County ranking · Census ACS 2024
Highest SNAP participation by county
The 100 U.S. counties where the largest share of households receive SNAP, the federal nutrition program, and how that maps onto food-access conditions. From the Census American Community Survey.
- 12.1%
- U.S. average SNAP
- 51.4%
- highest county
- 3,144
- counties ranked
The verdict
Kusilvak Census Area, AK leads with 51.4% of households on SNAP, against a national average of 12.1%.
- 0.0%
- Kusilvak Census Area low-access
- 67.6M
- low-access residents, U.S.
- 9,228
- food-desert tracts, U.S.
- 6.1%
- U.S. in food deserts
High SNAP participation is one of the strongest county-level signals of the low-income condition behind food deserts.
How SNAP participation is measured
SNAP, the Supplemental Nutrition Assistance Program, is administered by the U.S. Department of Agriculture's Food and Nutrition Service and operated state-by-state. Counties do not publish SNAP enrollment directly. Instead, the Census Bureau American Community Survey 5-Year Estimates ask a sample of households whether anyone received SNAP benefits in the previous 12 months, and the survey weights that response to produce a county-level estimate. The "SNAP %" column on this ranking is the share of households with at least one SNAP recipient over the previous 12 months, drawn from Census Table B22010 and aligned to the USDA Food Access Research Atlas county geography.
High-SNAP counties cluster in three regions of the United States. The Mississippi Delta, especially southern and western Mississippi and the Arkansas and Louisiana delta counties, ranks at the top by a wide margin. Native American reservations in the Plains and the Southwest follow, where median household incomes are below the federal poverty line and a substantial share of households qualify automatically. Appalachian counties in eastern Kentucky and West Virginia round out the top tier. In all three regions, SNAP participation tracks long-running structural poverty rather than a short-term cyclical downturn. Counties whose SNAP rate moves up or down sharply between ACS vintages usually do so because of an economic shock – a plant closure, a major hurricane, or a regional labor market collapse – rather than a sustained policy shift.
SNAP participation is one of the cleanest practical proxies for food insecurity at the county level. It is the same low-income signal the USDA layers on top of distance-to-supermarket to designate a "food desert" tract. That is why this ranking surfaces three additional columns alongside the SNAP rate: the official Census poverty rate, the share of population living in a USDA low-access tract, and the total number of SNAP households in absolute terms. A county that combines a high SNAP rate with a high low-access share is the canonical food desert profile, and that overlap is the actionable signal local agencies use to site supermarkets, subsidize SNAP-accepting retailers, or expand mobile food markets.
SNAP eligibility rules vary slightly by state, especially around asset tests and broad-based categorical eligibility. That state-level variation explains some of the spread between otherwise similar counties on either side of a state line. To read a single county in context, the per-county detail pages linked from the table below break out income, poverty, no-vehicle households, and low-access tracts on the same row as the SNAP figure, which lets a researcher reproduce the food desert overlap without leaving the page. The data citation at the bottom of this page lists every upstream URL.
Reading SNAP participation alongside the absolute SNAP household count is also useful, because a small rural county at sixty-percent SNAP participation may still have fewer total SNAP households than a large suburban county at twelve-percent participation. That distinction matters when local agencies design outreach and benefits-access programs: the percentage column flags how saturated a community is with the program, while the absolute count flags how many households the operational footprint needs to serve. Counties near the top of this list with both a high participation rate and a high absolute count are the largest leverage points for SNAP-accepting retailer expansion and for evaluating whether nearby supermarkets are equipped to process SNAP electronic benefit transfer transactions efficiently. The combination of percentage, absolute count, low-access overlap, and poverty rate on the same row is the canonical view food-policy researchers and state SNAP administrators use to identify counties where the program is doing the most work to close the food access gap.
Comparing this list to the worst-food-deserts ranking is instructive. The two lists overlap heavily but not perfectly. Counties that score high on both rankings are the canonical food desert profile: poverty, SNAP saturation, and structural distance to grocery stores reinforce each other. Counties that score high on SNAP but moderate on low-access tend to be dense urban tracts where supermarkets fall inside the one-mile urban threshold even though household incomes are low – the food access problem is more about price and product mix than geographic distance. Counties that score high on low-access but moderate on SNAP tend to be sparsely populated rural counties where the ten-mile threshold catches a wide swath of the land area but the resident population is small and stable enough that absolute SNAP household counts stay manageable. Reading the two rankings side by side is the cleanest way to distinguish a rural-distance food access problem from a low-income urban food access problem, and the per-county detail pages linked from the table below surface the indicators needed to make that distinction without leaving PlainFoodAccess.
The 15 highest-SNAP counties
Share of households receiving SNAP, ranked
- Kusilvak Census Area, AK
Kusilvak Census Area, Alaska
51.4 % on SNAP
- Oglala Lakota, SD
Oglala Lakota County, South Dakota
49.5 % on SNAP
- Starr, TX
Starr County, Texas
42.5 % on SNAP
- Bethel Census Area, AK
Bethel Census Area, Alaska
42.4 % on SNAP
- Guadalupe, NM
Guadalupe County, New Mexico
40.7 % on SNAP
- Zavala, TX
Zavala County, Texas
40.6 % on SNAP
- Todd, SD
Todd County, South Dakota
40.2 % on SNAP
- Randolph, GA
Randolph County, Georgia
39.4 % on SNAP
- Bronx, NY
Bronx County, New York
38.6 % on SNAP
- Owsley, KY
Owsley County, Kentucky
37.8 % on SNAP
- McKinley, NM
McKinley County, New Mexico
37.7 % on SNAP
- Magoffin, KY
Magoffin County, Kentucky
37.2 % on SNAP
- Wilcox, AL
Wilcox County, Alabama
37 % on SNAP
- Sioux, ND
Sioux County, North Dakota
36.8 % on SNAP
- Wolfe, KY
Wolfe County, Kentucky
36.5 % on SNAP
What this shows High-SNAP counties cluster in the Mississippi Delta, on Native American reservations in the Plains and Southwest, and in Appalachia, the same regions that anchor long-running structural poverty. The full 100-county table follows.
| # | County | SNAP |
|---|---|---|
| 1 | Kusilvak Census Area Alaska | 51.4% |
| 2 | Oglala Lakota County South Dakota | 49.5% |
| 3 | Starr County Texas | 42.5% |
| 4 | Bethel Census Area Alaska | 42.4% |
| 5 | Guadalupe County New Mexico | 40.7% |
| 6 | Zavala County Texas | 40.6% |
| 7 | Todd County South Dakota | 40.2% |
| 8 | Randolph County Georgia | 39.4% |
| 9 | Bronx County New York | 38.6% |
| 10 | Owsley County Kentucky | 37.8% |
| 11 | McKinley County New Mexico | 37.7% |
| 12 | Magoffin County Kentucky | 37.2% |
| 13 | Wilcox County Alabama | 37.0% |
| 14 | Sioux County North Dakota | 36.8% |
| 15 | Wolfe County Kentucky | 36.5% |
| 16 | McDowell County West Virginia | 36.5% |
| 17 | Mingo County West Virginia | 36.5% |
| 18 | Dallas County Alabama | 36.2% |
| 19 | Northwest Arctic Borough Alaska | 35.8% |
| 20 | Brooks County Texas | 35.7% |
| 21 | Hancock County Tennessee | 34.8% |
| 22 | Clay County Kentucky | 34.7% |
| 23 | Breathitt County Kentucky | 34.5% |
| 24 | Calhoun County Georgia | 34.3% |
| 25 | Taylor County Georgia | 34.1% |
| 26 | Emporia city Virginia | 34.1% |
| 27 | Bullock County Alabama | 34.0% |
| 28 | Washington County North Carolina | 33.9% |
| 29 | Claiborne Parish Louisiana | 33.8% |
| 30 | Anson County North Carolina | 33.8% |
| 31 | Stewart County Georgia | 33.7% |
| 32 | Perry County Alabama | 33.5% |
| 33 | McCreary County Kentucky | 33.3% |
| 34 | Sunflower County Mississippi | 33.1% |
| 35 | Holmes County Mississippi | 33.0% |
| 36 | Robeson County North Carolina | 32.6% |
| 37 | Clay County West Virginia | 32.6% |
| 38 | Webster County West Virginia | 32.6% |
| 39 | Turner County Georgia | 32.5% |
| 40 | Phillips County Arkansas | 32.1% |
| 41 | Greene County Alabama | 31.9% |
| 42 | Knox County Kentucky | 31.9% |
| 43 | Willacy County Texas | 31.9% |
| 44 | Tensas Parish Louisiana | 31.8% |
| 45 | Lake County Tennessee | 31.8% |
| 46 | Scotland County North Carolina | 31.6% |
| 47 | Noxubee County Mississippi | 31.3% |
| 48 | Zapata County Texas | 31.3% |
| 49 | Evangeline Parish Louisiana | 31.2% |
| 50 | Jim Hogg County Texas | 30.8% |
| 51 | St. Helena Parish Louisiana | 30.7% |
| 52 | Pemiscot County Missouri | 30.7% |
| 53 | Jackson County Kentucky | 30.6% |
| 54 | Sumter County Alabama | 30.5% |
| 55 | Apache County Arizona | 30.5% |
| 56 | Quay County New Mexico | 30.5% |
| 57 | Madison Parish Louisiana | 30.4% |
| 58 | Luna County New Mexico | 30.1% |
| 59 | Morehouse Parish Louisiana | 30.0% |
| 60 | Hancock County Georgia | 29.8% |
| 61 | Cottle County Texas | 29.8% |
| 62 | Norton city Virginia | 29.7% |
| 63 | East Carroll Parish Louisiana | 29.6% |
| 64 | Nome Census Area Alaska | 29.4% |
| 65 | Washington County Georgia | 29.4% |
| 66 | Lake and Peninsula Borough Alaska | 29.3% |
| 67 | Martin County Kentucky | 29.2% |
| 68 | San Miguel County New Mexico | 29.2% |
| 69 | Costilla County Colorado | 29.1% |
| 70 | Leslie County Kentucky | 29.1% |
| 71 | Halifax County North Carolina | 29.1% |
| 72 | Dougherty County Georgia | 29.0% |
| 73 | Lowndes County Alabama | 28.9% |
| 74 | Imperial County California | 28.7% |
| 75 | Webster County Georgia | 28.7% |
| 76 | Lee County Kentucky | 28.7% |
| 77 | Allendale County South Carolina | 28.7% |
| 78 | Maverick County Texas | 28.7% |
| 79 | Alamosa County Colorado | 28.6% |
| 80 | Hamilton County Florida | 28.6% |
| 81 | Perry County Kentucky | 28.6% |
| 82 | Richmond County North Carolina | 28.6% |
| 83 | Franklin city Virginia | 28.5% |
| 84 | Humphreys County Mississippi | 28.4% |
| 85 | Tunica County Mississippi | 28.3% |
| 86 | Malheur County Oregon | 28.3% |
| 87 | Washington Parish Louisiana | 28.2% |
| 88 | Lee County Virginia | 28.2% |
| 89 | Leflore County Mississippi | 28.1% |
| 90 | Dillingham Census Area Alaska | 27.9% |
| 91 | Telfair County Georgia | 27.9% |
| 92 | Avoyelles Parish Louisiana | 27.9% |
| 93 | Logan County West Virginia | 27.9% |
| 94 | Hale County Alabama | 27.8% |
| 95 | Lee County Arkansas | 27.8% |
| 96 | Esmeralda County Nevada | 27.8% |
| 97 | Hertford County North Carolina | 27.8% |
| 98 | Hidalgo County Texas | 27.8% |
| 99 | Cumberland County Kentucky | 27.6% |
| 100 | Letcher County Kentucky | 27.5% |
The bar beside each SNAP value scales to 100%. Showing the top 100 of 3,144 U.S. counties.
Source: USDA Economic Research Service, U.S. Census Bureau American Community Survey USDA Economic Research Service, U.S. Census Bureau American Community Survey
Data current as of (most recent source-data vintage; figures reflect the 2019 USDA Atlas and 2024 Census ACS).