A Consumer Food Data System for 2030 and Beyond (2020) / Chapter Skim
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1 Introduction
1 Introduction
Pages 15-36
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Congress; program administrators and managers; other federal agencies; state and local government officials; and organizations, including farm and industry groups and those studying food assistance. ERS research provides context for and informs the decisions that affect the agricultural sector, which in turn benefits everyone with efficient stewardship of our agricultural resources and the economic prosperity of the sector." See https://www.ers.
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Investments in the data used to inform those policy choices can yield large returns in program effectiveness. In an array of health-related policy areas, ERS research on agriculture, food, food assistance programs and the food environment, and nutrition programs advances the public good.
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They also include administrative data residing within USDA programs and proprietary commercial data, as well as products created by blending across all these sources. The desirable characteristics and qualities of a CFDS, and recommendations for achieving them, are examined in detail in Chapter 4.
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In their efforts to understand the population's health, their focus is not on the food environment or the food assistance landscape either. Hence, the responsibility for measuring and monitoring the food supply system and its impacts, including the ways it interacts with food assistance programs, falls to many different agencies.
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A consumer's (geographical) food access reflects the proximity and types of restaurants and stores present in his or her local environment, and where retail food stores are concerned an important feature is whether a store is authorized to participate in one of the USDA food assistance programs.
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• How do food assistance programs affect these choices? Concerning ease of access, USDA has provided mapping tools in applications such as the Healthy Food Finance Initiative and the Food Access Research Database.
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are largely focused on survey collections, enabling linkages between survey and nonsurvey data sources, establishing and maintaining searchable policy databases, and monitoring the quality and coverage of proprietary data sources. Supporting Program Policy and Administration for the Food and Nutrition Safety Net Food assistance programs serve a large share of the population.
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Since most food assistance programs are funded and administered through the USDA's Food and Nutrition Services (FNS) , it is natural that measuring and monitoring the impact of those programs should fall to a separate group within USDA, such as FED.
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Examples of proprietary data that have been used to study how people make choices about food acquisition are scanner data collected by IRI Worldwide and Nielsen on a panel of households or from a set of retailers. Analyses employing integrated or linked survey, commercial, and administrative approaches can take advantage of the wide-ranging outcome variables in surveys and the large sample sizes and geographical disaggregation of administrative data with high-frequency data from commercial sources.
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expanded tracking of state and local eligibility and implementation across all USDA food assistance programs, as well as tracking of stores where benefits are redeemed. Supporting Research on the Healthfulness of U.S.
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of CDC, which uses a dietary recall survey to collect information about food intake; the Consumer Expenditure Survey, sponsored by BLS, which collects data on expenditures for food at home and food away from home using two 1-week diary surveys; the food expenditure questions in PSID, funded by ERS; and the above-noted FoodAPS, sponsored by ERS, which collects household food expense data by asking selected households to scan their food receipts, as well through food diaries and telephone interviews. Other nonsurvey data sources that are increasingly being used to measure food acquisition include proprietary data, such as the Consumer Network by IRI Worldwide and HomeScans by Nielsen, which provide con 6 The same parent department, USDA, comprises both ERS and the Center for Nutrition P olicy and Promotion, which is the department's lead agency on the Dietary Guidelines for Americans.
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with commercial data, including acquiring it, assessing its quality, and using it to answer questions about food acquisition. For the most part, these data fall into one of three categories: proprietary retail scanner data,9 household panel and scanner data, and food store and restaurant name and location data.10 Retail scanner data are collected in stores during customer checkout, while household scanner data are collected using hand-held scanners provided to participating households.
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This joint project is a long-term effort to acquire state-level administrative data for USDA nutrition assistance programs -- especially SNAP and WIC -- and to make those data available for linkage to other administrative files and surveys. In this work, FNS contacts state SNAP and WIC offices to encourage them to share their USDA administrative data for the project, and the Census Bureau negotiates a data-sharing agreement that provides mutual benefits for all parties.13 For example, one anticipated research application of the program is the ability to evaluate SNAP and WIC participation and nonparticipation by county within a state, as well as by various demographic and other variables captured in the Census Bureau's American Community Survey.
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. While the acquisition data that FoodAPS provides are rich, they limit a researcher's ability to study some causal questions because the data derive from food acquisition at a single point in time.
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Beyond this arrangement, the agency is working with the Census Bureau, FNS, and state partners to make confidential administrative data and linked data available through the national network of Federal Statistical Research Data Centers. Data from modules and supplements cosponsored by ERS are available through the access procedures provided by the agency that collects the data.
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In particular, the panel that produced the 2005 report (NRC, 2005) supported collaborative interagency activities to create linkages between surveys, administrative data, and other data; to develop food-related modules to be used on relevant federal surveys; and to evaluate use of proprietary data (collected, owned, and made available by commercial firms)
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Data are needed to produce descriptive content, to serve monitoring functions, and to support causal and other kinds of policy research spanning topic areas ranging from the food environment, to informing program policy, to understanding the healthfulness of people's diets. The present report first describes the current ERS data infrastructure -- hich includes survey, administrative, commercial, w and combined data elements -- and then proposes data solutions to better answer questions that, as of now, cannot be satisfactorily addressed.
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, supplemental modules to existing federal surveys, administrative data residing in USDA and other agencies, commercial data sources, and the capacity to perform linkages across databases. The value of the CFDS program is realized from supporting research that informs high-priority current -- and anticipated future -- policy questions, some of which are state and locally focused (e.g., research on school lunch menus and nutrition, and on food choices of households given the distribution of retail outlets)
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The value (and limits) of linking SNAP or other food assistance ad ministrative data not to surveys but to other types of administrative data to provide a "universe" of people affected by the programs.
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Additionally, the panel heard several presentations about data needs from those in the policy environment studying nutrition or food assistance programs and those running the programs at a more local level. A topic of particular interest was the U.S.
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Implications for the survey component of the CFDS are discussed alongside opportunities and challenges associated with expanding the use of administrative records and commercial data sources. Finally, the chapter discusses the issues of data access and confidentiality constraints as they relate to a statistical system that is increasingly based on multiple sources of data, acknowledging that overcoming these constraints will require investment.
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