ESTIMATES OF STATE SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM 

PARTICIPATION RATES IN 2014

The Supplemental Nutrition Assistance Program (SNAP) 

is a central component of U.S. policy to alleviate hunger 

and poverty. The program’s main purpose is “to permit 

low-income households to obtain a more nutritious diet...

by increasing their purchasing power” (Food and Nutrition 

Act of 2008). SNAP is the largest of the domestic food 

and nutrition assistance programs administered by the U.S. 

Department of Agriculture’s Food and Nutrition Service. 

During fiscal year 2016, the program served over 44 

million people in an average month at a total annual cost 

of nearly $67 billion in benefits. 

SNAP provides an important support for “working poor” 

people—people who are eligible for SNAP benefits 

and live in households in which someone earns income 

from a job. In fiscal year 2015, 44 percent of all SNAP 

participants lived in households that had earned income. 

That was up from 30 percent of all participants in 1996, 

the year in which passage of the Personal Responsibility 

and Work Opportunity Reconciliation Act placed more 

emphasis on work for public assistance recipients.

The SNAP participation rate is the percentage of eligible 

people in the U. S. who actually participate in the program. 

Farson Gray and Cunnyngham (2016) examined national 

SNAP participation rates and rates for socioeconomic and 

demographic subgroups of people. This document presents 

estimates of State SNAP participation rates for all eligible 

people and working poor people for fiscal year 2014. 

These estimates can be used to assess recent program 

performance and focus efforts to improve access.

Participation rates in fiscal year 2014

An estimated 83 percent of eligible people received SNAP 

benefits in fiscal year 2014. Participation rates varied 

widely from State to State, however. In 22 States and the 

District of Columbia, the rates were significantly higher (in 

a statistical sense) than the national rate, and in 17 States, 

the rates were significantly lower. 

Among the regions, the Midwest Region had the highest 

participation rate. Its 95 percent rate was significantly 

higher than the rates for all of the other regions. The 

Western Region’s participation rate of 73 percent was 

significantly lower than the rates for all of the other 

regions except the Southwest Region. (See the last page 

for a map that shows regional boundaries.)

An estimated 70 percent of eligible working poor 

people participated in SNAP in fiscal year 2014. As with 

participation rates for all eligible people, rates for working 

poor people varied widely across States. In 23 States, 

SNAP participation rates for working poor people were 

significantly higher than the national rate for working poor 

people, and in 9 States and the District of Columbia they 

were significantly lower. 

In fiscal year 2014, the national SNAP participation rate 

for working poor people was significantly lower than the 

national rate for all eligible people. In 37 States and the 

District of Columbia, the participation rate for working 

poor people was likewise significantly lower than the rate 

for all eligible people. In 8 of these States and the District 

of Columbia, the difference between the rates for working 

poor people and all eligible people was significantly greater 

than the 12 percentage point difference between the national 

rates. In no State was the rate for working poor people 

significantly higher than the rate for all eligible people.

B Y KAREN CUNNYNGHAM • MATHEMATICA POLICY RESEARCH • JANUARY 2017

United States Department of Agriculture
How many were eligible in 2014? What percentage participated?

A confidence interval expresses our uncertainty about the true value of a participation rate. Each interval displayed here is a 90 percent confidence interval. One interpretation 

of such an interval is that there is a 90 percent chance that the true participation rate falls within the estimated bounds. For example, although our best estimate is that Alabama’s 

participation rate was 86 percent in 2014, the true rate may have been higher or lower. However, the chances are 90 in 100 that the true rate was between 81 and 90 percent.

See Estimation method section for information on participation rates of 100 percent.

2

Participation rates and confidence intervals (percent)

(Participation rate = 100 x number of people participating ÷ number of people eligible)

(Estimated participation rates are in red; estimated bounds of confidence intervals are in black.)

 
How many working poor people were eligible in 2014? What percentage participated?

A confidence interval expresses our uncertainty about the true value of a participation rate. Each interval displayed here is a 90 percent confidence interval. One interpretation of 

such an interval is that there is a 90 percent chance that the true participation rate falls within the estimated bounds. For example, although our best estimate is that Montana’s working poor participation rate was 73 percent in 2014, the true rate may have been higher or lower. However, the chances are 90 in 100 that the true rate was between 65 and 80 percent.

See Estimation method section for information on participation rates of 100 percent.

Participation rates and confidence intervals (percent)

(Participation rate = 100 x number of people participating ÷ number of people eligible)

(Estimated participation rates are in red; estimated bounds of confidence intervals are in black.)

 

3
State comparisons

The estimated SNAP participation rates presented here 

are based on fairly small samples of households in each 

State. Although there is substantial uncertainty associated 

with the estimates for some States and with comparisons 

of estimates from different States, the estimates show 

whether a State’s participation rate for all eligible people 

was probably at the top, at the bottom, or in the middle of 

the distribution. In fiscal year 2014, Oregon was very likely 

at the top, with a higher rate for all eligible people than all 

other States. In contrast, Wyoming likely had a lower rate 

than other States.

Similarly, it is possible to determine that some States were 

probably at the top, at the bottom, or in the middle of the 

distribution of rates for working poor people. In fiscal year 

2014, Wisconsin, Oregon, Michigan, Vermont, Iowa, South 

Dakota, and Maine were very likely at the top, with higher 

rates for working poor people than most States. In contrast, 

the District of Columbia and California likely had lower 

rates than most States.

How a State compares with other States may fluctuate 

over time due to both statistical variability in estimated 

rates and true changes in rates. The statistical variability is 

sufficiently great that a large change in a State’s rate from 

the prior year should be interpreted cautiously, as should 

differences between the rates of that State and other States. 

It may be incorrect to conclude that program performance 

in the State has improved or deteriorated dramatically. 

Despite this uncertainty, the estimated participation rates 

for all eligible people and working poor people suggest 

that some States have been fairly consistently in the top 

or bottom of the distribution of rates in recent years. In 

all 3 fiscal years from 2012 to 2014, Delaware, Maine, 

Michigan, Oregon, Tennessee, Vermont, Washington, 

and Wisconsin had significantly higher participation rates 

for all eligible people than two-thirds of the States. The 

District of Columbia, Iowa, Illinois, and New Mexico had 

significantly higher rates than half of the States. California, 

Kansas, North Dakota, New Jersey, Nevada, Texas, and 

Wyoming had significantly lower rates than two-thirds of 

the States in all three fiscal years, while Arkansas, Arizona, 

Colorado, Montana, and Nebraska had significantly lower 

rates than half of the States.

A State ranked near the top or bottom of the distribution 

of SNAP participation rates for all eligible people is likely 

to be ranked near the top or bottom, respectively, of the 

distribution of rates for working poor people. However, 

rankings of States by participation rates for working poor 

people and all eligible people are not always similar. Three 

States (Indiana, North Dakota, and South Dakota) are 

ranked significantly higher for all three fiscal years when 

ranked by their participation rate for working poor people 

than when ranked by their rate for all eligible people. 

In contrast, 5 States—Florida, Illinois, Massachusetts, 

Tennessee, and Washington—and the District of Columbia 

are ranked significantly lower for all 3 fiscal years when 

ranked by their participation rate for working poor people 

than when ranked by their rate for all eligible people.

Estimation method

We derived the estimates presented here using shrinkage 

estimation methods developed to improve precision when 

sample sizes are small (Cunnyngham et al. 2016). The 

shrinkage estimator averaged direct sample estimates 

of participation rates with predictions from a regression 

model, using data for all the States, all three years, and 

both groups (all eligible people and working poor people) 

to derive each estimate.

We obtained the direct sample estimates by applying 

SNAP eligibility rules to households in the Current 

Population Survey Annual Social and Economic 

Supplement to estimate numbers of eligible people and by 

using SNAP administrative data to estimate numbers of 

participating people. Farson Gray and Cunnyngham (2016) 

present details on the estimation methods used to derive 

the direct sample estimates.

4
The regression predictions of participation rates 

drew on data from the American Community Survey, 

individual tax returns, population estimates, and 

administrative records, and were based on indicators 

of socioeconomic conditions, such as the percentage 

of the total State population receiving SNAP benefits. 

Because of differences between the years being 

estimated, the regression model differs slightly from 

the one developed for Cunnyngham (2016). The 

regression model developed for this year’s report was 

chosen for its strong predictive ability for all 3 years 

and its consistency with the model developed for the 

prior report.

The shrinkage estimates presented here are substantially 

more precise than the direct sample estimates 

(Cunnyngham et al. 2016). Estimates for fiscal years 

2012 and 2013 differ from estimates presented in 

Cunnyngham (2016) because of differences in the 3 fiscal 

years being jointly estimated and the regression model.

The estimates for all eligible people include people 

in households that pass all applicable Federal SNAP 

income and asset tests or in which all members receive 

cash public assistance. People eligible solely through 

State categorical eligibility policies are not included in 

the estimates presented here. The estimates for eligible 

working poor people include people who are eligible 

for SNAP as defined above and live in a household in 

which a member earns money from a job.

Estimated participation rates of 100 percent are the 

result of differences between the data used to estimate 

the number of eligible people and the data used to 

estimate the number of participants; they should not 

be interpreted to mean that every eligible person is 

participating in SNAP. Using different data sources to 

estimate rate denominators and numerators can result in 

a preliminary estimate of eligible people in a particular 

State that is lower than the corresponding estimate of 

participants, leading to a participation rate that exceeds 

100 percent. We capped participation rates at 100 

percent by adjusting estimates of eligible people so no 

State had fewer eligible people than participants. See 

Cunnyngham et al. (2016) for details on how we made 

the adjustments. 

 Estimates of participation rates (percent)

All eligible people Working poor

2012 2013 2014 2012 2013 2014

Alabama 89 88 86 82 78 70

Alaska 86 85 86 71 74 75

Arizona 78 76 68 72 71 59

Arkansas 77 73 70 72 67 60

California 64 68 66 49 53 51

Colorado 73 79 76 65 71 69

Connecticut 88 90 94 76 79 79

Delaware 97 98 100 85 92 87

District of Columbia 95 97 96 50 63 49

Florida 91 92 90 75 75 72

Georgia 94 96 89 81 81 73

Hawaii 66 75 83 54 63 71

Idaho 89 89 84 84 84 82

Illinois 93 99 100 75 81 81

Indiana 85 89 88 85 86 84

Iowa 98 96 97 94 95 94

Kansas 72 77 74 65 70 68

Kentucky 89 88 84 76 73 67

Louisiana 84 88 76 74 78 64

Maine 100 100 100 98 96 92

Maryland 89 95 97 75 83 81

Massachusetts 87 87 85 64 68 65

Michigan 100 100 100 99 100 95

Minnesota 83 87 88 77 80 82

Mississippi 84 85 83 84 81 73

Missouri 92 92 86 82 80 74

Montana 77 81 77 72 77 73

Nebraska 72 78 77 67 73 74

Nevada 61 63 65 48 52 57

New Hampshire 84 85 84 80 80 79

New Jersey 73 78 75 69 72 69

New Mexico 92 94 92 88 90 85

New York 81 87 86 70 77 77

North Carolina 81 81 78 73 72 65

North Dakota 64 69 64 61 66 65

Ohio 85 92 87 77 82 76

Oklahoma 80 77 76 68 65 58

Oregon 100 100 100 90 96 95

Pennsylvania 86 89 88 78 79 78

Rhode Island 88 96 96 72 80 80

South Carolina 88 86 79 82 81 70

South Dakota 81 88 94 82 90 93

Tennessee 100 100 99 81 80 78

Texas 75 76 73 69 68 66

Utah 82 77 74 73 69 66

Vermont 100 100 100 93 99 95

Virginia 85 85 83 80 82 78

Washington 99 100 100 75 83 84

West Virginia 80 81 82 82 79 76

Wisconsin 96 100 100 89 94 97

Wyoming 63 58 59 63 57 56

Mid-Atlantic Region 84 87 86 76 79 76

Midwest Region 91 95 94 83 87 85

Mountain Plains Region 82 84 81 75 77 74

Northeast Region 84 88 88 71 77 77

Southeast Region 90 90 87 78 77 71

Southwest Region 78 78 74 71 70 66

Western Region 72 74 73 58 61 58

United States 83 85 83 72 74 70

There is substantial uncertainty associated with most of these estimates. Confidence 

intervals that measure the uncertainty in the estimates for 2012 and 2013 are 

presented in Cunnyngham et al. (2016). These confidence intervals are generally 

about as wide as the confidence intervals that are presented in this document for the 

2014 estimates.

See Estimation method section for information on participation rates of 100 percent.

5
How did your State rank in 2014?

A confidence interval expresses our uncertainty about the true value of a State’s rank. Each interval displayed here is a 90 percent confidence interval. One 

interpretation of such an interval is that there is a 90 percent chance that the true rank falls within the estimated bounds. For example, although our best estimate 

is that Alabama had the 26th highest participation rate in 2014, the true rank may have been higher or lower. However, the chances are 90 in 100 that the true 

rank was between 18 and 33 among all of the States. To determine how Alabama or your State compares with any other State, see the chart on page 7.

Rank and confidence intervals

(Estimated ranks are in red; estimated bounds of confidence intervals are in black.) 

 

6
How did your State compare with other States in 2014 for all eligibles?

Whether one State has a significantly higher participation rate than another State can be determined from this figure by finding the row for the first State at the left 

of the figure and the column for the second State at the top of the figure. If the box where the row and column intersect is red, there is at least a 90 percent chance 

that the first State (the row State) has a higher true participation rate. If the box is blue, there is at least a 90 percent chance that the second State (the column State) 

has a higher true participation rate. Equivalently, there is less than a 10-percent chance that the first State has a higher rate. If the box is tan, there is more than a 10 

percent chance but less than a 90 percent chance that the first State has a higher rate; thus, we conclude that neither estimated rate is significantly higher.

Taking Alabama, the State in the middle of the distribution, as an example, we see that it had a significantly lower participation rate than 16 States (Oregon, Vermont, Washington, Michigan, Wisconsin, Maine, Delaware, Illinois, Tennessee, Iowa, Maryland, the District of Columbia, Rhode Island, South Dakota, Connecticut, and New Mexico) and a significantly higher rate than 17 States (Wyoming, North Dakota, Nevada, California, Arizona, Arkansas, Texas, Kansas, Utah, New 

Jersey, Louisiana, Oklahoma, Colorado, Montana, Nebraska, North Carolina, and South Carolina). Its rate was neither significantly higher nor significantly lower 

than the rates for the other 18 States, suggesting that Alabama is probably in the broad center of the distribution, unlike, for example, Oregon and Wyoming, which 

were surely at or near the top and bottom of the distribution, respectively. Although we use the statistical definition of “significance” here, most of the significant 

differences were at least 10 percentage points, a difference that seems important as well as significant, and each was at least 4 percentage points.

See Estimation method section for information on participation rates of 100 percent.

 

7
 

Produced by Mathematica Policy Research for the Food and Nutrition Service under contract no. AG-3198-K-16-0011.

USDA is an equal opportunity provider and employer.

1About 1.3 million Supplemental Security Income recipients in California 

receive a small food assistance benefit through the State supplement. In the 

absence of the State rule excluding these people from receiving SNAP benefits, 

about 700,000 more California residents would be eligible for SNAP.

JANUARY 2017

Because the Current 

Population Survey 

does not collect data 

on participation in 

the Food Distribution 

Program on Indian 

Reservations, we did 

not adjust the estimates 

presented here to reflect 

the fact that participants 

in that program were 

not eligible to receive 

SNAP benefits at the 

same time (Farson 

Gray and Cunnyngham 

2016). The Food 

Distribution Program 

on Indian Reservations 

served about 85,000 

people in fiscal year 

2014, so the effects of 

such adjustments would 

be negligible in almost 

all States. Because the 

focus in this document 

is on participation 

submitted to the U.S. Department of Agriculture, Food

and Nutrition Service. Washington, DC: Mathematica 

Policy Research, 2017. Available at https://www.

mathematica-mpr.com/our-publications-and-findings/

publications/empirical-bayes-shrinkage-estimates-of-statesnap-participation-rates-in-2012to2014.

Cunnyngham, Karen. “Reaching Those in Need: 

State Supplemental Nutrition Assistance Program 

Participation Rates in 2013.” Final report submitted to 

the U.S. Department of Agriculture, Food and Nutrition 

Service. Washington, DC: Mathematica Policy Research, 

February 2016.

Farson Gray, Kelsey, and Karen Cunnyngham. “Trends in 

Supplemental Nutrition Assistance Program Participation 

Rates: Fiscal Year 2010 to Fiscal Year 2014.” Report 

submitted to the U.S. Department of Agriculture, Food and 

Nutrition Service. Washington, DC: Mathematica Policy 

Research, June 2016.

Estimates of participation rates varied widely

among people who were eligible for SNAP, we adjusted 

the estimates of eligible people using available data 

to reflect the fact that Supplemental Security Income 

recipients in California are not eligible to receive SNAP 

benefits because they receive cash instead.1

 However, 

in some other contexts, it might be useful to consider 

participation rates among those eligible for SNAP benefits 

or a cash substitute.

References

Cunnyngham, Karen, Amang Sukasih, and Laura 

Castner. “Empirical Bayes Shrinkage Estimates of State 

Supplemental Nutrition Assistance Program Participation 

Rates in Fiscal Year 2012 to Fiscal Year 2014 for All 

Eligible People and Working Poor People.” Final report