Skip to main content
×
×
Home

Detecting Election Fraud from Irregularities in Vote-Share Distributions

  • Arturas Rozenas
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.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Detecting Election Fraud from Irregularities in Vote-Share Distributions
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Detecting Election Fraud from Irregularities in Vote-Share Distributions
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Detecting Election Fraud from Irregularities in Vote-Share Distributions
      Available formats
      ×
Copyright
Corresponding author
* Email: ar199@nyu.edu
Footnotes
Hide All
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
Hide All
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.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
MathJax
Type Description Title
UNKNOWN
Supplementary materials

Rozenas supplementary material
Rozenas supplementary material 1

 Unknown (1.5 MB)
1.5 MB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed