Detecting Election Fraud from Irregularities in Vote-Share Distributions

Political Analysis, 25(1), p. 41-56

43 Pages Posted: 18 Mar 2017 Last revised: 5 Jun 2017

See all articles by Arturas Rozenas

Arturas Rozenas

New York University (NYU) - Wilf Family Department of Politics

Date Written: February 22, 2017

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.

Keywords: fraud, manipulation, Bayesian density estimation

Suggested Citation

Rozenas, Arturas, Detecting Election Fraud from Irregularities in Vote-Share Distributions (February 22, 2017). Political Analysis, 25(1), p. 41-56. Available at SSRN: https://ssrn.com/abstract=2934485 or http://dx.doi.org/10.2139/ssrn.2934485

Arturas Rozenas (Contact Author)

New York University (NYU) - Wilf Family Department of Politics ( email )

19 West 4
New York, NY 10012
United States

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