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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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  • 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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  • Chen Zhen & Justin L. Taylor & Mary K. Muth & Ephraim Leibtag, 2009. "Understanding Differences in Self-Reported Expenditures between Household Scanner Data and Diary Survey Data: A Comparison of Homescan and Consumer Expenditure Survey," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(3), pages 470-492, September.
  • Handle: RePEc:oup:revage:v:31:y:2009:i:3:p:470-492
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    Cited by:

    1. Andrew Leicester, 2012. "How might in-home scanner technology be used in budget surveys?," IFS Working Papers W12/01, Institute for Fiscal Studies.
    2. 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.
    3. 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 (IFAMA), vol. 15(1).
    4. Zhen, Chen & Muth, Mary K. & Karns, Shawn & Brown, Derick & Siegel, Peter, 2015. "Do Differences in Reported Expenditures between Commercial Household-based Scanner Data and Government Surveys Matter in a Structural Model of Food Demand?," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202702, European Association of Agricultural Economists.
    5. Taylor, Mykel & Klaiber, H. Allen & Kuchler, Fred, 2016. "Changes in U.S. consumer response to food safety recalls in the shadow of a BSE scare," Food Policy, Elsevier, vol. 62(C), pages 56-64.
    6. Carlson, Andrea & Jaenicke, Edward, 2016. "Changes in Retail Organic Price Premiums from 2004 to 2010," Economic Research Report 242448, United States Department of Agriculture, Economic Research Service.
    7. Andrew Leicester, 2014. "The Potential Use of In-Home Scanner Technology for Budget Surveys," NBER Chapters,in: Improving the Measurement of Consumer Expenditures, pages 441-491 National Bureau of Economic Research, Inc.
    8. Dharmasena, Senarath & Davis, George & Capps, Oral, Jr., 2014. "Partial versus General Equilibrium Calorie and Revenue Effects Associated with a Sugar-Sweetened Beverage Tax," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(2), August.
    9. Boonsaeng, Tullaya & Carpio, Carlos E., 2015. "Data Collection Period and Food Demand System Estimation using Cross Sectional Data," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205576, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    10. Tullaya, Boonsaeng & Carlos, Carpio, 2014. "A Comparison of Food Demand Estimation from Homescan and Consumer Expenditure Survey Data," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170543, Agricultural and Applied Economics Association.
    11. 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.
    12. 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.
    13. 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.
    14. repec:bla:econom:v:84:y:2017:i:333:p:34-53 is not listed on IDEAS
    15. Rachel Griffith & Martin O'Connell & Kate Smith, 2017. "The Importance of Product Reformulation Versus Consumer Choice in Improving Diet Quality," Economica, London School of Economics and Political Science, vol. 84(333), pages 34-53, January.

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