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Not-So-Classical Measurement Errors: A Validation Study of Homescan

Author

Listed:
  • Liran Einav

    () (Stanford University)

  • Ephraim Leibtag

    (Economic Research Service, Department of Agriculture, Government of the United States)

  • Aviv Nevoy

Abstract

We report results from a validation study of Nielsen Homescan data. We use data from a large grocery chain to match thousands of individual transactions that were recorded by both the retailer (at the store) and the Nielsen Homescan panelist (athome). First, we report how often shopping trips are not reported, and how often trip information, product information, price, and quantity are reported with error. We focus on recording errors in prices, which are more prevalent, and show that they can be classified to two categories, one due to standard recording errors, the other due to how Nielsen constructs the price data. We then show how the validation data can be used to correct the impact of recording errors on estimates obtained from Nielsen Homescan data. We use a simple application to illustrate the impact of recording errors as well as the ability to correct for these errors. The application suggests that while recording errors are present, and potentially impact results, corrections, like the one we employ, can be adopted by users of Homescan data to investigate the robustness of their results.

Suggested Citation

  • Liran Einav & Ephraim Leibtag & Aviv Nevoy, 2008. "Not-So-Classical Measurement Errors: A Validation Study of Homescan," Discussion Papers 08-007, Stanford Institute for Economic Policy Research.
  • Handle: RePEc:sip:dpaper:08-007
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    File URL: http://www-siepr.stanford.edu/repec/sip/08-007.pdf
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    References listed on IDEAS

    as
    1. Einav, Liran & Leibtag, Ephraim S. & Nevo, Aviv, 2008. "On the Accuracy of Nielsen Homescan Data," Economic Research Report 56490, United States Department of Agriculture, Economic Research Service.
    2. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 343-366.
    3. Christian Broda & David E. Weinstein, 2008. "Understanding International Price Differences Using Barcode Data," NBER Working Papers 14017, National Bureau of Economic Research, Inc.
    4. Jerry Hausman & Ephraim Leibtag, 2007. "Consumer benefits from increased competition in shopping outlets: Measuring the effect of Wal-Mart," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(7), pages 1157-1177.
    5. Mark Aguiar & Erik Hurst, 2007. "Life-Cycle Prices and Production," American Economic Review, American Economic Association, vol. 97(5), pages 1533-1559, December.
    6. Jean-Pierre Dubé, 2004. "Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks," Marketing Science, INFORMS, vol. 23(1), pages 66-81, September.
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    Cited by:

    1. Chad Cotti & Richard A. Dunn & Chad Cotti, 2015. "The Great Recession and Consumer Demand for Alcohol: A Dynamic Panel-Data Analysis of US Households," American Journal of Health Economics, MIT Press, vol. 1(3), pages 297-325, Summer.
    2. Cotti, Chad & Dunn, Richard A. & Tefft, Nathan, 2014. "Alcohol-impaired motor vehicle crash risk and the location of alcohol purchase," Social Science & Medicine, Elsevier, vol. 108(C), pages 201-209.
    3. Ferrier, Peyton & Zhen, Chen, 2014. "Explaining the Shift from Preserved to Fresh Vegetable Consumption," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170555, Agricultural and Applied Economics Association.
    4. Rhodes, Charles, 2010. "Demographic Variability In U.S. Consumer Responsiveness To Carbonated Soft-Drink Marketing Practices," 115th Joint EAAE/AAEA Seminar, September 15-17, 2010, Freising-Weihenstephan, Germany 116419, European Association of Agricultural Economists;Agricultural and Applied Economics Association.

    More about this item

    Keywords

    Measurement Error; Validation Study; Self-Reported Data;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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