Skip to main content
×
Home
    • Aa
    • Aa

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.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure coreplatform@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.

      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 Dropbox 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 Google Drive 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
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Adam L. Alter , and Hal E. Hershfield . 2014. People search for meaning when they approach a new decade in chronological age. Proceedings of the National Academy of Sciences 111(48):1706617070.

Bernd Beber , and Alexandra Scacco . 2012. What the numbers say: A digit-based test for election fraud. Political Analysis 20(2):211234.

Francisco Cantú , and Sebastián M. Saiegh . 2011. Fraudulent democracy? An analysis of Argentina’s infamous decade using Supervised Machine Learning. Political Analysis 19(4):409433.

Maria M. Febres Cordero , and Bernardo Márquez . 2006. A statistical approach to assess referendum results: The Venezuelan Recall Referendum 2004. International Statistical Review 74(3):379389.

Ursula E. Daxecker 2012. The cost of exposing cheating International election monitoring, fraud, and post-election violence in Africa. Journal of Peace Research 49(4):503516.

Joseph Deckert , Mikhail Myagkov , and Peter C. Ordeshook . 2011. Benford’s Law and the detection of election fraud. Political Analysis 19(3):245268.

Élise Janvresse , and Thierry De la Rue . 2004. From uniform distributions to Benford’s law. Journal of Applied Probability 41(4):12031210.

Peter Klimek , Yuri Yegorov , Rudolf Hanel , and Stefan Thurner . 2012. Statistical detection of systematic election irregularities. Proceedings of the National Academy of Sciences 109(41):1646916473.

Ines Levin , Julia Pomares , and R. Michael Alvarez . 2016. Using machine learning algorithms to detect election fraud. In Computation social science , ed. Michael R. Michael Alvarez . Cambridge University Press.

Beatriz Magaloni . 2006. Voting for autocracy: Hegemonic party survival and its demise in Mexico . New York: Cambridge University Press.

Walter R. Mebane 2011. Comment on “Benford’s law and the detection of election fraud”. Political Analysis 19(3):269272.

Walter R. Mebane , and Jasjeet S. Sekhon . 2004. Robust estimation and outlier detection for overdispersed multinomial models of count data. American Journal of Political Science 48(2):392411.

Juraj Medzihorsky . 2015. Election fraud: A latent class framework for digit-based tests. Political Analysis 23(4):506517.

Jacob M. Montgomery , Santiago Olivella , Joshua D. Potter , and Brian F. Crisp . 2015. An informed Forensics approach to detecting vote irregularities. Political Analysis 23(4):488505.

Mikhail Myagkov , Peter C. Ordeshook , and Dimitri Shakin . 2009. The forensics of election fraud: Russia and Ukraine . Cambridge University Press.

Devin Pope , and Uri Simonsohn . 2011. Round numbers as goals evidence from baseball, SAT takers, and the lab. Psychological science 22(1):7179.

Ashlea Rundlett , and Milan W. Svolik . 2016. Deliver the vote! micromotives and macrobehavior in electoral fraud. American Political Science Review 110(1):180197.

Alberto Simpser . 2013. Why governments and parties manipulate elections: theory, practice, and implications . Cambridge University Press.

Wendy K. Tam Cho , and Brian J. Gaines . 2007. Breaking the (Benford) law: Statistical fraud detection in campaign finance. The American Statistician 61(3):218223.

Vladimir Trifonov , Laura Pasqualucci , Riccardo Dalla-Favera , and Raul Rabadan . 2011. Fractal-like distributions over the rational numbers in high-throughput biological and clinical data. Scientific reports 1(191):17.

Joshua Tucker . 2007. Enough! electoral fraud, collective action problems, and post-communist Colored Revolutions. Perspectives on Politics 5(3):535551.

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: 19
Total number of PDF views: 259 *
Loading metrics...

Abstract views

Total abstract views: 443 *
Loading metrics...

* Views captured on Cambridge Core between 22nd February 2017 - 28th April 2017. This data will be updated every 24 hours.