IDEAS home Printed from https://ideas.repec.org/p/sip/dpaper/08-007.html
   My bibliography  Save this paper

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. Creation Date: 2008-09 Revision Date:

Suggested Citation

  • Liran Einav & Ephraim Leibtag & Aviv Nevoy, "undated". "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
    as

    Download full text from publisher

    File URL: http://www-siepr.stanford.edu/repec/sip/08-007.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christian Broda & David E. Weinstein, 2008. "Understanding International Price Differences Using Barcode Data," NBER Working Papers 14017, National Bureau of Economic Research, Inc.
    2. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    3. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    4. 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.
    5. 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.
    6. Mark Aguiar & Erik Hurst, 2007. "Life-Cycle Prices and Production," American Economic Review, American Economic Association, vol. 97(5), pages 1533-1559, December.
    7. Jean-Pierre Dubé, 2004. "Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks," Marketing Science, INFORMS, vol. 23(1), pages 66-81, September.
    8. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 343-366.
    9. Ashenfelter, Orley & Krueger, Alan B, 1994. "Estimates of the Economic Returns to Schooling from a New Sample of Twins," American Economic Review, American Economic Association, vol. 84(5), pages 1157-1173, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Edward C. Jaenicke & Andrea C. Carlson, 2015. "Estimating and Investigating Organic Premiums for Retail‐Level Food Products," Agribusiness, John Wiley & Sons, Ltd., vol. 31(4), pages 453-471, October.
    3. Chad Cotti & Richard A. Dunn & Nathan Tefft, 2013. "Alcohol-Related Motor Vehicle Crash Risk and the Location of Alcohol Purchase," Working Papers 23, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
    4. Madani, Fatima & Seenivasan, Satheesh & Ma, Junzhao, 2021. "Determinants of store patronage: The roles of political ideology, consumer and market characteristics," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    5. Noriko Amano, 2018. "Nutrition Inequality: The Role of Prices, Income, and Preferences," 2018 Meeting Papers 453, Society for Economic Dynamics.
    6. 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.
    7. 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.
    8. 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, University of Chicago Press, vol. 1(3), pages 297-325, Summer.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liran Einav & Ephraim Leibtag & Aviv Nevo, 2010. "Recording discrepancies in Nielsen Homescan data: Are they present and do they matter?," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 207-239, June.
    2. Gutknecht, Daniel, 2011. "Nonclassical Measurement Error in a Nonlinear (Duration) Model," Economic Research Papers 270763, University of Warwick - Department of Economics.
    3. Yingyao Hu & Geert Ridder, 2012. "Estimation of nonlinear models with mismeasured regressors using marginal information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 347-385, April.
    4. An, Yonghong & Hu, Yingyao, 2012. "Well-posedness of measurement error models for self-reported data," Journal of Econometrics, Elsevier, vol. 168(2), pages 259-269.
    5. Mitchell Hoffman & Steven Tadelis, 2021. "People Management Skills, Employee Attrition, and Manager Rewards: An Empirical Analysis," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 243-285.
    6. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    7. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    8. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers 41/12, Institute for Fiscal Studies.
    9. 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.
    10. Aaron Chalfin & Justin McCrary, 2013. "The Effect of Police on Crime: New Evidence from U.S. Cities, 1960-2010," NBER Working Papers 18815, National Bureau of Economic Research, Inc.
    11. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    12. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    13. Allison Lacko & Shu Wen Ng & Barry Popkin, 2020. "Urban vs. Rural Socioeconomic Differences in the Nutritional Quality of Household Packaged Food Purchases by Store Type," IJERPH, MDPI, vol. 17(20), pages 1-17, October.
    14. Richard Volpe & Edward C Jaenicke & Lauren Chenarides, 2018. "Store Formats, Market Structure, and Consumers’ Food Shopping Decisions," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 40(4), pages 672-694, December.
    15. Stella Martin & Kevin Stabenow & Mark Trede, 2024. "Measurement Error in Earnings," CQE Working Papers 10824, Center for Quantitative Economics (CQE), University of Muenster.
    16. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, vol. 175(2), pages 116-131.
    17. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.
    18. Abdurrahman Aydemir & George J. Borjas, 2011. "Attenuation Bias in Measuring the Wage Impact of Immigration," Journal of Labor Economics, University of Chicago Press, vol. 29(1), pages 69-113, January.
    19. Christopher R. Bollinger, 2001. "Response Error and the Union Wage Differential," Southern Economic Journal, John Wiley & Sons, vol. 68(1), pages 60-76, July.
    20. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.

    More about this item

    Keywords

    Measurement Error; Validation Study; Self-Reported Data;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sip:dpaper:08-007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anne Shor (email available below). General contact details of provider: https://edirc.repec.org/data/cestaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.