Principal–Agent Dynamics and Electoral Manipulation: Local Risks, Patronage and Tactical Variation in Russian Elections, 2003–2012

Abstract

Why are some elections manipulated more severely than others, and why do the techniques used to manipulate them vary over time and space? This article addresses these related questions by showing that patronage resources—not incumbent popularity—make manipulation appealing to frontline agents, while local political conditions can make manipulation personally risky for them. Agents can mitigate these risks by adopting more dispersed forms of manipulation like vote-buying, rather than more centralised falsification. These hypotheses are tested using forensic analysis of electoral data from more than 90,000 precincts per election across Russia’s 83 regions, from 2003 to 2012.

Notes

1 For a summary of the event and the video clip, see ‘Chlena gensoveta “Yedinoi Rossii” poimali na podkupe pensionerov’, newsru.com, available at: http://www.newsru.com/russia/30oct2011/agashin.html, accessed 24 July 2018.

2 These included efforts to remove him from office, and pressure on the regional prosecutor to bring criminal charges. For details see, ‘Chsobytiya, skandaly, katastrofy. Chem zapomnilsya 2011 god v Udmurtii’, dayudum.ru, available at: http://www.dayudm.ru/article/51302/, accessed 21 February 2017.

3 A website for the Honest Elections Public Council, a Kremlin-approved non-governmental agency, maintained a list of dozens of other incidents of arrests, administrative charges and criminal proceedings against individuals who have allegedly helped manipulate an election. The website is defunct as of 2017, but a record can be seen using the Internet Archive, available at: https://web.archive.org/web/20170502131220/http://www.chest-vibor.ru:80/chronicles/, accessed 13 September 2019.

4 See also Greene (2007).

5 This corresponds to Hale’s (2014, p. 10) conceptualisation of ‘single-pyramid’ and ‘multiple pyramid’ patronage systems.

6 I use the term ‘constraint’ rather than ‘competition’ since partisan competition is only one potential limitation on agents’ ability to manipulate, which also include the courts, civil-society monitoring groups and the media.

7 Rundlett and Svolik (2016) do not test this interactive hypothesis, since they only examine one election at a time (the 2012 Russian presidential election in the main article and the 2011 parliamentary election in an appendix). As a result, there is no variation in the incumbent’s national popularity in their empirical models.

8 Specifically, the legislative elections of 2003, 2007 and 2011 and the presidential elections of 2004, 2008 and 2012.

9 Supplementary materials can be found in the online appendix.

10 See also Weidmann and Callen (2013), Sjoberg (2013), Cantú (2014), Skovoroda and Lankina (2017).

11 The second-place party is almost always the Communist Party of the Russian Federation (Kommunisticheskaya Partiya Rossiskoi Federatsii—KPRF).

12 Assuming homogenous districts or appropriate statistical controls.

13 In this approach, about 5% of observations should appear significantly non-uniform purely due to chance; in the dataset approximately 9% and 20% of regions showed significantly non-uniform distributions for the first-place and second-place parties respectively. This suggests a non-random element to the distributions.

14 See also Moser and White (2017). Absolute vote-share refers to a party’s number of votes divided by the total number of registered voters.

15 Manipulation via absentee ballots is common in other post-Soviet countries; see, for example, D’Anieri (2005) and Herron (2010).

16 In particular, the estimated portion of variance of the DV not due to sampling error is modelled as $\sum _i\widehat{v_i^2}-\sum _i{\omega }_i^2+tr\left({\left(X^{\mathrm{\prime }}X\right)}^{-1}{X}^{\mathrm{\prime }}GX\right)/N-k$, where v refers to the residuals from the first level regression, ω² refers to the variance of the sampling error, and k refers to the number of parameters in the model. See Lewis and Linzer (2005, pp. 351–52) for more detail.

17 While the effective number of parties (ENP) would also be a plausible measure of constraint, margin of victory is a more useful measure for this purpose. First, margin of victory tracks the concept of constraint more directly, since a larger margin for the ruling party indicates reduced constraint in a straightforward way. In theory, a larger ENP should indicate increased constraint, while an ENP of 1 would indicate complete dominance by the ruling party. However, ENP can take on the same value based on widely different election results, making it less useful as a measurement of constraint on the ruling party. For example, using Golosov’s (2010) operationalisation, ENP takes on a value of approximately 2 when there are two parties that each take 50% of the vote (inverse margin of victory = 1, the highest level of constraint) and when one party takes 60% of the vote while eight other parties each take 5% (inverse margin of victory = 0.45, a relatively low level of constraint).

18 Similarly, the results of gubernatorial elections cannot be used for all elections, since Russia abolished gubernatorial elections between 2005 and 2012. There is a clear difference between the two types of elections that affects the value of competitiveness when measured in this way. The proportional nature of legislative elections allows more parties to be competitive, systematically narrowing the margin of victory in these elections. By contrast, the winner-take-all nature of the election tends to make these elections two-way contests, possibly producing wider margins of victory. To account for this difference, I first centre and scale each variable according to its variance before combining them. The distribution of these variables is sufficiently similar that the benefit of being able to make use of the data from the 2003 and 2004 elections in the study outweighs the cost of combining them in this fashion. Histograms for the raw and scaled data are available in the online appendix.

19 Federal’naya Sluzhba Gosydarstvennoi Statistiki 2016, available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/, accessed 12 December 2017.

20 As an additional robustness check, in the online appendix I present results from multilevel, partial-pooling models that help account for unobserved variables within regions, while still taking cross-national variation into account (Gelman & Hill 2007).

21 ‘Nikolai Merkushkin naznachen spetspredstavitelem Prezidenta po vzaimodeistviyu so Vsemirnym kongressom finno-ugorskikh narodov’, kremlin.ru, available at: http://www.kremlin.ru/events/president/news/55691, accessed 10 July 2018.

Additional information

Notes on contributors

Cole J. Harvey

Cole J. Harvey, Center for Russia, East Europe, and Central Asia, University of Wisconsin—Madison, 1155 Observatory Drive, Madison, WI 53706, USA. Email: [email protected]