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Benford's Law and the Detection of Election Fraud

Author

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  • Deckert, Joseph
  • Myagkov, Mikhail
  • Ordeshook, Peter C.

Abstract

The proliferation of elections in even those states that are arguably anything but democratic has given rise to a focused interest on developing methods for detecting fraud in the official statistics of a state's election returns. Among these efforts are those that employ Benford's Law, with the most common application being an attempt to proclaim some election or another fraud free or replete with fraud. This essay, however, argues that, despite its apparent utility in looking at other phenomena, Benford's Law is problematical at best as a forensic tool when applied to elections. Looking at simulations designed to model both fair and fraudulent contests as well as data drawn from elections we know, on the basis of other investigations, were either permeated by fraud or unlikely to have experienced any measurable malfeasance, we find that conformity with and deviations from Benford's Law follow no pattern. It is not simply that the Law occasionally judges a fraudulent election fair or a fair election fraudulent. Its “success rate” either way is essentially equivalent to a toss of a coin, thereby rendering it problematical at best as a forensic tool and wholly misleading at worst.

Suggested Citation

  • 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.
  • Handle: RePEc:cup:polals:v:19:y:2011:i:03:p:245-268_01
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    Cited by:

    1. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    2. Nermina Mumic & Peter Filzmoser, 2021. "A multivariate test for detecting fraud based on Benford’s law, with application to music streaming data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 819-840, September.
    3. Arezzo, Maria Felice & Cerqueti, Roy, 2023. "A Benford’s Law view of inspections’ reasonability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    4. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    5. Katherine M. Anderson & Kevin Dayaratna & Drew Gonshorowski & Steven J. Miller, 2022. "A New Benford Test for Clustered Data with Applications to American Elections," Stats, MDPI, vol. 5(3), pages 1-15, August.
    6. Ananyev, Maxim & Poyker, Michael, 2022. "Do dictators signal strength with electoral fraud?," European Journal of Political Economy, Elsevier, vol. 71(C).
    7. Hygor P M Melo & Nuno A M Araújo & José S Andrade Jr., 2019. "Fundraising and vote distribution: A non-equilibrium statistical approach," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-9, October.
    8. Bernhard Rauch & Max G�ttsche & Stephan Langenegger, 2014. "Detecting Problems in Military Expenditure Data Using Digital Analysis," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(2), pages 97-111, April.
    9. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    10. Gueron, Eduardo & Pellegrini, Jerônimo, 2022. "Application of Benford–Newcomb law with base change to electoral fraud detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Juan Fernández-Gracia & Lucas Lacasa, 2018. "Bipartisanship Breakdown, Functional Networks, and Forensic Analysis in Spanish 2015 and 2016 National Elections," Complexity, Hindawi, vol. 2018, pages 1-23, January.
    12. Cerqueti, Roy & Maggi, Mario, 2021. "Data validity and statistical conformity with Benford’s Law," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    13. Stéphane Blondeau Da Silva, 2022. "An Alternative to the Oversimplifying Benford’s Law in Experimental Fields," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 778-808, November.

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