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From Whence Commeth Data Misreporting? A Survey of Benford’s Law and Digit Analysis in the Time of the COVID-19 Pandemic

Author

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  • Călin Vâlsan

    (William School of Business, Bishop’s University, Sherbrooke, QC J1M 1Z7, Canada)

  • Andreea-Ionela Puiu

    (Department of Applied Economics and Quantitative Analysis, Faculty of Business and Administration, University of Bucharest, 030018 Bucharest, Romania)

  • Elena Druică

    (Department of Applied Economics and Quantitative Analysis, Faculty of Business and Administration, University of Bucharest, 030018 Bucharest, Romania)

Abstract

We survey the literature on the use of Benford’s distribution digit analysis applied to COVID-19 case data reporting. We combine a bibliometric analysis of 32 articles with a survey of their content and findings. In spite of combined efforts from teams of researchers across multiple countries and universities, using large data samples from a multitude of sources, there is no emerging consensus on data misreporting. We believe we are nevertheless able to discern a faint pattern in the segregation of findings. The evidence suggests that studies using very large, aggregate samples and a methodology based on hypothesis testing are marginally more likely to identify significant deviations from Benford’s distribution and to attribute this deviation to data tampering. Our results are far from conclusive and should be taken with a very healthy dose of skepticism. Academics and policymakers alike should remain mindful that the misreporting controversy is still far from being settled.

Suggested Citation

  • Călin Vâlsan & Andreea-Ionela Puiu & Elena Druică, 2024. "From Whence Commeth Data Misreporting? A Survey of Benford’s Law and Digit Analysis in the Time of the COVID-19 Pandemic," Mathematics, MDPI, vol. 12(16), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2579-:d:1460854
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    References listed on IDEAS

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    1. Fewster, R. M., 2009. "A Simple Explanation of Benford's Law," The American Statistician, American Statistical Association, vol. 63(1), pages 26-32.
    2. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    3. 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.
    4. Koch, Philipp, 2021. "Economic complexity and growth: Can value-added exports better explain the link?," Economics Letters, Elsevier, vol. 198(C).
    5. Druică, Elena & Oancea, Bogdan & Vâlsan, Călin, 2018. "Benford's law and the limits of digit analysis," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 75-82.
    6. Azevedo, Caio da Silva & Gonçalves, Rodrigo Franco & Gava, Vagner Luiz & Spinola, Mauro de Mesquita, 2021. "A Benford’s Law based methodology for fraud detection in social welfare programs: Bolsa Familia analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
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