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An Informed Forensics Approach to Detecting Vote Irregularities

Published online by Cambridge University Press:  04 January 2017

Jacob M. Montgomery ,
Santiago Olivella ,
Joshua D. Potter  and
Brian F. Crisp
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)
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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
Political Analysis , Volume 23 , Issue 4 , Autumn 2015 , pp. 488 - 505
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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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 the Political Analysis Web site.

References

Acemoglu, Daron, and Robinson, James A. 2006. Economic origins of dictatorship and democracy. New York: Cambridge University Press.Google Scholar
Beber, Bernd, and Scacco, Alexandra. 2012. What the numbers say: A digit-based test for election fraud. Political Analysis 20:211–34.Google Scholar
Beck, Thorsten, Clark, George, Groff, Alberto, Keefer, Philip, and Walsh, Patrick. 2001. New tools in comparative political economy: The database of political institutions. World Bank Economic Review 15:165–76.Google Scholar
Benford, Frank. 1938. The law of anomalous numbers. Proceedings of the American Philosophical Society 78:551–72.Google Scholar
Birch, Sarah. 2007. Electoral systems and electoral misconduct. Comparative Political Studies 40:1533–56.CrossRefGoogle Scholar
Birch, Sarah. 2012. Electoral malpractice. Oxford, UK: Oxford University Press.Google Scholar
Blais, André. 2006. What affects voter turnout? Annual Review of Political Science 9:111–25.Google Scholar
Boix, Carles. 1999. Setting the rules of the game: The choice of electoral systems in advanced democracies. American Political Science Review 93:609–24.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., and Schrodt, Philip A. 2014. Evaluating forecasts of political conflict dynamics. International Journal of Forecasting 30:944–62.Google Scholar
Cantú, Francisco, and Saiegh, Sebastián M. 2011. Fraudulent democracy? An analysis of Argentina's infamous decade using supervised machine learning. Political Analysis 19:409–33.CrossRefGoogle Scholar
Chipman, H. A., George, E. I., and McCulloch, R. E. 2010. BART: Bayesian additive regression trees. Annals of Applied Statistics 4:266–98.Google Scholar
Cho, W. K. Tam, and Gaines, B. J. 2007. Breaking the (Benford) law. American Statistician 61:218–23.Google Scholar
Cox, Gary W. 1999. Electoral rules and the calculus of mobilization. Legislative Studies Quarterly 24:387–419.Google Scholar
Cox, Gary W., and Morgan Kousser, J. 1981. Turnout and rural corruption: New York as a test case. American Journal of Political Science 25:646–63.CrossRefGoogle Scholar
Dardé, Carlos. 1996. Fraud and passivity of the electorate in Spain, 1875–1923. In Elections 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., and McCann, James A. 1996. Democratizing Mexico: Public opinion and electoral choices. Baltimore, MD: Johns Hopkins University Press.Google Scholar
Domínguez, Jorge I., and McCann, James A. 1998. Democratizing Mexico: Public opinion and electoral choices. Baltimore, MD: Johns Hopkins University Press.Google Scholar
Efron, Bradley, and Tibshirani, Robert. 1997. Improvements on cross-validation: The 632+ bootstrap method. Journal of the American Statistical Association 92:548–60.Google Scholar
Friedman, Jerome, Hastie, Trevor, and Tibshirani, Rob. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33:1.Google Scholar
Green, Donald P., and Kern, Holger L. 2012. Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public Opinion Quarterly 76:491–511.Google Scholar
Grendar, Marian, Judge, George, and Schechter, Laura. 2007. An empirical non-parametric likelihood family of data-based Benford-like distributions. Physica A 380:429–38.Google Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, 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 Statistics 10:217–40.Google Scholar
Hyde, Susan D., and Marinov, Nikolay. 2012. Which elections can be lost? Political Analysis 20:191–210.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, and Tóka, 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, and Backer, 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 Science 6:233–56.Google Scholar
Levitsky, Steven, and Way, Lucan A. 2002. Elections without democracy: The rise of competitive authoritarianism. Journal of Democracy 13:51–65.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 Science 26:528–42.Google Scholar
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, eds. Alvarez, Michael R., Hall, Thad E., and Hyde, Susan D. Washington, DC: Brookings Institute Press.Google Scholar
Mebane, Walter R. 2010. Fraud in the 2009 presidential election in Iran? Chance 23:6–15.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, and Kalinin, 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., and Crisp, 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
Schedler, Andreas. 2002. The menu of manipulation. Journal of Democracy 13:36–50.Google Scholar
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