A consumer food data system for 2030 and beyond

Government policy influences all parts of the food marketing chain, including farms, food manufacturers, retailers, restaurants, and diverse nutrition assistance programs. At every stage, sound policy-making depends on high-quality data.

The National Academies Press this month published a new consensus report from the Center for National Statistics (CNSTAT), entitled A Consumer Food Data System for 2030 and Beyond, with recommendations to help guide the federal government in consumer food data collection and dissemination. The report panel was chaired by UC Davis professor Marianne Bitler (I was a panel member).

As the report summary explains, trade-offs are essential, because it is challenging for any consumer food data system (CFDS) to achieve all of the characteristics that we would wish:

• Comprehensiveness. To monitor levels and trends in food behaviors and related outcomes, and to identify the effects of public programs and policies on those behaviors, a comprehensive data system requires a variety of sources spanning multiple topics.
• Representativeness. Data on food behaviors and outcomes is most useful if it is representative of the U.S. population, both nationally and sub-nationally.
• Timeliness. To have maximum program and policy impact, the system collects data at regular intervals, repeats over time at an appropriate frequency, and releases data without undue delay.
• Openness. Because data programs are maintained with taxpayer funds, data should be accessible to the public and to the research community. Security and privacy concerns must be addressed before making de-identified data available.
• Flexibility. A flexible data system recognizes that food and consumer data will be used for some research applications that were planned in advance, as well as for applications generated by a broad, entrepreneurial, and inventive community of research users studying unanticipated changes in policy, food retail markets, or consumer preferences.
• Accuracy. Accurate measurement and reporting are the foundation of effective evidence-based policy making, so a desirable data system is one that seeks continuous quality improvement. Given increased reliance on data produced by state and local governments and commercial entities for purposes other than scientific study, continual assessment and improvement of the quality of these sources will be a central part of the CFDS.
• Suitability for causal analysis. While some policy questions can be answered with descriptive information, others require cause-and-effect inference. With this in mind, data design efforts should include (i) the collection and sharing of policy variables for use in implementing quasi-experimental designs, (ii) the use of administrative data for potential program evaluations with random-assignment research designs, and (iii) the creation of longitudinal survey and administrative data (either repeated cross-sections or panel data) for use in statistical analyses that offer causal insight.
• Fiscal responsibility. The CFDS should maximize the research value of federal dollars invested in the data system (including staff time) through its combined impact in descriptive information, monitoring functions, and estimation of causal effects.

Looming behind the report is the panel's awareness of the increasing difficulties of collecting traditional survey data, due to rising costs and greater difficulty maintaining a high response rate. The data systems of the future will combine survey data with administrative data and proprietary data (such as retailer scanner data) in new ways.