"\n",
+ " ]\n",
+ " }\n",
+ " },\n",
+ " \"households\": {\n",
+ " \"your household\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\", \n",
+ " \"your first dependent\" # All live in the same household\n",
+ " ],\n",
+ " \"state_name\": {\n",
+ " \"2026\": \"NY\" # Located in New York state\n",
+ " },\n",
+ " \"county_fips\": {\n",
+ " \"2026\": \"36061\"\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"marital_units\": {\n",
+ " \"your marital unit\": {\n",
+ " \"members\": [\n",
+ " \"you\",\n",
+ " \"your partner\"\n",
+ " ]\n",
+ " },\n",
+ " \"your first dependent's marital unit\": {\n",
+ " \"members\": [\n",
+ " \"your first dependent\"\n",
+ " ],\n",
+ " \"marital_unit_id\": {\n",
+ " \"2026\": 1\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"axes\": [\n",
+ " [\n",
+ " {\n",
+ " \"name\": \"employment_income\",\n",
+ " \"count\": 800,\n",
+ " \"min\": 0,\n",
+ " \"max\": 400000\n",
+ " }\n",
+ " ]\n",
+ " ]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simulation_new_york = Simulation(\n",
+ " situation=situation_new_york,\n",
+ ")\n",
+ "reformed_simulation_new_york = Simulation(\n",
+ " situation=situation_new_york,\n",
+ " reform=reform,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " income_label | \n",
+ " income_usd | \n",
+ " ptc_baseline | \n",
+ " ptc_ira_reform | \n",
+ " medicaid_cost | \n",
+ " per_capita_chip | \n",
+ " SLCSP | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 154 % FPL ($42,039.50) | \n",
+ " 42039.50 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 16480.696289 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 200 % FPL ($54,596.76) | \n",
+ " 54596.76 | \n",
+ " 17926.822266 | \n",
+ " 20263.562500 | \n",
+ " 0.000000 | \n",
+ " 2489.789795 | \n",
+ " 21442.853516 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 300 % FPL ($81,895.14) | \n",
+ " 81895.14 | \n",
+ " 13662.815430 | \n",
+ " 16385.828125 | \n",
+ " 0.000000 | \n",
+ " 2489.789795 | \n",
+ " 21442.853516 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 405 % FPL ($110,558.43) | \n",
+ " 110558.43 | \n",
+ " 0.000000 | \n",
+ " 12045.386719 | \n",
+ " 0.000000 | \n",
+ " 2489.789795 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 406 % FPL ($110,831.42) | \n",
+ " 110831.42 | \n",
+ " 0.000000 | \n",
+ " 21135.390625 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Baseline MTR: %{y:.1%}'
+ ))
+
+ # Reform MTR (dashed line)
+ fig.add_trace(go.Scatter(
+ x=reform_mtr_incomes,
+ y=np.clip(reform_mtrs, -2.0, 2.0),
+ mode='lines',
+ name='IRA Extension',
+ line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),
+ hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'
+ ))
+
+ # Update layout
+ fig.update_layout(
+ title='Marginal tax rate including health benefits - New York household',
+ xaxis_title='Employment income',
+ yaxis_title='Marginal tax rate',
+ xaxis=dict(
+ tickformat='$,.0f',
+ range=[0, 200_000],
+ gridcolor='lightgray',
+ showgrid=True
+ ),
+ yaxis=dict(
+ tickformat='.0%',
+ range=[-2.0, 2.0],
+ gridcolor='lightgray',
+ showgrid=True,
+ zeroline=True,
+ zerolinewidth=1,
+ zerolinecolor='gray'
+ ),
+ height=600,
+ width=1000,
+ hovermode='x unified',
+ plot_bgcolor='white',
+ showlegend=True,
+ legend=dict(
+ orientation="h",
+ yanchor="bottom",
+ y=1.02,
+ xanchor="right",
+ x=1
+ )
+ )
+
+ fig = format_fig(fig)
+ fig.show()
+
+ # Save outputs with extra height for logo
+ fig.write_image("ny_mtr_baseline_vs_reform_fast.png", width=1000, height=650, scale=2)
+ print("Chart saved as ny_mtr_baseline_vs_reform_fast.png")
+
+ # Save data
+ combined_df = pd.DataFrame({
+ 'income': baseline_mtr_incomes,
+ 'baseline_mtr': baseline_mtrs,
+ 'baseline_mtr_display': np.clip(baseline_mtrs, -2.0, 2.0)
+ }).merge(
+ pd.DataFrame({
+ 'income': reform_mtr_incomes,
+ 'reform_mtr': reform_mtrs,
+ 'reform_mtr_display': np.clip(reform_mtrs, -2.0, 2.0)
+ }),
+ on='income',
+ how='outer'
+ ).sort_values('income')
+
+ combined_df.to_csv("ny_mtr_baseline_vs_reform_fast.csv", index=False)
+ print("Data saved to ny_mtr_baseline_vs_reform_fast.csv")
+
+ # Total time
+ total_time = time.time() - start_time
+ print(f"\n" + "=" * 70)
+ print(f"TOTAL RUNTIME: {total_time:.1f} seconds ({total_time/60:.1f} minutes)")
+ print(f"End time: {datetime.now().strftime('%H:%M:%S')}")
+ print(f"\nThis was ~{(25*60)/total_time:.0f}X faster than the loop-based approach!")
+ print("=" * 70)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
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