Hostname: page-component-5cf477f64f-mgq6s Total loading time: 0 Render date: 2025-04-03T11:16:58.555Z Has data issue: false hasContentIssue false
- English
- Français
Article contents
An Informed Forensics Approach to Detecting Vote Irregularities
Published online by Cambridge University Press: 04 January 2017
- Jacob M. Montgomery*
- Affiliation:Political Science, Washington University in St. Louis Campus, Box 1063, St. Louis, MO 63130, USA
- Santiago Olivella
- Affiliation:Political Science, University of Miami, 1300 Campo Sano Avenue, Coral Gables, FL 33146, USA, e-mail: olivella@miami.edu
- Joshua D. Potter
- Affiliation:Political Science, Louisiana State University, 240 Stubbs Hall, Baton Rouge, LA 70803, USA, e-mail: jpotter@lsu.edu
- Brian F. Crisp
- Affiliation:Political Science, Washington University in St. Louis Campus Box 1063, St. Louis, MO 63130, USA, e-mail: crisp@wustl.edu
- *
- e-mail:jacob.montgomery@wustl.edu (corresponding author)
Abstract
Electoral forensics involves examining election results for anomalies to efficiently identify patterns indicative of electoral irregularities. However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importance of different forensic and contextual features for identifying electoral fraud and results in a diagnostic tool that can be relatively easily implemented in cross-national research.
- Type
- Articles
- Information
- Copyright
- Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology
Access options
Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)Footnotes
Authors' note: Replication data and code are available at Montgomery et al. (2015). We are grateful for helpful comments we received from Chris Zorn and two anonymous reviewers. Supplementary Materials for this article are available on thePolitical Analysis Web site.
References
Acemoglu,Daron, andRobinson,James A.2006.Economic origins of dictatorship and democracy.New York:Cambridge University Press.Google Scholar
Beber,Bernd, andScacco,Alexandra.2012.What the numbers say: A digit-based test for election fraud.Political Analysis20:211–34.Google Scholar
Beck,Thorsten,Clark,George,Groff,Alberto,Keefer,Philip, andWalsh,Patrick.2001.New tools in comparative political economy: The database of political institutions.World Bank Economic Review15:165–76.Google Scholar
Benford,Frank.1938.The law of anomalous numbers.Proceedings of the American Philosophical Society78:551–72.Google Scholar
Birch,Sarah.2007.Electoral systems and electoral misconduct.Comparative Political Studies40:1533–56.CrossRefGoogle Scholar
Blais,André.2006.What affects voter turnout?Annual Review of Political Science9:111–25.Google Scholar
Boix,Carles.1999.Setting the rules of the game: The choice of electoral systems in advanced democracies.American Political Science Review93:609–24.CrossRefGoogle Scholar
Brancati,Dawn.2007.Constituency-Level Elections (CLE) dataset.New York:Constituency-Level Elections Dataset.http://www.cle.wustl.edu (accessed June 15, 2012).Google Scholar
Brandt,Patrick T.,Freeman,John R., andSchrodt,Philip A.2014.Evaluating forecasts of political conflict dynamics.International Journal of Forecasting30:944–62.Google Scholar
Cantú,Francisco, andSaiegh,Sebastián M.2011.Fraudulent democracy? An analysis of Argentina's infamous decade using supervised machine learning.Political Analysis19:409–33.CrossRefGoogle Scholar
Chipman,H. A.,George,E. I., andMcCulloch,R. E.2010.BART: Bayesian additive regression trees.Annals of Applied Statistics4:266–98.Google Scholar
Cho,W. K. Tam, andGaines,B. J.2007.Breaking the (Benford) law.American Statistician61:218–23.Google Scholar
Cox,Gary W.1999.Electoral rules and the calculus of mobilization.Legislative Studies Quarterly24:387–419.Google Scholar
Cox,Gary W., andMorgan Kousser,J.1981.Turnout and rural corruption: New York as a test case.American Journal of Political Science25:646–63.CrossRefGoogle Scholar
Dardé,Carlos.1996.Fraud and passivity of the electorate in Spain, 1875–1923. InElections before democracy: The history of elections in Europe and Latin America, ed.Eduardo,Posada-Carbó, 201–23.Baltimore, MD:MacMillan Press.Google Scholar
Domínguez,Jorge I., andMcCann,James A.1996.Democratizing Mexico: Public opinion and electoral choices.Baltimore, MD:Johns Hopkins University Press.Google Scholar
Domínguez,Jorge I., andMcCann,James A.1998.Democratizing Mexico: Public opinion and electoral choices.Baltimore, MD:Johns Hopkins University Press.Google Scholar
Efron,Bradley, andTibshirani,Robert.1997.Improvements on cross-validation: The 632+ bootstrap method.Journal of the American Statistical Association92:548–60.Google Scholar
Friedman,Jerome,Hastie,Trevor, andTibshirani,Rob.2010.Regularization paths for generalized linear models via coordinate descent.Journal of Statistical Software33:1.Google Scholar
Green,Donald P., andKern,Holger L.2012.Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees.Public Opinion Quarterly76:491–511.Google Scholar
Grendar,Marian,Judge,George, andSchechter,Laura.2007.An empirical non-parametric likelihood family of data-based Benford-like distributions.Physica A380:429–38.Google Scholar
Hastie,Trevor,Tibshirani,Robert, andFriedman,Jerome.2009.The elements of statistical learning: Data mining, inference, and prediction.New York:Springer.Google Scholar
Hill,Jennifer.2012.Bayesian nonparametric modeling for causal inference.Journal of Computational and Graphical Statistics10:217–40.Google Scholar
Hyde,Susan D., andMarinov,Nikolay.2012.Which elections can be lost?Political Analysis20:191–210.Google Scholar
Kelley,J. G.2012.Monitoring democracy: When international election observation works, and why it often fails.Princeton, NJ:Princeton University Press.Google Scholar
Kitschelt,Herbert,Mansfeldova, Radoslaw Markowski,Zdenka, andTóka,Gábor.1999.Post-communist party systems: Competition, representation, and inter-party cooperation.Cambridge, UK:Cambridge University Press.Google Scholar
Kollman,Ken,Hicken, Daniele Caramani,Allen, andBacker,David.2011.Constituency-Level Elections Archive (CLEA).Ann Arbor, MI:University of Michigan Center for Political Studies.www.electiondataarchive.org (accessed June 15, 2012).Google Scholar
Lehoucq,Fabrice.2003.Electoral fraud: Causes, types, and consequences.Annual Review of Political Science6:233–56.Google Scholar
Levitsky,Steven, andWay,Lucan A.2002.Elections without democracy: The rise of competitive authoritarianism.Journal of Democracy13:51–65.Google Scholar
Martín,Isbella.2011.2004 Venezuelan Presidential Recall Referendum (2004 PRR): A statistical analysis from the point of view of electronic voting data transmissions.Statistical Science26:528–42.Google Scholar
Mebane,Walter R.2008.Election forensics: The second-digit Benford's Law test and recent American presidential elections. InElection fraud: Detecting and deterring electoral manipulation, eds.Alvarez,Michael R.,Hall,Thad E., andHyde,Susan D.Washington, DC:Brookings Institute Press.Google Scholar
Mebane,Walter R.2012.Second-digit tests for voters’ election strategies and election fraud. Paper presented at the 2012 Annual Meeting of the Midwest Political Science Association, Chicago.Google Scholar
Mebane,Walter R, andKalinin,Kirill.2009.Comparative election fraud detection.Toronto, Canada:Prepared for the Annual Meeting of the American Political Science Association.Google Scholar
Montgomery,Jacob M.,Olivella,Santiago,Potter,Joshua D., andCrisp,Brian F.2015.Replication data for: An informed forensics approach to detecting vote irregularities,Harvard Dataverse,V1.http://dx.doi.org/10.7910/DVN/IZWWBC [UNF:6:eJuteNL3jtNycv8KscNNzA==].Google Scholar
Montgomery et al. supplementary material
Supplementary Material
PDF 314.2 KB
- 16
- Cited by
