diff --git a/obbba_district_impacts/Congressional-Hackathon-2025 b/obbba_district_impacts/Congressional-Hackathon-2025
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+++ b/obbba_district_impacts/Congressional-Hackathon-2025
@@ -0,0 +1 @@
+Subproject commit 3f6d05e76400c6e396a3a4eddd34a7b3f6919fc3
diff --git a/us/states/ny/nyc/mamdani_income_tax/data_exploration.ipynb b/us/states/ny/nyc/mamdani_income_tax/data_exploration.ipynb
new file mode 100644
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@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NYC Dataset Exploration\n",
+    "\n",
+    "This notebook explores the New York City (NYC) dataset to understand household counts, income distribution, and demographic characteristics relevant to the Mamdani Millionaire Income Tax analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from policyengine_us import Microsimulation\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "NYC_DATASET = \"hf://policyengine/policyengine-us-data/cities/NYC.h5\"\n",
+    "YEAR = 2026"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dcabd664b4d4de89a4fc47949c6fc63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "NYC.h5:   0%|          | 0.00/139M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load NYC dataset\n",
+    "sim = Microsimulation(dataset=NYC_DATASET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of households in dataset: 94,201\n",
+      "Household count (weighted): 3,146,018\n",
+      "Person count (weighted): 8,461,234\n",
+      "\n",
+      "County values in dataset: ['BRONX_COUNTY_NY' 'KINGS_COUNTY_NY' 'NEW_YORK_COUNTY_NY'\n",
+      " 'QUEENS_COUNTY_NY' 'RICHMOND_COUNTY_NY']...\n",
+      "\n",
+      "Households in NYC (in_nyc=True, weighted): 3,146,018\n",
+      "in_nyc unique values: [ True]\n",
+      "State names in dataset: ['NY']\n",
+      "\n",
+      "NY taxable income - median: $35,687\n",
+      "NY taxable income - max: $5,563,453\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check dataset size\n",
+    "household_weight = sim.calculate(\"household_weight\", period=YEAR)\n",
+    "household_count = sim.calculate(\"household_count\", period=YEAR, map_to=\"household\")\n",
+    "person_count = sim.calculate(\"person_count\", period=YEAR, map_to=\"household\")\n",
+    "\n",
+    "print(f\"Number of households in dataset: {len(household_weight):,}\")\n",
+    "print(f\"Household count (weighted): {household_count.sum():,.0f}\")\n",
+    "print(f\"Person count (weighted): {person_count.sum():,.0f}\")\n",
+    "\n",
+    "# Check county info (determines in_nyc)\n",
+    "county_str = sim.calculate(\"county_str\", period=YEAR, map_to=\"household\")\n",
+    "print(f\"\\nCounty values in dataset: {np.unique(county_str)[:10]}...\")  # First 10\n",
+    "\n",
+    "# Check if in_nyc is set\n",
+    "in_nyc = sim.calculate(\"in_nyc\", period=YEAR, map_to=\"household\")\n",
+    "in_nyc_weighted = (np.array(in_nyc) * np.array(household_weight)).sum()\n",
+    "print(f\"\\nHouseholds in NYC (in_nyc=True, weighted): {in_nyc_weighted:,.0f}\")\n",
+    "print(f\"in_nyc unique values: {np.unique(in_nyc)}\")\n",
+    "\n",
+    "# Check state\n",
+    "state_name = sim.calculate(\"state_name\", period=YEAR, map_to=\"household\")\n",
+    "print(f\"State names in dataset: {np.unique(state_name)}\")\n",
+    "\n",
+    "# Check NY taxable income\n",
+    "ny_taxable = sim.calculate(\"ny_taxable_income\", period=YEAR, map_to=\"household\")\n",
+    "print(f\"\\nNY taxable income - median: ${np.median(ny_taxable):,.0f}\")\n",
+    "print(f\"NY taxable income - max: ${np.max(ny_taxable):,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Income distribution:\n",
+      "  Median AGI: $48,746\n",
+      "  75th percentile: $110,770\n",
+      "  90th percentile: $227,831\n",
+      "  95th percentile: $384,892\n",
+      "  99th percentile: $3,972,415\n",
+      "  Max AGI: $5,626,860\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check household income distribution\n",
+    "agi = sim.calculate(\"adjusted_gross_income\", period=YEAR, map_to=\"household\")\n",
+    "print(f\"Income distribution:\")\n",
+    "print(f\"  Median AGI: ${agi.median():,.0f}\")\n",
+    "print(f\"  75th percentile: ${agi.quantile(0.75):,.0f}\")\n",
+    "print(f\"  90th percentile: ${agi.quantile(0.90):,.0f}\")\n",
+    "print(f\"  95th percentile: ${agi.quantile(0.95):,.0f}\")\n",
+    "print(f\"  99th percentile: ${agi.quantile(0.99):,.0f}\")\n",
+    "print(f\"  Max AGI: ${agi.max():,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NYC Taxable Income distribution:\n",
+      "  Median: $27,325\n",
+      "  75th percentile: $83,130\n",
+      "  90th percentile: $186,624\n",
+      "  95th percentile: $362,069\n",
+      "  99th percentile: $3,926,265\n",
+      "  Max: $5,563,453\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check NYC taxable income distribution (relevant for Mamdani tax)\n",
+    "nyc_taxable_income = sim.calculate(\"nyc_taxable_income\", period=YEAR, map_to=\"household\")\n",
+    "print(f\"NYC Taxable Income distribution:\")\n",
+    "print(f\"  Median: ${nyc_taxable_income.median():,.0f}\")\n",
+    "print(f\"  75th percentile: ${nyc_taxable_income.quantile(0.75):,.0f}\")\n",
+    "print(f\"  90th percentile: ${nyc_taxable_income.quantile(0.90):,.0f}\")\n",
+    "print(f\"  95th percentile: ${nyc_taxable_income.quantile(0.95):,.0f}\")\n",
+    "print(f\"  99th percentile: ${nyc_taxable_income.quantile(0.99):,.0f}\")\n",
+    "print(f\"  Max: ${nyc_taxable_income.max():,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "HIGH INCOME HOUSEHOLDS (NYC Taxable Income)\n",
+      "======================================================================\n",
+      "Households with income >= $500K:  119,432 (3.80%)\n",
+      "Households with income >= $1M:    100,112 (3.18%)\n",
+      "Households with income >= $2M:    75,965 (2.41%)\n",
+      "Households with income >= $5M:    250 (0.01%)\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# High income households (relevant for millionaire tax)\n",
+    "weights = np.array(sim.calculate(\"household_weight\", period=YEAR))\n",
+    "nyc_taxable = np.array(nyc_taxable_income)\n",
+    "total_households = weights.sum()\n",
+    "\n",
+    "# Households above $1M (Mamdani threshold)\n",
+    "above_1m_mask = nyc_taxable >= 1_000_000\n",
+    "above_1m_count = weights[above_1m_mask].sum()\n",
+    "\n",
+    "# Households in various high-income brackets\n",
+    "above_500k_mask = nyc_taxable >= 500_000\n",
+    "above_500k_count = weights[above_500k_mask].sum()\n",
+    "\n",
+    "above_2m_mask = nyc_taxable >= 2_000_000\n",
+    "above_2m_count = weights[above_2m_mask].sum()\n",
+    "\n",
+    "above_5m_mask = nyc_taxable >= 5_000_000\n",
+    "above_5m_count = weights[above_5m_mask].sum()\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)\n",
+    "print(\"HIGH INCOME HOUSEHOLDS (NYC Taxable Income)\")\n",
+    "print(\"=\"*70)\n",
+    "print(f\"Households with income >= $500K:  {above_500k_count:,.0f} ({above_500k_count/total_households*100:.2f}%)\")\n",
+    "print(f\"Households with income >= $1M:    {above_1m_count:,.0f} ({above_1m_count/total_households*100:.2f}%)\")\n",
+    "print(f\"Households with income >= $2M:    {above_2m_count:,.0f} ({above_2m_count/total_households*100:.2f}%)\")\n",
+    "print(f\"Households with income >= $5M:    {above_5m_count:,.0f} ({above_5m_count/total_households*100:.2f}%)\")\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "HOUSEHOLD COUNTS BY NYC TAXABLE INCOME BRACKET\n",
+      "======================================================================\n",
+      "Income Bracket Households % of All Households\n",
+      "       $0-$50k  1,959,036              62.27%\n",
+      "    $50k-$100k    535,118              17.01%\n",
+      "   $100k-$200k    355,081              11.29%\n",
+      "   $200k-$500k    177,351               5.64%\n",
+      "     $500k-$1M     19,320               0.61%\n",
+      "       $1M-$2M     24,147               0.77%\n",
+      "       $2M-$5M     75,715               2.41%\n",
+      "          $5M+        250               0.01%\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Household counts by income brackets (focus on high income for millionaire tax)\n",
+    "income_brackets = [\n",
+    "    (0, 50000, \"$0-$50k\"),\n",
+    "    (50000, 100000, \"$50k-$100k\"),\n",
+    "    (100000, 200000, \"$100k-$200k\"),\n",
+    "    (200000, 500000, \"$200k-$500k\"),\n",
+    "    (500000, 1000000, \"$500k-$1M\"),\n",
+    "    (1000000, 2000000, \"$1M-$2M\"),\n",
+    "    (2000000, 5000000, \"$2M-$5M\"),\n",
+    "    (5000000, float('inf'), \"$5M+\")\n",
+    "]\n",
+    "\n",
+    "bracket_data = []\n",
+    "for lower, upper, label in income_brackets:\n",
+    "    if upper == float('inf'):\n",
+    "        mask = nyc_taxable >= lower\n",
+    "    else:\n",
+    "        mask = (nyc_taxable >= lower) & (nyc_taxable < upper)\n",
+    "    count = weights[mask].sum()\n",
+    "    pct_of_total = (count / total_households) * 100\n",
+    "    \n",
+    "    bracket_data.append({\n",
+    "        \"Income Bracket\": label,\n",
+    "        \"Households\": f\"{count:,.0f}\",\n",
+    "        \"% of All Households\": f\"{pct_of_total:.2f}%\"\n",
+    "    })\n",
+    "\n",
+    "income_df = pd.DataFrame(bracket_data)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)\n",
+    "print(\"HOUSEHOLD COUNTS BY NYC TAXABLE INCOME BRACKET\")\n",
+    "print(\"=\"*70)\n",
+    "print(income_df.to_string(index=False))\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "NYC DATASET SUMMARY - WEIGHTED (Population Estimates)\n",
+      "============================================================\n",
+      "                              Metric      Value\n",
+      "          Household count (weighted)  3,146,018\n",
+      "             Person count (weighted)  8,461,234\n",
+      "                          Median AGI    $48,746\n",
+      "                 75th percentile AGI   $110,770\n",
+      "                 90th percentile AGI   $227,831\n",
+      "                 95th percentile AGI   $384,892\n",
+      "                 99th percentile AGI $3,972,415\n",
+      "                             Max AGI $5,626,860\n",
+      "           Median NYC Taxable Income    $27,325\n",
+      "  99th percentile NYC Taxable Income $3,926,265\n",
+      "       Households with income >= $1M    100,112\n",
+      "Pct of households with income >= $1M      3.18%\n",
+      "============================================================\n",
+      "\n",
+      "Summary saved to: nyc_dataset_summary_weighted.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create weighted summary table\n",
+    "weighted_summary_data = {\n",
+    "    'Metric': [\n",
+    "        'Household count (weighted)',\n",
+    "        'Person count (weighted)',\n",
+    "        'Median AGI',\n",
+    "        '75th percentile AGI',\n",
+    "        '90th percentile AGI',\n",
+    "        '95th percentile AGI',\n",
+    "        '99th percentile AGI',\n",
+    "        'Max AGI',\n",
+    "        'Median NYC Taxable Income',\n",
+    "        '99th percentile NYC Taxable Income',\n",
+    "        'Households with income >= $1M',\n",
+    "        'Pct of households with income >= $1M'\n",
+    "    ],\n",
+    "    'Value': [\n",
+    "        f\"{household_count.sum():,.0f}\",\n",
+    "        f\"{person_count.sum():,.0f}\",\n",
+    "        f\"${agi.median():,.0f}\",\n",
+    "        f\"${agi.quantile(0.75):,.0f}\",\n",
+    "        f\"${agi.quantile(0.90):,.0f}\",\n",
+    "        f\"${agi.quantile(0.95):,.0f}\",\n",
+    "        f\"${agi.quantile(0.99):,.0f}\",\n",
+    "        f\"${agi.max():,.0f}\",\n",
+    "        f\"${nyc_taxable_income.median():,.0f}\",\n",
+    "        f\"${nyc_taxable_income.quantile(0.99):,.0f}\",\n",
+    "        f\"{above_1m_count:,.0f}\",\n",
+    "        f\"{above_1m_count/total_households*100:.2f}%\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "weighted_df = pd.DataFrame(weighted_summary_data)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"NYC DATASET SUMMARY - WEIGHTED (Population Estimates)\")\n",
+    "print(\"=\"*60)\n",
+    "print(weighted_df.to_string(index=False))\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "# Save table\n",
+    "weighted_df.to_csv('nyc_dataset_summary_weighted.csv', index=False)\n",
+    "print(\"\\nSummary saved to: nyc_dataset_summary_weighted.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## County Discrepancy Investigation\n",
+    "\n",
+    "Ben reported seeing different county results when running the same dataset. His results showed multiple NYC counties (Queens, Bronx, etc.) instead of just Albany County. This may be related to local branch differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "County value counts:\n",
+      "QUEENS_COUNTY_NY      26719\n",
+      "KINGS_COUNTY_NY       26533\n",
+      "BRONX_COUNTY_NY       20787\n",
+      "NEW_YORK_COUNTY_NY    15029\n",
+      "RICHMOND_COUNTY_NY     5133\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "DataFrame columns and sample:\n",
+      "   household_id  household_weight  congressional_district_geoid  state_fips  \\\n",
+      "0       4550000          0.023717                          3603          36   \n",
+      "1       4550001         16.290739                          3603          36   \n",
+      "2       4550002          2.314028                          3603          36   \n",
+      "3       4550003         22.233847                          3603          36   \n",
+      "4       4550004          6.645908                          3603          36   \n",
+      "\n",
+      "             county        county_str  in_nyc  \n",
+      "0  QUEENS_COUNTY_NY  QUEENS_COUNTY_NY   False  \n",
+      "1  QUEENS_COUNTY_NY  QUEENS_COUNTY_NY   False  \n",
+      "2  QUEENS_COUNTY_NY  QUEENS_COUNTY_NY   False  \n",
+      "3  QUEENS_COUNTY_NY  QUEENS_COUNTY_NY   False  \n",
+      "4  QUEENS_COUNTY_NY  QUEENS_COUNTY_NY   False  \n",
+      "\n",
+      "Unique counties: ['QUEENS_COUNTY_NY' 'KINGS_COUNTY_NY' 'NEW_YORK_COUNTY_NY'\n",
+      " 'RICHMOND_COUNTY_NY' 'BRONX_COUNTY_NY']\n",
+      "in_nyc values: [False  True]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ben's results (from his local environment - may be related to local branches):\n",
+    "#\n",
+    "# In [5]: from policyengine_us import Microsimulation\n",
+    "#\n",
+    "# In [6]: sim = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/cities/NYC.h5\")\n",
+    "#\n",
+    "# In [7]: sim.calculate(\"county\")\n",
+    "# Out[7]: \n",
+    "#                   value      weight\n",
+    "# 0      QUEENS_COUNTY_NY    0.052011\n",
+    "# 1      QUEENS_COUNTY_NY    0.021776\n",
+    "# 2      QUEENS_COUNTY_NY    0.063064\n",
+    "# 3      QUEENS_COUNTY_NY    0.013337\n",
+    "# 4      QUEENS_COUNTY_NY   29.656933\n",
+    "# ...                 ...         ...\n",
+    "# 51490   BRONX_COUNTY_NY    0.050521\n",
+    "# 51491   BRONX_COUNTY_NY  249.701233\n",
+    "# 51492   BRONX_COUNTY_NY   10.212130\n",
+    "# 51493   BRONX_COUNTY_NY    0.592128\n",
+    "# 51494   BRONX_COUNTY_NY    0.095578\n",
+    "#\n",
+    "# [51495 rows x 2 columns]\n",
+    "\n",
+    "# Compare with our results\n",
+    "county = sim.calculate(\"county\", period=YEAR)\n",
+    "print(\"County value counts:\")\n",
+    "print(county.value_counts())\n",
+    "\n",
+    "# Filtering demonstration (from Ben's testing)\n",
+    "df = sim.calculate_dataframe(['household_id', 'household_weight', 'congressional_district_geoid', 'state_fips', 'county', 'county_str', 'in_nyc'])\n",
+    "pdf = pd.DataFrame(df)\n",
+    "print(\"\\nDataFrame columns and sample:\")\n",
+    "print(pdf.head())\n",
+    "print(f\"\\nUnique counties: {pdf['county'].unique()}\")\n",
+    "print(f\"in_nyc values: {pdf['in_nyc'].unique()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/us/states/ny/nyc/mamdani_income_tax/nyc_dataset_summary_weighted.csv b/us/states/ny/nyc/mamdani_income_tax/nyc_dataset_summary_weighted.csv
new file mode 100644
index 0000000..4cc5cc5
--- /dev/null
+++ b/us/states/ny/nyc/mamdani_income_tax/nyc_dataset_summary_weighted.csv
@@ -0,0 +1,13 @@
+Metric,Value
+Household count (weighted),"3,146,018"
+Person count (weighted),"8,461,234"
+Median AGI,"$48,746"
+75th percentile AGI,"$110,770"
+90th percentile AGI,"$227,831"
+95th percentile AGI,"$384,892"
+99th percentile AGI,"$3,972,415"
+Max AGI,"$5,626,860"
+Median NYC Taxable Income,"$27,325"
+99th percentile NYC Taxable Income,"$3,926,265"
+Households with income >= $1M,"100,112"
+Pct of households with income >= $1M,3.18%
diff --git a/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_decile_impacts.csv b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_decile_impacts.csv
new file mode 100644
index 0000000..c057173
--- /dev/null
+++ b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_decile_impacts.csv
@@ -0,0 +1,11 @@
+Decile,Average Income Change
+1,0.0
+2,0.0
+3,0.0
+4,0.0
+5,0.0
+6,0.0
+7,0.0
+8,0.0
+9,0.0
+10,-36149.48435367416
diff --git a/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_analysis.ipynb b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_analysis.ipynb
new file mode 100644
index 0000000..a02b4c2
--- /dev/null
+++ b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_analysis.ipynb
@@ -0,0 +1,458 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NYC Mamdani Millionaire Income Tax Analysis (2026)\n",
+    "\n",
+    "This notebook analyzes the fiscal and distributional impacts of Mayor-elect Zohran Mamdani's proposed income tax for New York City.\n",
+    "\n",
+    "## Baseline (Current Law)\n",
+    "- Current NYC income tax structure\n",
+    "\n",
+    "## Reform\n",
+    "- Mamdani Millionaire Income Tax proposal\n",
+    "\n",
+    "## Metrics\n",
+    "We calculate:\n",
+    "- Budgetary impact (revenue raised)\n",
+    "- Number and percentage of people/households affected\n",
+    "- Average change in net income for those affected\n",
+    "- Distributional analysis by income decile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from policyengine_us import Microsimulation\n",
+    "from policyengine_us.reforms.local.ny.mamdani_income_tax import nyc_mamdani_income_tax\n",
+    "from policyengine_core.reforms import Reform\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "NYC_DATASET = \"hf://policyengine/policyengine-us-data/cities/NYC.h5\"\n",
+    "YEAR = 2026\n",
+    "\n",
+    "# Create combined reform: structural reform + enable the parameter\n",
+    "param_reform = Reform.from_dict(\n",
+    "    {\n",
+    "        \"gov.local.ny.mamdani_income_tax.in_effect\": {\n",
+    "            \"2026-01-01.2100-12-31\": True\n",
+    "        }\n",
+    "    },\n",
+    "    country_id=\"us\",\n",
+    ")\n",
+    "\n",
+    "# Combine reforms: parameter reform first, then structural reform\n",
+    "mamdani_reform = (param_reform, nyc_mamdani_income_tax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_affected(baseline_sim, reform_sim, period=YEAR):\n",
+    "    \"\"\"\n",
+    "    Calculate people affected by the reform (losers who pay more taxes).\n",
+    "    Returns weighted counts, percentages, and average changes.\n",
+    "    \"\"\"\n",
+    "    # Get household-level income change\n",
+    "    baseline_income = np.array(baseline_sim.calculate(\"household_net_income\", period=period, map_to=\"household\"))\n",
+    "    reform_income = np.array(reform_sim.calculate(\"household_net_income\", period=period, map_to=\"household\"))\n",
+    "    household_weight = np.array(baseline_sim.calculate(\"household_weight\", period=period))\n",
+    "    income_change = reform_income - baseline_income\n",
+    "    \n",
+    "    # Get person-level data\n",
+    "    household_id_person = np.array(baseline_sim.calculate(\"household_id\", period=period, map_to=\"person\"))\n",
+    "    household_id_household = np.array(baseline_sim.calculate(\"household_id\", period=period, map_to=\"household\"))\n",
+    "    person_weight = np.array(baseline_sim.calculate(\"person_weight\", period=period))\n",
+    "    \n",
+    "    # Create mapping of household_id to income_change\n",
+    "    income_change_dict = dict(zip(household_id_household, income_change))\n",
+    "    \n",
+    "    # Map income change to each person\n",
+    "    person_income_change = np.array([income_change_dict.get(hh_id, 0) for hh_id in household_id_person])\n",
+    "    \n",
+    "    # Weighted count of people who are losers (lost more than $1 - paying more taxes)\n",
+    "    losers_mask = person_income_change < -1\n",
+    "    people_losing = person_weight[losers_mask].sum()\n",
+    "    \n",
+    "    total_people = person_weight.sum()\n",
+    "    \n",
+    "    # Calculate percentage\n",
+    "    pct_losers = (people_losing / total_people * 100) if total_people > 0 else 0\n",
+    "    \n",
+    "    # Households affected\n",
+    "    losing_hh_mask = income_change < -1\n",
+    "    households_losing = household_weight[losing_hh_mask].sum()\n",
+    "    total_households = household_weight.sum()\n",
+    "    pct_households_losing = (households_losing / total_households * 100) if total_households > 0 else 0\n",
+    "    \n",
+    "    # Average loss for affected households (weighted)\n",
+    "    avg_loss = np.average(income_change[losing_hh_mask], weights=household_weight[losing_hh_mask]) if losing_hh_mask.sum() > 0 else 0\n",
+    "    \n",
+    "    return {\n",
+    "        \"people_losing\": people_losing,\n",
+    "        \"total_people\": total_people,\n",
+    "        \"pct_losers\": pct_losers,\n",
+    "        \"households_losing\": households_losing,\n",
+    "        \"total_households\": total_households,\n",
+    "        \"pct_households_losing\": pct_households_losing,\n",
+    "        \"avg_loss\": avg_loss\n",
+    "    }\n",
+    "\n",
+    "def calculate_decile_impacts(baseline_sim, reform_sim, period=YEAR):\n",
+    "    \"\"\"\n",
+    "    Calculate average income change by income decile.\n",
+    "    \"\"\"\n",
+    "    from microdf import MicroSeries\n",
+    "    \n",
+    "    baseline_net_income = baseline_sim.calculate(\"household_net_income\", map_to=\"household\", period=period)\n",
+    "    reform_net_income = reform_sim.calculate(\"household_net_income\", map_to=\"household\", period=period)\n",
+    "    \n",
+    "    count_people = baseline_sim.calculate(\"household_count_people\", period=period)\n",
+    "    household_weight = baseline_sim.calculate(\"household_weight\", period=period)\n",
+    "    \n",
+    "    weighted_income = MicroSeries(\n",
+    "        baseline_net_income, weights=household_weight * count_people\n",
+    "    )\n",
+    "    decile = weighted_income.decile_rank().values\n",
+    "    \n",
+    "    household_income_decile = (np.where(baseline_net_income < 0, -1, decile)).astype(int)\n",
+    "    \n",
+    "    income_change = reform_net_income - baseline_net_income\n",
+    "    \n",
+    "    # Calculate average change by decile\n",
+    "    average_change = income_change.groupby(household_income_decile).mean()\n",
+    "    \n",
+    "    # Filter to valid deciles (1-10)\n",
+    "    average_change = average_change[average_change.index > 0]\n",
+    "    \n",
+    "    return average_change\n",
+    "\n",
+    "def format_currency(value):\n",
+    "    \"\"\"Format value as currency in millions or billions.\"\"\"\n",
+    "    if abs(value) >= 1e9:\n",
+    "        return f\"${value/1e9:.2f}B\"\n",
+    "    else:\n",
+    "        return f\"${value/1e6:.2f}M\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading baseline (current NYC income tax structure)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bf8b4830ba44333b462a21c996370b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "NYC.h5:   0%|          | 0.00/139M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline loaded\n",
+      "\n",
+      "Loading reform (Mamdani Millionaire Income Tax)...\n",
+      "Reform loaded\n",
+      "\n",
+      "============================================================\n",
+      "All simulations ready!\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Loading baseline (current NYC income tax structure)...\")\n",
+    "baseline = Microsimulation(dataset=NYC_DATASET)\n",
+    "print(\"Baseline loaded\")\n",
+    "\n",
+    "print(\"\\nLoading reform (Mamdani Millionaire Income Tax)...\")\n",
+    "reform_sim = Microsimulation(dataset=NYC_DATASET, reform=mamdani_reform)\n",
+    "print(\"Reform loaded\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"All simulations ready!\")\n",
+    "print(\"=\"*60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate Impacts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All impacts calculated\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Revenue impact - calculated as decrease in household net income (revenue to city)\n",
+    "baseline_hh_income = baseline.calculate(\"household_net_income\", period=YEAR, map_to=\"household\").sum()\n",
+    "reform_hh_income = reform_sim.calculate(\"household_net_income\", period=YEAR, map_to=\"household\").sum()\n",
+    "revenue_raised = baseline_hh_income - reform_hh_income  # Positive if reform raises revenue\n",
+    "\n",
+    "# Affected population\n",
+    "affected = calculate_affected(baseline, reform_sim)\n",
+    "\n",
+    "# Decile impacts\n",
+    "decile_impacts = calculate_decile_impacts(baseline, reform_sim)\n",
+    "\n",
+    "print(\"All impacts calculated\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "NYC MAMDANI MILLIONAIRE INCOME TAX IMPACTS (2026)\n",
+      "Baseline: Current NYC income tax | Reform: Mamdani Millionaire Income Tax\n",
+      "================================================================================\n",
+      "\n",
+      "================================BUDGETARY IMPACT================================\n",
+      "Revenue raised:                $8.87B\n",
+      "\n",
+      "==============================AFFECTED POPULATION===============================\n",
+      "People paying more taxes:      285,728 (3.38% of population)\n",
+      "Households paying more taxes:  100,089 (3.18% of households)\n",
+      "Average change for affected:   $-88,650.45\n",
+      "================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(f\"NYC MAMDANI MILLIONAIRE INCOME TAX IMPACTS ({YEAR})\")\n",
+    "print(\"Baseline: Current NYC income tax | Reform: Mamdani Millionaire Income Tax\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "print(f\"\\n{'BUDGETARY IMPACT':=^80}\")\n",
+    "print(f\"Revenue raised:                {format_currency(revenue_raised)}\")\n",
+    "\n",
+    "print(f\"\\n{'AFFECTED POPULATION':=^80}\")\n",
+    "print(f\"People paying more taxes:      {affected['people_losing']:,.0f} ({affected['pct_losers']:.2f}% of population)\")\n",
+    "print(f\"Households paying more taxes:  {affected['households_losing']:,.0f} ({affected['pct_households_losing']:.2f}% of households)\")\n",
+    "print(f\"Average change for affected:   ${affected['avg_loss']:,.2f}\")\n",
+    "print(\"=\"*80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distributional Analysis by Income Decile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "AVERAGE HOUSEHOLD INCOME CHANGE BY DECILE\n",
+      "================================================================================\n",
+      "Decile           Average Change\n",
+      "------------------------------\n",
+      "1          $              0.00\n",
+      "2          $              0.00\n",
+      "3          $              0.00\n",
+      "4          $              0.00\n",
+      "5          $              0.00\n",
+      "6          $              0.00\n",
+      "7          $              0.00\n",
+      "8          $              0.00\n",
+      "9          $              0.00\n",
+      "10         $        -36,149.48\n",
+      "================================================================================\n",
+      "Note: Negative values indicate income loss (higher taxes)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"AVERAGE HOUSEHOLD INCOME CHANGE BY DECILE\")\n",
+    "print(\"=\"*80)\n",
+    "print(f\"{'Decile':<10} {'Average Change':>20}\")\n",
+    "print(\"-\"*30)\n",
+    "for decile in range(1, 11):\n",
+    "    if decile in decile_impacts.index:\n",
+    "        change = decile_impacts[decile]\n",
+    "        print(f\"{decile:<10} ${change:>18,.2f}\")\n",
+    "print(\"=\"*80)\n",
+    "print(\"Note: Negative values indicate income loss (higher taxes)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==============================================================================================================\n",
+      "NYC MAMDANI INCOME TAX REFORM SUMMARY\n",
+      "==============================================================================================================\n",
+      "          Scenario                    Description  Year Revenue Raised % Population Affected % Households Affected Avg Change for Affected\n",
+      "Mamdani Income Tax Millionaire Income Tax for NYC  2026         $8.87B                 3.38%                 3.18%             $-88,650.45\n",
+      "==============================================================================================================\n",
+      "\n",
+      "Exported to: nyc_mamdani_income_tax_results.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create results DataFrame\n",
+    "results = [\n",
+    "    {\n",
+    "        \"Scenario\": \"Mamdani Income Tax\",\n",
+    "        \"Description\": \"Millionaire Income Tax for NYC\",\n",
+    "        \"Year\": YEAR,\n",
+    "        \"Revenue Raised\": format_currency(revenue_raised),\n",
+    "        \"% Population Affected\": f\"{affected['pct_losers']:.2f}%\",\n",
+    "        \"% Households Affected\": f\"{affected['pct_households_losing']:.2f}%\",\n",
+    "        \"Avg Change for Affected\": f\"${affected['avg_loss']:,.2f}\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "df_results = pd.DataFrame(results)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*110)\n",
+    "print(\"NYC MAMDANI INCOME TAX REFORM SUMMARY\")\n",
+    "print(\"=\"*110)\n",
+    "print(df_results.to_string(index=False))\n",
+    "print(\"=\"*110)\n",
+    "\n",
+    "# Export to CSV\n",
+    "df_results.to_csv(\"nyc_mamdani_income_tax_results.csv\", index=False)\n",
+    "print(\"\\nExported to: nyc_mamdani_income_tax_results.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Decile impacts exported to: nyc_mamdani_decile_impacts.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Export decile impacts\n",
+    "decile_df = pd.DataFrame({\n",
+    "    'Decile': decile_impacts.index,\n",
+    "    'Average Income Change': decile_impacts.values\n",
+    "})\n",
+    "decile_df.to_csv(\"nyc_mamdani_decile_impacts.csv\", index=False)\n",
+    "print(\"Decile impacts exported to: nyc_mamdani_decile_impacts.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_results.csv b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_results.csv
new file mode 100644
index 0000000..f3ed7e0
--- /dev/null
+++ b/us/states/ny/nyc/mamdani_income_tax/nyc_mamdani_income_tax_results.csv
@@ -0,0 +1,2 @@
+Scenario,Description,Year,Revenue Raised,% Population Affected,% Households Affected,Avg Change for Affected
+Mamdani Income Tax,Millionaire Income Tax for NYC,2026,$8.87B,3.38%,3.18%,"$-88,650.45"