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Benford's Law and the Detection of Election Fraud

Published online by Cambridge University Press:  04 January 2017

Joseph Deckert ,
Mikhail Myagkov  and
Peter C. Ordeshook
Joseph Deckert
Affiliation:
University of Oregon 97403 and California Institute of Technology 91124
Mikhail Myagkov
Affiliation:
University of Oregon 97403 and California Institute of Technology 91124
Peter C. Ordeshook*
Affiliation:
University of Oregon 97403 and California Institute of Technology 91124
*
e-mail: [email protected] (corresponding author)
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Abstract

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The proliferation of elections in even those states that are arguably anything but democratic has given rise to a focused interest on developing methods for detecting fraud in the official statistics of a state's election returns. Among these efforts are those that employ Benford's Law, with the most common application being an attempt to proclaim some election or another fraud free or replete with fraud. This essay, however, argues that, despite its apparent utility in looking at other phenomena, Benford's Law is problematical at best as a forensic tool when applied to elections. Looking at simulations designed to model both fair and fraudulent contests as well as data drawn from elections we know, on the basis of other investigations, were either permeated by fraud or unlikely to have experienced any measurable malfeasance, we find that conformity with and deviations from Benford's Law follow no pattern. It is not simply that the Law occasionally judges a fraudulent election fair or a fair election fraudulent. Its “success rate” either way is essentially equivalent to a toss of a coin, thereby rendering it problematical at best as a forensic tool and wholly misleading at worst.

Type
Research Article
Information
Political Analysis , Volume 19 , Issue 3 , Summer 2011 , pp. 245 - 268
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

References

Berber, Bernd, and Scacco, Alexandra. 2008. What the numbers say: A digit based test for election fraud using new data from Nigeria. Paper presented at the Annual Meeting of the Am. Political Science Association, Boston, MA, August 28-31.Google Scholar
Berezkin, Andrei V., Myagkov, Mikhail, and Ordeshook, Peter C. 1999. The urban rural divide in the Russian electorate and the effects of distance from urban centers. Post-Soviet Geography and Economics 40: 395–406.Google Scholar
Berezkin, Andrei V., Myagkov, Mikhail, and Ordeshook, Peter C. 2003. Location and political influence: A further elaboration of their effects on voting in recent Russian elections. Post-Soviet Geography and Economics 44: 169–83.Google Scholar
Brady, Henry E. 2005. Comments on Benfords Law and the Venezuelan election Unpublished manuscript, Stanford University, January 19, 2005.Google Scholar
Buttorf, Gail. 2008. Detecting fraud in America's gilded age. Unpublished manuscript, University of Iowa.Google Scholar
Buzin, Andrei, and Lubarev, Arkadii. 2008. Crime without punishment. Moscow, Russia: Nikkolo M.Google Scholar
Camerer, Colin. 2003. Behavioral game theory: Experiments in strategic interaction. Princeton, NJ: Princeton University Press.Google Scholar
Chaing, Lichun, and Ordeshook, Peter C. 2009. Fraud, elections and the American gene in Taiwan's democracy. Unpublished manuscript, California Institute of Technology.Google Scholar
Cox, Gary. 1997. Making votes count: Strategic coordination in the World's electoral systems. Cambridge, UK: Cambridge University Press.Google Scholar
Diekmann, Andreas. 2010. Not the first digit: Using Benford's Law to detect fraudlent data. Unpublished paper, Swiss Federal Institute of Technology, Zurich. Paper presented at the 2010 Conference on Plagiarism, 21-23 June, Newcastle, UK.Google Scholar
Ijiri, Yuri, and Simon, Herbert. 1977. Skew distributions and the sizes of business firms. New York, NY: North Holland Publishing Co.Google Scholar
Janvresse, Élise, and de la Rue, Thierry. 2004. From uniform distributions to Benford's Law. Journal of Applied Probability 41: 1203–10.CrossRefGoogle Scholar
Levin, Ines, Cohn, Gabe A., Michael Alvarez, R., and Ordeshook, Peter C. 2009. Detecting voter fraud in an electronic voting context: An analysis of the unlimited reelection vote in Venezuela. Electronic Voting Technology Workshop/Workshop on Trustworthy Elections. Online Proceedings.Google Scholar
Mebane, Walter. 2006. Election forensics: Vote counts and Benford's Law. Paper prepared for the 2006 Summer Meeting of the Political Methodology Society, University of California, Davis, July 20-22.Google Scholar
Mebane, Walter. 2007. Election forensics: Statistics, recounts and fraud Paper presented at the 2007 Annual Meeting of the Midwest Political Science Association. Chicago, IL, April 12-16.Google Scholar
Mebane, Walter. 2008. Election forensics: The Second Digit Benford's Law Test and recent American presidential elections. In Election fraud, ed. Alvarez, R. M., Hall, T. E., and Hyde, S. D. Washington, DC: Brookings.Google Scholar
Mebane, Walter, and Kalinin, Kirill. 2009. Electoral falsifications in Russia: Complex diagnostics selections 2003-2004, 2007-8. Electoral Policy REO, 57–70 (in Russian).Google Scholar
Myagkov, Mikhail, Ordeshook, Peter C., and Shakin, Dimitri. 2009. The forensics of election fraud. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Snyder, Timothy. 2010. Bloodlands: Europe between Hitler and Stalin. New York, NY: Basic Books.Google Scholar
Sobyanin, Alexandar, and Suchovolsky, V. 1993. Elections and the referendum December 11, 1993, in Russia. Unpublished report to the Administration of the President of the RF, Moscow.Google Scholar