From c604c3c58be10a2caae0ea1d93d1adea032088b0 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Thu, 4 Dec 2025 16:27:16 -0800
Subject: [PATCH 1/2] Improve county-to-household mapping for ACA rating areas
 Fixes #454

---
 .gitignore                                    |    1 +
 policyengine_us_data/datasets/cps/cps.py      |   33 +-
 .../geography/puma_county_crosswalk.py        |  437 ++
 .../storage/puma_county_crosswalk.csv         | 4702 +++++++++++++++++
 4 files changed, 5171 insertions(+), 2 deletions(-)
 create mode 100644 policyengine_us_data/geography/puma_county_crosswalk.py
 create mode 100644 policyengine_us_data/storage/puma_county_crosswalk.csv

diff --git a/.gitignore b/.gitignore
index 36301c6f..b5f27332 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,4 +23,5 @@ node_modules
 !soi_targets.csv
 !policyengine_us_data/storage/social_security_aux.csv
 !policyengine_us_data/storage/SSPopJul_TR2024.csv
+!policyengine_us_data/storage/puma_county_crosswalk.csv
 docs/.ipynb_checkpoints/
diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py
index f932e0d5..564207c5 100644
--- a/policyengine_us_data/datasets/cps/cps.py
+++ b/policyengine_us_data/datasets/cps/cps.py
@@ -631,9 +631,38 @@ def add_spm_variables(cps: h5py.File, spm_unit: DataFrame) -> None:
 
 
 def add_household_variables(cps: h5py.File, household: DataFrame) -> None:
-    cps["state_fips"] = household.GESTFIPS
-    cps["county_fips"] = household.GTCO
+    from policyengine_us_data.geography.puma_county_crosswalk import (
+        assign_county_from_state,
+    )
+
+    cps["state_fips"] = household.GESTFIPS.values
+
+    # CPS county codes (GTCO) are often 0 when suppressed for confidentiality.
+    # For households with missing county (GTCO == 0), we probabilistically
+    # assign a county based on state-level population weights.
+    raw_county = household.GTCO.values
+    missing_county_mask = raw_county == 0
+
+    if missing_county_mask.any():
+        logging.info(
+            f"Imputing county for {missing_county_mask.sum()} households "
+            f"({100 * missing_county_mask.mean():.1f}%) with missing GTCO"
+        )
+        # Use a fixed seed for reproducibility
+        random_state = np.random.RandomState(seed=42)
+        imputed_counties = assign_county_from_state(
+            cps["state_fips"][missing_county_mask],
+            random_state=random_state,
+        )
+        # Combine: use raw county where available, imputed where missing
+        county_fips = raw_county.copy()
+        county_fips[missing_county_mask] = imputed_counties
+        cps["county_fips"] = county_fips
+    else:
+        cps["county_fips"] = raw_county
+
     state_county_fips = cps["state_fips"] * 1e3 + cps["county_fips"]
+
     # Assign is_nyc here instead of as a variable formula so that it shows up
     # as toggleable in the webapp.
     # List county FIPS codes for each NYC county/borough.
diff --git a/policyengine_us_data/geography/puma_county_crosswalk.py b/policyengine_us_data/geography/puma_county_crosswalk.py
new file mode 100644
index 00000000..41ac330c
--- /dev/null
+++ b/policyengine_us_data/geography/puma_county_crosswalk.py
@@ -0,0 +1,437 @@
+"""
+Build a probabilistic crosswalk between PUMAs and counties.
+
+PUMAs (Public Use Microdata Areas) are geographic units used in Census
+microdata. They contain at least 100,000 people and are built from census
+tracts. A single PUMA may span multiple counties (especially in rural areas),
+and large counties may contain multiple PUMAs.
+
+This module creates population-weighted allocation factors that allow
+probabilistic assignment of counties to households that only have PUMA
+information.
+
+Methodology:
+1. Download Census tract-to-PUMA relationship file
+2. Download tract-level population from the 2020 Census
+3. Derive county FIPS from tract GEOID (first 5 digits)
+4. Calculate allocation factor = pop_in_county / total_puma_pop
+"""
+
+import io
+import requests
+import zipfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import us
+
+from policyengine_us_data.storage import STORAGE_FOLDER
+
+
+CROSSWALK_FILE = STORAGE_FOLDER / "puma_county_crosswalk.csv"
+
+
+def fetch_tract_to_puma() -> pd.DataFrame:
+    """
+    Fetches the 2020 Census Tract to 2020 PUMA relationship file.
+
+    Returns:
+        DataFrame with columns: state_fips, county_fips, tract, puma
+    """
+    url = "https://www2.census.gov/geo/docs/maps-data/data/rel2020/2020_Census_Tract_to_2020_PUMA.txt"
+    response = requests.get(url, timeout=120)
+    response.raise_for_status()
+
+    df = pd.read_csv(
+        io.BytesIO(response.content),
+        dtype=str,
+        encoding="utf-8-sig",  # Handle BOM
+    )
+
+    # The file has STATEFP, COUNTYFP, TRACTCE, PUMA5CE columns
+    df = df.rename(
+        columns={
+            "STATEFP": "state_fips",
+            "COUNTYFP": "county_fips",
+            "TRACTCE": "tract",
+            "PUMA5CE": "puma",
+        }
+    )
+
+    return df[["state_fips", "county_fips", "tract", "puma"]]
+
+
+def fetch_tract_population(state: str) -> pd.DataFrame:
+    """
+    Download & parse the 2020 PL-94-171 redistricting data for one state.
+
+    This provides population at the census tract level.
+
+    Args:
+        state: Two-letter state postal code or full state name.
+
+    Returns:
+        DataFrame with columns: tract_geoid, population
+    """
+    BASE = (
+        "https://www2.census.gov/programs-surveys/decennial/2020/data/"
+        "01-Redistricting_File--PL_94-171/{dir}/{abbr}2020.pl.zip"
+    )
+
+    # Handle DC specially since it's not in us.states
+    if state.upper() in ["DC", "DISTRICT OF COLUMBIA"]:
+        dir_name = "District_of_Columbia"
+        abbr = "dc"
+    else:
+        st = us.states.lookup(state)
+        if st is None:
+            raise ValueError(f"Unrecognised state name/abbr: {state}")
+        dir_name = st.name.replace(" ", "_")
+        abbr = st.abbr.lower()
+
+    url = BASE.format(dir=dir_name, abbr=abbr)
+
+    zbytes = requests.get(url, timeout=120).content
+    with zipfile.ZipFile(io.BytesIO(zbytes)) as z:
+        raw = z.read(f"{abbr}geo2020.pl")
+        try:
+            geo_lines = raw.decode("utf-8").splitlines()
+        except UnicodeDecodeError:
+            geo_lines = raw.decode("latin-1").splitlines()
+
+        p1_lines = z.read(f"{abbr}000012020.pl").decode("utf-8").splitlines()
+
+    # GEO file: keep tracts (SUMLEV 140)
+    geo_records = []
+    for ln in geo_lines:
+        parts = ln.split("|")
+        if parts[2] == "140":  # summary level 140 = tracts
+            logrecno = parts[7]
+            geoid = parts[8][-11:]  # 11-digit tract GEOID
+            geo_records.append((logrecno, geoid))
+
+    geo_df = pd.DataFrame(geo_records, columns=["LOGRECNO", "tract_geoid"])
+
+    # P-file: pull total-population
+    p1_records = [
+        (p[4], int(p[5])) for p in map(lambda x: x.split("|"), p1_lines)
+    ]
+    p1_df = pd.DataFrame(p1_records, columns=["LOGRECNO", "population"])
+
+    result = geo_df.merge(p1_df, on="LOGRECNO", how="left")
+    result["population"] = result["population"].fillna(0).astype(int)
+
+    return result[["tract_geoid", "population"]]
+
+
+def build_puma_county_crosswalk() -> pd.DataFrame:
+    """
+    Builds the PUMA-to-county crosswalk with population-weighted allocation
+    factors.
+
+    Returns:
+        DataFrame with columns:
+            - state_fips: 2-digit state FIPS
+            - puma: 5-digit PUMA code
+            - county_fips: 3-digit county FIPS within state
+            - county_fips_full: 5-digit full county FIPS (state + county)
+            - population: population in this PUMA-county intersection
+            - allocation_factor: proportion of PUMA population in this county
+    """
+    print("Fetching tract-to-PUMA relationship file...")
+    tract_puma = fetch_tract_to_puma()
+
+    # Build tract GEOID for joining
+    tract_puma["tract_geoid"] = (
+        tract_puma["state_fips"]
+        + tract_puma["county_fips"]
+        + tract_puma["tract"]
+    )
+
+    # Fetch tract populations for all states
+    print("Fetching tract-level population data...")
+    state_pops = []
+
+    # Include DC explicitly (not in us.states.STATES_AND_TERRITORIES)
+    states_to_fetch = list(us.states.STATES_AND_TERRITORIES) + ["DC"]
+
+    for s in states_to_fetch:
+        # Handle both State objects and string abbreviations
+        if hasattr(s, "abbr"):
+            if s.is_territory or s.abbr in ["ZZ"]:
+                continue
+            abbr = s.abbr
+            name = s.name
+        else:
+            abbr = s
+            name = s
+
+        print(f"  {name}")
+        try:
+            state_pops.append(fetch_tract_population(abbr))
+        except Exception as e:
+            print(f"    Warning: Could not fetch {name}: {e}")
+
+    tract_pop = pd.concat(state_pops, ignore_index=True)
+
+    # Join tract population to tract-PUMA mapping
+    merged = tract_puma.merge(tract_pop, on="tract_geoid", how="left")
+    merged["population"] = merged["population"].fillna(0).astype(int)
+
+    # Aggregate to PUMA-county level
+    print("Aggregating to PUMA-county level...")
+    puma_county = (
+        merged.groupby(["state_fips", "puma", "county_fips"])["population"]
+        .sum()
+        .reset_index()
+    )
+
+    # Calculate allocation factors (proportion of PUMA pop in each county)
+    puma_county["puma_total_pop"] = puma_county.groupby(
+        ["state_fips", "puma"]
+    )["population"].transform("sum")
+
+    puma_county["allocation_factor"] = (
+        puma_county["population"] / puma_county["puma_total_pop"]
+    )
+
+    # Handle edge case where PUMA has zero population
+    puma_county["allocation_factor"] = puma_county["allocation_factor"].fillna(
+        0
+    )
+
+    # Create full 5-digit county FIPS
+    puma_county["county_fips_full"] = (
+        puma_county["state_fips"] + puma_county["county_fips"]
+    )
+
+    # Sort by state, puma, allocation factor (descending)
+    puma_county = puma_county.sort_values(
+        ["state_fips", "puma", "allocation_factor"],
+        ascending=[True, True, False],
+    ).reset_index(drop=True)
+
+    # Select final columns
+    result = puma_county[
+        [
+            "state_fips",
+            "puma",
+            "county_fips",
+            "county_fips_full",
+            "population",
+            "allocation_factor",
+        ]
+    ]
+
+    return result
+
+
+def get_puma_county_crosswalk() -> pd.DataFrame:
+    """
+    Returns the PUMA-to-county crosswalk, loading from cache if available.
+
+    Returns:
+        DataFrame with PUMA-county allocation factors.
+    """
+    if CROSSWALK_FILE.exists():
+        return pd.read_csv(CROSSWALK_FILE, dtype=str).assign(
+            population=lambda df: df["population"].astype(int),
+            allocation_factor=lambda df: df["allocation_factor"].astype(float),
+        )
+
+    crosswalk = build_puma_county_crosswalk()
+    crosswalk.to_csv(CROSSWALK_FILE, index=False)
+    return crosswalk
+
+
+def assign_county_from_puma(
+    state_fips: np.ndarray,
+    puma: np.ndarray,
+    random_state: np.random.RandomState = None,
+) -> np.ndarray:
+    """
+    Probabilistically assigns county FIPS codes based on PUMA and state.
+
+    For each household, uses the PUMA-to-county allocation factors to
+    randomly assign a county, weighted by population.
+
+    Args:
+        state_fips: Array of 2-digit state FIPS codes (as strings or ints)
+        puma: Array of PUMA codes (as strings or ints)
+        random_state: Optional random state for reproducibility
+
+    Returns:
+        Array of 3-digit county FIPS codes (within state)
+    """
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    crosswalk = get_puma_county_crosswalk()
+
+    # Ensure consistent string types
+    state_fips = np.array([str(s).zfill(2) for s in state_fips])
+    puma = np.array([str(p).zfill(5) for p in puma])
+
+    n = len(state_fips)
+    county_fips = np.zeros(n, dtype=object)
+
+    # Group crosswalk by state-puma for efficient lookup
+    crosswalk_dict = {}
+    for (st, pm), group in crosswalk.groupby(["state_fips", "puma"]):
+        counties = group["county_fips"].values
+        probs = group["allocation_factor"].values
+        # Normalize probabilities (should already sum to 1, but be safe)
+        probs = probs / probs.sum() if probs.sum() > 0 else probs
+        crosswalk_dict[(st, pm)] = (counties, probs)
+
+    # Assign counties
+    for i in range(n):
+        key = (state_fips[i], puma[i])
+        if key in crosswalk_dict:
+            counties, probs = crosswalk_dict[key]
+            if len(counties) == 1:
+                county_fips[i] = counties[0]
+            else:
+                county_fips[i] = random_state.choice(counties, p=probs)
+        else:
+            # PUMA not found - assign 0 (unknown)
+            county_fips[i] = "000"
+
+    return county_fips
+
+
+def get_state_county_crosswalk() -> pd.DataFrame:
+    """
+    Creates a state-to-county crosswalk with population-weighted allocation
+    factors.
+
+    This is derived from the PUMA-county crosswalk by aggregating to state
+    level. Used for probabilistic county assignment when only state is known.
+
+    Returns:
+        DataFrame with columns:
+            - state_fips: 2-digit state FIPS
+            - county_fips: 3-digit county FIPS within state
+            - county_fips_full: 5-digit full county FIPS
+            - population: county population
+            - allocation_factor: proportion of state population in county
+    """
+    puma_county = get_puma_county_crosswalk()
+
+    # Aggregate to state-county level
+    state_county = (
+        puma_county.groupby(["state_fips", "county_fips", "county_fips_full"])[
+            "population"
+        ]
+        .sum()
+        .reset_index()
+    )
+
+    # Calculate state-level allocation factors
+    state_county["state_total_pop"] = state_county.groupby("state_fips")[
+        "population"
+    ].transform("sum")
+
+    state_county["allocation_factor"] = (
+        state_county["population"] / state_county["state_total_pop"]
+    )
+    state_county["allocation_factor"] = state_county[
+        "allocation_factor"
+    ].fillna(0)
+
+    # Sort by state, allocation factor (descending)
+    state_county = state_county.sort_values(
+        ["state_fips", "allocation_factor"],
+        ascending=[True, False],
+    ).reset_index(drop=True)
+
+    return state_county[
+        [
+            "state_fips",
+            "county_fips",
+            "county_fips_full",
+            "population",
+            "allocation_factor",
+        ]
+    ]
+
+
+def assign_county_from_state(
+    state_fips: np.ndarray,
+    random_state: np.random.RandomState = None,
+) -> np.ndarray:
+    """
+    Probabilistically assigns county FIPS codes based on state only.
+
+    For each household, uses state-to-county population weights to
+    randomly assign a county.
+
+    Args:
+        state_fips: Array of 2-digit state FIPS codes (as ints)
+        random_state: Optional random state for reproducibility
+
+    Returns:
+        Array of 3-digit county FIPS codes (as ints)
+    """
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    crosswalk = get_state_county_crosswalk()
+
+    # Ensure consistent string types for lookup
+    state_fips_str = np.array([str(int(s)).zfill(2) for s in state_fips])
+
+    n = len(state_fips)
+    county_fips = np.zeros(n, dtype=int)
+
+    # Group crosswalk by state for efficient lookup
+    crosswalk_dict = {}
+    for st, group in crosswalk.groupby("state_fips"):
+        counties = group["county_fips"].astype(int).values
+        probs = group["allocation_factor"].values
+        # Normalize probabilities
+        probs = probs / probs.sum() if probs.sum() > 0 else probs
+        crosswalk_dict[st] = (counties, probs)
+
+    # Assign counties
+    for i in range(n):
+        st = state_fips_str[i]
+        if st in crosswalk_dict:
+            counties, probs = crosswalk_dict[st]
+            if len(counties) == 1:
+                county_fips[i] = counties[0]
+            else:
+                county_fips[i] = random_state.choice(counties, p=probs)
+        else:
+            # State not found - assign 0 (unknown)
+            county_fips[i] = 0
+
+    return county_fips
+
+
+if __name__ == "__main__":
+    print("Building PUMA-to-county crosswalk...")
+    crosswalk = build_puma_county_crosswalk()
+    crosswalk.to_csv(CROSSWALK_FILE, index=False)
+    print(f"Saved crosswalk to {CROSSWALK_FILE}")
+
+    # Print summary statistics
+    print(f"\nTotal PUMA-county pairs: {len(crosswalk)}")
+    print(f"Unique PUMAs: {crosswalk.groupby(['state_fips', 'puma']).ngroups}")
+    print(f"Unique counties: {crosswalk['county_fips_full'].nunique()}")
+
+    # Show distribution of counties per PUMA
+    counties_per_puma = crosswalk.groupby(["state_fips", "puma"]).size()
+    print(f"\nCounties per PUMA:")
+    print(f"  Min: {counties_per_puma.min()}")
+    print(f"  Max: {counties_per_puma.max()}")
+    print(f"  Mean: {counties_per_puma.mean():.2f}")
+    print(f"  Median: {counties_per_puma.median():.0f}")
+
+    # Count single-county PUMAs (deterministic assignment)
+    single_county = (counties_per_puma == 1).sum()
+    print(
+        f"\nPUMAs with single county (deterministic): "
+        f"{single_county} ({100*single_county/len(counties_per_puma):.1f}%)"
+    )
diff --git a/policyengine_us_data/storage/puma_county_crosswalk.csv b/policyengine_us_data/storage/puma_county_crosswalk.csv
new file mode 100644
index 00000000..2ed1cf45
--- /dev/null
+++ b/policyengine_us_data/storage/puma_county_crosswalk.csv
@@ -0,0 +1,4702 @@
+state_fips,puma,county_fips,county_fips_full,population,allocation_factor
+01,00100,077,01077,93564,0.5115470410707256
+01,00100,033,01033,57227,0.31287998075493156
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+01,01100,063,01063,7730,0.0614018364947733
+01,01201,125,01125,101012,1.0
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+01,01301,127,01127,65342,0.5641831510054656
+01,01301,073,01073,50475,0.4358168489945345
+01,01401,073,01073,118409,1.0
+01,01402,073,01073,133581,1.0
+01,01403,073,01073,187166,1.0
+01,01404,073,01073,185090,1.0
+01,01501,117,01117,137436,1.0
+01,01502,117,01117,85588,0.6553345278020245
+01,01502,021,01021,45014,0.34466547219797555
+01,01600,121,01121,82149,0.6157726673063084
+01,01600,111,01111,21967,0.164660290237467
+01,01600,029,01029,15056,0.11285680019189254
+01,01600,027,01027,14236,0.10671024226433197
+01,01700,001,01001,58805,0.5065510082781315
+01,01700,047,01047,38462,0.33131476711833163
+01,01700,085,01085,10311,0.08881978482026721
+01,01700,105,01105,8511,0.07331443978326974
+01,01801,101,01101,109409,1.0
+01,01802,101,01101,105471,1.0
+01,01901,051,01051,87977,0.8620885635613565
+01,01901,101,01101,14074,0.13791143643864343
+01,02000,123,01123,41311,0.3550305519985562
+01,02000,017,01017,34772,0.29883378165848795
+01,02000,087,01087,19532,0.16785981316443077
+01,02000,037,01037,10387,0.0892668379755756
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+72,02000,045,72045,0,0.0
+72,02100,025,72025,0,0.0
+72,02200,063,72063,0,0.0
+72,02200,139,72139,0,0.0
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+72,02300,129,72129,0,0.0

From 0dcba52426420fa35d4268be039087a7e6117f65 Mon Sep 17 00:00:00 2001
From: daphnehanse11 <128793799+daphnehanse11@users.noreply.github.com>
Date: Mon, 8 Dec 2025 12:05:32 -0500
Subject: [PATCH 2/2] Improve county-to-household mapping for ACA rating areas
 Fixes #454

---
 changelog_entry.yaml                          |    6 +
 docs/long_term_projections.ipynb              |  495 +-
 docs/myst.yml                                 |    4 +-
 policyengine_us_data/datasets/cps/cps.py      |   32 +-
 .../geography/cd_county_crosswalk.py          |  595 +++
 .../geography/puma_county_crosswalk.py        |  437 --
 .../storage/puma_county_crosswalk.csv         | 4702 -----------------
 7 files changed, 622 insertions(+), 5649 deletions(-)
 create mode 100644 policyengine_us_data/geography/cd_county_crosswalk.py
 delete mode 100644 policyengine_us_data/geography/puma_county_crosswalk.py
 delete mode 100644 policyengine_us_data/storage/puma_county_crosswalk.csv

diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29b..de78c03a 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,6 @@
+- bump: minor
+  changes:
+    added:
+    - Probabilistic county assignment for CPS households with suppressed county data
+    - PUMA-to-county crosswalk with population-weighted allocation factors
+    - State-to-county crosswalk for CPS probabilistic assignment
diff --git a/docs/long_term_projections.ipynb b/docs/long_term_projections.ipynb
index 10b07b72..b99ab2ac 100644
--- a/docs/long_term_projections.ipynb
+++ b/docs/long_term_projections.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:56:10.232617Z",
@@ -33,139 +33,7 @@
      "shell.execute_reply": "2025-11-19T19:57:25.892382Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TEST_LITE == False\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "======================================================================\r\n",
-      "HOUSEHOLD-LEVEL INCOME TAX PROJECTION: 2025-2027\r\n",
-      "======================================================================\r\n",
-      "\r\n",
-      "Configuration:\r\n",
-      "  Base year: 2024 (CPS microdata)\r\n",
-      "  Projection: 2025-2027\r\n",
-      "  Calculation level: HOUSEHOLD ONLY (simplified)\r\n",
-      "  Calibration method: GREG\r\n",
-      "  Including Social Security benefits constraint: Yes\r\n",
-      "  Including taxable payroll constraint: Yes\r\n",
-      "  Saving year-specific .h5 files: Yes (to ./projected_datasets/)\r\n",
-      "  Years to process: 3\r\n",
-      "  Estimated time: ~9 minutes\r\n",
-      "\r\n",
-      "======================================================================\r\n",
-      "STEP 1: DEMOGRAPHIC PROJECTIONS\r\n",
-      "======================================================================\r\n",
-      "\r\n",
-      "Loaded SSA projections: 86 ages x 3 years\r\n",
-      "\r\n",
-      "Population projections:\r\n",
-      "  2025:  346.6M\r\n",
-      "  2027:  350.9M\r\n",
-      "\r\n",
-      "======================================================================\r\n",
-      "STEP 2: BUILDING HOUSEHOLD AGE COMPOSITION\r\n",
-      "======================================================================\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "Loaded 21,532 households\r\n",
-      "Household age matrix shape: (21532, 86)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "======================================================================\r\n",
-      "STEP 3: HOUSEHOLD-LEVEL PROJECTION\r\n",
-      "======================================================================\r\n",
-      "\r\n",
-      "Methodology (SIMPLIFIED):\r\n",
-      "  1. PolicyEngine uprates to each projection year\r\n",
-      "  2. Calculate all values at household level (map_to='household')\r\n",
-      "  3. IPF/GREG adjusts weights to match SSA demographics\r\n",
-      "  4. Apply calibrated weights directly (no aggregation needed)\r\n",
-      "\r\n",
-      "Initial memory usage: 1.13 GB\r\n",
-      "\r\n",
-      "Year    Population    Income Tax    Baseline Tax    Memory\r\n",
-      "-----------------------------------------------------------------\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  [DEBUG 2025] SS baseline: $1424.6B, target: $1609.0B\r\n",
-      "  [DEBUG 2025] Payroll baseline: $8950.9B, target: $10621.0B\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  [DEBUG 2025] SS achieved: $1609.0B (error: -0.0%)\r\n",
-      "  [DEBUG 2025] Payroll achieved: $10621.0B (error: -0.0%)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Saved 2025.h5\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2025      346.6M     $ 2543.1B     $ 1882.9B     4.32GB\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  [DEBUG 2027] SS baseline: $1495.1B, target: $1799.9B\r\n",
-      "  [DEBUG 2027] Payroll baseline: $9718.4B, target: $11627.0B\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  [DEBUG 2027] SS achieved: $1799.9B (error: -0.0%)\r\n",
-      "  [DEBUG 2027] Payroll achieved: $11627.0B (error: 0.0%)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Saved 2027.h5\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2027      350.9M     $ 2873.8B     $ 2125.0B     4.62GB\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Save calibrated datasets as .h5 files for each year\n",
     "!python ../policyengine_us_data/datasets/cps/long_term/run_household_projection.py 2027 --greg --use-ss --use-payroll --save-h5"
@@ -227,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:25.894248Z",
@@ -236,23 +104,7 @@
      "shell.execute_reply": "2025-11-19T19:57:28.259297Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/baogorek/envs/pe/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TEST_LITE == False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -264,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.260599Z",
@@ -273,149 +125,7 @@
      "shell.execute_reply": "2025-11-19T19:57:28.275040Z"
     }
    },
-   "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>Year</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>M Tot</th>\n",
-       "      <th>M Sin</th>\n",
-       "      <th>M Mar</th>\n",
-       "      <th>M Wid</th>\n",
-       "      <th>M Div</th>\n",
-       "      <th>F Tot</th>\n",
-       "      <th>F Sin</th>\n",
-       "      <th>F Mar</th>\n",
-       "      <th>F Wid</th>\n",
-       "      <th>F Div</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1941</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2492508</td>\n",
-       "      <td>1276328</td>\n",
-       "      <td>1276328</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1216180</td>\n",
-       "      <td>1216180</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2384290</td>\n",
-       "      <td>1218524</td>\n",
-       "      <td>1218524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1165766</td>\n",
-       "      <td>1165766</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1941</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2445054</td>\n",
-       "      <td>1246078</td>\n",
-       "      <td>1246078</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1198976</td>\n",
-       "      <td>1198976</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1941</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2395999</td>\n",
-       "      <td>1219543</td>\n",
-       "      <td>1219543</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1176456</td>\n",
-       "      <td>1176456</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1941</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2275425</td>\n",
-       "      <td>1157612</td>\n",
-       "      <td>1157612</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1117813</td>\n",
-       "      <td>1117813</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Year  Age    Total    M Tot    M Sin  M Mar  M Wid  M Div    F Tot  \\\n",
-       "0  1941    0  2492508  1276328  1276328      0      0      0  1216180   \n",
-       "1  1941    1  2384290  1218524  1218524      0      0      0  1165766   \n",
-       "2  1941    2  2445054  1246078  1246078      0      0      0  1198976   \n",
-       "3  1941    3  2395999  1219543  1219543      0      0      0  1176456   \n",
-       "4  1941    4  2275425  1157612  1157612      0      0      0  1117813   \n",
-       "\n",
-       "     F Sin  F Mar  F Wid  F Div  \n",
-       "0  1216180      0      0      0  \n",
-       "1  1165766      0      0      0  \n",
-       "2  1198976      0      0      0  \n",
-       "3  1176456      0      0      0  \n",
-       "4  1117813      0      0      0  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load SSA population data\n",
     "ssa_pop = pd.read_csv(STORAGE_FOLDER / 'SSPopJul_TR2024.csv')\n",
@@ -424,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.276266Z",
@@ -433,101 +143,7 @@
      "shell.execute_reply": "2025-11-19T19:57:28.279840Z"
     }
    },
-   "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>year</th>\n",
-       "      <th>oasdi_cost_in_billion_2025_usd</th>\n",
-       "      <th>cpi_w_intermediate</th>\n",
-       "      <th>oasdi_cost_in_billion_nominal_usd</th>\n",
-       "      <th>taxable_payroll_in_billion_nominal_usd</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025</td>\n",
-       "      <td>1609</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>1609.0000</td>\n",
-       "      <td>10621.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2026</td>\n",
-       "      <td>1660</td>\n",
-       "      <td>102.49</td>\n",
-       "      <td>1701.3340</td>\n",
-       "      <td>11129.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2027</td>\n",
-       "      <td>1715</td>\n",
-       "      <td>104.95</td>\n",
-       "      <td>1799.8925</td>\n",
-       "      <td>11627.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2028</td>\n",
-       "      <td>1763</td>\n",
-       "      <td>107.47</td>\n",
-       "      <td>1894.6961</td>\n",
-       "      <td>12159.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2029</td>\n",
-       "      <td>1810</td>\n",
-       "      <td>110.05</td>\n",
-       "      <td>1991.9050</td>\n",
-       "      <td>12696.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   year  oasdi_cost_in_billion_2025_usd  cpi_w_intermediate  \\\n",
-       "0  2025                            1609              100.00   \n",
-       "1  2026                            1660              102.49   \n",
-       "2  2027                            1715              104.95   \n",
-       "3  2028                            1763              107.47   \n",
-       "4  2029                            1810              110.05   \n",
-       "\n",
-       "   oasdi_cost_in_billion_nominal_usd  taxable_payroll_in_billion_nominal_usd  \n",
-       "0                          1609.0000                                 10621.0  \n",
-       "1                          1701.3340                                 11129.0  \n",
-       "2                          1799.8925                                 11627.0  \n",
-       "3                          1894.6961                                 12159.0  \n",
-       "4                          1991.9050                                 12696.0  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load Social Security auxiliary data\n",
     "ss_aux = pd.read_csv(STORAGE_FOLDER / 'social_security_aux.csv')\n",
@@ -590,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.280998Z",
@@ -609,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.283285Z",
@@ -618,18 +234,7 @@
      "shell.execute_reply": "2025-11-19T19:57:28.296600Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Age distribution targets for 2025:\n",
-      "Shape: (86, 1)\n",
-      "Total population: 346577.3M\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get SSA population targets for a specific year\n",
     "year = 2025\n",
@@ -641,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.297698Z",
@@ -650,16 +255,7 @@
      "shell.execute_reply": "2025-11-19T19:57:28.299938Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Social Security benefit target for 2025: $1609.0B\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get Social Security benefit target\n",
     "ss_target = load_ssa_benefit_projections(year)\n",
@@ -707,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2025-11-19T19:57:28.300920Z",
@@ -716,66 +312,7 @@
      "shell.execute_reply": "2025-11-19T19:59:04.747122Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "WHARTON COMPARISON PIPELINE - YEAR 2054\n",
-      "================================================================================\n",
-      "\n",
-      "Running PolicyEngine analysis...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: hf://policyengine/test/2054.h5\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ranking according to quantiles with: household_net_income\n",
-      "✓ Analysis complete\n",
-      "  Revenue impact: $-579.1B\n",
-      "\n",
-      "Generating comparison table...\n",
-      "\n",
-      "================================================================================\n",
-      "COMPARISON TABLE: 2054\n",
-      "================================================================================\n",
-      "\n",
-      "Average Tax Change (per household):\n",
-      "   Income Group PolicyEngine Wharton Difference\n",
-      " First quintile         $-95     $-5       $-90\n",
-      "Second quintile      $-1,054   $-275      $-779\n",
-      "Middle quintile      $-2,241 $-1,730      $-511\n",
-      "Fourth quintile      $-4,633 $-3,560    $-1,073\n",
-      "         80-90%      $-6,737 $-4,075    $-2,662\n",
-      "         90-95%     $-12,121 $-4,385    $-7,736\n",
-      "         95-99%      $-8,066 $-4,565    $-3,501\n",
-      "       99-99.9%      $-7,257 $-4,820    $-2,437\n",
-      "       Top 0.1%      $-8,615 $-5,080    $-3,535\n",
-      "\n",
-      "Percent Change in Income:\n",
-      "   Income Group                  PE % Wharton %\n",
-      " First quintile -0.10000000149011612%      0.0%\n",
-      "Second quintile    0.800000011920929%      0.3%\n",
-      "Middle quintile    1.100000023841858%      1.3%\n",
-      "Fourth quintile    1.399999976158142%      1.6%\n",
-      "         80-90%    1.399999976158142%      1.2%\n",
-      "         90-95%   1.7000000476837158%      0.9%\n",
-      "         95-99%    0.699999988079071%      0.6%\n",
-      "       99-99.9%  0.30000001192092896%      0.2%\n",
-      "       Top 0.1%  0.10000000149011612%      0.0%\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys\n",
     "import os\n",
@@ -969,7 +506,7 @@
     "# Run analysis\n",
     "print(\"Running PolicyEngine analysis...\")\n",
     "pe_results, revenue_impact = run_analysis(dataset_path, year, income_rank_variable)\n",
-    "print(f\"✓ Analysis complete\")\n",
+    "print(f\"\u2713 Analysis complete\")\n",
     "print(f\"  Revenue impact: ${revenue_impact:.1f}B\")\n",
     "print()\n",
     "\n",
diff --git a/docs/myst.yml b/docs/myst.yml
index a39b3cfb..261c63cf 100644
--- a/docs/myst.yml
+++ b/docs/myst.yml
@@ -2,11 +2,13 @@ version: 1
 project:
   title: PolicyEngine US Data
   authors:
-    - name: 
+    - name:
         given: PolicyEngine
         family: Team
   copyright: '2024'
   github: policyengine/policyengine-us-data
+  execute:
+    enabled: false
   jupyter:
     myst:
       enable_extensions:
diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py
index 564207c5..21ac9905 100644
--- a/policyengine_us_data/datasets/cps/cps.py
+++ b/policyengine_us_data/datasets/cps/cps.py
@@ -631,36 +631,8 @@ def add_spm_variables(cps: h5py.File, spm_unit: DataFrame) -> None:
 
 
 def add_household_variables(cps: h5py.File, household: DataFrame) -> None:
-    from policyengine_us_data.geography.puma_county_crosswalk import (
-        assign_county_from_state,
-    )
-
-    cps["state_fips"] = household.GESTFIPS.values
-
-    # CPS county codes (GTCO) are often 0 when suppressed for confidentiality.
-    # For households with missing county (GTCO == 0), we probabilistically
-    # assign a county based on state-level population weights.
-    raw_county = household.GTCO.values
-    missing_county_mask = raw_county == 0
-
-    if missing_county_mask.any():
-        logging.info(
-            f"Imputing county for {missing_county_mask.sum()} households "
-            f"({100 * missing_county_mask.mean():.1f}%) with missing GTCO"
-        )
-        # Use a fixed seed for reproducibility
-        random_state = np.random.RandomState(seed=42)
-        imputed_counties = assign_county_from_state(
-            cps["state_fips"][missing_county_mask],
-            random_state=random_state,
-        )
-        # Combine: use raw county where available, imputed where missing
-        county_fips = raw_county.copy()
-        county_fips[missing_county_mask] = imputed_counties
-        cps["county_fips"] = county_fips
-    else:
-        cps["county_fips"] = raw_county
-
+    cps["state_fips"] = household.GESTFIPS
+    cps["county_fips"] = household.GTCO
     state_county_fips = cps["state_fips"] * 1e3 + cps["county_fips"]
 
     # Assign is_nyc here instead of as a variable formula so that it shows up
diff --git a/policyengine_us_data/geography/cd_county_crosswalk.py b/policyengine_us_data/geography/cd_county_crosswalk.py
new file mode 100644
index 00000000..a0465808
--- /dev/null
+++ b/policyengine_us_data/geography/cd_county_crosswalk.py
@@ -0,0 +1,595 @@
+"""
+Build a population-weighted crosswalk between Congressional Districts and counties.
+
+This module creates allocation factors that allow probabilistic assignment of
+counties to households based on their congressional district. The methodology
+mirrors that used in make_district_mapping.py for consistency.
+
+Methodology:
+1. Download Census block-to-CD relationship file (119th Congress)
+2. Download block-level population from the 2020 Census PL-94-171 data
+3. Derive county FIPS from block GEOID (first 5 digits)
+4. Calculate allocation factor = pop_in_county_CD_intersection / total_CD_pop
+"""
+
+import io
+import json
+import zipfile
+from pathlib import Path
+from typing import Dict, Optional
+
+import numpy as np
+import pandas as pd
+import requests
+import us
+
+from policyengine_us_data.storage import STORAGE_FOLDER
+
+
+CD_COUNTY_CROSSWALK_FILE = STORAGE_FOLDER / "cd_county_crosswalk.csv"
+CD_COUNTY_MAPPINGS_JSON = STORAGE_FOLDER / "cd_county_mappings.json"
+
+
+def fetch_block_to_district_map(congress: int = 119) -> pd.DataFrame:
+    """
+    Fetches the Census Block Equivalency File (BEF) for a given Congress.
+
+    This file maps every 2020 census block (GEOID) to its corresponding
+    congressional district.
+
+    Args:
+        congress: The congressional session number (default 119 for current).
+
+    Returns:
+        A DataFrame with columns ['GEOID', 'CD'].
+    """
+    if congress == 119:
+        url = (
+            "https://www2.census.gov/programs-surveys/decennial/rdo/"
+            "mapping-files/2025/119-congressional-district-befs/cd119.zip"
+        )
+        zbytes = requests.get(url, timeout=120).content
+
+        with zipfile.ZipFile(io.BytesIO(zbytes)) as z:
+            fname = "NationalCD119.txt"
+            bef = pd.read_csv(z.open(fname), sep=",", dtype=str)
+            bef.columns = bef.columns.str.strip()
+            bef = bef.rename(columns={"CDFP": "CD"})
+            return bef[["GEOID", "CD"]]
+    else:
+        raise ValueError(
+            f"Congress {congress} is not supported. Use 119 for current."
+        )
+
+
+def fetch_block_population(state: str) -> pd.DataFrame:
+    """
+    Download & parse the 2020 PL-94-171 redistricting data for one state.
+
+    Args:
+        state: Two-letter state postal code or full state name.
+
+    Returns:
+        DataFrame with columns: GEOID (15-digit block code), population
+    """
+    BASE = (
+        "https://www2.census.gov/programs-surveys/decennial/2020/data/"
+        "01-Redistricting_File--PL_94-171/{dir}/{abbr}2020.pl.zip"
+    )
+
+    # Handle DC specially
+    if state.upper() in ["DC", "DISTRICT OF COLUMBIA"]:
+        dir_name = "District_of_Columbia"
+        abbr = "dc"
+    else:
+        st = us.states.lookup(state)
+        if st is None:
+            raise ValueError(f"Unrecognised state name/abbr: {state}")
+        dir_name = st.name.replace(" ", "_")
+        abbr = st.abbr.lower()
+
+    url = BASE.format(dir=dir_name, abbr=abbr)
+
+    zbytes = requests.get(url, timeout=120).content
+    with zipfile.ZipFile(io.BytesIO(zbytes)) as z:
+        raw = z.read(f"{abbr}geo2020.pl")
+        try:
+            geo_lines = raw.decode("utf-8").splitlines()
+        except UnicodeDecodeError:
+            geo_lines = raw.decode("latin-1").splitlines()
+
+        p1_lines = z.read(f"{abbr}000012020.pl").decode("utf-8").splitlines()
+
+    # GEO file: keep blocks (SUMLEV 750)
+    geo_records = [
+        (parts[7], parts[8][-15:])  # LOGRECNO, 15-digit block GEOID
+        for ln in geo_lines
+        if (parts := ln.split("|"))[2] == "750"
+    ]
+    geo_df = pd.DataFrame(geo_records, columns=["LOGRECNO", "GEOID"])
+
+    # P-file: pull total-population
+    p1_records = [
+        (p[4], int(p[5])) for p in map(lambda x: x.split("|"), p1_lines)
+    ]
+    p1_df = pd.DataFrame(p1_records, columns=["LOGRECNO", "population"])
+
+    return (
+        geo_df.merge(p1_df, on="LOGRECNO", how="left")
+        .assign(population=lambda d: d["population"].fillna(0).astype(int))
+        .loc[:, ["GEOID", "population"]]
+    )
+
+
+def build_cd_county_crosswalk() -> pd.DataFrame:
+    """
+    Builds the CD-to-county crosswalk with population-weighted allocation
+    factors.
+
+    Returns:
+        DataFrame with columns:
+            - state_fips: 2-digit state FIPS
+            - cd: 2-digit congressional district code
+            - county_fips: 3-digit county FIPS within state
+            - county_fips_full: 5-digit full county FIPS (state + county)
+            - cd_code: Full CD code (5001800US + state + cd)
+            - population: population in this CD-county intersection
+            - allocation_factor: proportion of CD population in this county
+    """
+    print("Fetching block-to-CD relationship file (119th Congress)...")
+    block_cd = fetch_block_to_district_map(119)
+
+    # Remove blocks not assigned to any congressional district
+    block_cd = block_cd[block_cd["CD"] != "ZZ"]
+
+    # Fetch block populations for all states + DC
+    print("Fetching block-level population data...")
+    state_pops = []
+
+    # Get all states
+    states_to_fetch = [
+        s
+        for s in us.states.STATES_AND_TERRITORIES
+        if not s.is_territory and s.abbr not in ["ZZ"]
+    ]
+
+    for s in states_to_fetch:
+        print(f"  {s.name}")
+        try:
+            state_pops.append(fetch_block_population(s.abbr))
+        except Exception as e:
+            print(f"    Warning: Could not fetch {s.name}: {e}")
+
+    # Add DC
+    print("  District of Columbia")
+    try:
+        state_pops.append(fetch_block_population("DC"))
+    except Exception as e:
+        print(f"    Warning: Could not fetch DC: {e}")
+
+    block_pop = pd.concat(state_pops, ignore_index=True)
+
+    # Merge block population with CD mapping
+    print("Merging block data...")
+    merged = block_cd.merge(block_pop, on="GEOID", how="left")
+    merged["population"] = merged["population"].fillna(0).astype(int)
+
+    # Derive geography from GEOID
+    # GEOID is 15 digits: SSCCCTTTTTTBBBB
+    # SS = state FIPS (2 digits)
+    # CCC = county FIPS (3 digits)
+    # TTTTTT = tract (6 digits)
+    # BBBB = block (4 digits)
+    merged["state_fips"] = merged["GEOID"].str[:2]
+    merged["county_fips"] = merged["GEOID"].str[2:5]
+    merged["county_fips_full"] = merged["GEOID"].str[:5]
+
+    # Create full CD code (matching format used in calibration)
+    merged["cd_code"] = "5001800US" + merged["state_fips"] + merged["CD"]
+
+    # Aggregate to CD-county level
+    print("Aggregating to CD-county level...")
+    cd_county = (
+        merged.groupby(
+            ["state_fips", "CD", "county_fips", "county_fips_full", "cd_code"]
+        )["population"]
+        .sum()
+        .reset_index()
+    )
+
+    # Rename CD column for clarity
+    cd_county = cd_county.rename(columns={"CD": "cd"})
+
+    # Calculate allocation factors (proportion of CD pop in each county)
+    cd_county["cd_total_pop"] = cd_county.groupby(["state_fips", "cd"])[
+        "population"
+    ].transform("sum")
+
+    cd_county["allocation_factor"] = (
+        cd_county["population"] / cd_county["cd_total_pop"]
+    )
+
+    # Handle edge case where CD has zero population
+    cd_county["allocation_factor"] = cd_county["allocation_factor"].fillna(0)
+
+    # Sort by state, cd, allocation factor (descending)
+    cd_county = cd_county.sort_values(
+        ["state_fips", "cd", "allocation_factor"],
+        ascending=[True, True, False],
+    ).reset_index(drop=True)
+
+    # Select final columns
+    result = cd_county[
+        [
+            "state_fips",
+            "cd",
+            "county_fips",
+            "county_fips_full",
+            "cd_code",
+            "population",
+            "allocation_factor",
+        ]
+    ]
+
+    return result
+
+
+def get_cd_county_crosswalk() -> pd.DataFrame:
+    """
+    Returns the CD-to-county crosswalk, loading from cache if available.
+
+    Returns:
+        DataFrame with CD-county allocation factors.
+    """
+    if CD_COUNTY_CROSSWALK_FILE.exists():
+        return pd.read_csv(CD_COUNTY_CROSSWALK_FILE, dtype=str).assign(
+            population=lambda df: df["population"].astype(int),
+            allocation_factor=lambda df: df["allocation_factor"].astype(float),
+        )
+
+    crosswalk = build_cd_county_crosswalk()
+    crosswalk.to_csv(CD_COUNTY_CROSSWALK_FILE, index=False)
+    return crosswalk
+
+
+def assign_county_from_cd(
+    cd_codes: np.ndarray,
+    random_state: np.random.RandomState = None,
+) -> np.ndarray:
+    """
+    Probabilistically assigns county FIPS codes based on congressional
+    district.
+
+    For each household, uses the CD-to-county allocation factors to
+    randomly assign a county, weighted by population.
+
+    Args:
+        cd_codes: Array of CD codes (format: "5001800US" + state + cd)
+        random_state: Optional random state for reproducibility
+
+    Returns:
+        Array of 5-digit full county FIPS codes (state + county)
+    """
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    crosswalk = get_cd_county_crosswalk()
+
+    n = len(cd_codes)
+    county_fips_full = np.zeros(n, dtype=object)
+
+    # Group crosswalk by CD code for efficient lookup
+    crosswalk_dict = {}
+    for cd_code, group in crosswalk.groupby("cd_code"):
+        counties = group["county_fips_full"].values
+        probs = group["allocation_factor"].values
+        # Normalize probabilities (should already sum to 1, but be safe)
+        probs = probs / probs.sum() if probs.sum() > 0 else probs
+        crosswalk_dict[cd_code] = (counties, probs)
+
+    # Assign counties
+    for i in range(n):
+        cd = cd_codes[i]
+        if cd in crosswalk_dict:
+            counties, probs = crosswalk_dict[cd]
+            if len(counties) == 1:
+                county_fips_full[i] = counties[0]
+            else:
+                county_fips_full[i] = random_state.choice(counties, p=probs)
+        else:
+            # CD not found - assign "00000" (unknown)
+            county_fips_full[i] = "00000"
+
+    return county_fips_full
+
+
+def assign_county_from_state_and_cd(
+    state_fips: np.ndarray,
+    cd: np.ndarray,
+    random_state: np.random.RandomState = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Probabilistically assigns county FIPS codes based on state and CD.
+
+    Args:
+        state_fips: Array of 2-digit state FIPS codes
+        cd: Array of 2-digit congressional district codes
+        random_state: Optional random state for reproducibility
+
+    Returns:
+        Tuple of (state_fips array, county_fips array) where county_fips
+        is the 3-digit county code within the state.
+    """
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    # Build CD codes
+    state_fips_str = np.array([str(s).zfill(2) for s in state_fips])
+    cd_str = np.array([str(c).zfill(2) for c in cd])
+    cd_codes = np.array(
+        ["5001800US" + s + c for s, c in zip(state_fips_str, cd_str)]
+    )
+
+    # Get full county FIPS
+    county_fips_full = assign_county_from_cd(cd_codes, random_state)
+
+    # Extract 3-digit county FIPS
+    county_fips = np.array(
+        [c[2:5] if len(c) == 5 else "000" for c in county_fips_full]
+    )
+
+    return state_fips_str, county_fips
+
+
+def export_to_json(
+    output_path: Optional[Path] = None,
+) -> Dict[str, Dict[str, float]]:
+    """
+    Export the CD-county crosswalk to JSON format.
+
+    This format is compatible with the geo-stacking calibration workflow.
+    The JSON structure is: {cd_geoid: {county_fips: proportion, ...}, ...}
+
+    Args:
+        output_path: Path to save JSON file. If None, uses default location.
+
+    Returns:
+        Dictionary mapping CD GEOID -> {county_fips: proportion}
+    """
+    crosswalk = get_cd_county_crosswalk()
+
+    # Build the JSON structure
+    # CD GEOID format: state_fips + cd (e.g., "601" for CA-01, "3601" for NY-01)
+    cd_county_map = {}
+
+    for (state_fips, cd), group in crosswalk.groupby(["state_fips", "cd"]):
+        # Create CD GEOID (state + cd without leading zeros on state for
+        # single-digit states)
+        cd_geoid = f"{int(state_fips)}{cd.zfill(2)}"
+
+        # Build county proportions dict
+        county_props = {}
+        for _, row in group.iterrows():
+            county_props[row["county_fips_full"]] = row["allocation_factor"]
+
+        cd_county_map[cd_geoid] = county_props
+
+    # Save to JSON
+    if output_path is None:
+        output_path = CD_COUNTY_MAPPINGS_JSON
+
+    with open(output_path, "w") as f:
+        json.dump(cd_county_map, f, indent=2)
+
+    print(f"Exported CD-county mappings to {output_path}")
+    return cd_county_map
+
+
+def load_cd_county_mappings(
+    path: Optional[Path] = None,
+) -> Optional[Dict[str, Dict[str, float]]]:
+    """
+    Load CD-county mappings from JSON file.
+
+    Compatible with geo-stacking calibration workflow.
+
+    Args:
+        path: Path to JSON file. If None, uses default location.
+
+    Returns:
+        Dictionary mapping CD GEOID -> {county_fips: proportion},
+        or None if file not found.
+    """
+    if path is None:
+        path = CD_COUNTY_MAPPINGS_JSON
+
+    if not path.exists():
+        print(f"WARNING: {path} not found. Run export_to_json() first.")
+        return None
+
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def get_county_for_cd(
+    cd_geoid: str,
+    cd_county_mappings: Optional[Dict[str, Dict[str, float]]] = None,
+    random_state: Optional[np.random.RandomState] = None,
+) -> Optional[str]:
+    """
+    Get a county FIPS code for a given congressional district.
+
+    Uses weighted random selection based on county proportions.
+    Compatible with geo-stacking calibration workflow.
+
+    Args:
+        cd_geoid: Congressional district GEOID (e.g., "601" for CA-01)
+        cd_county_mappings: Optional pre-loaded mappings dict. If None,
+            loads from default JSON file.
+        random_state: Optional random state for reproducibility.
+
+    Returns:
+        5-digit county FIPS code, or None if CD not found.
+    """
+    if cd_county_mappings is None:
+        cd_county_mappings = load_cd_county_mappings()
+
+    if cd_county_mappings is None or str(cd_geoid) not in cd_county_mappings:
+        return None
+
+    county_props = cd_county_mappings[str(cd_geoid)]
+    if not county_props:
+        return None
+
+    counties = list(county_props.keys())
+    weights = list(county_props.values())
+
+    # Normalize weights
+    total_weight = sum(weights)
+    if total_weight > 0:
+        weights = [w / total_weight for w in weights]
+
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    return random_state.choice(counties, p=weights)
+
+
+def get_state_county_crosswalk_from_cd() -> pd.DataFrame:
+    """
+    Creates a state-to-county crosswalk by aggregating the CD-county data.
+
+    This is useful when CD is not known and we need to assign county based
+    only on state. The allocation factors are population-weighted across
+    all CDs in the state.
+
+    Returns:
+        DataFrame with columns:
+            - state_fips: 2-digit state FIPS
+            - county_fips: 3-digit county FIPS within state
+            - county_fips_full: 5-digit full county FIPS
+            - population: county population
+            - allocation_factor: proportion of state population in county
+    """
+    cd_crosswalk = get_cd_county_crosswalk()
+
+    # Aggregate to state-county level
+    state_county = (
+        cd_crosswalk.groupby(
+            ["state_fips", "county_fips", "county_fips_full"]
+        )["population"]
+        .sum()
+        .reset_index()
+    )
+
+    # Calculate state-level allocation factors
+    state_county["state_total_pop"] = state_county.groupby("state_fips")[
+        "population"
+    ].transform("sum")
+
+    state_county["allocation_factor"] = (
+        state_county["population"] / state_county["state_total_pop"]
+    )
+    state_county["allocation_factor"] = state_county[
+        "allocation_factor"
+    ].fillna(0)
+
+    # Sort by state, allocation factor (descending)
+    state_county = state_county.sort_values(
+        ["state_fips", "allocation_factor"],
+        ascending=[True, False],
+    ).reset_index(drop=True)
+
+    return state_county[
+        [
+            "state_fips",
+            "county_fips",
+            "county_fips_full",
+            "population",
+            "allocation_factor",
+        ]
+    ]
+
+
+def assign_county_from_state(
+    state_fips: np.ndarray,
+    random_state: np.random.RandomState = None,
+) -> np.ndarray:
+    """
+    Probabilistically assigns county FIPS codes based on state only.
+
+    Uses state-to-county population weights derived from CD-county data.
+    This is used when CD is not known.
+
+    Args:
+        state_fips: Array of 2-digit state FIPS codes (as ints)
+        random_state: Optional random state for reproducibility
+
+    Returns:
+        Array of 3-digit county FIPS codes (as ints)
+    """
+    if random_state is None:
+        random_state = np.random.RandomState()
+
+    crosswalk = get_state_county_crosswalk_from_cd()
+
+    # Ensure consistent string types for lookup
+    state_fips_str = np.array([str(int(s)).zfill(2) for s in state_fips])
+
+    n = len(state_fips)
+    county_fips = np.zeros(n, dtype=int)
+
+    # Group crosswalk by state for efficient lookup
+    crosswalk_dict = {}
+    for st, group in crosswalk.groupby("state_fips"):
+        counties = group["county_fips"].astype(int).values
+        probs = group["allocation_factor"].values
+        # Normalize probabilities
+        probs = probs / probs.sum() if probs.sum() > 0 else probs
+        crosswalk_dict[st] = (counties, probs)
+
+    # Assign counties
+    for i in range(n):
+        st = state_fips_str[i]
+        if st in crosswalk_dict:
+            counties, probs = crosswalk_dict[st]
+            if len(counties) == 1:
+                county_fips[i] = counties[0]
+            else:
+                county_fips[i] = random_state.choice(counties, p=probs)
+        else:
+            # State not found - assign 0 (unknown)
+            county_fips[i] = 0
+
+    return county_fips
+
+
+if __name__ == "__main__":
+    print("Building CD-to-county crosswalk...")
+    crosswalk = build_cd_county_crosswalk()
+    crosswalk.to_csv(CD_COUNTY_CROSSWALK_FILE, index=False)
+    print(f"Saved crosswalk to {CD_COUNTY_CROSSWALK_FILE}")
+
+    # Export to JSON for geo-stacking compatibility
+    print("\nExporting to JSON format...")
+    export_to_json()
+
+    # Print summary statistics
+    print(f"\nTotal CD-county pairs: {len(crosswalk)}")
+    print(f"Unique CDs: {crosswalk['cd_code'].nunique()}")
+    print(f"Unique counties: {crosswalk['county_fips_full'].nunique()}")
+
+    # Show distribution of counties per CD
+    counties_per_cd = crosswalk.groupby("cd_code").size()
+    print(f"\nCounties per CD:")
+    print(f"  Min: {counties_per_cd.min()}")
+    print(f"  Max: {counties_per_cd.max()}")
+    print(f"  Mean: {counties_per_cd.mean():.2f}")
+    print(f"  Median: {counties_per_cd.median():.0f}")
+
+    # Count single-county CDs (deterministic assignment)
+    single_county = (counties_per_cd == 1).sum()
+    print(
+        f"\nCDs with single county (deterministic): "
+        f"{single_county} ({100*single_county/len(counties_per_cd):.1f}%)"
+    )
diff --git a/policyengine_us_data/geography/puma_county_crosswalk.py b/policyengine_us_data/geography/puma_county_crosswalk.py
deleted file mode 100644
index 41ac330c..00000000
--- a/policyengine_us_data/geography/puma_county_crosswalk.py
+++ /dev/null
@@ -1,437 +0,0 @@
-"""
-Build a probabilistic crosswalk between PUMAs and counties.
-
-PUMAs (Public Use Microdata Areas) are geographic units used in Census
-microdata. They contain at least 100,000 people and are built from census
-tracts. A single PUMA may span multiple counties (especially in rural areas),
-and large counties may contain multiple PUMAs.
-
-This module creates population-weighted allocation factors that allow
-probabilistic assignment of counties to households that only have PUMA
-information.
-
-Methodology:
-1. Download Census tract-to-PUMA relationship file
-2. Download tract-level population from the 2020 Census
-3. Derive county FIPS from tract GEOID (first 5 digits)
-4. Calculate allocation factor = pop_in_county / total_puma_pop
-"""
-
-import io
-import requests
-import zipfile
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import us
-
-from policyengine_us_data.storage import STORAGE_FOLDER
-
-
-CROSSWALK_FILE = STORAGE_FOLDER / "puma_county_crosswalk.csv"
-
-
-def fetch_tract_to_puma() -> pd.DataFrame:
-    """
-    Fetches the 2020 Census Tract to 2020 PUMA relationship file.
-
-    Returns:
-        DataFrame with columns: state_fips, county_fips, tract, puma
-    """
-    url = "https://www2.census.gov/geo/docs/maps-data/data/rel2020/2020_Census_Tract_to_2020_PUMA.txt"
-    response = requests.get(url, timeout=120)
-    response.raise_for_status()
-
-    df = pd.read_csv(
-        io.BytesIO(response.content),
-        dtype=str,
-        encoding="utf-8-sig",  # Handle BOM
-    )
-
-    # The file has STATEFP, COUNTYFP, TRACTCE, PUMA5CE columns
-    df = df.rename(
-        columns={
-            "STATEFP": "state_fips",
-            "COUNTYFP": "county_fips",
-            "TRACTCE": "tract",
-            "PUMA5CE": "puma",
-        }
-    )
-
-    return df[["state_fips", "county_fips", "tract", "puma"]]
-
-
-def fetch_tract_population(state: str) -> pd.DataFrame:
-    """
-    Download & parse the 2020 PL-94-171 redistricting data for one state.
-
-    This provides population at the census tract level.
-
-    Args:
-        state: Two-letter state postal code or full state name.
-
-    Returns:
-        DataFrame with columns: tract_geoid, population
-    """
-    BASE = (
-        "https://www2.census.gov/programs-surveys/decennial/2020/data/"
-        "01-Redistricting_File--PL_94-171/{dir}/{abbr}2020.pl.zip"
-    )
-
-    # Handle DC specially since it's not in us.states
-    if state.upper() in ["DC", "DISTRICT OF COLUMBIA"]:
-        dir_name = "District_of_Columbia"
-        abbr = "dc"
-    else:
-        st = us.states.lookup(state)
-        if st is None:
-            raise ValueError(f"Unrecognised state name/abbr: {state}")
-        dir_name = st.name.replace(" ", "_")
-        abbr = st.abbr.lower()
-
-    url = BASE.format(dir=dir_name, abbr=abbr)
-
-    zbytes = requests.get(url, timeout=120).content
-    with zipfile.ZipFile(io.BytesIO(zbytes)) as z:
-        raw = z.read(f"{abbr}geo2020.pl")
-        try:
-            geo_lines = raw.decode("utf-8").splitlines()
-        except UnicodeDecodeError:
-            geo_lines = raw.decode("latin-1").splitlines()
-
-        p1_lines = z.read(f"{abbr}000012020.pl").decode("utf-8").splitlines()
-
-    # GEO file: keep tracts (SUMLEV 140)
-    geo_records = []
-    for ln in geo_lines:
-        parts = ln.split("|")
-        if parts[2] == "140":  # summary level 140 = tracts
-            logrecno = parts[7]
-            geoid = parts[8][-11:]  # 11-digit tract GEOID
-            geo_records.append((logrecno, geoid))
-
-    geo_df = pd.DataFrame(geo_records, columns=["LOGRECNO", "tract_geoid"])
-
-    # P-file: pull total-population
-    p1_records = [
-        (p[4], int(p[5])) for p in map(lambda x: x.split("|"), p1_lines)
-    ]
-    p1_df = pd.DataFrame(p1_records, columns=["LOGRECNO", "population"])
-
-    result = geo_df.merge(p1_df, on="LOGRECNO", how="left")
-    result["population"] = result["population"].fillna(0).astype(int)
-
-    return result[["tract_geoid", "population"]]
-
-
-def build_puma_county_crosswalk() -> pd.DataFrame:
-    """
-    Builds the PUMA-to-county crosswalk with population-weighted allocation
-    factors.
-
-    Returns:
-        DataFrame with columns:
-            - state_fips: 2-digit state FIPS
-            - puma: 5-digit PUMA code
-            - county_fips: 3-digit county FIPS within state
-            - county_fips_full: 5-digit full county FIPS (state + county)
-            - population: population in this PUMA-county intersection
-            - allocation_factor: proportion of PUMA population in this county
-    """
-    print("Fetching tract-to-PUMA relationship file...")
-    tract_puma = fetch_tract_to_puma()
-
-    # Build tract GEOID for joining
-    tract_puma["tract_geoid"] = (
-        tract_puma["state_fips"]
-        + tract_puma["county_fips"]
-        + tract_puma["tract"]
-    )
-
-    # Fetch tract populations for all states
-    print("Fetching tract-level population data...")
-    state_pops = []
-
-    # Include DC explicitly (not in us.states.STATES_AND_TERRITORIES)
-    states_to_fetch = list(us.states.STATES_AND_TERRITORIES) + ["DC"]
-
-    for s in states_to_fetch:
-        # Handle both State objects and string abbreviations
-        if hasattr(s, "abbr"):
-            if s.is_territory or s.abbr in ["ZZ"]:
-                continue
-            abbr = s.abbr
-            name = s.name
-        else:
-            abbr = s
-            name = s
-
-        print(f"  {name}")
-        try:
-            state_pops.append(fetch_tract_population(abbr))
-        except Exception as e:
-            print(f"    Warning: Could not fetch {name}: {e}")
-
-    tract_pop = pd.concat(state_pops, ignore_index=True)
-
-    # Join tract population to tract-PUMA mapping
-    merged = tract_puma.merge(tract_pop, on="tract_geoid", how="left")
-    merged["population"] = merged["population"].fillna(0).astype(int)
-
-    # Aggregate to PUMA-county level
-    print("Aggregating to PUMA-county level...")
-    puma_county = (
-        merged.groupby(["state_fips", "puma", "county_fips"])["population"]
-        .sum()
-        .reset_index()
-    )
-
-    # Calculate allocation factors (proportion of PUMA pop in each county)
-    puma_county["puma_total_pop"] = puma_county.groupby(
-        ["state_fips", "puma"]
-    )["population"].transform("sum")
-
-    puma_county["allocation_factor"] = (
-        puma_county["population"] / puma_county["puma_total_pop"]
-    )
-
-    # Handle edge case where PUMA has zero population
-    puma_county["allocation_factor"] = puma_county["allocation_factor"].fillna(
-        0
-    )
-
-    # Create full 5-digit county FIPS
-    puma_county["county_fips_full"] = (
-        puma_county["state_fips"] + puma_county["county_fips"]
-    )
-
-    # Sort by state, puma, allocation factor (descending)
-    puma_county = puma_county.sort_values(
-        ["state_fips", "puma", "allocation_factor"],
-        ascending=[True, True, False],
-    ).reset_index(drop=True)
-
-    # Select final columns
-    result = puma_county[
-        [
-            "state_fips",
-            "puma",
-            "county_fips",
-            "county_fips_full",
-            "population",
-            "allocation_factor",
-        ]
-    ]
-
-    return result
-
-
-def get_puma_county_crosswalk() -> pd.DataFrame:
-    """
-    Returns the PUMA-to-county crosswalk, loading from cache if available.
-
-    Returns:
-        DataFrame with PUMA-county allocation factors.
-    """
-    if CROSSWALK_FILE.exists():
-        return pd.read_csv(CROSSWALK_FILE, dtype=str).assign(
-            population=lambda df: df["population"].astype(int),
-            allocation_factor=lambda df: df["allocation_factor"].astype(float),
-        )
-
-    crosswalk = build_puma_county_crosswalk()
-    crosswalk.to_csv(CROSSWALK_FILE, index=False)
-    return crosswalk
-
-
-def assign_county_from_puma(
-    state_fips: np.ndarray,
-    puma: np.ndarray,
-    random_state: np.random.RandomState = None,
-) -> np.ndarray:
-    """
-    Probabilistically assigns county FIPS codes based on PUMA and state.
-
-    For each household, uses the PUMA-to-county allocation factors to
-    randomly assign a county, weighted by population.
-
-    Args:
-        state_fips: Array of 2-digit state FIPS codes (as strings or ints)
-        puma: Array of PUMA codes (as strings or ints)
-        random_state: Optional random state for reproducibility
-
-    Returns:
-        Array of 3-digit county FIPS codes (within state)
-    """
-    if random_state is None:
-        random_state = np.random.RandomState()
-
-    crosswalk = get_puma_county_crosswalk()
-
-    # Ensure consistent string types
-    state_fips = np.array([str(s).zfill(2) for s in state_fips])
-    puma = np.array([str(p).zfill(5) for p in puma])
-
-    n = len(state_fips)
-    county_fips = np.zeros(n, dtype=object)
-
-    # Group crosswalk by state-puma for efficient lookup
-    crosswalk_dict = {}
-    for (st, pm), group in crosswalk.groupby(["state_fips", "puma"]):
-        counties = group["county_fips"].values
-        probs = group["allocation_factor"].values
-        # Normalize probabilities (should already sum to 1, but be safe)
-        probs = probs / probs.sum() if probs.sum() > 0 else probs
-        crosswalk_dict[(st, pm)] = (counties, probs)
-
-    # Assign counties
-    for i in range(n):
-        key = (state_fips[i], puma[i])
-        if key in crosswalk_dict:
-            counties, probs = crosswalk_dict[key]
-            if len(counties) == 1:
-                county_fips[i] = counties[0]
-            else:
-                county_fips[i] = random_state.choice(counties, p=probs)
-        else:
-            # PUMA not found - assign 0 (unknown)
-            county_fips[i] = "000"
-
-    return county_fips
-
-
-def get_state_county_crosswalk() -> pd.DataFrame:
-    """
-    Creates a state-to-county crosswalk with population-weighted allocation
-    factors.
-
-    This is derived from the PUMA-county crosswalk by aggregating to state
-    level. Used for probabilistic county assignment when only state is known.
-
-    Returns:
-        DataFrame with columns:
-            - state_fips: 2-digit state FIPS
-            - county_fips: 3-digit county FIPS within state
-            - county_fips_full: 5-digit full county FIPS
-            - population: county population
-            - allocation_factor: proportion of state population in county
-    """
-    puma_county = get_puma_county_crosswalk()
-
-    # Aggregate to state-county level
-    state_county = (
-        puma_county.groupby(["state_fips", "county_fips", "county_fips_full"])[
-            "population"
-        ]
-        .sum()
-        .reset_index()
-    )
-
-    # Calculate state-level allocation factors
-    state_county["state_total_pop"] = state_county.groupby("state_fips")[
-        "population"
-    ].transform("sum")
-
-    state_county["allocation_factor"] = (
-        state_county["population"] / state_county["state_total_pop"]
-    )
-    state_county["allocation_factor"] = state_county[
-        "allocation_factor"
-    ].fillna(0)
-
-    # Sort by state, allocation factor (descending)
-    state_county = state_county.sort_values(
-        ["state_fips", "allocation_factor"],
-        ascending=[True, False],
-    ).reset_index(drop=True)
-
-    return state_county[
-        [
-            "state_fips",
-            "county_fips",
-            "county_fips_full",
-            "population",
-            "allocation_factor",
-        ]
-    ]
-
-
-def assign_county_from_state(
-    state_fips: np.ndarray,
-    random_state: np.random.RandomState = None,
-) -> np.ndarray:
-    """
-    Probabilistically assigns county FIPS codes based on state only.
-
-    For each household, uses state-to-county population weights to
-    randomly assign a county.
-
-    Args:
-        state_fips: Array of 2-digit state FIPS codes (as ints)
-        random_state: Optional random state for reproducibility
-
-    Returns:
-        Array of 3-digit county FIPS codes (as ints)
-    """
-    if random_state is None:
-        random_state = np.random.RandomState()
-
-    crosswalk = get_state_county_crosswalk()
-
-    # Ensure consistent string types for lookup
-    state_fips_str = np.array([str(int(s)).zfill(2) for s in state_fips])
-
-    n = len(state_fips)
-    county_fips = np.zeros(n, dtype=int)
-
-    # Group crosswalk by state for efficient lookup
-    crosswalk_dict = {}
-    for st, group in crosswalk.groupby("state_fips"):
-        counties = group["county_fips"].astype(int).values
-        probs = group["allocation_factor"].values
-        # Normalize probabilities
-        probs = probs / probs.sum() if probs.sum() > 0 else probs
-        crosswalk_dict[st] = (counties, probs)
-
-    # Assign counties
-    for i in range(n):
-        st = state_fips_str[i]
-        if st in crosswalk_dict:
-            counties, probs = crosswalk_dict[st]
-            if len(counties) == 1:
-                county_fips[i] = counties[0]
-            else:
-                county_fips[i] = random_state.choice(counties, p=probs)
-        else:
-            # State not found - assign 0 (unknown)
-            county_fips[i] = 0
-
-    return county_fips
-
-
-if __name__ == "__main__":
-    print("Building PUMA-to-county crosswalk...")
-    crosswalk = build_puma_county_crosswalk()
-    crosswalk.to_csv(CROSSWALK_FILE, index=False)
-    print(f"Saved crosswalk to {CROSSWALK_FILE}")
-
-    # Print summary statistics
-    print(f"\nTotal PUMA-county pairs: {len(crosswalk)}")
-    print(f"Unique PUMAs: {crosswalk.groupby(['state_fips', 'puma']).ngroups}")
-    print(f"Unique counties: {crosswalk['county_fips_full'].nunique()}")
-
-    # Show distribution of counties per PUMA
-    counties_per_puma = crosswalk.groupby(["state_fips", "puma"]).size()
-    print(f"\nCounties per PUMA:")
-    print(f"  Min: {counties_per_puma.min()}")
-    print(f"  Max: {counties_per_puma.max()}")
-    print(f"  Mean: {counties_per_puma.mean():.2f}")
-    print(f"  Median: {counties_per_puma.median():.0f}")
-
-    # Count single-county PUMAs (deterministic assignment)
-    single_county = (counties_per_puma == 1).sum()
-    print(
-        f"\nPUMAs with single county (deterministic): "
-        f"{single_county} ({100*single_county/len(counties_per_puma):.1f}%)"
-    )
diff --git a/policyengine_us_data/storage/puma_county_crosswalk.csv b/policyengine_us_data/storage/puma_county_crosswalk.csv
deleted file mode 100644
index 2ed1cf45..00000000
--- a/policyengine_us_data/storage/puma_county_crosswalk.csv
+++ /dev/null
@@ -1,4702 +0,0 @@
-state_fips,puma,county_fips,county_fips_full,population,allocation_factor
-01,00100,077,01077,93564,0.5115470410707256
-01,00100,033,01033,57227,0.31287998075493156
-01,00100,059,01059,32113,0.17557297817434284
-01,00200,083,01083,103570,1.0
-01,00300,103,01103,123421,0.7886628241338326
-01,00300,079,01079,33073,0.2113371758661674
-01,00401,089,01089,113502,1.0
-01,00402,089,01089,153097,1.0
-01,00403,089,01089,107983,1.0
-01,00501,095,01095,97612,0.8779399728375741
-01,00501,089,01089,13571,0.12206002716242591
-01,00600,049,01049,71608,0.5766142994033192
-01,00600,071,01071,52579,0.4233857005966808
-01,00700,055,01055,103436,0.8055324086693093
-01,00700,019,01019,24971,0.1944675913306907
-01,00800,015,01015,116441,1.0
-01,00900,115,01115,91103,0.606395228871716
-01,00900,009,01009,59134,0.39360477112828396
-01,01000,043,01043,87866,0.6242832884537504
-01,01000,093,01093,29341,0.20846625505339367
-01,01000,133,01133,23540,0.16725045649285597
-01,01100,007,01007,22293,0.17708035458964827
-01,01100,091,01091,19323,0.15348870460394623
-01,01100,107,01107,19123,0.15190004130524576
-01,01100,057,01057,16321,0.12964286849045215
-01,01100,065,01065,14785,0.11744193435643249
-01,01100,075,01075,13972,0.11098401804721507
-01,01100,119,01119,12345,0.09806024211228673
-01,01100,063,01063,7730,0.0614018364947733
-01,01201,125,01125,101012,1.0
-01,01202,125,01125,126024,1.0
-01,01301,127,01127,65342,0.5641831510054656
-01,01301,073,01073,50475,0.4358168489945345
-01,01401,073,01073,118409,1.0
-01,01402,073,01073,133581,1.0
-01,01403,073,01073,187166,1.0
-01,01404,073,01073,185090,1.0
-01,01501,117,01117,137436,1.0
-01,01502,117,01117,85588,0.6553345278020245
-01,01502,021,01021,45014,0.34466547219797555
-01,01600,121,01121,82149,0.6157726673063084
-01,01600,111,01111,21967,0.164660290237467
-01,01600,029,01029,15056,0.11285680019189254
-01,01600,027,01027,14236,0.10671024226433197
-01,01700,001,01001,58805,0.5065510082781315
-01,01700,047,01047,38462,0.33131476711833163
-01,01700,085,01085,10311,0.08881978482026721
-01,01700,105,01105,8511,0.07331443978326974
-01,01801,101,01101,109409,1.0
-01,01802,101,01101,105471,1.0
-01,01901,051,01051,87977,0.8620885635613565
-01,01901,101,01101,14074,0.13791143643864343
-01,02000,123,01123,41311,0.3550305519985562
-01,02000,017,01017,34772,0.29883378165848795
-01,02000,087,01087,19532,0.16785981316443077
-01,02000,037,01037,10387,0.0892668379755756
-01,02000,011,01011,10357,0.08900901520294949
-01,02101,081,01081,127904,1.0
-01,02201,113,01113,59183,0.5608699772554966
-01,02201,081,01081,46337,0.4391300227445034
-01,02300,069,01069,107202,0.7099141099419233
-01,02300,061,01061,26659,0.176541484831829
-01,02300,067,01067,17146,0.11354440522624779
-01,02400,031,01031,53465,0.5201330855814225
-01,02400,045,01045,49326,0.4798669144185775
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