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Understanding Differences in Self-Reported Expenditures between Household Scanner Data and Diary Survey Data: A Comparison of Homescan and Consumer Expenditure Survey

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Author Info

  • Chen Zhen
  • Justin L. Taylor
  • Mary K. Muth
  • Ephraim Leibtag

Abstract

Household scanner data contain rich information on household demographics and transactions in actual markets over a long time period. To more fully understand the characteristics of these data, we conducted an analysis to determine whether household expenditures in the Nielsen Homescan panel are similar to the Bureau of Labor Statistic's Consumer Expenditure Diary Survey. We found that many differences in reported expenditures across the two datasets can be explained by such household demographics as female head, income, and household size, for example. The largest degrees of discrepancies across datasets occur for food categories containing more random-weight foods without universal product codes. Copyright 2009 Agricultural and Applied Economics Association

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Bibliographic Info

Article provided by Agricultural and Applied Economics Association in its journal Review of Agricultural Economics.

Volume (Year): 31 (2009)
Issue (Month): 3 (09)
Pages: 470-492

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Handle: RePEc:oup:revage:v:31:y:2009:i:3:p:470-492

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Cited by:
  1. Andrew Leicester, 2013. "The Potential use of In-home Scanner Technology for Budget Surveys," NBER Working Papers 19536, National Bureau of Economic Research, Inc.
  2. Andrew Leicester, 2012. "How might in-home scanner technology be used in budget surveys?," IFS Working Papers W12/01, Institute for Fiscal Studies.
  3. Todd, Jessica E. & Leibtag, Ephraim S. & Penberthy, Corttney, 2011. "Geographic Differences in the Relative Price of Healthy Foods," Economic Information Bulletin 117976, United States Department of Agriculture, Economic Research Service.
  4. Davis, Christopher G. & Dong, Diansheng & Blayney, Donald P. & Yen, Steven T. & Stillman, Richard, 2012. "U.S. Fluid Milk Demand: A Disaggregated Approach," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association (IAMA), vol. 15(1).
  5. Leffler, Kristyn K. & Carpio, Carlos E. & Boonsaeng, Tullaya, 2012. "Temporal Aggregation and Treatment of Zero Dependent Variables in the Estimation of Food Demand using Cross-Sectional Data," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124913, Agricultural and Applied Economics Association.
  6. Buzby, Jean C. & Hyman, Jeffrey, 2012. "Total and per capita value of food loss in the United States," Food Policy, Elsevier, vol. 37(5), pages 561-570.
  7. Andrew Leicester, 2014. "The Potential Use of In-Home Scanner Technology for Budget Surveys," NBER Chapters, in: Improving the Measurement of Consumer Expenditures National Bureau of Economic Research, Inc.
  8. Rhodes, Charles, 2012. "An Empirical Analysis of Socio-Demographic Stratification in Sweetened Carbonated Soft-Drink Purchasing," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124678, Agricultural and Applied Economics Association.

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