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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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Copyright
Corresponding author
* Email: ar199@nyu.edu
Footnotes
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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
Footnotes
References
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Alter Adam L., and Hershfield Hal E.. 2014. People search for meaning when they approach a new decade in chronological age. Proceedings of the National Academy of Sciences 111(48):1706617070.
Beber Bernd, and Scacco Alexandra. 2012. What the numbers say: A digit-based test for election fraud. Political Analysis 20(2):211234.
Berman Daniel, and Rintoul Thomas. 2009. Preliminary analysis of the voting figures in Iran’s 2009 presidential preliminary analysis of the voting figures in Iran’s 2009 presidential election . London: Chatham House.
Cantú Francisco, and Saiegh Sebastián M.. 2011. Fraudulent democracy? An analysis of Argentina’s infamous decade using Supervised Machine Learning. Political Analysis 19(4):409433.
Collier Paul. 2009. Wars, guns, and votes. democracy in dangerous places . New York: Harper.
Cordero Maria M. Febres, and Márquez Bernardo. 2006. A statistical approach to assess referendum results: The Venezuelan Recall Referendum 2004. International Statistical Review 74(3):379389.
Daxecker Ursula E. 2012. The cost of exposing cheating International election monitoring, fraud, and post-election violence in Africa. Journal of Peace Research 49(4):503516.
Deckert Joseph, Myagkov Mikhail, and Ordeshook Peter C.. 2011. Benford’s Law and the detection of election fraud. Political Analysis 19(3):245268.
Gehlbach Scott. 2012. Electoral Fraud in Russia: Report from the Russian Blogosphere. The Monkey Cage, Jan 27 .
Gelman Andrew, Carlin John B., Stern Hal B., and Rubin Donald B.. 2003. Bayesian data analysis . 2 ed. Chapman and Hall.
Hyde Susan, and Marinov Nikolay. 2012. Which Elections Can Be Lost? Political Analysis 2(20):191210.
Janvresse Élise, and De la Rue Thierry et al. . 2004. From uniform distributions to Benford’s law. Journal of Applied Probability 41(4):12031210.
Kalinin Kirill, and Mebane Walter R.. 2012. Understanding electoral frauds through evolution of Russian federalism: The emergence of ‘Signaling Loyalty. Available at SSRN 1668154.
Klimek Peter, Yegorov Yuri, Hanel Rudolf, and Thurner Stefan. 2012. Statistical detection of systematic election irregularities. Proceedings of the National Academy of Sciences 109(41):1646916473.
Levin Ines, Pomares Julia, and Alvarez R. Michael. 2016. Using machine learning algorithms to detect election fraud. In Computation social science , ed. Alvarez Michael R. Michael. Cambridge University Press.
Magaloni Beatriz. 2006. Voting for autocracy: Hegemonic party survival and its demise in Mexico . New York: Cambridge University Press.
Monica Martinez-Bravo. 2014. The role of local officials in new democracies: Evidence from Indonesia. The American Economic Review 104(4):12441287.
Mebane Walter R. 2008. Election forensics: The second-digit Benford’s law test and recent American Presidential elections. In Election fraud: Detecting and deterring electoral manipulation , ed. Alvarez Michael R. Michael, Thad E. Hall, and Susan D Hyde. Brookings Press.
Mebane Walter R. 2009. Note on the presidential election in Iran, June 2009 . Michigan: University of Michigan.
Mebane Walter R. 2011. Comment on “Benford’s law and the detection of election fraud”. Political Analysis 19(3):269272.
Mebane Walter R.2013. Using vote count’s digits to diagnose strategies and frauds: Russia. University of Michigan, Mimeo.
Mebane Walter R., and Sekhon Jasjeet S.. 2004. Robust estimation and outlier detection for overdispersed multinomial models of count data. American Journal of Political Science 48(2):392411.
Medzihorsky Juraj. 2015. Election fraud: A latent class framework for digit-based tests. Political Analysis 23(4):506517.
Meyersson Erik. 2014. Trouble in Turkey’s elections . Stockholm: SITE.
Montgomery Jacob M., Olivella Santiago, Potter Joshua D., and Crisp Brian F.. 2015. An informed Forensics approach to detecting vote irregularities. Political Analysis 23(4):488505.
Myagkov Mikhail, Ordeshook Peter C., and Shakin Dimitri. 2009. The forensics of election fraud: Russia and Ukraine . Cambridge University Press.
Pope Devin, and Simonsohn Uri. 2011. Round numbers as goals evidence from baseball, SAT takers, and the lab. Psychological science 22(1):7179.
R Core Team. 2016. R: A language and environment for statistical computing. https://www.R-project.org/.
Rozenas Arturas. 2016a. Replication data for: Detecting election fraud from irregularities in vote-share distributions. , Harvard Dataverse, V1.
Rozenas Arturas. 2016b. spikes: Detecting election fraud from irregularities in vote-share distributions. R package version 1.0, https://CRAN.R-project.org/package=spikes.
Rundlett Ashlea, and Svolik Milan W.. 2016. Deliver the vote! micromotives and macrobehavior in electoral fraud. American Political Science Review 110(1):180197.
Simpser Alberto. 2013. Why governments and parties manipulate elections: theory, practice, and implications . Cambridge University Press.
Tam Cho Wendy K., and Gaines Brian J.. 2007. Breaking the (Benford) law: Statistical fraud detection in campaign finance. The American Statistician 61(3):218223.
Trifonov Vladimir, Pasqualucci Laura, Dalla-Favera Riccardo, and Rabadan Raul. 2011. Fractal-like distributions over the rational numbers in high-throughput biological and clinical data. Scientific reports 1(191):17.
Tucker Joshua. 2007. Enough! electoral fraud, collective action problems, and post-communist Colored Revolutions. Perspectives on Politics 5(3):535551.
Weisbrot Mark, Rosnick David, and Tucker Todd. 2004. Black swans, conspiracy theories, and the quixotic search for fraud: A look at Hausmann and Rigobon’s analysis of Venezuela’s referendum vote . Washington, DC: CEPR.
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Political Analysis
  • ISSN: 1047-1987
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