A Consumer Food Data System for 2030 and Beyond (2020) / Chapter Skim
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Appendix C: Summary, Third Meeting, September 21, 2018
Appendix C: Summary, Third Meeting, September 21, 2018
Pages 175-208
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From page 175... ...
, Medicaid, and K–12 education; the limits of existing survey data; use of retail panel loyalty card data and Rhode Island state administrative records (housed in a secure facility at Brown University) to analyze how SNAP benefits are spent; evidence needed to design a "smarter SNAP"; and food consumption data needs for obesity and other health research.
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Mary Muth of RTI discussed types, sources, and considerations in using store scanner data, household scanner data, and nutrition data from labels for food policy research. Helen Jensen of Iowa State described the use of proprietary (scanner)
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From page 177... ...
Panel member Dianne Schanzenbach stated her concern that critical administrative data products produced in conjunction with other federal agencies, specifically with the Census Bureau, will be impacted if ERS staff relocate. Variyam and Denbaly did not speculate on what those impacts might be and stressed that the roadmap they seek from the panel will be key for the future of the Consumer Food Data Program.
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From page 178... ...
Heflin noted that the limitation of administrative data in relation to survey data decline when data are linked across programs. Administrative data from SNAP only include participants, but linking the types of datasets mentioned above to SNAP administrative records allows for more coverage of the total population, undermining a key limitation of most administrative data.
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From page 179... ...
All of this results in high costs to researchers to use state administrative data. The costs may be summarized this way: • time to get access (which can take months to years)
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Justine Hastings of Brown University and Research Improving People's Lives (RIPL) talked about her work combining SNAP data with grocery store scanner data in Rhode Island.
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. Courtemarche looked at the less-studied phenomenon of measurement error in administrative data using data from FoodAPS.
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Data characteristics provided by state administrative data include • state caseload information from March to November 2012 (not quite a match to survey dates of April 2012 to January 2013) ; • variation in quality of data across states (e.g., monthly versus non monthly data, disbursement date availability, period of caseload data)
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During open discussion, panel member Michael Link asked the three presenters to consider the quality of matching administrative records. He pointed out that there is reasonable agreement on what quality survey methodology is, but the linkages as described by the three panelists are
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C.3. DATA INTEGRATION AND LINKAGES FOR POLICY RESEARCH USING ADMINISTRATIVE DATA Panel member Amy O'Hara began the session by describing the international set of best practices for the handling of sensitive data, especially the use of administrative data or health data.
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a Rachel Shattuck of the Census Bureau described work being done at the bureau in estimating SNAP and WIC eligibility and participation. Congress authorizes the bureau to collect administrative records to improve survey operations.
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From page 186... ...
The Census Bureau acquires administrative records via legal agreements with states and the data are encrypted when transmitted. When the files arrive at the Census Bureau they are placed on a secure, isolated server where a very small number of staff who have authorization to see these data create matching identifiers and remove all personally identifiable information (PII)
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While administrative records are not representative of the U.S. population, they can have information on hard to count populations, such as low-income children who do not appear in the decennial census.
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Below the federal level there are implications for management and integration of regional data sources, especially the costs incurred in linking datasets. These costs borne by agencies and researchers can be substantial, so they must be included in budgets Eltinge concluded that in the "old world" when sample surveys were a dominant mode of data collection, there was a high degree of control over nearly everything that took place in data collection, analysis, and inference, but this is not the case when linking multiple data sources that include administrative records.
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The motivation for the linkage lies in the strategic goals of both NCHS and HUD, results of the Foundations for Evidence-Based Policymaking Act, and several directives on the use of administrative records issued by the U.S. Office of Management and Budget (OMB)
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From page 190... ...
Many reports have also been produced by HUD that describe adults and children who receive HUD benefits.2 Access to the linked data is similar to accessing Census Bureau data described by Rachel Shattuck. NCHS has research data centers in Atlanta and Washington, DC, and they are affiliated with FSRDCs around the country as well.
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From page 191... ...
C.4. ADDITIONAL NONGOVERNMENTAL SOURCES FOR FILLING DATA GAPS IN ERS'S CONSUMER FOOD DATA SYSTEM PROGRAM Rob Santos, The Urban Institute, spoke about projects and reports coming out of Feeding America's (FA)
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From page 192... ...
The new process Santos outlined may be beneficial for the panel in thinking through ways of getting the types of data that ERS needs to make decisions without necessarily making it a point estimate with a margin of error. Alessandro Bonanno, Colorado State University, provided some thoughts and insights improving geospatial information in ERS's food data system.
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It has the largest amount of information to help answer questions about food insecurity, SNAP participation, and how SNAP benefits are used along with information on store location and distance traveled to stores. FoodAPS includes the geocoded location where the food acquisition event took place, including whether it was a SNAP authorized store, and the geocodes (latitude and longitude)
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C.5. USING PROPRIETARY DATA FOR FOOD POLICY RESEARCH Mary Muth, Research Triangle Institute, described proprietary data: the types, sources, and considerations in using store scanner data, household scanner data, and nutrition data from labels for food policy research.
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• IRI provides household data in its Consumer Network and store data, in InfoScan. It collects data in 10 other countries.
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Muth said that she recently learned that Gladstone, Label Insight, and Nielsen are offering these data products to retailers to help them optimize location of products on shelves. Analyzing Scanner Data Muth described her own experiences in assessing and analyzing scanner data.
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compared hedonic models using the nutrition data from the IRI food label database versus Gladstone label data. Muth said that in collaboration with ERS, she analyzed the differences in reported expenditures between commercial household scanner data and Consumer Expenditure Survey matter in a food demand system.
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From page 198... ...
households, lower income households, Black and Hispanic households, and households with children are less likely to meet static panel criteria • prices are typically not exact prices paid by the household o prices are assigned using store scanner data based on where household shopped • data are weighted based on demographics, not shipment or expen diture totals For Store Data, researchers should remember that • Not all stores are represented in the data o Data collection process is not designed to capture sales at smaller, independent stores (data may be collected but not avail able for research) • Private-label product data (about 18 percent of all food)
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From page 199... ...
However, loyalty card data should not be considered a replacement for panel data because a household may shop at multiple outlets. Muth noted that as part of her research she and her colleagues have identified about 150 peer-reviewed publications on food policy research projects using some form of scanner or label data.
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The three main goals of the WIC analysis were, first, to study household purchase behavior; second, to determine prices and price indices for WIC food items; and third, to evaluate the cost of alternative WIC food packages, assess package design, and conduct a regulatory impact analysis. Because of the flexibility of using the Homescan scanner data, Jensen and colleagues were able to evaluate various food package contents meeting food item specifications (types of milk, types of yogurt, etc.)
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Jensen said that one of the advantages of the scanner data for the purpose of her study was the ability to construct detailed prices for food items with the characteristics that the WIC program was dictating for those items. This was based on searching food label databases and identifying keywords associated with approved products.
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From page 202... ...
Census Bureau, described the Census Bureau's work on using web scraping and machine learning to discover, collect, and process data from the web, with a goal to improve economic statistics. First, Hogue provided the big data context and some web-scraping background.
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It is available on the Census Bureau's GitHub account.4 Census has used SABLE to seek out and collect information from state Comprehensive Annual Financial Reports (CAFR) and other online publications that contain tax revenue data.
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Census would like to develop the SEC filing product, discussed above. After that, next steps will be guided by a new working group to address policy issues regarding web scraping and web crawling.
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- Colleen Heflin, Syracuse University • Use of retail panel loyalty card data and Rhode Island state administrative records (housed in a secure facility at Brown University) to analyze how SNAP benefits are spent.
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- Chuck Courtemanche, Georgia State University • Open discussion 11:15 Data Integration and linkages for policy research, use of ad ministrative data There is high value to ERS's Consumer Food Data System of linkages to external data sets -- e.g., to NHANES, Nielsen datasets, IRI datasets, SNAP administrative data, CPS, SIPP, ACS, BRFSS, CEX, Nationwide Food Consumption Sur vey, PSID, state and local-level datasets with information on low-income households, etc. During this session, we discuss practices being developed by the statistical agencies for com bining data sources.
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Member of Feeding R America Technical Advisory Group • Improving geospatial information in ERS's food data sys tem (for example, for assessing the role of accessibility of food outlets role in SNAP participation and effectiveness) - Alessandro Bonanno, Colorado State University • Open discussion 2:30 Using proprietary data for food policy research • Types, sources, and considerations in using store scanner data, household scanner data, and nutrition data from labels for food policy research.
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- Carma Hogue, U.S. Census Bureau • Open discussion 4:00 Adjourn
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