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.

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.

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.

The annual research and development expenditure of Snap Inc in 2024 was 1.7 billion U.S. dollars, down from 1.9 billion U.S. dollars in 2023.

This public dataset published by USDA summarizes the total number of enrollees in the Supplemental Nutrition Assistance Program (SNAP) by region. SNAP provides nutrition benefits to supplement the food budget of families and persons meeting eligibility criteria related to monthly income. Program enrollment data offers a direct look into some of the most important underlying social determinants of health (SDoH) by county, including financial insecurity and food insecurity. Analysis of this data can also provide information about the characteristics of the subsidy program’s reach and market penetration over time. As an objective marker of the welfare benefit program’s utilization, these data also offer a complementary view of these SDoH alongside the survey-based questions about SNAP that are included in the ACS dataset. States report these administrative data to the USDA twice a year. The dataset includes total count of people, households and issuance of SNAP benefits by county or county/program. For more information, please refer to the USDA’s SNAP website (link )

This dataset enlists the monthly listings of households, recipients and expenditures for SNAP (Supplemental Nutrition Assistance Program). Data is from the New York State Office of Temporary and Disability Assistance and the NYC Open Data.

This dataset provides monthly statistics for Supplemental Nutrition Assistance Program (SNAP) participation in Virginia by month for the period 2012-2021.

Graph and download economic data for SNAP Benefits Recipients in Indiana (BR18000INA647NCEN) from 1989 to 2022 about SNAP, nutrition, food stamps, benefits, IN, food, and USA.

This dataset provides monthly statistics for Supplemental Nutrition Assistance Program (SNAP) participation in Virginia by month and by Local Department of Social Services (LDSS) for the period 2012-2021.

These data are monthly listings of households, recipients and expenditures for the Supplemental Nutrition Assistance Program.

This report examines patterns of SNAP benefit redemption in Fiscal Year 2017, particularly related to the timing and amount of transactions and the rate at which households​ exhaust their benefits. Patterns in Fiscal Year 2017 are compared with findings from two similar studies conducted for Fiscal Years 2003 and 2009. This study also examines spending behaviors identified as atypical – months in which a benefit is received but the household does not make a transaction, an ending monthly balance greater than $200, and out-of-state transactions that occur beyond the state’s border counties.​

This paper investigates how Supplemental Nutrition Assistance Program (SNAP) eligibility affects food expenditures. A 2019 policy change in California granted SNAP eligibility to previously ineligible Supplemental Security Income (SSI) recipients. Using the Consumer Expenditure Survey, we find that after the policy change, affected SSI recipients increased their food at home'' budget share between 2.5 to 4.3 percentage points ($120 to $206 per quarter). The SNAP effect on total food expenditures is dampened by a decrease infood away from home'' which SNAP benefits cannot be spent on.

This statistic shows change in SNAP food spending in the United States in 2013 according to food departments. In 2013, SNAP food spending fell an average 2.8 percent. In the same year, the most sizable cuts has been made to spending on liquor, where SNAP households fell more than 12 percent.

This paper investigates the effect of Supplemental Nutrition Assistance Program(SNAP) benefit disbursement on intra monthly household level purchases made from a supermarket retailer. Using variation in the timing of benefit receipt, we find that spending and the likelihood of shopping increase by $2 and 1.5 percentage points, respectively, on the day that SNAP benefits are disbursed. We also find that the bulk(national brand) expenditure share increases by 2 (0.6) percentage points on the day of benefit receipt. We find little evidence for cyclicality in the healthfulness of food purchases.Finally, we compare and contrast estimates that use variation in the indicator for benefit receipt (benefit receipt estimates) to estimates that utilize variation in the probability of SNAP benefit receipt (likelihood of benefit receipt estimates). We find that the likelihood of benefit receipt estimates are statistically distinguishable from and 2.1 to 2.8 times larger than the benefit receipt estimates for the outcome of spending.We decompose these differences and provide guidance to researchers in the event that only the likelihood of benefit receipt is known.

This dataset contains Food Assistance metrics displayed in the U.S. Department of Agriculture (USDA) Food Environment Atlas website, including statistics for SNAP, National School Lunch Program, School Breakfast Program, Summer Food Service Program, WIC, FDPIR, and food banks. USDA's domestic food and nutrition assistance programs affect the daily lives of millions of people, with about one in four Americans participating in at least one program at some point during a given year. These programs represent a significant investment, accounting for over two-thirds of USDA's annual budget.

Data was last updated on the USDA website in September 2020.

Any data elements with numerical values reflect figures at the locality-level unless otherwise specified with an asterisk (*). See column descriptions for details. For more information on all metrics in this dataset, see the Food Environment Atlas Food Assistance documentation.

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).

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.

City of Dallas Households Receiving Supplemental Nutrition Assistance Program (SNAP) Benefits aggregated by Zip Code. SNAP provides food benefits to low-income families to supplement their grocery budget so they can afford the nutritious food essential to health and well-being.

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 ---