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

Graph and download economic data for Government social benefits: to persons: Federal: Supplemental Nutrition Assistance Program (SNAP) (TRP6001A027NBEA) from 1961 to 2023 about assistance, social assistance, nutrition, food stamps, benefits, federal, food, government, GDP, and USA.

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

The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload. The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual. This dataset includes separate SNAP QC files for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months. Resources in this dataset:Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 2). File Name: qc_pub_fy2020_per2.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 1). File Name: qc_pub_fy2020_per1.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Technical Documentation for the Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database and the QC Minimodel. File Name: FY2020TechDoc.pdfResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.

United States SNAP: Food Stamps: Benefit: Costs data was reported at 4.797 USD bn in Aug 2018. This records a decrease from the previous number of 4.798 USD bn for Jul 2018. United States SNAP: Food Stamps: Benefit: Costs data is updated monthly, averaging 2.041 USD bn from Oct 1989 (Median) to Aug 2018, with 347 observations. The data reached an all-time high of 6,471.723 USD bn in Nov 2012 and a record low of 1.144 USD bn in Nov 1989. United States SNAP: Food Stamps: Benefit: Costs data remains active status in CEIC and is reported by Food and Nutrition Service. The data is categorized under Global Database’s United States – Table US.G086: 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.

This report contains key data regarding the cost of FNS' food assistance programs. The report summarizes data submitted by various reporting agencies for the United States during fiscal year 2013 and fiscal year 2014.

This statistic depicts the shopping value of products that were purchased with food stamps of the Supplemental Nutrition Assistance Program (SNAP) at U.S. farmers markets and through direct marketing from FY 2012 to 2020. In 2020, the value of farmers' market and direct marketing redemptions amounted to nearly 33 million U.S. dollars.

Provide total food cost of the Emergency Food Assistance Program on State level.

Many safety-net programs issue benefits as monthly lump-sum payments. We investigate how the timing of Supplemental Nutrition Assistance Program (SNAP) benefit issuance affects food purchases and the incidence of the transfer. Using scanner data from a large sample of grocery stores and state and time variation in SNAP issuance schedules, we document large, SNAP-induced intra-month cycles in food expenditures. However, we find that retailers do not adjust prices based on these predictable patterns of demand. Our results therefore suggest that reforming issuance schedules may reduce costs from SNAP-induced demand surges but are unlikely to affect the incidence of SNAP benefits.

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).

Analysis of ‘Supplemental Nutrition Assistance Program (SNAP) Authorized Retailers’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cad8dddd-9fc1-498d-91d0-dbc43c66ccfc on 26 January 2022.

--- Dataset description provided by original source is as follows ---

A listing of retailers in Conneticut authorized to accept SNAP EBT benefits. The Supplemental Nutrition Assistance Program (SNAP), formerly known as Food Stamps, helps eligible individuals and families afford the cost of food at supermarkets, grocery stores and farmers’ markets.

--- Original source retains full ownership of the source dataset ---

A listing of retailers in Conneticut authorized to accept SNAP EBT benefits. The Supplemental Nutrition Assistance Program (SNAP), formerly known as Food Stamps, helps eligible individuals and families afford the cost of food at supermarkets, grocery stores and farmers’ markets.

{"definition": "Percent change in the per capita average monthly dollar amount of SNAP (Supplemental Nutrition Assistance Program, previously called Food Stamp Program) benefits in a county. Percent change indicators are calculated as [((Year 2 - Year 1) / Year 1) * 100]. For indicators where Year 1 has a value of zero, or Year 1 or Year 2 have missing values, the percent change value is set to -9999 to denote no value.", "availableYears": "2008-2010", "name": "SNAP benefits per capita (% change), 2008-10", "units": "% change ", "shortName": "PCH_PC_SNAPBEN_08_10", "geographicLevel": "County", "dataSources": "SNAP benefits from the U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic Accounts Directorate, as reported in the SNAP Data System at ERS (http://www.ers.usda.gov/data-products/supplemental-nutrition-assistance-program-(snap)-data-system.aspx). Population data from the U.S. Census Bureau, County Intercensal Estimates (2000-2010)."}

© PCH_PC_SNAPBEN_08_10

This layer is sourced from gis.ers.usda.gov.

Trafficking of Supplemental Nutrition Assistance Program (SNAP) benefits occurs when SNAP recipients sell their benefits for cash to food retailers, often at a discount. Although trafficking does not increase costs to the Federal Government, it is a diversion of program benefits from their intended purpose of helping low-income families access a nutritious diet. This report, the latest in a series of periodic analyses, provides estimates of the extent of trafficking during the period 2009 through 2011.

The Emergency Food Assistance Program (TEFAP) is a U.S. Department of Agriculture (USDA) program that for three decades has helped supplement the diets of low-income Americans, including seniors, by providing them with emergency food and nutrition assistance at no cost. This white paper explains the program and describes some of its key results.

Analysis of ‘US Public Food Assistance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jpmiller/publicassistance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset focuses on public assistance programs in the United States that provide food, namely SNAP and WIC. If you are interested in a broader picture of food security across the world, please see Food Security Indicators for the World 2016-2020.

Initial coverage was for the Special Supplemental Nutrition Program for Women, Infants, and Children Program, or simply WIC. The program allocates Federal and State funds to help low-income women and children up to age five who are at nutritional risk. Funds are used to provide supplemental foods, baby formula, health care, and nutrition education.

Starting with version 5, the dataset also covers the US Supplemental Nutrition Assistance Program, more commonly known as SNAP. The program is the successor to the Food Stamps program previously in place. The program provides food assistance to low-income families in the form of a debit card. A 2016 study using POS data from SNAP-eligible vendors showed the three most purchased types of food to be meats, sweetened beverages, and vegetables.

Content

Files may include participation data and spending for state programs, and poverty data for each state. Data for WIC covers fiscal years 2013-2016, which is actually October 2012 through September 2016. Data for SNAP covers 2015 to 2020.

Motivation

My original purpose here is two-fold:

• Explore various aspects of US Public Assistance. Show trends over recent years and better understand differences across state agencies. Although the federal government sponsors the program and provides funding, program are administered at the state level and can widely vary. Indian nations (native Americans) also administer their own programs.

• Share with the Kaggle Community the joy - and pain - of working with government data. Data is often spread across numerous agency sites and comes in a variety of formats. Often the data is provided in Excel, with the files consisting of multiple tabs. Also, files are formatted as reports and contain aggregated data (sums, averages, etc.) along with base data.

As of March 2nd, I am expanding the purpose to support the M5 Forecasting Challenges here on Kaggle. Store sales are partly driven by participation in Public Assistance programs. Participants typically receive the items free of charge. The store then recovers the sale price from the state agencies administering the program.

Additional Content Ideas

The dataset can benefit greatly from additional content. Economics, additional demographics, administrative costs and more. I'd like to eventually explore the money trail from taxes and corporate subsidies, through the government agencies, and on to program participants. All community ideas are welcome!

--- Original source retains full ownership of the source dataset ---

BackgroundLarge socio-economic disparities exist in US dietary habits and cardiovascular disease (CVD) mortality. While economic incentives have demonstrated success in improving dietary choices, the quantitative impact of different dietary policies on CVD disparities is not well established. We aimed to quantify and compare the potential effects on total CVD mortality and disparities of specific dietary policies to increase fruit and vegetable (F&V) consumption and reduce sugar-sweetened beverage (SSB) consumption in the US.Methods and findingsUsing the US IMPACT Food Policy Model and probabilistic sensitivity analyses, we estimated and compared the reductions in CVD mortality and socio-economic disparities in the US population potentially achievable from 2015 to 2030 with specific dietary policy scenarios: (a) a national mass media campaign (MMC) aimed to increase consumption of F&Vs and reduce consumption of SSBs, (b) a national fiscal policy to tax SSBs to increase prices by 10%, (c) a national fiscal policy to subsidise F&Vs to reduce prices by 10%, and (d) a targeted policy to subsidise F&Vs to reduce prices by 30% among Supplemental Nutrition Assistance Program (SNAP) participants only. We also evaluated a combined policy approach, combining all of the above policies. Data sources included the Surveillance, Epidemiology, and End Results Program, National Vital Statistics System, National Health and Nutrition Examination Survey, and published meta-analyses.Among the individual policy scenarios, a national 10% F&V subsidy was projected to be most beneficial, potentially resulting in approximately 150,500 (95% uncertainty interval [UI] 141,400–158,500) CVD deaths prevented or postponed (DPPs) by 2030 in the US. This far exceeds the approximately 35,100 (95% UI 31,700–37,500) DPPs potentially attributable to a 30% F&V subsidy targeting SNAP participants, the approximately 25,800 (95% UI 24,300–28,500) DPPs for a 1-y MMC, or the approximately 31,000 (95% UI 26,800–35,300) DPPs for a 10% SSB tax.Neither the MMC nor the individual national economic policies would significantly reduce CVD socio-economic disparities. However, the SNAP-targeted intervention might potentially reduce CVD disparities between SNAP participants and SNAP-ineligible individuals, by approximately 8% (10 DPPs per 100,000 population). The combined policy approach might save more lives than any single policy studied (approximately 230,000 DPPs by 2030) while also significantly reducing disparities, by approximately 6% (7 DPPs per 100,000 population).Limitations include our effect estimates in the model; these estimates use interventional and prospective observational studies (not exclusively randomised controlled trials). They are thus imperfect and should be interpreted as the best available evidence. Another key limitation is that we considered only CVD outcomes; the policies we explored would undoubtedly have additional beneficial effects upon other diseases. Further, we did not model or compare the cost-effectiveness of each proposed policy.ConclusionsFiscal strategies targeting diet might substantially reduce CVD burdens. A national 10% F&V subsidy would save by far the most lives, while a 30% F&V subsidy targeting SNAP participants would most reduce socio-economic disparities. A combined policy would have the greatest overall impact on both mortality and socio-economic disparities.