Shifting Votes on Shifting Sands: Opposition Party Electoral Performance in Dominant Party Authoritarian Regimes

Abstract

When competition is real but unfair, how do opposition parties make electoral inroads at the local level? I investigate patterns of opposition party support in the largest and most influential dominant party authoritarian regime—Russia—and focus on the most prominent opposition, the Communist Party of the Russian Federation (KPRF). Drawing on evidence from regimes around the world, I generate hypotheses regarding the electoral environments conducive to opposition success. Using county-level data, I test these hypotheses on the KPRF, whose electoral patterns have a low probability of conforming to my expectations. My findings highlight the electoral-geographic effects of party dominance.

Acknowledgments

I would like to thank Inga Saikkonen for generously sharing demographic data with me.

Data

The data that support the findings of this study were derived from the following resources available in the public domain: Central Election Commission of Russia [http://www.cikrf.ru/] and the All-Russia National Census [http://www.perepis2002.ru/index.html?id=87].

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Naturally, the degree to which parties oppose dominant parties in electoral authoritarian regimes varies across the world. Some may argue that, in the Putin era, the KPRF does not function as a genuine opposition party. However, the KPRF has been treated as an opposition party in the existing literature across the 2000s (see, for Gel’man 2005, 2011; Turovsky 2014; Enikolopov et al. 2013), and the party fields candidates throughout the country in each legislative election. The KPRF does not coordinate with United Russia to field or avoid fielding candidates in certain areas. It is for these reasons that I classify the KPRF as an opposition party.

2. Previous research demonstrates that the LPDR’s bases of support have been less coherent than the KPRF’s along two electoral cleavages explored here, namely the urban–rural divide and economic prosperity (Clem and Craumer 1997a, 1997b; March 2002; Hale 2006). Given that the LPDR’s electorate was less defined before United Russia’s emergence, including this opposition party would jeopardize the least-likely case study approach (see Gerring 2007). Therefore, compared to the KPRF, the utility of the LPDR as a case for studying opposition party electoral patterns in dominant party authoritarian regimes is more limited.

3. In contrast to Golosov’s (2014) article, which uses regional-level data to identify continuities in the “territorial genealogies” of various parties from 1993 to 2007, I leverage raion-level data to reveal both continuities and discontinuities in electoral patterns from 1995 to 2016 based on the theoretical predictions of cross-national scholarship. Golosov’s primary point of theoretical reference is the literature on political machines, while I draw on a different strand of scholarship, i.e. opposition party electoral outcomes in dominant party authoritarian regimes, which yields different research objectives, approaches, and conclusions. With respect to the KPRF, my hypotheses are centered around three electoral environments, i.e. urban centers, wealthy areas, and areas with higher levels of education, rather than concentrating on the urban–rural divide up to the time of the 2007 election. Instead of focusing exclusively on territorial continuities and excluding explanatory variables negatively associated with the dependent variables (Golosov 2014, 470), I am interested in how the bases of support for Russia’s strongest opposition party changed, or remained the same, with the emergence of United Russia, and therefore my approach to statistical modeling differs as a result.

4. Russia has a federal system with 83 regions, discounting the two federal subjects (the Republic of Crimea and the federal city of Sevastopol) that were annexed in 2014.

5. Russia’s federal regions are divided between ethnic Russian regions and ethnic non-Russian regions that are designated as ethnic homelands for a titular minority group, e.g. Chechens, Bashkirs, or Tatars.

6. When effects are present at more than one level, it may be challenging for an ordinary least squares (OLS) regression model to meet the assumptions of classical regression (Luke 2004). Multilevel models using maximum likelihood estimation (MLE) are therefore more appropriate. The statistical analyses were conducted using STATA.

7. Since voter turnout is correlated with some of the control variables, such as ethnicity and location in the Caucasus, I also conducted the analysis excluding the high turnout variable. While the magnitude of effects pertaining to my variables of interest shifted slightly, the sign of the coefficients did not change, which lends robustness to my findings.