The great majority of households in the United States were food-secure in 2024. Over **** percent of households in the country were food-insecure comprised of both adults and children.

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

{"definition": "Prevalence of household-level food insecurity by State. Food-insecure households were unable, at times during the year, to provide adequate food for one or more household members because the household lacked money and other resources for food. For most food-insecure households, inadequacy was in quality and variety of foods; for about a third\u2014those with very low food security\u2014amounts were also inadequate.", "availableYears": "2000-02 (aggregate data)", "name": "Household food insecurity (%, three-year average), 2000-02*", "units": "Percent", "shortName": "FOODINSEC_00_02", "geographicLevel": "State", "dataSources": "ERS estimates using 3 years of data from the Current Population Survey Food Security Supplement, as reported in Table 5 in Coleman-Jensen, Alisha, Mark Nord, and Anita Singh, Household Food Security in the United States in 2012, ERR-155, USDA/ERS, September 2013 (http://www.ers.usda.gov/publications/err-economic-research-report/err155.aspx). The food security survey asks one adult respondent in each household a series of questions about experiences and behaviors that indicate food insecurity. The food security status of the household was assessed based on the number of food-insecure conditions reported (such as being unable to afford balanced meals, cutting the size of meals because of too little money for food, or being hungry because of too little money for food). Note: margins of error are substantial for some States; comparisons between States should take into consideration margins of error published in the source report."}

© FOODINSEC_00_02 This layer is sourced from gis.ers.usda.gov.

USDA's National Household Food Acquisition and Purchase Survey (FoodAPS) will provide unique and detailed data about household food choices that are not available from any other survey. FoodAPS is a nationally representative survey of household food purchases and acquisitions.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to documents For complete information, please visit https://data.gov.

These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – pending). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay close attention to the amounts of different foods their household throws away, feeds to animals or composts because the food is past date, spoiled or no longer wanted for other reasons. They are told to exclude items they would normally not eat, such as bones, pits, and shells. Approximately 7 days later they received the follow-up survey, which elicited the amount of waste in up to 24 categories of food and included other questions (see supplemental materials for core survey questions). Waste amounts in each category are reported by selecting from one of several ranges of possible amounts. The gram weight for categories with volumetric ranges (e.g., listed in cups) were derived by assigning an appropriate mass to the midpoint of the selected range consistent with the food category. For the categories with highly variable weight per volume (e.g., a cup of raw asparagus weighs about 7 times more than a cup of raw chopped arugula), we use the profile of items most consumed in the United States to determine the appropriate gram weight. For display purposes, the 24 categories are consolidated into 8 more general categories. Total weekly household food waste is calculated by summing up reported gram amounts across all categories. We divide this total by the number of household members to generate the per person weekly food waste amount.

Household food insecurity, by age group and sex, Canada, provinces, territories, health regions (2013 boundaries) and peer groups.

Household food security status, by living arrangement, Canada, provinces and territories.

The table Food-at-Home Event Data – Public Use File is part of the dataset FoodAPS National Household Food Acquisition and Purchase Survey, available at https://redivis.com/datasets/bw26-1km85fqpd. It contains 15998 rows across 73 variables.

Abstract

The National Household Food Acquisition and Purchase Survey (FoodAPS) collected information on foods purchased or otherwise acquired, and the prices and nutrient characteristics of those foods, for a nationally representative sample of U.S. households.

This statistic depicts the average household food expenditure in the United States in 2023, by ethnicity. In that year, Asian households spent ****** U.S. dollars on food on average.

Abstract

Multiple factors contributed to high and volatile food prices in Bangladesh during the 2007/2008 period. A “perfect storm” of international, regional, and national conditions delivered a powerful economic shock to the country's food security. Rising global food and fuel prices, regional trade barriers for food exports from South and Southeast Asia, and efforts to ensure macro-economic stability within Bangladesh, all played important roles as the shock of high food prices reverberated throughout the economy.

These important information and knowledge gaps were the major impetus for UNICEF, WFP and the Institute of Public Health Nutrition to jointly undertake a national Household Food Security and Nutrition Assessment of the Situation. The assessment aimed to analyse the current impact of the food price hikes on food security and nutrition and health status through the capture of changes in household food and nutrition security over time in order to suggest response options and recommendations for the short and medium term. More specific objectives pertained to understanding in greater detail, aspects of food security and nutrition, including food markets, household food access and food utilization, nutrition and health, and water and sanitation. The food security component and market analysis were led by WFP and the nutritional component by UNICEF and IPHN.

Geographic coverage

National coverage

Analysis unit

• Household
• Nutrition
• Trader

Universe

The survey covered household heads, women between 15-49 years plus their pre-school children (0-59 months) resident of that household. A household was defined as persons routinely sharing food from the same cooking pot and living in the same compound or physical location or dependent family member living home or abroad.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling size estimates were made to ensure that key indicators would be statistically representative at the national, urban, rural, and divisional levels. Sample sizes were calculated with a 0.05 statistical significance (95% confidence interval) for the key indicators. Based on previous surveys, assumptions were made that each household would have an average of one child aged 6 to 59 months of age, a household size of six members and one mother. Prevalence estimates were based on the BDHS 2007 survey, which estimated acute malnutrition at 16 %, stunting at 50%, and an underweight prevalence of 48%.

A two-stage cluster sampling was used, the sample size was increased by a factor that would allow for the design effect; thus, design effects of 1.5% for acute malnutrition and 2% for stunting and underweight were used, and the 5% desired precision was based on the estimated prevalence of the BDHS 2007.

Sample clusters were used as the first-stage sample, and 361 EAs were selected with probability proportional to the EA size. Some of the selected EAs were of a large size. Therefore, EAs having more than 300 households were further segmented and only one segment was selected for the assessment, with probability proportional to the segment size. Thus, a cluster was either an EA or a segment of an EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

MACROECONOMIC PERFORMANCE ANALYSES: Events and developments pertaining to public food stocks, imports, procurement (both domestic and international), and production were examined. Regional trade, including barriers to trade, price trends, and macroeconomic stability. MARKET ANALYSES: An analysis of market performance was conducted using a combination of primary data from the trader's survey and secondary price data. The analysis of the traders survey data focused on numerous topics including the availability of food on local markets, food flows (including volumes and quantities sold), prices (both actual and expected trends), and perceived reasons for price increases. Other topics analyse were profit margins, access to credit, constraints to trade, and the capacity of food markets to respond to increased demand. HOUSEHOLD FOOD SECURITY: The analysis of the household food security survey data focused on numerous topics including changes in livelihoods and income sources, the effects of inflation on income, changes in wages, salaries and purchasing power, and changes in the “net seller or net buyer status” of agricultural households. The impact of higher food prices on food and non-food expenditures was also examine, as was the impact of other “shocks” and the seasonality and timing of such “shocks”. Extensive analysis was undertaken on household coping strategies and food consumption, using a food consumption score. The score was based on both dietary diversity and the frequency of various foods consumed. NUTRITION and HEALTH STATUS of WOMEN: Mid-upper arm circumferences were taken from the mothers of children aged from birth to 59 months of age or from pregnant women to measure their nutritional status. Information was also collected regarding micronutrient supplementation with Vitamin A post-partum and iron and folate supplementation during pregnancy. Vitamin A capsules and iron and folate tables were shown to the women in order to avoid any misunderstanding. INFANT and YOUNG CHILD FEEDING PRACTICES: Enumerators asked questions regarding infant and young-child feeding practices to all mothers with a child aged from birth to 23 months in the surveyed household. The indicators were related to breastfeeding practices and the introduction of complementary food in time, quantity, and quality (diet diversity). Exclusive breastfeeding, continued breastfeeding at one year and two years, proportion of infants 6 to 8 months of age who received solid, semi-solid, or soft foods, minimum meal frequency, minimum diet diversity and minimum acceptable diet. HEALTH of CHILDREN: Caregivers were asked if the child had been ill during the two weeks prior to the assessment, what illness the child presented with, and if the child had been taken to a health facility. The coverage of Vitamin A supplementation in children from 9 to 59 months was also assessed. HEALTH of GENERAL POPULATION: Households were asked if any household member had been ill in the two weeks prior to the assessment, the main cause of illness, and if treatment had been sought outside the house. MORTALITY: Mortality was assessed using the retrospective household census method. Respondents were asked the following information: number of deaths in the family in the six months prior to the assessment, and how many were children under five years of age; and presumed cause of death. WATER and SANITATION: Access to safe water sources, types of toilet facilities, treatment of drinking water, use of toilet facility and sharing latrine at household level.

All interviews were conducted in Bangla or in a local dialect and data was recorded onto paper questionnaire

All questionnaires and modules are provided as external resources.

Cleaning operations

Following the field data collection period from November 2008 to January 2009, Mitra and Associates carried out data entry in February 2009.

Response rate

In Barisal and Sylhet response rates were 76.5% and 74% respectively.

Number and percentage of persons based on the level of household food insecurity, by age group and sex, for 2004 only.

The table Food-at-Home (FAH) Nutrient Data is part of the dataset FoodAPS National Household Food Acquisition and Purchase Survey, available at https://redivis.com/datasets/bw26-1km85fqpd. It contains 143050 rows across 130 variables.

From 2020 to 2023, the average annual household food expenditure in the United States increased by almost ** percent. The average household expenditure in 2020 was about ***** U.S. dollars and has since grown to nearly ******.

Supplementary files for article "Changes and correlates of household food insecurity during COVID-19: a repeated cross-sectional survey of low-income households in peri-urban Peru"National lockdowns and containment measures to control the spread of COVID-19 led to increased unemployment, lower household incomes and reduced access to affordable and nutritious foods globally. This study aimed to examine changes and correlates of household food insecurity experience and mitigation strategies adopted in peri-urban Peru during the COVID-19 pandemic. Low income households with children age < 2 years in Lima and Huánuco participated in three repeated cross-sectional surveys from 2020 to 2022 (n = 759). We assessed changes in household food insecurity experience using the Food Insecurity Experience Scale. Correlates of moderate-severe food insecurity were analysed using univariate and multivariable linear mixed-effect regressions. We also assessed perceived impacts of the pandemic on livelihoods, coping strategies and receipt of financial or food assistance. Moderate-severe food insecurity was 47.0% in 2020 (survey 1) decreasing to 31.1% in 2022 (survey 3). In adjusted analyses, food insecurity was higher in households with perceived reduced income (β = 12.69 [6.82; 18.56]); in the lower socio-economic status (SES) tertiles (compared to the relatively highest SES tertile; middle tertile (β = 20.91 [9.89; 31.93]), lowest tertile (β = 39.37 [28.35; 50.40]); in households with ≥ 2 children < 5 years (β = 8.78 [2.05; 15.50]); and in Lima (compared to Huánuco; β = 10.47 [1.27; 19.67]). Food insecurity improved more among the relatively lowest SES compared to the relatively highest SES households between survey 1 and 3 (interaction p = 0.007). In conclusion, almost half of households experienced moderate-severe food insecurity mid-pandemic with greater risk observed in the most socio-economically disadvantaged households. The inequality gap in food insecurity associated with SES narrowed over time likely due to household coping strategies and reduced poverty.©The Authors, CC BY 4.0

Sweden - Final consumption expenditure of households: Food and non-alcoholic beverages was 12.80% in December of 2022, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Sweden - Final consumption expenditure of households: Food and non-alcoholic beverages - last updated from the EUROSTAT on July of 2025. Historically, Sweden - Final consumption expenditure of households: Food and non-alcoholic beverages reached a record high of 14.30% in December of 1994 and a record low of 11.80% in December of 2007.

Brazil CES: Household Food Acquisition per Capita: Food Prepared & Mixed Industrial: Food Prepared data was reported at 3.214 kg in 2008. This records an increase from the previous number of 2.365 kg for 2002. Brazil CES: Household Food Acquisition per Capita: Food Prepared & Mixed Industrial: Food Prepared data is updated yearly, averaging 2.790 kg from Jun 2002 (Median) to 2008, with 2 observations. The data reached an all-time high of 3.214 kg in 2008 and a record low of 2.365 kg in 2002. Brazil CES: Household Food Acquisition per Capita: Food Prepared & Mixed Industrial: Food Prepared data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Domestic Trade and Household Survey – Table BR.HC018: Consumer Expenditure Survey: Household Food Acquisition per Capita.

Background: Household food purchasing behavior has gained interest as an intervention to improve nutrition and nutrition-associated outcomes. However, evaluating food expenditures is challenging in epidemiological studies. Assessment methods that are both valid and feasible for use among diverse, low-income populations are needed. We therefore developed a novel simple annotated receipt method to assess household food purchasing. First, we describe and evaluate the extent to which the method captures food purchasing information. We then evaluate within- and between-household variation in weekly food purchasing to determine sample sizes and the number of weeks of data needed to measure household food purchasing with adequate precision.Methods: Four weeks of food purchase receipt data were collected from 260 low-income households in the Minneapolis-St. Paul metropolitan area. The proportion of receipt line items that could not be coded into one of 11 food categories (unidentified) was calculated, and a zero-inflated negative binomial regression was used to evaluate the association between unidentified receipt items and participant characteristics and store type. Within- and between-household coefficients of variation were calculated for total food expenditures and several food categories.Results: A low proportion of receipt line items (1.6%) could not be coded into a food category and the incidence of unidentified items did not appreciably vary by participant characteristics. Weekly expenditures on foods high in added sugar had higher within- and between-household coefficients of variation than weekly fruit and vegetable expenditures. To estimate mean weekly food expenditures within 20% of the group's usual (“true”) expenditures, 72 households were required. Nine weeks of data were required to achieve an r = 0.90 between observed and usual weekly food expenditures.Conclusions: The simple annotated receipt method may be a feasible tool for use in assessing food expenditures of low-income, diverse populations. Within- and between-household coefficients of variation suggest that the number of weeks of data or group sizes required to precisely estimate usual household expenditures is higher for foods high in added sugar compared to fruits and vegetables.