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Detecting Election Fraud from Irregularities in Vote-Share Distributions

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.

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* Email: ar199@nyu.edu
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Author’s note: I thank Walter Mebane, Denis Stukal, Milan Svolik, participants of the 2015 Political Methodology Annual Meeting at the University of Rochester, the reviewers and the editor for comments and suggestions. The method developed in this paper can be implemented in R software (R Core Team 2016) package spikes (Rozenas 2016b). The replication materials for this article are available online (Rozenas 2016a).
Contributing Editor: R. Michael Alvarez
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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Rozenas supplementary material 1

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