diff --git a/.DS_Store b/.DS_Store index ce253bd..0e5be69 100644 Binary files a/.DS_Store and b/.DS_Store differ 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/debug_ptc_54k.py b/debug_ptc_54k.py new file mode 100644 index 0000000..2103580 --- /dev/null +++ b/debug_ptc_54k.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 +""" +Debug the REAL discrepancy: At ~$54k income, the chart shows ~20k PTC +but the table shows 0 PTC! +""" + +from policyengine_us import Simulation +from policyengine_core.reforms import Reform +import numpy as np +import copy + +# Define the reform (same as notebook) +reform = Reform.from_dict({ + "gov.aca.ptc_phase_out_rate[0].amount": {"2026-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[1].amount": {"2025-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[2].amount": {"2026-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[3].amount": {"2026-01-01.2100-12-31": 0.02}, + "gov.aca.ptc_phase_out_rate[4].amount": {"2026-01-01.2100-12-31": 0.04}, + "gov.aca.ptc_phase_out_rate[5].amount": {"2026-01-01.2100-12-31": 0.06}, + "gov.aca.ptc_phase_out_rate[6].amount": {"2026-01-01.2100-12-31": 0.085}, + "gov.aca.ptc_income_eligibility[2].amount": {"2026-01-01.2100-12-31": True} +}, country_id="us") + +# Base situation for New York family +base_situation_ny = { + "people": { + "you": {"age": {"2026": 30}}, + "your partner": {"age": {"2026": 30}}, + "your first dependent": {"age": {"2026": 3}} + }, + "families": { + "your family": {"members": ["you", "your partner", "your first dependent"]} + }, + "spm_units": { + "your household": {"members": ["you", "your partner", "your first dependent"]} + }, + "tax_units": { + "your tax unit": {"members": ["you", "your partner", "your first dependent"]} + }, + "households": { + "your household": { + "members": ["you", "your partner", "your first dependent"], + "state_name": {"2026": "NY"}, + "county_fips": {"2026": "36061"} + } + }, + "marital_units": { + "your marital unit": {"members": ["you", "your partner"]}, + "your first dependent's marital unit": { + "members": ["your first dependent"], + "marital_unit_id": {"2026": 1} + } + } +} + +# Test income: 200% FPL = $53,300 +test_income = 53_300 + +print("=" * 80) +print(f"DEBUGGING PTC DISCREPANCY AT $53,300 (200% FPL)") +print("Table shows: PTC = $0") +print("Chart apparently shows: PTC = ~$20,000") +print("=" * 80) + +# METHOD 1: Table approach (50/50 income split) +print("\nMETHOD 1: Table Approach (50/50 income split)") +print("-" * 60) + +situation_5050 = copy.deepcopy(base_situation_ny) +situation_5050["people"]["you"]["employment_income"] = {"2026": test_income / 2} +situation_5050["people"]["your partner"]["employment_income"] = {"2026": test_income / 2} + +sim_5050 = Simulation(situation=situation_5050, reform=reform) +ptc_5050 = sim_5050.calculate("aca_ptc", map_to="household", period=2026)[0] +medicaid_5050 = sim_5050.calculate("medicaid_cost", map_to="household", period=2026)[0] +slcsp_5050 = sim_5050.calculate("slcsp", map_to="household", period=2026)[0] +print(f"PTC under reform: ${ptc_5050:,.2f}") +print(f"Medicaid cost: ${medicaid_5050:,.2f}") +print(f"SLCSP: ${slcsp_5050:,.2f}") + +# METHOD 2: All income on first person (like axes does) +print("\nMETHOD 2: All income on first person (axes default)") +print("-" * 60) + +situation_all_on_you = copy.deepcopy(base_situation_ny) +situation_all_on_you["people"]["you"]["employment_income"] = {"2026": test_income} +situation_all_on_you["people"]["your partner"]["employment_income"] = {"2026": 0} + +sim_all_on_you = Simulation(situation=situation_all_on_you, reform=reform) +ptc_all = sim_all_on_you.calculate("aca_ptc", map_to="household", period=2026)[0] +medicaid_all = sim_all_on_you.calculate("medicaid_cost", map_to="household", period=2026)[0] +slcsp_all = sim_all_on_you.calculate("slcsp", map_to="household", period=2026)[0] +is_medicaid_eligible_all = sim_all_on_you.calculate("is_medicaid_eligible", period=2026) + +print(f"PTC under reform: ${ptc_all:,.2f}") +print(f"Medicaid cost: ${medicaid_all:,.2f}") +print(f"SLCSP: ${slcsp_all:,.2f}") +print(f"Medicaid eligible - You: {is_medicaid_eligible_all['you']}") +print(f"Medicaid eligible - Partner: {is_medicaid_eligible_all['your partner']}") +print(f"Medicaid eligible - Child: {is_medicaid_eligible_all['your first dependent']}") + +# Check chart simulation at this exact income point +print("\nMETHOD 3: Chart simulation (axes with 800 points)") +print("-" * 60) + +situation_axes = copy.deepcopy(base_situation_ny) +situation_axes["axes"] = [[{ + "name": "employment_income", + "count": 800, + "min": 0, + "max": 400000 +}]] + +sim_axes = Simulation(situation=situation_axes, reform=reform) +household_income = sim_axes.calculate("employment_income", map_to="household", period=2026) +ptc_array = sim_axes.calculate("aca_ptc", map_to="household", period=2026) +medicaid_array = sim_axes.calculate("medicaid_cost", map_to="household", period=2026) +slcsp_array = sim_axes.calculate("slcsp", map_to="household", period=2026) + +# Find values around $53,300 +indices = np.where((household_income > 52000) & (household_income < 55000))[0] +print("\nIncome points near $53,300:") +for idx in indices: + print(f" Income: ${household_income[idx]:,.2f} -> PTC: ${ptc_array[idx]:,.2f}, " + f"Medicaid: ${medicaid_array[idx]:,.2f}, SLCSP: ${slcsp_array[idx]:,.2f}") + +# Look at the exact Medicaid eligibility thresholds +print("\n" + "=" * 80) +print("KEY INSIGHT:") +print("-" * 60) +print("The difference is that when one person has all the income, their") +print("individual income might exceed Medicaid eligibility thresholds differently") +print("than when income is split 50/50!") +print() +print("In New York, adult Medicaid eligibility cuts off at 138% FPL.") +print("For a family of 3, that's about $36,570 total household income.") +print("BUT individual eligibility might be calculated differently!") + +# Check individual incomes and FPL percentages +print("\n" + "=" * 80) +print("MEDICAID ELIGIBILITY ANALYSIS") +print("-" * 60) + +for method_name, sim in [("50/50 split", sim_5050), ("All on one", sim_all_on_you)]: + print(f"\n{method_name}:") + + # Get FPL percentages + fpl_individual = sim.calculate("fpl", period=2026) + tax_unit_fpl = sim.calculate("tax_unit_fpl", map_to="tax_unit", period=2026)[0] + + print(f" Tax unit FPL %: {tax_unit_fpl:.1%}") + print(f" Individual FPL - You: {fpl_individual['you']:.1%}") + print(f" Individual FPL - Partner: {fpl_individual['your partner']:.1%}") + print(f" Individual FPL - Child: {fpl_individual['your first dependent']:.1%}") + + # Check if they're on employer health insurance + has_employer_coverage = sim.calculate("has_employer_coverage", period=2026) + print(f" Has employer coverage - You: {has_employer_coverage['you']}") + print(f" Has employer coverage - Partner: {has_employer_coverage['your partner']}") + print(f" Has employer coverage - Child: {has_employer_coverage['your first dependent']}") + +print("\n" + "=" * 80) +print("CONCLUSION:") +print("The discrepancy occurs because Medicaid eligibility in New York") +print("makes the family eligible for Medicaid at 200% FPL, which makes them") +print("INELIGIBLE for PTC (you can't get both). That's why the table shows") +print("PTC = $0 and Medicaid = $12,930.") +print() +print("However, the chart might be showing different results if the axes") +print("simulation is handling Medicaid eligibility differently!") +print("=" * 80) \ No newline at end of file diff --git a/debug_ptc_discrepancy.py b/debug_ptc_discrepancy.py new file mode 100644 index 0000000..81cb9e7 --- /dev/null +++ b/debug_ptc_discrepancy.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +""" +Debug script to identify the discrepancy between block 7 table and block 10 chart +in the ACA reform households notebook. +""" + +from policyengine_us import Simulation +from policyengine_core.reforms import Reform +import numpy as np +import copy + +# Define the reform (same as notebook) +reform = Reform.from_dict({ + "gov.aca.ptc_phase_out_rate[0].amount": {"2026-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[1].amount": {"2025-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[2].amount": {"2026-01-01.2100-12-31": 0}, + "gov.aca.ptc_phase_out_rate[3].amount": {"2026-01-01.2100-12-31": 0.02}, + "gov.aca.ptc_phase_out_rate[4].amount": {"2026-01-01.2100-12-31": 0.04}, + "gov.aca.ptc_phase_out_rate[5].amount": {"2026-01-01.2100-12-31": 0.06}, + "gov.aca.ptc_phase_out_rate[6].amount": {"2026-01-01.2100-12-31": 0.085}, + "gov.aca.ptc_income_eligibility[2].amount": {"2026-01-01.2100-12-31": True} +}, country_id="us") + +# Base situation for New York family +base_situation_ny = { + "people": { + "you": {"age": {"2026": 30}}, + "your partner": {"age": {"2026": 30}}, + "your first dependent": {"age": {"2026": 3}} + }, + "families": { + "your family": {"members": ["you", "your partner", "your first dependent"]} + }, + "spm_units": { + "your household": {"members": ["you", "your partner", "your first dependent"]} + }, + "tax_units": { + "your tax unit": {"members": ["you", "your partner", "your first dependent"]} + }, + "households": { + "your household": { + "members": ["you", "your partner", "your first dependent"], + "state_name": {"2026": "NY"}, + "county_fips": {"2026": "36061"} + } + }, + "marital_units": { + "your marital unit": {"members": ["you", "your partner"]}, + "your first dependent's marital unit": { + "members": ["your first dependent"], + "marital_unit_id": {"2026": 1} + } + } +} + +# Test income: 300% FPL = $79,950 +test_income = 79_950 + +print("=" * 60) +print("DEBUGGING PTC DISCREPANCY AT 300% FPL ($79,950)") +print("=" * 60) + +# METHOD 1: Block 7 approach (single-point with explicit income split) +print("\nMETHOD 1: Block 7 Table Approach (50/50 income split)") +print("-" * 40) + +situation_method1 = copy.deepcopy(base_situation_ny) +# Split income 50/50 between adults +situation_method1["people"]["you"]["employment_income"] = {"2026": test_income / 2} +situation_method1["people"]["your partner"]["employment_income"] = {"2026": test_income / 2} + +sim_method1 = Simulation(situation=situation_method1, reform=reform) +ptc_method1 = sim_method1.calculate("aca_ptc", map_to="household", period=2026)[0] +print(f"PTC under reform: ${ptc_method1:,.2f}") + +# METHOD 2: Block 10 chart approach (axes-based simulation) +print("\nMETHOD 2: Block 10 Chart Approach (axes simulation)") +print("-" * 40) + +situation_method2 = copy.deepcopy(base_situation_ny) +situation_method2["axes"] = [[{ + "name": "employment_income", + "count": 800, + "min": 0, + "max": 400000 +}]] + +sim_method2 = Simulation(situation=situation_method2, reform=reform) +household_income = sim_method2.calculate("employment_income", map_to="household", period=2026) +ptc_array = sim_method2.calculate("aca_ptc", map_to="household", period=2026) + +# Find the closest income point to our test income +closest_idx = np.argmin(np.abs(household_income - test_income)) +actual_income = household_income[closest_idx] +ptc_method2 = ptc_array[closest_idx] + +print(f"Closest income point: ${actual_income:,.2f}") +print(f"PTC under reform: ${ptc_method2:,.2f}") + +# METHOD 3: Check how axes distributes income +print("\nMETHOD 3: Investigating income distribution in axes simulation") +print("-" * 40) + +# The axes parameter by default applies the variation to the first person in the first entity +# Since we used "employment_income" which is a person-level variable, it's applied to "you" +print("Note: When using axes with 'employment_income', PolicyEngine applies") +print("the entire income variation to the first person ('you') by default.") +print("This is different from block 7 which explicitly splits income 50/50.") + +# METHOD 4: Test with all income on one person +print("\nMETHOD 4: All income on one person") +print("-" * 40) + +situation_method4 = copy.deepcopy(base_situation_ny) +situation_method4["people"]["you"]["employment_income"] = {"2026": test_income} +situation_method4["people"]["your partner"]["employment_income"] = {"2026": 0} + +sim_method4 = Simulation(situation=situation_method4, reform=reform) +ptc_method4 = sim_method4.calculate("aca_ptc", map_to="household", period=2026)[0] +print(f"PTC under reform (all on 'you'): ${ptc_method4:,.2f}") + +# Check some key variables that might affect PTC calculation +print("\n" + "=" * 60) +print("KEY VARIABLES COMPARISON") +print("=" * 60) + +for method_name, sim in [("Method 1 (50/50)", sim_method1), + ("Method 4 (all on one)", sim_method4)]: + print(f"\n{method_name}:") + print("-" * 30) + + # Tax unit AGI + agi = sim.calculate("adjusted_gross_income", map_to="tax_unit", period=2026)[0] + print(f"Tax Unit AGI: ${agi:,.2f}") + + # SLCSP + slcsp = sim.calculate("slcsp", map_to="household", period=2026)[0] + print(f"SLCSP: ${slcsp:,.2f}") + + # Try to get expected contribution if it exists + try: + expected_contribution = sim.calculate("aca_expected_contribution", map_to="tax_unit", period=2026)[0] + print(f"Expected ACA contribution: ${expected_contribution:,.2f}") + except: + pass + + # Try to get PTC phase-out if it exists + try: + ptc_phase_out = sim.calculate("aca_ptc_phase_out", map_to="tax_unit", period=2026)[0] + print(f"PTC phase-out amount: ${ptc_phase_out:,.2f}") + except: + pass + +print("\n" + "=" * 60) +print("CONCLUSION:") +print("The discrepancy occurs because:") +print("1. Block 7 explicitly splits income 50/50 between spouses") +print("2. Block 10 uses axes which assigns all income to the first person") +print("3. This affects tax calculations and potentially PTC eligibility/amounts") +print("=" * 60) \ No newline at end of file diff --git a/us/.DS_Store b/us/.DS_Store new file mode 100644 index 0000000..c83b29c Binary files /dev/null and b/us/.DS_Store differ diff --git a/us/blog_posts/aca_california.ipynb b/us/blog_posts/aca_california.ipynb new file mode 100644 index 0000000..7241e06 --- /dev/null +++ b/us/blog_posts/aca_california.ipynb @@ -0,0 +1,23696 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "cell-0", + "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": 14, + "id": "cell-1", + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform2 = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_simplified_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform3 = 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" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cell-2", + "metadata": {}, + "outputs": [], + "source": [ + "situation_california = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\n", + " \"2026\": 64\n", + " }\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\n", + " \"2026\": 62\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"CA\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"06069\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 400000\n", + " }\n", + " ]\n", + " ]\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cell-3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_california = Simulation(\n", + " situation=situation_california,\n", + ")\n", + "reformed_simulation_california = Simulation(\n", + " situation=situation_california,\n", + " reform=reform,\n", + ")\n", + "reformed_simulation_california2 = Simulation(\n", + " situation=situation_california,\n", + " reform=reform2,\n", + ")\n", + "reformed_simulation_california3 = Simulation(\n", + " situation=situation_california,\n", + " reform=reform3,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "cell-4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_stepped_proposalptc_linear_proposalptc_ira_extension
0138 % FPL ($29,897.10)29897.100.0000000.0000000.0000000.000000
1300 % FPL ($64,993.70)64993.7036241.79687538633.56250037333.68750038701.808594
2400 % FPL ($86,658.27)86658.270.00000033737.37109432004.20507835349.214844
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_stepped_proposal \\\n", + "0 138 % FPL ($29,897.10) 29897.10 0.000000 0.000000 \n", + "1 300 % FPL ($64,993.70) 64993.70 36241.796875 38633.562500 \n", + "2 400 % FPL ($86,658.27) 86658.27 0.000000 33737.371094 \n", + "\n", + " ptc_linear_proposal ptc_ira_extension \n", + "0 0.000000 0.000000 \n", + "1 37333.687500 38701.808594 \n", + "2 32004.205078 35349.214844 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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 California couple simulation\n", + "# ------------------------------------------------------------------\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"\n", + " Return ACA PTC (household-level) for the given income and year.\n", + " \"\"\"\n", + " sit = copy.deepcopy(base_situation)\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\"] = {str(period): income / 2}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\n", + "fpg_2026 = get_tax_unit_fpg(situation_california, 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, 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_california, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_stepped_proposal\": calc_ptc_for_income(situation_california, inc, reform_to_use=reform, period=PERIOD),\n", + " \"ptc_linear_proposal\": calc_ptc_for_income(situation_california, inc, reform_to_use=reform2, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_california, inc, reform_to_use=reform3, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values for California\n", + "household_income_california = simulation_california.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_california_per_capita_chip = simulation_california.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "baseline_california_aca_ptc = simulation_california.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_california_medicaid_cost = simulation_california.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_california_net_income_including_health_benefits = simulation_california.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "baseline_california_slcsp = simulation_california.calculate(\"slcsp\", map_to=\"household\", period=2026)\n", + "\n", + "reform_california_per_capita_chip = reformed_simulation_california.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform_california_aca_ptc = reformed_simulation_california.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_california_medicaid_cost = reformed_simulation_california.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_california_net_income_including_health_benefits = reformed_simulation_california.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform2_california_per_capita_chip = reformed_simulation_california2.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform2_california_aca_ptc = reformed_simulation_california2.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform2_california_medicaid_cost = reformed_simulation_california2.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform2_california_net_income_including_health_benefits = reformed_simulation_california2.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform3_california_per_capita_chip = reformed_simulation_california3.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform3_california_aca_ptc = reformed_simulation_california3.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform3_california_medicaid_cost = reformed_simulation_california3.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform3_california_net_income_including_health_benefits = reformed_simulation_california3.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate total benefits for each scenario\n", + "baseline_california_total = [sum(x) for x in zip(baseline_california_per_capita_chip, baseline_california_aca_ptc, baseline_california_medicaid_cost)]\n", + "reform_california_total = [sum(x) for x in zip(reform_california_per_capita_chip, reform_california_aca_ptc, reform_california_medicaid_cost)]\n", + "reform2_california_total = [sum(x) for x in zip(reform2_california_per_capita_chip, reform2_california_aca_ptc, reform2_california_medicaid_cost)]\n", + "reform3_california_total = [sum(x) for x in zip(reform3_california_per_capita_chip, reform3_california_aca_ptc, reform3_california_medicaid_cost)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cell-6", + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"\n", + "PURPLE = \"#9467BD\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cell-7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "ACA PTC (Baseline)", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 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(Baseline)', \n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Add reform traces\n", + "fig_california.add_trace(go.Scatter(\n", + " x=household_income_california, \n", + " y=reform_california_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Stepped Proposal)', \n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_california.add_trace(go.Scatter(\n", + " x=household_income_california, \n", + " y=reform2_california_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Linear Proposal)', \n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_california.add_trace(go.Scatter(\n", + " x=household_income_california, \n", + " y=reform3_california_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (IRA Extension)', \n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "\n", + "# Update layout\n", + "fig_california.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) - Program Benefits by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Benefit Amount',\n", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "fig_california = format_fig(fig_california)\n", + "fig_california.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cell-8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "Baseline", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 6589.57763671875, + 7138.708984375, + 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----------\n", + "fig_ca = go.Figure()\n", + "\n", + "# Baseline (solid)\n", + "fig_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=baseline_california_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Reform 1 (stepped)\n", + "fig_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=reform_california_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# Reform 2 (linear)\n", + "fig_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=reform2_california_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "# Reform 3 (IRA extension)\n", + "fig_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=reform3_california_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "# Layout\n", + "fig_ca.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) – 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", + "fig_ca = format_fig(fig_ca)\n", + "fig_ca.show()\n", + "\n", + "# --- Δ Health-adjusted net income (Reform – Baseline) ---\n", + "delta_ca = (\n", + " reform_california_net_income_including_health_benefits\n", + " - baseline_california_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_ca2 = (\n", + " reform2_california_net_income_including_health_benefits\n", + " - baseline_california_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_ca3 = (\n", + " reform3_california_net_income_including_health_benefits\n", + " - baseline_california_net_income_including_health_benefits\n", + ")\n", + "\n", + "fig_delta_ca = go.Figure()\n", + "\n", + "fig_delta_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=delta_ca,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_delta_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=delta_ca2,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_delta_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=delta_ca3,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_delta_ca.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) – Impact of Premium Tax Credit Proposals',\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_ca = format_fig(fig_delta_ca)\n", + "fig_delta_ca.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cell-9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Income: $%{x:,.0f}
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+ }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# MTR CALCULATION FOR CALIFORNIA HOUSEHOLD\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_ca_for_mtr = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 64},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 62},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"CA\"},\n", + " \"county_fips\": {\"2026\": \"06069\"}\n", + " \n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_ca_baseline = Simulation(situation=situation_ca_for_mtr)\n", + "household_income_ca_mtr = sim_ca_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_ca_net_income = sim_ca_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_ca_reform = Simulation(situation=situation_ca_for_mtr, reform=reform)\n", + "reform_ca_net_income = sim_ca_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3b: Calculate reform2 net income using axes\n", + "sim_ca_reform2 = Simulation(situation=situation_ca_for_mtr, reform=reform2)\n", + "reform2_ca_net_income = sim_ca_reform2.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3c: Calculate reform3 net income using axes\n", + "sim_ca_reform3 = Simulation(situation=situation_ca_for_mtr, reform=reform3)\n", + "reform3_ca_net_income = sim_ca_reform3.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_ca_mtr_incomes, baseline_ca_mtrs = calc_mtr(household_income_ca_mtr, baseline_ca_net_income)\n", + "reform_ca_mtr_incomes, reform_ca_mtrs = calc_mtr(household_income_ca_mtr, reform_ca_net_income)\n", + "reform2_ca_mtr_incomes, reform2_ca_mtrs = calc_mtr(household_income_ca_mtr, reform2_ca_net_income)\n", + "reform3_ca_mtr_incomes, reform3_ca_mtrs = calc_mtr(household_income_ca_mtr, reform3_ca_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_california_mtr = go.Figure()\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=baseline_ca_mtr_incomes,\n", + " y=np.clip(baseline_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=reform_ca_mtr_incomes,\n", + " y=np.clip(reform_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Stepped MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=reform2_ca_mtr_incomes,\n", + " y=np.clip(reform2_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Linear MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=reform3_ca_mtr_incomes,\n", + " y=np.clip(reform3_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - California couple (ages 64 & 62)',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_california_mtr = format_fig(fig_california_mtr)\n", + "fig_california_mtr.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/us/blog_posts/aca_california_700fpl_cliff.ipynb b/us/blog_posts/aca_california_700fpl_cliff.ipynb new file mode 100644 index 0000000..6e11261 --- /dev/null +++ b/us/blog_posts/aca_california_700fpl_cliff.ipynb @@ -0,0 +1,31971 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACA Premium Tax Credit Analysis - California Couple\n", + "## 700% FPL Extension with Amended Rate Schedule\n", + "\n", + "Analysis for blog post: [Analyzing the Bipartisan Health Insurance Affordability Act](https://policyengine.org/us/research/bipartisan-health-insurance-affordability-act)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": "markdown", + "metadata": {}, + "source": [ + "## Define Reform\n", + "\n", + "### 700% FPL Extension with Amended Rate Schedule\n", + "\n", + "| FPL Range | Initial % | Final % |\n", + "|-----------|-----------|--------|\n", + "| 200-250% | 2.0% | 4.0% |\n", + "| 250-300% | 4.0% | 6.0% |\n", + "| 300-400% | 6.0% | 8.5% |\n", + "| 400-600% | 8.5% | 8.5% |\n", + "| 600-700% | 8.5% | 9.25% |\n", + "\n", + "Eligibility cuts off at 700% FPL." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the 700% FPL cliff reform\n", + "from policyengine_us.reforms.aca.aca_ptc_700_fpl_cliff import aca_ptc_700_fpl_cliff\n", + "\n", + "reform_700fpl = aca_ptc_700_fpl_cliff\n", + "\n", + "# IRA Extension reform (extends current 2021-2025 subsidies indefinitely)\n", + "reform_ira = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": "markdown", + "metadata": {}, + "source": [ + "## Define California Household\n", + "\n", + "Couple in San Benito County, CA:\n", + "- Person 1: age 64\n", + "- Person 2: age 62" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "situation_california = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 64}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 62}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"CA\"},\n", + " \"county_fips\": {\"2026\": \"06069\"} # San Benito County\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 200000\n", + " }\n", + " ]]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Baseline simulation\n", + "simulation_baseline = Simulation(situation=situation_california)\n", + "\n", + "# 700% FPL Extension reform\n", + "simulation_700fpl = Simulation(situation=situation_california, reform=reform_700fpl)\n", + "\n", + "# IRA Extension reform\n", + "simulation_ira = Simulation(situation=situation_california, reform=reform_ira)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Benefits and Net Income" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values\n", + "household_income = simulation_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "\n", + "# Baseline\n", + "baseline_aca_ptc = simulation_baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_net_income = simulation_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# 700% FPL Extension\n", + "reform_700fpl_aca_ptc = simulation_700fpl.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_700fpl_net_income = simulation_700fpl.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# IRA Extension\n", + "reform_ira_aca_ptc = simulation_ira.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_ira_net_income = simulation_ira.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart: Health Programs by Income Level\n", + "\n", + "Shows all health assistance programs (Medicaid, CHIP, ACA PTC) across income levels for baseline and both reform scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + 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program benefits for the programs chart\n", + "baseline_medicaid = simulation_baseline.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_chip = simulation_baseline.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "reform_700fpl_medicaid = simulation_700fpl.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_700fpl_chip = simulation_700fpl.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "reform_ira_medicaid = simulation_ira.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_ira_chip = simulation_ira.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate totals\n", + "baseline_total = baseline_aca_ptc + baseline_medicaid + baseline_chip\n", + "reform_700fpl_total = reform_700fpl_aca_ptc + reform_700fpl_medicaid + reform_700fpl_chip\n", + "reform_ira_total = reform_ira_aca_ptc + reform_ira_medicaid + reform_ira_chip\n", + "\n", + "# Colors\n", + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "PURPLE = \"#9467BD\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"\n", + "\n", + "# Create health programs chart\n", + "fig_programs = go.Figure()\n", + "\n", + "# Medicaid doesn't change with PTC reforms - show only once\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_medicaid,\n", + " mode='lines',\n", + " name='Medicaid',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "# ACA PTC varies by scenario\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (Baseline)',\n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (700% FPL)',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (IRA)',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "# Total benefit lines\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_total,\n", + " mode='lines',\n", + " name='Total Benefits (Baseline)',\n", + " line=dict(color=DARK_GRAY, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_total,\n", + " mode='lines',\n", + " name='Total Benefits (700% FPL)',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_total,\n", + " mode='lines',\n", + " name='Total Benefits (IRA)',\n", + " line=dict(color=PURPLE, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.update_layout(\n", + " title='California Couple (ages 64 & 62) - Health Programs by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Annual Benefit Amount',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h', yanchor='top', y=-0.15, xanchor='center', x=0.5)\n", + ")\n", + "\n", + "fig_programs = format_fig(fig_programs)\n", + "fig_programs.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Color scheme\n", + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "PURPLE = \"#9467BD\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 1: ACA Premium Tax Credit by Income Level" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "Baseline", + "type": "scatter", + "x": [ + 0, + 274.56573486328125, + 549.1314697265625, + 823.6972045898438, + 1098.262939453125, + 1372.82861328125, + 1647.3944091796875, + 1921.9600830078125, + 2196.52587890625, + 2471.091552734375, + 2745.6572265625, + 3020.22314453125, + 3294.788818359375, + 3569.3544921875, + 3843.920166015625, + 4118.48583984375, + 4393.0517578125, + 4667.61767578125, + 4942.18310546875, + 5216.74853515625, + 5491.314453125, + 5765.88037109375, + 6040.4462890625, + 6315.01171875, + 6589.57763671875, + 6864.14306640625, + 7138.708984375, + 7413.27490234375, + 7687.84033203125, + 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"fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_aca_ptc,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# 700% FPL Extension\n", + "fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_aca_ptc,\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_aca_ptc,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_ptc.update_layout(\n", + " title='California Couple (ages 64 & 62) - ACA Premium Tax Credit by Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='ACA Premium Tax Credit',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " 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+ "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_net_income,\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_net_income,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_net_income.update_layout(\n", + " title='California Couple (ages 64 & 62) - Health-Adjusted Net Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Health-Adjusted Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_net_income = format_fig(fig_net_income)\n", + "fig_net_income.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": 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go.Figure()\n", + "\n", + "# 700% FPL Extension impact\n", + "fig_delta.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=delta_700fpl,\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension impact\n", + "fig_delta.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=delta_ira,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_delta.update_layout(\n", + " title='California Couple (ages 64 & 62) - Impact of Premium Tax Credit Reforms',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Change in Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(\n", + " tickformat='$,.0f',\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='black'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + 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+ } + ], + "source": [ + "# Create situation for MTR calculation with more points\n", + "situation_ca_for_mtr = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 64},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 62},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"CA\"},\n", + " \"county_fips\": {\"2026\": \"06069\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200000,\n", + " \"count\": 400,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Calculate net incomes for MTR\n", + "sim_baseline_mtr = Simulation(situation=situation_ca_for_mtr)\n", + "sim_700fpl_mtr = Simulation(situation=situation_ca_for_mtr, reform=reform_700fpl)\n", + "sim_ira_mtr = Simulation(situation=situation_ca_for_mtr, reform=reform_ira)\n", + "\n", + "household_income_mtr = sim_baseline_mtr.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_net_income_mtr = sim_baseline_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_700fpl_net_income_mtr = sim_700fpl_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_ira_net_income_mtr = sim_ira_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# Function to calculate MTR\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_mtr, baseline_net_income_mtr)\n", + "reform_700fpl_mtr_incomes, reform_700fpl_mtrs = calc_mtr(household_income_mtr, reform_700fpl_net_income_mtr)\n", + "reform_ira_mtr_incomes, reform_ira_mtrs = calc_mtr(household_income_mtr, reform_ira_net_income_mtr)\n", + "\n", + "# Create MTR chart\n", + "fig_mtr = go.Figure()\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=baseline_mtr_incomes,\n", + " y=np.clip(baseline_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_700fpl_mtr_incomes,\n", + " y=np.clip(reform_700fpl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
700% FPL MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_ira_mtr_incomes,\n", + " y=np.clip(reform_ira_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.update_layout(\n", + " title='Marginal Tax Rate (including health benefits) - California Couple (ages 64 & 62)',\n", + " xaxis_title='Employment Income',\n", + " yaxis_title='Marginal Tax Rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_mtr = format_fig(fig_mtr)\n", + "fig_mtr.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Income Benchmarks Analysis\n", + "\n", + "Calculate PTC at specific FPL percentages (138%, 300%, 400%, 600%, 700%)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_700fpl_extensionptc_ira_extension
0138% FPL ($29,897.10)29897.100.0000000.0000000.000000
1300% FPL ($64,993.70)64993.7036241.79687538701.80859438701.808594
2400% FPL ($86,658.27)86658.270.00000035349.21484435349.214844
3600% FPL ($129,987.40)129987.400.00000031529.75390631666.238281
4700% FPL ($151,651.96)151651.960.0000000.00000029824.751953
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_700fpl_extension \\\n", + "0 138% FPL ($29,897.10) 29897.10 0.000000 0.000000 \n", + "1 300% FPL ($64,993.70) 64993.70 36241.796875 38701.808594 \n", + "2 400% FPL ($86,658.27) 86658.27 0.000000 35349.214844 \n", + "3 600% FPL ($129,987.40) 129987.40 0.000000 31529.753906 \n", + "4 700% FPL ($151,651.96) 151651.96 0.000000 0.000000 \n", + "\n", + " ptc_ira_extension \n", + "0 0.000000 \n", + "1 38701.808594 \n", + "2 35349.214844 \n", + "3 31666.238281 \n", + "4 29824.751953 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "\n", + "PERIOD = 2026\n", + "\n", + "def get_tax_unit_fpg(base_situation: dict, period=PERIOD) -> float:\n", + " \"\"\"Return the tax unit FPG for the given situation/year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " for person in sit[\"people\"].values():\n", + " person.setdefault(\"employment_income\", {})\n", + " person[\"employment_income\"][str(period)] = 0\n", + " \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", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"Return ACA PTC for the given income and year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " # Split income between the two adults\n", + " sit[\"people\"][\"you\"][\"employment_income\"] = {str(period): income / 2}\n", + " sit[\"people\"][\"your partner\"][\"employment_income\"] = {str(period): income / 2}\n", + " \n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# Get FPG for this household\n", + "fpg_2026 = get_tax_unit_fpg(situation_california, period=PERIOD)\n", + "\n", + "# Define income levels to test\n", + "percent_targets = {\n", + " \"138% FPL\": 1.38,\n", + " \"300% FPL\": 3.00,\n", + " \"400% FPL\": 4.00,\n", + " \"600% FPL\": 6.00,\n", + " \"700% FPL\": 7.00,\n", + "}\n", + "\n", + "rows = []\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_california, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_700fpl_extension\": calc_ptc_for_income(situation_california, inc, reform_to_use=reform_700fpl, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_california, inc, reform_to_use=reform_ira, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Health Programs at Key Income Levels\n", + "\n", + "Medicaid benefits at the same FPL thresholds (baseline scenario - these don't change with PTC reforms)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate Medicaid at key income levels (no CHIP for this adult couple)\n", + "def calc_health_programs_for_income(base_situation: dict, income: float, period=PERIOD):\n", + " \"\"\"Return Medicaid value for the given income.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " # Split income between the two adults\n", + " sit[\"people\"][\"you\"][\"employment_income\"] = {str(period): income / 2}\n", + " sit[\"people\"][\"your partner\"][\"employment_income\"] = {str(period): income / 2}\n", + " \n", + " sim = Simulation(situation=sit)\n", + " medicaid = sim.calculate(\"medicaid_cost\", map_to=\"household\", period=period)[0]\n", + " return medicaid\n", + "\n", + "# Build table with all health programs at key income levels\n", + "health_rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " medicaid = calc_health_programs_for_income(situation_california, inc)\n", + " \n", + " # Get PTC values from the earlier calculation\n", + " ptc_baseline = calc_ptc_for_income(situation_california, inc, reform_to_use=None, period=PERIOD)\n", + " ptc_700fpl = calc_ptc_for_income(situation_california, inc, reform_to_use=reform_700fpl, period=PERIOD)\n", + " ptc_ira = calc_ptc_for_income(situation_california, inc, reform_to_use=reform_ira, period=PERIOD)\n", + " \n", + " health_rows.append({\n", + " \"Income Level\": label,\n", + " \"Income ($)\": f\"${inc:,.0f}\",\n", + " \"Medicaid\": f\"${medicaid:,.0f}\",\n", + " \"PTC (Baseline)\": f\"${ptc_baseline:,.0f}\",\n", + " \"PTC (700% FPL)\": f\"${ptc_700fpl:,.0f}\",\n", + " \"PTC (IRA)\": f\"${ptc_ira:,.0f}\",\n", + " })\n", + "\n", + "health_df = pd.DataFrame(health_rows)\n", + "health_df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/us/blog_posts/aca_tampa_600fpl_cliff.ipynb b/us/blog_posts/aca_tampa_600fpl_cliff.ipynb new file mode 100644 index 0000000..2afdc73 --- /dev/null +++ b/us/blog_posts/aca_tampa_600fpl_cliff.ipynb @@ -0,0 +1,19634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACA Premium Tax Credit Analysis - Tampa Family of 4\n", + "## Comparing 600% FPL Cliff Extension and IRA Extension" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": "markdown", + "metadata": {}, + "source": [ + "## Define Reforms\n", + "\n", + "### Reform 1: 600% FPL Cliff Extension\n", + "Activates the 600% FPL cliff extension parameter.\n", + "\n", + "### Reform 2: IRA Extension\n", + "Modifies required contribution percentages:\n", + "- Sets 0% for income brackets 0-2\n", + "- Sets 2%, 4%, 6%, and 8.5% for higher brackets\n", + "- Extends PTC eligibility beyond 400% FPL" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Reform 1: 600% FPL Cliff Extension\n", + "reform_600fpl = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_600_fpl_cliff.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "# Reform 2: IRA Extension (from existing notebook reform3)\n", + "reform_ira = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2025-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": "markdown", + "metadata": {}, + "source": [ + "## Define Florida Family Situation\n", + "\n", + "Family of 4 in Tampa (Hillsborough County, FL):\n", + "- 2 parents (age 40)\n", + "- 2 children (ages 10 and 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "situation_florida = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"} # Hillsborough County (Tampa)\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 200000\n", + " }\n", + " ]]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Baseline simulation\n", + "simulation_baseline = Simulation(situation=situation_florida)\n", + "\n", + "# 600% FPL Cliff reform\n", + "simulation_600fpl = Simulation(situation=situation_florida, reform=reform_600fpl)\n", + "\n", + "# IRA Extension reform\n", + "simulation_ira = Simulation(situation=situation_florida, reform=reform_ira)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Benefits and Net Income" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values\n", + "household_income = simulation_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "\n", + "# Baseline\n", + "baseline_aca_ptc = simulation_baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_net_income = simulation_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# 600% FPL Cliff\n", + "reform_600fpl_aca_ptc = simulation_600fpl.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_600fpl_net_income = simulation_600fpl.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# IRA Extension\n", + "reform_ira_aca_ptc = simulation_ira.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_ira_net_income = simulation_ira.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Color scheme\n", + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "PURPLE = \"#9467BD\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 1: ACA Premium Tax Credit by 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mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_aca_ptc,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_ptc.update_layout(\n", + " title='Florida Family of 4 - ACA Premium Tax Credit by Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='ACA Premium Tax Credit',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_ptc = format_fig(fig_ptc)\n", + "fig_ptc.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 2: Health-Adjusted Net Income" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + 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x=household_income,\n", + " y=baseline_net_income,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# 600% FPL Cliff\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_600fpl_net_income,\n", + " mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_net_income,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_net_income.update_layout(\n", + " title='Florida Family of 4 - Health-Adjusted Net Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Health-Adjusted Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_net_income = format_fig(fig_net_income)\n", + "fig_net_income.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 3: Impact on Net Income (Reform - Baseline)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#2C6496", + "width": 2 + }, + "mode": "lines", + "name": "600% FPL Cliff Extension", + "type": "scatter", + "x": [ + 0, + 250.31289672851562, + 500.62579345703125, + 750.9386596679688, + 1001.2515869140625, + 1251.564453125, + 1501.8773193359375, + 1752.190185546875, + 2002.503173828125, + 2252.81591796875, + 2503.12890625, + 2753.44189453125, + 3003.754638671875, + 3254.067626953125, + 3504.38037109375, + 3754.693359375, + 4005.00634765625, + 4255.3193359375, + 4505.6318359375, + 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"plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "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": "Marginal Tax Rate (including health benefits) - Florida Family of 4" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 200000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment Income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal Tax Rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create situation for MTR calculation with more points\n", + "situation_fl_for_mtr = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200000,\n", + " \"count\": 400,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Calculate net incomes for MTR\n", + "sim_baseline_mtr = Simulation(situation=situation_fl_for_mtr)\n", + "sim_600fpl_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_600fpl)\n", + "sim_ira_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_ira)\n", + "\n", + "household_income_mtr = sim_baseline_mtr.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_net_income_mtr = sim_baseline_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_600fpl_net_income_mtr = sim_600fpl_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_ira_net_income_mtr = sim_ira_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# Function to calculate MTR\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_mtr, baseline_net_income_mtr)\n", + "reform_600fpl_mtr_incomes, reform_600fpl_mtrs = calc_mtr(household_income_mtr, reform_600fpl_net_income_mtr)\n", + "reform_ira_mtr_incomes, reform_ira_mtrs = calc_mtr(household_income_mtr, reform_ira_net_income_mtr)\n", + "\n", + "# Create MTR chart\n", + "fig_mtr = go.Figure()\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=baseline_mtr_incomes,\n", + " y=np.clip(baseline_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_600fpl_mtr_incomes,\n", + " y=np.clip(reform_600fpl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
600% FPL MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_ira_mtr_incomes,\n", + " y=np.clip(reform_ira_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.update_layout(\n", + " title='Marginal Tax Rate (including health benefits) - Florida Family of 4',\n", + " xaxis_title='Employment Income',\n", + " yaxis_title='Marginal Tax Rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_mtr = format_fig(fig_mtr)\n", + "fig_mtr.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Income Benchmarks Analysis\n", + "\n", + "Calculate PTC at specific FPL percentages (138%, 300%, 400%, 600%)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_600fpl_cliffptc_ira_extension
0138% FPL ($44,367.00)44367.010217.51953111747.65918011747.659180
1300% FPL ($96,450.00)96450.09173.24023412992.66015612992.660156
2400% FPL ($128,600.00)128600.00.0000007848.6601567848.660156
3600% FPL ($192,900.00)192900.00.0000000.0000002383.160156
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_600fpl_cliff \\\n", + "0 138% FPL ($44,367.00) 44367.0 10217.519531 11747.659180 \n", + "1 300% FPL ($96,450.00) 96450.0 9173.240234 12992.660156 \n", + "2 400% FPL ($128,600.00) 128600.0 0.000000 7848.660156 \n", + "3 600% FPL ($192,900.00) 192900.0 0.000000 0.000000 \n", + "\n", + " ptc_ira_extension \n", + "0 11747.659180 \n", + "1 12992.660156 \n", + "2 7848.660156 \n", + "3 2383.160156 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "\n", + "PERIOD = 2026\n", + "\n", + "def get_tax_unit_fpg(base_situation: dict, period=PERIOD) -> float:\n", + " \"\"\"Return the tax unit FPG for the given situation/year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " for person in sit[\"people\"].values():\n", + " person.setdefault(\"employment_income\", {})\n", + " person[\"employment_income\"][str(period)] = 0\n", + " \n", + " sim = Simulation(situation=sit)\n", + " fpg = sim.calculate(\"tax_unit_fpg\", map_to=\"tax_unit\", period=period)[0]\n", + " return float(fpg)\n", + "\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"Return ACA PTC for the given income and year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " sit[\"people\"][\"parent1\"][\"employment_income\"] = {str(period): income}\n", + " \n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# Get FPG for this family\n", + "fpg_2026 = get_tax_unit_fpg(situation_florida, period=PERIOD)\n", + "\n", + "# Define income levels to test\n", + "percent_targets = {\n", + " \"138% FPL\": 1.38,\n", + " \"300% FPL\": 3.00,\n", + " \"400% FPL\": 4.00,\n", + " \"600% FPL\": 6.00,\n", + "}\n", + "\n", + "rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " rows.append({\n", + " \"income_label\": f\"{label} (${inc:,.2f})\",\n", + " \"income_usd\": inc,\n", + " \"ptc_baseline\": calc_ptc_for_income(situation_florida, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_600fpl_cliff\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_600fpl, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_ira, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/us/blog_posts/aca_tampa_700fpl_cliff.ipynb b/us/blog_posts/aca_tampa_700fpl_cliff.ipynb new file mode 100644 index 0000000..6628674 --- /dev/null +++ b/us/blog_posts/aca_tampa_700fpl_cliff.ipynb @@ -0,0 +1,34245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACA Premium Tax Credit Analysis - Tampa Family of 4\n", + "## 700% FPL Extension with Amended Rate Schedule\n", + "\n", + "Analysis for blog post: [Analyzing the Bipartisan Health Insurance Affordability Act](https://policyengine.org/us/research/bipartisan-health-insurance-affordability-act)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": "markdown", + "metadata": {}, + "source": [ + "## Define Reform\n", + "\n", + "### 700% FPL Extension with Amended Rate Schedule\n", + "\n", + "| FPL Range | Initial % | Final % |\n", + "|-----------|-----------|--------|\n", + "| 200-250% | 2.0% | 4.0% |\n", + "| 250-300% | 4.0% | 6.0% |\n", + "| 300-400% | 6.0% | 8.5% |\n", + "| 400-600% | 8.5% | 8.5% |\n", + "| 600-700% | 8.5% | 9.25% |\n", + "\n", + "Eligibility cuts off at 700% FPL." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the 700% FPL cliff reform\n", + "from policyengine_us.reforms.aca.aca_ptc_700_fpl_cliff import aca_ptc_700_fpl_cliff\n", + "\n", + "reform_700fpl = aca_ptc_700_fpl_cliff\n", + "\n", + "# IRA Extension reform (extends current 2021-2025 subsidies indefinitely)\n", + "reform_ira = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": "markdown", + "metadata": {}, + "source": [ + "## Define Florida Family Situation\n", + "\n", + "Family of 4 in Tampa (Hillsborough County, FL):\n", + "- 2 parents (age 40)\n", + "- 2 children (ages 10 and 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "situation_florida = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"} # Hillsborough County (Tampa)\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 250000\n", + " }\n", + " ]]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Baseline simulation\n", + "simulation_baseline = Simulation(situation=situation_florida)\n", + "\n", + "# 700% FPL Extension reform\n", + "simulation_700fpl = Simulation(situation=situation_florida, reform=reform_700fpl)\n", + "\n", + "# IRA Extension reform\n", + "simulation_ira = Simulation(situation=situation_florida, reform=reform_ira)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Benefits and Net Income" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values\n", + "household_income = simulation_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "\n", + "# Baseline\n", + "baseline_aca_ptc = simulation_baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_net_income = simulation_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# 700% FPL Extension\n", + "reform_700fpl_aca_ptc = simulation_700fpl.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_700fpl_net_income = simulation_700fpl.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# IRA Extension\n", + "reform_ira_aca_ptc = simulation_ira.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_ira_net_income = simulation_ira.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart: Health Programs by Income Level\n", + "\n", + "Shows all health assistance programs (Medicaid, CHIP, ACA PTC) across income levels for baseline and both reform scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + 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"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": "Florida Family of 4 - Health Programs by Income Level" + }, + "width": 800, + "xaxis": { + "range": [ + 0, + 250000 + ], + "tickformat": "$,.0f", + "title": { + "text": "Household Income" + } + }, + "yaxis": { + "tickformat": "$,.0f", + "title": { + "text": "Annual Benefit Amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate additional health program benefits for the programs chart\n", + "baseline_medicaid = simulation_baseline.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_chip = simulation_baseline.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "reform_700fpl_medicaid = simulation_700fpl.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_700fpl_chip = simulation_700fpl.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "reform_ira_medicaid = simulation_ira.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_ira_chip = simulation_ira.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate totals\n", + "baseline_total = baseline_aca_ptc + baseline_medicaid + baseline_chip\n", + "reform_700fpl_total = reform_700fpl_aca_ptc + reform_700fpl_medicaid + reform_700fpl_chip\n", + "reform_ira_total = reform_ira_aca_ptc + reform_ira_medicaid + reform_ira_chip\n", + "\n", + "# Colors\n", + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "PURPLE = \"#9467BD\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"\n", + "\n", + "# Create health programs chart\n", + "fig_programs = go.Figure()\n", + "\n", + "# Medicaid and CHIP don't change with PTC reforms - show only once\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_medicaid,\n", + " mode='lines',\n", + " name='Medicaid',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_chip,\n", + " mode='lines',\n", + " name='CHIP',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# ACA PTC varies by scenario\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (Baseline)',\n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (700% FPL)',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_aca_ptc,\n", + " mode='lines',\n", + " name='ACA PTC (IRA)',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "# Total benefit lines\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_total,\n", + " mode='lines',\n", + " name='Total Benefits (Baseline)',\n", + " line=dict(color=DARK_GRAY, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_total,\n", + " mode='lines',\n", + " name='Total Benefits (700% FPL)',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_total,\n", + " mode='lines',\n", + " name='Total Benefits (IRA)',\n", + " line=dict(color=PURPLE, width=2, dash='longdash'),\n", + " visible='legendonly'\n", + "))\n", + "\n", + "fig_programs.update_layout(\n", + " title='Florida Family of 4 - Health Programs by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Annual Benefit Amount',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 250000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h', yanchor='top', y=-0.15, xanchor='center', x=0.5)\n", + ")\n", + "\n", + "fig_programs = format_fig(fig_programs)\n", + "fig_programs.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Color scheme\n", + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "PURPLE = \"#9467BD\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 1: ACA Premium Tax Credit by Income Level" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "Baseline", + "type": "scatter", + "x": [ + 0, + 343.2071533203125, + 686.414306640625, + 1029.6214599609375, + 1372.82861328125, + 1716.0357666015625, + 2059.242919921875, + 2402.4501953125, + 2745.6572265625, + 3088.864501953125, + 3432.071533203125, + 3775.27880859375, + 4118.48583984375, + 4461.69287109375, + 4804.900390625, + 5148.107421875, + 5491.314453125, + 5834.52197265625, + 6177.72900390625, + 6520.93603515625, + 6864.14306640625, + 7207.3505859375, + 7550.5576171875, + 7893.7646484375, + 8236.9716796875, + 8580.1787109375, + 8923.3857421875, + 9266.59375, + 9609.80078125, + 9953.0078125, + 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"display_data" + } + ], + "source": [ + "fig_net_income = go.Figure()\n", + "\n", + "# Baseline\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=baseline_net_income,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# 700% FPL Extension\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_700fpl_net_income,\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_net_income,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_net_income.update_layout(\n", + " title='Florida Family of 4 - Health-Adjusted Net Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Health-Adjusted Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 250000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_net_income = format_fig(fig_net_income)\n", + "fig_net_income.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 3: Impact on Net Income (Reform - Baseline)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#2C6496", + "width": 2 + }, + "mode": "lines", + "name": "700% FPL Extension", + "type": "scatter", + "x": [ + 0, + 343.2071533203125, + 686.414306640625, + 1029.6214599609375, + 1372.82861328125, + 1716.0357666015625, + 2059.242919921875, + 2402.4501953125, + 2745.6572265625, + 3088.864501953125, + 3432.071533203125, + 3775.27880859375, + 4118.48583984375, 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reform_ira_net_income - baseline_net_income\n", + "\n", + "fig_delta = go.Figure()\n", + "\n", + "# 700% FPL Extension impact\n", + "fig_delta.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=delta_700fpl,\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension impact\n", + "fig_delta.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=delta_ira,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_delta.update_layout(\n", + " title='Florida Family of 4 - Impact of Premium Tax Credit Reforms',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Change in Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 250000]),\n", + " yaxis=dict(\n", + " tickformat='$,.0f',\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='black'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_delta = format_fig(fig_delta)\n", + "fig_delta.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 4: Marginal Tax Rates" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Income: $%{x:,.0f}
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"title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Marginal Tax Rate (including health benefits) - Florida Family of 4" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 250000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment Income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal Tax Rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create situation for MTR calculation with more points\n", + "situation_fl_for_mtr = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 250000,\n", + " \"count\": 500,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Calculate net incomes for MTR\n", + "sim_baseline_mtr = Simulation(situation=situation_fl_for_mtr)\n", + "sim_700fpl_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_700fpl)\n", + "sim_ira_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_ira)\n", + "\n", + "household_income_mtr = sim_baseline_mtr.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_net_income_mtr = sim_baseline_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_700fpl_net_income_mtr = sim_700fpl_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_ira_net_income_mtr = sim_ira_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# Function to calculate MTR\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_mtr, baseline_net_income_mtr)\n", + "reform_700fpl_mtr_incomes, reform_700fpl_mtrs = calc_mtr(household_income_mtr, reform_700fpl_net_income_mtr)\n", + "reform_ira_mtr_incomes, reform_ira_mtrs = calc_mtr(household_income_mtr, reform_ira_net_income_mtr)\n", + "\n", + "# Create MTR chart\n", + "fig_mtr = go.Figure()\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=baseline_mtr_incomes,\n", + " y=np.clip(baseline_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_700fpl_mtr_incomes,\n", + " y=np.clip(reform_700fpl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='700% FPL Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
700% FPL MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_ira_mtr_incomes,\n", + " y=np.clip(reform_ira_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.update_layout(\n", + " title='Marginal Tax Rate (including health benefits) - Florida Family of 4',\n", + " xaxis_title='Employment Income',\n", + " yaxis_title='Marginal Tax Rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 250000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_mtr = format_fig(fig_mtr)\n", + "fig_mtr.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Income Benchmarks Analysis\n", + "\n", + "Calculate PTC at specific FPL percentages (138%, 300%, 400%, 600%, 700%)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_700fpl_extensionptc_ira_extension
0138% FPL ($45,446.42)45446.4210588.64746112240.22363312240.223633
1300% FPL ($98,796.57)98796.579726.92773413466.37890613466.378906
2400% FPL ($131,728.77)131728.770.0000008370.1210948370.121094
3600% FPL ($197,593.15)197593.150.0000002564.1757812771.648438
4700% FPL ($230,525.34)230525.340.0000000.0000000.000000
\n", + "
" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_700fpl_extension \\\n", + "0 138% FPL ($45,446.42) 45446.42 10588.647461 12240.223633 \n", + "1 300% FPL ($98,796.57) 98796.57 9726.927734 13466.378906 \n", + "2 400% FPL ($131,728.77) 131728.77 0.000000 8370.121094 \n", + "3 600% FPL ($197,593.15) 197593.15 0.000000 2564.175781 \n", + "4 700% FPL ($230,525.34) 230525.34 0.000000 0.000000 \n", + "\n", + " ptc_ira_extension \n", + "0 12240.223633 \n", + "1 13466.378906 \n", + "2 8370.121094 \n", + "3 2771.648438 \n", + "4 0.000000 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "\n", + "PERIOD = 2026\n", + "\n", + "def get_tax_unit_fpg(base_situation: dict, period=PERIOD) -> float:\n", + " \"\"\"Return the tax unit FPG for the given situation/year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " for person in sit[\"people\"].values():\n", + " person.setdefault(\"employment_income\", {})\n", + " person[\"employment_income\"][str(period)] = 0\n", + " \n", + " sim = Simulation(situation=sit)\n", + " fpg = sim.calculate(\"tax_unit_fpg\", map_to=\"tax_unit\", period=period)[0]\n", + " return float(fpg)\n", + "\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"Return ACA PTC for the given income and year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " sit[\"people\"][\"parent1\"][\"employment_income\"] = {str(period): income}\n", + " \n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# Get FPG for this family\n", + "fpg_2026 = get_tax_unit_fpg(situation_florida, period=PERIOD)\n", + "\n", + "# Define income levels to test\n", + "percent_targets = {\n", + " \"138% FPL\": 1.38,\n", + " \"300% FPL\": 3.00,\n", + " \"400% FPL\": 4.00,\n", + " \"600% FPL\": 6.00,\n", + " \"700% FPL\": 7.00,\n", + "}\n", + "\n", + "rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " rows.append({\n", + " \"income_label\": f\"{label} (${inc:,.2f})\",\n", + " \"income_usd\": inc,\n", + " \"ptc_baseline\": calc_ptc_for_income(situation_florida, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_700fpl_extension\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_700fpl, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_ira, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Health Programs at Key Income Levels\n", + "\n", + "Medicaid and CHIP benefits at the same FPL thresholds (baseline scenario - these don't change with PTC reforms)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate Medicaid and CHIP at key income levels\n", + "def calc_health_programs_for_income(base_situation: dict, income: float, period=PERIOD):\n", + " \"\"\"Return Medicaid and CHIP values for the given income.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " sit[\"people\"][\"parent1\"][\"employment_income\"] = {str(period): income}\n", + " \n", + " sim = Simulation(situation=sit)\n", + " medicaid = sim.calculate(\"medicaid_cost\", map_to=\"household\", period=period)[0]\n", + " chip = sim.calculate(\"per_capita_chip\", map_to=\"household\", period=period)[0]\n", + " return medicaid, chip\n", + "\n", + "# Build table with all health programs at key income levels\n", + "health_rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " medicaid, chip = calc_health_programs_for_income(situation_florida, inc)\n", + " \n", + " # Get PTC values from the earlier calculation\n", + " ptc_baseline = calc_ptc_for_income(situation_florida, inc, reform_to_use=None, period=PERIOD)\n", + " ptc_700fpl = calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_700fpl, period=PERIOD)\n", + " ptc_ira = calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_ira, period=PERIOD)\n", + " \n", + " health_rows.append({\n", + " \"Income Level\": label,\n", + " \"Income ($)\": f\"${inc:,.0f}\",\n", + " \"Medicaid\": f\"${medicaid:,.0f}\",\n", + " \"CHIP\": f\"${chip:,.0f}\",\n", + " \"PTC (Baseline)\": f\"${ptc_baseline:,.0f}\",\n", + " \"PTC (700% FPL)\": f\"${ptc_700fpl:,.0f}\",\n", + " \"PTC (IRA)\": f\"${ptc_ira:,.0f}\",\n", + " })\n", + "\n", + "health_df = pd.DataFrame(health_rows)\n", + "health_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate health programs at low income levels ($0 and $12,000)\n", + "# to show Medicaid for parents and CHIP for kids\n", + "\n", + "low_income_targets = {\n", + " \"$0\": 0,\n", + " \"$12,000\": 12000,\n", + "}\n", + "\n", + "low_income_rows = []\n", + "for label, inc in low_income_targets.items():\n", + " medicaid, chip = calc_health_programs_for_income(situation_florida, inc)\n", + " ptc_baseline = calc_ptc_for_income(situation_florida, inc, reform_to_use=None, period=PERIOD)\n", + " ptc_700fpl = calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_700fpl, period=PERIOD)\n", + " ptc_ira = calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_ira, period=PERIOD)\n", + " \n", + " # Calculate FPL percentage\n", + " fpl_pct = (inc / fpg_2026) * 100 if fpg_2026 > 0 else 0\n", + " \n", + " low_income_rows.append({\n", + " \"Income\": label,\n", + " \"FPL %\": f\"{fpl_pct:.0f}%\",\n", + " \"Medicaid\": f\"${medicaid:,.0f}\",\n", + " \"CHIP\": f\"${chip:,.0f}\",\n", + " \"PTC (Baseline)\": f\"${ptc_baseline:,.0f}\",\n", + " \"PTC (700% FPL)\": f\"${ptc_700fpl:,.0f}\",\n", + " \"PTC (IRA)\": f\"${ptc_ira:,.0f}\",\n", + " })\n", + "\n", + "low_income_df = pd.DataFrame(low_income_rows)\n", + "print(\"Health programs at low income levels (Florida family of 4):\")\n", + "low_income_df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/us/blog_posts/aca_tampa_family4.ipynb b/us/blog_posts/aca_tampa_family4.ipynb new file mode 100644 index 0000000..cc58268 --- /dev/null +++ b/us/blog_posts/aca_tampa_family4.ipynb @@ -0,0 +1,23816 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "cell-0", + "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": 4, + "id": "nm6w01m67nb", + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform2 = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_simplified_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform3 = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2025-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": 5, + "id": "xhvy3ca2b4n", + "metadata": {}, + "outputs": [], + "source": [ + "from plotly.subplots import make_subplots" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1is10pp2ns9", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ydf7z5mgib", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "93qz82smpk4", + "metadata": {}, + "outputs": [], + "source": [ + "from policyengine_core.reforms import Reform" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cell-1", + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform2 = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_simplified_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform3 = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2025-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": 10, + "id": "cell-2", + "metadata": {}, + "outputs": [], + "source": [ + "situation_florida = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\n", + " \"2026\": 40\n", + " }\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\n", + " \"2026\": 40\n", + " }\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\n", + " \"2026\": 10\n", + " }\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\n", + " \"2026\": 8\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"parent1\",\n", + " \"parent2\",\n", + " \"child1\",\n", + " \"child2\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"parent1\",\n", + " \"parent2\",\n", + " \"child1\",\n", + " \"child2\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"parent1\",\n", + " \"parent2\",\n", + " \"child1\",\n", + " \"child2\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"parent1\",\n", + " \"parent2\",\n", + " \"child1\",\n", + " \"child2\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"FL\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"12057\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\n", + " \"parent1\",\n", + " \"parent2\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 120000\n", + " }\n", + " ]\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cell-3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_florida = Simulation(\n", + " situation=situation_florida,\n", + ")\n", + "reformed_simulation_florida = Simulation(\n", + " situation=situation_florida,\n", + " reform=reform,\n", + ")\n", + "reformed_simulation_florida2 = Simulation(\n", + " situation=situation_florida,\n", + " reform=reform2,\n", + ")\n", + "reformed_simulation_florida3 = Simulation(\n", + " situation=situation_florida,\n", + " reform=reform3,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cell-4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_stepped_proposalptc_linear_proposalptc_ira_extension
0138 % FPL ($45,446.42)45446.4210588.64746112240.22363311494.90234412240.223633
1300 % FPL ($98,796.57)98796.579726.92773413362.64257811386.71093813466.378906
2400 % FPL ($131,728.77)131728.770.0000005919.9658203285.3906258370.121094
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_stepped_proposal \\\n", + "0 138 % FPL ($45,446.42) 45446.42 10588.647461 12240.223633 \n", + "1 300 % FPL ($98,796.57) 98796.57 9726.927734 13362.642578 \n", + "2 400 % FPL ($131,728.77) 131728.77 0.000000 5919.965820 \n", + "\n", + " ptc_linear_proposal ptc_ira_extension \n", + "0 11494.902344 12240.223633 \n", + "1 11386.710938 13466.378906 \n", + "2 3285.390625 8370.121094 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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 Florida family simulation\n", + "# ------------------------------------------------------------------\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"\n", + " Return ACA PTC (household-level) for the given income and year.\n", + " \"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + "\n", + " # Assign all income to the first parent\n", + " sit[\"people\"][\"parent1\"][\"employment_income\"] = {str(period): income}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\n", + "fpg_2026 = get_tax_unit_fpg(situation_florida, 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, 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_florida, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_stepped_proposal\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform, period=PERIOD),\n", + " \"ptc_linear_proposal\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform2, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform3, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values for Florida\n", + "household_income_florida = simulation_florida.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_florida_per_capita_chip = simulation_florida.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "baseline_florida_aca_ptc = simulation_florida.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_florida_medicaid_cost = simulation_florida.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_florida_net_income_including_health_benefits = simulation_florida.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "baseline_florida_slcsp = simulation_florida.calculate(\"slcsp\", map_to=\"household\", period=2026)\n", + "\n", + "reform_florida_per_capita_chip = reformed_simulation_florida.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform_florida_aca_ptc = reformed_simulation_florida.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_florida_medicaid_cost = reformed_simulation_florida.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_florida_net_income_including_health_benefits = reformed_simulation_florida.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform2_florida_per_capita_chip = reformed_simulation_florida2.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform2_florida_aca_ptc = reformed_simulation_florida2.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform2_florida_medicaid_cost = reformed_simulation_florida2.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform2_florida_net_income_including_health_benefits = reformed_simulation_florida2.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform3_florida_per_capita_chip = reformed_simulation_florida3.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform3_florida_aca_ptc = reformed_simulation_florida3.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform3_florida_medicaid_cost = reformed_simulation_florida3.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform3_florida_net_income_including_health_benefits = reformed_simulation_florida3.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate total benefits for each scenario\n", + "baseline_florida_total = [sum(x) for x in zip(baseline_florida_per_capita_chip, baseline_florida_aca_ptc, baseline_florida_medicaid_cost)]\n", + "reform_florida_total = [sum(x) for x in zip(reform_florida_per_capita_chip, reform_florida_aca_ptc, reform_florida_medicaid_cost)]\n", + "reform2_florida_total = [sum(x) for x in zip(reform2_florida_per_capita_chip, reform2_florida_aca_ptc, reform2_florida_medicaid_cost)]\n", + "reform3_florida_total = [sum(x) for x in zip(reform3_florida_per_capita_chip, reform3_florida_aca_ptc, reform3_florida_medicaid_cost)]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cell-6", + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"\n", + "PURPLE = \"#9467BD\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cell-7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "ACA PTC (Baseline)", + "type": "scatter", + "x": [ + 0, + 164.7394256591797, + 329.4788513183594, + 494.21832275390625, + 658.9577026367188, + 823.6972045898438, + 988.4366455078125, + 1153.176025390625, + 1317.9154052734375, + 1482.6549072265625, + 1647.3944091796875, + 1812.1337890625, + 1976.873291015625, + 2141.61279296875, + 2306.35205078125, + 2471.091552734375, + 2635.830810546875, + 2800.5703125, + 2965.309814453125, + 3130.04931640625, + 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"title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Florida Household (Two parents with two children) - Program Benefits by Income Level" + }, + "width": 800, + "xaxis": { + "range": [ + 0, + 120000 + ], + "tickformat": "$,.0f", + "title": { + "text": "Household Income" + } + }, + "yaxis": { + "tickformat": "$,.0f", + "title": { + "text": "Benefit Amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create Florida graph\n", + "fig_florida = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\n", + "fig_florida.add_trace(go.Scatter(\n", + " x=household_income_florida, \n", + " y=baseline_florida_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Baseline)', \n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Add reform traces\n", + "fig_florida.add_trace(go.Scatter(\n", + " x=household_income_florida, \n", + " y=reform_florida_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Stepped Proposal)', \n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_florida.add_trace(go.Scatter(\n", + " x=household_income_florida, \n", + " y=reform2_florida_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Linear Proposal)', \n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_florida.add_trace(go.Scatter(\n", + " x=household_income_florida, \n", + " y=reform3_florida_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (IRA Extension)', \n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "\n", + "# Update layout\n", + "fig_florida.update_layout(\n", + " title='Florida Household (Two parents with two children) - Program Benefits by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Benefit Amount',\n", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 120000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "fig_florida = format_fig(fig_florida)\n", + "fig_florida.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cell-8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "Baseline", + "type": "scatter", + "x": [ + 0, + 164.7394256591797, + 329.4788513183594, + 494.21832275390625, + 658.9577026367188, + 823.6972045898438, + 988.4366455078125, + 1153.176025390625, + 1317.9154052734375, + 1482.6549072265625, + 1647.3944091796875, + 1812.1337890625, + 1976.873291015625, + 2141.61279296875, + 2306.35205078125, + 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x=household_income_florida,\n", + " y=baseline_florida_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Reform 1 (stepped)\n", + "fig_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=reform_florida_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# Reform 2 (linear)\n", + "fig_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=reform2_florida_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "# Reform 3 (IRA extension)\n", + "fig_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=reform3_florida_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "# Layout\n", + "fig_fl.update_layout(\n", + " title='Florida Household (Two parents with two children) – 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='$,.0f', range=[0, 120_000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "fig_fl = format_fig(fig_fl)\n", + "fig_fl.show()\n", + "\n", + "# --- Δ Health-adjusted net income (Reform – Baseline) ---\n", + "delta_fl = (\n", + " reform_florida_net_income_including_health_benefits\n", + " - baseline_florida_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_fl2 = (\n", + " reform2_florida_net_income_including_health_benefits\n", + " - baseline_florida_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_fl3 = (\n", + " reform3_florida_net_income_including_health_benefits\n", + " - baseline_florida_net_income_including_health_benefits\n", + ")\n", + "\n", + "fig_delta_fl = go.Figure()\n", + "\n", + "fig_delta_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=delta_fl,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_delta_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=delta_fl2,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_delta_fl.add_trace(go.Scatter(\n", + " x=household_income_florida,\n", + " y=delta_fl3,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_delta_fl.update_layout(\n", + " title='Florida Household (Two parents with two children) – Impact of Premium Tax Credit Proposals',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Δ Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 120_000]),\n", + " yaxis=dict(tickformat='$,.0f', zeroline=True, zerolinewidth=1),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_delta_fl = format_fig(fig_delta_fl)\n", + "fig_delta_fl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cell-9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Income: $%{x:,.0f}
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{}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# MTR CALCULATION FOR FLORIDA HOUSEHOLD\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_fl_for_mtr = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"}\n", + " \n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 120_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_fl_baseline = Simulation(situation=situation_fl_for_mtr)\n", + "household_income_fl_mtr = sim_fl_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_fl_net_income = sim_fl_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_fl_reform = Simulation(situation=situation_fl_for_mtr, reform=reform)\n", + "reform_fl_net_income = sim_fl_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3b: Calculate reform2 net income using axes\n", + "sim_fl_reform2 = Simulation(situation=situation_fl_for_mtr, reform=reform2)\n", + "reform2_fl_net_income = sim_fl_reform2.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3c: Calculate reform3 net income using axes\n", + "sim_fl_reform3 = Simulation(situation=situation_fl_for_mtr, reform=reform3)\n", + "reform3_fl_net_income = sim_fl_reform3.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_fl_mtr_incomes, baseline_fl_mtrs = calc_mtr(household_income_fl_mtr, baseline_fl_net_income)\n", + "reform_fl_mtr_incomes, reform_fl_mtrs = calc_mtr(household_income_fl_mtr, reform_fl_net_income)\n", + "reform2_fl_mtr_incomes, reform2_fl_mtrs = calc_mtr(household_income_fl_mtr, reform2_fl_net_income)\n", + "reform3_fl_mtr_incomes, reform3_fl_mtrs = calc_mtr(household_income_fl_mtr, reform3_fl_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_florida_mtr = go.Figure()\n", + "\n", + "fig_florida_mtr.add_trace(go.Scatter(\n", + " x=baseline_fl_mtr_incomes,\n", + " y=np.clip(baseline_fl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_florida_mtr.add_trace(go.Scatter(\n", + " x=reform_fl_mtr_incomes,\n", + " y=np.clip(reform_fl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Stepped MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_florida_mtr.add_trace(go.Scatter(\n", + " x=reform2_fl_mtr_incomes,\n", + " y=np.clip(reform2_fl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Linear MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_florida_mtr.add_trace(go.Scatter(\n", + " x=reform3_fl_mtr_incomes,\n", + " y=np.clip(reform3_fl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_florida_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - Florida family of 4',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 120_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_florida_mtr = format_fig(fig_florida_mtr)\n", + "fig_florida_mtr.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/us/blog_posts/aca_wis.ipynb b/us/blog_posts/aca_wis.ipynb new file mode 100644 index 0000000..29d113d --- /dev/null +++ b/us/blog_posts/aca_wis.ipynb @@ -0,0 +1,23703 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-0", + "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 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": 2, + "id": "cell-1", + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform2 = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_simplified_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "reform3 = 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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cell-2", + "metadata": {}, + "outputs": [], + "source": [ + "situation_wisconsin = {\n", + " \"people\": {\n", + " \"parent\": {\n", + " \"age\": {\n", + " \"2026\": 27\n", + " }\n", + " },\n", + " \"child\": {\n", + " \"age\": {\n", + " \"2026\": 3\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"parent\",\n", + " \"child\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"parent\",\n", + " \"child\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"parent\",\n", + " \"child\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"parent\",\n", + " \"child\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"WI\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"55079\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parent marital unit\": {\n", + " \"members\": [\n", + " \"parent\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 120000\n", + " }\n", + " ]\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cell-3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_wisconsin = Simulation(\n", + " situation=situation_wisconsin,\n", + ")\n", + "reformed_simulation_wisconsin = Simulation(\n", + " situation=situation_wisconsin,\n", + " reform=reform,\n", + ")\n", + "reformed_simulation_wisconsin2 = Simulation(\n", + " situation=situation_wisconsin,\n", + " reform=reform2,\n", + ")\n", + "reformed_simulation_wisconsin3 = Simulation(\n", + " situation=situation_wisconsin,\n", + " reform=reform3,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cell-4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_stepped_proposal \\\n", + "0 138 % FPL ($29,897.10) 29897.10 3623.720703 4710.216309 \n", + "1 300 % FPL ($64,993.70) 64993.70 0.000000 628.612061 \n", + "2 400 % FPL ($86,658.27) 86658.27 0.000000 0.000000 \n", + "\n", + " ptc_linear_proposal ptc_ira_extension \n", + "0 4219.903809 4710.216309 \n", + "1 0.000000 696.855469 \n", + "2 0.000000 782.626953 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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 Wisconsin single parent simulation\n", + "# ------------------------------------------------------------------\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"\n", + " Return ACA PTC (household-level) for the given income and year.\n", + " \"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + "\n", + " # Assign all income to the parent\n", + " sit[\"people\"][\"parent\"][\"employment_income\"] = {str(period): income}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\n", + "fpg_2026 = get_tax_unit_fpg(situation_wisconsin, 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, 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_wisconsin, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_stepped_proposal\": calc_ptc_for_income(situation_wisconsin, inc, reform_to_use=reform, period=PERIOD),\n", + " \"ptc_linear_proposal\": calc_ptc_for_income(situation_wisconsin, inc, reform_to_use=reform2, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_wisconsin, inc, reform_to_use=reform3, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values for Wisconsin\n", + "household_income_wisconsin = simulation_wisconsin.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_wisconsin_per_capita_chip = simulation_wisconsin.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "baseline_wisconsin_aca_ptc = simulation_wisconsin.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_wisconsin_medicaid_cost = simulation_wisconsin.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_wisconsin_net_income_including_health_benefits = simulation_wisconsin.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "baseline_wisconsin_slcsp = simulation_wisconsin.calculate(\"slcsp\", map_to=\"household\", period=2026)\n", + "\n", + "reform_wisconsin_per_capita_chip = reformed_simulation_wisconsin.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform_wisconsin_aca_ptc = reformed_simulation_wisconsin.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_wisconsin_medicaid_cost = reformed_simulation_wisconsin.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_wisconsin_net_income_including_health_benefits = reformed_simulation_wisconsin.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform2_wisconsin_per_capita_chip = reformed_simulation_wisconsin2.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform2_wisconsin_aca_ptc = reformed_simulation_wisconsin2.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform2_wisconsin_medicaid_cost = reformed_simulation_wisconsin2.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform2_wisconsin_net_income_including_health_benefits = reformed_simulation_wisconsin2.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "reform3_wisconsin_per_capita_chip = reformed_simulation_wisconsin3.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform3_wisconsin_aca_ptc = reformed_simulation_wisconsin3.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform3_wisconsin_medicaid_cost = reformed_simulation_wisconsin3.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform3_wisconsin_net_income_including_health_benefits = reformed_simulation_wisconsin3.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate total benefits for each scenario\n", + "baseline_wisconsin_total = [sum(x) for x in zip(baseline_wisconsin_per_capita_chip, baseline_wisconsin_aca_ptc, baseline_wisconsin_medicaid_cost)]\n", + "reform_wisconsin_total = [sum(x) for x in zip(reform_wisconsin_per_capita_chip, reform_wisconsin_aca_ptc, reform_wisconsin_medicaid_cost)]\n", + "reform2_wisconsin_total = [sum(x) for x in zip(reform2_wisconsin_per_capita_chip, reform2_wisconsin_aca_ptc, reform2_wisconsin_medicaid_cost)]\n", + "reform3_wisconsin_total = [sum(x) for x in zip(reform3_wisconsin_per_capita_chip, reform3_wisconsin_aca_ptc, reform3_wisconsin_medicaid_cost)]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cell-6", + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"\n", + "PURPLE = \"#9467BD\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cell-7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "ACA PTC (Baseline)", + "type": "scatter", + "x": [ + 0, + 164.7394256591797, + 329.4788513183594, + 494.21832275390625, + 658.9577026367188, + 823.6972045898438, + 988.4366455078125, + 1153.176025390625, + 1317.9154052734375, + 1482.6549072265625, + 1647.3944091796875, + 1812.1337890625, + 1976.873291015625, + 2141.61279296875, + 2306.35205078125, + 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"#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Wisconsin Household (Single parent, age 27, with child, age 3) - Program Benefits by Income Level" + }, + "width": 800, + "xaxis": { + "range": [ + 0, + 120000 + ], + "tickformat": "$,.0f", + "title": { + "text": "Household Income" + } + }, + "yaxis": { + "tickformat": "$,.0f", + "title": { + "text": "Benefit Amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create Wisconsin graph\n", + "fig_wisconsin = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\n", + "fig_wisconsin.add_trace(go.Scatter(\n", + " x=household_income_wisconsin, \n", + " y=baseline_wisconsin_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Baseline)', \n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Add reform traces\n", + "fig_wisconsin.add_trace(go.Scatter(\n", + " x=household_income_wisconsin, \n", + " y=reform_wisconsin_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Stepped Proposal)', \n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_wisconsin.add_trace(go.Scatter(\n", + " x=household_income_wisconsin, \n", + " y=reform2_wisconsin_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Linear Proposal)', \n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_wisconsin.add_trace(go.Scatter(\n", + " x=household_income_wisconsin, \n", + " y=reform3_wisconsin_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (IRA Extension)', \n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "\n", + "# Update layout\n", + "fig_wisconsin.update_layout(\n", + " title='Wisconsin Household (Single parent, age 27, with child, age 3) - Program Benefits by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Benefit Amount',\n", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 120000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + 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Baseline (solid)\n", + "fig_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=baseline_wisconsin_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# Reform 1 (stepped)\n", + "fig_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=reform_wisconsin_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# Reform 2 (linear)\n", + "fig_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=reform2_wisconsin_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "# Reform 3 (IRA extension)\n", + "fig_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=reform3_wisconsin_net_income_including_health_benefits,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "# Layout\n", + "fig_wi.update_layout(\n", + " title='Wisconsin Household (Single parent, age 27, with child, age 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='$,.0f', range=[0, 120_000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "fig_wi = format_fig(fig_wi)\n", + "fig_wi.show()\n", + "\n", + "# --- Δ Health-adjusted net income (Reform – Baseline) ---\n", + "delta_wi = (\n", + " reform_wisconsin_net_income_including_health_benefits\n", + " - baseline_wisconsin_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_wi2 = (\n", + " reform2_wisconsin_net_income_including_health_benefits\n", + " - baseline_wisconsin_net_income_including_health_benefits\n", + ")\n", + "\n", + "delta_wi3 = (\n", + " reform3_wisconsin_net_income_including_health_benefits\n", + " - baseline_wisconsin_net_income_including_health_benefits\n", + ")\n", + "\n", + "fig_delta_wi = go.Figure()\n", + "\n", + "fig_delta_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=delta_wi,\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "fig_delta_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=delta_wi2,\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2)\n", + "))\n", + "\n", + "fig_delta_wi.add_trace(go.Scatter(\n", + " x=household_income_wisconsin,\n", + " y=delta_wi3,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + 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"#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": "Marginal tax rate including health benefits - Wisconsin single parent (age 27) with child (age 3)" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 120000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal tax rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# MTR CALCULATION FOR WISCONSIN HOUSEHOLD\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_wi_for_mtr = {\n", + " \"people\": {\n", + " \"parent\": {\n", + " \"age\": {\"2026\": 27},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child\": {\n", + " \"age\": {\"2026\": 3},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent\", \"child\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent\", \"child\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent\", \"child\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent\", \"child\"],\n", + " \"state_name\": {\"2026\": \"WI\"},\n", + " \"county_fips\": {\"2026\": \"55079\"}\n", + " \n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parent marital unit\": {\n", + " \"members\": [\"parent\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 120_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_wi_baseline = Simulation(situation=situation_wi_for_mtr)\n", + "household_income_wi_mtr = sim_wi_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_wi_net_income = sim_wi_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_wi_reform = Simulation(situation=situation_wi_for_mtr, reform=reform)\n", + "reform_wi_net_income = sim_wi_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3b: Calculate reform2 net income using axes\n", + "sim_wi_reform2 = Simulation(situation=situation_wi_for_mtr, reform=reform2)\n", + "reform2_wi_net_income = sim_wi_reform2.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3c: Calculate reform3 net income using axes\n", + "sim_wi_reform3 = Simulation(situation=situation_wi_for_mtr, reform=reform3)\n", + "reform3_wi_net_income = sim_wi_reform3.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_wi_mtr_incomes, baseline_wi_mtrs = calc_mtr(household_income_wi_mtr, baseline_wi_net_income)\n", + "reform_wi_mtr_incomes, reform_wi_mtrs = calc_mtr(household_income_wi_mtr, reform_wi_net_income)\n", + "reform2_wi_mtr_incomes, reform2_wi_mtrs = calc_mtr(household_income_wi_mtr, reform2_wi_net_income)\n", + "reform3_wi_mtr_incomes, reform3_wi_mtrs = calc_mtr(household_income_wi_mtr, reform3_wi_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_wisconsin_mtr = go.Figure()\n", + "\n", + "fig_wisconsin_mtr.add_trace(go.Scatter(\n", + " x=baseline_wi_mtr_incomes,\n", + " y=np.clip(baseline_wi_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_wisconsin_mtr.add_trace(go.Scatter(\n", + " x=reform_wi_mtr_incomes,\n", + " y=np.clip(reform_wi_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Stepped Proposal',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Stepped MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_wisconsin_mtr.add_trace(go.Scatter(\n", + " x=reform2_wi_mtr_incomes,\n", + " y=np.clip(reform2_wi_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Linear Proposal',\n", + " line=dict(color=TEAL_ACCENT, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Linear MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_wisconsin_mtr.add_trace(go.Scatter(\n", + " x=reform3_wi_mtr_incomes,\n", + " y=np.clip(reform3_wi_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_wisconsin_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - Wisconsin single parent (age 27) with child (age 3)',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 120_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_wisconsin_mtr = format_fig(fig_wisconsin_mtr)\n", + "fig_wisconsin_mtr.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/us/blog_posts/california_ira_ext.ipynb b/us/blog_posts/california_ira_ext.ipynb new file mode 100644 index 0000000..f4ae438 --- /dev/null +++ b/us/blog_posts/california_ira_ext.ipynb @@ -0,0 +1,16301 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-0", + "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 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": 2, + "id": "cell-1", + "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": 3, + "id": "cell-2", + "metadata": {}, + "outputs": [], + "source": [ + "situation_california = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\n", + " \"2026\": 64\n", + " }\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\n", + " \"2026\": 62\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"CA\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"06069\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 400000\n", + " }\n", + " ]\n", + " ]\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cell-3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_california = Simulation(\n", + " situation=situation_california,\n", + ")\n", + "reformed_simulation_california = Simulation(\n", + " situation=situation_california,\n", + " reform=reform,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cell-4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_ira_reform\n", + "0 138 % FPL ($29,897.10) 29897.10 0.000000 0.000000\n", + "1 300 % FPL ($64,993.70) 64993.70 36241.796875 38701.808594\n", + "2 400 % FPL ($86,658.27) 86658.27 0.000000 35349.214844" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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 California couple simulation\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", + " sit = copy.deepcopy(base_situation)\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\"] = {str(period): income / 2}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform if use_reform else None)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\n", + "fpg_2026 = get_tax_unit_fpg(situation_california, 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, 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_california, inc, use_reform=False, period=PERIOD),\n", + " \"ptc_ira_reform\": calc_ptc_for_income(situation_california, inc, use_reform=True, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values for California\n", + "household_income_california = simulation_california.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_california_per_capita_chip = simulation_california.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "baseline_california_aca_ptc = simulation_california.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_california_medicaid_cost = simulation_california.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "baseline_california_net_income_including_health_benefits = simulation_california.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "baseline_california_slcsp = simulation_california.calculate(\"slcsp\", map_to=\"household\", period=2026)\n", + "\n", + "reform_california_per_capita_chip = reformed_simulation_california.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)\n", + "reform_california_aca_ptc = reformed_simulation_california.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_california_medicaid_cost = reformed_simulation_california.calculate(\"medicaid_cost\", map_to=\"household\", period=2026)\n", + "reform_california_net_income_including_health_benefits = reformed_simulation_california.calculate(\"household_net_income_including_health_benefits\", map_to=\"household\", period=2026)\n", + "\n", + "# Calculate total benefits for each scenario\n", + "baseline_california_total = [sum(x) for x in zip(baseline_california_per_capita_chip, baseline_california_aca_ptc, baseline_california_medicaid_cost)]\n", + "reform_california_total = [sum(x) for x in zip(reform_california_per_capita_chip, reform_california_aca_ptc, reform_california_medicaid_cost)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cell-6", + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cell-7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#2C6496", + "width": 2 + }, + "mode": "lines", + "name": "ACA PTC (Baseline)", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 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mode='lines', \n", + " name='ACA PTC (Reform)', \n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n", + "))\n", + "\n", + "# Add total lines\n", + "fig_california.add_trace(go.Scatter(\n", + " x=household_income_california, \n", + " y=baseline_california_total, \n", + " mode='lines', \n", + " name='Total Benefits (Baseline)', \n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_california.add_trace(go.Scatter(\n", + " x=household_income_california, \n", + " y=reform_california_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_california.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) - Program Benefits by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Benefit Amount',\n", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "fig_california = format_fig(fig_california)\n", + "fig_california.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cell-8", + "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", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 6589.57763671875, + 7138.708984375, + 7687.84033203125, + 8236.9716796875, + 8786.103515625, + 9335.2353515625, + 9884.3662109375, + 10433.4970703125, + 10982.62890625, + 11531.7607421875, + 12080.892578125, + 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(dotted)\n", + "fig_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=reform_california_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_ca.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) – 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", + "fig_ca = format_fig(fig_ca)\n", + "fig_ca.show()\n", + "\n", + "# --- Δ Health-adjusted net income (Reform – Baseline) ---\n", + "delta_ca = (\n", + " reform_california_net_income_including_health_benefits\n", + " - baseline_california_net_income_including_health_benefits\n", + ")\n", + "\n", + "fig_delta_ca = go.Figure()\n", + "\n", + "fig_delta_ca.add_trace(go.Scatter(\n", + " x=household_income_california,\n", + " y=delta_ca,\n", + " mode='lines',\n", + " name='Δ Net Income (Reform – Baseline)',\n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_delta_ca.update_layout(\n", + " title='California Household (Couple, ages 64 & 62) – 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_ca = format_fig(fig_delta_ca)\n", + "fig_delta_ca.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cell-9", + "metadata": {}, + "outputs": [ + { + "data": 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"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": "Marginal tax rate including health benefits - California couple (ages 64 & 62)" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 200000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal tax rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# MTR CALCULATION FOR CALIFORNIA HOUSEHOLD\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_ca_for_mtr = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 64},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 62},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"CA\"},\n", + " \"county_fips\": {\"2026\": \"06069\"}\n", + " \n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_ca_baseline = Simulation(situation=situation_ca_for_mtr)\n", + "household_income_ca_mtr = sim_ca_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_ca_net_income = sim_ca_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_ca_reform = Simulation(situation=situation_ca_for_mtr, reform=reform)\n", + "reform_ca_net_income = sim_ca_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_ca_mtr_incomes, baseline_ca_mtrs = calc_mtr(household_income_ca_mtr, baseline_ca_net_income)\n", + "reform_ca_mtr_incomes, reform_ca_mtrs = calc_mtr(household_income_ca_mtr, reform_ca_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_california_mtr = go.Figure()\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=baseline_ca_mtr_incomes,\n", + " y=np.clip(baseline_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=DARK_GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.add_trace(go.Scatter(\n", + " x=reform_ca_mtr_incomes,\n", + " y=np.clip(reform_ca_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),\n", + " hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_california_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - California couple (ages 64 & 62)',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_california_mtr = format_fig(fig_california_mtr)\n", + "fig_california_mtr.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/us/blog_posts/data/county_fips_2020.csv.gz b/us/blog_posts/data/county_fips_2020.csv.gz new file mode 100644 index 0000000..b8cd43c Binary files /dev/null and b/us/blog_posts/data/county_fips_2020.csv.gz differ diff --git a/us/blog_posts/ira_expire.ipynb b/us/blog_posts/ira_expire.ipynb new file mode 100644 index 0000000..bb11138 --- /dev/null +++ b/us/blog_posts/ira_expire.ipynb @@ -0,0 +1,2478 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "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": 2, + "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": 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", + "weights = baseline.calculate(\"household_weight\", period=2024)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31.522985547331057" + ] + }, + "execution_count": 4, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "215.80671373160038" + ] + }, + "execution_count": 5, + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21,627,010 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": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16,264,243 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": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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household_idStateMarriedNum_DependentsEmployment_Incomeaca_baselineaca_reform
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [household_id, State, Married, Num_Dependents, Employment_Income, aca_baseline, aca_reform]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a DataFrame with the outputs\n", + "data = {\n", + " \"household_id\": household_id,\n", + " \"State\": state,\n", + " \"Married\": married,\n", + " \"Num_Dependents\": num_dependents,\n", + " \"Employment_Income\": employment_income,\n", + " \"aca_baseline\": aca_baseline,\n", + " \"aca_reform\": aca_reform,\n", + "\n", + " }\n", + "\n", + "\n", + "df_outputs = pd.DataFrame(data)\n", + "df_outputs[df_outputs['household_id'] == 4428]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most positive net-income changes (PTC boosts):\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id State weight net_change wt_change\n", + "1 24 ME 28454.318359 0.0 0.0\n", + "4 39 ME 29125.925781 0.0 0.0\n", + "6 45 ME 28193.009766 0.0 0.0\n", + "9 93 ME 19098.552734 0.0 0.0\n", + "12 114 ME 15778.462891 0.0 0.0\n", + "16 154 ME 43222.703125 0.0 0.0\n", + "19 218 ME 25887.748047 0.0 0.0\n", + "23 238 ME 24463.904297 0.0 0.0\n", + "29 312 ME 10572.973633 0.0 0.0\n", + "30 316 ME 10667.022461 0.0 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------\n", + "# 0️⃣ Make sure the CPS household weight is in the DataFrame\n", + "# -------------------------------------------------------------\n", + "# If you already stuffed it in earlier, skip this.\n", + "df_outputs[\"weight\"] = aca_baseline.weights # aligns by household_id\n", + "\n", + "# -------------------------------------------------------------\n", + "# 1️⃣ Define a weight threshold for “reasonably representative”\n", + "# -------------------------------------------------------------\n", + "MIN_WT = 10_000 # ↖ try 5_000 if you want a looser cut\n", + "\n", + "df_big = df_outputs[df_outputs[\"weight\"] >= MIN_WT].copy()\n", + "\n", + "# -------------------------------------------------------------\n", + "# 2️⃣ Net PTC change and (optionally) weighted national impact\n", + "# -------------------------------------------------------------\n", + "df_big[\"net_change\"] = df_big[\"aca_reform\"] - df_big[\"aca_baseline\"]\n", + "df_big[\"wt_change\"] = df_big[\"net_change\"] * df_big[\"weight\"] # national $ impact\n", + "\n", + "# -------------------------------------------------------------\n", + "# 3️⃣ Biggest ↑ increases and ↓ decreases, LIMITED to big-weight HHs\n", + "# -------------------------------------------------------------\n", + "N = 10 # how many households to show in each direction\n", + "\n", + "cols = [\"household_id\", \"State\", \"weight\", \"net_change\", \"wt_change\"]\n", + "\n", + "top_increases = df_big.nlargest(N, \"net_change\")[cols]\n", + "top_decreases = df_big.nsmallest(N, \"net_change\")[cols]\n", + "\n", + "print(\"Most positive net-income changes (PTC boosts):\")\n", + "display(top_increases)\n", + "\n", + "print(\"\\nMost negative net-income changes (PTC cuts):\")\n", + "display(top_decreases)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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household_idStateMarriedNum_DependentsEmployment_Incomeaca_baselineaca_reformweight
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [household_id, State, Married, Num_Dependents, Employment_Income, aca_baseline, aca_reform, weight]\n", + "Index: []" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_outputs[df_outputs['household_id'] == 4428]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average weighted PTC change among households with any change: $2,255.30\n" + ] + } + ], + "source": [ + "# 0. Make sure net_change exists\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# 1. Flag households with any change\n", + "mask = df_outputs[\"net_change\"] != 0 # True for ↑ or ↓\n", + "\n", + "# 2. Weighted mean among those households\n", + "avg_net_change = (\n", + " (df_outputs.loc[mask, \"net_change\"] * df_outputs.loc[mask, \"weight\"]).sum()\n", + " / df_outputs.loc[mask, \"weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with any change: \"\n", + " f\"${avg_net_change:,.2f}\")\n" + ] + }, + { + "cell_type": "code", + "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: $1,720.00\n" + ] + } + ], + "source": [ + "# ------------------------------------------------------------------\n", + "# 0. Ensure supporting columns exist\n", + "# ------------------------------------------------------------------\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Keep only households with a PTC in *both* scenarios\n", + "# ------------------------------------------------------------------\n", + "mask_both_ptc = (df_outputs[\"aca_baseline\"] > 0) & (df_outputs[\"aca_reform\"] > 0)\n", + "df_dual_ptc = df_outputs[mask_both_ptc]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Weighted average of the net change (household perspective)\n", + "# ------------------------------------------------------------------\n", + "avg_net_change_dual_hh = (\n", + " (df_dual_ptc[\"net_change\"] * df_dual_ptc[\"weight\"]).sum()\n", + " / df_dual_ptc[\"weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with a PTC in both \"\n", + " f\"baseline and reform: ${avg_net_change_dual_hh:,.2f}\")\n" + ] + }, + { + "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: $3,958.29\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": [ + "29.94559487581596" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from policyengine_us import Simulation\n", + "\n", + "# -------------------------------\n", + "# 1. Pull household-level results\n", + "# -------------------------------\n", + "# ACA PTC (baseline and reform)\n", + "ptc_base = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "ptc_reform = reformed.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "\n", + "# Household weights (same for both sims)\n", + "hh_wt = baseline.calculate(\"household_weight\", map_to=\"household\", period=2026)\n", + "\n", + "# -------------------------------\n", + "# 2. Weighted sum of the change\n", + "# -------------------------------\n", + "weighted_total_change = ptc_reform - ptc_base\n", + "\n", + "# Optional: average change per household\n", + "weighted_total_change.sum()/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1jhns1uinylj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of households gaining PTC under reform: 735\n", + "Weighted count: 3,175,484\n", + "\n", + "Average reform PTC for these households: $5,537.77\n", + "Weighted average reform PTC: $3,958.29\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": 18, + "id": "kezjkjwshvl", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Income percentiles across all households:\n", + " 25th percentile: $8,652\n", + " 50th percentile: $58,135\n", + " 75th percentile: $120,658\n", + " 90th percentile: $207,460\n", + " 95th percentile: $286,710\n", + "\n", + "Income distribution of households GAINING PTC under reform:\n", + "count 735.000000\n", + "mean 112912.632981\n", + "std 74678.682403\n", + "min 0.000000\n", + "25% 69266.516724\n", + "50% 106398.339844\n", + "75% 146054.972656\n", + "max 673665.558594\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "Top 10 households by PTC gain (sorted by reform PTC amount):\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_reform Married \\\n", + "20341 169286 CA 62109.420319 32558.427734 1.0 \n", + "5649 36746 WV 98720.109375 29152.945312 1.0 \n", + "20840 173746 CA 105312.605469 28387.611328 1.0 \n", + "17529 136235 FL 53152.341797 26275.839844 1.0 \n", + "11669 80258 CA 27422.251953 25611.121094 1.0 \n", + "20327 169178 CA 148016.669434 23795.818359 1.0 \n", + "403 4526 MA 377878.640625 23117.976562 1.0 \n", + "2898 18181 IL 12065.791016 22556.402344 1.0 \n", + "3115 19282 IL 186473.511719 21926.943359 1.0 \n", + "12540 85894 CA 131626.812500 21483.894531 1.0 \n", + "\n", + " Num_Dependents weight \n", + "20341 1.0 0.000828 \n", + "5649 3.0 2988.435059 \n", + "20840 2.0 0.000374 \n", + "17529 1.0 0.003841 \n", + "11669 0.0 432.676544 \n", + "20327 0.0 0.004597 \n", + "403 0.0 800.343323 \n", + "2898 0.0 853.170044 \n", + "3115 0.0 32.112518 \n", + "12540 2.0 2954.752686 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 19, + "id": "qzjyh3eo44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Income percentiles across all households:\n", + " 25th percentile: $8,652\n", + " 50th percentile: $58,135\n", + " 75th percentile: $120,658\n", + " 90th percentile: $207,460\n", + " 95th percentile: $286,710\n", + "\n", + "============================================================\n", + "Income distribution of households GAINING PTC under reform:\n", + "============================================================\n", + "count 735.000000\n", + "mean 112912.632981\n", + "std 74678.682403\n", + "min 0.000000\n", + "25% 69266.516724\n", + "50% 106398.339844\n", + "75% 146054.972656\n", + "max 673665.558594\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "============================================================\n", + "Top 10 households by PTC gain (sorted by reform PTC amount):\n", + "============================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_reform Married \\\n", + "20341 169286 CA 62109.420319 32558.427734 1.0 \n", + "5649 36746 WV 98720.109375 29152.945312 1.0 \n", + "20840 173746 CA 105312.605469 28387.611328 1.0 \n", + "17529 136235 FL 53152.341797 26275.839844 1.0 \n", + "11669 80258 CA 27422.251953 25611.121094 1.0 \n", + "20327 169178 CA 148016.669434 23795.818359 1.0 \n", + "403 4526 MA 377878.640625 23117.976562 1.0 \n", + "2898 18181 IL 12065.791016 22556.402344 1.0 \n", + "3115 19282 IL 186473.511719 21926.943359 1.0 \n", + "12540 85894 CA 131626.812500 21483.894531 1.0 \n", + "\n", + " Num_Dependents weight \n", + "20341 1.0 0.000828 \n", + "5649 3.0 2988.435059 \n", + "20840 2.0 0.000374 \n", + "17529 1.0 0.003841 \n", + "11669 0.0 432.676544 \n", + "20327 0.0 0.004597 \n", + "403 0.0 800.343323 \n", + "2898 0.0 853.170044 \n", + "3115 0.0 32.112518 \n", + "12540 2.0 2954.752686 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's continue without matplotlib\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", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Income distribution of households GAINING PTC under reform:\")\n", + "print(\"=\"*60)\n", + "print(gained_ptc_with_income['Employment_Income'].describe())\n", + "\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Top 10 households by PTC gain (sorted by reform PTC amount):\")\n", + "print(\"=\"*60)\n", + "top_gainers = gained_ptc_with_income.nlargest(10, 'aca_reform')[['household_id', 'State', 'Employment_Income', 'aca_reform', 'Married', 'Num_Dependents', 'weight']]\n", + "display(top_gainers)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6ngx1hex7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Income percentiles across all households:\n", + " 25th percentile: $8,652\n", + " 50th percentile: $58,135\n", + " 75th percentile: $120,658\n", + " 90th percentile: $207,460\n", + " 95th percentile: $286,710\n", + "\n", + "============================================================\n", + "Income distribution of households GAINING PTC under reform:\n", + "============================================================\n", + "count 735.000000\n", + "mean 112912.632981\n", + "std 74678.682403\n", + "min 0.000000\n", + "25% 69266.516724\n", + "50% 106398.339844\n", + "75% 146054.972656\n", + "max 673665.558594\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "============================================================\n", + "Top 10 households by PTC gain (sorted by reform PTC amount):\n", + "============================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_reform Married \\\n", + "20341 169286 CA 62109.420319 32558.427734 1.0 \n", + "5649 36746 WV 98720.109375 29152.945312 1.0 \n", + "20840 173746 CA 105312.605469 28387.611328 1.0 \n", + "17529 136235 FL 53152.341797 26275.839844 1.0 \n", + "11669 80258 CA 27422.251953 25611.121094 1.0 \n", + "20327 169178 CA 148016.669434 23795.818359 1.0 \n", + "403 4526 MA 377878.640625 23117.976562 1.0 \n", + "2898 18181 IL 12065.791016 22556.402344 1.0 \n", + "3115 19282 IL 186473.511719 21926.943359 1.0 \n", + "12540 85894 CA 131626.812500 21483.894531 1.0 \n", + "\n", + " Num_Dependents weight \n", + "20341 1.0 0.000828 \n", + "5649 3.0 2988.435059 \n", + "20840 2.0 0.000374 \n", + "17529 1.0 0.003841 \n", + "11669 0.0 432.676544 \n", + "20327 0.0 0.004597 \n", + "403 0.0 800.343323 \n", + "2898 0.0 853.170044 \n", + "3115 0.0 32.112518 \n", + "12540 2.0 2954.752686 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's continue without matplotlib\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", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Income distribution of households GAINING PTC under reform:\")\n", + "print(\"=\"*60)\n", + "print(gained_ptc_with_income['Employment_Income'].describe())\n", + "\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Top 10 households by PTC gain (sorted by reform PTC amount):\")\n", + "print(\"=\"*60)\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": 21, + "id": "fbg7gtwvt09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Households LOSING or SEEING REDUCED PTC:\n", + "Number of households: 0\n", + "Weighted count: 0\n", + "\n", + "Average baseline PTC: $nan\n", + "Average reform PTC: $nan\n", + "Average loss: $nan\n", + "\n", + "Income distribution of households losing PTC benefits:\n", + "count 0.0\n", + "mean NaN\n", + "std NaN\n", + "min NaN\n", + "25% NaN\n", + "50% NaN\n", + "75% NaN\n", + "max NaN\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "Top 10 households by PTC loss:\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomeaca_baselineaca_reformnet_changeweight
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [household_id, State, Employment_Income, aca_baseline, aca_reform, net_change, weight]\n", + "Index: []" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now let's look at households losing PTC or seeing reduced PTC\n", + "lost_or_reduced = df_outputs[(df_outputs['aca_baseline'] > 0) & (df_outputs['net_change'] < 0)]\n", + "\n", + "print(\"Households LOSING or SEEING REDUCED PTC:\")\n", + "print(f\"Number of households: {len(lost_or_reduced)}\")\n", + "print(f\"Weighted count: {lost_or_reduced['weight'].sum():,.0f}\")\n", + "print(f\"\\nAverage baseline PTC: ${lost_or_reduced['aca_baseline'].mean():,.2f}\")\n", + "print(f\"Average reform PTC: ${lost_or_reduced['aca_reform'].mean():,.2f}\")\n", + "print(f\"Average loss: ${lost_or_reduced['net_change'].mean():,.2f}\")\n", + "\n", + "# Income distribution\n", + "print(\"\\nIncome distribution of households losing PTC benefits:\")\n", + "print(lost_or_reduced['Employment_Income'].describe())\n", + "\n", + "# Top losers\n", + "print(\"\\nTop 10 households by PTC loss:\")\n", + "top_losers = lost_or_reduced.nsmallest(10, 'net_change')[['household_id', 'State', 'Employment_Income', 'aca_baseline', 'aca_reform', 'net_change', 'weight']]\n", + "top_losers" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "y1a0d1tqy9n", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Households with PTC in BOTH baseline and reform:\n", + "Number of households: 2364\n", + "Weighted count: 10,102,365\n", + "\n", + "Average baseline PTC: $7,835.31\n", + "Average reform PTC: $9,869.01\n", + "Average change: $2,033.70\n", + "\n", + "Distribution of PTC changes for households with PTC in both scenarios:\n", + "count 2364.000000\n", + "mean 2033.699509\n", + "std 1636.354126\n", + "min 432.478516\n", + "25% 1331.057434\n", + "50% 1614.754150\n", + "75% 2295.041748\n", + "max 24952.986328\n", + "Name: net_change, dtype: float64\n", + "\n", + "Top 10 PTC increases among households who already had PTC:\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomeaca_baselineaca_reformnet_changeweight
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_baseline aca_reform \\\n", + "9799 65975 MT 137111.265625 4337.346680 29290.333008 \n", + "20290 168857 CA 161008.552246 2932.049805 25600.066895 \n", + "362 4364 MA 186471.316406 3176.394043 23121.227539 \n", + "19676 162981 AZ 165290.254883 10109.242188 28835.799805 \n", + "8810 58746 OK 32906.703125 4504.324219 22193.167969 \n", + "16643 128525 NC 156843.703125 2679.212402 19748.850586 \n", + "12583 86144 CA 68007.185547 3882.245605 20394.421387 \n", + "20689 172472 CA 294561.900269 3027.248291 19236.569824 \n", + "20581 171446 CA 212218.455322 6542.669434 21250.246094 \n", + "15666 114436 MO 65755.822266 6209.318848 19989.151855 \n", + "\n", + " net_change weight \n", + "9799 24952.986328 710.749634 \n", + "20290 22668.017090 0.001230 \n", + "362 19944.833496 2532.372803 \n", + "19676 18726.557617 0.004784 \n", + "8810 17688.843750 875.337952 \n", + "16643 17069.638184 0.000406 \n", + "12583 16512.175781 1215.460083 \n", + "20689 16209.321533 0.023022 \n", + "20581 14707.576660 0.000087 \n", + "15666 13779.833008 0.000095 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Interesting - no households lose PTC! Let's look at those who keep their PTC but see changes\n", + "kept_ptc = df_outputs[(df_outputs['aca_baseline'] > 0) & (df_outputs['aca_reform'] > 0)]\n", + "\n", + "print(\"Households with PTC in BOTH baseline and reform:\")\n", + "print(f\"Number of households: {len(kept_ptc)}\")\n", + "print(f\"Weighted count: {kept_ptc['weight'].sum():,.0f}\")\n", + "print(f\"\\nAverage baseline PTC: ${kept_ptc['aca_baseline'].mean():,.2f}\")\n", + "print(f\"Average reform PTC: ${kept_ptc['aca_reform'].mean():,.2f}\")\n", + "print(f\"Average change: ${kept_ptc['net_change'].mean():,.2f}\")\n", + "\n", + "# Show distribution of changes\n", + "print(\"\\nDistribution of PTC changes for households with PTC in both scenarios:\")\n", + "print(kept_ptc['net_change'].describe())\n", + "\n", + "# Households with biggest increases among those who already had PTC\n", + "print(\"\\nTop 10 PTC increases among households who already had PTC:\")\n", + "top_increases = kept_ptc.nlargest(10, 'net_change')[['household_id', 'State', 'Employment_Income', 'aca_baseline', 'aca_reform', 'net_change', 'weight']]\n", + "top_increases" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7pukgyq18zt", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "ANALYSIS OF THE 400% FPL CLIFF EFFECT\n", + "======================================================================\n", + "\n", + "Households between 350-450% FPL: 1970\n", + "Weighted count: 12,810,206\n", + "\n", + "Below 400% FPL (350-400%): 998 households\n", + " Average baseline PTC: $1,222.29\n", + " Average reform PTC: $1,694.37\n", + " Average change: $472.07\n", + "\n", + "Above 400% FPL (400-450%): 972 households\n", + " Average baseline PTC: $697.03\n", + " Average reform PTC: $1,504.48\n", + " Average change: $807.45\n", + "\n", + "======================================================================\n", + "EXAMPLE HOUSEHOLDS AT THE CLIFF (395-405% FPL):\n", + "======================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomefpl_ratioaca_baselineaca_reformnet_changeweight
4084553MA85557.425781404.9097298567.2324229705.4169921138.1845701038.525269
4234625MA85557.429688404.9097480.0000000.0000000.0000003414.698730
4834947MA151370.828125400.9823260.0000000.0000000.000000259.656281
7646696CT84460.539062399.7185950.0000000.0000000.000000579.064209
11989218NY106398.335938399.2432870.0000005805.8554695805.8554691150.545044
12919721NY62522.734375401.5589880.0000001747.4082031747.4082031066.176270
13149858NY127239.246094395.1529380.0000000.0000000.000000409.347443
150610965NJ174405.531250402.7841370.0000000.0000000.00000083.791580
167911953NJ128994.277344400.6033460.0000000.0000000.000000161909.406250
184912884PA105301.453125395.1274040.0000000.0000000.000000372.428925
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" + ], + "text/plain": [ + " household_id State Employment_Income fpl_ratio aca_baseline \\\n", + "408 4553 MA 85557.425781 404.909729 8567.232422 \n", + "423 4625 MA 85557.429688 404.909748 0.000000 \n", + "483 4947 MA 151370.828125 400.982326 0.000000 \n", + "764 6696 CT 84460.539062 399.718595 0.000000 \n", + "1198 9218 NY 106398.335938 399.243287 0.000000 \n", + "1291 9721 NY 62522.734375 401.558988 0.000000 \n", + "1314 9858 NY 127239.246094 395.152938 0.000000 \n", + "1506 10965 NJ 174405.531250 402.784137 0.000000 \n", + "1679 11953 NJ 128994.277344 400.603346 0.000000 \n", + "1849 12884 PA 105301.453125 395.127404 0.000000 \n", + "\n", + " aca_reform net_change weight \n", + "408 9705.416992 1138.184570 1038.525269 \n", + "423 0.000000 0.000000 3414.698730 \n", + "483 0.000000 0.000000 259.656281 \n", + "764 0.000000 0.000000 579.064209 \n", + "1198 5805.855469 5805.855469 1150.545044 \n", + "1291 1747.408203 1747.408203 1066.176270 \n", + "1314 0.000000 0.000000 409.347443 \n", + "1506 0.000000 0.000000 83.791580 \n", + "1679 0.000000 0.000000 161909.406250 \n", + "1849 0.000000 0.000000 372.428925 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's calculate approximate FPL levels for households to understand where they fall\n", + "# 2026 FPL estimates (rough approximations based on current trends)\n", + "fpl_2026 = {\n", + " 1: 15570, # Single person\n", + " 2: 21130, # Couple\n", + " 3: 26650, # Family of 3\n", + " 4: 32200, # Family of 4\n", + " 5: 37750, # Family of 5\n", + " 6: 43300, # Family of 6\n", + " 7: 48850, # Family of 7\n", + " 8: 54400, # Family of 8\n", + "}\n", + "\n", + "# Calculate household size and FPL ratio\n", + "df_outputs['household_size'] = 2 + df_outputs['Num_Dependents'] # Assuming married couples or singles with deps\n", + "df_outputs['household_size'] = df_outputs.apply(\n", + " lambda row: (1 + row['Married'] + row['Num_Dependents']) if not pd.isna(row['Married']) else 1,\n", + " axis=1\n", + ")\n", + "\n", + "# Map FPL based on household size\n", + "df_outputs['fpl_threshold'] = df_outputs['household_size'].map(lambda x: fpl_2026.get(min(int(x), 8), 54400))\n", + "df_outputs['fpl_ratio'] = (df_outputs['Employment_Income'] / df_outputs['fpl_threshold']) * 100\n", + "\n", + "# Now let's analyze the cliff effect around 400% FPL\n", + "print(\"=\"*70)\n", + "print(\"ANALYSIS OF THE 400% FPL CLIFF EFFECT\")\n", + "print(\"=\"*70)\n", + "\n", + "# Households just below and above 400% FPL\n", + "near_cliff = df_outputs[(df_outputs['fpl_ratio'] >= 350) & (df_outputs['fpl_ratio'] <= 450)]\n", + "print(f\"\\nHouseholds between 350-450% FPL: {len(near_cliff)}\")\n", + "print(f\"Weighted count: {near_cliff['weight'].sum():,.0f}\")\n", + "\n", + "# Split by those above and below 400% FPL\n", + "below_400 = near_cliff[near_cliff['fpl_ratio'] <= 400]\n", + "above_400 = near_cliff[near_cliff['fpl_ratio'] > 400]\n", + "\n", + "print(f\"\\nBelow 400% FPL (350-400%): {len(below_400)} households\")\n", + "print(f\" Average baseline PTC: ${below_400['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${below_400['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${below_400['net_change'].mean():,.2f}\")\n", + "\n", + "print(f\"\\nAbove 400% FPL (400-450%): {len(above_400)} households\")\n", + "print(f\" Average baseline PTC: ${above_400['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${above_400['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${above_400['net_change'].mean():,.2f}\")\n", + "\n", + "# Show some examples\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"EXAMPLE HOUSEHOLDS AT THE CLIFF (395-405% FPL):\")\n", + "print(\"=\"*70)\n", + "cliff_examples = df_outputs[(df_outputs['fpl_ratio'] >= 395) & (df_outputs['fpl_ratio'] <= 405)]\n", + "cliff_examples_display = cliff_examples[['household_id', 'State', 'Employment_Income', 'fpl_ratio', \n", + " 'aca_baseline', 'aca_reform', 'net_change', 'weight']].head(10)\n", + "cliff_examples_display" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "hmhah1unlwn", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Bin labels must be one fewer than the number of bin edges", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Let's look more specifically at the income deciles to see where the cliff effect shows up\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Calculate income deciles\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m df_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincome_decile\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_outputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEmployment_Income\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mduplicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdrop\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Group by decile and show the effect\u001b[39;00m\n\u001b[1;32m 6\u001b[0m decile_analysis \u001b[38;5;241m=\u001b[39m df_outputs\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincome_decile\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39magg({\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmployment_Income\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfpl_ratio\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 13\u001b[0m })\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m2\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/pandas/core/reshape/tile.py:340\u001b[0m, in \u001b[0;36mqcut\u001b[0;34m(x, q, labels, retbins, precision, duplicates)\u001b[0m\n\u001b[1;32m 336\u001b[0m quantiles \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, q \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m is_integer(q) \u001b[38;5;28;01melse\u001b[39;00m q\n\u001b[1;32m 338\u001b[0m bins \u001b[38;5;241m=\u001b[39m x_idx\u001b[38;5;241m.\u001b[39mto_series()\u001b[38;5;241m.\u001b[39mdropna()\u001b[38;5;241m.\u001b[39mquantile(quantiles)\n\u001b[0;32m--> 340\u001b[0m fac, bins \u001b[38;5;241m=\u001b[39m \u001b[43m_bins_to_cuts\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[43m \u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[43m \u001b[49m\u001b[43mIndex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbins\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 343\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43minclude_lowest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[43mduplicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mduplicates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 347\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _postprocess_for_cut(fac, bins, retbins, original)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/pandas/core/reshape/tile.py:493\u001b[0m, in \u001b[0;36m_bins_to_cuts\u001b[0;34m(x_idx, bins, right, labels, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(bins) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 493\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 494\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBin labels must be one fewer than the number of bin edges\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m )\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(labels, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m), CategoricalDtype):\n\u001b[1;32m 498\u001b[0m labels \u001b[38;5;241m=\u001b[39m Categorical(\n\u001b[1;32m 499\u001b[0m labels,\n\u001b[1;32m 500\u001b[0m categories\u001b[38;5;241m=\u001b[39mlabels \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mset\u001b[39m(labels)) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 501\u001b[0m ordered\u001b[38;5;241m=\u001b[39mordered,\n\u001b[1;32m 502\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Bin labels must be one fewer than the number of bin edges" + ] + } + ], + "source": [ + "# Let's look more specifically at the income deciles to see where the cliff effect shows up\n", + "# Calculate income deciles\n", + "df_outputs['income_decile'] = pd.qcut(df_outputs['Employment_Income'], 10, labels=range(1, 11), duplicates='drop')\n", + "\n", + "# Group by decile and show the effect\n", + "decile_analysis = df_outputs.groupby('income_decile').agg({\n", + " 'Employment_Income': ['min', 'max', 'mean'],\n", + " 'fpl_ratio': 'mean',\n", + " 'aca_baseline': 'mean',\n", + " 'aca_reform': 'mean',\n", + " 'net_change': 'mean',\n", + " 'weight': 'sum'\n", + "}).round(2)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"PTC EFFECTS BY INCOME DECILE\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome ranges and average PTC changes by decile:\")\n", + "decile_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "w09m1i1mc5q", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "PTC EFFECTS BY INCOME DECILE\n", + "======================================================================\n", + "\n", + "Income ranges and average PTC changes by decile:\n" + ] + }, + { + "data": { + "text/html": [ + "
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Employment_Incomefpl_ratioaca_baselineaca_reformnet_changeweight
minmaxmeanmeanmeanmeanmeansum
decile_num
10.0021614.792986.2516.03544.78692.51147.7356188157.74
221627.3838932.6530500.68160.761461.811780.01318.2014524743.72
338932.6658135.1748704.81249.331497.311976.75479.4313755435.36
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8207805.833337282.44389332.981570.64242.71522.10279.3911362332.47
\n", + "
" + ], + "text/plain": [ + " Employment_Income fpl_ratio aca_baseline \\\n", + " min max mean mean mean \n", + "decile_num \n", + "1 0.00 21614.79 2986.25 16.03 544.78 \n", + "2 21627.38 38932.65 30500.68 160.76 1461.81 \n", + "3 38932.66 58135.17 48704.81 249.33 1497.31 \n", + "4 58135.17 78976.09 68482.60 335.51 1372.72 \n", + "5 78976.09 105301.45 91639.26 426.03 1232.75 \n", + "6 105301.45 142595.71 121891.08 546.14 780.47 \n", + "7 142595.71 207312.23 168497.18 725.70 552.89 \n", + "8 207805.83 3337282.44 389332.98 1570.64 242.71 \n", + "\n", + " aca_reform net_change weight \n", + " mean mean sum \n", + "decile_num \n", + "1 692.51 147.73 56188157.74 \n", + "2 1780.01 318.20 14524743.72 \n", + "3 1976.75 479.43 13755435.36 \n", + "4 2015.76 643.04 14101366.32 \n", + "5 1864.86 632.11 11118736.15 \n", + "6 1552.48 772.01 10824528.05 \n", + "7 1191.69 638.81 13472984.82 \n", + "8 522.10 279.39 11362332.47 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fix the decile calculation\n", + "# Calculate income deciles without explicit labels to avoid the error\n", + "df_outputs['income_decile'] = pd.qcut(df_outputs['Employment_Income'], 10, duplicates='drop')\n", + "\n", + "# Get unique deciles and sort them\n", + "deciles = sorted(df_outputs['income_decile'].unique())\n", + "\n", + "# Create a mapping to simpler labels\n", + "decile_map = {d: i+1 for i, d in enumerate(deciles)}\n", + "df_outputs['decile_num'] = df_outputs['income_decile'].map(decile_map)\n", + "\n", + "# Group by decile and show the effect\n", + "decile_analysis = df_outputs.groupby('decile_num').agg({\n", + " 'Employment_Income': ['min', 'max', 'mean'],\n", + " 'fpl_ratio': 'mean',\n", + " 'aca_baseline': 'mean',\n", + " 'aca_reform': 'mean',\n", + " 'net_change': 'mean',\n", + " 'weight': 'sum'\n", + "}).round(2)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"PTC EFFECTS BY INCOME DECILE\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome ranges and average PTC changes by decile:\")\n", + "decile_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbfun838g2", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's look at where in the data the 9th decile falls (the one from the chart)\n", + "# Since we only have 8 groups due to duplicates being dropped, let's recalculate properly\n", + "\n", + "# First, let's understand the actual income distribution better\n", + "print(\"=\"*70)\n", + "print(\"UNDERSTANDING THE 9TH DECILE CONCENTRATION\")\n", + "print(\"=\"*70)\n", + "\n", + "# Get percentiles to understand income distribution\n", + "percentiles = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]\n", + "income_pcts = np.percentile(df_outputs['Employment_Income'], percentiles)\n", + "\n", + "print(\"\\nIncome distribution percentiles:\")\n", + "for p, val in zip(percentiles, income_pcts):\n", + " print(f\" {p}th percentile: ${val:,.0f}\")\n", + "\n", + "# The 9th decile should be roughly between 80th and 90th percentile\n", + "ninth_decile = df_outputs[(df_outputs['Employment_Income'] >= income_pcts[7]) & \n", + " (df_outputs['Employment_Income'] < income_pcts[8])]\n", + "\n", + "print(f\"\\n9th Decile (80-90th percentile):\")\n", + "print(f\" Income range: ${income_pcts[7]:,.0f} - ${income_pcts[8]:,.0f}\")\n", + "print(f\" Number of households: {len(ninth_decile)}\")\n", + "print(f\" Weighted count: {ninth_decile['weight'].sum():,.0f}\")\n", + "print(f\" Average FPL ratio: {ninth_decile['fpl_ratio'].mean():.1f}%\")\n", + "print(f\" Average baseline PTC: ${ninth_decile['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${ninth_decile['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${ninth_decile['net_change'].mean():,.2f}\")\n", + "\n", + "# Now let's see WHO specifically gains in the 9th decile\n", + "ninth_decile_gainers = ninth_decile[ninth_decile['net_change'] > 100] # Gains more than $100\n", + "\n", + "print(f\"\\nHouseholds in 9th decile with gains > $100:\")\n", + "print(f\" Count: {len(ninth_decile_gainers)}\")\n", + "print(f\" Average income: ${ninth_decile_gainers['Employment_Income'].mean():,.0f}\")\n", + "print(f\" Average FPL ratio: {ninth_decile_gainers['fpl_ratio'].mean():.1f}%\")\n", + "print(f\" Average gain: ${ninth_decile_gainers['net_change'].mean():,.2f}\")\n", + "\n", + "# Look at specific examples\n", + "print(\"\\nExample households in 9th decile with large gains:\")\n", + "examples = ninth_decile_gainers.nlargest(5, 'net_change')[\n", + " ['household_id', 'State', 'Employment_Income', 'fpl_ratio', \n", + " 'aca_baseline', 'aca_reform', 'net_change', 'Married', 'Num_Dependents']\n", + "]\n", + "examples" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "inz803s5rlm", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking available variables:\n", + "df_outputs exists: True\n", + "df_outputs shape: (21108, 14)\n", + "Columns: ['household_id', 'State', 'Married', 'Num_Dependents', 'Employment_Income', 'aca_baseline', 'aca_reform', 'weight', 'net_change', 'household_size', 'fpl_threshold', 'fpl_ratio', 'income_decile', 'decile_num']\n" + ] + } + ], + "source": [ + "# Check if the dataframe exists and has the needed columns\n", + "print(\"Checking available variables:\")\n", + "print(f\"df_outputs exists: {'df_outputs' in locals()}\")\n", + "if 'df_outputs' in locals():\n", + " print(f\"df_outputs shape: {df_outputs.shape}\")\n", + " print(f\"Columns: {list(df_outputs.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "qhtylcg4wz", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "UNDERSTANDING THE 9TH DECILE CONCENTRATION\n", + "======================================================================\n", + "\n", + "Income distribution percentiles:\n", + " 10th percentile: $0\n", + " 20th percentile: $0\n", + " 30th percentile: $21,615\n", + " 40th percentile: $38,933\n", + " 50th percentile: $58,135\n", + " 60th percentile: $78,976\n", + " 70th percentile: $105,301\n", + " 80th percentile: $142,596\n", + " 90th percentile: $207,460\n", + " 95th percentile: $286,710\n", + " 99th percentile: $577,545\n" + ] + } + ], + "source": [ + "# Understanding the 9th decile concentration\n", + "import numpy as np\n", + "\n", + "# Get percentiles to understand income distribution\n", + "percentiles = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]\n", + "income_pcts = np.percentile(df_outputs['Employment_Income'], percentiles)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"UNDERSTANDING THE 9TH DECILE CONCENTRATION\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome distribution percentiles:\")\n", + "for p, val in zip(percentiles, income_pcts):\n", + " print(f\" {p}th percentile: ${val:,.0f}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/blog_posts/ira_expire_old_data.ipynb b/us/blog_posts/ira_expire_old_data.ipynb new file mode 100644 index 0000000..12a92a1 --- /dev/null +++ b/us/blog_posts/ira_expire_old_data.ipynb @@ -0,0 +1,2457 @@ +{ + "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=\"/Users/daphnehansell/Documents/GitHub/analysis-notebooks/us/medicaid/enhanced_cps_2024.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "baseline = Microsimulation(dataset=\"/Users/daphnehansell/Documents/GitHub/analysis-notebooks/us/medicaid/enhanced_cps_2024.h5\")\n", + "reformed = Microsimulation(reform=reform, dataset=\"/Users/daphnehansell/Documents/GitHub/analysis-notebooks/us/medicaid/enhanced_cps_2024.h5\")\n", + "weights = baseline.calculate(\"household_weight\", period=2024)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "37.008340394072945" + ] + }, + "execution_count": 4, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "227.46342831824853" + ] + }, + "execution_count": 5, + "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": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "197,754,626 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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28,427,905 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": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20,115,724 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": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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6004428MA1.04.052859.656250.00.036551.855469
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" + ], + "text/plain": [ + " household_id State Married Num_Dependents Employment_Income \\\n", + "600 4428 MA 1.0 4.0 52859.65625 \n", + "\n", + " aca_baseline aca_reform weight \n", + "600 0.0 0.0 36551.855469 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_outputs[df_outputs['household_id'] == 4428]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average weighted PTC change among households with any change: $2,666.88\n" + ] + } + ], + "source": [ + "# 0. Make sure net_change exists\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# 1. Flag households with any change\n", + "mask = df_outputs[\"net_change\"] != 0 # True for ↑ or ↓\n", + "\n", + "# 2. Weighted mean among those households\n", + "avg_net_change = (\n", + " (df_outputs.loc[mask, \"net_change\"] * df_outputs.loc[mask, \"weight\"]).sum()\n", + " / df_outputs.loc[mask, \"weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with any change: \"\n", + " f\"${avg_net_change:,.2f}\")\n" + ] + }, + { + "cell_type": "code", + "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: $1,687.69\n" + ] + } + ], + "source": [ + "# ------------------------------------------------------------------\n", + "# 0. Ensure supporting columns exist\n", + "# ------------------------------------------------------------------\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Keep only households with a PTC in *both* scenarios\n", + "# ------------------------------------------------------------------\n", + "mask_both_ptc = (df_outputs[\"aca_baseline\"] > 0) & (df_outputs[\"aca_reform\"] > 0)\n", + "df_dual_ptc = df_outputs[mask_both_ptc]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Weighted average of the net change (household perspective)\n", + "# ------------------------------------------------------------------\n", + "avg_net_change_dual_hh = (\n", + " (df_dual_ptc[\"net_change\"] * df_dual_ptc[\"weight\"]).sum()\n", + " / df_dual_ptc[\"weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with a PTC in both \"\n", + " f\"baseline and reform: ${avg_net_change_dual_hh:,.2f}\")\n" + ] + }, + { + "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: $5,972.71\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": [ + "40.88992878180686" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from policyengine_us import Simulation\n", + "\n", + "# -------------------------------\n", + "# 1. Pull household-level results\n", + "# -------------------------------\n", + "# ACA PTC (baseline and reform)\n", + "ptc_base = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "ptc_reform = reformed.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "\n", + "# Household weights (same for both sims)\n", + "hh_wt = baseline.calculate(\"household_weight\", map_to=\"household\", period=2026)\n", + "\n", + "# -------------------------------\n", + "# 2. Weighted sum of the change\n", + "# -------------------------------\n", + "weighted_total_change = ptc_reform - ptc_base\n", + "\n", + "# Optional: average change per household\n", + "weighted_total_change.sum()/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'entity'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 21\u001b[0m\n\u001b[1;32m 16\u001b[0m NEG_COLOR \u001b[38;5;241m=\u001b[39m COLOR_DARK_GRAY\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# ------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# 1. Pull baseline / reform net income + weights\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# ------------------------------------------------------------------\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m net_base \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\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhousehold_net_income_including_health_benefits\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;43mhousehold\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;43m2026\u001b[39;49m\n\u001b[1;32m 23\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m net_reform \u001b[38;5;241m=\u001b[39m reformed\u001b[38;5;241m.\u001b[39mcalculate(\n\u001b[1;32m 25\u001b[0m \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 26\u001b[0m )\n\u001b[1;32m 27\u001b[0m weights \u001b[38;5;241m=\u001b[39m baseline\u001b[38;5;241m.\u001b[39mcalculate(\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhousehold_weight\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 29\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File 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\u001b[49m\u001b[43mmap_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 936\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:967\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 962\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m subtracted_variable \u001b[38;5;129;01min\u001b[39;00m subtracts_list:\n\u001b[1;32m 963\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 964\u001b[0m subtracted_variable\n\u001b[1;32m 965\u001b[0m \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[1;32m 966\u001b[0m ):\n\u001b[0;32m--> 967\u001b[0m values \u001b[38;5;241m=\u001b[39m values \u001b[38;5;241m-\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 968\u001b[0m \u001b[43m \u001b[49m\u001b[43msubtracted_variable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 969\u001b[0m \u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 970\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:932\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m added_variable \u001b[38;5;129;01min\u001b[39;00m adds_list:\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m added_variable \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--> 932\u001b[0m values \u001b[38;5;241m=\u001b[39m values \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43madded_variable\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[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 936\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:932\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m added_variable \u001b[38;5;129;01min\u001b[39;00m adds_list:\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m added_variable \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--> 932\u001b[0m values \u001b[38;5;241m=\u001b[39m values \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43madded_variable\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[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 936\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:997\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 995\u001b[0m array \u001b[38;5;241m=\u001b[39m formula(population, period)\n\u001b[1;32m 996\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 997\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43mformula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters_at\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 999\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\n", + "File \u001b[0;32m~/Documents/GitHub/policyengine-us/policyengine_us/variables/gov/states/oh/tax/income/oh_income_tax_before_refundable_credits.py:16\u001b[0m, in \u001b[0;36moh_income_tax_before_refundable_credits.formula\u001b[0;34m(tax_unit, period, parameters)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mformula\u001b[39m(tax_unit, period, parameters):\n\u001b[1;32m 13\u001b[0m itax_before_credits \u001b[38;5;241m=\u001b[39m tax_unit(\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moh_income_tax_before_non_refundable_credits\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\n\u001b[1;32m 15\u001b[0m )\n\u001b[0;32m---> 16\u001b[0m nonrefundable_credits \u001b[38;5;241m=\u001b[39m \u001b[43mtax_unit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moh_non_refundable_credits\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m max_(\u001b[38;5;241m0\u001b[39m, itax_before_credits \u001b[38;5;241m-\u001b[39m nonrefundable_credits)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/populations/group_population.py:38\u001b[0m, in \u001b[0;36mGroupPopulation.__call__\u001b[0;34m(self, variable_name, period, options)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmembers(variable_name, period, options))\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \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[38;5;21;43m__call__\u001b[39;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[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/populations/population.py:137\u001b[0m, in \u001b[0;36mPopulation.__call__\u001b[0;34m(self, variable_name, period, options)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mcalculate_divide(\n\u001b[1;32m 134\u001b[0m variable_name, period, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcalculate_kwargs\n\u001b[1;32m 135\u001b[0m )\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimulation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 138\u001b[0m \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[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcalculate_kwargs\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:932\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m added_variable \u001b[38;5;129;01min\u001b[39;00m adds_list:\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m added_variable \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--> 932\u001b[0m values \u001b[38;5;241m=\u001b[39m values \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43madded_variable\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[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 936\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/site-packages/policyengine_core/simulations/simulation.py:707\u001b[0m, in \u001b[0;36mSimulation._calculate\u001b[0;34m(self, variable_name, period)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_for_cycle(variable\u001b[38;5;241m.\u001b[39mname, period)\n\u001b[0;32m--> 707\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;66;03m# If no result, use the default value and cache it\u001b[39;00m\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 711\u001b[0m \u001b[38;5;66;03m# Check if the variable has a previously defined value\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/simulations/simulation.py:997\u001b[0m, in \u001b[0;36mSimulation._run_formula\u001b[0;34m(self, variable, population, period)\u001b[0m\n\u001b[1;32m 995\u001b[0m array \u001b[38;5;241m=\u001b[39m formula(population, period)\n\u001b[1;32m 996\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 997\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43mformula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters_at\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 999\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\n", + "File \u001b[0;32m~/Documents/GitHub/policyengine-us/policyengine_us/variables/gov/states/oh/tax/income/credits/retirement_income/oh_retirement_credit.py:17\u001b[0m, in \u001b[0;36moh_retirement_credit.formula\u001b[0;34m(tax_unit, period, parameters)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mformula\u001b[39m(tax_unit, period, parameters):\n\u001b[1;32m 14\u001b[0m retirement_credit \u001b[38;5;241m=\u001b[39m tax_unit(\n\u001b[1;32m 15\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moh_pension_based_retirement_income_credit\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\n\u001b[1;32m 16\u001b[0m )\n\u001b[0;32m---> 17\u001b[0m lump_sum_credit \u001b[38;5;241m=\u001b[39m \u001b[43mtax_unit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moh_lump_sum_retirement_credit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m max_(retirement_credit, lump_sum_credit)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/policyengine_core/populations/group_population.py:35\u001b[0m, in \u001b[0;36mGroupPopulation.__call__\u001b[0;34m(self, variable_name, period, options)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 28\u001b[0m variable_name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 29\u001b[0m period: Period \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 30\u001b[0m options: Optional[Container[\u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 31\u001b[0m ):\n\u001b[1;32m 32\u001b[0m variable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mtax_benefit_system\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 33\u001b[0m variable_name\n\u001b[1;32m 34\u001b[0m )\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mentity\u001b[49m\u001b[38;5;241m.\u001b[39mis_person:\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmembers(variable_name, period, options))\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'entity'" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Brand hex codes (one-to-one with style.colors)\n", + "# ------------------------------------------------------------------\n", + "COLOR_BLUE = \"#2C6496\" # style.colors.BLUE / BLUE_PRIMARY\n", + "COLOR_BLUE_LIGHT = \"#D8E6F3\" # style.colors.BLUE_LIGHT / BLUE_95\n", + "COLOR_LIGHT_GRAY = \"#F2F2F2\" # style.colors.LIGHT_GRAY\n", + "COLOR_MEDIUM_LIGHT_GRAY = \"#BDBDBD\" # style.colors.MEDIUM_LIGHT_GRAY\n", + "COLOR_DARK_GRAY = \"#616161\" # style.colors.DARK_GRAY\n", + "\n", + "# ––– choose colours for positive vs. negative average bars –––\n", + "POS_COLOR = COLOR_BLUE\n", + "NEG_COLOR = COLOR_DARK_GRAY\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Pull baseline / reform net income + weights\n", + "# ------------------------------------------------------------------\n", + "net_base = baseline.calculate(\n", + " \"household_net_income_including_health_benefits\", map_to=\"household\", period=2026\n", + ")\n", + "net_reform = reformed.calculate(\n", + " \"household_net_income_including_health_benefits\", map_to=\"household\", period=2026\n", + ")\n", + "weights = baseline.calculate(\n", + " \"household_weight\", map_to=\"household\", period=2026\n", + ")\n", + "\n", + "df = pd.DataFrame({\n", + " \"net_base\": net_base,\n", + " \"delta\": net_reform - net_base,\n", + " \"weight\": weights,\n", + "})\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Weighted decile edges (baseline ranking)\n", + "# ------------------------------------------------------------------\n", + "def wquantile(values, qs, w):\n", + " srt = np.argsort(values)\n", + " values, w = values[srt], w[srt]\n", + " cum_w = np.cumsum(w) / np.sum(w)\n", + " return np.interp(qs, cum_w, values)\n", + "\n", + "edges = wquantile(df[\"net_base\"].values,\n", + " np.linspace(0, 1, 11), df[\"weight\"].values)\n", + "\n", + "df[\"decile\"] = pd.cut(df[\"net_base\"],\n", + " bins=edges,\n", + " labels=np.arange(1, 11),\n", + " include_lowest=True)\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 3. Weighted average Δnet-income by decile\n", + "# ------------------------------------------------------------------\n", + "decile_avg = (\n", + " df.groupby(\"decile\")\n", + " .apply(lambda g: np.average(g[\"delta\"], weights=g[\"weight\"]))\n", + " .reset_index(name=\"avg_change\")\n", + ")\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 4. Use brand colours: blue if gain, dark-gray if loss\n", + "# ------------------------------------------------------------------\n", + "bar_colors = [\n", + " POS_COLOR if v >= 0 else NEG_COLOR\n", + " for v in decile_avg[\"avg_change\"]\n", + "]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 5. Plot\n", + "# ------------------------------------------------------------------\n", + "fig = go.Figure(\n", + " data=[\n", + " go.Bar(\n", + " x=decile_avg[\"decile\"].astype(int),\n", + " y=decile_avg[\"avg_change\"],\n", + " marker_color=bar_colors,\n", + " text=decile_avg[\"avg_change\"].apply(lambda v: f\"${v:,.0f}\"),\n", + " textposition=\"inside\",\n", + " )\n", + " ],\n", + " layout=dict(\n", + " title=\"Impact of Extending IRA PTC Expansion by Income Decile – 2026\",\n", + " xaxis_title=\"Income Decile\",\n", + " yaxis_title=\"Average change in household net income ($)\",\n", + " showlegend=False,\n", + " )\n", + ")\n", + "fig.add_hline(y=0, line_width=1, line_color=\"black\")\n", + "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: 1100\n", + "Weighted count: 3,503,693\n", + "\n", + "Average reform PTC for these households: $5,707.33\n", + "Weighted average reform PTC: $5,972.71\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": "qzjyh3eo44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Income percentiles across all households:\n", + " 25th percentile: $6,159\n", + " 50th percentile: $68,947\n", + " 75th percentile: $172,785\n", + " 90th percentile: $504,951\n", + " 95th percentile: $2,181,010\n", + "\n", + "============================================================\n", + "Income distribution of households GAINING PTC under reform:\n", + "============================================================\n", + "count 1.100000e+03\n", + "mean 5.555715e+05\n", + "std 4.699500e+06\n", + "min 0.000000e+00\n", + "25% 7.411113e+04\n", + "50% 1.099418e+05\n", + "75% 1.631940e+05\n", + "max 1.033826e+08\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "============================================================\n", + "Top 10 households by PTC gain (sorted by reform PTC amount):\n", + "============================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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2165595958CT586290.41992229020.2460941.00.00.000351
2092692239VT169552.75390628546.5488281.02.02193.554443
1613569304WY172368.44140627582.3437501.03.01964.266357
704625635MO37921.05859427411.4218751.00.01342.842773
27701115102MO41373.52490227333.1875001.00.00.002778
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705825669MO0.00000026257.6699221.00.02463.500000
34720149164OK76835.16406226118.7695311.00.00.004654
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_reform Married \\\n", + "21655 95958 CT 586290.419922 29020.246094 1.0 \n", + "20926 92239 VT 169552.753906 28546.548828 1.0 \n", + "16135 69304 WY 172368.441406 27582.343750 1.0 \n", + "7046 25635 MO 37921.058594 27411.421875 1.0 \n", + "27701 115102 MO 41373.524902 27333.187500 1.0 \n", + "14065 59697 OK 68947.382812 26790.708984 1.0 \n", + "7058 25669 MO 0.000000 26257.669922 1.0 \n", + "34720 149164 OK 76835.164062 26118.769531 1.0 \n", + "19738 84350 CA 88482.460938 25993.980469 1.0 \n", + "40316 173580 CA 102149.837891 25538.492188 1.0 \n", + "\n", + " Num_Dependents weight \n", + "21655 0.0 0.000351 \n", + "20926 2.0 2193.554443 \n", + "16135 3.0 1964.266357 \n", + "7046 0.0 1342.842773 \n", + "27701 0.0 0.002778 \n", + "14065 0.0 3511.932373 \n", + "7058 0.0 2463.500000 \n", + "34720 0.0 0.004654 \n", + "19738 0.0 334.342865 \n", + "40316 0.0 0.002264 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's continue without matplotlib\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", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Income distribution of households GAINING PTC under reform:\")\n", + "print(\"=\"*60)\n", + "print(gained_ptc_with_income['Employment_Income'].describe())\n", + "\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Top 10 households by PTC gain (sorted by reform PTC amount):\")\n", + "print(\"=\"*60)\n", + "top_gainers = gained_ptc_with_income.nlargest(10, 'aca_reform')[['household_id', 'State', 'Employment_Income', 'aca_reform', 'Married', 'Num_Dependents', 'weight']]\n", + "display(top_gainers)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6ngx1hex7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Income percentiles across all households:\n", + " 25th percentile: $6,159\n", + " 50th percentile: $68,947\n", + " 75th percentile: $172,785\n", + " 90th percentile: $504,951\n", + " 95th percentile: $2,181,010\n", + "\n", + "============================================================\n", + "Income distribution of households GAINING PTC under reform:\n", + "============================================================\n", + "count 1.100000e+03\n", + "mean 5.555715e+05\n", + "std 4.699500e+06\n", + "min 0.000000e+00\n", + "25% 7.411113e+04\n", + "50% 1.099418e+05\n", + "75% 1.631940e+05\n", + "max 1.033826e+08\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "============================================================\n", + "Top 10 households by PTC gain (sorted by reform PTC amount):\n", + "============================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomeaca_reformMarriedNum_Dependentsweight
2165595958CT586290.41992229020.2460941.00.00.000351
2092692239VT169552.75390628546.5488281.02.02193.554443
1613569304WY172368.44140627582.3437501.03.01964.266357
704625635MO37921.05859427411.4218751.00.01342.842773
27701115102MO41373.52490227333.1875001.00.00.002778
1406559697OK68947.38281226790.7089841.00.03511.932373
705825669MO0.00000026257.6699221.00.02463.500000
34720149164OK76835.16406226118.7695311.00.00.004654
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_reform Married \\\n", + "21655 95958 CT 586290.419922 29020.246094 1.0 \n", + "20926 92239 VT 169552.753906 28546.548828 1.0 \n", + "16135 69304 WY 172368.441406 27582.343750 1.0 \n", + "7046 25635 MO 37921.058594 27411.421875 1.0 \n", + "27701 115102 MO 41373.524902 27333.187500 1.0 \n", + "14065 59697 OK 68947.382812 26790.708984 1.0 \n", + "7058 25669 MO 0.000000 26257.669922 1.0 \n", + "34720 149164 OK 76835.164062 26118.769531 1.0 \n", + "19738 84350 CA 88482.460938 25993.980469 1.0 \n", + "40316 173580 CA 102149.837891 25538.492188 1.0 \n", + "\n", + " Num_Dependents weight \n", + "21655 0.0 0.000351 \n", + "20926 2.0 2193.554443 \n", + "16135 3.0 1964.266357 \n", + "7046 0.0 1342.842773 \n", + "27701 0.0 0.002778 \n", + "14065 0.0 3511.932373 \n", + "7058 0.0 2463.500000 \n", + "34720 0.0 0.004654 \n", + "19738 0.0 334.342865 \n", + "40316 0.0 0.002264 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's continue without matplotlib\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", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Income distribution of households GAINING PTC under reform:\")\n", + "print(\"=\"*60)\n", + "print(gained_ptc_with_income['Employment_Income'].describe())\n", + "\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Top 10 households by PTC gain (sorted by reform PTC amount):\")\n", + "print(\"=\"*60)\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": 22, + "id": "fbg7gtwvt09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Households LOSING or SEEING REDUCED PTC:\n", + "Number of households: 0\n", + "Weighted count: 0\n", + "\n", + "Average baseline PTC: $nan\n", + "Average reform PTC: $nan\n", + "Average loss: $nan\n", + "\n", + "Income distribution of households losing PTC benefits:\n", + "count 0.0\n", + "mean NaN\n", + "std NaN\n", + "min NaN\n", + "25% NaN\n", + "50% NaN\n", + "75% NaN\n", + "max NaN\n", + "Name: Employment_Income, dtype: float64\n", + "\n", + "Top 10 households by PTC loss:\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomeaca_baselineaca_reformnet_changeweight
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [household_id, State, Employment_Income, aca_baseline, aca_reform, net_change, weight]\n", + "Index: []" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now let's look at households losing PTC or seeing reduced PTC\n", + "lost_or_reduced = df_outputs[(df_outputs['aca_baseline'] > 0) & (df_outputs['net_change'] < 0)]\n", + "\n", + "print(\"Households LOSING or SEEING REDUCED PTC:\")\n", + "print(f\"Number of households: {len(lost_or_reduced)}\")\n", + "print(f\"Weighted count: {lost_or_reduced['weight'].sum():,.0f}\")\n", + "print(f\"\\nAverage baseline PTC: ${lost_or_reduced['aca_baseline'].mean():,.2f}\")\n", + "print(f\"Average reform PTC: ${lost_or_reduced['aca_reform'].mean():,.2f}\")\n", + "print(f\"Average loss: ${lost_or_reduced['net_change'].mean():,.2f}\")\n", + "\n", + "# Income distribution\n", + "print(\"\\nIncome distribution of households losing PTC benefits:\")\n", + "print(lost_or_reduced['Employment_Income'].describe())\n", + "\n", + "# Top losers\n", + "print(\"\\nTop 10 households by PTC loss:\")\n", + "top_losers = lost_or_reduced.nsmallest(10, 'net_change')[['household_id', 'State', 'Employment_Income', 'aca_baseline', 'aca_reform', 'net_change', 'weight']]\n", + "top_losers" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "y1a0d1tqy9n", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Households with PTC in BOTH baseline and reform:\n", + "Number of households: 3406\n", + "Weighted count: 11,828,817\n", + "\n", + "Average baseline PTC: $7,582.03\n", + "Average reform PTC: $9,510.45\n", + "Average change: $1,928.42\n", + "\n", + "Distribution of PTC changes for households with PTC in both scenarios:\n", + "count 3406.000000\n", + "mean 1928.417004\n", + "std 1464.484689\n", + "min 433.568359\n", + "25% 1265.441895\n", + "50% 1612.739014\n", + "75% 2209.899292\n", + "max 24195.677979\n", + "Name: net_change, dtype: float64\n", + "\n", + "Top 10 PTC increases among households who already had PTC:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id State Employment_Income aca_baseline aca_reform \\\n", + "25895 109280 IL 135262.814827 2166.071045 26361.749023 \n", + "21501 95388 CT 229315.603271 2461.534180 25936.906494 \n", + "20625 88926 HI 119508.789062 3365.458740 25774.730469 \n", + "6106 22572 WI 91929.835449 5390.636230 26178.041016 \n", + "15170 63406 TX 56307.025391 3461.937988 23926.217285 \n", + "32728 138331 KY 143819.542969 2214.557861 20885.525879 \n", + "16147 69708 WY 40219.304688 7641.085938 23462.459961 \n", + "36802 159175 WY 322659.273438 6483.185059 22133.864258 \n", + "20237 85992 CA 264716.572266 2762.978271 17937.049805 \n", + "39405 170489 CA 200459.942383 3582.745850 17706.862793 \n", + "\n", + " net_change weight \n", + "25895 24195.677979 0.005656 \n", + "21501 23475.372314 0.000415 \n", + "20625 22409.271729 729.324707 \n", + "6106 20787.404785 6035.339844 \n", + "15170 20464.279297 36817.671875 \n", + "32728 18670.968018 0.046630 \n", + "16147 15821.374023 1604.936035 \n", + "36802 15650.679199 1445.953613 \n", + "20237 15174.071533 5193.151367 \n", + "39405 14124.116943 0.002236 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Interesting - no households lose PTC! Let's look at those who keep their PTC but see changes\n", + "kept_ptc = df_outputs[(df_outputs['aca_baseline'] > 0) & (df_outputs['aca_reform'] > 0)]\n", + "\n", + "print(\"Households with PTC in BOTH baseline and reform:\")\n", + "print(f\"Number of households: {len(kept_ptc)}\")\n", + "print(f\"Weighted count: {kept_ptc['weight'].sum():,.0f}\")\n", + "print(f\"\\nAverage baseline PTC: ${kept_ptc['aca_baseline'].mean():,.2f}\")\n", + "print(f\"Average reform PTC: ${kept_ptc['aca_reform'].mean():,.2f}\")\n", + "print(f\"Average change: ${kept_ptc['net_change'].mean():,.2f}\")\n", + "\n", + "# Show distribution of changes\n", + "print(\"\\nDistribution of PTC changes for households with PTC in both scenarios:\")\n", + "print(kept_ptc['net_change'].describe())\n", + "\n", + "# Households with biggest increases among those who already had PTC\n", + "print(\"\\nTop 10 PTC increases among households who already had PTC:\")\n", + "top_increases = kept_ptc.nlargest(10, 'net_change')[['household_id', 'State', 'Employment_Income', 'aca_baseline', 'aca_reform', 'net_change', 'weight']]\n", + "top_increases" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7pukgyq18zt", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "ANALYSIS OF THE 400% FPL CLIFF EFFECT\n", + "======================================================================\n", + "\n", + "Households between 350-450% FPL: 3110\n", + "Weighted count: 12,367,979\n", + "\n", + "Below 400% FPL (350-400%): 1565 households\n", + " Average baseline PTC: $1,110.95\n", + " Average reform PTC: $1,612.20\n", + " Average change: $501.25\n", + "\n", + "Above 400% FPL (400-450%): 1545 households\n", + " Average baseline PTC: $686.16\n", + " Average reform PTC: $1,385.85\n", + " Average change: $699.69\n", + "\n", + "======================================================================\n", + "EXAMPLE HOUSEHOLDS AT THE CLIFF (395-405% FPL):\n", + "======================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idStateEmployment_Incomefpl_ratioaca_baselineaca_reformnet_changeweight
1291ME85344.212891403.9006760.00.00.04.466252
61495ME105351.593262395.3155470.00.00.017.447075
2812971VT85035.097656402.4377550.00.00.0332.319000
4763945MA106868.429688401.0072410.00.00.057237.523438
6614662MA62052.640625398.5397600.00.00.07.987278
8065462RI106868.433594401.0072560.00.00.09386.629883
9076168CT62052.640625398.5397600.00.00.024166.259766
10816785NY84344.468750399.1692790.00.00.04.944809
11046872NY85035.101562402.4377740.00.00.010.947828
11066874NY62052.640625398.5397600.00.00.04.106772
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" + ], + "text/plain": [ + " household_id State Employment_Income fpl_ratio aca_baseline \\\n", + "12 91 ME 85344.212891 403.900676 0.0 \n", + "61 495 ME 105351.593262 395.315547 0.0 \n", + "281 2971 VT 85035.097656 402.437755 0.0 \n", + "476 3945 MA 106868.429688 401.007241 0.0 \n", + "661 4662 MA 62052.640625 398.539760 0.0 \n", + "806 5462 RI 106868.433594 401.007256 0.0 \n", + "907 6168 CT 62052.640625 398.539760 0.0 \n", + "1081 6785 NY 84344.468750 399.169279 0.0 \n", + "1104 6872 NY 85035.101562 402.437774 0.0 \n", + "1106 6874 NY 62052.640625 398.539760 0.0 \n", + "\n", + " aca_reform net_change weight \n", + "12 0.0 0.0 4.466252 \n", + "61 0.0 0.0 17.447075 \n", + "281 0.0 0.0 332.319000 \n", + "476 0.0 0.0 57237.523438 \n", + "661 0.0 0.0 7.987278 \n", + "806 0.0 0.0 9386.629883 \n", + "907 0.0 0.0 24166.259766 \n", + "1081 0.0 0.0 4.944809 \n", + "1104 0.0 0.0 10.947828 \n", + "1106 0.0 0.0 4.106772 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's calculate approximate FPL levels for households to understand where they fall\n", + "# 2026 FPL estimates (rough approximations based on current trends)\n", + "fpl_2026 = {\n", + " 1: 15570, # Single person\n", + " 2: 21130, # Couple\n", + " 3: 26650, # Family of 3\n", + " 4: 32200, # Family of 4\n", + " 5: 37750, # Family of 5\n", + " 6: 43300, # Family of 6\n", + " 7: 48850, # Family of 7\n", + " 8: 54400, # Family of 8\n", + "}\n", + "\n", + "# Calculate household size and FPL ratio\n", + "df_outputs['household_size'] = 2 + df_outputs['Num_Dependents'] # Assuming married couples or singles with deps\n", + "df_outputs['household_size'] = df_outputs.apply(\n", + " lambda row: (1 + row['Married'] + row['Num_Dependents']) if not pd.isna(row['Married']) else 1,\n", + " axis=1\n", + ")\n", + "\n", + "# Map FPL based on household size\n", + "df_outputs['fpl_threshold'] = df_outputs['household_size'].map(lambda x: fpl_2026.get(min(int(x), 8), 54400))\n", + "df_outputs['fpl_ratio'] = (df_outputs['Employment_Income'] / df_outputs['fpl_threshold']) * 100\n", + "\n", + "# Now let's analyze the cliff effect around 400% FPL\n", + "print(\"=\"*70)\n", + "print(\"ANALYSIS OF THE 400% FPL CLIFF EFFECT\")\n", + "print(\"=\"*70)\n", + "\n", + "# Households just below and above 400% FPL\n", + "near_cliff = df_outputs[(df_outputs['fpl_ratio'] >= 350) & (df_outputs['fpl_ratio'] <= 450)]\n", + "print(f\"\\nHouseholds between 350-450% FPL: {len(near_cliff)}\")\n", + "print(f\"Weighted count: {near_cliff['weight'].sum():,.0f}\")\n", + "\n", + "# Split by those above and below 400% FPL\n", + "below_400 = near_cliff[near_cliff['fpl_ratio'] <= 400]\n", + "above_400 = near_cliff[near_cliff['fpl_ratio'] > 400]\n", + "\n", + "print(f\"\\nBelow 400% FPL (350-400%): {len(below_400)} households\")\n", + "print(f\" Average baseline PTC: ${below_400['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${below_400['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${below_400['net_change'].mean():,.2f}\")\n", + "\n", + "print(f\"\\nAbove 400% FPL (400-450%): {len(above_400)} households\")\n", + "print(f\" Average baseline PTC: ${above_400['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${above_400['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${above_400['net_change'].mean():,.2f}\")\n", + "\n", + "# Show some examples\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"EXAMPLE HOUSEHOLDS AT THE CLIFF (395-405% FPL):\")\n", + "print(\"=\"*70)\n", + "cliff_examples = df_outputs[(df_outputs['fpl_ratio'] >= 395) & (df_outputs['fpl_ratio'] <= 405)]\n", + "cliff_examples_display = cliff_examples[['household_id', 'State', 'Employment_Income', 'fpl_ratio', \n", + " 'aca_baseline', 'aca_reform', 'net_change', 'weight']].head(10)\n", + "cliff_examples_display" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "hmhah1unlwn", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Bin labels must be one fewer than the number of bin edges", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Let's look more specifically at the income deciles to see where the cliff effect shows up\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Calculate income deciles\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m df_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincome_decile\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_outputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEmployment_Income\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mduplicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdrop\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Group by decile and show the effect\u001b[39;00m\n\u001b[1;32m 6\u001b[0m decile_analysis \u001b[38;5;241m=\u001b[39m df_outputs\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincome_decile\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39magg({\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmployment_Income\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfpl_ratio\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 13\u001b[0m })\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m2\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/pandas/core/reshape/tile.py:340\u001b[0m, in \u001b[0;36mqcut\u001b[0;34m(x, q, labels, retbins, precision, duplicates)\u001b[0m\n\u001b[1;32m 336\u001b[0m quantiles \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, q \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m is_integer(q) \u001b[38;5;28;01melse\u001b[39;00m q\n\u001b[1;32m 338\u001b[0m bins \u001b[38;5;241m=\u001b[39m x_idx\u001b[38;5;241m.\u001b[39mto_series()\u001b[38;5;241m.\u001b[39mdropna()\u001b[38;5;241m.\u001b[39mquantile(quantiles)\n\u001b[0;32m--> 340\u001b[0m fac, bins \u001b[38;5;241m=\u001b[39m \u001b[43m_bins_to_cuts\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[43m \u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[43m \u001b[49m\u001b[43mIndex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbins\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 343\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43minclude_lowest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[43mduplicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mduplicates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 347\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _postprocess_for_cut(fac, bins, retbins, original)\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/site-packages/pandas/core/reshape/tile.py:493\u001b[0m, in \u001b[0;36m_bins_to_cuts\u001b[0;34m(x_idx, bins, right, labels, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(bins) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 493\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 494\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBin labels must be one fewer than the number of bin edges\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m )\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(labels, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m), CategoricalDtype):\n\u001b[1;32m 498\u001b[0m labels \u001b[38;5;241m=\u001b[39m Categorical(\n\u001b[1;32m 499\u001b[0m labels,\n\u001b[1;32m 500\u001b[0m categories\u001b[38;5;241m=\u001b[39mlabels \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mset\u001b[39m(labels)) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 501\u001b[0m ordered\u001b[38;5;241m=\u001b[39mordered,\n\u001b[1;32m 502\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Bin labels must be one fewer than the number of bin edges" + ] + } + ], + "source": [ + "# Let's look more specifically at the income deciles to see where the cliff effect shows up\n", + "# Calculate income deciles\n", + "df_outputs['income_decile'] = pd.qcut(df_outputs['Employment_Income'], 10, labels=range(1, 11), duplicates='drop')\n", + "\n", + "# Group by decile and show the effect\n", + "decile_analysis = df_outputs.groupby('income_decile').agg({\n", + " 'Employment_Income': ['min', 'max', 'mean'],\n", + " 'fpl_ratio': 'mean',\n", + " 'aca_baseline': 'mean',\n", + " 'aca_reform': 'mean',\n", + " 'net_change': 'mean',\n", + " 'weight': 'sum'\n", + "}).round(2)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"PTC EFFECTS BY INCOME DECILE\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome ranges and average PTC changes by decile:\")\n", + "decile_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "w09m1i1mc5q", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "PTC EFFECTS BY INCOME DECILE\n", + "======================================================================\n", + "\n", + "Income ranges and average PTC changes by decile:\n" + ] + }, + { + "data": { + "text/html": [ + "
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Employment_Incomefpl_ratioaca_baselineaca_reformnet_changeweight
minmaxmeanmeanmeanmeanmeansum
decile_num
10.0022082.703081.2316.50481.01618.01136.9956060974.39
222096.5640219.3031811.30167.521638.771989.05350.2718267145.22
340220.4559168.3450102.24254.531575.922062.44486.5213547056.92
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6106597.34142491.25122908.07554.42816.631518.36701.7213853349.38
7142491.25205693.01169652.75727.25489.021177.16688.1410689611.79
8205693.023305428.97382158.511548.38210.36482.04271.6711524346.53
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" + ], + "text/plain": [ + " Employment_Income fpl_ratio aca_baseline \\\n", + " min max mean mean mean \n", + "decile_num \n", + "1 0.00 22082.70 3081.23 16.50 481.01 \n", + "2 22096.56 40219.30 31811.30 167.52 1638.77 \n", + "3 40220.45 59168.34 50102.24 254.53 1575.92 \n", + "4 59179.83 80438.61 69904.57 343.95 1437.25 \n", + "5 80438.61 106574.53 93253.97 434.80 1303.70 \n", + "6 106597.34 142491.25 122908.07 554.42 816.63 \n", + "7 142491.25 205693.01 169652.75 727.25 489.02 \n", + "8 205693.02 3305428.97 382158.51 1548.38 210.36 \n", + "\n", + " aca_reform net_change weight \n", + " mean mean sum \n", + "decile_num \n", + "1 618.01 136.99 56060974.39 \n", + "2 1989.05 350.27 18267145.22 \n", + "3 2062.44 486.52 13547056.92 \n", + "4 2029.91 592.66 12499143.93 \n", + "5 2060.30 756.60 11057517.34 \n", + "6 1518.36 701.72 13853349.38 \n", + "7 1177.16 688.14 10689611.79 \n", + "8 482.04 271.67 11524346.53 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fix the decile calculation\n", + "# Calculate income deciles without explicit labels to avoid the error\n", + "df_outputs['income_decile'] = pd.qcut(df_outputs['Employment_Income'], 10, duplicates='drop')\n", + "\n", + "# Get unique deciles and sort them\n", + "deciles = sorted(df_outputs['income_decile'].unique())\n", + "\n", + "# Create a mapping to simpler labels\n", + "decile_map = {d: i+1 for i, d in enumerate(deciles)}\n", + "df_outputs['decile_num'] = df_outputs['income_decile'].map(decile_map)\n", + "\n", + "# Group by decile and show the effect\n", + "decile_analysis = df_outputs.groupby('decile_num').agg({\n", + " 'Employment_Income': ['min', 'max', 'mean'],\n", + " 'fpl_ratio': 'mean',\n", + " 'aca_baseline': 'mean',\n", + " 'aca_reform': 'mean',\n", + " 'net_change': 'mean',\n", + " 'weight': 'sum'\n", + "}).round(2)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"PTC EFFECTS BY INCOME DECILE\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome ranges and average PTC changes by decile:\")\n", + "decile_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbfun838g2", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's look at where in the data the 9th decile falls (the one from the chart)\n", + "# Since we only have 8 groups due to duplicates being dropped, let's recalculate properly\n", + "\n", + "# First, let's understand the actual income distribution better\n", + "print(\"=\"*70)\n", + "print(\"UNDERSTANDING THE 9TH DECILE CONCENTRATION\")\n", + "print(\"=\"*70)\n", + "\n", + "# Get percentiles to understand income distribution\n", + "percentiles = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]\n", + "income_pcts = np.percentile(df_outputs['Employment_Income'], percentiles)\n", + "\n", + "print(\"\\nIncome distribution percentiles:\")\n", + "for p, val in zip(percentiles, income_pcts):\n", + " print(f\" {p}th percentile: ${val:,.0f}\")\n", + "\n", + "# The 9th decile should be roughly between 80th and 90th percentile\n", + "ninth_decile = df_outputs[(df_outputs['Employment_Income'] >= income_pcts[7]) & \n", + " (df_outputs['Employment_Income'] < income_pcts[8])]\n", + "\n", + "print(f\"\\n9th Decile (80-90th percentile):\")\n", + "print(f\" Income range: ${income_pcts[7]:,.0f} - ${income_pcts[8]:,.0f}\")\n", + "print(f\" Number of households: {len(ninth_decile)}\")\n", + "print(f\" Weighted count: {ninth_decile['weight'].sum():,.0f}\")\n", + "print(f\" Average FPL ratio: {ninth_decile['fpl_ratio'].mean():.1f}%\")\n", + "print(f\" Average baseline PTC: ${ninth_decile['aca_baseline'].mean():,.2f}\")\n", + "print(f\" Average reform PTC: ${ninth_decile['aca_reform'].mean():,.2f}\")\n", + "print(f\" Average change: ${ninth_decile['net_change'].mean():,.2f}\")\n", + "\n", + "# Now let's see WHO specifically gains in the 9th decile\n", + "ninth_decile_gainers = ninth_decile[ninth_decile['net_change'] > 100] # Gains more than $100\n", + "\n", + "print(f\"\\nHouseholds in 9th decile with gains > $100:\")\n", + "print(f\" Count: {len(ninth_decile_gainers)}\")\n", + "print(f\" Average income: ${ninth_decile_gainers['Employment_Income'].mean():,.0f}\")\n", + "print(f\" Average FPL ratio: {ninth_decile_gainers['fpl_ratio'].mean():.1f}%\")\n", + "print(f\" Average gain: ${ninth_decile_gainers['net_change'].mean():,.2f}\")\n", + "\n", + "# Look at specific examples\n", + "print(\"\\nExample households in 9th decile with large gains:\")\n", + "examples = ninth_decile_gainers.nlargest(5, 'net_change')[\n", + " ['household_id', 'State', 'Employment_Income', 'fpl_ratio', \n", + " 'aca_baseline', 'aca_reform', 'net_change', 'Married', 'Num_Dependents']\n", + "]\n", + "examples" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "inz803s5rlm", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking available variables:\n", + "df_outputs exists: True\n", + "df_outputs shape: (21607, 14)\n", + "Columns: ['household_id', 'State', 'Married', 'Num_Dependents', 'Employment_Income', 'aca_baseline', 'aca_reform', 'weight', 'net_change', 'household_size', 'fpl_threshold', 'fpl_ratio', 'income_decile', 'decile_num']\n" + ] + } + ], + "source": [ + "# Check if the dataframe exists and has the needed columns\n", + "print(\"Checking available variables:\")\n", + "print(f\"df_outputs exists: {'df_outputs' in locals()}\")\n", + "if 'df_outputs' in locals():\n", + " print(f\"df_outputs shape: {df_outputs.shape}\")\n", + " print(f\"Columns: {list(df_outputs.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qhtylcg4wz", + "metadata": {}, + "outputs": [], + "source": [ + "# Understanding the 9th decile concentration\n", + "import numpy as np\n", + "\n", + "# Get percentiles to understand income distribution\n", + "percentiles = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]\n", + "income_pcts = np.percentile(df_outputs['Employment_Income'], percentiles)\n", + "\n", + "print(\"=\"*70)\n", + "print(\"UNDERSTANDING THE 9TH DECILE CONCENTRATION\")\n", + "print(\"=\"*70)\n", + "print(\"\\nIncome distribution percentiles:\")\n", + "for p, val in zip(percentiles, income_pcts):\n", + " print(f\" {p}th percentile: ${val:,.0f}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/blog_posts/new_reform_aca_households.ipynb b/us/blog_posts/new_reform_aca_households.ipynb new file mode 100644 index 0000000..ffa48db --- /dev/null +++ b/us/blog_posts/new_reform_aca_households.ipynb @@ -0,0 +1,39008 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "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", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "situation_texas = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\n", + " \"2026\": 25\n", + " }\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\n", + " \"2026\": 28\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"TX\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"48015\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 400000\n", + " }\n", + " ]\n", + " ]\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: >\n", + "Traceback (most recent call last):\n", + " File \"/Users/daphnehansell/miniconda3/envs/policyengine/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n", + " def _clean_thread_parent_frames(\n", + "KeyboardInterrupt: \n" + ] + } + ], + "source": [ + "simulation_new_york = Simulation(\n", + " situation=situation_new_york,\n", + ")\n", + "simulation_texas = Simulation(\n", + " situation=situation_texas,\n", + ")\n", + "reformed_simulation_new_york = Simulation(\n", + " situation=situation_new_york,\n", + " reform=reform,\n", + ")\n", + "reformed_simulation_texas = Simulation(\n", + " situation=situation_texas,\n", + " reform=reform,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_ira_reform
0138 % FPL ($29,897.10)29897.109268.48339810354.979492
1300 % FPL ($64,993.70)64993.703881.6069346273.375000
2400 % FPL ($86,658.27)86658.270.0000001377.181641
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_ira_reform\n", + "0 138 % FPL ($29,897.10) 29897.10 9268.483398 10354.979492\n", + "1 300 % FPL ($64,993.70) 64993.70 3881.606934 6273.375000\n", + "2 400 % FPL ($86,658.27) 86658.27 0.000000 1377.181641" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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", + " sit = copy.deepcopy(base_situation)\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\"] = {str(period): income / 2}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform if use_reform else None)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\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, 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" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_ira_reformmedicaid_costper_capita_chipSLCSP
0154 % FPL ($41,041)410410.0000000.00000016480.6962890.0000000.000000
1200 % FPL ($53,300)533000.0000000.00000012930.671875829.9299320.000000
2300 % FPL ($79,950)7995014042.51757817208.5371090.000000829.92993222005.537109
3405 % FPL ($107,933)1079330.00000010996.3701170.000000829.9299320.000000
\n", + "
" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_ira_reform \\\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 14042.517578 17208.537109 \n", + "3 405 % FPL ($107,933) 107933 0.000000 10996.370117 \n", + "\n", + " 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 22005.537109 \n", + "3 0.000000 829.929932 0.000000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Helper: run a one-point simulation and collect metrics\n", + "# ------------------------------------------------------------------\n", + "def get_metrics_for_income(base_situation: dict, income: float):\n", + " \"\"\"\n", + " Returns baseline & reform PTC plus baseline Medicaid and CHIP metrics\n", + " for a New York family of three at the specified annual income.\n", + "\n", + " Parameters\n", + " ----------\n", + " base_situation : dict\n", + " Your `situation_ny` dictionary.\n", + " income : float\n", + " Total household employment income to test (USD, annual).\n", + "\n", + " Returns\n", + " -------\n", + " dict with keys\n", + " ptc_baseline – ACA PTC in baseline\n", + " ptc_ira_reform – ACA PTC under IRA-style reform\n", + " medicaid_cost – household Medicaid benefit (baseline)\n", + " per_capita_chip – CHIP benefit ÷ household size (baseline)\n", + " \"\"\"\n", + " # ---------------- Copy & inject the income --------------------\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None) # single-point sim only\n", + "\n", + " # Split income equally between both adults\n", + " for person in [\"you\", \"your partner\"]:\n", + " sit[\"people\"][person][\"employment_income\"] = {\"2026\": income / 2}\n", + "\n", + " hh_size = len(sit[\"people\"])\n", + "\n", + " # ---------------- Run simulations ----------------------------\n", + " sim_base = Simulation(situation=sit)\n", + " sim_reform = Simulation(situation=sit, reform=reform)\n", + "\n", + " # ---------------- Pull variables -----------------------------\n", + " # 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_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", + " # * Otherwise use `chip` (total CHIP dollars) or adjust as needed.\n", + " chip_total = sim_base.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)[0]\n", + " per_capita_chip = chip_total / hh_size if hh_size else 0\n", + "\n", + " return dict(\n", + " ptc_baseline = ptc_base,\n", + " ptc_ira_reform = ptc_reform,\n", + " medicaid_cost = medicaid_cost,\n", + " per_capita_chip = per_capita_chip,\n", + " SLCSP = SLCSP\n", + " )\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Income targets (family of 3, 2026 FPL thresholds you supplied)\n", + "# ------------------------------------------------------------------\n", + "targets_ny = {\n", + " \"154 % FPL ($41,041)\" : 41_041,\n", + " \"200 % FPL ($53,300)\" : 53_300,\n", + " \"300 % FPL ($79,950)\": 79_950,\n", + " \"405 % FPL ($107,933)\": 107_933,\n", + "}\n", + "\n", + "rows = []\n", + "for label, inc in targets_ny.items():\n", + " metrics = get_metrics_for_income(situation_new_york, inc)\n", + " rows.append(\n", + " dict(income_label = label, income_usd = inc, **metrics)\n", + " )\n", + "\n", + "ny_ptc_df = pd.DataFrame(rows)\n", + "ny_ptc_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "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", + "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_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", + "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_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", + "\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_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", + "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_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_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", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "CHIP (Baseline)", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 6589.57763671875, 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Amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create NY graph\n", + "fig_new_york = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\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_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_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_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_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_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_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_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_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", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "# Create Texas graph\n", + "fig_texas = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=baseline_texas_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Baseline)', \n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=reform_texas_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Reform)', \n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n", + "))\n", + "\n", + "\n", + "\n", + "# Add total lines\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=baseline_texas_total, \n", + " mode='lines', \n", + " name='Total Benefits (Baseline)', \n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=reform_texas_total, \n", + " mode='lines', \n", + " name='Total Benefits (Reform)', \n", + " line=dict(color=DARK_GRAY, width=2, dash='dot')\n", + "))\n", + "\n", + "# Update layout\n", + "fig_texas.update_layout(\n", + " title='Texas Household (Couple) - Program Benefits by Income Level',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Benefit Amount',\n", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "# Apply your format_fig function if it exists\n", + "# If you don't have this function defined, you can remove these lines\n", + "fig_new_york = format_fig(fig_new_york)\n", + "fig_texas = format_fig(fig_texas)\n", + "\n", + "# Display the figures\n", + "fig_new_york.show()\n", + "fig_texas.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#616161", + "width": 2 + }, + "mode": "lines", + "name": "Health Net Income (Baseline)", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 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"#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "New York Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits" + }, + "width": 800, + "xaxis": { + "range": [ + 0, + 400000 + ], + "tickformat": "$,.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='$,.0f', zeroline=True, zerolinewidth=1),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_delta_ny = format_fig(fig_delta_ny) # if you use the helper elsewhere\n", + "fig_delta_ny.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + 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Income by Household Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Health-Adjusted Net Income',\n", + " legend_title='Scenario',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200_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", + "# --- 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 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\"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"],\n", + " \"state_name\": {\"2026\": \"NY\"},\n", + " \"county_fips\": {\"2026\": \"36061\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " },\n", + " \"your first dependent's marital unit\": {\n", + " \"members\": [\"your first dependent\"],\n", + " \"marital_unit_id\": {\"2026\": 1}\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_ny_baseline = Simulation(situation=situation_ny_for_mtr)\n", + "household_income_ny_mtr = sim_ny_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_ny_net_income = sim_ny_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_ny_reform = Simulation(situation=situation_ny_for_mtr, reform=reform)\n", + "reform_ny_net_income = sim_ny_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_ny_mtr_incomes, baseline_ny_mtrs = calc_mtr(household_income_ny_mtr, baseline_ny_net_income)\n", + "reform_ny_mtr_incomes, reform_ny_mtrs = calc_mtr(household_income_ny_mtr, reform_ny_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_new_york_mtr = go.Figure()\n", + "\n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=baseline_ny_mtr_incomes,\n", + " y=np.clip(baseline_ny_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=DARK_GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=reform_ny_mtr_incomes,\n", + " y=np.clip(reform_ny_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),\n", + " hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_new_york_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - New York household',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_new_york_mtr = format_fig(fig_new_york_mtr)\n", + "fig_new_york_mtr.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Income: $%{x:,.0f}
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[ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal tax rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# CORRECT MTR CALCULATION FOR TEXAS COUPLE\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_tx_for_mtr = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 25},\n", + " \"employment_income\": {\"2026\": 0}, # Will be set by axes\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 28},\n", + " \"employment_income\": {\"2026\": 0}, # Will be set by axes\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"TX\"},\n", + " \"county_fips\": {\"2026\": \"48015\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_tx_baseline = Simulation(situation=situation_tx_for_mtr)\n", + "household_income_tx_mtr = sim_tx_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_tx_net_income = sim_tx_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_tx_reform = Simulation(situation=situation_tx_for_mtr, reform=reform)\n", + "reform_tx_net_income = sim_tx_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points (reuse function from NY cell)\n", + "baseline_tx_mtr_incomes, baseline_tx_mtrs = calc_mtr(household_income_tx_mtr, baseline_tx_net_income)\n", + "reform_tx_mtr_incomes, reform_tx_mtrs = calc_mtr(household_income_tx_mtr, reform_tx_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_texas_mtr = go.Figure()\n", + "\n", + "fig_texas_mtr.add_trace(go.Scatter(\n", + " x=baseline_tx_mtr_incomes,\n", + " y=np.clip(baseline_tx_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=DARK_GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_texas_mtr.add_trace(go.Scatter(\n", + " x=reform_tx_mtr_incomes,\n", + " y=np.clip(reform_tx_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),\n", + " hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_texas_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - Texas couple',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_texas_mtr = format_fig(fig_texas_mtr)\n", + "fig_texas_mtr.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/blog_posts/new_reform_microsim_aca.ipynb b/us/blog_posts/new_reform_microsim_aca.ipynb new file mode 100644 index 0000000..fe5a5b0 --- /dev/null +++ b/us/blog_posts/new_reform_microsim_aca.ipynb @@ -0,0 +1,398 @@ +{ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "reform = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_additional_bracket.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "baseline = Microsimulation()\n", + "reformed = Microsimulation(reform=reform)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18,014,459 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": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16,253,747 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": 6, + "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)\n", + "household_weight = baseline.calculate(\"household_weight\", map_to=\"household\", period=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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household_idStateMarriedNum_DependentsEmployment_Incomeaca_baselineaca_reformhousehold_weight
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [household_id, State, Married, Num_Dependents, Employment_Income, aca_baseline, aca_reform, household_weight]\n", + "Index: []" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Create a DataFrame with the outputs\n", + "data = {\n", + " \"household_id\": household_id,\n", + " \"State\": state,\n", + " \"Married\": married,\n", + " \"Num_Dependents\": num_dependents,\n", + " \"Employment_Income\": employment_income,\n", + " \"aca_baseline\": aca_baseline,\n", + " \"aca_reform\": aca_reform,\n", + " \"household_weight\": household_weight,\n", + "\n", + " }\n", + "\n", + "\n", + "df_outputs = pd.DataFrame(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average weighted PTC change among households with any change: $1,943.97\n" + ] + } + ], + "source": [ + "# 0. Make sure net_change exists\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# 1. Flag households with any change\n", + "mask = df_outputs[\"net_change\"] != 0 # True for ↑ or ↓\n", + "\n", + "# 2. Weighted mean among those households\n", + "avg_net_change = (\n", + " (df_outputs.loc[mask, \"net_change\"] * df_outputs.loc[mask, \"household_weight\"]).sum()\n", + " / df_outputs.loc[mask, \"household_weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with any change: \"\n", + " f\"${avg_net_change:,.2f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average weighted PTC change among households with a PTC in both baseline and reform: $1,704.48\n" + ] + } + ], + "source": [ + "# ------------------------------------------------------------------\n", + "# 0. Ensure supporting columns exist\n", + "# ------------------------------------------------------------------\n", + "df_outputs[\"net_change\"] = df_outputs[\"aca_reform\"] - df_outputs[\"aca_baseline\"]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Keep only households with a PTC in *both* scenarios\n", + "# ------------------------------------------------------------------\n", + "mask_both_ptc = (df_outputs[\"aca_baseline\"] > 0) & (df_outputs[\"aca_reform\"] > 0)\n", + "df_dual_ptc = df_outputs[mask_both_ptc]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Weighted average of the net change (household perspective)\n", + "# ------------------------------------------------------------------\n", + "avg_net_change_dual_hh = (\n", + " (df_dual_ptc[\"net_change\"] * df_dual_ptc[\"household_weight\"]).sum()\n", + " / df_dual_ptc[\"household_weight\"].sum()\n", + ")\n", + "\n", + "print(f\"Average weighted PTC change among households with a PTC in both \"\n", + " f\"baseline and reform: ${avg_net_change_dual_hh:,.2f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average weighted PTC change among households that newly receive a PTC under the reform: $4,092.65\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[\"household_weight\"]).sum()\n", + " / df_reform_only[\"household_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": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "One year cost of the reform in billions under the reform: $21.80\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from policyengine_us import Simulation\n", + "\n", + "# -------------------------------\n", + "# 1. Pull household-level results\n", + "# -------------------------------\n", + "# ACA PTC (baseline and reform)\n", + "ptc_base = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "ptc_reform = reformed.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "\n", + "# Household weights (same for both sims)\n", + "hh_wt = baseline.calculate(\"household_weight\", map_to=\"household\", period=2026)\n", + "\n", + "# -------------------------------\n", + "# 2. Weighted sum of the change\n", + "# -------------------------------\n", + "weighted_total_change = ptc_reform - ptc_base\n", + "\n", + "# Optional: average change per household\n", + "weighted_total_change.sum()/1e9\n", + "\n", + "\n", + "print(f\"One year cost of the reform in billions \"\n", + " f\"under the reform: ${weighted_total_change.sum()/1e9:,.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of households gaining PTC under reform: 334\n", + "Weighted count: 1,121,658\n", + "\n", + "Average reform PTC for these households: $4,771.23\n", + "Weighted average reform PTC: $4,092.65\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['household_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['household_weight']).sum() / gained_ptc['household_weight'].sum():,.2f}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "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..8dded8f --- /dev/null +++ b/us/medicaid/aca_reform.ipynb @@ -0,0 +1,471 @@ +{ + "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" + ] + }, + { + "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": [ + "19.8896926531077" + ] + }, + "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": [ + "31.542351631973787" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_aca_enrollment/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "51.43204428508149" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reformed_aca_enrollment/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": [ + "41.48141013389625" + ] + }, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "85.14359348561398" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_medicaid_enrollment = baseline.calculate(\"is_medicaid_eligible\", map_to=\"person\", period=2024 ).sum()\n", + "baseline_medicaid_enrollment/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "170.0861678993786" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_has_esi/1e6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.364080432275241" + ] + }, + "execution_count": 17, + "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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.617468693659635" + ] + }, + "execution_count": 18, + "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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.705994718609649" + ] + }, + "execution_count": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.45083694065887" + ] + }, + "execution_count": 20, + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "356.50153524161357" + ] + }, + "execution_count": 21, + "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": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024 0.054597097187702254\n", + "2025 1.0\n", + "2026 0.05459709726223681\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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "131288173629.472" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_ptc = baseline.calculate(\"aca_ptc\", map_to=\"person\", period=2026).sum()\n", + "\n", + "baseline_ptc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/medicaid/aca_reform_households.ipynb b/us/medicaid/aca_reform_households.ipynb new file mode 100644 index 0000000..0d45421 --- /dev/null +++ b/us/medicaid/aca_reform_households.ipynb @@ -0,0 +1,39024 @@ +{ + "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 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": 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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "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", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "situation_texas = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\n", + " \"2026\": 25\n", + " }\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\n", + " \"2026\": 28\n", + " }\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ],\n", + " \"state_name\": {\n", + " \"2026\": \"TX\"\n", + " },\n", + " \"county_fips\": {\n", + " \"2026\": \"48015\"\n", + " }\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\n", + " \"you\",\n", + " \"your partner\"\n", + " ]\n", + " }\n", + " },\n", + " \"axes\": [\n", + " [\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 400000\n", + " }\n", + " ]\n", + " ]\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "simulation_new_york = Simulation(\n", + " situation=situation_new_york,\n", + ")\n", + "simulation_texas = Simulation(\n", + " situation=situation_texas,\n", + ")\n", + "reformed_simulation_new_york = Simulation(\n", + " situation=situation_new_york,\n", + " reform=reform,\n", + ")\n", + "reformed_simulation_texas = Simulation(\n", + " situation=situation_texas,\n", + " reform=reform,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_ira_reform
0138 % FPL ($29,897.10)29897.109052.59570310090.201172
1300 % FPL ($64,993.70)64993.703915.7998056076.840332
2400 % FPL ($86,658.27)86658.270.0000002724.248047
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" + ], + "text/plain": [ + " 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, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "PERIOD = 2026\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: get the tax unit's 2026 FPG from the situation\n", + "# ------------------------------------------------------------------\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", + " # 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", + " 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", + " sit = copy.deepcopy(base_situation)\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\"] = {str(period): income / 2}\n", + "\n", + " sim = Simulation(situation=sit, reform=reform if use_reform else None)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2) Build targets from model-derived FPG and compute PTCs\n", + "# ------------------------------------------------------------------\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, 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" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_ira_reformmedicaid_costper_capita_chipSLCSP
0154 % FPL ($41,041)410410.0000000.00000016480.6962890.0000000.000000
1200 % FPL ($53,300)533000.0000000.00000012930.671875829.9299320.000000
2300 % FPL ($79,950)7995013847.60351616645.8535160.000000829.92993221442.853516
3405 % FPL ($107,933)1079330.00000012268.5488280.000000829.9299320.000000
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" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_ira_reform \\\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 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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 1. Helper: run a one-point simulation and collect metrics\n", + "# ------------------------------------------------------------------\n", + "def get_metrics_for_income(base_situation: dict, income: float):\n", + " \"\"\"\n", + " Returns baseline & reform PTC plus baseline Medicaid and CHIP metrics\n", + " for a New York family of three at the specified annual income.\n", + "\n", + " Parameters\n", + " ----------\n", + " base_situation : dict\n", + " Your `situation_ny` dictionary.\n", + " income : float\n", + " Total household employment income to test (USD, annual).\n", + "\n", + " Returns\n", + " -------\n", + " dict with keys\n", + " ptc_baseline – ACA PTC in baseline\n", + " ptc_ira_reform – ACA PTC under IRA-style reform\n", + " medicaid_cost – household Medicaid benefit (baseline)\n", + " per_capita_chip – CHIP benefit ÷ household size (baseline)\n", + " \"\"\"\n", + " # ---------------- Copy & inject the income --------------------\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None) # single-point sim only\n", + "\n", + " # Split income equally between both adults\n", + " for person in [\"you\", \"your partner\"]:\n", + " sit[\"people\"][person][\"employment_income\"] = {\"2026\": income / 2}\n", + "\n", + " hh_size = len(sit[\"people\"])\n", + "\n", + " # ---------------- Run simulations ----------------------------\n", + " sim_base = Simulation(situation=sit)\n", + " sim_reform = Simulation(situation=sit, reform=reform)\n", + "\n", + " # ---------------- Pull variables -----------------------------\n", + " # 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_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", + " # * Otherwise use `chip` (total CHIP dollars) or adjust as needed.\n", + " chip_total = sim_base.calculate(\"per_capita_chip\", map_to=\"household\", period=2026)[0]\n", + " per_capita_chip = chip_total / hh_size if hh_size else 0\n", + "\n", + " return dict(\n", + " ptc_baseline = ptc_base,\n", + " ptc_ira_reform = ptc_reform,\n", + " medicaid_cost = medicaid_cost,\n", + " per_capita_chip = per_capita_chip,\n", + " SLCSP = SLCSP\n", + " )\n", + "\n", + "# ------------------------------------------------------------------\n", + "# 2. Income targets (family of 3, 2026 FPL thresholds you supplied)\n", + "# ------------------------------------------------------------------\n", + "targets_ny = {\n", + " \"154 % FPL ($41,041)\" : 41_041,\n", + " \"200 % FPL ($53,300)\" : 53_300,\n", + " \"300 % FPL ($79,950)\": 79_950,\n", + " \"405 % FPL ($107,933)\": 107_933,\n", + "}\n", + "\n", + "rows = []\n", + "for label, inc in targets_ny.items():\n", + " metrics = get_metrics_for_income(situation_new_york, inc)\n", + " rows.append(\n", + " dict(income_label = label, income_usd = inc, **metrics)\n", + " )\n", + "\n", + "ny_ptc_df = pd.DataFrame(rows)\n", + "ny_ptc_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "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", + "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_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", + "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_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", + "\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_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", + "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_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_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", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "GRAY = \"#808080\"\n", + "BLUE_PRIMARY = \"#2C6496\"\n", + "TEAL_ACCENT = \"#39C6C0\"\n", + "DARK_GRAY = \"#616161\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#808080", + "width": 2 + }, + "mode": "lines", + "name": "CHIP (Baseline)", + "type": "scatter", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 6589.57763671875, 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+ "title": { + "text": "Benefit Amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create NY graph\n", + "fig_new_york = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\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_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_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_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_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_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_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_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_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", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "# Create Texas graph\n", + "fig_texas = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=baseline_texas_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Baseline)', \n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=reform_texas_aca_ptc, \n", + " mode='lines', \n", + " name='ACA PTC (Reform)', \n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\n", + "))\n", + "\n", + "\n", + "\n", + "# Add total lines\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=baseline_texas_total, \n", + " mode='lines', \n", + " name='Total Benefits (Baseline)', \n", + " line=dict(color=DARK_GRAY, width=2)\n", + "))\n", + "\n", + "fig_texas.add_trace(go.Scatter(\n", + " x=household_income_texas, \n", + " y=reform_texas_total, \n", + " mode='lines', \n", + " name='Total Benefits (Reform)', \n", + " line=dict(color=DARK_GRAY, width=2, dash='dot')\n", + "))\n", + "\n", + "# Update layout\n", + "fig_texas.update_layout(\n", + " title='Texas Household (Couple) - Program Benefits by Income Level',\n", + " 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+ "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "New York Household (Family of 3) – Impact of Extending Enhanced Premium Tax Credits" + }, + "width": 800, + "xaxis": { + "range": [ + 0, + 400000 + ], + "tickformat": "$,.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": 12, + "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", + "x": [ + 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 3843.920166015625, + 4393.0517578125, + 4942.18310546875, + 5491.314453125, + 6040.4462890625, + 6589.57763671875, + 7138.708984375, + 7687.84033203125, + 8236.9716796875, + 8786.103515625, + 9335.2353515625, + 9884.3662109375, + 10433.4970703125, + 10982.62890625, + 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"text": "Δ Net Income" + }, + "zeroline": true, + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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, 200_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", + "# --- 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 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dependent\": {\n", + " \"age\": {\"2026\": 3},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\", \"your first dependent\"],\n", + " \"state_name\": {\"2026\": \"NY\"},\n", + " \"county_fips\": {\"2026\": \"36061\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " },\n", + " \"your first dependent's marital unit\": {\n", + " \"members\": [\"your first dependent\"],\n", + " \"marital_unit_id\": {\"2026\": 1}\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_ny_baseline = Simulation(situation=situation_ny_for_mtr)\n", + "household_income_ny_mtr = sim_ny_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_ny_net_income = sim_ny_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_ny_reform = Simulation(situation=situation_ny_for_mtr, reform=reform)\n", + "reform_ny_net_income = sim_ny_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_ny_mtr_incomes, baseline_ny_mtrs = calc_mtr(household_income_ny_mtr, baseline_ny_net_income)\n", + "reform_ny_mtr_incomes, reform_ny_mtrs = calc_mtr(household_income_ny_mtr, reform_ny_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_new_york_mtr = go.Figure()\n", + "\n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=baseline_ny_mtr_incomes,\n", + " y=np.clip(baseline_ny_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=DARK_GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=reform_ny_mtr_incomes,\n", + " y=np.clip(reform_ny_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),\n", + " hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_new_york_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - New York household',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_new_york_mtr = format_fig(fig_new_york_mtr)\n", + "fig_new_york_mtr.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Income: $%{x:,.0f}
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"mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Marginal tax rate including health benefits - Texas couple" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 200000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal tax rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ========================================================================\n", + "# CORRECT MTR CALCULATION FOR TEXAS COUPLE\n", + "# Uses axes-based simulation for net income, then calculates MTR manually\n", + "# ========================================================================\n", + "\n", + "# Step 1: Create situation with axes for vectorized net income calculation\n", + "situation_tx_for_mtr = {\n", + " \"people\": {\n", + " \"you\": {\n", + " \"age\": {\"2026\": 25},\n", + " \"employment_income\": {\"2026\": 0}, # Will be set by axes\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"your partner\": {\n", + " \"age\": {\"2026\": 28},\n", + " \"employment_income\": {\"2026\": 0}, # Will be set by axes\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"you\", \"your partner\"],\n", + " \"state_name\": {\"2026\": \"TX\"},\n", + " \"county_fips\": {\"2026\": \"48015\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"your marital unit\": {\n", + " \"members\": [\"you\", \"your partner\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200_000,\n", + " \"count\": 200,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Step 2: Calculate baseline net income using axes\n", + "sim_tx_baseline = Simulation(situation=situation_tx_for_mtr)\n", + "household_income_tx_mtr = sim_tx_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_tx_net_income = sim_tx_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 3: Calculate reform net income using axes\n", + "sim_tx_reform = Simulation(situation=situation_tx_for_mtr, reform=reform)\n", + "reform_tx_net_income = sim_tx_reform.calculate(\n", + " \"household_net_income_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", + "\n", + "# Step 4: Calculate MTR from adjacent points (reuse function from NY cell)\n", + "baseline_tx_mtr_incomes, baseline_tx_mtrs = calc_mtr(household_income_tx_mtr, baseline_tx_net_income)\n", + "reform_tx_mtr_incomes, reform_tx_mtrs = calc_mtr(household_income_tx_mtr, reform_tx_net_income)\n", + "\n", + "# Step 5: Create the chart\n", + "fig_texas_mtr = go.Figure()\n", + "\n", + "fig_texas_mtr.add_trace(go.Scatter(\n", + " x=baseline_tx_mtr_incomes,\n", + " y=np.clip(baseline_tx_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=DARK_GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_texas_mtr.add_trace(go.Scatter(\n", + " x=reform_tx_mtr_incomes,\n", + " y=np.clip(reform_tx_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2, dash='dash'),\n", + " hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_texas_mtr.update_layout(\n", + " title='Marginal tax rate including health benefits - Texas couple',\n", + " xaxis_title='Employment income',\n", + " yaxis_title='Marginal tax rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200_000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_texas_mtr = format_fig(fig_texas_mtr)\n", + "fig_texas_mtr.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/medicaid/medicaid_households.ipynb b/us/medicaid/aca_reform_ny_only.ipynb similarity index 66% rename from us/medicaid/medicaid_households.ipynb rename to us/medicaid/aca_reform_ny_only.ipynb index 8b27a19..13133e8 100644 --- a/us/medicaid/medicaid_households.ipynb +++ b/us/medicaid/aca_reform_ny_only.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 80, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -16,11 +16,10 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 12, "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", @@ -46,18 +45,16 @@ " \"gov.aca.ptc_income_eligibility[2].amount\": {\n", " \"2026-01-01.2100-12-31\": True\n", " }\n", - "}, country_id=\"us\")\n", - "\n" + "}, country_id=\"us\")" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "\n", - "situation_vermont = {\n", + "situation_new_york = {\n", " \"people\": {\n", " \"you\": {\n", " \"age\": {\n", @@ -103,17 +100,20 @@ " }\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", @@ -140,148 +140,268 @@ " }\n", " ]\n", " ]\n", - "}\n", - "\n" + "}" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "situation_texas = {\n", - " \"people\": {\n", - " \"you\": {\n", - " \"age\": {\n", - " \"2026\": 25\n", - " }\n", - " },\n", - " \"your partner\": {\n", - " \"age\": {\n", - " \"2026\": 28\n", - " }\n", - " }\n", - " },\n", - " \"families\": {\n", - " \"your family\": {\n", - " \"members\": [\n", - " \"you\",\n", - " \"your partner\"\n", - " ]\n", - " }\n", - " },\n", - " \"spm_units\": {\n", - " \"your household\": {\n", - " \"members\": [\n", - " \"you\",\n", - " \"your partner\"\n", - " ]\n", - " }\n", - " },\n", - " \"tax_units\": {\n", - " \"your tax unit\": {\n", - " \"members\": [\n", - " \"you\",\n", - " \"your partner\"\n", - " ]\n", - " }\n", - " },\n", - " \"households\": {\n", - " \"your household\": {\n", - " \"members\": [\n", - " \"you\",\n", - " \"your partner\"\n", - " ],\n", - " \"state_name\": {\n", - " \"2026\": \"TX\"\n", - " },\n", - " \"county_fips\": {\n", - " \"2026\": \"48015\"\n", - " }\n", - " }\n", - " },\n", - " \"marital_units\": {\n", - " \"your marital unit\": {\n", - " \"members\": [\n", - " \"you\",\n", - " \"your partner\"\n", - " ]\n", - " }\n", - " },\n", - " \"axes\": [\n", - " [\n", - " {\n", - " \"name\": \"employment_income\",\n", - " \"count\": 800,\n", - " \"min\": 0,\n", - " \"max\": 400000\n", - " }\n", - " ]\n", - " ]\n", - "}\n", - "\n" + "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", - 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income_labelincome_usdptc_baselineptc_ira_reformmedicaid_costper_capita_chipSLCSP
0154 % FPL ($42,039.50)42039.500.0000000.00000016480.6962890.0000000.000000
1200 % FPL ($54,596.76)54596.7617926.82226620263.5625000.0000002489.78979521442.853516
2300 % FPL ($81,895.14)81895.1413662.81543016385.8281250.0000002489.78979521442.853516
3405 % FPL ($110,558.43)110558.430.00000012045.3867190.0000002489.7897950.000000
4406 % FPL ($110,831.42)110831.420.00000021135.3906250.0000000.0000000.000000
\n", + "
" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_ira_reform \\\n", + "0 154 % FPL ($42,039.50) 42039.50 0.000000 0.000000 \n", + "1 200 % FPL ($54,596.76) 54596.76 17926.822266 20263.562500 \n", + "2 300 % FPL ($81,895.14) 81895.14 13662.815430 16385.828125 \n", + "3 405 % FPL ($110,558.43) 110558.43 0.000000 12045.386719 \n", + "4 406 % FPL ($110,831.42) 110831.42 0.000000 21135.390625 \n", + "\n", + " medicaid_cost per_capita_chip SLCSP \n", + "0 16480.696289 0.000000 0.000000 \n", + "1 0.000000 2489.789795 21442.853516 \n", + "2 0.000000 2489.789795 21442.853516 \n", + "3 0.000000 2489.789795 0.000000 \n", + "4 0.000000 0.000000 0.000000 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "simulation_vermont = Simulation(\n", - " situation=situation_vermont,\n", - ")\n", - "simulation_texas = Simulation(\n", - " situation=situation_texas,\n", - ")\n", - "reformed_simulation_vermont = Simulation(\n", - " situation=situation_vermont,\n", - " reform=reform,\n", - ")\n", - "reformed_simulation_texas = Simulation(\n", - " situation=situation_texas,\n", - " reform=reform,\n", - ")" + "import copy\n", + "import pandas as pd\n", + "from policyengine_us import Simulation\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Helper: compute the tax unit's FPG (annual $) from the situation\n", + "# ------------------------------------------------------------------\n", + "def get_tax_unit_fpg(base_situation: dict, period=2026) -> float:\n", + " \"\"\"\n", + " Returns the tax unit FPG for the given situation and year.\n", + " Assumes one relevant tax unit; returns the first element.\n", + " \"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + "\n", + " # Zero out earned income so FPG isn't influenced by any prior values\n", + " for person in sit[\"people\"].values():\n", + " person.setdefault(\"employment_income\", {})\n", + " person[\"employment_income\"][str(period)] = 0\n", + "\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", + "# Helper: run a one-point simulation and collect metrics\n", + "# ------------------------------------------------------------------\n", + "def get_metrics_for_income(base_situation: dict, income: float, period=2026):\n", + " \"\"\"\n", + " Returns baseline & reform PTC plus baseline Medicaid and CHIP metrics\n", + " for a New York family of three at the specified annual income.\n", + " \"\"\"\n", + " # ---------------- Copy & inject the income --------------------\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None) # single-point sim only\n", + "\n", + " # Split income equally between both adults\n", + " for person in [\"you\", \"your partner\"]:\n", + " sit[\"people\"][person][\"employment_income\"] = {str(period): income / 2}\n", + "\n", + " hh_size = len(sit[\"people\"])\n", + "\n", + " # ---------------- Run simulations ----------------------------\n", + " sim_base = Simulation(situation=sit)\n", + " sim_reform = Simulation(situation=sit, reform=reform)\n", + "\n", + " # ---------------- Pull variables -----------------------------\n", + " # ACA PTC\n", + " ptc_base = sim_base.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + " ptc_reform = sim_reform.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + " SLCSP = sim_base.calculate(\"slcsp\", map_to=\"household\", period=period)[0]\n", + "\n", + " # Medicaid benefit\n", + " medicaid_cost = sim_base.calculate(\"medicaid_cost\", map_to=\"household\", period=period)[0]\n", + "\n", + " # CHIP benefit\n", + " # Prefer total CHIP $ at household, fall back to per-capita if that's what's available.\n", + " try:\n", + " chip_total = sim_base.calculate(\"chip_cost\", map_to=\"household\", period=period)[0]\n", + " per_capita_chip = chip_total / hh_size if hh_size else 0\n", + " except Exception:\n", + " # If only per_capita_chip exists in your build:\n", + " per_capita_chip = sim_base.calculate(\"per_capita_chip\", map_to=\"household\", period=period)[0]\n", + "\n", + " return dict(\n", + " ptc_baseline = ptc_base,\n", + " ptc_ira_reform = ptc_reform,\n", + " medicaid_cost = medicaid_cost,\n", + " per_capita_chip = per_capita_chip,\n", + " SLCSP = SLCSP,\n", + " )\n", + "\n", + "# ------------------------------------------------------------------\n", + "# Build income targets from model-derived FPG (family of 3, NY, 2026)\n", + "# ------------------------------------------------------------------\n", + "period = 2026\n", + "fpg_2026 = get_tax_unit_fpg(situation_new_york, period=period)\n", + "\n", + "percent_targets = {\n", + " \"154 % FPL\": 1.54,\n", + " \"200 % FPL\": 2.00,\n", + " \"300 % FPL\": 3.00,\n", + " \"405 % FPL\": 4.05,\n", + " \"406 % FPL\": 4.06,\n", + "\n", + "}\n", + "\n", + "rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " metrics = get_metrics_for_income(situation_new_york, inc, period=period)\n", + " rows.append(dict(income_label=f'{label} (${inc:,.2f})',\n", + " income_usd=inc,\n", + " **metrics))\n", + "\n", + "ny_ptc_df = pd.DataFrame(rows)\n", + "ny_ptc_df\n" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, "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", - "\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", + "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_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", + "baseline_new_york_slcsp = simulation_new_york.calculate(\"slcsp\", 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", + "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_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", - "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", - "\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", - "\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", - "\n" + "# Calculate total benefits for New York\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)]" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 17, "metadata": {}, 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29.84375, 0, 0, 0, @@ -14081,7 +14201,103 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# Create NY graph\n", + "fig_new_york = go.Figure()\n", + "\n", + "# Add baseline traces (solid lines)\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_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_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_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_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_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_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_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_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", + " legend_title='Programs',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000\n", + ")\n", + "\n", + "# Apply format_fig function\n", + "fig_new_york = format_fig(fig_new_york)\n", + "\n", + "# Display the figure\n", + "fig_new_york.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { "data": { "application/vnd.plotly.v1+json": 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+ }, + "yaxis": { + "tickformat": "$,.0f", + "title": { + "text": "Δ Net Income" + }, + "zeroline": true, + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Household net income graphs\n", + "import plotly.graph_objects as go\n", + "\n", + "# ---------- NY family ----------\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)\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", + "fig_ny = format_fig(fig_ny)\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)\n", + "fig_delta_ny.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#616161", + "width": 2 + }, + "mode": "lines", + "name": "Marginal Tax Rate (Baseline)", + "type": "scatter", + "x": [ 0, + 549.1314697265625, + 1098.262939453125, + 1647.3944091796875, + 2196.52587890625, + 2745.6572265625, + 3294.788818359375, + 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- "text": "Programs" + "text": "Scenario" } }, "modebar": { @@ -27843,13 +25051,13 @@ } }, "title": { - "text": "Texas Household (Couple) - Program Benefits by Income Level" + "text": "New York Household (Family of 3) – Marginal Tax Rate Including Health Benefits by Household Income" }, "width": 800, "xaxis": { "range": [ 0, - 400000 + 200000 ], "tickformat": "$,.0f", "title": { @@ -27857,9 +25065,13 @@ } }, "yaxis": { - "tickformat": "$,.0f", + "range": [ + -1, + 1 + ], + "tickformat": ".0%", "title": { - "text": "Benefit Amount" + "text": "Marginal Tax Rate (Including Health Benefits)" } } } @@ -27870,191 +25082,65 @@ } ], "source": [ - "# Create Vermont graph\n", - "fig_vermont = 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " title='New York Household (Family of 3) - Program Benefits by Income Level',\n", - " xaxis_title='Household Income',\n", - " yaxis_title='Benefit Amount',\n", - " legend_title='Programs',\n", - " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", - " yaxis=dict(tickformat='$,.0f'),\n", - " height=600,\n", - " width=1000\n", + "# ---------- Pull the inputs ----------\n", + "household_income_new_york = simulation_new_york.calculate(\n", + " \"employment_income\", map_to=\"household\", period=2026\n", ")\n", "\n", - "# Create Texas graph\n", - "fig_texas = go.Figure()\n", - "\n", - "# Add baseline traces (solid lines)\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=baseline_texas_per_capita_chip, \n", - " mode='lines', \n", - " name='CHIP (Baseline)', \n", - " line=dict(color=GRAY, width=2)\n", - "))\n", - "\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=baseline_texas_aca_ptc, \n", - " mode='lines', \n", - " name='ACA PTC (Baseline)', \n", - " line=dict(color=BLUE_PRIMARY, width=2)\n", - "))\n", - "\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=baseline_texas_medicaid_cost, \n", - " mode='lines', \n", - " name='Medicaid (Baseline)', \n", - " line=dict(color=TEAL_ACCENT, width=2)\n", - "))\n", + "baseline_raw = simulation_new_york.calculate(\n", + " \"marginal_tax_rate_including_health_benefits\",\n", + " map_to=\"household\",\n", + " period=2026\n", + ")\n", "\n", - "# Add reform traces (dotted lines)\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=reform_texas_per_capita_chip, \n", - " mode='lines', \n", - " name='CHIP (Reform)', \n", - " line=dict(color=GRAY, width=2, dash='dot')\n", - "))\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", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=reform_texas_aca_ptc, \n", - " mode='lines', \n", - " name='ACA PTC (Reform)', \n", - " line=dict(color=BLUE_PRIMARY, width=2, dash='dot')\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", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=reform_texas_medicaid_cost, \n", - " mode='lines', \n", - " name='Medicaid (Reform)', \n", - " line=dict(color=TEAL_ACCENT, width=2, dash='dot')\n", - "))\n", + "# ---------- Build the graph ----------\n", + "fig_new_york_mtr = go.Figure()\n", "\n", - "# Add total lines\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=baseline_texas_total, \n", - " mode='lines', \n", - " name='Total Benefits (Baseline)', \n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=household_income_new_york,\n", + " y=baseline_mtr,\n", + " mode='lines',\n", + " name='Marginal Tax Rate (Baseline)',\n", " line=dict(color=DARK_GRAY, width=2)\n", "))\n", "\n", - "fig_texas.add_trace(go.Scatter(\n", - " x=household_income_texas, \n", - " y=reform_texas_total, \n", - " mode='lines', \n", - " name='Total Benefits (Reform)', \n", - " line=dict(color=DARK_GRAY, width=2, dash='dot')\n", + "fig_new_york_mtr.add_trace(go.Scatter(\n", + " x=household_income_new_york,\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", - "# Update layout\n", - "fig_texas.update_layout(\n", - " title='Texas Household (Couple) - Program Benefits by Income Level',\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='Benefit Amount',\n", - " legend_title='Programs',\n", - " xaxis=dict(tickformat='$,.0f', range=[0, 400000]),\n", - " yaxis=dict(tickformat='$,.0f'),\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%', range=[-1, 1]), # keep the same visual bounds\n", " height=600,\n", " width=1000\n", ")\n", "\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_texas = format_fig(fig_texas)\n", - "\n", - "# Display the figures\n", - "fig_vermont.show()\n", - "fig_texas.show()" + "fig_new_york_mtr = format_fig(fig_new_york_mtr)\n", + "fig_new_york_mtr.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "pe", + "display_name": "policyengine", "language": "python", "name": "python3" }, @@ -28068,9 +25154,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.16" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/us/medicaid/claude_help.md b/us/medicaid/claude_help.md new file mode 100644 index 0000000..f75acb9 --- /dev/null +++ b/us/medicaid/claude_help.md @@ -0,0 +1,48 @@ + + +## Using Your Old Outputs as Ground Truth + +**Recreate key statistics and compare:** +- Total ACA enrollment (you noted ~2M drop - that's a red flag) +- Income distribution of enrollees +- Average subsidy amounts by income level +- Count of people near the subsidy cliff (138-400% FPL) + +**Reverse-engineer expected values:** +- If effects were in deciles 4-6 before, calculate what income range that implied +- Check if those income ranges in the new data still contain the population that should be affected by the cliff +- The subsidy cliff at 400% FPL should hit people around $50-60k (single) or $100-120k (family of 4) - verify which decile that falls into now + +## Critical Checks Without Old Data + +**Sanity check the new results:** +```python +# Check income percentiles to see if decile 9 makes sense +print(df.groupby('income_decile')['income'].describe()) +print(f"400% FPL for single: {fpl_single * 4}") +print(f"400% FPL for family of 4: {fpl_family4 * 4}") + +# Verify ACA enrollment totals +print(f"Total ACA enrollees: {df[df['aca_enrolled']==1]['weight'].sum()}") +# Compare to known ~14-15 million marketplace enrollment +``` + +**Look for data/code misalignment:** +- Variable name changes (income vs magi vs adjusted_income) +- Changes in categorical coding (0/1 vs 1/2 for enrollment) +- Unit changes (annual vs monthly income) +- Weight scaling issues (person weights in thousands vs ones) + +## Most Likely Culprits + +Given your symptoms, check these first: + +1. **Income definition changed** - The decile shift suggests income might be calculated differently (e.g., excluding certain sources, or using post-tax instead of pre-tax) + +2. **Weight rescaling** - A 2M drop in enrollment could be a simple weight scaling issue + +3. **Sample universe** - The data team might have restricted to a different population (e.g., only tax filers, or excluded dependents) + +4. **Decile calculation scope** - Are you calculating deciles over the full population or just ACA-eligible? This could drastically shift which incomes fall in decile 9 + +Would it help if I wrote some diagnostic code to check these specific issues? Also, what specific outputs did you document from your previous run - do you have things like mean income by decile, or counts of people at different FPL thresholds? \ No newline at end of file diff --git a/us/medicaid/copy_of_aca_tampa.ipynb b/us/medicaid/copy_of_aca_tampa.ipynb new file mode 100644 index 0000000..aff0f8e --- /dev/null +++ b/us/medicaid/copy_of_aca_tampa.ipynb @@ -0,0 +1,19643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACA Premium Tax Credit Analysis - Tampa Family of 4\n", + "## Comparing 600% FPL Cliff Extension and IRA Extension" + ] + }, + { + "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 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": "markdown", + "metadata": {}, + "source": [ + "## Define Reforms\n", + "\n", + "### Reform 1: 600% FPL Cliff Extension\n", + "Activates the 600% FPL cliff extension parameter.\n", + "\n", + "### Reform 2: IRA Extension\n", + "Modifies required contribution percentages:\n", + "- Sets 0% for income brackets 0-2\n", + "- Sets 2%, 4%, 6%, and 8.5% for higher brackets\n", + "- Extends PTC eligibility beyond 400% FPL" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Reform 1: 600% FPL Cliff Extension\n", + "reform_600fpl = Reform.from_dict(\n", + " {\n", + " \"gov.contrib.aca.ptc_600_fpl_cliff.in_effect\": {\n", + " \"2026-01-01.2100-12-31\": True\n", + " }\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "# Reform 2: IRA Extension (from existing notebook reform3)\n", + "reform_ira = Reform.from_dict({\n", + " \"gov.aca.required_contribution_percentage[0].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[1].amount\": {\n", + " \"2025-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[2].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0\n", + " },\n", + " \"gov.aca.required_contribution_percentage[3].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.02\n", + " },\n", + " \"gov.aca.required_contribution_percentage[4].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.04\n", + " },\n", + " \"gov.aca.required_contribution_percentage[5].amount\": {\n", + " \"2026-01-01.2100-12-31\": 0.06\n", + " },\n", + " \"gov.aca.required_contribution_percentage[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": "markdown", + "metadata": {}, + "source": [ + "## Define Florida Family Situation\n", + "\n", + "Family of 4 in Tampa (Hillsborough County, FL):\n", + "- 2 parents (age 40)\n", + "- 2 children (ages 10 and 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "situation_florida = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"} # Hillsborough County (Tampa)\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"count\": 800,\n", + " \"min\": 0,\n", + " \"max\": 200000\n", + " }\n", + " ]]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Baseline simulation\n", + "simulation_baseline = Simulation(situation=situation_florida)\n", + "\n", + "# 600% FPL Cliff reform\n", + "simulation_600fpl = Simulation(situation=situation_florida, reform=reform_600fpl)\n", + "\n", + "# IRA Extension reform\n", + "simulation_ira = Simulation(situation=situation_florida, reform=reform_ira)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Benefits and Net Income" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Get household-level values\n", + "household_income = simulation_baseline.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "\n", + "# Baseline\n", + "baseline_aca_ptc = simulation_baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "baseline_net_income = simulation_baseline.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# 600% FPL Cliff\n", + "reform_600fpl_aca_ptc = simulation_600fpl.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_600fpl_net_income = simulation_600fpl.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# IRA Extension\n", + "reform_ira_aca_ptc = simulation_ira.calculate(\"aca_ptc\", map_to=\"household\", period=2026)\n", + "reform_ira_net_income = simulation_ira.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization Setup" + ] + }, + { + "cell_type": "code", + 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"fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_600fpl_aca_ptc,\n", + " mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_ptc.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_aca_ptc,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_ptc.update_layout(\n", + " title='Florida Family of 4 - ACA Premium Tax Credit by Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='ACA Premium Tax Credit',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_ptc = format_fig(fig_ptc)\n", + "fig_ptc.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 2: Health-Adjusted Net 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line=dict(color=GRAY, width=2)\n", + "))\n", + "\n", + "# 600% FPL Cliff\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_600fpl_net_income,\n", + " mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2)\n", + "))\n", + "\n", + "# IRA Extension\n", + "fig_net_income.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=reform_ira_net_income,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_net_income.update_layout(\n", + " title='Florida Family of 4 - Health-Adjusted Net Income',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Health-Adjusted Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(tickformat='$,.0f'),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_net_income = format_fig(fig_net_income)\n", + 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impact\n", + "fig_delta.add_trace(go.Scatter(\n", + " x=household_income,\n", + " y=delta_ira,\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2)\n", + "))\n", + "\n", + "fig_delta.update_layout(\n", + " title='Florida Family of 4 - Impact of Premium Tax Credit Reforms',\n", + " xaxis_title='Household Income',\n", + " yaxis_title='Change in Net Income',\n", + " xaxis=dict(tickformat='$,.0f', range=[0, 200000]),\n", + " yaxis=dict(\n", + " tickformat='$,.0f',\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='black'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " legend=dict(orientation='h')\n", + ")\n", + "\n", + "fig_delta = format_fig(fig_delta)\n", + "fig_delta.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chart 4: Marginal Tax Rates" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + 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"gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Marginal Tax Rate (including health benefits) - Florida Family of 4" + }, + "width": 800, + "xaxis": { + "gridcolor": "lightgray", + "range": [ + 0, + 200000 + ], + "showgrid": true, + "tickformat": "$,.0f", + "title": { + "text": "Employment Income" + } + }, + "yaxis": { + "gridcolor": "lightgray", + "range": [ + -1, + 1 + ], + "showgrid": true, + "tickformat": ".0%", + "title": { + "text": "Marginal Tax Rate" + }, + "zeroline": true, + "zerolinecolor": "gray", + "zerolinewidth": 1 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create situation for MTR calculation with more points\n", + "situation_fl_for_mtr = {\n", + " \"people\": {\n", + " \"parent1\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"parent2\": {\n", + " \"age\": {\"2026\": 40},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child1\": {\n", + " \"age\": {\"2026\": 10},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " },\n", + " \"child2\": {\n", + " \"age\": {\"2026\": 8},\n", + " \"employment_income\": {\"2026\": 0},\n", + " \"self_employment_income\": {\"2026\": 0}\n", + " }\n", + " },\n", + " \"families\": {\n", + " \"your family\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"spm_units\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"tax_units\": {\n", + " \"your tax unit\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"]\n", + " }\n", + " },\n", + " \"households\": {\n", + " \"your household\": {\n", + " \"members\": [\"parent1\", \"parent2\", \"child1\", \"child2\"],\n", + " \"state_name\": {\"2026\": \"FL\"},\n", + " \"county_fips\": {\"2026\": \"12057\"}\n", + " }\n", + " },\n", + " \"marital_units\": {\n", + " \"parents marital unit\": {\n", + " \"members\": [\"parent1\", \"parent2\"]\n", + " }\n", + " },\n", + " \"axes\": [[\n", + " {\n", + " \"name\": \"employment_income\",\n", + " \"min\": 0,\n", + " \"max\": 200000,\n", + " \"count\": 400,\n", + " \"period\": \"2026\"\n", + " }\n", + " ]]\n", + "}\n", + "\n", + "# Calculate net incomes for MTR\n", + "sim_baseline_mtr = Simulation(situation=situation_fl_for_mtr)\n", + "sim_600fpl_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_600fpl)\n", + "sim_ira_mtr = Simulation(situation=situation_fl_for_mtr, reform=reform_ira)\n", + "\n", + "household_income_mtr = sim_baseline_mtr.calculate(\"employment_income\", map_to=\"household\", period=2026)\n", + "baseline_net_income_mtr = sim_baseline_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_600fpl_net_income_mtr = sim_600fpl_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "reform_ira_net_income_mtr = sim_ira_mtr.calculate(\n", + " \"household_net_income_including_health_benefits\", \n", + " map_to=\"household\", \n", + " period=2026\n", + ")\n", + "\n", + "# Function to calculate MTR\n", + "def calc_mtr(incomes, net_incomes):\n", + " \"\"\"Calculate MTR between adjacent income points.\"\"\"\n", + " mtrs = []\n", + " mtr_incomes = []\n", + " for i in range(len(incomes) - 1):\n", + " income_change = incomes[i + 1] - incomes[i]\n", + " net_change = net_incomes[i + 1] - net_incomes[i]\n", + " if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]):\n", + " mtr = 1 - (net_change / income_change)\n", + " mtrs.append(mtr)\n", + " mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2)\n", + " return np.array(mtr_incomes), np.array(mtrs)\n", + "\n", + "baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_mtr, baseline_net_income_mtr)\n", + "reform_600fpl_mtr_incomes, reform_600fpl_mtrs = calc_mtr(household_income_mtr, reform_600fpl_net_income_mtr)\n", + "reform_ira_mtr_incomes, reform_ira_mtrs = calc_mtr(household_income_mtr, reform_ira_net_income_mtr)\n", + "\n", + "# Create MTR chart\n", + "fig_mtr = go.Figure()\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=baseline_mtr_incomes,\n", + " y=np.clip(baseline_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='Baseline',\n", + " line=dict(color=GRAY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_600fpl_mtr_incomes,\n", + " y=np.clip(reform_600fpl_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='600% FPL Cliff Extension',\n", + " line=dict(color=BLUE_PRIMARY, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
600% FPL MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.add_trace(go.Scatter(\n", + " x=reform_ira_mtr_incomes,\n", + " y=np.clip(reform_ira_mtrs, -1.0, 1.0),\n", + " mode='lines',\n", + " name='IRA Extension',\n", + " line=dict(color=PURPLE, width=2),\n", + " hovertemplate='Income: $%{x:,.0f}
IRA MTR: %{y:.1%}'\n", + "))\n", + "\n", + "fig_mtr.update_layout(\n", + " title='Marginal Tax Rate (including health benefits) - Florida Family of 4',\n", + " xaxis_title='Employment Income',\n", + " yaxis_title='Marginal Tax Rate',\n", + " xaxis=dict(\n", + " tickformat='$,.0f',\n", + " range=[0, 200000],\n", + " gridcolor='lightgray',\n", + " showgrid=True\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='.0%',\n", + " range=[-1.0, 1.0],\n", + " gridcolor='lightgray',\n", + " showgrid=True,\n", + " zeroline=True,\n", + " zerolinewidth=1,\n", + " zerolinecolor='gray'\n", + " ),\n", + " height=600,\n", + " width=1000,\n", + " hovermode='x unified',\n", + " plot_bgcolor='white',\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=1.02,\n", + " xanchor=\"right\",\n", + " x=1\n", + " )\n", + ")\n", + "\n", + "fig_mtr = format_fig(fig_mtr)\n", + "fig_mtr.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Income Benchmarks Analysis\n", + "\n", + "Calculate PTC at specific FPL percentages (138%, 300%, 400%, 600%)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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income_labelincome_usdptc_baselineptc_600fpl_cliffptc_ira_extension
0138% FPL ($45,446.42)45446.4210588.64746112240.22363312240.223633
1300% FPL ($98,796.57)98796.579726.92773413466.37890613466.378906
2400% FPL ($131,728.77)131728.770.0000008370.1210948370.121094
3600% FPL ($197,593.15)197593.150.0000000.0000002771.648438
\n", + "
" + ], + "text/plain": [ + " income_label income_usd ptc_baseline ptc_600fpl_cliff \\\n", + "0 138% FPL ($45,446.42) 45446.42 10588.647461 12240.223633 \n", + "1 300% FPL ($98,796.57) 98796.57 9726.927734 13466.378906 \n", + "2 400% FPL ($131,728.77) 131728.77 0.000000 8370.121094 \n", + "3 600% FPL ($197,593.15) 197593.15 0.000000 0.000000 \n", + "\n", + " ptc_ira_extension \n", + "0 12240.223633 \n", + "1 13466.378906 \n", + "2 8370.121094 \n", + "3 2771.648438 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "import pandas as pd\n", + "\n", + "PERIOD = 2026\n", + "\n", + "def get_tax_unit_fpg(base_situation: dict, period=PERIOD) -> float:\n", + " \"\"\"Return the tax unit FPG for the given situation/year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " for person in sit[\"people\"].values():\n", + " person.setdefault(\"employment_income\", {})\n", + " person[\"employment_income\"][str(period)] = 0\n", + " \n", + " sim = Simulation(situation=sit)\n", + " fpg = sim.calculate(\"tax_unit_fpg\", map_to=\"tax_unit\", period=period)[0]\n", + " return float(fpg)\n", + "\n", + "def calc_ptc_for_income(base_situation: dict, income: float, *, reform_to_use=None, period=PERIOD):\n", + " \"\"\"Return ACA PTC for the given income and year.\"\"\"\n", + " sit = copy.deepcopy(base_situation)\n", + " sit.pop(\"axes\", None)\n", + " \n", + " sit[\"people\"][\"parent1\"][\"employment_income\"] = {str(period): income}\n", + " \n", + " sim = Simulation(situation=sit, reform=reform_to_use)\n", + " return sim.calculate(\"aca_ptc\", map_to=\"household\", period=period)[0]\n", + "\n", + "# Get FPG for this family\n", + "fpg_2026 = get_tax_unit_fpg(situation_florida, period=PERIOD)\n", + "\n", + "# Define income levels to test\n", + "percent_targets = {\n", + " \"138% FPL\": 1.38,\n", + " \"300% FPL\": 3.00,\n", + " \"400% FPL\": 4.00,\n", + " \"600% FPL\": 6.00,\n", + "}\n", + "\n", + "rows = []\n", + "for label, mult in percent_targets.items():\n", + " inc = round(fpg_2026 * mult, 2)\n", + " rows.append({\n", + " \"income_label\": f\"{label} (${inc:,.2f})\",\n", + " \"income_usd\": inc,\n", + " \"ptc_baseline\": calc_ptc_for_income(situation_florida, inc, reform_to_use=None, period=PERIOD),\n", + " \"ptc_600fpl_cliff\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_600fpl, period=PERIOD),\n", + " \"ptc_ira_extension\": calc_ptc_for_income(situation_florida, inc, reform_to_use=reform_ira, period=PERIOD),\n", + " })\n", + "\n", + "ptc_df = pd.DataFrame(rows)\n", + "ptc_df" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/us/medicaid/debug_aca.ipynb b/us/medicaid/debug_aca.ipynb new file mode 100644 index 0000000..bc2e7de --- /dev/null +++ b/us/medicaid/debug_aca.ipynb @@ -0,0 +1,475 @@ +{ + "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", + "baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/cps_2023.h5\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.428581703125" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_aca_enrollment = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2025).sum()\n", + "\n", + "baseline_aca_enrollment/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.30790088671875" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_has_coverage = baseline.calculate(\"has_marketplace_health_coverage\", map_to=\"person\", period=2025)\n", + "baseline_is_eligible = baseline.calculate(\"is_aca_ptc_eligible\", map_to=\"person\", period=2025)\n", + "\n", + "baseline_aca = ((baseline_has_coverage & baseline_is_eligible)*baseline_has_coverage.weights).sum()\n", + "\n", + "baseline_aca/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "627 observations (weighted: 4,120,681) do not overlap.\n", + " household_id weight adjusted_gross_income age has_esi\n", + "37586 65629 28516.822266 26300.667969 40.0 False\n", + "37587 65629 28516.822266 26300.667969 58.0 False\n", + "4998 10045 23764.017578 20978.705078 27.0 False\n", + "37976 65209 19011.214844 0.000000 30.0 False\n", + "4497 9340 19011.214844 0.000000 22.0 False\n", + "24938 43163 19011.214844 31465.369141 9.0 False\n", + "43445 77870 19011.214844 7706.610352 26.0 False\n", + "43677 78173 19011.214844 19480.806641 35.0 True\n", + "43678 78173 19011.214844 19480.806641 6.0 False\n", + "43679 78173 19011.214844 19480.806641 1.0 False\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "period = 2025 # keep this in one place so it’s easy to change\n", + "sim = baseline # just so the code is a bit shorter\n", + "\n", + "# 1. Pull the two flags (and the person-level sampling weight) into one DataFrame\n", + "has_cov = sim.calculate(\"has_marketplace_health_coverage\",\n", + " map_to=\"person\", period=period)\n", + "is_elg = sim.calculate(\"is_aca_ptc_eligible\",\n", + " map_to=\"person\", period=period)\n", + "\n", + "df = pd.DataFrame({\n", + " \"has_cov\" : has_cov,\n", + " \"is_elg\" : is_elg,\n", + " \"weight\" : has_cov.weights # the Series carries its own CPS weight\n", + "})\n", + "\n", + "# 2. Keep only people who HAVE Marketplace coverage but FAIL the eligibility flag\n", + "problem = df[(df.has_cov) & (~df.is_elg)]\n", + "print(f\"{problem.shape[0]:,} observations \"\n", + " f\"(weighted: {problem.weight.sum():,.0f}) do not overlap.\")\n", + "\n", + "# 3. (Optional) Bring in a few explanatory variables so you can see *why*\n", + "extra_vars = [\n", + " \n", + " \"has_esi\", # or whatever ESI flag you rely on\n", + " \"age\",\n", + " \"household_id\",\n", + " \"aca_ptc\",\n", + " \"adjusted_gross_income\"\n", + " \n", + "]\n", + "\n", + "for v in extra_vars:\n", + " problem[v] = sim.calculate(v, map_to=\"person\", period=period).loc[problem.index]\n", + "\n", + "# 4. Quickly eyeball the 10 highest-weight cases\n", + "cols_to_show = [\"household_id\", \"weight\", \"adjusted_gross_income\", \"age\",\n", + " \"has_esi\", \n", + " \n", + " \n", + " ]\n", + "print(problem.sort_values(\"weight\", ascending=False)[cols_to_show].head(10))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "171.7710788408203" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_has_esi = baseline.calculate(\"has_esi\", map_to=\"person\", period=2025)\n", + "baseline_has_esi.sum() / 1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.465774763671875" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "double_coverage = ((baseline_has_coverage & baseline_has_esi)*baseline_has_coverage.weights).sum()\n", + "double_coverage/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "558 observations remain (weighted pop: 3.7 million)\n", + " household_id weight tax_unit_earned_income age tax_unit_dependents\n", + " 65629 28516.822266 28300.000000 40.0 6\n", + " 65629 28516.822266 28300.000000 58.0 6\n", + " 10045 23764.017578 20976.912109 27.0 0\n", + " 43163 19011.214844 31465.369141 9.0 3\n", + " 77870 19011.214844 2726.998779 26.0 0\n", + " 78173 19011.214844 20976.912109 6.0 2\n", + " 78173 19011.214844 20976.912109 1.0 2\n", + " 9340 19011.214844 0.000000 22.0 0\n", + " 43163 19011.214844 31465.369141 4.0 3\n", + " 43163 19011.214844 31465.369141 2.0 3\n", + " 65209 19011.214844 0.000000 30.0 0\n", + " 22644 14258.411133 0.000000 57.0 0\n", + " 43237 14258.411133 46673.632812 6.0 2\n", + " 43237 14258.411133 46673.632812 11.0 2\n", + " 82028 14258.411133 0.000000 24.0 0\n", + " 9547 14258.411133 52442.281250 19.0 1\n", + " 9547 14258.411133 52442.281250 21.0 1\n", + " 9547 14258.411133 52442.281250 54.0 1\n", + " 60998 14258.411133 13634.993164 21.0 2\n", + " 43784 14258.411133 10488.456055 48.0 0\n", + " 38683 14258.411133 56637.664062 7.0 2\n", + " 43554 14258.411133 0.000000 54.0 0\n", + " 39013 14258.411133 0.000000 58.0 0\n", + " 38683 14258.411133 56637.664062 5.0 2\n", + " 47389 14258.411133 0.000000 60.0 1\n", + " 38683 14258.411133 56637.664062 32.0 2\n", + " 71141 14258.411133 26221.140625 58.0 2\n", + " 71141 14258.411133 26221.140625 47.0 2\n", + " 71141 14258.411133 26221.140625 30.0 2\n", + " 17419 14258.411133 29367.677734 53.0 0\n", + " 17419 14258.411133 29367.677734 48.0 0\n", + " 71141 14258.411133 26221.140625 18.0 2\n", + " 73982 14258.411133 0.000000 47.0 3\n", + " 22661 14258.411133 19928.066406 50.0 0\n", + " 44036 14258.411133 17830.376953 37.0 2\n", + " 22644 14258.411133 0.000000 57.0 0\n", + " 47870 14258.411133 29996.986328 40.0 3\n", + " 45101 14258.411133 104814.812500 17.0 5\n", + " 46450 14258.411133 68174.968750 16.0 3\n", + " 59325 14258.411133 13634.993164 51.0 0\n", + " 59663 14258.411133 0.000000 35.0 2\n", + " 45328 14258.411133 12586.147461 40.0 0\n", + " 45275 14258.411133 62888.890625 70.0 0\n", + " 45333 14258.411133 37758.441406 15.0 1\n", + " 45101 14258.411133 104814.812500 15.0 5\n", + " 45101 14258.411133 104814.812500 16.0 5\n", + " 46206 14258.411133 419.259277 39.0 0\n", + " 45101 14258.411133 104814.812500 17.0 5\n", + " 7906 14258.411133 27269.986328 29.0 0\n", + " 25606 9505.607422 48172.429688 6.0 2\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "period = 2025\n", + "sim = baseline # shorthand\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. Line up the three core flags plus the CPS weight\n", + "# ------------------------------------------------------------\n", + "has_cov = sim.calculate(\"has_marketplace_health_coverage\",\n", + " map_to=\"person\", period=period)\n", + "is_elg = sim.calculate(\"is_aca_ptc_eligible\",\n", + " map_to=\"person\", period=period)\n", + "has_esi = sim.calculate(\"has_esi\",\n", + " map_to=\"person\", period=period)\n", + "\n", + "df = pd.DataFrame({\n", + " \"has_cov\" : has_cov,\n", + " \"is_elg\" : is_elg,\n", + " \"has_esi\" : has_esi,\n", + " \"weight\" : has_cov.weights,\n", + "})\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. Keep Marketplace ✔️ AND ACA-eligible ❌ AND ESI ❌\n", + "# ------------------------------------------------------------\n", + "problem_no_esi = df[(df.has_cov) & (~df.is_elg) & (~df.has_esi)]\n", + "\n", + "print(\n", + " f\"{problem_no_esi.shape[0]:,} observations remain \"\n", + " f\"(weighted pop: {problem_no_esi.weight.sum()/1e6:,.1f} million)\"\n", + ")\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. Pull in only *your* existing explanatory vars\n", + "# ------------------------------------------------------------\n", + "extra_vars = [\n", + " \"tax_unit_earned_income\",\n", + "\n", + " \"age\",\n", + " \"household_id\",\n", + " \"tax_unit_dependents\",\n", + "]\n", + "\n", + "for v in extra_vars:\n", + " problem_no_esi[v] = (\n", + " sim.calculate(v, map_to=\"person\", period=period)\n", + " .loc[problem_no_esi.index]\n", + " )\n", + "\n", + "# ------------------------------------------------------------\n", + "# 4. Quick look at the heaviest-weighted cases\n", + "# ------------------------------------------------------------\n", + "cols_to_show = [\n", + " \"household_id\",\n", + " \"weight\",\n", + " \"tax_unit_earned_income\",\n", + " \n", + " \"age\",\n", + " \"tax_unit_dependents\",\n", + "]\n", + "top50 = (problem_no_esi\n", + " .sort_values(\"weight\", ascending=False)\n", + " [cols_to_show]\n", + " .head(50))\n", + "\n", + "print(top50.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "132094.3153464236" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aca_premiums = baseline.calculate(\"aca_ptc\", map_to=\"household\", period=2025)\n", + "\n", + "aca_premiums.sum() / 1e6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Variable medicaid 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[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m medicaid \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;43mmedicaid\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\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 2\u001b[0m medicaid\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1e9\u001b[39m\n\u001b[1;32m 5\u001b[0m medicaid_per_capita \u001b[38;5;241m=\u001b[39m baseline\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedicaid_per_capita_cost\u001b[39m\u001b[38;5;124m\"\u001b[39m, period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2025\u001b[39m)\u001b[38;5;241m.\u001b[39msum()\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/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 medicaid does not exist." + ] + } + ], + "source": [ + "medicaid = baseline.calculate(\"medicaid\", map_to=\"person\", period=2025).sum()\n", + "medicaid/1e9\n", + "\n", + "\n", + "medicaid_per_capita = baseline.calculate(\"medicaid_per_capita_cost\", period=2025).sum()\n", + "\n", + "medicaid_per_capita/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "has_medicaid = baseline.calculate(\"medicaid\", map_to=\"person\", period=2025) >0\n", + "has_medicaid_per_capita = baseline.calculate(\"medicaid_per_capita_cost\", period=2025) >0\n", + "\n", + "(~has_medicaid & has_medicaid_per_capita).sum() \n", + "\n", + "target = has_medicaid & ~has_medicaid_per_capita" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['WI', 'NC'], dtype=object)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "medicaid_cat = baseline.calculate(\"medicaid_group\", map_to=\"person\", period=2025)\n", + "medicaid_cat[target]\n", + "\n", + "\n", + "df = baseline.calculate_dataframe([\"medicaid\", \"medicaid_per_capita_cost\", \"medicaid_group\", \"state_code\"], map_to=\"person\", period=2025)\n", + "df[\"target\"] = (df.medicaid > 0 )& (df.medicaid_per_capita_cost == 0)\n", + "df[df.target].state_code.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "122636.457876885" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many households have aca_ptc > 0\n", + "aca_ptc = baseline.calculate(\"aca_ptc\", map_to=\"household\",\n", + "period=2025)\n", + "\n", + "# Count households with aca_ptc > 0\n", + "households_with_ptc = (aca_ptc > 0).sum()\n", + "\n", + "\n", + "# Weighted count\n", + "weighted_households_with_ptc = ((aca_ptc > 0) *\n", + "aca_ptc.weights).sum()\n", + "\n", + "weighted_households_with_ptc/1e6" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/medicaid/medicaid_calculation_example.ipynb b/us/medicaid/medicaid_calculation_example.ipynb index b90fc03..77a8d6d 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 +207,110 @@ "# 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" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "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", + "# ---------- 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": { "kernelspec": { - "display_name": "pe", + "display_name": "base", "language": "python", "name": "python3" }, @@ -217,7 +324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/us/medicaid/mtr.ipynb b/us/medicaid/mtr.ipynb new file mode 100644 index 0000000..fb3500c --- /dev/null +++ b/us/medicaid/mtr.ipynb @@ -0,0 +1,252 @@ +{ + "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 Simulation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.12789845 -0.12789845 0. -0.12699997 -0.12699997 0.\n", + " -0.21553326 -0.21553326 0. -0.07592583 -0.07592583 0.\n", + " 0.06478518 0.06478518 0. 0.02499998 0.02499998 0.\n", + " 0.02499998 0.02499998 0. 0.02499998 0.02499998 0.\n", + " 0.02499998 0.02499998 0. 0.0656094 0.0656094 0.\n", + " 0.07450002 0.07450002 0. -0.2516328 -0.2516328 0.\n", + " 0.12963283 0.12963283 0. 0.29822654 0.29822654 0.\n", + " 0.3176992 0.3176992 0. 0.31410158 0.31410158 0.\n", + " 0.35033596 0.35033596 0. 0.35769922 0.35769922 0.\n", + " 0.35679686 0.35679686 0. 0.3550039 0.3550039 0.\n", + " 0.35770315 0.35770315 0. 0.35770315 0.35770315 0.\n", + " 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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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/medicaid/ny_mtr_axes_fast.py b/us/medicaid/ny_mtr_axes_fast.py new file mode 100644 index 0000000..c1b35f1 --- /dev/null +++ b/us/medicaid/ny_mtr_axes_fast.py @@ -0,0 +1,320 @@ +#!/usr/bin/env python3 +""" +FAST MTR calculation using axes-based simulations for net income. +This should be MUCH faster than looping - potentially under 2 minutes total. +""" + +from policyengine_us import Simulation +from policyengine_core.reforms import Reform +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from policyengine_core.charts import format_fig +import time +from datetime import datetime + +# PolicyEngine app colors +DARK_GRAY = "#616161" +BLUE_PRIMARY = "#2C6496" + +# Define the IRA extension reform +reform = Reform.from_dict({ + "gov.aca.ptc_phase_out_rate[0].amount": { + "2026-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[1].amount": { + "2025-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[2].amount": { + "2026-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[3].amount": { + "2026-01-01.2100-12-31": 0.02 + }, + "gov.aca.ptc_phase_out_rate[4].amount": { + "2026-01-01.2100-12-31": 0.04 + }, + "gov.aca.ptc_phase_out_rate[5].amount": { + "2026-01-01.2100-12-31": 0.06 + }, + "gov.aca.ptc_phase_out_rate[6].amount": { + "2026-01-01.2100-12-31": 0.085 + }, + "gov.aca.ptc_income_eligibility[2].amount": { + "2026-01-01.2100-12-31": True + } +}, country_id="us") + +def create_ny_household_situation_with_axes(period=2026, count=100, max_income=200_000): + """Creates a NY household situation WITH AXES for vectorized calculation.""" + return { + "people": { + "you": { + "age": {str(period): 30}, + "employment_income": {str(period): 0}, # Will be set by axes + "self_employment_income": {str(period): 0} + }, + "your partner": { + "age": {str(period): 30}, + "employment_income": {str(period): 0}, # Will be set by axes + "self_employment_income": {str(period): 0} + }, + "your first dependent": { + "age": {str(period): 3}, + "employment_income": {str(period): 0}, + "self_employment_income": {str(period): 0} + } + }, + "families": { + "your family": { + "members": ["you", "your partner", "your first dependent"] + } + }, + "spm_units": { + "your household": { + "members": ["you", "your partner", "your first dependent"] + } + }, + "tax_units": { + "your tax unit": { + "members": ["you", "your partner", "your first dependent"] + } + }, + "households": { + "your household": { + "members": ["you", "your partner", "your first dependent"], + "state_name": {str(period): "NY"}, + "county_fips": {str(period): "36061"} + } + }, + "marital_units": { + "your marital unit": { + "members": ["you", "your partner"] + }, + "your first dependent's marital unit": { + "members": ["your first dependent"], + "marital_unit_id": {str(period): 1} + } + }, + "axes": [[ + { + "name": "employment_income", + "min": 0, + "max": max_income, + "count": count, + "period": str(period) + } + ]] + } + +def main(): + print("=" * 70) + print("FAST MTR CALCULATION USING AXES") + print("Expected runtime: 1-2 minutes (vs 25+ minutes with loops!)") + print("=" * 70) + + start_time = time.time() + print(f"\nStart time: {datetime.now().strftime('%H:%M:%S')}\n") + + # Use 200 points for good resolution + num_points = 200 + + # Step 1: Baseline with axes + print(f"STEP 1: Calculating baseline at {num_points} points using axes...") + baseline_start = time.time() + + situation_baseline = create_ny_household_situation_with_axes(period=2026, count=num_points, max_income=200_000) + + try: + sim_baseline = Simulation(situation=situation_baseline) + + # Get the income values from the axes + household_income_baseline = sim_baseline.calculate("employment_income", map_to="household", period=2026) + + # Calculate net income including health benefits + baseline_net_income = sim_baseline.calculate( + "household_net_income_including_health_benefits", + map_to="household", + period=2026 + ) + + # Also get ACA PTC for verification + baseline_aca_ptc = sim_baseline.calculate("aca_ptc", map_to="household", period=2026) + + baseline_time = time.time() - baseline_start + print(f"✓ Baseline calculated in {baseline_time:.1f} seconds!") + print(f" Income shape: {household_income_baseline.shape}") + print(f" Net income shape: {baseline_net_income.shape}") + print(f" Sample incomes: {household_income_baseline[:5]}") + print(f" Sample net incomes: {baseline_net_income[:5]}\n") + + except Exception as e: + print(f"✗ Baseline failed: {e}") + return + + # Step 2: Reform with axes + print(f"STEP 2: Calculating reform at {num_points} points using axes...") + print("This is the moment of truth - will it be fast?") + reform_start = time.time() + + situation_reform = create_ny_household_situation_with_axes(period=2026, count=num_points, max_income=200_000) + + try: + sim_reform = Simulation(situation=situation_reform, reform=reform) + + # Get the income values from the axes + household_income_reform = sim_reform.calculate("employment_income", map_to="household", period=2026) + + # Calculate net income including health benefits WITH REFORM + reform_net_income = sim_reform.calculate( + "household_net_income_including_health_benefits", + map_to="household", + period=2026 + ) + + # Also get ACA PTC for verification + reform_aca_ptc = sim_reform.calculate("aca_ptc", map_to="household", period=2026) + + reform_time = time.time() - reform_start + print(f"✓ Reform calculated in {reform_time:.1f} seconds!") + print(f"🎉 THIS IS {(25*60)/reform_time:.0f}X FASTER THAN LOOPING!\n") + + except Exception as e: + print(f"✗ Reform failed: {e}") + print("Falling back to loop-based approach would be needed.") + return + + # Step 3: Calculate MTRs from adjacent points + print("STEP 3: Calculating MTRs from adjacent points...") + + def calc_mtr(incomes, net_incomes): + mtrs = [] + mtr_incomes = [] + for i in range(len(incomes) - 1): + income_change = incomes[i + 1] - incomes[i] + net_change = net_incomes[i + 1] - net_incomes[i] + if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]): + mtr = 1 - (net_change / income_change) + mtrs.append(mtr) + mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2) + return np.array(mtr_incomes), np.array(mtrs) + + baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_baseline, baseline_net_income) + reform_mtr_incomes, reform_mtrs = calc_mtr(household_income_reform, reform_net_income) + + print(f"✓ MTRs calculated\n") + + # Verify the calculations + print("VERIFICATION:") + print(f"Baseline average MTR: {baseline_mtrs.mean():.2%}") + print(f"Reform average MTR: {reform_mtrs.mean():.2%}") + + # Check at $150k + idx_150k = np.argmin(np.abs(baseline_mtr_incomes - 150_000)) + if idx_150k < len(baseline_mtrs): + print(f"\nAt ~$150k income:") + print(f" Baseline MTR: {baseline_mtrs[idx_150k]:.2%}") + print(f" Reform MTR: {reform_mtrs[idx_150k]:.2%}") + print(f" Difference: {reform_mtrs[idx_150k] - baseline_mtrs[idx_150k]:.2%}") + print(f" (Should be ~8.5% higher for reform)") + + # Check ACA amounts + idx_107k = np.argmin(np.abs(household_income_baseline - 107_000)) + print(f"\nAt ~$107k income (near cliff):") + print(f" Baseline ACA PTC: ${baseline_aca_ptc[idx_107k]:,.2f}") + print(f" Reform ACA PTC: ${reform_aca_ptc[idx_107k]:,.2f}") + + # Step 4: Create visualization + print("\nSTEP 4: Creating visualization...") + + fig = go.Figure() + + # Baseline MTR (solid line) + fig.add_trace(go.Scatter( + x=baseline_mtr_incomes, + y=np.clip(baseline_mtrs, -1.0, 1.0), + mode='lines', + name='Baseline', + line=dict(color=DARK_GRAY, width=2), + hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}' + )) + + # Reform MTR (dashed line) + fig.add_trace(go.Scatter( + x=reform_mtr_incomes, + y=np.clip(reform_mtrs, -1.0, 1.0), + 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=[-1.0, 1.0], + 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, -1.0, 1.0) + }).merge( + pd.DataFrame({ + 'income': reform_mtr_incomes, + 'reform_mtr': reform_mtrs, + 'reform_mtr_display': np.clip(reform_mtrs, -1.0, 1.0) + }), + 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 diff --git a/us/medicaid/ny_mtr_baseline_vs_reform_fast.csv b/us/medicaid/ny_mtr_baseline_vs_reform_fast.csv new file mode 100644 index 0000000..754e053 --- /dev/null +++ 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a/us/medicaid/old_dataset.ipynb b/us/medicaid/old_dataset.ipynb new file mode 100644 index 0000000..14142f2 --- /dev/null +++ b/us/medicaid/old_dataset.ipynb @@ -0,0 +1,126 @@ +{ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sim = Microsimulation(dataset=\"/Users/daphnehansell/Documents/GitHub/analysis-notebooks/us/medicaid/enhanced_cps_2024.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " value weight\n", + "0 107805.242188 7.193183\n", + "1 67854.984375 5.331448\n", + "2 20952.380859 5.331448\n", + "3 1.457558 1523.087158\n", + "4 0.000000 1523.087158\n", + "... ... ...\n", + "56763 -1044.119141 0.019413\n", + "56764 31625.267578 0.001958\n", + "56765 15550.738281 0.002303\n", + "56766 24284.789062 0.002303\n", + "56767 86636.476562 0.003094\n", + "\n", + "[56768 rows x 2 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.calculate('adjusted_gross_income')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " value weight\n", + "0 98.845360 608.083069\n", + "1 0.790763 608.083069\n", + "2 0.000000 608.083069\n", + "3 43622.523438 1576.879639\n", + "4 0.000000 1160.135986\n", + "... ... ...\n", + "77421 30575.771484 709.115173\n", + "77422 0.000000 586.208740\n", + "77423 26181.121094 387.369049\n", + "77424 110852.523438 387.369049\n", + "77425 120745.625000 723.155579\n", + "\n", + "[77426 rows x 2 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_new = Microsimulation()\n", + "sim_new.calculate('adjusted_gross_income')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/medicaid/tx_mtr_axes_fast.py b/us/medicaid/tx_mtr_axes_fast.py new file mode 100644 index 0000000..6bfd399 --- /dev/null +++ b/us/medicaid/tx_mtr_axes_fast.py @@ -0,0 +1,305 @@ +#!/usr/bin/env python3 +""" +FAST MTR calculation using axes-based simulations for net income. +Texas couple (ages 25 & 28, no kids) - baseline vs IRA extension reform. +""" + +from policyengine_us import Simulation +from policyengine_core.reforms import Reform +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from policyengine_core.charts import format_fig +import time +from datetime import datetime + +# PolicyEngine app colors +DARK_GRAY = "#616161" +BLUE_PRIMARY = "#2C6496" + +# Define the IRA extension reform +reform = Reform.from_dict({ + "gov.aca.ptc_phase_out_rate[0].amount": { + "2026-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[1].amount": { + "2025-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[2].amount": { + "2026-01-01.2100-12-31": 0 + }, + "gov.aca.ptc_phase_out_rate[3].amount": { + "2026-01-01.2100-12-31": 0.02 + }, + "gov.aca.ptc_phase_out_rate[4].amount": { + "2026-01-01.2100-12-31": 0.04 + }, + "gov.aca.ptc_phase_out_rate[5].amount": { + "2026-01-01.2100-12-31": 0.06 + }, + "gov.aca.ptc_phase_out_rate[6].amount": { + "2026-01-01.2100-12-31": 0.085 + }, + "gov.aca.ptc_income_eligibility[2].amount": { + "2026-01-01.2100-12-31": True + } +}, country_id="us") + +def create_tx_household_situation_with_axes(period=2026, count=100, max_income=200_000): + """Creates a TX couple situation WITH AXES for vectorized calculation.""" + return { + "people": { + "you": { + "age": {str(period): 25}, + "employment_income": {str(period): 0}, # Will be set by axes + "self_employment_income": {str(period): 0} + }, + "your partner": { + "age": {str(period): 28}, + "employment_income": {str(period): 0}, # Will be set by axes + "self_employment_income": {str(period): 0} + } + }, + "families": { + "your family": { + "members": ["you", "your partner"] + } + }, + "spm_units": { + "your household": { + "members": ["you", "your partner"] + } + }, + "tax_units": { + "your tax unit": { + "members": ["you", "your partner"] + } + }, + "households": { + "your household": { + "members": ["you", "your partner"], + "state_name": {str(period): "TX"}, + "county_fips": {str(period): "48015"} + } + }, + "marital_units": { + "your marital unit": { + "members": ["you", "your partner"] + } + }, + "axes": [[ + { + "name": "employment_income", + "min": 0, + "max": max_income, + "count": count, + "period": str(period) + } + ]] + } + +def main(): + print("=" * 70) + print("FAST MTR CALCULATION USING AXES - TEXAS COUPLE") + print("Expected runtime: 1-2 minutes (vs 25+ minutes with loops!)") + print("=" * 70) + + start_time = time.time() + print(f"\nStart time: {datetime.now().strftime('%H:%M:%S')}\n") + + # Use 200 points for good resolution + num_points = 200 + + # Step 1: Baseline with axes + print(f"STEP 1: Calculating baseline at {num_points} points using axes...") + baseline_start = time.time() + + situation_baseline = create_tx_household_situation_with_axes(period=2026, count=num_points, max_income=200_000) + + try: + sim_baseline = Simulation(situation=situation_baseline) + + # Get the income values from the axes + household_income_baseline = sim_baseline.calculate("employment_income", map_to="household", period=2026) + + # Calculate net income including health benefits + baseline_net_income = sim_baseline.calculate( + "household_net_income_including_health_benefits", + map_to="household", + period=2026 + ) + + # Also get ACA PTC for verification + baseline_aca_ptc = sim_baseline.calculate("aca_ptc", map_to="household", period=2026) + + baseline_time = time.time() - baseline_start + print(f"✓ Baseline calculated in {baseline_time:.1f} seconds!\n") + + except Exception as e: + print(f"✗ Baseline failed: {e}") + return + + # Step 2: Reform with axes + print(f"STEP 2: Calculating reform at {num_points} points using axes...") + reform_start = time.time() + + situation_reform = create_tx_household_situation_with_axes(period=2026, count=num_points, max_income=200_000) + + try: + sim_reform = Simulation(situation=situation_reform, reform=reform) + + # Get the income values from the axes + household_income_reform = sim_reform.calculate("employment_income", map_to="household", period=2026) + + # Calculate net income including health benefits WITH REFORM + reform_net_income = sim_reform.calculate( + "household_net_income_including_health_benefits", + map_to="household", + period=2026 + ) + + # Also get ACA PTC for verification + reform_aca_ptc = sim_reform.calculate("aca_ptc", map_to="household", period=2026) + + reform_time = time.time() - reform_start + print(f"✓ Reform calculated in {reform_time:.1f} seconds!") + print(f"🎉 THIS IS {(25*60)/reform_time:.0f}X FASTER THAN LOOPING!\n") + + except Exception as e: + print(f"✗ Reform failed: {e}") + return + + # Step 3: Calculate MTRs from adjacent points + print("STEP 3: Calculating MTRs from adjacent points...") + + def calc_mtr(incomes, net_incomes): + mtrs = [] + mtr_incomes = [] + for i in range(len(incomes) - 1): + income_change = incomes[i + 1] - incomes[i] + net_change = net_incomes[i + 1] - net_incomes[i] + if income_change > 0 and not np.isnan(net_incomes[i]) and not np.isnan(net_incomes[i + 1]): + mtr = 1 - (net_change / income_change) + mtrs.append(mtr) + mtr_incomes.append((incomes[i] + incomes[i + 1]) / 2) + return np.array(mtr_incomes), np.array(mtrs) + + baseline_mtr_incomes, baseline_mtrs = calc_mtr(household_income_baseline, baseline_net_income) + reform_mtr_incomes, reform_mtrs = calc_mtr(household_income_reform, reform_net_income) + + print(f"✓ MTRs calculated\n") + + # Verify the calculations + print("VERIFICATION:") + print(f"Baseline average MTR: {baseline_mtrs.mean():.2%}") + print(f"Reform average MTR: {reform_mtrs.mean():.2%}") + + # Check at $150k + idx_150k = np.argmin(np.abs(baseline_mtr_incomes - 150_000)) + if idx_150k < len(baseline_mtrs): + print(f"\nAt ~$150k income:") + print(f" Baseline MTR: {baseline_mtrs[idx_150k]:.2%}") + print(f" Reform MTR: {reform_mtrs[idx_150k]:.2%}") + print(f" Difference: {reform_mtrs[idx_150k] - baseline_mtrs[idx_150k]:.2%}") + print(f" (Should be ~8.5% higher for reform)") + + # Check ACA amounts at 400% FPL (around $87k for couple) + idx_87k = np.argmin(np.abs(household_income_baseline - 87_000)) + print(f"\nAt ~$87k income (near 400% FPL cliff):") + print(f" Baseline ACA PTC: ${baseline_aca_ptc[idx_87k]:,.2f}") + print(f" Reform ACA PTC: ${reform_aca_ptc[idx_87k]:,.2f}") + + # Step 4: Create visualization + print("\nSTEP 4: Creating visualization...") + + fig = go.Figure() + + # Baseline MTR (solid line) + fig.add_trace(go.Scatter( + x=baseline_mtr_incomes, + y=np.clip(baseline_mtrs, -1.0, 1.0), + mode='lines', + name='Baseline', + line=dict(color=DARK_GRAY, width=2), + hovertemplate='Income: $%{x:,.0f}
Baseline MTR: %{y:.1%}' + )) + + # Reform MTR (dashed line) + fig.add_trace(go.Scatter( + x=reform_mtr_incomes, + y=np.clip(reform_mtrs, -1.0, 1.0), + mode='lines', + name='IRA Extension', + line=dict(color=BLUE_PRIMARY, width=2, dash='dash'), + hovertemplate='Income: $%{x:,.0f}
Reform MTR: %{y:.1%}' + )) + + # Update layout + fig.update_layout( + title='Marginal tax rate including health benefits - Texas Couple', + xaxis_title='Employment income', + yaxis_title='Marginal tax rate', + xaxis=dict( + tickformat='$,.0f', + range=[0, 200_000], + gridcolor='lightgray', + showgrid=True + ), + yaxis=dict( + tickformat='.0%', + range=[-1.0, 1.0], + 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("tx_mtr_baseline_vs_reform_fast.png", width=1000, height=650, scale=2) + print("Chart saved as tx_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, -1.0, 1.0) + }).merge( + pd.DataFrame({ + 'income': reform_mtr_incomes, + 'reform_mtr': reform_mtrs, + 'reform_mtr_display': np.clip(reform_mtrs, -1.0, 1.0) + }), + on='income', + how='outer' + ).sort_values('income') + + combined_df.to_csv("tx_mtr_baseline_vs_reform_fast.csv", index=False) + print("Data saved to tx_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 diff --git a/us/medicaid/tx_mtr_baseline_vs_reform_fast.csv b/us/medicaid/tx_mtr_baseline_vs_reform_fast.csv new file mode 100644 index 0000000..e2b75ba --- /dev/null +++ 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+199497.484375,0.23450763346910852,0.23450763346910852,0.23450763346910852,0.23450763346910852 diff --git a/us/medicaid/tx_mtr_baseline_vs_reform_fast.png b/us/medicaid/tx_mtr_baseline_vs_reform_fast.png new file mode 100644 index 0000000..2073d34 Binary files /dev/null and b/us/medicaid/tx_mtr_baseline_vs_reform_fast.png differ diff --git a/us/nyt/ira_ptc.ipynb b/us/nyt/ira_ptc.ipynb new file mode 100644 index 0000000..dbec8da --- /dev/null +++ b/us/nyt/ira_ptc.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'baseline_2025' 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[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# STEP 0 — run this right after you define baseline_2025\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m coverage_flag_2025 \u001b[38;5;241m=\u001b[39m \u001b[43mbaseline_2025\u001b[49m\u001b[38;5;241m.\u001b[39mcalculate(\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_marketplace_health_coverage\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[39mYEAR_FILTER\n\u001b[1;32m 4\u001b[0m )\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Convert to Boolean explicitly and keep only the TRUEs\u001b[39;00m\n\u001b[1;32m 7\u001b[0m ptc_households \u001b[38;5;241m=\u001b[39m coverage_flag_2025\u001b[38;5;241m.\u001b[39mindex[coverage_flag_2025\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mbool\u001b[39m)]\n", + "\u001b[0;31mNameError\u001b[0m: name 'baseline_2025' is not defined" + ] + } + ], + "source": [ + "# STEP 0 — run this right after you define baseline_2025\n", + "coverage_flag_2025 = baseline_2025.calculate(\n", + " \"has_marketplace_health_coverage\", map_to=\"household\", period=YEAR_FILTER\n", + ")\n", + "\n", + "# Convert to Boolean explicitly and keep only the TRUEs\n", + "ptc_households = coverage_flag_2025.index[coverage_flag_2025.astype(bool)]\n", + "\n", + "print(\"Households flagged as Marketplace in 2025:\", len(ptc_households))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total households with ANY PTC in 2026 baseline: 0.0\n", + "…inside Marketplace subset: 0\n" + ] + } + ], + "source": [ + "# STEP 1 — run this right after you create baseline_2026 (before the reform)\n", + "\n", + "ptc_all = baseline_2026.calculate(\"aca_ptc\", map_to=\"household\", period=YEAR_ANALYZE)\n", + "print(\"Total households with ANY PTC in 2026 baseline:\", (ptc_all > 0).sum())\n", + "\n", + "# Check inside the Marketplace subset\n", + "baseline_ptc_subset = ptc_all.loc[ptc_households]\n", + "print(\"…inside Marketplace subset:\", (baseline_ptc_subset > 0).sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'baseline_2026' 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[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Ensure baseline_2026 is defined in a previous cell\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mbaseline_2026\u001b[49m\u001b[38;5;241m.\u001b[39mcalculate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msecond_lowest_cost_silver_premium\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m 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)\u001b[38;5;241m.\u001b[39mdescribe()\n", + "\u001b[0;31mNameError\u001b[0m: name 'baseline_2026' is not defined" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2,330 households enrolled in Marketplace coverage in 2025\n", + "Saved 2,330 rows to aca_ptc_extension_impacts.csv\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------------\n", + "# 0. Imports & constants\n", + "# ---------------------------------------------------------------------\n", + "from policyengine_us import Microsimulation\n", + "from policyengine_core.reforms import Reform\n", + "import pandas as pd\n", + "\n", + "DATASET = \"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\"\n", + "YEAR_ANALYZE = 2026 # policy year we care about\n", + "YEAR_FILTER = 2025 # year used to flag Marketplace households\n", + "OUTPUT_CSV = \"aca_ptc_extension_impacts.csv\"\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 1. Reform: keep the ARPA / IRA enhanced PTC schedule past 2025\n", + "# ---------------------------------------------------------------------\n", + "aca_extension_reform = Reform.from_dict(\n", + " {\n", + " # 0 – 150 % FPL: zero expected contribution\n", + " \"gov.aca.ptc_phase_out_rate[0].amount\": {\"2026-01-01.2100-12-31\": 0},\n", + " \"gov.aca.ptc_phase_out_rate[1].amount\": {\"2026-01-01.2100-12-31\": 0},\n", + "\n", + " # 150 – 400 % FPL glide-path 0 → 8.5 %\n", + " \"gov.aca.ptc_phase_out_rate[2].amount\": {\"2026-01-01.2100-12-31\": 0},\n", + " \"gov.aca.ptc_phase_out_rate[3].amount\": {\"2026-01-01.2100-12-31\": 0.02},\n", + " \"gov.aca.ptc_phase_out_rate[4].amount\": {\"2026-01-01.2100-12-31\": 0.04},\n", + " \"gov.aca.ptc_phase_out_rate[5].amount\": {\"2026-01-01.2100-12-31\": 0.06},\n", + " \"gov.aca.ptc_phase_out_rate[6].amount\": {\"2026-01-01.2100-12-31\": 0.085},\n", + "\n", + " # Keep subsidies available above 400 % FPL\n", + " \"gov.aca.ptc_income_eligibility[2].amount\": {\"2026-01-01.2100-12-31\": True},\n", + " },\n", + " country_id=\"us\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 2. Identify Marketplace households in 2025\n", + "# ---------------------------------------------------------------------\n", + "baseline_2025 = Microsimulation(dataset=DATASET)\n", + "\n", + "coverage_flag_2025 = baseline_2025.calculate(\n", + " \"has_marketplace_health_coverage\", map_to=\"household\", period=YEAR_FILTER\n", + ")\n", + "ptc_households = coverage_flag_2025.index[coverage_flag_2025.astype(bool)]\n", + "\n", + "print(f\"{len(ptc_households):,} households enrolled in Marketplace coverage in {YEAR_FILTER}\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 3. Run baseline and reform for 2026\n", + "# ---------------------------------------------------------------------\n", + "baseline_2026 = Microsimulation(dataset=DATASET)\n", + "reform_2026 = Microsimulation(reform=aca_extension_reform, dataset=DATASET)\n", + "\n", + "def pull(sim, var):\n", + " \"\"\"Convenience: grab a household variable for YEAR_ANALYZE, keep only PTC households.\"\"\"\n", + " return sim.calculate(var, map_to=\"household\", period=YEAR_ANALYZE).loc[ptc_households]\n", + "\n", + "state = pull(baseline_2026, \"state_code\")\n", + "employment_income = pull(baseline_2026, \"employment_income\")\n", + "\n", + "baseline_ptc = pull(baseline_2026, \"aca_ptc\")\n", + "reform_ptc = pull(reform_2026, \"aca_ptc\")\n", + "\n", + "baseline_income_hb = pull(baseline_2026, \"household_net_income_including_health_benefits\")\n", + "reform_income_hb = pull(reform_2026, \"household_net_income_including_health_benefits\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 4. Assemble results\n", + "# ---------------------------------------------------------------------\n", + "df = pd.DataFrame({\n", + " \"state\" : state,\n", + " \"employment_income\" : employment_income,\n", + " \"baseline_aca_ptc\" : baseline_ptc,\n", + " \"reform_aca_ptc\" : reform_ptc,\n", + " \"aca_ptc_change\" : reform_ptc - baseline_ptc,\n", + " \"baseline_net_income_including_hb\" : baseline_income_hb,\n", + " \"reform_net_income_including_hb\" : reform_income_hb,\n", + " \"net_income_change_including_hb\" : reform_income_hb - baseline_income_hb,\n", + "})\n", + "\n", + "df.to_csv(OUTPUT_CSV, index=False)\n", + "print(f\"Saved {len(df):,} rows to {OUTPUT_CSV}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Variable second_lowest_cost_silver_premium 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[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Ensure baseline_2026 is defined in a previous cell\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mbaseline_2026\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;43msecond_lowest_cost_silver_premium\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \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;43mhousehold\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;43m2026\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdescribe()\n", + "File \u001b[0;32m~/miniconda3/envs/policyengine/lib/python3.10/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/python3.10/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/python3.10/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 second_lowest_cost_silver_premium does not exist." + ] + } + ], + "source": [ + "# Ensure baseline_2026 is defined in a previous cell\n", + "baseline_2026.calculate(\"second_lowest_cost_silver_premium\",\n", + " map_to=\"household\", period=2026).describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/nyt/medicaid_work_req.ipynb b/us/nyt/medicaid_work_req.ipynb new file mode 100644 index 0000000..844b87e --- /dev/null +++ b/us/nyt/medicaid_work_req.ipynb @@ -0,0 +1,57 @@ +{ + "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 plotly.graph_objects as go\n", + "import pandas as pd\n", + "from policyengine_core.charts import format_fig \n", + "import plotly.express as px" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aca_baseline = " + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/nyt/nyt_bug.ipynb b/us/nyt/nyt_bug.ipynb new file mode 100644 index 0000000..d33511f --- /dev/null +++ b/us/nyt/nyt_bug.ipynb @@ -0,0 +1,275 @@ +{ + "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": 4, + "metadata": {}, + "outputs": [], + "source": [ + "year = 2025\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_magi = baseline.calculate(\"aca_magi\", map_to=\"household\", period=year)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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household_idaca_magiStateMarriedNum_DependentsEmployment_IncomeSelf_Employment_Incomeaca_baselinerating_area
23890103176186612.625PA1.03.080329.0846250.039122.4453129
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" + ], + "text/plain": [ + " household_id aca_magi State Married Num_Dependents \\\n", + "23890 103176 186612.625 PA 1.0 3.0 \n", + "\n", + " Employment_Income Self_Employment_Income aca_baseline rating_area \n", + "23890 80329.084625 0.0 39122.445312 9 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a DataFrame with the outputs\n", + "data = {\n", + " \"household_id\": household_id,\n", + " \"aca_magi\": aca_magi,\n", + " \"State\": state,\n", + " \"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", + " \"rating_area\": rating_area,\n", + "}\n", + "\n", + "df_outputs = pd.DataFrame(data)\n", + "df_outputs[df_outputs['household_id'] == 103176]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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person_idhousehold_idagemedicaid_eligiblemedicaid_per_capita_cost
587245872410317659.0False0.000000
587255872510317656.0False0.000000
587265872610317626.0False0.000000
587275872710317618.0False0.000000
587285872810317616.0True4179.962891
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" + ], + "text/plain": [ + " person_id household_id age medicaid_eligible \\\n", + "58724 58724 103176 59.0 False \n", + "58725 58725 103176 56.0 False \n", + "58726 58726 103176 26.0 False \n", + "58727 58727 103176 18.0 False \n", + "58728 58728 103176 16.0 True \n", + "\n", + " medicaid_per_capita_cost \n", + "58724 0.000000 \n", + "58725 0.000000 \n", + "58726 0.000000 \n", + "58727 0.000000 \n", + "58728 4179.962891 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get Medicaid eligibility for each person\n", + "medicaid_eligibility = baseline.calculate(\"is_medicaid_eligible\", map_to=\"person\", period=year)\n", + "person_household_id = baseline.calculate(\"household_id\", map_to=\"person\", period=year)\n", + "age = baseline.calculate(\"age\", map_to=\"person\", period=year)\n", + "medicaid_per_capita_cost = baseline.calculate(\"medicaid_per_capita_cost\", map_to=\"person\", period=year)\n", + "\n", + "# Create person-level dataframe\n", + "person_data = pd.DataFrame({\n", + " \"person_id\": range(len(medicaid_eligibility)),\n", + " \"household_id\": person_household_id,\n", + " \"age\": age,\n", + " \"medicaid_eligible\": medicaid_eligibility,\n", + " \"medicaid_per_capita_cost\": medicaid_per_capita_cost\n", + "\n", + "})\n", + "\n", + "# Filter for household 103176\n", + "household_103176_medicaid = person_data[person_data['household_id'] ==\n", + "103176]\n", + "household_103176_medicaid = person_data[person_data['household_id'] == 103176]\n", + "\n", + "\n", + "household_103176_medicaid\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/nyt/work_req.ipynb b/us/nyt/work_req.ipynb new file mode 100644 index 0000000..1b6715d --- /dev/null +++ b/us/nyt/work_req.ipynb @@ -0,0 +1,611 @@ +{ + "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": null, + "metadata": { + "vscode": { + "languageId": "javascript" + } + }, + "outputs": [], + "source": [] + }, + { + "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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "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", + "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=\"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": 5, + "metadata": {}, + "outputs": [], + "source": [ + "medicaid_reform = reformed.calculate(\"medicaid_enrolled\", map_to=\"household\", period=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "88.03708112231564" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "medicaid_baseline.sum()/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66.17075737452024" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "medicaid_reform.sum()/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "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": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "754.2560939202893" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "medicaid_cost_baseline.sum()/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "583.9976766367972" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "medicaid_cost_reform.sum()/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 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", + " 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, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {\n", + " \"household_id\": household_id,\n", + " \"age\": age,\n", + " \"State\": state,\n", + " \"Married\": married,\n", + " \"Num_Dependents\": num_dependents,\n", + " \"net_income\": net_income,\n", + " \"medicaid_baseline\": medicaid_baseline,\n", + " \"medicaid_reform\": medicaid_reform,\n", + " \"disability\": disability,\n", + "}\n", + "\n", + "df_outputs = pd.DataFrame(data)\n", + "df_outputs.sample(n=10) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " household_id age State Married Num_Dependents net_income \\\n", + "39714 171625 190.0 CA 1.0 0.0 266112.437500 \n", + "25746 108895 162.0 IL 1.0 1.0 113722.921875 \n", + "34089 145258 123.0 AR 1.0 0.0 52366.300781 \n", + "21260 93908 60.0 MA 0.0 0.0 12498.601562 \n", + "39141 169644 31.0 CA 0.0 1.0 20294.919922 \n", + "5781 21507 87.0 MI 0.0 1.0 126903.640625 \n", + "25970 109575 151.0 IL 1.0 1.0 77474.335938 \n", + "35709 152511 145.0 TX 1.0 3.0 54013.031250 \n", + "21664 96019 147.0 CT 0.0 0.0 105085.656250 \n", + "25351 107589 52.0 IL 0.0 2.0 24615.585938 \n", + "\n", + " medicaid_baseline medicaid_reform disability \n", + "39714 2.0 1.0 2.0 \n", + "25746 1.0 0.0 1.0 \n", + "34089 1.0 0.0 1.0 \n", + "21260 2.0 1.0 0.0 \n", + "39141 2.0 2.0 0.0 \n", + "5781 1.0 0.0 1.0 \n", + "25970 2.0 1.0 0.0 \n", + "35709 2.0 2.0 0.0 \n", + "21664 1.0 1.0 0.0 \n", + "25351 3.0 3.0 1.0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_medicaid = df_outputs[df_outputs[\"medicaid_baseline\"].gt(0)]\n", + "df_medicaid.sample(n=10) \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "policyengine", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/us/snap_twitter.ipynb b/us/snap_twitter.ipynb new file mode 100644 index 0000000..cef92ea --- /dev/null +++ b/us/snap_twitter.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "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": {}, + "outputs": [], + "source": [ + "year =2024\n", + "\n", + "household_size = baseline.calculate(\"household_size\", map_to= \"household\", period=year)\n", + "snap = baseline.calculate(\"snap\", map_to= \"household\", period=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = {\n", + " \"household_size\": household_size,\n", + " \"snap\": snap,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Households getting maximum snap: 51343.10517691474\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Single month keeps it simple\n", + "period = \"2024-01\"\n", + "\n", + "snap = baseline.calculate(\"snap\", period=period) # SPM-unit level\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period) # size of that SPM unit\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period) # population weights\n", + "\n", + "mask = (unit_size == 4) & np.isclose(snap, 973, atol=1)\n", + "\n", + "print(\"Households getting maximum snap: \", float((mask * weights).sum()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.11511349654758" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Single month keeps it simple\n", + "period = \"2024-01\"\n", + "\n", + "snap = baseline.calculate(\"snap\", period=period) # SPM-unit level\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period) # size of that SPM unit\n", + "\n", + "(unit_size == 4)[snap >= 1].sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1455.7" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snap[unit_size == 4].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.000000 4561\n", + "973.000000 184\n", + "95.000000 3\n", + "363.399963 2\n", + "705.400024 2\n", + "548.199951 2\n", + "744.700012 2\n", + "790.299988 2\n", + "573.400024 2\n", + "488.499969 2\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(snap[unit_size == 4]).value_counts().head(10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 Person Households that get Maximum SNAP Allotment : 2271.6703824460506\n", + "Median SNAP for Family of 4: $704\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import microdf as mdf\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Single month keeps it simple\n", + "period = \"2024-01\"\n", + "\n", + "snap = baseline.calculate(\"snap\", period=period) # SPM-unit level\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period) # size of that SPM unit\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period) # population weights\n", + "\n", + "mask = (unit_size == 4) & np.isclose(snap, 750, atol=1)\n", + "\n", + "print(\"4 Person Households that get Maximum SNAP Allotment : \", float((mask * weights).sum()))\n", + "\n", + "period = \"2024-01\" # keep benefits in monthly dollars\n", + "\n", + "# ── Pull variables at the SPM-unit level ──\n", + "snap = baseline.calculate(\"snap\", period=period)\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period)\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period)\n", + "\n", + "# ── Keep 4-person units that actually get SNAP (> $0) ──\n", + "mask = (unit_size == 4) & (snap > 0)\n", + "\n", + "# Tiny 2-column frame for microdf\n", + "df = pd.DataFrame({\"benefit\": snap[mask], \"wt\": weights[mask]})\n", + "\n", + "# Unweighted and weighted medians\n", + "median_unwtd = float(df[\"benefit\"].median())\n", + "median_wtd = float(mdf.weighted_median(df, \"benefit\", \"wt\"))\n", + "\n", + "print(f\"Median SNAP for Family of 4: ${median_wtd:,.0f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Median SNAP for Family of 2: $270\n", + "Median SNAP for Family of 1: $114\n" + ] + } + ], + "source": [ + "period = \"2024-01\" # keep benefits in monthly dollars\n", + "\n", + "# ── Pull variables at the SPM-unit level ──\n", + "snap = baseline.calculate(\"snap\", period=period)\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period)\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period)\n", + "\n", + "# ── Keep 4-person units that actually get SNAP (> $0) ──\n", + "mask = (unit_size == 2) & (snap > 0)\n", + "\n", + "# Tiny 2-column frame for microdf\n", + "df = pd.DataFrame({\"benefit\": snap[mask], \"wt\": weights[mask]})\n", + "\n", + "# Unweighted and weighted medians\n", + "median_unwtd = float(df[\"benefit\"].median())\n", + "median_wtd = float(mdf.weighted_median(df, \"benefit\", \"wt\"))\n", + "\n", + "print(f\"Median SNAP for Family of 2: ${median_wtd:,.0f}\")\n", + "\n", + "period = \"2024-01\" # keep benefits in monthly dollars\n", + "\n", + "# ── Pull variables at the SPM-unit level ──\n", + "snap = baseline.calculate(\"snap\", period=period)\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period)\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period)\n", + "\n", + "# ── Keep 4-person units that actually get SNAP (> $0) ──\n", + "mask = (unit_size == 1) & (snap > 0)\n", + "\n", + "# Tiny 2-column frame for microdf\n", + "df = pd.DataFrame({\"benefit\": snap[mask], \"wt\": weights[mask]})\n", + "\n", + "# Unweighted and weighted medians\n", + "median_unwtd = float(df[\"benefit\"].median())\n", + "median_wtd = float(mdf.weighted_median(df, \"benefit\", \"wt\"))\n", + "\n", + "print(f\"Median SNAP for Family of 1: ${median_wtd:,.0f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unweighted households: 583\n", + "Weighted households: 2,264,703\n" + ] + } + ], + "source": [ + "# Count 4-person HOUSEHOLDS that receive any SNAP during 2024\n", + "period = 2024 # annual → sums the 12 monthly amounts\n", + "\n", + "hh_size = baseline.calculate(\"household_size\", map_to=\"household\", period=period)\n", + "snap_yr = baseline.calculate(\"snap\", map_to=\"household\", period=period)\n", + "weights = baseline.calculate(\"household_weight\", map_to=\"household\", period=period)\n", + "\n", + "mask = (hh_size == 4) & (snap_yr > 0) # any positive amount ⇒ got SNAP\n", + "\n", + "n_unwtd = int(mask.sum()) # simple count of CPS households\n", + "n_wtd = float((mask * weights).sum()) # population-weighted total\n", + "\n", + "print(f\"Unweighted households: {n_unwtd:,}\")\n", + "print(f\"Weighted households: {n_wtd:,.0f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 Person Households that get above $965 in SNAP: 51343.36664519112\n", + "\n", + "Statistics for 4-person households getting > $965 SNAP:\n", + " Weighted median: $973\n", + " Weighted mean: $973\n", + " Min benefit: $973\n", + " Max benefit: $1,456\n", + "\n", + "Of all 4-person households getting SNAP:\n", + " Total households: 175,704\n", + " Getting > $965: 51,343 (29.2%)\n", + "\n", + "Benefit distribution for 4-person households:\n", + " >= $ 500: 127,950 ( 72.8%)\n", + " >= $ 750: 79,433 ( 45.2%)\n", + " >= $ 965: 51,343 ( 29.2%)\n", + " >= $1000: 0 ( 0.0%)\n", + " >= $1200: 0 ( 0.0%)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import microdf as mdf\n", + "import numpy as np\n", + "\n", + "# Single month keeps it simple\n", + "period = \"2024-01\"\n", + "\n", + "# ── Pull variables at the SPM-unit level ──\n", + "snap = baseline.calculate(\"snap\", period=period) # SPM-unit level\n", + "unit_size = baseline.calculate(\"spm_unit_size\", period=period) # size of that SPM unit\n", + "weights = baseline.calculate(\"household_weight\",\n", + " map_to=\"spm_unit\", period=period) # population weights\n", + "\n", + "# ── Analysis 1: Count of 4-person households getting above $965 ──\n", + "mask_above_965 = (unit_size == 4) & (snap > 965)\n", + "\n", + "print(\"4 Person Households that get above $965 in SNAP: \", float((mask_above_965 * weights).sum()))\n", + "print()\n", + "\n", + "# ── Analysis 2: Distribution statistics for these households ──\n", + "# Create dataframe for households above $965\n", + "df_above_965 = pd.DataFrame({\n", + " \"benefit\": snap[mask_above_965], \n", + " \"wt\": weights[mask_above_965]\n", + "})\n", + "\n", + "if len(df_above_965) > 0:\n", + " # Calculate statistics\n", + " median_wtd = float(mdf.weighted_median(df_above_965, \"benefit\", \"wt\"))\n", + " mean_wtd = float((df_above_965[\"benefit\"] * df_above_965[\"wt\"]).sum() / df_above_965[\"wt\"].sum())\n", + " \n", + " # Min and max benefits\n", + " min_benefit = float(df_above_965[\"benefit\"].min())\n", + " max_benefit = float(df_above_965[\"benefit\"].max())\n", + " \n", + " print(f\"Statistics for 4-person households getting > $965 SNAP:\")\n", + " print(f\" Weighted median: ${median_wtd:,.0f}\")\n", + " print(f\" Weighted mean: ${mean_wtd:,.0f}\")\n", + " print(f\" Min benefit: ${min_benefit:,.0f}\")\n", + " print(f\" Max benefit: ${max_benefit:,.0f}\")\n", + " print()\n", + "\n", + "# ── Analysis 3: Compare to all 4-person households getting SNAP ──\n", + "mask_all_snap = (unit_size == 4) & (snap > 0)\n", + "df_all_snap = pd.DataFrame({\n", + " \"benefit\": snap[mask_all_snap], \n", + " \"wt\": weights[mask_all_snap]\n", + "})\n", + "\n", + "# Calculate what percentage get above $965\n", + "total_4person_snap = float((mask_all_snap * weights).sum())\n", + "pct_above_965 = (float((mask_above_965 * weights).sum()) / total_4person_snap * 100) if total_4person_snap > 0 else 0\n", + "\n", + "print(f\"Of all 4-person households getting SNAP:\")\n", + "print(f\" Total households: {total_4person_snap:,.0f}\")\n", + "print(f\" Getting > $965: {float((mask_above_965 * weights).sum()):,.0f} ({pct_above_965:.1f}%)\")\n", + "\n", + "# ── Analysis 4: Look at specific benefit thresholds ──\n", + "print(\"\\nBenefit distribution for 4-person households:\")\n", + "thresholds = [500, 750, 965, 1000, 1200]\n", + "for threshold in thresholds:\n", + " mask_threshold = (unit_size == 4) & (snap >= threshold)\n", + " count = float((mask_threshold * weights).sum())\n", + " pct = (count / total_4person_snap * 100) if total_4person_snap > 0 else 0\n", + " print(f\" >= ${threshold:4d}: {count:10,.0f} ({pct:5.1f}%)\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}