Name: US SNAP participation intelligence (monthly)
Creator: USDA Food and Nutrition Service
License: https://www.usda.gov/node/6232
Keywords: us, snap, households, food, financial-stress, monthly, signals, demand-forecasting

Monthly US SNAP participation intelligence from the USDA Food and Nutrition Service monthly state participation and costs workbooks (keyless FNS program-data ZIP, FY2020 to present): participating persons, households and benefit costs for the 50 states, DC, Guam and the Virgin Islands, with MoM and YoY percent changes, each state's share of the national caseload, a documented 0-100 household-budget stress score with per-month ranks and tiers, rising / surge / 12-month-record-high flags, and month-level context (national average benefit per person). Joinable to orders/customers by ISO month + state for demand and churn modelling. FNS material is U.S. federal public domain (commercial reuse allowed); source: USDA Food and Nutrition Service.

Source: USDA Food and Nutrition Service
Rows: 4,240
Columns: 27
Source cadence: Monthly
Last refreshed: Sep 29, 2026
Theme: economy

Column	Type	Description
month	string	Reference month: first day of the month, ISO date. The panel runs from the start of FY2020 (2019-10) to the latest published month of the FNS data bank; unpublished months ('--') produce no row. (unit: ISO date)
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
state_fips	string	Census FIPS code (e.g. 06 = California, 11 = District of Columbia, 66 = Guam).
geo_code	string	Stable geography code: USPS postal abbreviation, lowercase (ca, tx, ny, ...; dc, gu, vi). Primary join key with month.
geo_name	string	Workbook state/territory block label (e.g. California, District of Columbia, Virgin Islands).
persons	integer	Published participating persons (Total Participation, regular ongoing SNAP only — disaster supplements and replacements excluded per workbook footnote 1). (unit: persons)
households	integer	Published participating households (regular ongoing SNAP only). (unit: households)
benefits_usd	float	Published total benefit costs for the month. A few state-months are negative (publisher accounting adjustments, e.g. Wyoming 2022-08) — kept verbatim. (unit: USD)
avg_benefit_per_person_usd	float	Published average monthly benefit per person, kept verbatim. (unit: USD/person)
avg_benefit_per_household_usd	float	Published average monthly benefit per household, kept verbatim. (unit: USD/household)
persons_per_household	float	persons / households — the derived average caseload size. (unit: persons/household)
persons_mom_pct	float	Month-over-month percent change of participating persons. (unit: percent)
persons_yoy_pct	float	Year-over-year percent change of participating persons (warm-up months are null, never interpolated). (unit: percent)
households_mom_pct	float	Month-over-month percent change of participating households. (unit: percent)
households_yoy_pct	float	Year-over-year percent change of participating households. (unit: percent)
benefits_mom_pct	float	Month-over-month percent change of benefit costs. (unit: percent)
benefits_yoy_pct	float	Year-over-year percent change of benefit costs. (unit: percent)
persons_share_of_us_total_pct	float	State persons / national persons (the US Summary sheet), percent — the state's weight in the national caseload. (unit: percent)
us_avg_benefit_per_person_usd	float	Month-level context: national benefit cost / national persons. (unit: USD/person)
snap_stress_score	float	Documented 0-100 household-budget stress gauge: 100 * (0.60 * min-max(winsorized persons_mom_pct, -10..+25) + 0.40 * min-max(winsorized (avg_benefit_per_person_usd - national avg), -60..+60)), min-maxed within each month across the 53 geos. Higher = most stressed caseload versus peers this month. (unit: 0-100 score)
stress_rank	integer	Per-month rank of snap_stress_score across the 53 geos (1 = most stressed). (unit: rank)
stress_tier	string	Quartile bucket of snap_stress_score within the month: t1 (least stressed) .. t4 (most stressed). (unit: tier)
rising_flag	integer	1 when persons_yoy_pct is positive (caseload growing year over year). (unit: binary)
surge_flag	integer	1 when persons_yoy_pct >= 5.0 (caseload growing 5%+ year over year). (unit: binary)
record_high_12m_flag	integer	1 when persons equals the trailing-12-month maximum (inclusive, minimum 12 observations). (unit: binary)
source_url	string	Effective download URL of the FNS history ZIP behind this snapshot.
row_hash	string	Deterministic 16-hex row hash of geo_code + month (idempotency).

First 10 sample rows — a preview, not the complete dataset.

month	country_code	state_fips	geo_code	geo_name	persons	households	benefits_usd	avg_benefit_per_person_usd	avg_benefit_per_household_usd	persons_per_household	persons_mom_pct	persons_yoy_pct	households_mom_pct	households_yoy_pct	benefits_mom_pct	benefits_yoy_pct	persons_share_of_us_total_pct	us_avg_benefit_per_person_usd	snap_stress_score	stress_rank	stress_tier	source_url	row_hash
2019-11-01	USA	01	al	Alabama	716,466	339,535	85,899,443	119.893	252.991	2.11	-0.324	—	-0.439	—	-0.47	—	1.912	121.436	18.894	20	t3	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	d3eb1c833c2f7814
2019-11-01	USA	02	ak	Alaska	81,320	37,206	14,052,128	172.8	377.685	2.186	11.199	—	10.119	—	10.043	—	0.217	121.436	95.593	1	t4	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	1b6f082b987a6e8e
2019-11-01	USA	04	az	Arizona	784,993	370,704	95,305,482	121.409	257.093	2.118	-1.298	—	-1.14	—	-1.895	—	2.095	121.436	15.466	36	t2	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	83f72c76d525a918
2019-11-01	USA	05	ar	Arkansas	348,052	157,594	38,418,565	110.382	243.782	2.209	-0.683	—	-0.292	—	-0.484	—	0.929	121.436	12.488	43	t1	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	272dc58db6c4ddef
2019-11-01	USA	06	ca	California	4,042,531	2,148,970	497,653,867	123.105	231.578	1.881	0.047	—	0.341	—	-1.761	—	10.79	121.436	22.129	12	t4	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	9e159fdc6e5f0e66
2019-11-01	USA	08	co	Colorado	435,937	221,741	52,624,032	120.715	237.322	1.966	-0.786	—	-0.671	—	-0.712	—	1.164	121.436	17.319	26	t3	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	a7b77c02795d9ec7
2019-11-01	USA	09	ct	Connecticut	363,535	212,893	48,574,247	133.616	228.163	1.708	-0.308	—	-0.193	—	-0.627	—	0.97	121.436	25.962	7	t4	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	6604a6c7e789cdc8
2019-11-01	USA	10	de	Delaware	118,796	58,485	13,787,845	116.063	235.75	2.031	-2.616	—	-2.294	—	-3.386	—	0.317	121.436	7.052	52	t1	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	bb0bf0f531a787b4
2019-11-01	USA	11	dc	District of Columbia	110,471	65,263	14,254,278	129.032	218.413	1.693	-0.515	—	-0.691	—	-1.235	—	0.295	121.436	22.731	10	t4	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	23f2c4d3c062aeaf
2019-11-01	USA	12	fl	Florida	2,765,924	1,509,611	331,524,432	119.86	219.609	1.832	-0.482	—	-0.236	—	-0.999	—	7.383	121.436	18.195	22	t3	https://www.fns.usda.gov/sites/default/files/resource-files/snap-zip-fy69tocurrent-9.zip	523bce234f4d3b73

Sep 29, 2026Current
20260929T084321Z-016d5f13ac45 · sha256 016d5f13ac45…
4,240 rows · first snapshot

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/us_snap_participation_intel/us_snap_participation_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_snap_participation_intel/us_snap_participation_monthly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/us_snap_participation_intel/us_snap_participation_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

Cite this snapshot

Pinned to snapshot 20260929T084321Z-016d5f13ac45 and its content hash, so readers get exactly the data you used.

APA

USDA Food and Nutrition Service. (2026). US SNAP participation intelligence (monthly) [Data set, snapshot 20260929T084321Z-016d5f13ac45, sha256 016d5f13ac45]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/us_snap_participation_intel/us_snap_participation_monthly?snapshot=20260929T084321Z-016d5f13ac45

BibTeX

@misc{dz_us_snap_participation_intel_us_snap_part_016d5f13,
  title        = {{US SNAP participation intelligence (monthly)}},
  author       = {{USDA Food and Nutrition Service}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_snap_participation_intel/us_snap_participation_monthly?snapshot=20260929T084321Z-016d5f13ac45}},
  note         = {Snapshot 20260929T084321Z-016d5f13ac45, sha256 016d5f13ac45d37d57a0fdc493cf3e8520b7b8e727902ae03a559614a17f3f5c; accessed 2026-09-30}
}

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US SNAP participation intelligence (monthly)

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