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Violence and Election Fraud: Evidence from Afghanistan

Published online by Cambridge University Press:  03 July 2012

Nils B. Weidmann  and
Michael Callen
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Abstract

What explains local variation in electoral manipulation in countries with ongoing internal conflict? The theory of election fraud developed in this article relies on the candidates’ loyalty networks as the agents manipulating the electoral process. It predicts (i) that the relationship between violence and fraud follows an inverted U-shape and (ii) that loyalty networks of both incumbent and challenger react differently to the security situation on the ground. Disaggregated violence and election results data from the 2009 Afghanistan presidential election provide empirical results consistent with this theory. Fraud is measured both by a forensic measure, and by using results from a visual inspection of a random sample of the ballot boxes. The results align with the two predicted relationships, and are robust to other violence and fraud measures.

Type
Articles
Information
British Journal of Political Science , Volume 43 , Issue 1 , January 2013 , pp. 53 - 75
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

Centre for the Study of Civil War, Peace Research Institute Oslo (email: [email protected]); and Department of Economics, University of California, San Diego (email: [email protected]), respectively. The authors thank Eli Berman, Tiffany Chou, Rex Douglass, Hanne Fjelde, Marjorie Flavin, Susan Hyde, Radha Iyengar, Patrick Kuhn, Jason Lyall, Craig McIntosh, Kyle Pizzey, Gerald Schneider, Jacob Shapiro, Jessica Trisko, Choon Wang, Hal White and their colleagues at UCSD and Princeton for helpful comments and assistance with data. They also thank Israel Malkin for excellent research assistance. This material is based upon work supported by the Air Force Office of Scientific Research (AFOSR) under Award No. FA9550-09-1-0314 and by the European Commission under a Marie Curie Fellowship. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of any of the funders. Replication data will be made available at http://dvn.iq.harvard.edu/dvn/dv/nilsw upon publication of this article. An appendix containing additional information is available at: http://dx.doi.org/10.1017/S0007123412000191

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18 We provide some evidence against the second explanation below by showing that our results are robust to the inclusion of the share of planned polling stations that actually operated into the regression.

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23 National Democratic Institute, The 2009 Presidential and Provincial Council Elections in Afghanistan.

24 See e.g. ‘Afghanistan imposes censorship on election day’, New York Times, 18 August 2009. http://www.nytimes.com/2009/08/19/world/asia/19afghan.html.

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38 Beber and Scacco (‘What the Numbers Say’) propose additional forensic tests. However, we find that they correlate less well with our recount-based fraud measure (see next section), so we do not use it in the core empirical analysis.

39 Beber and Scacco, ‘What the Numbers Say’.

40 Mebane, ‘Election Forensics’.

41 Lacking information about the precise location of polling stations, we are unable to estimate fraud at lower levels, for example, cities. Moreover, many of the covariates used in our regression analysis below are available only at the district level.

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43 Beber and Scacco, ‘What the Numbers Say’; Deckert, Myagkov and Ordeshook, ‘Benford's Law and the Detection of Election Fraud’.

44 The stations were divided into five categories, as follows: (A1) 600 or more valid votes were cast, (A2) 600 or more total votes were cast, (B1) more than 100 votes were cast and one candidate received 95 per cent or more of the total votes, (B2) one candidate received 95 per cent or more of the total valid votes cast, and (C1) 600 or more valid votes were cast and one candidate received 95 per cent or more of the total votes.

45 The IEC reports investigating 343 boxes, but categories B1 and C1 described above contain a mutual observation which was overlooked.

46 In the recount, the shares of boxes exhibiting some physical evidence of fraud are as follows. A1: 0.655 (sd = 0.480); A2: 0.7 (sd = 0.466); B1: 0.721 (sd = 0.450); B2: 0.462 (sd = 0.505); C1: 0.939 (sd = 0.241). The sample of 342 stations was drawn randomly and so by the weak law of large numbers the sample mean for each category provides a consistent estimate of the share of fraudulent polling stations in that category at the national level.

47 The IEC publicly posted the data in three waves. They reported returns from 27,163 distinct polling stations on 19 September, 23,300 stations on 10 October and 22,853 polling stations on 20 October. Of the original 27,163, 4,305 are missing or record no votes. This brings us to 22,858 (almost the 20 October number). We use the earliest data release in order to be able to control for the number of missing/closed polling stations.

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52 Our results are additionally robust to clustering at the province level; there are a sufficient number of provinces that clustered standard errors are likely to resemble their asymptotic distribution.

53 For examples, see Ellman and Wantchekon, ‘Electoral Competition under the Threat of Political Unrest’; Wilkinson, Votes and Violence.

54 C. Christine Fair and Seth G. Jones, ‘Securing Afghanistan: Getting on Track’ (Working paper, United States Institute of Peace, available at http://library.usip.org/articles/1012068.1022/1.PDF,2009).

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