Why did USDA conduct the FoodAPS survey?

USDA’s National Household Food Acquisition and Purchase Survey (FoodAPS) was designed to fill a critical data gap and support research to inform policymaking on key national priorities—such as health and obesity, food insecurity, and food and nutrition assistance policy.

The bulk of economic research in these areas previously had to rely on individual-level dietary recall data, consumer expenditure surveys, or retail-store purchase data. However, these surveys do not provide a complete picture of the way in which food prices, local food environments, and participation in USDA’s food and nutrition assistance programs affect the amount and types of foods that households acquire or the extent to which low-income households rely on alternative sources, such as food pantries or relatives, to supplement their food purchases.

While dietary recall data provide detailed information on what individuals consume and the nutritional quality of those foods, the data provide no information on food prices or food access. Consumer expenditure surveys typically lack detail on the form in which foods are purchased and there is little information on the food items that are purchased from restaurants, fast-food places, and other eating places (food away from home). Retail purchase data, which provide detailed information about food items and their prices, tend to underrepresent lower-income households. These data also lack information on food-away-from-home purchases and food acquired for free.

To provide a complete picture of the foods that consumers buy or acquire and the factors that influence their food choices, FoodAPS collected information about quantities and expenditures for the foods and beverages acquired by all household members over a 1-week period from grocery stores and other food retailers (food at home); from restaurants, fast food places, and other eating places (food away from home); and from other sources such as schools, community food pantries, and gardens.

What benefits has FoodAPS provided beyond its main objectives?

In addition to its unique and comprehensive content, FoodAPS was innovative in its data collection strategies, providing lessons for other surveys:

  • Hand-held scanners were used to collect item-level information, reducing the respondent burden.
  • Supplemental Nutrition Assistance Program (SNAP) administrative records were used to sample SNAP households more efficiently and to improve data quality, reducing costs and the response burden on U.S. households.
  • The field-collected data were enriched by linking product characteristics and the nutrient content of food items, and details about households' food environment and community characteristics.

How does FoodAPS improve USDA and Federal programs?

Agencies are expected to use evidence-based approaches to design, implement, evaluate, and improve Federal programs and policies. FoodAPS provides insights into the challenges facing low-income consumers, including those participating in USDA food and nutrition assistance programs. This information can improve Federal food and nutrition assistance programs and other programs designed to assist low-income people in the United States.

FoodAPS’s detailed information on food assistance program participation; types and amounts of foods purchased; prices paid; the influence of the surrounding environment and of household characteristics; and nutritional knowledge and attitudes allow investigation of critical policy issues such as:

  • How local food prices influence the cost of healthy diets and food assistance benefit adequacy;
  • How local food environments influence the ability to purchase an economical healthful diet;
  • How nutritional knowledge and attitudes influence the nutritional quality of food purchases and acquisitions.

FoodAPS research is also useful to other Federal programs that provide assistance to low-income people in the United States:

  • Information on where low-income U.S. consumers shop for food and how they travel to shop for food can inform Federal efforts to understand the connection between geography and access to healthy, affordable foods.
  • Information on how much low-income U.S. consumers spend on food (along with the additional socioeconomic information collected) can be useful to program and policy officials in the design of other income support programs, such as housing and heating assistance.
  • Information on product characteristics and nutrient content of foods purchased and acquired can be useful for targeting nutrition education efforts to food assistance program participants and efforts to reduce health disparities.

What policy questions can be addressed through FoodAPS research?

FoodAPS was designed to allow researchers to address important policy questions about food insecurity, food and nutrition assistance policy, the food retail environment, and dietary health and obesity. FoodAPS data are being used to address the following policy-related questions asked by ERS researchers or through external research funded by ERS:

  • Are SNAP benefits adequate for purchasing a healthy diet, and how does price variation across geographic areas affect the adequacy of SNAP benefits?
  • How does price variation of specific foods affect the healthfulness of foods acquired by SNAP and other low-income households?
  • How does the issuance of SNAP benefits on a monthly basis affect food spending and shopping among SNAP households?
  • Do SNAP households cut back on food spending and meals as their monthly benefits are fully used?
  • How does access to large grocery stores and other aspects of the food retail environment
    (including prices) affect food security, food spending, and diet quality?
  • What factors affect households’ decisions about where to shop for food and their preferences for food at home versus food away from home?
  • Are WIC (Special Supplemental Nutrition Program for Women, Infants, and Children) households less price sensitive when the households use WIC benefits, as compared with when they use their own money or SNAP benefits?

What is referenced by the term FoodAPS-2? How does the term differ from the terms FoodAPS and FoodAPS-1?

FoodAPS refers to the data collection efforts as a whole. FoodAPS-1 refers to the specific data collection that was fielded in 2012–2013. FoodAPS-2 refers to the second round of data collection in the National Household Food Acquisition and Purchase Survey, which is currently in development. Several enhancements are planned for FoodAPS-2 which are currently being tested.

Is there a need for a second round of FoodAPS (FoodAPS-2)?

Innovations in food production, processing, and marketing (along with consumer concerns about healthy choices, changing product offerings in the food-at-home and food-away-from-home markets, and changing food retail structure and delivery systems) are contributing to an evolving food environment. Additionally, in the last decade, food acquisition patterns may have changed in response to global events. At the same time, top policy concerns still include: Access to affordable healthy foods, childhood obesity, food security, and the effectiveness of Federal food and nutrition assistance programs.

Efforts for developing a second round of FoodAPS data collection are underway. FoodAPS-2 will provide updated, timely, and relevant information on key policy issues (such as inequity and nutritional insecurity) related to the changing food environment and consumer food choices. USDA plans to expand the focus of FoodAPS-2 to cover other population groups of importance to policy and program officials, such as those participating in the WIC program and child nutrition programs. In addition, FoodAPS-2 data will enable researchers to link food acquisitions and food commodities in order to assess the farm production implications of alternative baskets of food choices.

Examples of questions related to the changing food landscape are as follows:

  • Have the 2020–2025 Dietary Guidelines for Americans changed food purchasing patterns? In what ways?
  • How has the changing number and characteristics of SNAP participants affected overall demand for food and the food security of children in the United States?
  • How has the food acquisition and food landscape been impacted by the rise of food delivery, on-line grocery shopping and meal kits?
  • How have factors such as COVID-19 and inflation impacted food acquisition patterns?

What were the challenges in FoodAPS-1 and how will the challenges be addressed in FoodAPS-2?

There were some challenges in conducting FoodAPS-1 in terms of collecting reliable and complete data, as well as in making those data available to researchers on a timely schedule and within budget. The survey contractor conducted a systematic study of the lessons learned from the contractor’s perspective. This report (along with evaluation studies by an independent contractor) has contributed to ERS’s institutional knowledge and have been highly useful in designing FoodAPS-2 and in monitoring survey operations and progress when the survey is in the field. See the Data Quality and Accuracy page for more information.

Scheduling and budget issues are more tractable than issues of data quality. FoodAPS-2 will address the following data quality challenges:

  • Better distinguish between days for which no food was acquired for the respondent and days for which the respondent failed to report acquisitions.
  • Develop methods to more accurately identify all foods acquired by survey respondents.

What additional value will be achieved in conducting FoodAPS-2?

No single survey can address all policy-relevant data needs related to food-choice research. FoodAPS-1 represented a major step in fulfilling many of those needs, but USDA expects to do even better in FoodAPS-2. ERS has and will continue to work (and listen to research peers, stakeholders, and FoodAPS-1 data users) to understand what would make FoodAPS-2 better in promoting food policy research. ERS is planning to implement the following in FoodAPS-2:

  • FoodAPS-1 made a special effort to collect data from a large number of SNAP households and low-income households not participating in SNAP. FoodAPS-2 will also collect data from a large sample of households containing WIC participants.
  • A large number of children are represented in FoodAPS-1, but the sample may not be nationally representative of all U.S. children by age group. Weighted data from FoodAPS-2 will be nationally representative of children within multiple age categories.
  • ERS expects that the income and food data to be collected from FoodAPS-2 will be more accurate and complete than the data from FoodAPS-1. Lessons learned from prior experience should lead to improved results in the future.
  • Finally, most of the FoodAPS-1 data were collected between mid-April and late December of 2012. No data were collected from mid-January through mid-April. Because the food supply is seasonal and food acquisitions reflect this seasonality, ERS plans to expand FoodAPS-2 to a year-long survey.

Data Access

Sample Design, Survey Operations, and Protocols

Data Contents

Data Access

Who owns the FoodAPS data?

The FoodAPS data were collected by the U.S. Department of Agriculture (USDA) under authority of U.S.C, Title 7, Section 2026 (a)(1) and are owned by USDA. Mathematica, a private research firm with experience conducting large-scale surveys, conducted the survey that is under contract to ERS. In the field, the survey was called the National Food Study for simplicity (see Informational brochure). The OMB clearance number for FoodAPS-1 was 0536-0068.

Due to the sensitive nature and confidentiality of the FoodAPS data (the data were collected under the Confidential Information Protection and Statistical Efficiency Act, or CIPSEA), the full survey files may be accessed by external researchers only through a secure data enclave. See Data Access for more information.

Are free, public-use data available?

Yes. Free, public-use files were released in November 2016. The public-use files are stripped of data that pose a risk of disclosing confidential information. However, depending on the research project, the data may be just as viable and informative as the restricted-use data. See the Overview page to view and download the public-use files.

How can I access the restricted-use data?

Instructions for accessing the restricted-use data are available on the Data Access page.

How much does it cost to use the restricted-use data?

Researchers with approved project agreements will receive access to a web-based data enclave. Researchers interested in accessing the restricted-use data should contact David Dudgeon (David.Dudgeon@usda.gov) for information about costs.

How long does it typically take to receive approval and gain access to the restricted data?

It normally takes 1 to 2 weeks to receive approval, and it will take approximately 3 weeks to set up accounts and provide training on data enclave use.

How can I access summary tables and aggregate figures for the FoodAPS-1 data?

Users may access public-use files from the Overview page and create summaries on their own. Figures and tables created by ERS can be viewed on the Interactive Charts and Summary Findings pages.

To access detailed aggregate figures, you will need to gain access to the restricted-use data files (see Data Access).

How can students gain access to the restricted-use FoodAPS data?

Students cannot serve as project directors. To access the restricted-use data set, students need to collaborate with a professor to submit a proposal.

As the cost of access can be prohibitive, especially for students, you may want to consider using the FoodAPS public-use files available on the Overview page.

For restricted-use data, the Project Agreement form states that the Project Leader cannot be a student. However, if a student will be conducting the research, who should be designated as the Project Leader? Will payment be for two accounts, even though only one person—the student—will need access to the data?

If applicable, the dissertation chair or the student’s academic adviser should be designated as the Project Leader. The data enclave account will be set up for the student, and payment will be due for only the one account. However, the professor supervising the work is also required to be CIPSEA-certified and sign a confidentiality agreement.

How can a new researcher be added to a FoodAPS restricted-data agreement?

To add a new researcher to a data agreement, please amend the project agreement and contact ERS (see Data Access page for more details).

What do I need to do if the scope of my restricted-data project has changed?

If the research team decides to make any changes to its approved Project Agreement, the original Project Agreement will need to be amended (see the Data Access page for more information).

What information can be removed from the data enclave?

Before any type of output—including tables, charts, graphs, slide presentations, draft reports, and final reports—can be downloaded from the secure data enclave, the output will be reviewed by ERS for disclosure risk and adherence to outputs specified in the Project Agreement. This review requirement includes preliminary or interim results meant to be shared outside of the data enclave with other CIPSEA-trained and approved colleagues on the research team.

If it is determined from a review that disclosure risk is present, the research team and the ERS reviewer may work together to reach an agreement on output content that would be approved for download from the data enclave. If there is a disagreement between the research team and the ERS reviewer as to whether any proposed output poses a disclosure risk, the research team may request a review by the ERS Confidentiality Officer. The Confidentiality Officer’s decision regarding disclosure risk will be final.

Neither subsets of any FoodAPS data file nor sections of codebooks containing distributions of variable responses may be exported from the data enclave. Standard output from statistical packages often contains information that is not planned for publication (for example, minimum and maximum values of a variable) that could increase disclosure risk, and requests to export such output from the data enclave are usually rejected. Researchers may copy and paste relevant portions of a statistical output into a text file for export or have their statistical package output Excel files for subsequent review and export.

Does ERS need to review a summary version of a previously-reviewed-and-approved report that uses restricted data for publication in a journal?

Yes, any alterations to a previously-approved report must be reviewed again before publication.

Does a new project agreement need to be submitted for an additional study if the researcher(s) already has/have access to the restricted-use data?

If the new project is a separate study, a new project agreement will need to be submitted. If you are making an adjustment to an already-approved study OR if you are conducting new research in a related area, you may submit a project agreement amendment (see Data Access for detailed instructions).

Is there a procedure for merging an outside dataset with the data in the data enclave?

Researchers can upload datasets to their workspaces, so researchers are able to combine external or outside datasets to datasets within the enclave. Researchers must submit a ticket to have the dataset added to their workspace. Once the dataset is added, all researchers working on the project team (per the project agreement) will have access to the uploaded external dataset.

What is included in the IRI data, and how can a researcher gain access?

There are multiple data sets that comprise the Information Resources, Inc. (IRI) data. At a broad level, the IRI Consumer Network data includes information about where households shopped, when they shopped, and what they purchased for food at home. Another data set (InfoScan) contains weekly-product Universal Product Code (UPC) level sales for individual store locations, although some sales information is at a more aggregate level. More information about scanner data available at ERS can be found on the Using Proprietary Data web page. The available product dictionaries include product descriptions with information such as brand and flavor for items with Universal Product Codes (UPC) and nutrition facts and health-related claims (such as lower cholesterol) for UPC items. For more information, see:

Understanding IRI Household-Based and Store-Based Scanner Data

Use of proprietary data is governed by contractual agreements between USDA and data owners. In order to gain access to the proprietary IRI data, it is necessary for a researcher to collaborate with an ERS researcher on a USDA-sponsored project. USDA-sponsored projects include USDA grants, non-USDA funded collaborative agreements, ERS cooperative agreements, and direct collaboration with ERS researchers.

Usually, external collaborations are formed independently between ERS and external researchers, based on similar research interests. If you have someone in mind you would like to work with, please contact the person directly.

Would a grantee have access to the weekly point-of-sale data (including UPC and product description) from FoodAPS retailers through IRI?

If a USDA-sponsored research project requires access to a specific proprietary data source, the institution with which the research collaborator is affiliated, must enter into a Third-Party Agreement (TPA) with the data provider.

Can proprietary IRI data be linked to household food purchases?

Point-of-sale data from IRI can be matched to FoodAPS retailers and places that FoodAPS households visited, but the IRI data cannot be matched to specific purchasers or FoodAPS households. In addition, the IRI point-of-sale data have poor coverage of stores in some of the survey areas.

Sample Design, Survey Operations, and Protocols

What is the survey’s sample design?

FoodAPS-1 is a nationally representative, stratified sample of 4,826 households surveyed between April 2012 and January 2013. Stratification was based on participation in the Supplemental Nutrition Assistance Program (SNAP) and poverty level. The four strata are as follows:

  1. Households receiving SNAP benefits;
  2. Non-SNAP households with incomes less than the poverty level guideline;
  3. Non-SNAP households with incomes at or above the poverty guideline and less than 185 percent of that level; and
  4. Non-SNAP households with incomes equal to or greater than 185 percent of the poverty guideline.

Prior to sampling, 948 Primary Sampling Units (PSUs) within the continental United States were defined as counties or groups of contiguous counties. In forming PSUs, metropolitan statistical area (MSA) boundaries were used (some MSAs were split into multiple PSUs, but in no case was part of one MSA joined to part of another MSA to form a PSU). One large PSU was sampled with certainty, and 49 non-certainty PSUs were selected using probability proportional to size (PPS) with implicit stratification based on the metropolitan status of the PSU and its Food and Nutrition Service (FNS) region. Eight secondary sampling units (SSUs) were formed within each of the 50 sampled PSUs; each SSU is a census block group (CBG), or a group of contiguous block groups if the CBG did not meet minimum size requirements.

SSUs were selected using PPS sampling as well. Within sampled SSUs, addresses for screening were selected from two primary sampling frames: (1) A list of addresses of all SNAP participants active in February 2012; and (2) a list of addresses in the Address Based Sampling (ABS) list, obtained from the United States Postal Service Delivery Sequence File, that were not on the SNAP list of addresses. In States for which SNAP administrative data could not be obtained, the ABS list was used as a single sample frame. Finally, field listing of addresses was done in a few rural areas, and the field listing was used as a single address frame.

Residential units were sampled in two phases. FoodAPS used this two-phase sampling approach for conducting the screener interview, as a way to reduce the potential of non-response bias. In the first phase, attempts were made to screen all released sample addresses. If no contact was made after a pre-specified number of attempts at different hours of the day and week, the address was moved to a sample frame for Phase 2. Addresses in this frame were sampled for additional contact efforts toward the end of the survey period.

For more information, see the User's Guide.

How does a person account for the sample design when using the data?

Each household and individual observation has a final sampling weight that makes the sample nationally representative of all non-institutionalized households in the contiguous United States.

The household weights were constructed in three stages. In the first stage, the weights accounted for the differences in the probability of selection across households and then were adjusted to account for unit nonresponse. The second stage of the weighting process involved post-stratifying the weights to replicate external estimates of the number of households in the United States and the distribution by specific demographic and economic characteristics using a raking process (iterative proportional fitting). The final stage of the weighting process involved trimming the weights to reduce the variability of the weights and the overall design effect.

Each household is given a final sampling weight (given by the variable HHWGT in the Household data set). The weights were constructed for the household, but can be applied to individual-level analyses as well. Software such as SUDAAN, STATA and SAS can be used to estimate sampling errors by the Taylor series method (linearization)—using the HHWGT along with the stratum variable, TSSTRATA, and the PSU variable, TSPSU. The variables necessary for Taylor series variance estimation are attached to both the faps_household and faps_individual data file.

Section 6.1 of the User's Guide outlines the construction of the sampling weights and how users can apply these weights to their analyses. Appendix C includes detailed examples of variance estimation.

Who is the primary respondent, and how was the respondent selected?

The primary respondent (PR) is the main food shopper or meal planner in the household. The PR was identified during the screener interview and was asked to complete two in-person interviews, three telephone interviews, and a 7-day diary over the course of the survey.

Over what time period were FoodAPS-1 data collected?

The first initial interviews were conducted in mid-April, 2012, and the last initial interviews were conducted in mid-January, 2013. The distribution of initial interviews by month of data collection can be found on page 53 of the Household Codebook.

How were height and weight measured/recorded?

The primary respondent was asked to provide the height and weight of each individual in the household in either English or metric units. If respondents were reluctant to tell the interviewer weights, the respondents were allowed to enter this information directly using a laptop. All heights and weights have been converted to English units of inches or pounds, respectively. Extreme values of height and weight (based on age-specific height and weight distributions) have been set to missing.

Are the names of places from which a household purchased food ever omitted for confidentiality reasons?

In the restricted-use data, names of places from which households purchased food are available for analysis, but these names cannot be used in any presentations or papers (based on the data). Place names are excluded in the public-use data files, but a measure of the place type—for example, supermarket, convenience store, coffee shop, or buffet restaurant—is available.

When working in the secure environment, users can gain access to the geographic coordinates of each geo-coded food store or public eating place that were reported by a household in the survey. However, not all PRs provided enough information to identify the specific location for their primary and alternate stores or the stores visited during the week. For example, 6.6 percent of primary stores (317 stores) could not be geo-coded because a unique and valid address could not be determined.

A project must have an analytic need for these retailer geo-codes in order to gain access to the retailers. See the Data Access page to learn how to gain access to the FoodAPS data.

Can researchers find out which counties or States are included in the FoodAPS data?

To reduce the risk of disclosure of confidential FoodAPS information about survey respondents, ERS does not include in the Public-Use Files any specific information about the PSUs or SSUs in which the survey was fielded. A list of States included in the survey is available for projects needing this information (for example, to construct an external file of county-level data to upload to the data enclave and subsequent merging with the FoodAPS data). Researchers needing county or census block group information for an approved or proposed project may request access to the Geography Component data, within the restricted-use data enclave.

Note that the FoodAPS data were collected to be nationally representative of the continental United States and were not designed for State- or county-level analyses.

Data Contents

Where are the definitions/distinctions between terms such as household, family, gueststype1, guesttype2, food at home (FAH), food away from home (FAFH), and other terms?

For in-depth definitions and distinctions, please refer to the FoodAPS User's Guide, Household Codebook, and Individual Codebook.

Is there a way to distinguish between urban/rural and metro/nonmetro; how many observations are in each area?

There are two variables to identify metro/nonmetro and rural/urban status. In the household data, the variable NONMETRO identifies if a household resides in a census core-based statistical area (CBSA), and the variable RURAL identifies if a household resides in a rural census tract. The NONMETRO indicator and the RURAL indicator do not necessarily coincide. The NONMETRO indicator is based on whether or not the county in which the household lives is within a CBSA, while the RURAL indicator is based on the census tract in which the household lives. An unweighted crosstab of the variables is displayed in the table below. For more information, see the Household Codebook.

Table of NONMETRO variable by RURAL variable
NON­METRO RURAL (FARA: rural tract)
(Household does not reside in a CBSA) 0 1 Total
0 3,408 992 4,400
1 107 319 426
Total 3,515 1,311 4,826

Is there a question on respondent shopping frequency?

No, the survey did not ask respondents about the usual number of times they went shopping per week.

How can individuals with disabilities be identified in the study?

The primary respondent was asked if they have difficulty using the phone because of a disability (PRDISPHONE), if they have difficulty writing because of disability (PRDISWRITING), and if they have difficulty with memory/concentration/making decisions. The survey did not ask questions about specific disabilities, outside of the questions mentioned above. See the Household Codebook for more information.

Depending on specific research needs, disability income may be identified by determining if the individual or household reported income from disability payments (INCRETDISIND, INCAMOUNT4). Additionally, if an individual was not working, a follow-up question was asked to determine why. Researchers may use the variable REASONNOWORK to determine disability status for those who are unemployed. Furthermore, the SCHLEVEL variable may be used to identify children with a disability who are not attending school. See the Individual Codebook for more information on disability income.

How was SNAP participation confirmed?

To confirm respondents’ reports of SNAP participation, records of households that had given consent for data matching were matched against two sets of SNAP administrative data: State-level enrollment files for March through November 2012 and transaction records from the program’s electronic benefit transfer (EBT) ALERT database. ALERT data contain one record for each swipe of an EBT card and include information on: State, store ID, date/time, EBT account number, EBT card number, dollar amount of purchase, and balance remaining in the account. Although SNAP issuance dates—the dates at which SNAP benefits are transferred to recipients—are not in the ALERT database, they often may be closely approximated by seeing when the remaining balance increases between two consecutive transactions.

For more information, see the Household Codebook.

How was SNAP eligibility of each household determined?

SNAP eligibility was estimated four times, using different assumptions about income and composition of the SNAP unit. See the Household Codebook for more information.

Does one of the four alternatives for SNAP eligibility (from Mathematica) closely match how SNAP eligibility would be defined with respect to an individual child?

The alternatives include different assumptions for the estimates. Users can review the Household Codebook for more information. Section of the Household Codebook details the performance of these estimates from a number of different metrics:

Among the 1,581 FoodAPS households with current SNAP participants (SNAPNOWHH=1), the estimations identify about 79 to 87 percent of households as having at least one SNAP-eligible unit (column b of table 7). This means that about 13 to 21 percent of households with SNAP participants are estimated to not be eligible, depending on the treatment of income and identification of SNAP units within the household. Run 3, which allows for multiple SNAP units per household and does adjust reported net earnings, performs the best in minimizing such "false negatives".

Section 2.4.10 of the Household Codebook also includes information on known data anomalies that could be useful:

As noted earlier, the estimations treated foster children differently in the four runs. In runs 1 and 2, the 17 foster children in the sample were included as part of the SNAP unit, even though SNAP regulations exclude foster children from being part of a unit. In runs 3 and 4, foster children were not assigned to a SNAP unit.

It appears that there are households in the data set that are receiving SNAP but that are also above the SNAP Federal Poverty Level requirement. Is this correct?

Yes, it is possible to see households with incomes above the gross income limit for SNAP eligibility. Some reasons for this include the following:

  1. Categorical eligibility eliminates the gross income test.
  2. Some members of the household with income may not be part of the SNAP unit.
  3. There could be multiple SNAP units within the same household—the household unit in the FoodAPS does not necessarily correspond to the unit that the Food and Nutrition Service (FNS) uses to determine eligibility for SNAP. Specifically, there may be multiple SNAP units within a FoodAPS household.
  4. SNAP eligibility was determined at an earlier point in time, and there have been variations in monthly household income over time.

Can a reimbursable school meal be identified?

No—school meals cannot be identified as reimbursable. There are cases of children in the same household reporting different costs of school meals, and even some children with different costs for school breakfasts and lunches. No attempt to edit this information has been made. It is possible that children who attend different schools have access to different levels of subsidies. For example, one child may attend a school that offers universal free breakfast and lunch, while another child in the same household attends a different school that does not offer free meals to all students and the student is only eligible for reduced-price or full-priced meals. Moreover, ERS does not know how eligibility for school meals was determined.

What are the 10 food security questions in the Household data based on? How is the composite score calculated?

The 10 food security questions are based on USDA’s 30-day Adult Food Security Scale. The ADLTFSCAT variable is calculated according to the methodology described in USDA’s Revised Guide to Measuring Household Food Security. Imputations for missing responses are based on a household’s responses to other valid items (page 36-7 of guide). Section 2.3.7 of the Household Codebook has further details about the food security questions and variables.

How were driving and walking distances and time determined when there are multiple routes to get to a location?

Distance measures were calculated once all geocoding of places was completed. Straight-line distances from each household to each place were calculated by an SAS function, while walking and driving distances and times were obtained from the Google Maps Application Programming Interface (API). When multiple routes were possible, the default provided by Google was selected.

Is there a variable indicating whether a FAFH acquisition was purchased from a fast-food restaurant?

No, there is no fast-food restaurant indicator. The PLACETYPE variable may be a rough indicator, but “Mexican Restaurant” could include everything from a fast-food taco chain to a formal, sit-down Mexican restaurant.

Why does the sum of item-level expenditures (either FAH or FAFH) not match event-level expenditures?

The sum of item expenditures and the total expenditures in the event data may not match for several reasons. The TOTALPAID in the event data is the total amount of the purchase reported for the event and may include nonfood items. TOTALPAID also includes food (and possibly non-food) taxes and, if applicable, container deposit fees. If there are any imputed item prices for the event, the imprecision of the imputation process may cause the sum of item prices to not equal TOTALPAID. Finally, there are some items for which missing price data were not imputed, and it is possible that some purchased food items were not reported at all. For more information, see Section 2.4.6. of the FAH Events Codebook and the Supplementary Documentation Food-Away-From-Home.

Regarding item level-data, is there one record per unique item or multiple records for two or more of the same thing?

Each record in the item-level files does not necessarily represent a unique product. Because the receipt sometimes served as a guide to entering items into the database, the purchase of multiple units of the same item (such as two boxes of a specific cereal) may appear in the data two different ways. If the barcodes were scanned or the receipt recorded the purchase on two separate lines (one for each box), the FAH item data will include two line records, one for each box of cereal. However, if the barcodes were not scanned and the receipt recorded the two boxes on only one line, the data will include one record for the two boxes, but the quantity will be marked as "2". FAFH items are recorded as reported by respondents and, just as with FAH items, each record does not necessarily represent a unique food item. Food items can be linked to the event record using EVENTID, which is unique across all FAH and FAFH events and all households.

In a hypothetical scenario, if a family purchased four burgers for dinner at a fast-food restaurant, and the mother (also the primary respondent) purchased all of the food, would she write down that she purchased four burgers for dinner? Or would her husband and two teenage children who were also tracking their food acquisitions write that they each acquired one "FREE" burger for dinner from the fast-food restaurant?

Respondents were asked to record each event once, so the primary respondent should have recorded the food event for all household attendees, while the other household members would not. However, it is possible that multiple household members would have recorded the same event and entered only their portion. The contractor (Mathematica) tried to remove such duplications, but there is always a chance that they missed some.

Is it possible to determine if someone purchased a bundled meal such as a kid's meal, as well as the individual components?

Yes, one can identify kid combo meals and the respective items obtained in that combo. However, the ability to link the items in a combo (and to see all the items in a combo) depends on how the household reported the food information and (if limited detail was reported) on whether the meal/combo could be assumed with near certainty. For example, if the household reported a "kid’s chicken nugget meal" at a fast-food restaurant, but not the separate items, the contractor filled in the items that come standard with such a meal. If the household reported a kid's meal from a different kind of restaurant, such as a dine-in only restaurant, and did not report the items, then ERS would not be able to fill in such data. Additionally, if multiple combos were purchased, the contractor may not have been able to link the individual items to specific combo meals.

For the most part, the various caveats or limitations mentioned here are a small share of the data.

Can barcodes in FoodAPS data be matched with barcodes in other data sets?

European Article Numbers (EAN) typically match with barcodes in FoodAPS, but the EAN variable is 13 digits, not 12. If FoodAPS barcodes are 12 digits, try dropping the first digit from EAN. Users should be able to match about 60 percent of FAH products with IRI point-of-sale (POS) products.

For perishables, users may be able to trim a Price Look-Up (PLU) code out of the BARCODE and BARCODE_ORIGINAL variables but may have to match by category/product/type.

What are the ERS FoodAPS-1 Market Basket Groups? How can FoodAPS-1 data be matched and what match rate can be expected?

The ERS FoodAPS-1 Market Basket Groups describes retail food price data that enable FoodAPS data users to compare prices (faced by) and options available to households across different store types and different geographical locations. Yes, FoodAPS places can be linked to the ERS FoodAPS-1 Market Basket Groups data, using the linker file PlaceID_TempERSID.xlsx (available to researchers with access to the restricted-use data enclave). This file matches the PlaceID (from faps_places) to an ERS-created ID that is included in the basket prices data set. This match allows a user to link IRI store-week price to places visited by FoodAPS households. Note that basket price data match only to about 30 percent of non-restaurant food retailers that FoodAPS households visited (i.e., non-restaurant places in faps_places) because the IRI data used to construct basket prices are not inclusive of all stores. More information about this project and data can be found on the FoodAPS Geography Component page.

Which of the basket cost measures should be used?

There are two basket cost measures, and it is up to the user to determine which is best for their research. For more information, see the Construction of Weekly Store-Level Food Basket Costs Documentation.

Are there measures of access to stores that are not SNAP authorized?

There are several summary measures of access to all stores aggregated to the census block group and tract level, as well as at the county level. The Retail Environment Study Codebook details these measures, which are based on a combined list of stores from the TDLinx and the SNAP-authorized store list, STARS. Additionally, users may obtain access to TDLinx store locations if they obtain a Third-Party Agreement with ERS and Nielsen to use the TDLinx data for a USDA-sponsored project.

What is the significance of FLAG variables?

The Office of Management and Budget (OMB) requires any data modifications be flagged. OMB’s Standards and Guidelines for Statistical Surveys specifies that:

Agencies must add codes to collected data to identify aspects of data quality from the collection (e.g., missing data) in order to allow users to appropriately analyze the data. Codes that are added to convert information collected as text (into a form that permits immediate analysis) must use standardized codes, when available, to enhance comparability.

Please review the guidelines for additional information.

Information about the construction and significance of specific variables can be found in the FoodAPS Codebooks and User Guide. For more information about variables related to frequently asked topics below, see the suggested Codebook locations (all Codebooks and the FoodAPS User Guide are available for download on the Overview page)

  • Individual respondent-related topics:
    • Body Mass Index (BMI) (Individual Codebook pages 5, 32-33)
    • Disability Status (Individual Codebook pages 6, 36, also see Household Codebook)
    • Income (Individual Codebook pages 6-7, 34-38)
  • Food item-related topics:
    • Food Categories
      • For Food-at-home (FAH Nutrient Codebook pages 9-19)
      • For Food-away-from-home (FAFH Nutrient Codebook pages 9-17)
    • Item Descriptions
      • Food-at-home (FAH Item Codebook pages 3-4, 8, 32)
      • Food-away-from-home (FAFH Items Codebook pages 5-12)
    • Item Weight
      • Food-at-home (FAH Items Codebook pages 3-7, 13-17)
      • Food-away-from-home (FAH Items Codebook pages 10-11, 21)
  • Food Access-related topics:
    • General Access to Food Stores (Access Codebook)
    • Driving Distance to Primary FAH Retailer (Household Codebook pages 41-42, 82, 96)
    • Walking Distances to Primary FAH Retailer (Household Codebook pages 41-42, 83, 96-97)
  • Event-related topics:
    • Geography (User Guide pages 2, 16-17, 31)
    • Payment
      • FAH Event Payment (FAH Event Codebook pages 4, 7-9, 23-30)
      • FAFH Event Payment (FAFH Event Codebook pages 5, 8-10, 25-35)
    • Store Categories & Name
      • FAH Event (FAH Event Codebook pages 4-6, 16-22)
      • FAFH Event (FAFH Event Codebook pages 4-6, 17-18)
    • School Meals (FAFH Events Codebook pages 9-10; FAFH Supplemental Codebook 4-10)
  • Variable suppression in the PUFs:
    • Variables available in the Restricted-Use Files (User Guide, pages 16-18, 35-48)