In 2021, the total cost of the U.S. Supplemental Nutrition Assistance Program (SNAP) was around 113.74 billion U.S. dollars. This is a significant increase from the previous year, when the total cost of SNAP amounted to 79.1 billion U.S. dollars.

Supplemental Nutrition Assistance Program (SNAP) is the new name for the federal Food Stamp Program. This data set contains participation and cost data for SNAP. The data is furthered divided by annual, state, and monthly levels categorized by persons participating, households participating, benefits provided, average monthly benefits per person and average monthly benefits per household.

This dataset provides the Supplemental Nutrition Assistance Program (SNAP) benefits costs for each state.

The statistic shows total U.S. government spending for the Supplemental Nutrition Assistance Program (SNAP, formerly called Food Stamps) from 1995 to 2020. In 2010, about 70 billion U.S. dollars were spent for the Supplemental Nutrition Assistance Program.

This report describes "churning" as a policy concern in regards to the Supplemental Nutrition Assistance Program (SNAP). “Churning” in the Supplemental Nutrition Assistance Program (SNAP) is defined as when a household exits SNAP and then re-enters the program within 4 months. Churning is a policy concern due to the financial and administrative burden incurred by both SNAP households and State agencies that administer SNAP. This study explores the circumstances of churning in SNAP by determining the rates and patterns of churn, examining the causes of caseload churn, and calculating costs of churn to both participants and administering agencies in six States.

BackgroundThe Supplemental Nutrition Assistance Program (SNAP) provides approximately US$70 billion annually to support food purchases by low-income households, supporting approximately 1 in 7 Americans. In the 2018 Farm Bill, potential SNAP revisions to improve diets and health could include financial incentives, disincentives, or restrictions for certain foods. However, the overall and comparative impacts on health outcomes and costs are not established. We aimed to estimate the health impact, program and healthcare costs, and cost-effectiveness of food incentives, disincentives, or restrictions in SNAP.Methods and findingsWe used a validated microsimulation model (CVD-PREDICT), populated with national data on adult SNAP participants from the National Health and Nutrition Examination Survey (NHANES) 2009–2014, policy effects from SNAP pilots and food pricing meta-analyses, diet–disease effects from meta-analyses, and policy, food, and healthcare costs from published literature to estimate the overall and comparative impacts of 3 dietary policy interventions: (1) a 30% incentive for fruits and vegetables (F&V), (2) a 30% F&V incentive with a restriction of sugar-sweetened beverages (SSBs), and (3) a broader incentive/disincentive program for multiple foods that also preserves choice (SNAP-plus), combining 30% incentives for F&V, nuts, whole grains, fish, and plant-based oils and 30% disincentives for SSBs, junk food, and processed meats. Among approximately 14.5 million adults on SNAP at baseline with mean age 52 years, our simulation estimates that the F&V incentive over 5 years would prevent 38,782 cardiovascular disease (CVD) events, gain 18,928 quality-adjusted life years (QALYs), and save $1.21 billion in healthcare costs. Adding SSB restriction increased gains to 93,933 CVD events prevented, 45,864 QALYs gained, and $4.33 billion saved. For SNAP-plus, corresponding gains were 116,875 CVD events prevented, 56,056 QALYs gained, and $5.28 billion saved. Over a lifetime, the F&V incentive would prevent approximately 303,900 CVD events, gain 649,000 QALYs, and save $6.77 billion in healthcare costs. Adding SSB restriction increased gains to approximately 797,900 CVD events prevented, 2.11 million QALYs gained, and $39.16 billion in healthcare costs saved. For SNAP-plus, corresponding gains were approximately 940,000 CVD events prevented, 2.47 million QALYs gained, and $41.93 billion saved. From a societal perspective (including programmatic costs but excluding food subsidy costs as an intra-societal transfer), all 3 scenarios were cost-saving. From a government affordability perspective (i.e., incorporating food subsidy costs, including for children and young adults for whom no health gains were modeled), the F&V incentive was of low cost-effectiveness at 5 years (incremental cost-effectiveness ratio: $548,053/QALY) but achieved cost-effectiveness ($66,525/QALY) over a lifetime. Adding SSB restriction, the intervention was cost-effective at 10 years ($68,857/QALY) and very cost-effective at 20 years ($26,435/QALY) and over a lifetime ($5,216/QALY). The combined incentive/disincentive program produced the largest health gains and reduced both healthcare and food costs, with net cost-savings of $10.16 billion at 5 years and $63.33 billion over a lifetime. Results were consistent in probabilistic sensitivity analyses: for example, from a societal perspective, 1,000 of 1,000 iterations (100%) were cost-saving for all 3 interventions. Due to the nature of simulation studies, the findings cannot prove the health and cost impacts of national SNAP interventions.ConclusionsLeveraging healthier eating through SNAP could generate substantial health benefits and be cost-effective or cost-saving. A combined food incentive/disincentive program appears most effective and may be most attractive to policy-makers.

The mission of FNS is to provide children and needy families better access to food and a more healthful diet through its food assistance programs and comprehensive nutrition education efforts. These dataset provides a summary of all the FNS School Food Program combined into one dataset. It contains cash payments and commodity costs for the National School Lunch Program, School Breakfast Program and the Special Milk Program. (format: html, xls)

Estimated health gains, costs, and cost-effectiveness of SNAP food subsidies, restrictions, and combined incentives/disincentives over 5, 10, 20 years and over a lifetimea.

The SNAP participation rate shows how many households in Champaign County receive SNAP benefits, as a percentage of the total number of households in the county. The SNAP participation rate can serve as an indicator of poverty and need in the area, as income-based thresholds establish SNAP eligibility. However, not every household in poverty receives SNAP benefits, as can be determined by comparing the poverty rate between 2005 and 2023 and the percentage of households receiving SNAP benefits between 2005 and 2023.

The number of households and the percentage of households receiving SNAP benefits was higher in 2023 than in 2005, but we cannot establish a trend based on year-to-year changes, as in many years these changes are not statistically significant.

SNAP participation data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Receipt of Food Stamps/SNAP in the Past 12 Months by Presence of Children Under 18 Years for Households.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (26 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (5 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This dataset shows the list of retailers in Connecticut authorized to accept The Supplemental Nutrition Assistance Program (SNAP) Electronic Benefit Transfer (EBT) benefits. The SNAP, formerly known as Food Stamps, helps eligible individuals and families afford the cost of food at supermarkets, grocery stores and farmers’ markets.

The percentage of each county population in Maryland that participate in the Supplemental Nutrition Assistance Program (SNAP) (previously known as food stamps), specifically among those that are eligible, i.e. they have an income low enough to qualify for benefits.

The number of eligible people not participating in SNAP in each county in state fiscal year 2017 was generated by comparing the number of people living below 185 percent of the federal poverty level - a proxy for those who are likely eligible for SNAP — to those who are currently participating in the program. Nationally, the eligibility cut-off for SNAP is 130 percent of the federal poverty level, but this dataset uses 185 percent of the federal poverty level due to the high cost of living in Maryland.

Data source: United States Census Bureau, Maryland Department of Human Resources

Date: 2017

16.95 (%) in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

17.33 (%) in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

Baseline consumption levels and estimated changes in consumption per person at 5 years for 3 intervention scenarios in SNAPa.

10.11 (%) in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

28,02 (%) in 2001. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

Note: Due to the COVID-19 pandemic, in-person health insurance enrollment services will be suspended until further notice. During this period, our enrollment staff will continue to help New Yorkers sign up for low- and no-cost health insurance by phone. Health insurance enrollment and assistance with SNAP benefits (Food Stamps) Data collected to promote health insurance enrollment among uninsured New Yorkers. Data collected manually. Each record represents a health center location where health insurance enrollment and assistance with SNAP benefits (Food Stamps) are offered. Data can be used by general public seeking assistance with signing up for health insurance or SNAP. Data may change as program needs are changed, e.g., opening of new site or change in hours of operation at a particular site.

18.87 (%) in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

SNAP recipiency rate of Illinois declined by 1.14% from 15.83 % in 2013 to 15.65 % in 2014. Since the 4.11% growth in 2012, SNAP recipiency rate surged by 7.73% in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year

14,29 (%) in 2014. The ratio of average monthly number of program participants in an area to that area's total resident population count; percentage of total resident population as of July 1 of that year