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Identifying odometer fraud in used car market data

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  • Montag, Josef

Abstract

This paper investigates the presence of odometer fraud in the used car market using a large dataset of car sale advertisements from the Czech Republic. The strategic aspects of sale decisions and the practice of rounding odometer readings, however, render the standard statistical tests for fabricated data invalid. I therefore develop and employ a modification of the last-digit test, which has been used to detect fraud in election data. Simulations using the data from advertisements and travel survey data from the United States support the validity of this test under alternative distributional assumptions. The results suggest that suspicious patterns are more prevalent in the segment of cars imported from abroad. I also show that this test can be used at the seller level, which may be of interest to authorities and market participants.

Suggested Citation

  • Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    • Handle: RePEc:eee:trapol:v:60:y:2017:i:c:p:10-23
    DOI: 10.1016/j.tranpol.2017.07.018
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    References listed on IDEAS

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    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. Deckert, Joseph & Myagkov, Mikhail & Ordeshook, Peter C., 2011. "Benford's Law and the Detection of Election Fraud," Political Analysis, Cambridge University Press, vol. 19(3), pages 245-268, July.
    3. Montag, Josef, 2015. "The simple economics of motor vehicle pollution: A case for fuel tax," Energy Policy, Elsevier, vol. 85(C), pages 138-149.
    4. 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.
    5. 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.
    6. Mebane, Walter R., 2011. "Comment on “Benford's Law and the Detection of Election Fraudâ€," Political Analysis, Cambridge University Press, vol. 19(3), pages 269-272, July.
    7. Fewster, R. M., 2009. "A Simple Explanation of Benford's Law," The American Statistician, American Statistical Association, vol. 63(1), pages 26-32.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
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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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