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Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic

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  • Montag, Josef

Abstract

This paper investigates the presence of odometer fraud in the used-car market in the Czech Republic using a unique dataset of 250,000 car-sale ads. Alternative identification techniques are also discussed. However, selection into the market as well as the practice of rounding odometer readings---possibly strategic yet innocent---render the standard statistical tests unusable. A modification of the last-digit test, which was previously used to detect fraud in election and accounting data, is therefore developed and employed. The results suggest that suspicious patterns are more prevalent in the segment of cars imported from abroad. I also show that this methodology can be used at the firm-level, which may be of interest to authorities and market participants.

Suggested Citation

  • Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:65182
    as

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    File URL: https://mpra.ub.uni-muenchen.de/65182/1/MPRA_paper_65182.pdf
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    References listed on IDEAS

    as
    1. Bernhard Rauch & Max Göttsche & Gernot Brähler & Stefan Engel, 2011. "Fact and Fiction in EU‐Governmental Economic Data," German Economic Review, Verein für Socialpolitik, vol. 12(3), pages 243-255, August.
    2. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    3. Beber, Bernd & Scacco, Alexandra, 2012. "What the Numbers Say: A Digit-Based Test for Election Fraud," Political Analysis, Cambridge University Press, vol. 20(2), pages 211-234, April.
    4. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(3), pages 488-500.
    5. Andreas Diekmann, 2007. "Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 321-329.
    6. Brian A. Jacob & Steven D. Levitt, 2003. "Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 843-877.
    7. Nye John & Moul Charles, 2007. "The Political Economy of Numbers: On the Application of Benford's Law to International Macroeconomic Statistics," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-14, July.
    8. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    9. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Used car market; odometer fraud; digit tests.;
    All these keywords.

    JEL classification:

    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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