Using Scanner Data
This page provides information on the following topics:
To address policy and programmatic issues of interest to USDA, ERS acquires proprietary household and retail scanner data from Information Resources, Inc. (IRI), a market research firm. These data include the Consumer Network household-based scanner data and InfoScan retail point-of-sale scanner data.
IRI Consumer Network household scanner data
The Consumer Network survey is a nationally representative household panel survey that contains self-reported information on household food purchases. Over 120,000 households use a handheld scanner or mobile application to record what food products they purchased and where they shopped on an ongoing basis. Households record all food-at-home (grocery store) purchases, including random weight fresh foods. The purchase data links to detailed product characteristics and nutrition information. The data include household demographic information, and a subset of households also report health information and prescription drug purchases. The data are available for the years 2008-18.
IRI Infoscan retail data
The InfoScan data are retailer point-of-sale records of consumer food purchases. Retailers collect weekly revenues and quantities of each UPC (Universal Product Code) sold by store; the data set includes detailed information for both packaged food and random weight fresh food. The retail sales data are available for individual store locations or market areas, covering a variety of outlet types including grocery, club, convenience, dollar, drug, and mass merchandiser stores. The purchase data are linked to detailed product characteristics and nutrition data for food products. The InfoScan data is a big data set with billions of transaction records, covering a large portion of retail food-at-home sales in the United States. The data are available for the years 2008-18.
For more information, see:Understanding IRI Household-Based and Store-Based Scanner Data
Access to proprietary IRI data is limited to researchers collaborating on USDA-sponsored projects. USDA-sponsored projects include USDA grants, USDA cooperative agreements, and/or direct collaboration with USDA researchers on an issue of interest to the Department of Agriculture. For a sponsored research project, the institution affiliated with the research collaborator must enter into a Third Party Agreement (TPA) with the data provider. The language of the TPA is specified by the Vendor and must be signed as is. (See the TPA Template for language).
Steps to obtain a TPA:
- The ERS point of contact works with collaborators to complete the USDA TPA Input Sheet. The form is returned to the ERS Data Steward to review and forward the form to IRI.
- IRI will populate the actual TPA based on the information provided in the Input Sheet and send the populated TPA in pdf form to ERS for signatures.
- A senior official needs to sign the TPA for the institution. The confidentiality clause holds the collaborator/institution responsible for the use of the licensed materials by any third party that they share the data with. ERS and collaborators shall maintain a record of each recipient requiring access to the data.
ERS has established an efficient and secure data enclave at NORC that meets the Federal Information Security Management Act (FISMA), where access to external users is provided only through secure channels from NORC.
The annual cost to obtain a lead account to access the data enclave is $4350 for one principal investigator (PI) or one researcher. PIs can add an unlimited number of researchers; however, for each additional researcher with an account to access the data enclave, an additional cost of $2,250 per year will be incurred. Users will cover the cost of accessing the data with their own funds.
Several reports provide detailed information about proprietary retail scanner data, including methodology, characteristics, and statistical properties of the data:Understanding IRI Household-Based and Store-Based Scanner Data
A report by Mary K. Muth, Megan Sweitzer, Derick Brown, Kristen Capogrossi, Shawn A. Karns, David Levin, Abigail Okrent, Peter Siegel, and Chen Zhen examines commercial scanner data from market research firm IRI for use in food economics research. The report examines the methodology, characteristics, and statistical properties of the data sets. It also provides an introduction to the data for new users and important considerations for advanced users (April 2016).Linking USDA Nutrition Databases to IRI Household-Based and Store-Based Scanner Data
In this report by Andrea Carlson, Elina T. Page, Thea Palmer Zimmerman, Carina E. Tornow, and Sigurd Hermansen, the researchers created a purchase-to-plate “crosswalk,” linking data between USDA data and household and retail scanner data to measure the overall healthfulness of Americans' food-at-home (FAH) purchases. Substantial improvements in the healthfulness of Americans' FAH purchases would be needed to comply with Federal dietary guidance (March 2019).
Other technical reports provide independent assessments of data quality.Examining Food Store Scanner Data: A Comparison of the IRI InfoScan Data with Other Data Sets, 2008–2012
A report by David Levin, Danton Noriega, Chris Dicken, Abigail Okrent, Matt Harding, and Michael Lovenheim looks at proprietary retail scanner data (InfoScan) that are used to examine food policy questions. To determine how representative the data are, this report compares the number of stores and sales revenue reported in the InfoScan data with the same information from other datasets (October 2018).Food-at-Home Expenditures: Comparing Commercial Household Scanner Data From IRI and Government Survey Data
Megan Sweitzer, Derick Brown, Shawn A. Karns, Mary K. Muth, Peter Siegel, and Chen Zhen's report compares proprietary household scanner data to nationally representative Government survey data and finds that reported household food-at-home expenditures in commercial scanner data were lower than in two Government surveys. The report details the comparison methodology and describes implications for using the commercial data in food economics research (September 2017).