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Difficulties Detecting Fraud? The Use of Benford’s Law on Regression Tables

Author

Listed:
  • Bauer Johannes

    (Ludwig-Maximilians-Universität München, Institut für Soziologie Konradstr. 6, 80539 München, Germany)

  • Groß Jochen

    (Senior Quantitative Consultant, Roland Berger Strategy Consultants Holding GmbH, Mies-van-der-Rohe-Str. 6, 80807 München, Germany)

Abstract

The occurrence of scientific fraud damages the credibility of science. An instrument to discover deceit was proposed with Benford’s law, a distribution which describes the probability of significant digits in many empirical observations. If Benford-distributed digits are expected and empirical observations deviate from this law, the difference yields evidence for fraud.This article analyses the practicability and capability of the digit distribution to investigate scientific counterfeit. In our context, capability means that little data is required to discover forgery. Furthermore, we present a Benford-based method which is more effective in detecting deceit and can also be extended to several other fields of digit analysis. We also restrict this article to the research area of non-standardized regressions. The results reproduce and extend the finding that non-standardized regression coefficients follow Benford’s law. Moreover, the data show that investigating regressions from different subjects demands more observations and hence is less effective than investigating regressions from single persons. Consequently, the digit distribution can discover indications for fraud, but only if the percentage of forgery in the data is large. With a decreasing proportion of fabricated values, the number of required cases to detect a significant difference between real and fraudulent regressions rises. Under the condition that only few scientists forge results, the investigation method becomes ineffective and inapplicable.

Suggested Citation

  • Bauer Johannes & Groß Jochen, 2011. "Difficulties Detecting Fraud? The Use of Benford’s Law on Regression Tables," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 733-748, October.
    • Handle: RePEc:jns:jbstat:v:231:y:2011:i:5-6:p:733-748
    DOI: 10.1515/jbnst-2011-5-611
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    References listed on IDEAS

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    1. 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.
    2. repec:bla:germec:v:10:y:2009:i::p:339-351 is not listed on IDEAS
    3. Andreas Diekmann, 2005. "Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data," Others 0507001, University Library of Munich, Germany.
    4. David Giles, 2007. "Benford's law and naturally occurring prices in certain ebaY auctions," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 157-161.
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    Cited by:

    1. Walter R. Schumm & Duane W. Crawford & Lorenza Lockett & Asma bin Ateeq & Abdullah AlRashed, 2023. "Can Retracted Social Science Articles Be Distinguished from Non-Retracted Articles by Some of the Same Authors, Using Benford’s Law or Other Statistical Methods?," Publications, MDPI, vol. 11(1), pages 1-13, March.
    2. Florian El Mouaaouy & Jan Riepe, 2018. "Benford and the Internal Capital Market: A Useful Indicator of Managerial Engagement," German Economic Review, Verein für Socialpolitik, vol. 19(3), pages 309-329, August.
    3. Teddy Lazebnik & Dan Gorlitsky, 2023. "Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?," Data, MDPI, vol. 8(11), pages 1-11, October.
    4. Horton, Joanne & Krishna Kumar, Dhanya & Wood, Anthony, 2020. "Detecting academic fraud using Benford law: The case of Professor James Hunton," Research Policy, Elsevier, vol. 49(8).

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