MaxGhenis · May 5, 2026 00:17
diff --git a/ny_supplemental_recapture_household_types.ipynb b/ny_supplemental_recapture_household_types.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c1d83f55",
   "metadata": {},
   "source": [
    "# NY supplemental tax recapture cliff by household type\n",
    "\n",
    "This notebook uses PolicyEngine US to simulate 2026 New York households around the supplemental income tax high-AGI branch. The x-axis is annual employment income and the y-axis is household net income after federal, state, and local taxes and benefits modeled by PolicyEngine.\n",
    "\n",
    "In PolicyEngine US, `ny_supplemental_tax` switches to the high-AGI calculation when New York AGI is greater than the last finite NY main income tax threshold. In 2026 that threshold is $25,000,000. PolicyEngine calculations use `float32`, so an input of $25,000,001 rounds to $25,000,000; the first simulated income above the branch is therefore $25,000,002."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4e1c732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T00:16:52.525910Z",
     "iopub.status.busy": "2026-05-05T00:16:52.525814Z",
     "iopub.status.idle": "2026-05-05T00:17:04.286931Z",
     "shell.execute_reply": "2026-05-05T00:17:04.286176Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maxghenis/PolicyEngine/policyengine-us/.venv/lib/python3.14/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 pathlib import Path\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from matplotlib.font_manager import FontProperties, fontManager\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "from policyengine_us import CountryTaxBenefitSystem, Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8461e954",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T00:17:04.291869Z",
     "iopub.status.busy": "2026-05-05T00:17:04.291683Z",
     "iopub.status.idle": "2026-05-05T00:17:04.302689Z",
     "shell.execute_reply": "2026-05-05T00:17:04.301413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib font: Inter Variable\n",
      "float32($25,000,001) == float32($25,000,000): True\n"
     ]
    }
   ],
   "source": [
    "PERIOD = \"2026\"\n",
    "CENTER = 25_000_000\n",
    "OUTPUT_DIR = Path(\".\")\n",
    "ASSET_DIR = OUTPUT_DIR / \"assets\"\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "ASSET_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# PolicyEngine design-system tokens, mirrored from policyengine-app-v2/packages/design-system.\n",
    "PE_COLORS = {\n",
    "    \"primary_500\": \"#319795\",\n",
    "    \"primary_700\": \"#285E61\",\n",
    "    \"primary_300\": \"#4FD1C5\",\n",
    "    \"gray_500\": \"#6B7280\",\n",
    "    \"gray_700\": \"#344054\",\n",
    "    \"border_light\": \"#E2E8F0\",\n",
    "    \"border_medium\": \"#CBD5E1\",\n",
    "    \"text_primary\": \"#000000\",\n",
    "    \"text_secondary\": \"#5A5A5A\",\n",
    "    \"white\": \"#FFFFFF\",\n",
    "    \"error\": \"#EF4444\",\n",
    "}\n",
    "SERIES_COLORS = [\n",
    "    PE_COLORS[\"primary_500\"],\n",
    "    PE_COLORS[\"gray_500\"],\n",
    "    PE_COLORS[\"primary_700\"],\n",
    "    PE_COLORS[\"gray_700\"],\n",
    "    PE_COLORS[\"primary_300\"],\n",
    "]\n",
    "\n",
    "FONT_URL = \"https://raw.githubusercontent.com/rsms/inter/master/docs/font-files/InterVariable.ttf\"\n",
    "LOGO_URL = \"https://raw.githubusercontent.com/PolicyEngine/policyengine-app-v2/main/app/public/assets/logos/policyengine/teal.png\"\n",
    "font_path = ASSET_DIR / \"InterVariable.ttf\"\n",
    "logo_path = ASSET_DIR / \"policyengine-teal.png\"\n",
    "\n",
    "if not font_path.exists():\n",
    "    urlretrieve(FONT_URL, font_path)\n",
    "if not logo_path.exists():\n",
    "    urlretrieve(LOGO_URL, logo_path)\n",
    "\n",
    "fontManager.addfont(font_path)\n",
    "INTER = FontProperties(fname=font_path).get_name()\n",
    "plt.rcParams.update({\n",
    "    \"font.family\": INTER,\n",
    "    \"figure.facecolor\": PE_COLORS[\"white\"],\n",
    "    \"axes.facecolor\": PE_COLORS[\"white\"],\n",
    "    \"axes.edgecolor\": PE_COLORS[\"border_medium\"],\n",
    "    \"axes.labelcolor\": PE_COLORS[\"text_primary\"],\n",
    "    \"xtick.color\": PE_COLORS[\"text_secondary\"],\n",
    "    \"ytick.color\": PE_COLORS[\"text_secondary\"],\n",
    "})\n",
    "\n",
    "print(f\"Using matplotlib font: {INTER}\")\n",
    "print(f\"float32($25,000,001) == float32($25,000,000): {np.float32(25_000_001) == np.float32(25_000_000)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa3e4df4",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-05-05T00:17:04.304388Z",
     "iopub.status.idle": "2026-05-05T00:17:04.309194Z",
     "shell.execute_reply": "2026-05-05T00:17:04.308685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1153, np.float64(24980000.0), np.float64(25020000.0))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CASES = [\n",
    "    {\n",
    "        \"case\": \"Single\",\n",
    "        \"case_slug\": \"single\",\n",
    "        \"subtitle\": \"One adult, no dependents\",\n",
    "        \"filing_status\": \"SINGLE\",\n",
    "        \"members\": \"single\",\n",
    "    },\n",
    "    {\n",
    "        \"case\": \"Married filing separately\",\n",
    "        \"case_slug\": \"separate\",\n",
    "        \"subtitle\": \"Separated adult, one earner\",\n",
    "        \"filing_status\": \"SEPARATE\",\n",
    "        \"members\": \"single\",\n",
    "    },\n",
    "    {\n",
    "        \"case\": \"Head of household\",\n",
    "        \"case_slug\": \"head_of_household\",\n",
    "        \"subtitle\": \"One adult with one child dependent\",\n",
    "        \"filing_status\": \"HEAD_OF_HOUSEHOLD\",\n",
    "        \"members\": \"child\",\n",
    "    },\n",
    "    {\n",
    "        \"case\": \"Married filing jointly\",\n",
    "        \"case_slug\": \"joint\",\n",
    "        \"subtitle\": \"Two adults, one earner\",\n",
    "        \"filing_status\": \"JOINT\",\n",
    "        \"members\": \"spouse\",\n",
    "    },\n",
    "    {\n",
    "        \"case\": \"Surviving spouse\",\n",
    "        \"case_slug\": \"surviving_spouse\",\n",
    "        \"subtitle\": \"Surviving spouse with one child dependent\",\n",
    "        \"filing_status\": \"SURVIVING_SPOUSE\",\n",
    "        \"members\": \"surviving_child\",\n",
    "    },\n",
    "]\n",
    "\n",
    "# Coarse grid over the full view, dense $2 steps across the exact branch.\n",
    "earnings_grid = np.r_[\n",
    "    np.arange(24_980_000, 24_999_000, 250),\n",
    "    np.arange(24_999_000, 25_001_001, 2),\n",
    "    np.arange(25_001_250, 25_020_001, 250),\n",
    "].astype(float)\n",
    "\n",
    "len(earnings_grid), earnings_grid.min(), earnings_grid.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5a71b05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T00:17:04.311244Z",
     "iopub.status.busy": "2026-05-05T00:17:04.311090Z",
     "iopub.status.idle": "2026-05-05T00:17:21.825195Z",
     "shell.execute_reply": "2026-05-05T00:17:21.824795Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>case_slug</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>input_employment_income</th>\n",
       "      <th>filing_status_code</th>\n",
       "      <th>filing_status</th>\n",
       "      <th>ny_agi</th>\n",
       "      <th>ny_taxable_income</th>\n",
       "      <th>ny_main_income_tax</th>\n",
       "      <th>ny_supplemental_tax</th>\n",
       "      <th>ny_income_tax</th>\n",
       "      <th>household_net_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>single</td>\n",
       "      <td>One adult, no dependents</td>\n",
       "      <td>24980000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>24980000.0</td>\n",
       "      <td>24972000.0</td>\n",
       "      <td>2506830.00</td>\n",
       "      <td>65286.0</td>\n",
       "      <td>2572116.00</td>\n",
       "      <td>12618079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Single</td>\n",
       "      <td>single</td>\n",
       "      <td>One adult, no dependents</td>\n",
       "      <td>24980250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>24980250.0</td>\n",
       "      <td>24972250.0</td>\n",
       "      <td>2506855.75</td>\n",
       "      <td>65286.0</td>\n",
       "      <td>2572141.75</td>\n",
       "      <td>12618204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Single</td>\n",
       "      <td>single</td>\n",
       "      <td>One adult, no dependents</td>\n",
       "      <td>24980500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>24980500.0</td>\n",
       "      <td>24972500.0</td>\n",
       "      <td>2506881.50</td>\n",
       "      <td>65286.0</td>\n",
       "      <td>2572167.50</td>\n",
       "      <td>12618331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Single</td>\n",
       "      <td>single</td>\n",
       "      <td>One adult, no dependents</td>\n",
       "      <td>24980750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>24980750.0</td>\n",
       "      <td>24972750.0</td>\n",
       "      <td>2506907.25</td>\n",
       "      <td>65286.0</td>\n",
       "      <td>2572193.25</td>\n",
       "      <td>12618456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Single</td>\n",
       "      <td>single</td>\n",
       "      <td>One adult, no dependents</td>\n",
       "      <td>24981000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>24981000.0</td>\n",
       "      <td>24973000.0</td>\n",
       "      <td>2506933.00</td>\n",
       "      <td>65286.0</td>\n",
       "      <td>2572219.00</td>\n",
       "      <td>12618582.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     case case_slug                  subtitle  input_employment_income  \\\n",
       "0  Single    single  One adult, no dependents               24980000.0   \n",
       "1  Single    single  One adult, no dependents               24980250.0   \n",
       "2  Single    single  One adult, no dependents               24980500.0   \n",
       "3  Single    single  One adult, no dependents               24980750.0   \n",
       "4  Single    single  One adult, no dependents               24981000.0   \n",
       "\n",
       "   filing_status_code filing_status      ny_agi  ny_taxable_income  \\\n",
       "0                   0        Single  24980000.0         24972000.0   \n",
       "1                   0        Single  24980250.0         24972250.0   \n",
       "2                   0        Single  24980500.0         24972500.0   \n",
       "3                   0        Single  24980750.0         24972750.0   \n",
       "4                   0        Single  24981000.0         24973000.0   \n",
       "\n",
       "   ny_main_income_tax  ny_supplemental_tax  ny_income_tax  \\\n",
       "0          2506830.00              65286.0     2572116.00   \n",
       "1          2506855.75              65286.0     2572141.75   \n",
       "2          2506881.50              65286.0     2572167.50   \n",
       "3          2506907.25              65286.0     2572193.25   \n",
       "4          2506933.00              65286.0     2572219.00   \n",
       "\n",
       "   household_net_income  \n",
       "0            12618079.0  \n",
       "1            12618204.0  \n",
       "2            12618331.0  \n",
       "3            12618456.0  \n",
       "4            12618582.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_situation(cases, earnings_values):\n",
    "    people = {}\n",
    "    tax_units = {}\n",
    "    households = {}\n",
    "    spm_units = {}\n",
    "    families = {}\n",
    "    marital_units = {}\n",
    "    records = []\n",
    "\n",
    "    for case in cases:\n",
    "        for earnings in earnings_values:\n",
    "            row_id = f\"{case['case_slug']}_{int(earnings)}\"\n",
    "            adult = f\"adult_{row_id}\"\n",
    "            members = [adult]\n",
    "            people[adult] = {\n",
    "                \"age\": {PERIOD: 30},\n",
    "                \"employment_income\": {PERIOD: float(earnings)},\n",
    "            }\n",
    "\n",
    "            if case[\"members\"] == \"spouse\":\n",
    "                spouse = f\"spouse_{row_id}\"\n",
    "                members.append(spouse)\n",
    "                people[spouse] = {\n",
    "                    \"age\": {PERIOD: 30},\n",
    "                    \"employment_income\": {PERIOD: 0},\n",
    "                }\n",
    "\n",
    "            if case[\"members\"] in (\"child\", \"surviving_child\"):\n",
    "                child = f\"child_{row_id}\"\n",
    "                members.append(child)\n",
    "                people[child] = {\n",
    "                    \"age\": {PERIOD: 8},\n",
    "                    \"is_tax_unit_dependent\": {PERIOD: True},\n",
    "                }\n",
    "\n",
    "            if case[\"members\"] == \"surviving_child\":\n",
    "                people[adult][\"is_surviving_spouse\"] = {PERIOD: True}\n",
    "\n",
    "            tax_unit = f\"tu_{row_id}\"\n",
    "            household = f\"hh_{row_id}\"\n",
    "            spm_unit = f\"spm_{row_id}\"\n",
    "            family = f\"family_{row_id}\"\n",
    "            marital_unit = f\"mu_{row_id}\"\n",
    "\n",
    "            tax_units[tax_unit] = {\n",
    "                \"members\": members,\n",
    "                \"filing_status\": {PERIOD: case[\"filing_status\"]},\n",
    "            }\n",
    "            households[household] = {\n",
    "                \"members\": members,\n",
    "                \"state_code\": {PERIOD: \"NY\"},\n",
    "            }\n",
    "            spm_units[spm_unit] = {\"members\": members}\n",
    "            families[family] = {\"members\": members}\n",
    "            marital_units[marital_unit] = {\n",
    "                \"members\": members if case[\"members\"] == \"spouse\" else [adult]\n",
    "            }\n",
    "\n",
    "            records.append({\n",
    "                \"case\": case[\"case\"],\n",
    "                \"case_slug\": case[\"case_slug\"],\n",
    "                \"subtitle\": case[\"subtitle\"],\n",
    "                \"input_employment_income\": float(earnings),\n",
    "            })\n",
    "\n",
    "    situation = {\n",
    "        \"people\": people,\n",
    "        \"tax_units\": tax_units,\n",
    "        \"households\": households,\n",
    "        \"spm_units\": spm_units,\n",
    "        \"families\": families,\n",
    "        \"marital_units\": marital_units,\n",
    "    }\n",
    "    return situation, pd.DataFrame(records)\n",
    "\n",
    "\n",
    "def simulate(cases=CASES, earnings_values=earnings_grid):\n",
    "    system = CountryTaxBenefitSystem()\n",
    "    situation, df = build_situation(cases, earnings_values)\n",
    "    sim = Simulation(tax_benefit_system=system, situation=situation)\n",
    "    df[\"filing_status_code\"] = sim.calculate(\"filing_status\", PERIOD)\n",
    "    filing_status_labels = {\n",
    "        \"SINGLE\": \"Single\",\n",
    "        \"SEPARATE\": \"Separate\",\n",
    "        \"HEAD_OF_HOUSEHOLD\": \"Head of household\",\n",
    "        \"JOINT\": \"Joint\",\n",
    "        \"SURVIVING_SPOUSE\": \"Surviving spouse\",\n",
    "    }\n",
    "    df[\"filing_status\"] = df[\"case_slug\"].map({\n",
    "        case[\"case_slug\"]: filing_status_labels[case[\"filing_status\"]]\n",
    "        for case in CASES\n",
    "    })\n",
    "    for variable in [\n",
    "        \"ny_agi\",\n",
    "        \"ny_taxable_income\",\n",
    "        \"ny_main_income_tax\",\n",
    "        \"ny_supplemental_tax\",\n",
    "        \"ny_income_tax\",\n",
    "        \"household_net_income\",\n",
    "    ]:\n",
    "        df[variable] = sim.calculate(variable, PERIOD)\n",
    "    return df\n",
    "\n",
    "\n",
    "df = simulate()\n",
    "df.to_csv(OUTPUT_DIR / \"ny_supplemental_recapture_household_types.csv\", index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89467f0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T00:17:21.828692Z",
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     "iopub.status.idle": "2026-05-05T00:17:21.846059Z",
     "shell.execute_reply": "2026-05-05T00:17:21.845631Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Household type</th>\n",
       "      <th>Net income at $25,000,000 earnings</th>\n",
       "      <th>Net income at $25,000,002 earnings</th>\n",
       "      <th>Net change over next $2</th>\n",
       "      <th>Estimated cliff size</th>\n",
       "      <th>NY supplemental tax jump</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>$12,628,149</td>\n",
       "      <td>$12,478,198</td>\n",
       "      <td>$-149,951</td>\n",
       "      <td>$149,953</td>\n",
       "      <td>$149,952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married filing separately</td>\n",
       "      <td>$12,622,349</td>\n",
       "      <td>$12,472,398</td>\n",
       "      <td>$-149,951</td>\n",
       "      <td>$149,953</td>\n",
       "      <td>$149,952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Head of household</td>\n",
       "      <td>$12,633,367</td>\n",
       "      <td>$12,483,442</td>\n",
       "      <td>$-149,925</td>\n",
       "      <td>$149,925</td>\n",
       "      <td>$149,927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Married filing jointly</td>\n",
       "      <td>$12,669,177</td>\n",
       "      <td>$12,519,275</td>\n",
       "      <td>$-149,902</td>\n",
       "      <td>$149,903</td>\n",
       "      <td>$149,903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Surviving spouse</td>\n",
       "      <td>$12,668,830</td>\n",
       "      <td>$12,518,934</td>\n",
       "      <td>$-149,896</td>\n",
       "      <td>$149,897</td>\n",
       "      <td>$149,897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Household type Net income at $25,000,000 earnings  \\\n",
       "0                     Single                        $12,628,149   \n",
       "1  Married filing separately                        $12,622,349   \n",
       "2          Head of household                        $12,633,367   \n",
       "3     Married filing jointly                        $12,669,177   \n",
       "4           Surviving spouse                        $12,668,830   \n",
       "\n",
       "  Net income at $25,000,002 earnings Net change over next $2  \\\n",
       "0                        $12,478,198               $-149,951   \n",
       "1                        $12,472,398               $-149,951   \n",
       "2                        $12,483,442               $-149,925   \n",
       "3                        $12,519,275               $-149,902   \n",
       "4                        $12,518,934               $-149,896   \n",
       "\n",
       "  Estimated cliff size NY supplemental tax jump  \n",
       "0             $149,953                 $149,952  \n",
       "1             $149,953                 $149,952  \n",
       "2             $149,925                 $149,927  \n",
       "3             $149,903                 $149,903  \n",
       "4             $149,897                 $149,897  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_summary(df):\n",
    "    rows = []\n",
    "    for case in CASES:\n",
    "        case_df = df[df[\"case_slug\"] == case[\"case_slug\"]].set_index(\"input_employment_income\")\n",
    "        before = case_df.loc[float(CENTER)]\n",
    "        after = case_df.loc[float(CENTER + 2)]\n",
    "        prior = case_df.loc[float(CENTER - 2)]\n",
    "        normal_gain = before[\"household_net_income\"] - prior[\"household_net_income\"]\n",
    "        net_change = after[\"household_net_income\"] - before[\"household_net_income\"]\n",
    "        rows.append({\n",
    "            \"case\": case[\"case\"],\n",
    "            \"case_slug\": case[\"case_slug\"],\n",
    "            \"filing_status\": str(before[\"filing_status\"]).replace(\"_\", \" \").title(),\n",
    "            \"before_input_employment_income\": CENTER,\n",
    "            \"after_input_employment_income\": CENTER + 2,\n",
    "            \"before_ny_agi\": before[\"ny_agi\"],\n",
    "            \"after_ny_agi\": after[\"ny_agi\"],\n",
    "            \"before_net_income\": before[\"household_net_income\"],\n",
    "            \"after_net_income\": after[\"household_net_income\"],\n",
    "            \"net_income_change\": net_change,\n",
    "            \"normal_gain_estimate\": normal_gain,\n",
    "            \"cliff_size\": normal_gain - net_change,\n",
    "            \"before_supplemental_tax\": before[\"ny_supplemental_tax\"],\n",
    "            \"after_supplemental_tax\": after[\"ny_supplemental_tax\"],\n",
    "            \"supplemental_tax_jump\": after[\"ny_supplemental_tax\"] - before[\"ny_supplemental_tax\"],\n",
    "        })\n",
    "    return pd.DataFrame(rows).sort_values(\"cliff_size\", ascending=False)\n",
    "\n",
    "summary = make_summary(df)\n",
    "summary.to_csv(OUTPUT_DIR / \"ny_supplemental_recapture_household_types_summary.csv\", index=False)\n",
    "\n",
    "summary_display = summary[[\n",
    "    \"case\",\n",
    "    \"before_net_income\",\n",
    "    \"after_net_income\",\n",
    "    \"net_income_change\",\n",
    "    \"cliff_size\",\n",
    "    \"supplemental_tax_jump\",\n",
    "]].copy()\n",
    "for col in summary_display.columns[1:]:\n",
    "    summary_display[col] = summary_display[col].map(lambda x: f\"${x:,.0f}\")\n",
    "summary_display = summary_display.rename(columns={\n",
    "    \"case\": \"Household type\",\n",
    "    \"before_net_income\": \"Net income at $25,000,000 earnings\",\n",
    "    \"after_net_income\": \"Net income at $25,000,002 earnings\",\n",
    "    \"net_income_change\": \"Net change over next $2\",\n",
    "    \"cliff_size\": \"Estimated cliff size\",\n",
    "    \"supplemental_tax_jump\": \"NY supplemental tax jump\",\n",
    "})\n",
    "display(summary_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1aeb967",
   "metadata": {},
   "source": [
    "The largest estimated cliff is about $150,000. It is the additional NY supplemental tax from switching to the high-AGI formula just above $25 million of NY AGI, net of the ordinary net-income gain from the next $2 of earnings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1d2cce",
   "metadata": {},
   "source": [
    "## Tax form and law cross-check\n",
    "\n",
    "The 2025 Form IT-201-I instructions show the tax-form workflow: taxpayers with New York AGI above $107,650 use the tax computation worksheets rather than only the rate schedule. For AGI above $25,000,000, the worksheet says to multiply taxable income by 10.9% and enter that as New York State tax. For AGI above $5,000,000 but not above $25,000,000, the worksheet instead adds the normal rate-schedule tax, a recapture base, and the incremental benefit phased in over $50,000.\n",
    "\n",
    "Because this notebook simulates tax year 2026, the dollar amounts are checked against the FY2026 budget bill rather than the 2025 form. S3009C creates a 2026 recapture subsection and keeps the same high-AGI rule: above $25,000,000 of NY AGI, supplemental tax equals the difference between 10.9% of NY taxable income and the regular tax-table computation. That is exactly what PolicyEngine implements as `ny_supplemental_tax = 0.109 * ny_taxable_income - ny_main_income_tax` above the threshold.\n",
    "\n",
    "Sources: [2025 Form IT-201-I instructions](https://www.tax.ny.gov/forms/current-forms/it/it201i.htm), [FY2026 budget bill S3009C](https://legislation.nysenate.gov/pdf/bills/2025/S3009C), and [NY Tax Expenditure report](https://www.tax.ny.gov/data/stats/ter/fiscal-year26/personal-income-tax.htm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "049a40d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T00:17:21.847709Z",
     "iopub.status.busy": "2026-05-05T00:17:21.847585Z",
     "iopub.status.idle": "2026-05-05T00:17:21.858483Z",
     "shell.execute_reply": "2026-05-05T00:17:21.858089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Household type</th>\n",
       "      <th>Earnings</th>\n",
       "      <th>Branch</th>\n",
       "      <th>NY AGI</th>\n",
       "      <th>NY taxable income</th>\n",
       "      <th>PE state tax before credits</th>\n",
       "      <th>Form-law calculation</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$5M &lt; AGI &lt;= $25M worksheet</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$24,992,000</td>\n",
       "      <td>$2,574,176</td>\n",
       "      <td>$2,574,176</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Single</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>AGI &gt; $25M worksheet</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>$24,992,002</td>\n",
       "      <td>$2,724,128</td>\n",
       "      <td>$2,724,128</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Married filing separately</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$5M &lt; AGI &lt;= $25M worksheet</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$24,992,000</td>\n",
       "      <td>$2,574,176</td>\n",
       "      <td>$2,574,176</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Married filing separately</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>AGI &gt; $25M worksheet</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>$24,992,002</td>\n",
       "      <td>$2,724,128</td>\n",
       "      <td>$2,724,128</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Head of household</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$5M &lt; AGI &lt;= $25M worksheet</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$24,987,800</td>\n",
       "      <td>$2,573,744</td>\n",
       "      <td>$2,573,744</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Head of household</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>AGI &gt; $25M worksheet</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>$24,987,802</td>\n",
       "      <td>$2,723,670</td>\n",
       "      <td>$2,723,670</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Married filing jointly</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$5M &lt; AGI &lt;= $25M worksheet</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$24,983,950</td>\n",
       "      <td>$2,573,348</td>\n",
       "      <td>$2,573,348</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Married filing jointly</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>AGI &gt; $25M worksheet</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>$24,983,952</td>\n",
       "      <td>$2,723,251</td>\n",
       "      <td>$2,723,251</td>\n",
       "      <td>$-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Surviving spouse</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$5M &lt; AGI &lt;= $25M worksheet</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$24,982,950</td>\n",
       "      <td>$2,573,244</td>\n",
       "      <td>$2,573,244</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Surviving spouse</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>AGI &gt; $25M worksheet</td>\n",
       "      <td>$25,000,002</td>\n",
       "      <td>$24,982,952</td>\n",
       "      <td>$2,723,142</td>\n",
       "      <td>$2,723,142</td>\n",
       "      <td>$-0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Household type     Earnings                       Branch  \\\n",
       "0                     Single  $25,000,000  $5M < AGI <= $25M worksheet   \n",
       "1                     Single  $25,000,002         AGI > $25M worksheet   \n",
       "2  Married filing separately  $25,000,000  $5M < AGI <= $25M worksheet   \n",
       "3  Married filing separately  $25,000,002         AGI > $25M worksheet   \n",
       "4          Head of household  $25,000,000  $5M < AGI <= $25M worksheet   \n",
       "5          Head of household  $25,000,002         AGI > $25M worksheet   \n",
       "6     Married filing jointly  $25,000,000  $5M < AGI <= $25M worksheet   \n",
       "7     Married filing jointly  $25,000,002         AGI > $25M worksheet   \n",
       "8           Surviving spouse  $25,000,000  $5M < AGI <= $25M worksheet   \n",
       "9           Surviving spouse  $25,000,002         AGI > $25M worksheet   \n",
       "\n",
       "        NY AGI NY taxable income PE state tax before credits  \\\n",
       "0  $25,000,000       $24,992,000                  $2,574,176   \n",
       "1  $25,000,002       $24,992,002                  $2,724,128   \n",
       "2  $25,000,000       $24,992,000                  $2,574,176   \n",
       "3  $25,000,002       $24,992,002                  $2,724,128   \n",
       "4  $25,000,000       $24,987,800                  $2,573,744   \n",
       "5  $25,000,002       $24,987,802                  $2,723,670   \n",
       "6  $25,000,000       $24,983,950                  $2,573,348   \n",
       "7  $25,000,002       $24,983,952                  $2,723,251   \n",
       "8  $25,000,000       $24,982,950                  $2,573,244   \n",
       "9  $25,000,002       $24,982,952                  $2,723,142   \n",
       "\n",
       "  Form-law calculation Difference  \n",
       "0           $2,574,176      $0.00  \n",
       "1           $2,724,128      $0.00  \n",
       "2           $2,574,176      $0.00  \n",
       "3           $2,724,128      $0.00  \n",
       "4           $2,573,744      $0.00  \n",
       "5           $2,723,670      $0.00  \n",
       "6           $2,573,348      $0.00  \n",
       "7           $2,723,251     $-0.00  \n",
       "8           $2,573,244      $0.00  \n",
       "9           $2,723,142     $-0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FORM_RECAPTURE_BASE_2026 = {\n",
    "    \"single\": 32_786,\n",
    "    \"separate\": 32_786,\n",
    "    \"head_of_household\": 48_606,\n",
    "    \"joint\": 64_561,\n",
    "    \"surviving_spouse\": 64_561,\n",
    "}\n",
    "FORM_INCREMENTAL_BENEFIT_TOP_2026 = 32_500\n",
    "\n",
    "check_rows = []\n",
    "for case in CASES:\n",
    "    case_df = df[\n",
    "        (df[\"case_slug\"] == case[\"case_slug\"])\n",
    "        & df[\"input_employment_income\"].isin([CENTER, CENTER + 2])\n",
    "    ].copy()\n",
    "    for _, row in case_df.iterrows():\n",
    "        pe_total = row[\"ny_main_income_tax\"] + row[\"ny_supplemental_tax\"]\n",
    "        if row[\"ny_agi\"] > CENTER:\n",
    "            branch = \"AGI > $25M worksheet\"\n",
    "            form_total = 0.109 * row[\"ny_taxable_income\"]\n",
    "        else:\n",
    "            branch = \"$5M < AGI <= $25M worksheet\"\n",
    "            form_total = (\n",
    "                row[\"ny_main_income_tax\"]\n",
    "                + FORM_RECAPTURE_BASE_2026[case[\"case_slug\"]]\n",
    "                + FORM_INCREMENTAL_BENEFIT_TOP_2026\n",
    "            )\n",
    "        check_rows.append({\n",
    "            \"Household type\": row[\"case\"],\n",
    "            \"Earnings\": f\"${row['input_employment_income']:,.0f}\",\n",
    "            \"Branch\": branch,\n",
    "            \"NY AGI\": f\"${row['ny_agi']:,.0f}\",\n",
    "            \"NY taxable income\": f\"${row['ny_taxable_income']:,.0f}\",\n",
    "            \"PE state tax before credits\": f\"${pe_total:,.0f}\",\n",
    "            \"Form-law calculation\": f\"${form_total:,.0f}\",\n",
    "            \"Difference\": f\"${pe_total - form_total:,.2f}\",\n",
    "        })\n",
    "\n",
    "form_check = pd.DataFrame(check_rows)\n",
    "display(form_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842c3908",
   "metadata": {},
   "source": [
    "## Has anyone written about this?\n",
    "\n",
    "Yes, but mostly about the broader tax benefit recapture mechanism rather than the exact 2026 $25M edge. New York State itself describes the supplemental tax as recapturing the benefit of income being taxed below the top marginal rate. Tax Foundation has also described New York as one of the only states with tax benefit recapture, where the benefit of lower brackets is phased out so the top rate ultimately applies to all income. Empire Center wrote about New York's income tax cliffs in 2003, including examples where one additional dollar of income triggered hundreds or thousands of dollars of additional tax.\n",
    "\n",
    "Sources: [NY Tax Expenditure report](https://www.tax.ny.gov/data/stats/ter/fiscal-year26/personal-income-tax.htm), [Tax Foundation New York state tax index](https://taxfoundation.org/de/statetaxindex/states/new-york/), and [Empire Center, New York's Ugly Stealth Tax Hikes](https://www.empirecenter.org/publications/new-yorks-ugly-stealth-tax-hikes/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d2bae3a",
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     "iopub.status.idle": "2026-05-05T00:17:22.404421Z",
     "shell.execute_reply": "2026-05-05T00:17:22.403944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LyZs3r0/JT999951s3Lgx0fSAS5cuNdfNmjVLVlsAAED40ASluPh4e2KRxnd0ANoVx2QjKznJvo5zcpKnxCbdL8KbJhJpspFrMlLi5KTI60lMkVaCkmNik64T6ZCsFG3iT64D2QAAwYXkJwAAAPjs282bpXiuXM7luZ1Kdbu/P3SKxSItREVE+FU9yZrqzek+h8QkK9FJr3XfAACkyvtVVJTcdttt5vLJJ5+YakZVq1aV7du3y+zZs2XVqlVmWrzChQun6HGmTJkiy5cvl1y5cknv3r39rvo0c+ZMU7VJE4208lNynD592hyfPnaHDh182kaTnbQK1tq1a03727Zta+7X6e6OHTtm2pKc6fscjR071uOydTsO2CskhkLyvzViuHjBnFR0BOhvQEjLkS2zx2WLj+0z12YAmxUH+meaN3uV7n8GuNmnhGMgW9iLjoi4Hh+y4jyuA9NcB7A5recymC2DDmTj8yRAf4P/SH4CAACAz37cvo2zlUFFZsrktiJSjMM0bonu81g96fp91xOdoiQ6MjLQhwcAgF8KFiwoBQoUkFdffVVOnDhhKjVt3brVJOe88soryT6b8+fPl2nTpplqUs8//7ypOOWPU6dOmYpNOg1fly5dkt2OX375xVSQatSokRQqVMjn7Zo3b26Sn7R6upX8lN5Vn1JlSpB0pO0NtTYDoYr+BqRR3/KybMzvqzjtYTCQzVPcJ1FyUqKEpX8HvTGQLfl4fwPSD/0ttIV18pOW9tZRbsuWLTMBLNWrVy+pVKmStGzZUipXrpxom7i4OBk/frwpg67uvvtu6dy5c6L1tmzZIrNmzTJl0q9cuWICZg0aNJB27dqZAFVK7d69WwYNGmRuN23aVJ566imn5W+//bYZmZiUKlWqyMCBA81tDeb17dvX3I6MjDSl3R1LmHs6hzpKUUcMKg0K6qhIAAAApE3A0aqMFOMQVLISkWIcRrwlqp7kksRkrXv9vmgz6o4f5gAAcJ8I1adPH3n22WdN/EjjPMmJ7axbt07GjRtnqi317NnTTHnnL53uLjY2Vrp27Sp58uRJ1tN14cIFk4Sl7/sdO3b0a1ut/KSPu3//fjl48KAULVpUfv/9d4mOjpaGDRsmqz0AAABI2UA2EyPyUHHbigWZ+xzWcRzg5nqfXjOQDQAQasI2+eny5cvy/vvvm9Lcjs6ePSsrVqwwl/bt20u3bt3sy44ePSojRowwAR5vZsyYId9//70pSZgjRw7JmTOnHDlyRH788UdZuXKlSVrS+5JL9/vVV195XUdHDmoAypOLFy/KuXPnPC6Pj483I/c0WcsbTfKyEp8AAEDGE88o9WT5d7Sbh+pJ/yQlZfM2Ms7N6LkskZEkKAEAkMq0ipEOXrvpppukevXqbtfJnj27ZMmSxVRL0ou/yU86iG306NFmENkTTzxhHis5sSydek99++235uKODvTTS6lSpWTw4MGJls+ZM8ckcNWpU0dKlizpVxt0sFzjxo3l559/NklPFStWNMlU9evXN+cIAABkbMSJkj+QzeMUbu6qJ7lOB2dV13aZ5i0zcSIAAOzCNvlJR9pp4lP58uXlscces5fs1oQovZ44caLMnDlTSpcubUqAb968WUaOHCmXLl0ywSEd9afBIle6T0180vLlTz/9tNStW9f8QHXy5En59NNPZdu2beaxtVpScmn7NGjmjT62N++8844Zragj9lxpezXBSgNlSSU/6TpKk7w02AUAAEJfwezZzPXR7NnlsdYtJaPSUXHeRsZZZbntlZP+SUpyGjHnZmRclqgoiSBpDACAkKGDuubOnSuHDh3ymPykg7807qEVsv0d0KaD6YYOHWoqNulUdS1atEh2W70NdNOBd47ruZvOTpOefv31V3P7zjvvTFYbtP2a/LR+/XozuC49p7wDAADpr0C28IkTuYv72O9zrK7tMqDNHiNyk9DEQDYAANJHWCY/nT9/XpYvX25G6fXv398k7mjyk9KkJZ3yTpN/dHo7LQOuyU8bNmwwASItK64VobSKkzvz5s0z2951111Sr149+/067Z2WSNekp9WrV5s2JKf6kyZfaXJVVFSUSUyaPn263/vQxClNfNIReXqsrrStWbNmNRWu9u7dK2XLlvU44lDPmyaC6Ta+TLMHAACC303Fikv26Gi5ePVqoJtiRrD9G0hKnGiUaGScS8Ul+7X9vn+CVVFREhkREejDAwAAQUCTebSKtw5YGzNmjNx77732ZXFxcaaK94QJE8zfbdu29WvfWnV7yJAh5rpNmzY+TzP30UcfmZiLxqFatWpl7tNYjSZRebJo0SL57LPPpGnTpvLUU0+5Xee3334zSVw33nhjsqbdU8WKFZMKFSrIn3/+KceOHTPT4NWoUSNZ+wIAAMGvSanS8vHq3yXeZgt0U0wikfe4j3O1pMT3J15HY00MZAMAIPSFZfKTBmY0QUmDNZr45I6W8NYEo2z/ZLRrIOeVV16RKlWqeN33wYMHzXXNmjUTLdPHKlGihEk+On78eLKSnzTp6syZM9KhQwcpXLiwJIeVMNW6dWu3Zdr13Gi5ck1+0ipTnpKftNS6jlrU6lb79u1LVlsAAEBwjuh7+7aW8saC+XI2NjbJ9aMiIvyqnuR2tJzLyDgz2i462uwbAAAgLWk1p379+smwYcPMYDm9WJ588kkzzZ3SpKKkKmS7GjVqlIlDqY0bN5qLOxqj0YvSAXPLli2zD7Kzkp9SSo/jl19+Mbd9TcLypHnz5rJr1y5T+UnPSQSf2QAAyLAqFiggLzVtJkOXL5PYa9eSXD86IuKf6kn/xneuJxldjxV5ixG5DnqLcVyHgWwAAMCLsEx+shKetBz42bNnTZDLlSY93XLLLfa/NdnIF4MHD/a6XANYKkuWLH62WuTw4cOmNLkmYnXq1MkkH/lLk7PWrVtnHl9HHHpy8803y5QpU2TFihXSvXt3kwjmaco7XZfkJwAAMpbmZcrKtG7dZcmuv+TC1TgpkT+nZMucOdHIOA08RUdGBrq5AAAAKVKtWjVTVWn27NmyadMmMyBM5cqVS8qXL2+SfWrVquX3fnXwm7tp6TzFi5QOltNBeVr5yV3F7uTSAW46oE6PR483JRo0aGCqYemgOKa8AwAg4+tc9UZpVrqMLN+93yRAFS+QS7JFZ3aa6o2BbAAAIJDCMvmpSJEipjy3jlB77bXX5MEHH5SEhIQ0f9ydO3eaoJcmL2nVKX99/fXXEh8fb0qeu6vY5Ast466VnW699VaPVa+Utq9UqVIm2Ld+/XqnKfzUiRMn5I8//pB8+fKZc5mWtL3BzrGNodBeIJTR34D0kzNzZqlZqIi5XbxgTsmUKVOS/RJA8vD+BqSfUO1vnt6HkXry5s0r3bp1M5epU6eaZKERI0b4tG3nzp3NxZWv27t65pln/N5GE7T04sltt91mLr4qWLCgTJo0ye0ynYLvyy+/9LjtwIEDfX4cAAAQOpXC6xS+/ttWiUK5+HwKAACCSlgmP6levXrJ8OHDZc+ePebasnLlSjOSL7nJRZ7ExcXZg0Lt27f3uxz4mjVrZPPmzWZ0XpMmTZLVBk280kpO0dHRPpVp18eZPHmyCfa5Jj9p1ScNEutIxJQEYHv06OF5Wc+XJEtMFjl4/JyEkkMn/h2tCYD+BoSyTKdPS8z3k83tUyISe9/9YsubN9DNAjI8Pk8C9Dd3ShZOXLUaAAAASC8Jp05JzKefmduXskZL1scek4j8+XkCAABAUAjb5Kf8+fPL66+/LosWLZKFCxeaJCg1atQoyZw5s6mMpFPLaanx1PD555/LgQMH5IYbbpDbb7/dr22vXr1qRtppktHDDz+c7GSjn376yVS4atGihRnN6Evy03fffScbN25MND3g0qVLzTWlzQEAyMCuxknkrj+c/gYAAAgHnio5AQAAhK24f+NE8VERYosjTgQAAIJH2CY/qaioKHvJ708++cRUM6pataps375dZs+eLatWrTLT4hUuXDhFjzNlyhRZvny55MqVS3r37u131aeZM2eaqk2aaKSVn5Lj9OnT5vj0sTt06ODTNprspFWw1q5da9rftm1bc79Od3fs2DHTluRM3+do7NixHpet23HAXj412GkVLGuEvrdpgQDQ34BQEh9/Uaz6i1FREVI0fw6JCIH3ZSAU8XkSoL8BAAAAAAAASJ6wTn5yVLBgQSlQoIC8+uqrcuLECVOpaevWrSY555VXXkn2fufPny/Tpk0z1aSef/55U3HKH6dOnTIVm3Qavi5duiS7Hb/88oupINWoUSMpVKiQz9s1b97cJD/p1HdW8lN6V30KtUQibW+otRkIVfQ3II37WOJOx3sckA54fwPSD/0NAAAAAAAACH3+lSAKo0SoPn36SHR0tEmAunLlSrL2s27dOhk3bpypttSzZ08z5Z2/dLq72NhYMwVfnjx5ktWOCxcumCQsDep27NjRr2218pM+7v79++XgwYMSHx8vv//+uzk3DRs2TFZ7AAAAAAAAAAAAAAAAgNQQlpWftIrRnj175KabbpLq1au7XSd79uySJUsWUy1JL1p5yR+7d++W0aNHS0JCgjzxxBPmsfx1+fJlM/We+vbbb83FHZ3OTi+lSpWSwYMHJ1o+Z84ck8BVp04dKVmypF9tiIyMlMaNG8vPP/9skp4qVqxokqnq169vzhEAAAAAAAAAAAAAAAAQKGGZ/HT69GmZO3euHDp0yGPy05YtW0yST+7cuSVnzpx+7f/o0aMydOhQU7FJp6pr0aJFsttatGhRj8uOHDnitJ676ew06enXX381t++8885ktUHbr8lP69evl4sXL6brlHcAAAAAAAAAAAAAAACAJ2GZ/KTJPDNmzJBt27bJmDFj5N5777Uvi4uLk5UrV8qECRPM323btvVr3+fOnZMhQ4aY6zZt2vg8zdxHH30ka9eula5du0qrVq3MfVmzZjVJVJ4sWrRIPvvsM2natKk89dRTbtf57bffTBLXjTfemKxp91SxYsWkQoUK8ue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L8upEvi5LITpy+weUqBPlXKHrRMlM55ZMQMm/G5OmkEOEzwd1IgAAkIXl6PDTmTNnbPbs2TZ37lxbs2aNu65nz55Wo0YNa926tdWsWTPJOvHx8TZmzBibOXOm+/ftt99unTp1SrLc6tWrbcqUKbZ582Y7efKklS5d2po0aWLt27e32NjYVO3vvHnzbNq0abZt2zY32qVSpUrWqlUra968ech1tmzZYhMnTrT169fbr7/+aiVLlrQGDRrYLbfcYkWKFEmy/L/+9S9bt26du/zHP/7R7W9KPvnkE/vqq6/cZe1L9+7dU/X4AKQPtdj2LxilPqCUuFilddUaHDlT/piYlANKeQKmdwtVoPIrPGm7jI7LGgrHxdmQGbOsVFycCyMezZs3s3cJAAAAAJCGdaJQASVft6REAaXE9aJzoaRz/3ZdlqgT5WiR1In8a0GJAkq+7krUibKiC44ds5H/+84K/TZo9VC+fJm9SwAAAInk2PDTiRMnbODAgbZhw4ZE1x8+fNjmz5/vTh06dLC7777bd9vu3btt0KBBLnyUnAkTJtjYsWNdQKlw4cIuZLRr1y4bP368LViwwJ555pmgwaPkjBw50he40jZl48aN7rRixQp76KGHLHfASIU5c+a49RISEqxAgQJWrFgx279/v02ePNk9vgEDBli5cuVC3qeCYSmFnxQgU3gMwPnR/xdqs+0fUDoZON1bcgGlZIpVKmghZ4rJlStRkSlU8Ciw8ORfaApWeNLlGEbHZXtPLFnmgk/eSMsip05ZyRMnMnu3AAAAACBH1Yl8nZACuin5B5R+rycFDHbzBZR+rzFRJ8q5cufK5ZuaLbKBbL/XhILVibQ9uihlf/9csMgFn7w6UYn4eMuX4HqFAwAAZAk5Nvw0evRoF3yqWrWqde3a1ZYsWWKzZs1ygSidf/DBB65jUuXKla1p06a2atUqGzx4sB0/ftzq169vZcqUsa+//jrJdrVNBZ/y5ctnPXr0sIYNG7oOFgodjRgxwtauXevuu1evXmHv69SpU13wqWzZsq6rkjpTibpVDR061AWZLr30Umvbtq1vnb1799o777zjvih369bNWrRoYTExMXb06FHXuUohrGHDhtlzzz0X9D61z9u3b3edoy6++OKQ+6Z9OHjwoAtkHTt2LOzHBESrhDNnfp+e7fRpvzCS36i3VASUtEzCWSZ6y6lUJDr/gJL/uueWzZs7N12UkCr5jh+3q3bvTnSdCludftzEEQUAAAAAvy5KiToh+XdTCtZZ27+elEJAiW7bObtOdD4BpXP1pHN1Iv96Uj66bSOVSh44aFUPH05SJ2qeQqMAAACAjJQjw08KAGkKOU0/98QTT7jgjsJPotCSprxTaEghoenTp7vw0/Lly930dZ07d3YdodTFKRhNS6d1b7vtNmvUqJHvek1717t3bxd6Wrx4sduHcLo/6T4///xzy5s3r/Xr18/Kly/vu6127dr2wAMP2Kuvvuq6OfmHn2bMmGGnTp1yj6Vly5a+63WfCmWpY9SmTZvsp59+cgGvQAp4eYGw5MJP6g4lmkrP60wFZDa9B+MTEoIElPxGxQW27g4zoBTHaJYcSyPY0iSg5Df1mzfaTiPvgKyk1rx5ljdIILPZrt1mBw6YVSyYKfsFAAAAAKmtEyUNKCWtE0UaUKKLUs7l3237XABJgaXfL6ccUPo93ORfT1KdiG7byGqunj/fhZ0Ctd+02c6ePGm5YmMzYa8AAAAyMPykLknfffedff/9967zkYI8b7/9trtNHYVKlSplRYsWtYy2Z88e96X3wgsv9E0hF6hZs2aWR19ACp77ca948eLWv39/q1WrVrLb3rFjhzuvW7duktt0XxUrVnShI3VmCif8pACSOiopwOQffPJceeWV1qVLFzf93OnTp90+i7o2hdoPLVOtWjU7cOCAm44vWPipcePGtnTpUhcSu+eee3zbDZw6UM+tnkN1niL8hEhpBJuKSie94tJvRaSQASW/25MLKGkdjbxDzhRqZFyKASW/AlWwcFPemJjMfmhAhjhz5IjVnL8g6G35zpyxs+PGmfV+lGcDAABkmzoRgKzTbTuwW5Kv23YYAaXfl0scUNJl6kQ5l9dtO1hAyVf/8Qau+deLvC5LvoCSf72JbtvIORK2b7fL1q4LeluJuDiLmzTJYjt1yvD9AgAAyLDwk7oRvfjii3bo0CHfdZp2zStqaXq5//73vzZu3Dhr3ry5ZSQv8KTgz+HDh61YsWJJllHo6frrr/f9++abbw5r23rMyVHHJ8mfP39Y29N0e17IKdT0dOruFKhv377Jbtebok7dr4IpWbKkmxLwxx9/tGXLliXqYuVZtGiRxcXFuc5Y2g9kTwoKeiPZggeU/MNIfi29gwWUAgpYGnWHnNtFKWkA6fdRb0kCSok6LuWxY8fiLTYmj1UuW9wK5vs93JSfLkrAeYsbP97yxcWFXuDrb+zMPfda7tKlOdoAACBb1IkARFYnUlfs4AGl32tC4QSUvNqRF1CiTpRzed223VRtrkt2Mp21/Qe3KZSUJ4/9+mvSOtG57dBtGzhfJz/80HInM9A47tNPLX/79nR/AgAA2TP81L17d1e80pfhEiVK2DXXXGNfffVVomU0jZxG+Wl6uNWrVwftapRedF/Vq1d3U78NGDDA7rvvPtc5Kb398MMPruOTukip61Q4vA5O6s6krlKfffaZrV271u1vpUqVXPBJxzcS+/bts/Xr17up9HQcQmnSpIkLP2nqu2DhJ2/KOy2nLlLpRa+jrM5/HzNrf08lJJwLKCUbOvq9yHQ85HRwiTso6TzrPwNID4o0FggoKAUbFRc4tdvvI+GCd1BS4el8uijpPbZz37kgaYWSRZKEL6Ph/wwgK3d9ivv882SXyaUfJz75xAo8/HCG7ReQE2SFz5NAThGt77doHnSU1etEQHbutp18QMm/Q1KQztoBASW6bcM/gJS483bimlFgQClUl23v8vnWiXbsPeIuVyxVNKr/XgJZsevTqe++S3aZs4cO0f0JAABkz/DTxIkTbeTIkW6aNI3ae/jhh93l3LlzJ1muTZs2rrj1xhtv2H/+8x/LSD179nT3u3nzZnfuWbBggdWrVy9kR6TUio+Pt3fffddd7tChQ5LjEYoXKlL798GDB1tCQoJrA68p5xTe0klFQRUSw/0yOGrUKLcdHf9ChQqFXPbqq6+2jz/+2FasWJGkQ5YXoFLR8rLLLvMFoSLVrVu30Lc98nfLH5vf9+U1WnihjORGx51MOBdMUuHIjZY7fcriFEZy1587179VnIpL+P3c68Dku/23c/1bRS3k4NFxMXlc1yMVnjTSzTvP7/u3ilIxv192t8X4LscGWTdfTEzaFIz0e1K82an4M3bY4twpI95vACKX77OPLf/x4ykuFzd5sh1sdZOdLVmKwwykA/6+ARknmt5vlcol7VodDaKlTgRkZrdtXyjJP4AUEFBSHenc7Ym7KwULKOnyKepEOVZe1Yn8AkoFggaQfuuy5OpFQaaBCxJQots2kDO7PlkYf0/o/gQAALJl+OnNN990P5Y/99xz1rt375DLaVq1l19+2RW2Jk2alOFFLQWInn32WZs5c6bNmDHDhaBkyJAhli9fPmvZsqV17NjRihQpkib3pxGO6uJUrVo1u+GGG8Je7+TJk+58+PDhrvtTjx49XNcoFUcWL17stqvg0SWXXGJt27ZNcXtjx451U+mVLVvW7rjjjmSXVYequnXr2tKlS23evHl24403+m6bM2eO2weN1gw3yJVdJZw5Yyv27bale362w5rj2gsrBQkz6Rw5kyJE/oEkVzSK8Q8f/X7598CS33mePJY/RuudO/cPK8Xk8PcggDRy7Kjl+3pyWIuq+1O+iV9Y3P2hQ8wAAADRVCcC0orqP/O2b7PpGzfbsVPxZjFmJ0MElHQ5evrPIT26bScfUPLvvn3u38E6LflPD3e+3bYBIJKuTx66PwEAgGwZflq4cKE779q1a4rLNm7c2NfVKDNopGGrVq3c6a233nIhInUxWrdunU2dOtU9Fk2LV65cufO6H01Vp/BQ0aJFrVevXhGFhbzp+AoWLGhPPvmkOxcVDnX8dK4RkSoMagq85LatkNeECRMsf/78ruBYoECBFO+/RYsWLvykqe8Cw0/SvHlzOx/qQhXK0vXnpvyrWLaoZVVnzp61Z7+bbpM3/pDZu4I04gJJvxWNvIKSm8YtcKq3324PuqxfUcorSGm7tN1OG4mmvSuTdNo7AKlzcvJ4izt5Iuzl8303zUr9+T7LXbo0hxxIA/x9AzIO77eMFU11IuB8xSckWO8pk23Rzh0czGxCXbGDBZSCdkvymxLOVxfyDy75dV2iTgQgu3R98tD9CQAAZLvwk6Zj80bspXjnec7dfd68edN6NyJWpkwZK126tD311FNuSjd1VFqzZo0L5/Tv3z/V250+fbp98cUXrptU3759XcepSCjsdPToURc88oJP/ho2bOimntu/f787qaNTMGobr2n3FBJ46KGHrEqVKmHd/5VXXulCW9u2bbOtW7e69TTV3u7du61q1apWoUIFS29ZOdgwY8tmgk+ZIHeuXL7CU3IFpUTBJP/gkq9Ylfg6bU9TyCF66P+HrPx/BBAtzp48aXFffBHZSqdOWfznn1uBBx9Mr90Cciz+vgG837KTaK0TAakxdvUqgk+ZVCf6fbDa7x2SUg4o/V4TCgwoeWEn6kQAcqIze/Yk6fqkToW5Uuj+FP/NN5b/llvSff8AAAAyJPykcMz69ett5cqVVr9+/WSX1bRtouncshIFodQZ6eGHH3YBKE09FxsbG/F21DFp9OjRrhvTI4884qa8i5SKgwo/hQo16YcRLXPo0CE7fPhw0OU0pd/gwYMtISHB/vznP7vAVLhiYmLc1HaTJ0923av0/Opcrr32WsvpVu7Zndm7kKV5U7OlNqB0rnX3uQJVrF9hSqPuCLwAQNpJ2LHD7PjxRNcdyJ/fSsXFJbve6Q0beBoAAEC2rxMB4VpBnShZ6nYULKCUaDo3/4FrwQJKQTpxUycCgLR1+ocfknR92h8ba2VOnkx2vQTqRAAAIDuFn9ShSNPGabq4iRMnhgwoaDq3Z5991t3esWNHy0iawk2BoAYNGlidOnWCLlOoUCE3PdypU6fcKdLw06ZNm2zo0KHucT7wwAPuvlJD3ZV++ukn27t3b9DbtX11fJLixYsnuV3rDRw40OLi4uy2226zNm3aRLwPmvpO4adFixbZ3XffbUuWLHGjMK+++mrL6S4oknWn5AtXjEbH+YJGSVt3pxxQ8p/y7ffClEJPMXRRAoCokLtMGTNNh/tbZ4azuXLZupIl7JpdiUO+q0qWtDoHD/r+HXPRRRm+rwAAILpEQ50ISCvlCxfJVnWicAJKwcJIXo3J/zrqRAAQPWIqVdLIe80X7f59OibGdhYulCT8tK1IEbvo6FHfv3NTJwIAANkp/PTYY4+5bkdTp061W2+91V555RW79NJLk3RE6tevn82cOdN1WdI0bBlJXZK+/fZb27lzZ8jw0+rVq+3YsWNWrFgxK1IkssKFpoTT41bg6K677rLrrrsu1fvapEkTN3Xed99954JLmj7Pn7owqeNT+fLl3bH0p45RL730kh05csRatmxpd9xxR6r2oWLFii6EpUCX7u/AgQN21VVXuYBYTtfq4kts1JLv7cBvPxanJ9duO0RAKdnW3QEFqlj/YlXevJY3d266KAFADpe7WDEr2K+fnRw1yv17TsOGtn/F8iTLfVjjUnvxrFnu9estT716Fnv//ZmwtwAAIJpEQ50ISCs3X1rDxq1dYydPn86wOlGiulCEAaXEHbrP1ZOoEwEAYqpUsQK9etnJjz6yXIUK2ecNG1ilOXOSHJhBDRvYoEO/WMKWLZb3mmss/623cvAAAED2CT9deOGFNn78eLvlllvciL5Jkya5YI5G7mnatVKlStkvv/xiZ8+edaGicePGhZzSLb0ojDRhwgRbu3atDRs2zO68807fbfHx8bZgwQJ7//33fSMUI6Gg0csvv+zO27Vr545DOIYPH+46KnXu3DlRd6batWtbrVq13CjJ1157zbp16+YKgRoROXfuXFdAlMD7UfBKHZ8UxGrUqJF16dLFzveYKfz0f//3f+7fTHl3TplChezdjrfby3Nm2/Lduy3u9OnfC02/jYxL2qI7cUDJdUtKrlj12+i43CFGxwIAkBbyNWvmTrJp4QIrEiT8FJcnj519/HErVqAgBx0AAGSbOhGQVi4tXdpG3HyrDV4w31bv3WOnz5zx66L9e83HN7jNqwtFGFCiTgQASG/5O3RwJ9k87Zug4adfYmOt8MCBPBkAACB7hp+8adLWrFnjRu19+eWXtmvXrkRdlzSFnIpeCgldlAltMNXNqU+fPvb666+7TkY6eR588EE3zZ00b97c2rdvH9G2hwwZYnv27HGXV6xY4U7BtG3b1p28Dk0KMsm0adOSTE3Xo0cPe+GFF2zVqlVuv0uWLGnHjx+3E791G1LISsfc33vvvefCSrJ161Z74okngu5Hw4YNXeAqnA5UH3zwgeuGpen1rrjiijCORs5QqVgxG3xTe9u+57Ar3lYsW5ROSgAAAAAAREmdCEhLdcqVs5G33EqdCAAAAACAaA8/eVOlffjhhy6gs3z5clfYUreiCy64wOrWrWuFCxe2zHT55Ze7Vutqu75y5Urbtm2bu75o0aJuijcV5urVqxfxdvfu3eu77F/MC6TAk0cjG5s1a+Y6P7Vu3TrJshoF+fzzz9vkyZNt4cKFtm/fPitQoIBVr17dBaXq16+f7H5o+VA0ujIcBQsWtMaNG9vs2bPdvubOnTus9XISBZ8AAAAAAED01YmAtEadCAAAAACAjJPrrPqKw7VVnzVrlg0aNIijkUUsXb/dndevWcmyOr2Nduw94i7T+Qng/QZEuyGa9u7dd63j5i2Jru/T/Bp79fHHrSTT3gFpjs+TQMbh/QZEJ+pEAILh7zqQ/p6c9o21eO//rMnuc7OeeLrd2M7GPdaXpwBIB/x9AzIO77fsg/Y9AAAACBuxeQAAkFMGyfXu3TuzdwMAAAAAAACZOe3d/v373ZRyO3futLi4uBSXf+aZZywzderUyZ0AAAAAAACQs+tEAAAAAAAAyOHhp//85z/27LPP2unTp8NqI5YrVy6KWgAAAFncL/nzZfYuAACAKESdCAAAIPs5UCA2s3cBAAAg/cJPH330kfXv399dLlWqlNWpU8cKFSqU1ncDAACADHYqJoZjDgAAsm2d6MyZMzZ79mybO3eurVmzxl3Xs2dPq1GjhrVu3dpq1qyZZJ34+HgbM2aMzZw50/379ttvD9pZfPXq1TZlyhTbvHmznTx50kqXLm1NmjSx9u3bW2xs6n44nDdvnk2bNs22bdvmBhdWqlTJWrVqZc2bNw+6/KOPPmr79u0Lub1atWrZ008/7fu3ltU6EhMTY0OGDLFixYqleAx79eplhw4dcv9+6qmn7LLLLkvV4wMAAFnbaepEAAAgO4efXn/9dXfeoUMH++STT6xgwYJpfRcAAABIR7k4ugAAIIfViU6cOGEDBw60DRs2JLr+8OHDNn/+fHfSY7j77rt9t+3evdsGDRrkwkfJmTBhgo0dO9YFlAoXLmxFihSxXbt22fjx423BggWuG7qui8TIkSN9gSttUzZu3OhOK1assIceeshy587tW173rUCSuq+XL18+6DYVTgslISHB5syZ48JayVHIyws+AQCA7CEXlSIAAJATw09r1651hZRXXnklyxa0AAAAAAAAkP6ipU40evRoF3yqWrWqde3a1ZYsWWKzZs1ygSidf/DBBzZx4kSrXLmyNW3a1FatWmWDBw+248ePW/369a1MmTL29ddfJ9mutqngU758+axHjx7WsGFDdzz2799vI0aMcMdH961uSeGaOnWqCz6VLVvWunfv7jpTibpVDR061AW1Lr30Umvbtm2iENfp06fdOnouIqH9VXhKXbFSCj9pGS+QdezYsYjuBwAAAAAAILV+HwKWRrzW5SoWAQAAAAAAIOeKhjrR0aNH3RRymn7uiSeesCpVqvhuU2hJU97dc8897t/Tp09358uXL3fT13Xu3Nkee+yxkFP5aVo6BYduu+02a9SokQsSiaa96927t+XPn98WL17s9iEcus/PP//c8ubNa/369fMFn6R27dr2wAMPuMuTJ09OtN6BAwfceYkSJSI8Ouf29aKLLrLt27fbli1bku2epdCYgmCagg8AAAAAACBqw0+tWrVy5xoBBwAAgOyj0KlTmb0LAAAgykRDnWjPnj0uoHThhRf6ppAL1KxZM/vLX/5ibdq0cf8uXry49e/f326++WZfoCmYHTt2uPO6desmuU33VbFiRTtz5ozt3bs3rH1VuEgdlZo3bx50+rorr7zSunTpYjfddJPr9OQ5ePCgOy9ZsqRFSsfmqquucpfVBSuUhQsXWlxcnOtuBQAAsr/CcfGZvQsAAADpF376xz/+4Ua7/fWvf7V9+/al9eYBAACQSQqe+v0HNAAAgOxSJ/ICT7t27XLTwwWjKfuuv/56XwhIoadatWqluO0XX3zRPvzwQ9c5KRiv45M6QIXDC5Ep5BSMgljqVKUp7/LkyZOk81Nqwk/SuHFjd64p9fxDVcGmvPOWBQAA2RuD5AAAQFbyexUkjajd9rfffmtdu3Z1I9A0Eq1o0aLJrqPCzJdffpnWuwIAAAAAAIBMFA11Iu1X9erVbePGjTZgwAC77777XDem9PbDDz+4jk/qIqWuU+HQ1HNSuXJl11Xqs88+s7Vr17r91VRzCj5dc801SdbzOj8lJCTYW2+9ZStXrrRff/3VTWnXpEkT69ChgxUoUCDk/Wr/FODatm2bLVu2zE3h50/Btg0bNrhwlY5lelInqqzOfx+jYX+BaMb7DciA95kF/1uma/k7B6TT+47Pk0CGidb3W3JdqHOqNA8/ybp16+zIkSPuxZFcO2wPTwwAAAAAAED2FA11op49e9obb7xhmzdvdueeBQsWWL169Sw2NjZN7y8+Pt7effddd1nBo9y5w2vO7nVw2rJliw0ePNiFmUqVKmUnTpxw4S2dVq9ebd27dw+63tSpU9251lE3q927d9sXX3xh33//vT399NNWpEiRkPetUNVHH33knsPA8JO6Pun51fSA5/P8devWLfRtj/zd8sfmtx17j1g02bnvXHcvALzfgGh14uSpoNfrb3+0/V0GohGfJwHeb8FUKleMl0Z6h5/GjBnjRvOp0NGiRQu7+uqrw27dDQAAAAAAgOwjWupECgM9++yzNnPmTJsxY4YLQcmQIUMsX7581rJlS+vYsWOy4aBIvP32266LU7Vq1eyGG24Ie72TJ0+68+HDh7vuTz169HBdmfTj4+LFi912FUS65JJL3NR3njp16lhMTIzr9NSmTRsrUaKEu/7HH3+0N99803WReuedd6xPnz7Jhp8+/fRTW7FihZsesFix3wutc+bMcefXXnttqo4HAAAAAABAlgo/DRw40BW0Hn30UXv11VfTevMAAAAAAACIEtFUJ8qTJ4+1atXKnTQ1nEJEl112metcpY5JCxcudNPilStX7rzuR1PVzZs3z03/16tXr7C7Pok3HZ+6Nj355JPuXHSMGzdu7M7VuWrSpEluCjxv29ddd507BVL4ql+/fvbEE0+47k8///xzyCn4FHZSF6wlS5a4/b/xxhvd9Zrubs+ePVa1atWwp+8LZdSoUSFvW7r+3JR/FcsmP21iVqAwmjdCv0KZInS9B3i/AVGtQGzeoNfrc0c0/F0GohGfJwHeb8gC4Se13RYVTgAAAAAAAJBzRWudqEyZMq5L0lNPPWX79u1zHZXWrFnjwjn9+/dP9XanT5/upplTN6m+ffu6jlORUNjp6NGjLnjkBZ/8NWzY0HV12r9/vzuVLVs2xW1qmfr167tw1/r165MNMKl7l8JPmvrOCz9ldNenzJgW8Xz3N9r2GYhWvN+AdHp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      "text/plain": [
       "<Figure size 2380x2108 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved chart to ny_supplemental_recapture_household_types_policyengine_inter_logo.png\n"
     ]
    }
   ],
   "source": [
    "def dollars_millions(value, _pos=None):\n",
    "    return f\"${value / 1_000_000:,.2f}M\"\n",
    "\n",
    "case_order = [case[\"case_slug\"] for case in CASES]\n",
    "summary_by_slug = summary.set_index(\"case_slug\").loc[case_order]\n",
    "\n",
    "fig = plt.figure(figsize=(14, 12.4), dpi=170)\n",
    "grid = fig.add_gridspec(\n",
    "    3,\n",
    "    2,\n",
    "    left=0.07,\n",
    "    right=0.965,\n",
    "    top=0.80,\n",
    "    bottom=0.13,\n",
    "    hspace=0.62,\n",
    "    wspace=0.20,\n",
    ")\n",
    "\n",
    "axes = [fig.add_subplot(grid[i, j]) for i in range(3) for j in range(2)]\n",
    "plot_axes = axes[:5]\n",
    "summary_ax = axes[5]\n",
    "\n",
    "for ax, case, color in zip(plot_axes, CASES, SERIES_COLORS):\n",
    "    case_df = df[df[\"case_slug\"] == case[\"case_slug\"]].sort_values(\"input_employment_income\")\n",
    "    ax.plot(\n",
    "        case_df[\"input_employment_income\"],\n",
    "        case_df[\"household_net_income\"],\n",
    "        color=PE_COLORS[\"primary_500\"],\n",
    "        linewidth=2.8,\n",
    "        solid_capstyle=\"round\",\n",
    "    )\n",
    "\n",
    "    before = case_df[case_df[\"input_employment_income\"] == CENTER].iloc[0]\n",
    "    after = case_df[case_df[\"input_employment_income\"] == CENTER + 2].iloc[0]\n",
    "    cliff = summary.loc[summary[\"case_slug\"] == case[\"case_slug\"], \"cliff_size\"].iloc[0]\n",
    "\n",
    "    ax.axvline(CENTER, color=PE_COLORS[\"error\"], linewidth=1.4, linestyle=\"--\", alpha=0.85)\n",
    "    ax.annotate(\n",
    "        \"\",\n",
    "        xy=(CENTER, before[\"household_net_income\"]),\n",
    "        xytext=(CENTER, after[\"household_net_income\"]),\n",
    "        arrowprops={\"arrowstyle\": \"<->\", \"color\": PE_COLORS[\"error\"], \"lw\": 2.0},\n",
    "    )\n",
    "    ax.text(\n",
    "        CENTER + 950,\n",
    "        (before[\"household_net_income\"] + after[\"household_net_income\"]) / 2,\n",
    "        f\"${cliff:,.0f} cliff\",\n",
    "        fontsize=10.5,\n",
    "        va=\"center\",\n",
    "        ha=\"left\",\n",
    "        bbox={\n",
    "            \"boxstyle\": \"round,pad=0.22,rounding_size=0.04\",\n",
    "            \"facecolor\": PE_COLORS[\"white\"],\n",
    "            \"edgecolor\": PE_COLORS[\"border_light\"],\n",
    "            \"linewidth\": 0.8,\n",
    "        },\n",
    "    )\n",
    "\n",
    "    y_min = min(case_df[\"household_net_income\"].min(), after[\"household_net_income\"]) - 9_000\n",
    "    y_max = max(case_df[\"household_net_income\"].max(), before[\"household_net_income\"]) + 13_000\n",
    "    ax.set_ylim(y_min, y_max)\n",
    "    ax.xaxis.set_major_locator(mticker.FixedLocator([\n",
    "        24_980_000,\n",
    "        24_990_000,\n",
    "        25_000_000,\n",
    "        25_010_000,\n",
    "        25_020_000,\n",
    "    ]))\n",
    "    ax.xaxis.set_major_formatter(mticker.FuncFormatter(dollars_millions))\n",
    "    ax.yaxis.set_major_formatter(mticker.FuncFormatter(dollars_millions))\n",
    "    ax.grid(True, color=PE_COLORS[\"border_light\"], linewidth=0.85)\n",
    "    ax.set_axisbelow(True)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"left\"].set_color(PE_COLORS[\"border_medium\"])\n",
    "    ax.spines[\"bottom\"].set_color(PE_COLORS[\"border_medium\"])\n",
    "    ax.tick_params(axis=\"both\", labelsize=9.5)\n",
    "    ax.set_xlabel(\"Employment income\", fontsize=10.5, labelpad=8)\n",
    "    ax.set_ylabel(\"Household net income\", fontsize=10.5, labelpad=8)\n",
    "    ax.set_title(case[\"case\"], loc=\"left\", fontsize=15, fontweight=500, pad=20)\n",
    "    ax.text(\n",
    "        0,\n",
    "        1.035,\n",
    "        case[\"subtitle\"],\n",
    "        transform=ax.transAxes,\n",
    "        ha=\"left\",\n",
    "        va=\"bottom\",\n",
    "        color=PE_COLORS[\"text_secondary\"],\n",
    "        fontsize=9.5,\n",
    "    )\n",
    "\n",
    "summary_ax.axis(\"off\")\n",
    "summary_ax.text(\n",
    "    0.0,\n",
    "    0.88,\n",
    "    \"PE-simulated boundary\",\n",
    "    fontsize=15,\n",
    "    fontweight=600,\n",
    "    color=PE_COLORS[\"text_primary\"],\n",
    "    ha=\"left\",\n",
    ")\n",
    "y = 0.76\n",
    "for _, row in summary_by_slug.iterrows():\n",
    "    summary_ax.text(0.0, y, row[\"case\"], fontsize=10.5, color=PE_COLORS[\"text_primary\"], ha=\"left\")\n",
    "    summary_ax.text(\n",
    "        0.94,\n",
    "        y,\n",
    "        f\"${row['cliff_size']:,.0f}\",\n",
    "        fontsize=10.5,\n",
    "        color=PE_COLORS[\"error\"],\n",
    "        ha=\"right\",\n",
    "    )\n",
    "    y -= 0.115\n",
    "summary_ax.text(\n",
    "    0.0,\n",
    "    0.13,\n",
    "    \"At $25,000,000 AGI, PE uses the lower branch. Because these arrays are float32,\\n\"\n",
    "    \"$25,000,001 rounds to $25,000,000; the first simulated value above the branch is $25,000,002.\",\n",
    "    fontsize=9.5,\n",
    "    color=PE_COLORS[\"text_secondary\"],\n",
    "    ha=\"left\",\n",
    "    va=\"bottom\",\n",
    "    linespacing=1.35,\n",
    ")\n",
    "\n",
    "fig.text(\n",
    "    0.07,\n",
    "    0.93,\n",
    "    \"NY Supplemental Tax Recapture\",\n",
    "    fontsize=25,\n",
    "    fontweight=500,\n",
    "    color=PE_COLORS[\"text_primary\"],\n",
    "    ha=\"left\",\n",
    ")\n",
    "fig.text(\n",
    "    0.07,\n",
    "    0.895,\n",
    "    \"By household type, PolicyEngine US 2026 simulations varying employment income around the $25M AGI branch.\",\n",
    "    fontsize=12.5,\n",
    "    color=PE_COLORS[\"text_secondary\"],\n",
    "    ha=\"left\",\n",
    ")\n",
    "\n",
    "fig.text(\n",
    "    0.07,\n",
    "    0.045,\n",
    "    \"Rendered with Inter Variable and @policyengine/design-system tokens.\",\n",
    "    fontsize=8.5,\n",
    "    color=\"#98A2B3\",\n",
    "    ha=\"left\",\n",
    ")\n",
    "logo = mpimg.imread(logo_path)\n",
    "logo_ax = fig.add_axes([0.83, 0.035, 0.14, 0.07], anchor=\"SE\")\n",
    "logo_ax.imshow(logo)\n",
    "logo_ax.axis(\"off\")\n",
    "\n",
    "png_path = OUTPUT_DIR / \"ny_supplemental_recapture_household_types_policyengine_inter_logo.png\"\n",
    "fig.savefig(png_path, bbox_inches=\"tight\", facecolor=PE_COLORS[\"white\"], dpi=220)\n",
    "plt.show()\n",
    "print(f\"Saved chart to {png_path}\")"
   ]
  }
 ],
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