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Merge pull request #327 from PolicyEngine/snap-states
State Level SNAP targets from the USDA
2 parents f9808d8 + be871e4 commit 8807c57

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‎.github/workflows/manual_tests.yaml‎

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name: Manual tests
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on:
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workflow_dispatch:
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inputs:
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test_lite:
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description: 'Run in lite mode'
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required: true
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default: true
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type: boolean
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jobs:
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test:
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uses: ./.github/workflows/pr_changelog.yaml
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with:
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TEST_LITE: ${{ github.event.inputs.test_lite }}
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secrets: inherit

‎Makefile‎

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@@ -23,6 +23,7 @@ download:
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python policyengine_us_data/storage/download_public_prerequisites.py
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python policyengine_us_data/storage/pull_age_targets.py
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python policyengine_us_data/storage/pull_soi_state_targets.py
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python policyengine_us_data/storage/pull_snap_state_targets.py
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python policyengine_us_data/storage/download_private_prerequisites.py
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upload:

‎changelog_entry.yaml‎

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- bump: minor
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changes:
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added:
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- State SNAP calibration targets.

‎policyengine_us_data/storage/pull_snap_state_targets.py‎

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import requests
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import zipfile
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import io
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import pandas as pd
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from policyengine_us_data.storage import STORAGE_FOLDER
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STATE_NAME_TO_FIPS = {
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"Alabama": "01",
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"Alaska": "02",
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"Arizona": "04",
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"Arkansas": "05",
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"California": "06",
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"Colorado": "08",
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"Connecticut": "09",
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"District of Columbia": "11",
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"Delaware": "10",
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"Florida": "12",
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"Georgia": "13",
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"Hawaii": "15",
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"Idaho": "16",
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"Illinois": "17",
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"Indiana": "18",
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"Iowa": "19",
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"Kansas": "20",
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"Kentucky": "21",
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"Louisiana": "22",
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"Maine": "23",
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"Maryland": "24",
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"Massachusetts": "25",
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"Michigan": "26",
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"Minnesota": "27",
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"Mississippi": "28",
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"Missouri": "29",
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"Montana": "30",
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"Nebraska": "31",
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"Nevada": "32",
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"New Hampshire": "33",
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"New Jersey": "34",
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"New Mexico": "35",
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"New York": "36",
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"North Carolina": "37",
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"North Dakota": "38",
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"Ohio": "39",
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"Oklahoma": "40",
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"Oregon": "41",
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"Pennsylvania": "42",
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"Rhode Island": "44",
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"South Carolina": "45",
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"South Dakota": "46",
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"Tennessee": "47",
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"Texas": "48",
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"Utah": "49",
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"Vermont": "50",
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"Virginia": "51",
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"Washington": "53",
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"West Virginia": "54",
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"Wisconsin": "55",
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"Wyoming": "56",
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}
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def extract_usda_snap_data(year=2023):
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"""
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Downloads and extracts annual state-level SNAP data from the USDA FNS zip file.
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"""
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url = "https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-6.zip"
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try:
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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print(f"Error downloading file: {e}")
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return None
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zip_file = zipfile.ZipFile(io.BytesIO(response.content))
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filename = f"FY{str(year)[-2:]}.xlsx"
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with zip_file.open(filename) as f:
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xls = pd.ExcelFile(f)
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tab_results = []
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for sheet_name in [
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"NERO",
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"MARO",
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"SERO",
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"MWRO",
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"SWRO",
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"MPRO",
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"WRO",
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]:
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df_raw = pd.read_excel(
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xls, sheet_name=sheet_name, header=None, dtype={0: str}
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)
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state_row_mask = (
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df_raw[0].notna()
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& df_raw[1].isna()
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& ~df_raw[0].str.contains("Total", na=False)
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& ~df_raw[0].str.contains("Footnote", na=False)
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)
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df_raw["State"] = df_raw.loc[state_row_mask, 0]
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df_raw["State"] = df_raw["State"].ffill()
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total_rows = df_raw[df_raw[0].eq("Total")].copy()
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total_rows = total_rows.rename(
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columns={
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1: "Households",
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2: "Persons",
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3: "Cost",
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4: "CostPerHousehold",
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5: "CostPerPerson",
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}
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)
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state_totals = total_rows[
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[
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"State",
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"Households",
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"Persons",
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"Cost",
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"CostPerHousehold",
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"CostPerPerson",
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]
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]
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tab_results.append(state_totals)
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results_df = pd.concat(tab_results)
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df_states = results_df.loc[
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results_df["State"].isin(STATE_NAME_TO_FIPS.keys())
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].copy()
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df_states["STATE_FIPS"] = df_states["State"].map(STATE_NAME_TO_FIPS)
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df_states = (
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df_states.loc[~df_states["STATE_FIPS"].isna()]
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.sort_values("STATE_FIPS")
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.reset_index(drop=True)
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)
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df_states["GEO_ID"] = "0400000US" + df_states["STATE_FIPS"]
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return df_states[["GEO_ID", "Households", "Cost"]]
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def main() -> None:
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out_dir = STORAGE_FOLDER
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state_df = extract_usda_snap_data(2024)
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state_df.to_csv(out_dir / "snap_state.csv", index=False)
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if __name__ == "__main__":
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main()

‎policyengine_us_data/tests/test_datasets/test_enhanced_cps.py‎

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@@ -15,9 +15,9 @@ def test_ecps_has_tips():
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from policyengine_us import Microsimulation
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sim = Microsimulation(dataset=EnhancedCPS_2024)
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# Ensure we impute at least $45 billion in tip income.
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# Ensure we impute at least $40 billion in tip income.
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# We currently target $38 billion * 1.4 = $53.2 billion.
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TIP_INCOME_MINIMUM = 45e9
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TIP_INCOME_MINIMUM = 40e9
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assert sim.calculate("tip_income").sum() > TIP_INCOME_MINIMUM
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]
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assert (
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jct_rows.rel_abs_error.max() < 0.4
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jct_rows.rel_abs_error.max() < 0.5
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), "JCT tax expenditure targets not met (see the calibration log for details). Max relative error: {:.2%}".format(
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jct_rows.rel_abs_error.max()
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)
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from policyengine_core.reforms import Reform
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TARGET_COUNT = 2e6
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TOLERANCE = 4 # Allow ±400% error
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TOLERANCE = 4.5 # Allow +/-450% error
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# Define the CTC reform
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ctc_reform = Reform.from_dict(

‎policyengine_us_data/utils/loss.py‎

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import pandas as pd
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from .soi import pe_to_soi, get_soi
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import numpy as np
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from policyengine_us_data.storage import STORAGE_FOLDER
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from policyengine_us_data.storage.pull_soi_state_targets import (
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STATE_ABBR_TO_FIPS,
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)
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from policyengine_core.reforms import Reform
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from policyengine_us_data.utils.soi import pe_to_soi, get_soi
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def fmt(x):
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loss_matrix, targets_array, sim
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)
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snap_state_target_names, snap_state_targets = _add_snap_state_targets(sim)
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targets_array.extend(snap_state_targets)
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loss_matrix = _add_snap_metric_columns(loss_matrix, sim)
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return loss_matrix, np.array(targets_array)
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loss_matrix[label] = real_estate_taxes * in_state
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return targets_list, loss_matrix
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def _add_snap_state_targets(sim):
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"""
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Add snap targets at the state level, adjusted in aggregate to the sim
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"""
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snap_targets = pd.read_csv(STORAGE_FOLDER / "snap_state.csv")
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time_period = sim.default_calculation_period
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national_cost_target = sim.tax_benefit_system.parameters(
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time_period
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).calibration.gov.cbo._children["snap"]
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ratio = snap_targets[["Cost"]].sum().values[0] / national_cost_target
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snap_targets[["CostAdj"]] = snap_targets[["Cost"]] / ratio
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assert (
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np.round(snap_targets[["CostAdj"]].sum().values[0])
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== national_cost_target
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)
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743+
cost_targets = snap_targets.copy()[["GEO_ID", "CostAdj"]]
744+
cost_targets["target_name"] = (
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cost_targets["GEO_ID"].str[-4:] + "/snap-cost"
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)
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748+
hh_targets = snap_targets.copy()[["GEO_ID", "Households"]]
749+
hh_targets["target_name"] = snap_targets["GEO_ID"].str[-4:] + "/snap-hhs"
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target_names = (
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cost_targets["target_name"].tolist()
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+ hh_targets["target_name"].tolist()
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)
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target_values = (
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cost_targets["CostAdj"].astype(float).tolist()
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+ hh_targets["Households"].astype(float).tolist()
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)
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return target_names, target_values
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def _add_snap_metric_columns(
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loss_matrix: pd.DataFrame,
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sim,
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):
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"""
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Add SNAP metric columns to the loss_matrix.
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"""
769+
snap_targets = pd.read_csv(STORAGE_FOLDER / "snap_state.csv")
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snap_cost = sim.calculate("snap_reported", map_to="household").values
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snap_hhs = (
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sim.calculate("snap_reported", map_to="household").values > 0
774+
).astype(int)
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state = sim.calculate("state_code", map_to="person").values
777+
state = sim.map_result(
778+
state, "person", "household", how="value_from_first_person"
779+
)
780+
STATE_ABBR_TO_FIPS["DC"] = 11
781+
state_fips = pd.Series(state).apply(lambda s: STATE_ABBR_TO_FIPS[s])
782+
783+
for _, r in snap_targets.iterrows():
784+
in_state = state_fips == r.GEO_ID[-2:]
785+
metric = np.where(in_state, snap_cost, 0.0)
786+
col_name = f"{r.GEO_ID[-4:]}/snap-cost"
787+
loss_matrix[col_name] = metric
788+
789+
for _, r in snap_targets.iterrows():
790+
in_state = state_fips == r.GEO_ID[-2:]
791+
metric = np.where(in_state, snap_hhs, 0.0)
792+
col_name = f"{r.GEO_ID[-4:]}/snap-hhs"
793+
loss_matrix[col_name] = metric
794+
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return loss_matrix

‎pyproject.toml‎

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"google-auth",
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"scipy<1.13",
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"statsmodels>=0.14.0",
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"openpyxl>=3.1.5",
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"tables>=3.10.2",
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"torch>=2.7.1",
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]
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[project.optional-dependencies]
3538
dev = [
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"black",
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"pytest",
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"quantile-forest",
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"torch",
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"tables",
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"tabulate",
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"furo",
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"jupyter-book",

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