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Detecting Election Fraud from Irregularities in Vote-Share Distributions

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  • Rozenas, Arturas

Abstract

I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.

Suggested Citation

  • Rozenas, Arturas, 2017. "Detecting Election Fraud from Irregularities in Vote-Share Distributions," Political Analysis, Cambridge University Press, vol. 25(1), pages 41-56, January.
  • Handle: RePEc:cup:polals:v:25:y:2017:i:01:p:41-56_00
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    Cited by:

    1. Koenig, Christoph, 2019. "Patronage and Election Fraud: Insights from Russia’s Governors 2000–2012," CAGE Online Working Paper Series 433, Competitive Advantage in the Global Economy (CAGE).
    2. Ananyev, Maxim & Poyker, Michael, 2022. "Do dictators signal strength with electoral fraud?," European Journal of Political Economy, Elsevier, vol. 71(C).
    3. Peter Klimek & Raúl Jiménez & Manuel Hidalgo & Abraham Hinteregger & Stefan Thurner, 2018. "Forensic analysis of Turkish elections in 2017–2018," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    4. Christoph Koenig, 2024. "With a Little Help From the Crowd: Estimating Election Fraud with Forensic Methods," CEIS Research Paper 584, Tor Vergata University, CEIS, revised 28 Oct 2024.

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