Analyzing the Results of the 2019 Moscow City Duma Election: A Preface to the Discussion



The paper points out special features of the 2019 Moscow City Duma election campaign. As a lead-up to the discussion of how "Smart Voting" has influenced the results of the election, the author makes a series of remarks on its potentially different influence on vote returns and election results as well as on different understanding of the "Smart Voting" phenomenon. The paper also gives an overview of the main data on vote returns and election results.

There are several remarkable things about the 2019 Moscow City Duma election. First, a relatively recent trend of rejecting popular candidates for running in the election by refusing to register them after checking signature sheet was most evident this time. This gave rise to unprecedented protest activity among Moscow citizens: it was the first time when protest rallies against mobilizing administrative resource in the election took place during the campaign and not after it [4].

Second, it was the first election to have utilized a so-called "Smart Voting" technology. "Smart Voting" aims to concentrate opposition votes in every constituency around the candidate with the best chance of winning [6]. The technology was spearheaded by Alexey Navalny – a politician who lost his right of passive suffrage due to the sentence in Kirovles case and who does not have an official party, as any attempt to establish one was cut short at various stages by the Ministry of Justice. Nevertheless, Navalny wields significant authority among the opposition, as evidenced by the 2013 Moscow mayoral election results in particular, where he became the runner-up, receiving 27.2% of votes – the highest number ever for an oppositional candidate in Moscow mayoral election.

Third, the election results themselves are quite curious. Contrary to all the previous Moscow City Duma campaigns [5], the "party of power" won nearly "on the verge" by getting 25 seats while the notional opposition got 20 seats (the overall number of seats is 45). That said, all 20 opposition winners were endorsed by "Smart Voting" campaign.

This result sparked a discussion among experts. On the one hand, it is obvious that the result of the "party of power" dropped largely because of it generally starting to lose popularity following the 2018 pension reform [3], which went further downhill during the Moscow protests. On the other hand, the role of "Smart Voting" in the opposition's relative success became the most debated issue. Opinions varied greatly in this case, from "Smart Voting" being called a decisive contribution to denying its significance.

This is an interesting discussion for reasons other than assessing the last Moscow election. It is important from a methodological standpoint as an attempt to devise models that would allow to assess the contribution of various factors and technologies to the success of any specific political power. I hope this discussion will facilitate the development of electoral studies in political science.

The papers published in this issue continue the discussion that began in 2019 mostly in blogosphere, but has already moved on to the pages of political science journals [1]. It is unlikely this discussion will stay within the confines of our journal only.

Before we begin, I would like to draw a methodological line between two aspects. It is important to see the difference between vote returns and election results. Vote returns are the number of votes gained by candidates. Election results imply the decision of who is elected deputy. "Smart Voting" campaign sought to influence the election results in particular. That said, it could not but influence vote returns as well; still, its influence extended over vote returns of the opposition candidates, but not the "party of power" vote returns.

However, influencing vote returns in a plurality voting system does not always end favorably. The main line of the discussion concerns this aspect in particular. Did "Smart Voting" actually help certain opposition candidates win, or would they have won without this campaign anyway? In an attempt to answer this question, researchers use different models, which we can make sure of by looking through their papers.

Another issue stems from different understanding of "Smart Voting" as a political phenomenon. It can be viewed through three different aspects. The first aspect is the well-known "tactical" (or "strategic", as other researchers put it) voting [7; 2], which voters in Russia intuitively used before, as evidenced by liberal voters supporting CPRF candidates in particular (A.Ye. Klychkov in the 2014 Moscow City Duma election, D.A. Parfyonov in Medvedkovo constituency in the 2016 legislative election, S.G. Levchenko in the 2015 Irkutsk Oblast gubernatorial election, A.S. Ishchenko and V.O. Konovalov in the 2018 gubernatorial election). The second aspect is the idea of "Smart Voting" proposed by Navalny back in 2018, which developed a life of its own, largely detached from Navalny himself (for example, certain candidates being endorsed by opinion leaders, including unregistered candidates, discussing candidates' chances on social media). The third aspect is Navalny's project as such and his specific recommendations for all single-member constituencies.

Readers with only superficial understanding of the Moscow campaign might find it useful to take a look at the general information on election results. This information can be found in Tables 1 and 2. That said, my classification offers the following grouping of candidates. Candidates nominated by the four parties that covered most of the constituencies are grouped separately: CPRF, LDPR, A Just Russia (JR) and the Communist Party of Communists of Russia (CPCR). Three Yabloko party candidates in Table 2 are grouped with three independent candidates from the liberal pool (Daria Besedina, Dmitry Klochkov, Roman Yuneman). They are defined as "Opposition independents" in Table 1. Candidates from other parties (Rodina, Party of Growth, the Greens, Civilian Power) are grouped together in Table 1 ("Other parties"). The group called "Administration candidates" includes independent candidates endorsed by the mayor's office as well as two independents endorsed by United Russia, but having had a purely technical purpose. The remaining independent candidates are grouped under "Technical independents" in Table 1, while in Table 2 they are grouped together with candidates from "Other parties" under "Other". A separate row in Table 1 includes candidates endorsed by "Smart Voting"; in Table 2, these candidates are marked by an asterisk.

Table 1. Average results of different candidate groups
Candidates The no. of candidates Average
in the ballot winners runners-up
CPRF 42 13 20 34.1%
LDPR 45 0 2 8.7%
A Just Russia 39 3 5 12.8%
Yabloko 3 3 0 41.5%
Communists of Russia 31 0 0 7.1%
Other parties 7 0 1 6.9%
Technical independents 13 0 0 6.6%
Opposition independents 3 1 1 23.4%
Administration independents 42 25 16 34.5%
Smart Voting 45 20 24 36.9%

Table 2. Vote returns by constituency
 Constituency Adm. ind. CPRF LDPR JR Liberals** CPCR Other
1 38.8% 34.0%* 7.6% 8.2%  — 8.0%  —
2 37.1% 39.8%* 9.1% 7.4%  —  — 2.8%
3 32.9%  — 7.2% 35.0%*  — 20.6%  —
4 41.3% 35.1%* 6.5% 3.7%  — 10.0%  —
5 47.2% 39.7%* 4.3% 5.6%  —  —  —
6 27.8% 16.8% 5.7%  — 40.6%* 6.1%  —
7 35.8% 35.4%* 9.3% 8.7%  — 7.4%  —
8 6.8% 31.4% 5.3% 14.2% 36.6%*  — 3.1%
9 40.0% 38.7%* 6.2% 5.0%  — 6.3%  —
10 40.9% 28.5%* 9.9% 10.3%  — 6.9%  —
11 33.3% 43.1%* 12.6% 7.4%  —  —  —
12 40.8% 37.6%* 7.5% 6.8%  — 4.2%  —
13 31.9% 25.3% 9.3% 25.6%*  — 4.5%  —
14 22.8%  — 5.9% 13.1% 39.3%* + 5.2%  — 8.6%
15 31.9% 42.2%* 9.1%  —  — 5.8% 6.8%
16 30.9% 17.3% 4.1% 36.4%*  — 4.3% 3.8%
17 28.3% 45.9%* 9.0% 12.2%  —  —  —
18 23.2% 42.2%* 12.8% 5.3%  —  — 12.5%
19 38.2% 40.2%* 9.8%  —  — 7.4%  —

The table is not fully displayed Show table

* Candidate endorsed by "Smart Voting" project.
** Candidates nominated by Russian United Democratic Party (RUDP) Yabloko or those we refer to as opposition independents.

Received 25.05.2020, revision received 01.06.2020.


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