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An Informed Forensics Approach to Detecting Vote Irregularities

  • Jacob M. Montgomery (a1), Santiago Olivella (a2), Joshua D. Potter (a3) and Brian F. Crisp (a4)
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.

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Corresponding author
e-mail: jacob.montgomery@wustl.edu (corresponding author)
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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.

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References
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Acemoglu, Daron, and Robinson, James A. 2006. Economic origins of dictatorship and democracy. New York: Cambridge University Press.
Beber, Bernd, and Scacco, Alexandra. 2012. What the numbers say: A digit-based test for election fraud. Political Analysis 20:211–34.
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.
Benford, Frank. 1938. The law of anomalous numbers. Proceedings of the American Philosophical Society 78:551–72.
Birch, Sarah. 2007. Electoral systems and electoral misconduct. Comparative Political Studies 40:1533–56.
Birch, Sarah. 2012. Electoral malpractice. Oxford, UK: Oxford University Press.
Blais, André. 2006. What affects voter turnout? Annual Review of Political Science 9:111–25.
Boix, Carles. 1999. Setting the rules of the game: The choice of electoral systems in advanced democracies. American Political Science Review 93:609–24.
Brancati, Dawn. 2007. Constituency-Level Elections (CLE) dataset. New York: Constituency-Level Elections Dataset. http://www.cle.wustl.edu (accessed June 15, 2012).
Brandt, Patrick T., Freeman, John R., and Schrodt, Philip A. 2014. Evaluating forecasts of political conflict dynamics. International Journal of Forecasting 30:944–62.
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.
Chipman, H. A., George, E. I., and McCulloch, R. E. 2010. BART: Bayesian additive regression trees. Annals of Applied Statistics 4:266–98.
Cho, W. K. Tam, and Gaines, B. J. 2007. Breaking the (Benford) law. American Statistician 61:218–23.
Cox, Gary W. 1999. Electoral rules and the calculus of mobilization. Legislative Studies Quarterly 24:387419.
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.
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.
Domínguez, Jorge I., and McCann, James A. 1996. Democratizing Mexico: Public opinion and electoral choices. Baltimore, MD: Johns Hopkins University Press.
Domínguez, Jorge I., and McCann, James A. 1998. Democratizing Mexico: Public opinion and electoral choices. Baltimore, MD: Johns Hopkins University Press.
Efron, Bradley, and Tibshirani, Robert. 1997. Improvements on cross-validation: The 632+ bootstrap method. Journal of the American Statistical Association 92:548–60.
Friedman, Jerome, Hastie, Trevor, and Tibshirani, Rob. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33:1.
Green, Donald P., and Kern, Holger L. 2012. Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public Opinion Quarterly 76:491511.
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.
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2009. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Hill, Jennifer. 2012. Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics 10:217–40.
Hyde, Susan D., and Marinov, Nikolay. 2012. Which elections can be lost? Political Analysis 20:191210.
Kelley, J. G. 2012. Monitoring democracy: When international election observation works, and why it often fails. Princeton, NJ: Princeton University Press.
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.
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).
Lehoucq, Fabrice. 2003. Electoral fraud: Causes, types, and consequences. Annual Review of Political Science 6:233–56.
Levitsky, Steven, and Way, Lucan A. 2002. Elections without democracy: The rise of competitive authoritarianism. Journal of Democracy 13:5165.
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.
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.
Mebane, Walter R. 2010. Fraud in the 2009 presidential election in Iran? Chance 23:615.
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.
Mebane, Walter R, and Kalinin, Kirill. 2009. Comparative election fraud detection. Toronto, Canada: Prepared for the Annual Meeting of the American Political Science Association.
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==].
Schedler, Andreas. 2002. The menu of manipulation. Journal of Democracy 13:3650.
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Political Analysis
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