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.

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, food, federal, 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 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.

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 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.

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.

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Part 2 of the dataset is complete (for now!) There you'll find data specific to the Supplemental Nutrition Assistance (SNAP) Program. The US SNAP program provides food benefits to low-income families to supplement their grocery budget.

Link: US Public Food Assistance 2 - SNAP Please click on the ▲ if you find it useful -- it has almost 500 downloads!

Context

This dataset, Part 1, addresses another US program, 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.

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.

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.

The demand for community food services during the pandemic prompted significant government aid and led to innovative distribution methods, such as temporary food banks in parking lots and drive-through pick-up points. Innovations continued to enhance collection (online ordering systems) and funding (virtual donations), boosting revenue and profit in both 2020 and 2021. However, significant revenue volatility eroded gains and industry revenue is expected to climb at a CAGR of 0.6% through 2025, with revenue expected to strengthen 2.2% in 2025 alone and reach $21.3 billion by 2025. Federal, state and the private sector participate in implementing and funding innovative practices that benefit the community food sector. The National Science Foundation Convergence Accelerator is financing a team at the University of Houston to develop an AI platform for food ecosystems. Drone and self-driving technologies developed in the private sector are in the early stages of food transport use and have the potential to provide solutions for delivering food to remote, underserved, food-insecure areas. However, innovations do not erase the volatility of donations, which plays a role in industry performance and will continue to do so. Individual contributions and volunteering, which fluctuate yearly and result from changing economic conditions, can reduce essential funding. Future volatility is expected because of uncertain federal policy and the ability of states and localities to meet increasing demand. The Trump administration's 2025 FY cuts and potential cuts in the FY 2026 budget have created uncertainty. Program cuts to SNAP (Supplemental Nutrition Assistance Program) and TEFAP (The Emergency Food Assistance Program) threaten meal availability. At the same time, as of May 2025, the Farm Bill extension leads to uncertainty concerning funding, eligibility, purchase and distribution of surplus. To mitigate possible cuts, community food organizations must further diversify support, strengthen partnerships and advocate for stable policies. While declines in unemployment and poverty could relieve pressure on community food service providers, industry revenue is forecast to climb at a CAGR of 1.2% through 2030 to total $22.6 billion, as profit continues to dip to 2.6% of revenue.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Project Overview This project is designed to bridge the gap in medical education by integrating the SNAP Challenge, an experiential learning exercise where participants live on the average Supplemental Nutrition Assistance Program (SNAP) budget—about $4–5 per day—to understand the realities of food insecurity. Most medical students come from affluent backgrounds, limiting their first-hand experience with poverty. Traditional curricula rely on lectures, but experiential learning fosters deeper understanding and motivation for change. The Transformative Care Continuum (TCC) program at Ohio University’s Heritage College of Osteopathic Medicine (OUHCOM) immerses first-year medical students in this structured experience, incorporating journaling, clinical case logs, and quality improvement initiatives to connect learning to real-world patient care. Unlike voluntary initiatives, this program embeds the challenge into training, enhancing empathy, clinical decision-making, and awareness of social determinants of health to better prepare future family medicine physicians. Data and Data Collection Overview This study collected data in August 2024 from forty medical students who had participated in the SNAP exercise as first-year students (between 2018 and 2023) through reflective journals, patient-centered observation forms, clinical case logs, and follow-up surveys to assess the impact of the SNAP Challenge on medical students' understanding of food insecurity. Students documented their daily experiences on a limited SNAP budget through reflective journals, capturing challenges and personal insights. Faculty assessed students’ ability to apply motivational interviewing and empathy using patient-centered observation forms during clinical encounters. Clinical case logs tracked students’ recognition of food insecurity in patient interactions and documentation in electronic health records. A long-term follow-up survey, conducted 9 months to 5 years post- participation, evaluated knowledge retention, continued engagement with food insecurity issues, and application of skills in clinical practice, with a 50% response rate (21/40). Results showed that students increased their awareness of food insecurity, improved patient screening, and initiated community-based interventions to address food insecurity in healthcare settings. Shared Data Organization The data file shared here contains the responses (including free-text entries) to the follow-up survey. The documentation files include the recruitment email, informed consent form and survey questionnaire used for the study, as well as this Data Narrative and an administrative README file.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills Scheme: Expenses on Marketing Costs data was reported at 35,000.000 INR mn in 2022. India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills Scheme: Expenses on Marketing Costs data is updated yearly, averaging 35,000.000 INR mn from Mar 2022 (Median) to 2022, with 1 observations. The data reached an all-time high of 35,000.000 INR mn in 2022 and a record low of 35,000.000 INR mn in 2022. India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills Scheme: Expenses on Marketing Costs data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FB035: Union Budget: Central Sector Schemes. 2025-2026 – Budget Estimates 2024-2025 – Revised Estimates 2023-2022 & Before – Actuals

How much do fruits and vegetables cost? The USDA, Economic Research Service (ERS) estimated average prices for more than 150 commonly consumed fresh and processed fruits and vegetables. Reported estimates include each product's average retail price and price per edible cup equivalent (i.e., the unit of measurement for Federal recommendations for fruit and vegetable consumption). Average retail prices are reported per pound or per pint. For many fruits and vegetables, a 1-cup equivalent equals the weight of enough edible food to fill a measuring cup. USDA, ERS calculated average prices at retail stores using 2013, 2016, 2020, and 2022 retail scanner data from Circana (formerly Information Resources Inc. (IRI)). A selection of retail establishments—grocery stores, supermarkets, supercenters, convenience stores, drug stores, and liquor stores—across the United States provides Circana with weekly retail sales data (revenue and quantity).

USDA, ERS reports average prices per edible cup equivalent to inform policymakers and nutritionists about how much money it costs U.S. households to eat a sufficient quantity and variety of fruits and vegetables. Every 5 years the Departments of Agriculture and Health and Human Services release a new version of the Dietary Guidelines for Americans with information about how individuals can achieve a healthy diet. However, the average consumer falls short in meeting these recommendations. Many people consume too many calories from refined grains, solid fats, and added sugars, and do not eat enough whole grains, fruits, and vegetables. Are food prices a barrier to eating a healthy diet? USDA, ERS research using this dataset examines the quantity and variety of fruits and vegetables that a household can afford with a limited budget. See:

• The Cost of Satisfying Fruit and Vegetable Recommendations in the Dietary Guidelines
• For Supplemental Nutrition Assistance Program (SNAP) Households, Fruit and Vegetable Affordability Is Partly a Question of Budgeting
• Americans Still Can Meet Fruit and Vegetable Dietary Guidelines for $2.10-$2.60 per Day

USDA, ERS fruit and vegetable prices will be updated each year, subject to data availability. When generating estimates using 2013, 2016, 2020, and 2022 data, USDA, ERS researchers priced similar fruit and vegetable products. However, because of different methods for coding the underlying Circana data, the entry of new products into the market, the exit of old products from the market, and other factors, the data are not suitable for making year-to-year comparisons. These data should not be used for making inferences about price changes over time.

For data on retail food price trends, see the USDA, ERS’ Food Price Outlook (FPO). The FPO provides food price data and forecasts changes in the Consumer Price Index (CPI) and Producer Price Index (PPI) for food.

For additional data on food costs, see the USDA, ERS’ Purchase to Plate (PP-Suite). The PP-Suite reports a U.S. household’s costs to consume other categories of foods in addition to fruits and vegetables, such as meats, seafood, and cereal and bakery products. Food groupings in the PP-Suite are based on the USDA, Agricultural Research Service’s (ARS) Food and Nutrient Database for Dietary Studies (FNDDS). This allows users to import price estimates for foods found in USDA dietary survey data. USDA, ARS’ FNDDS food groupings are broader than the specific food products priced for constructing this data product. They also include both conventional and organic products. For example, the PP-Suite average price to consume broccoli purchased raw is the average price paid for organic and conventional heads, crowns, and florets. By contrast, this data product distinguishes and separately reports the average costs to consume conventional raw broccoli purchased as heads and florets.

• Fruit
• Vegetables

。Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills Scheme: Expenses on Marketing Costs数据按每年更新，2022至2022期间平均值为35,000.000INR mn，共1份观测结果。该数据的历史最高值出现于2022，达35,000.000INR mn，而历史最低值则出现于2022，为35,000.000INR mn。CEIC提供的Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills Scheme: Expenses on Marketing Costs数据处于定期更新的状态，数据来源于Ministry of Finance，数据归类于India Premium Database的Government and Public Finance – Table IN.FB035: Union Budget: Central Sector Schemes。

Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season在2023达83.200INR mn，相较于2022的21,208.600INR mn有所下降。Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season数据按每年更新，2020至2023期间平均值为11,104.300INR mn，共4份观测结果。该数据的历史最高值出现于2021，达39,000.000INR mn，而历史最低值则出现于2023，为83.200INR mn。CEIC提供的Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season数据处于定期更新的状态，数据来源于Ministry of Finance，数据归类于India Premium Database的Government and Public Finance – Table IN.FB035: Union Budget: Central Sector Schemes。

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season data was reported at 83.200 INR mn in 2023. This records a decrease from the previous number of 21,208.600 INR mn for 2022. India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season data is updated yearly, averaging 11,104.300 INR mn from Mar 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 39,000.000 INR mn in 2021 and a record low of 83.200 INR mn in 2023. India Union Budget: Central Sector Schemes: Department of Food and Public Distribution: Assistance to Sugar Mills for 2019-20 season data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FB035: Union Budget: Central Sector Schemes. 2025-2026 – Budget Estimates 2024-2025 – Revised Estimates 2023-2022 & Before – Actuals