Staying Out of Trouble: Criminal Cases Against Russian Mayors

Abstract

Although repression against elites is a common occurrence in authoritarian regimes, we know little about which elites are targeted. This paper uses an original dataset on the prosecution of mayors in large Russian cities to examine the factors that make elites more likely to be arrested. We argue that in electoral authoritarian regimes like Russia, regime leaders are reluctant to arrest popular officials. Such officials command political capital that is useful to the regime, and arrests of prominent officials can produce popular backlash. We examine this argument using an original dataset on all arrests of municipal leaders in Russia’s 221 largest cities between 2002 and 2018. We find that mayors who won their elections by large margins are less likely to be arrested. In addition, we document several other substantively important patterns: (1) a mayor’s professional background is not related to the likelihood of arrest, (2) opposition mayors are four times more likely to be arrested, and (3) mobilization of votes for the regime is not protective against arrest.

JEL Classifications P16, P30, R59

Keywords

authoritarian politics repression anti-corruption campaigns subnational political elites Russia

Introduction

Repression of political elites is a common occurrence under autocracy. Office-holders in authoritarian regimes are much more likely to lose office via force (i.e., arrest or violence) than are elites in democracies (Svolik, 2009). Subnational officials are a particularly frequent target of such repression. Indeed, in the past 5 years, there have been major arrest campaigns against local officials in settings as diverse as Venezuela, China, Tanzania, Turkey, Uganda, and Russia.¹ In this paper, we examine why some subnational elites are arrested or charged with criminal wrongdoing, while others are not.

There is a vast literature on elite repression under autocracy, but relatively few empirical studies examine which subnational officials are actually targeted with repression (Lorentzen & Lu, 2018 and Zhu & Zhang, 2017 are two exceptions). Generally speaking, the literature on elite repression can be classified into two categories. First, there is scholarship on purges—large-scale repression campaigns, usually targeted at entire elite groups, factions, or categories. In addition to a voluminous historical literature describing famous purges, this literature focuses on uncovering the factors that lead top regime leaders to launch a purge campaign, usually against members of the security services or top officials in the central government (Brzezinski, 1958; Bueno de Mesquita & Smith, 2009; Sudduth, 2017). This literature does not make clear predictions about which elites will be repressed, nor does it help us understand patterns of elite repression in “normal” times, outside of well-defined purge campaigns. We take up both tasks in this paper.

Second, there is a large and growing literature on anti-corruption campaigns, primarily focusing on China (Chen & Kung, 2019; Li, 2019; Li et al., 2018; Manion, 2016; Pan & Tian, 2020; Wedeman, 2005; 2017; Xi et al., 2020; Zhu et al., 2019). Much of this literature focuses on determining the purpose of these campaigns. Are they really aimed at stamping out corruption, or are they merely a tool for leaders to eliminate political threats, consolidate their power, and strengthen the political legitimacy of the regime? In recent years, a number of innovative papers have made progress on these questions, generally coming to the conclusion that all three factors—anti-corruption efforts, political vendettas, as well as concerns about the public image of the party—drive the pattern of arrests in China (Lorentzen & Lu, 2018; Zhu et al., 2019; Zhu & Zhang, 2017).

We adopt a different approach in this paper. We start from the premise that authoritarian politics is opaque. The decision-making processes of the security services are particularly inscrutable (Soldatov & Rochlitz, 2018; Wang & Minzner, 2016). Thus, the specific, idiosyncratic motivation for a particular arrest—whether it is punishment for corruption, personal disputes, or economic conflicts—is very difficult to discern. Rather than trying to determine the idiosyncratic causes of arrests, we theorize the systematic, generalizable factors that are likely to protect officials from being arrested. Whatever the particular “offense”—be it corruption or being on the wrong end of a political struggle—what are the factors that make it less likely that a given official will be targeted with repression?

Drawing on insights from both the elite repression literature and the literature on subnational politics in Russia, we examine several plausible explanations for variation in the arrests of officials. We train our theoretical focus on examining how the personal popularity of officials affects their chance of being arrested. Some research suggests that popular officials pose a threat to regime leaders, and, as such, are targets for repression (Egorov & Sonin, 2011; Gueorguiev & Schuler, 2016; Zakharov, 2016). We advance the opposing view, suggesting that a base of popular support can protect an official from arrest. In electoral authoritarian regimes such as Russia, regime leaders need popular officials to help them win elections (Reuter & Robertson, 2012; Reuter, 2013). In addition, arresting popular officials can produce popular backlash, which can seriously undermine the ability of the regime to win elections. Thus, we argue that more popular officials in electoral authoritarian regimes are—ceteris paribus—less likely to be arrested than are less popular officials.

We examine these propositions using an original dataset of 1051 mayors in Russia’s 220 largest cities between 2002 and 2018. During this period, over 10% of these mayors (108 out of 1051) were arrested for corruption-related offenses. Thirty-eight percent of large Russian cities saw at least one of their mayors arrested. We present the first empirical analysis of these arrests. The true reasons for these arrests are opaque, with even local observers and journalists often having a difficult time understanding the motives behind an arrest.² In this regard, large-N analysis excels because it can uncover trends and patterns that close analysis of a few cases may miss.

Our data offers a rare opportunity to leverage high quality, comprehensive data on repression of a homogenous set of elites under autocracy. In many of the other settings studied in the literature, the targets of repression vary wildly in position and authority. Our focus on one elite group—mayors—allows us to examine actors of equal statutory authority.

Focusing on the arrests of Russian mayors is useful for theory-building because it allows us to exclude some prominent explanations in the literature. For instance, while we show that political factors play a major role, we can exclude explanations that focus on repression as a way to prevent coups or conspiracies (Braithwaite & Sudduth, 2016), as mayors in Russia are not in a position to stage palace coups in the Kremlin. We can also exclude explanations based on factional politics in the top leadership. A number of studies of China find that those with personal ties to Xi Jinping were less likely to be targeted in the latest anti-corruption campaign (see, for example, Lorentzen & Lu, 2018). Russian mayors are not linked to the top leadership or embedded in national-level factions, so we can exclude this as an explanation. Finally, we can also exclude explanations based on ethnic rivalry, as ethnicity is not an important factor in Russian elite politics (Giuliano & Gorenburg, 2012; Rutland, 2010).

In addition, because mayors govern concrete political-administrative jurisdictions, we can parameterize and measure many structural correlates of repression, a task that would be very difficult in an analysis of repression in the security services. Relatedly, since mayors are public officials, there are many aspects of their biographies and backgrounds that can be gathered and analyzed in a way that could be difficult for other parts of the authoritarian elite.

Our analyses reveal a number of substantively important findings. First, we find that popular mayors, as proxied by their margin of victory in their most recent election, are much less likely to be arrested—irrespective of their political allegiance. We interpret this finding as suggesting that the authorities are hesitant to arrest popular mayors. In additional analyses, we find that these results hold among mayors not affiliated with the regime, which suggests that the findings are not just driven by the need to retain popular pro-regime mayors who can help the regime mobilize votes, but also by a desire to avoid popular backlash.

Second, using occupational backgrounds as a proxy for connections to regional officials, the security services, and the Kremlin—all possible sources of protection—we find no evidence that mayors with such connections are less likely to be arrested. It seems that, at least for this set of authoritarian elites, personal characteristics like popularity are more important than having connections.

We also present several other empirical findings that will be of particular interest to those who follow Russian and post-communist politics. First, empirically confirming commonly held wisdom, opposition mayors are almost four times as likely as regime-affiliated mayors to be arrested. Second, we find that mayors are more likely to be arrested in large cities, although this finding is not fully robust to model specification. Third, we do not find evidence that performing well at mobilizing votes for United Russia helps mayors avoid arrest. Finally, we find no evidence that arrests are more likely after a turnover of governor.

These findings highlight the complex nature of repression in autocracies. We find that popularity can be just as important as professional connections or regime loyalty. We advance the literature on the role of personality, charisma and popularity (Bittner, 2018; Breslin, 2008; Eatwell, 2006; Feuchtwang & Mingming, 2001; Jones & Olken, 2005; Rahat & Sheafer, 2007) by emphasizing that personal popularity can matter just as that backstabbing maneuvers between political clans or following the party line can. In this way, our study highlights the complicated relationship that regime leaders have with popular officials. On the one hand, their popularity may be a threat, but this also makes them costly to dislodge.

Literature

The literature on elite repression in autocracies can be grouped into two broad categories: a literature on political purges, and a group of more recent studies on the purposes and effects of anti-corruption campaigns in authoritarian regimes. An extensive historical literature has documented the political purges of the 20th century, from the repeated mass arrests in the early Soviet Union (Pipes, 1994; Siegel, 1954) and Chiang Kai-shek’s repression of the Communists in Shanghai in 1927 (Fenby, 2003) to purges in Saddam Hussein’s Iraq (Aburish, 1999), Hafez al-Assad’s Syria (Van Dam, 2011), or Ethiopia under Mengistu (Wiebel, 2015).³

Drawing on these cases, an analytical literature has tried to understand why and when dictators carry out purges of the political or military elite. Keeping elites in check is a central concern in modern autocracies, as dictators are more likely to be overthrown in an elite coup than they are to be ousted in a mass uprising (Svolik, 2009). Across countries, elite purges are more likely to occur in non-democratic states (Carey, 2010; Hill & Jones, 2014), when the elite is temporarily weakened (Sudduth, 2017), or when the country is still relatively poor and cooptation is too costly (Bove et al., 2017). In addition to coup threats per se, the intensity of political purges also seems to depend on a number of additional strategic concerns, such as rules of political succession (Egorov & Sonin, 2015), the existence of multiple parallel threats to the ruler (Bueno de Mesquita & Smith, 2017), the need to enforce top-down accountability (Montagnes & Wolton, 2019), or the availability of information (see Gehlbach et al., 2016 for an overview).

This literature on purges is illuminating, but it suffers from several shortcomings. While the literature provides a wealth of theory and evidence to explain why a dictator might launch a purge, most studies lack a clear explanation for why certain elites are arrested, and others not. It also does little to help us explain patterns of elite repression in “normal” times, outside of well-defined purges. We seek to address these questions in this paper.

In addition to the literature on purges, a growing literature on anti-corruption campaigns has tried to understand why and when authoritarian regimes undertake mass arrests of political elites to fight corruption. Conceptually, one might expect a trade-off between tolerating corruption as a tool to coopt the elite (Gandhi & Przeworski, 2006) and the economic harm being done by excessive elite predation (Acemoglu et al., 2004; Dawisha, 2014; Pei, 2006; 2016; Rochlitz et al., 2020). An additional factor might be a desire to increase regime legitimacy in view of excessive elite corruption, independent from corruption being perceived as a direct threat to the regime or not (Burger & Gitau, 2010; Dai, 2018; Zhu et al., 2019).

While fighting excessive corruption may be an important motivation behind recent, intense anti-corruption campaigns in places like Russia, China, Saudi Arabia, Vietnam, and Iran, it is likely that these campaigns—at least in part—are also a cover for more typical purges of elites by the authoritarian leadership (Hubbard, 2020; Jiang & Xu, 2015; Lorentzen & Lu, 2018; Petrov & Rochlitz, 2019). Indeed, much of the literature on anti-corruption in China is focused on determining whether arrests are motivated by real corruption concerns or by factional disputes. A number of these studies suggest that personal connections to the top leadership can help protect officials from persecution (Goh et al., 2019; Jiang & Xu, 2015; Lorentzen & Lu, 2018).

Studies of purges and anti-corruption campaigns, often in China, make it clear that at least some arrests are politically motivated. Ultimately, however, it is very difficult to discern the proximate causes for arrests: fighting corruption and intra-elite political fighting frequently blur together. In this paper, we do not attempt to disentangle these two as such. Rather, we seek to explore the systematic political factors that make some officials more prone to being arrested, whatever the proximate cause. In other words, we do not attempt to disentangle whether a particular arrest is driven by true corruption, personal disputes, factional struggle, or some mixture of these factors. Instead, we analyze the factors that protect elites from prosecution, focusing attention on one factor in particular: personal popularity.

Popularity and Repression

Our main focus is on how the personal popularity of subnational officials affects their likelihood of undergoing criminal prosecution. Many scholars have argued that autocrats fear competent officials who might have the skills, resources, charisma and/or following to challenge their rule (Egorov & Sonin, 2011; Zakharov, 2016). Popularity may be a particularly threatening form of “competence.”⁴ Dictators may fear that popular leaders will use their influence to plot a coup, rally the masses, or aid the opposition. Indeed, history is replete with examples of high-level officials who are purged because of their charisma and influence. Leon Trotsky in Stalin’s Soviet Union, Ernst Roehm in Nazi Germany, Khin Nyunt in Myanmar, Jang Song-thaek in North Korea, and Bo Xilai and Zhou Yongkang in contemporary China are just a few such examples of elites who were eliminated largely because they were perceived to be too popular to be trusted. Consistent with this perspective, Gueorguiev and Schuler (2016) find that officials who are well-known in China and Vietnam—as measured by internet searches—are less likely to be promoted.

So, the idea that popular officials are particularly vulnerable to repression due to their popularity has substantial theoretical and historical weight behind it. But there is a flip side to such arguments. First, it has long been recognized that competent officials are an asset as well as a liability for autocrats. Dictators need competent officials who can get things done: implement policies, grow the economy, attract investment, mobilize the masses, win elections, outsmart rivals, and so on. As Huber and McCarty’s (2004) model of bureaucratic delegation shows, politicians find it hard to secure the implementation of their preferred policies when bureaucratic capacity is low.⁵

Another reason popular officials may be less vulnerable to repression than unpopular officials is that popular officials can serve more effectively as political surrogates for the regime. Such elites’ popularity is an asset that makes them well-positioned to mobilize the masses, quell protest, defend the regime, and generally implement regime policy. They can use their influence over other elites to help shore up elite support, or they may use their public popularity to help win mass support for the regime. This latter factor may be particularly important in electoral authoritarian regimes, where subnational officials are often tasked with mobilizing votes on behalf of the regime (Blaydes, 2010; Buckley & Reuter, 2019; Reuter & Robertson, 2012).

There is a second reason that popular officials may be more protected from prosecution. If an official is popular, jailing them can produce a popular backlash, especially if voters perceive that the arrest was politically motivated. This may be especially true for elected officials. Popular elected officials enjoy a mandate to govern and voters may retaliate against the regime if regime officials subvert their will by removing an elected official in handcuffs. Such backlash may come in the form of declining approval ratings for the regime, electoral punishment, or ,most worryingly for the regime, popular protest.⁶ In Russia, the most prominent recent example of popular backlash after the arrest of an elected official were the month-long protests following the arrest of the popular governor of Khabarovsk Krai, Sergei Furgal, in July 2020. Weekly protests with often many thousands of participants went on until the end of the year, and threatened to spill over into other regions.⁷

Several times, mass protests have also erupted in support of arrested mayors in Russian regions. For example, the 2013 arrest of Yaroslavl mayor Evgenii Urlashov sparked large-scale demonstrations that drew together disparate opposition forces. Prominent national opposition leaders from Moscow—including Ilya Yashin and Irina Prokhorova—traveled to Yaroslavl to address the protesters.⁸ Three years later, small scale protest in support of the mayor were still a semi-regular occurrence.⁹

In sum, there are two compelling sets of arguments about the relationship between an official’s popularity and their chance of being repressed. We argue that the application of a particular logic will depend on two factors: (1) the importance of public opinion to the regime and (2) the type of official.

Beginning with the type of official, the argument that dictators will repress popular elites applies better to national-level elites than it does to subnational officials. Aside, perhaps, from the leaders of capital cities and large provinces, subnational officials lack the platform and audience to effectively challenge the regime on their own. Moreover, subnational officials face significant collective action problems if they attempt to mount a unified challenge (see, for example, Shvetsova, 1999). Thus, leaders should be less concerned about the threat posed by popular subnational officials.

The relative importance of public opinion for regime stability also matters. While many things determine the relevance of public opinion for regime stability, one important distinction is the difference between regimes that hold multi-party elections and those that do not. In electoral authoritarian regimes, the regime cannot rely solely on coercion and fraud to win elections.¹⁰ Rather, such regimes need millions of voters to come to the polls and vote for the regime (or at least not vote for the opposition). This has two implications for our argument. First, the backlash caused by arresting popular officials is likely to be more costly in electoral authoritarian regimes than in single-party dictatorships. Second, because regime leaders in electoral authoritarian regimes rely heavily on subnational officials for vote mobilization, sacking a popular local leader will be particularly damaging for electoral authoritarian regimes (Blaydes, 2010; Reuter & Robertson, 2012; Reuter, 2013).

Thus, when it comes to repression of subnational officials in electoral authoritarian regimes—the empirical scope of this paper—we argue that popular officials will be less likely to suffer arrest. Since electoral authoritarian regimes are by far the most common type of contemporary autocracy and subnational officials are frequent targets of repression in these regimes (Freeman, 2018; Gel’man, 2016; Kwong, 2018; Turchenko, 2017), we think this perspective has the potential to generalize to other country cases. In the sections below, we examine and test this argument in the context of Putin-era Russia.

Data

Our study focuses on one prominent example of an electoral authoritarian regime, the Russian Federation. A comprehensive original biographical dataset covering the universe of mayors of Russian cities with a population over 75,000 from 2000 to 2018 forms the core of our analysis, including 1051 unique mayors from 220 Russian cities.¹¹ A team of research assistants collected a wide-ranging set of variables on these mayors: demographics, career histories, electoral outcomes and term-level details such as partisan affiliation. The data also classifies mayors according to their method of selection (i.e., elected or appointed). Thus, our data includes both appointed and elected mayors (glavy gorodov), as well as appointed city managers, who serve in cities with dual executive systems. For convenience and clarity, we informally refer to any of these city executives as “mayors,” even while their formal titles vary.

Eightymillion people, more than half of Russia’s population, live in the 220 most populous cities that we study in this paper. For this reason alone, the regime takes a keen interest in the heads of these cities. Because urban centers facilitate communication and collective action, large urban centers have historically been threats to authoritarian regimes (Hobsbawm, 1973; Wallace, 2013). In the past decade, support for the authorities has tended to be lower in cities than it has been in small towns and rural areas (Greene, 2014; Gabowitsch, 2016; Garrels, 2016; Greene & Robertson, 2019). Elections in cities therefore require special political attention and management. As a consequence, the Kremlin invests considerable energy in ensuring that pro-regime mayors win these elections, and win them well. As several studies have shown, the Kremlin relies in particular on United Russia mayors to help it mobilize votes, check the opposition, and stem protest in large cites (Gel’man & Ryzhenkov, 2011; Reuter et al., 2016).

To this mayor-level dataset, we added information on criminal cases against these mayors. The authors worked with research assistants to identify all instances of a criminal charge being brought against these mayors between 2000 and 2018. We also gathered data on whether the mayor was arrested, convicted, or both after being charged, as well as data on the specific charge and the ultimate sentence.¹² All data was gathered for criminal investigations that occurred while the mayor was in office and, to ensure all relevant cases are captured, for investigations that occurred within 4 years of a mayor leaving office. Not all charges result in arrest or conviction: sixty-seven percent of charged mayors whose case concluded prior to the end of our data were convicted. Being arrested is more likely to lead to conviction: 83 percent of those arrested are eventually convicted. As discussed below, we drop from our main analysis cases where the mayor is exonerated. For brevity, though at the cost of some precision, we refer to all cases in our analysis as “arrests” even if they were not formally arrested (arestovan/a).

Utmost care was taken to locate every criminal case against these mayors. Research assistants entered all 1051 mayors’ names and relevant search terms (arrest, charge, court case, etc.) into Google and leading Russian search engine Yandex in order to gather this information, with every case being coded by two RAs independently. In any case, criminal cases against mayors are high-profile events, certain to make news. For mayors that are arrested, news stories about the arrest almost always appear on the first page of search engine results when searching just on the mayor’s name. Still, it is possible that a few cases escaped our attention, especially in smaller cities or in the early 2000s (more on this below). Nevertheless, we are confident in having captured the overwhelming majority of criminal cases. To the best of our knowledge, this dataset and the extensive qualitative data it draws from are the first time that comprehensive information has been collected on criminal cases against authoritarian elites in Russia or neighboring countries.

In Table 1, we can see that by far the most common charge brought against these officials is corruption or closely related abuse of office. Economic crimes such as fraud make up much of the remainder. A small handful of cases concern violent offenses (murder, kidnapping)—we exclude these from the analysis. We do this because they are unlikely to be fabricated or politically motivated, and the extreme nature of these crimes means that no amount of popularity or political connection is likely to get a mayor out of criminal prosecution for murder or kidnapping.

Table 1.

Types of Charges Brought against Mayors.

Type of Charge	Proportion
Corruption	0.78
Economic crimes (e.g., fraud)	0.15
Miscellaneous	0.04
Violence	0.03

Our main dependent variable is constructed as follows. We first exclude all cases where the charges were dropped or the mayors were exonerated.¹³ Our only exception to this is that we include 13 cases where the year of the charge coincided with the year that the mayor left office. We make this exception because close examination of these cases revealed that the charge was likely a way to induce the mayor to resign, and the charges were later dropped as a condition of this. We also exclude charges that were filed more than 1 year after the mayor leaves office. On the other hand, we include charges that occurred just 1 year after leaving office because many cases are begun as ways of removing mayors from office, but they are only formally charged after the mayor is removed or resigns. Finally, we also drop all years prior to 2002. Since internet penetration was quite low in Russia’s smaller, more far-flung cities in the very early 2000s and many archival sources on today’s internet are less comprehensive for these years, the quality of information for this period is much lower. As a result, we cannot be fully confident that we have captured all arrests in 2000–2001. We drop these 2 years entirely so as to avoid possible bias from missed cases of arrests. As shown in the Supplemental appendix, results are robust to relaxing these coding rules in various ways.

The data contains, some interesting descriptive patterns. Figures 1 and 2 show the geographic distribution of the cities in our dataset, with darker coloration indicating more arrests. As one can see, these arrests are widespread across Russia. There do not appear to be obvious geographic patterns.

Figure 1.

Map of frequency of Mayor Arrests by city.

Figure 2.

Map of frequency of Mayor Arrests by city, detail.

Figure 3 shows how the number of mayoral arrests in Russia has changed over time. In 2015, there was significant discussion among political commentators about an uptick in repression against regional elites (Hale et al., 2019). This pattern is borne out by our data and arrests have remained elevated since then. A second, less recognized pattern, however, is the uptick in arrests that occurred in the mid-2000s. As we discuss below, this was a period when arrests were often used to remove oppositional or independent elected mayors.¹⁴

Figure 3.

Arrests of Mayors, 2002–2018.

Table 2 begins to explore some of the correlates of arrest. Observers of Russian politics will not be surprised to discover that opposition mayors are much more likely to be arrested than are independent or regime-affiliated mayors.¹⁵ The numbers are striking. One-third of all opposition mayors in Russia end up behind bars.¹⁶ Independent mayors fare somewhat better but are still almost three times as likely to end up behind bars as regime-affiliated mayors. Still, a full 8% of mayors affiliated with pro-regime political party United Russia also end up in jail. Indeed, since regime-affiliated mayors far outnumber opposition mayors, this is the vast majority of the sample. This clearly shows that mayoral arrests in Russia are not purely about targeting regime opponents.

Table 2.

Proportion of Mayors Ever Arrested, by Mayor Characteristics.

Mayor Characteristic	Total mayors	Number arrested	Proportion arrested
Regime affiliation
Oppositional	44	14	0.32
Independent	95	20	0.21
Regime-affiliated	771	62	0.08
Selection mechanism
Popularly elected mayors	265	54	0.20
Non-elected mayors	645	42	0.07
Executive status
Heads of cities	220	7	0.03
City managers	248	21	0.08

Table 2 shows that popularly elected mayors are nearly three times more likely than non-elected mayors to be arrested. This makes some sense since appointees who are anointed by the regime are less likely to be targeted with repression. This variable, however, is highly collinear with regime affiliation, so it makes more sense to examine these relationships in a multivariate setting, which we do in the following sections.

Finally, Table 2 also examines appointed mayors in cities with dual executives. In these cities, a city manager, selected by a local commission and local legislature, is responsible for issues of governance (khozyaistvo), while a head of city (glava goroda) is chosen from among the deputies in the legislature to serve in a mostly ceremonial function as the chief political executive. The table shows that city managers are about three times more likely to be arrested than are formal heads of cities (glava goroda). There are two possible interpretations. First, this could be evidence that actual corruption plays a role in arrests. City managers deal with the details of budgets, state contracts, and regulation, giving them much more opportunity than city heads to abuse office. Alternatively, this could simply be evidence that city managers have the real power in these dual executive cities.

Empirical Approach

This section describes how we set up the data for testing our argument. After combining the above mayor- and arrest-level information, we transform our dataset into mayor-year format whereby the officeholder on January 1 is counted as the mayor for that calendar year. Time-invariant mayor characteristics and term-varying characteristics like electoral margin of victory in each term are included in the appropriate years, as are arrest events and a set of city- and region-level measures. The dependent variable, Mayor Arrest, is the arrest variable described above. It takes a value of 1 in years when a mayor is arrested, 0 when they are not. For instances in which the mayor is arrested after leaving office, the variable takes a value of 1 in his or her last year in office.

In order to examine how popularity affects a mayor’s chances of arrest, we use data (for elected mayors) on their margin of victory in their most recent election. Unfortunately, public opinion data on the popularity of Russia’s mayors is unavailable, so we use electoral margins as a proxy. Thus, we are only able to examine this hypothesis for the subset of mayors who are elected.

Does vote mobilization help protect mayors from arrest? Russian subnational officials are often enlisted to help the regime mobilize votes (Beazer & Reuter, 2019; Reuter & Robertson, 2012). It may be that those who perform well at mobilizing votes for United Russia are more protected from arrest. We use data on the share of the vote received by United Russia in that city during the most recent regional elections held under the sitting mayor’s tenure. This leads to a large reduction in observations since we are not able to include data for the years that come after a new mayor enters office but before the next regional election.

In order to get a fuller picture of how mayors’ personal characteristics beyond personal popularity may affect their probability of being arrested, we also examine mayors’ professional backgrounds. To do this, we rely on a series of binary variables drawn from mayors’ career histories.¹⁷ For each type of professional history, we use two dummy variables: one that is equal to one if the given mayor ever worked in a given professional sphere and a second that is equal to one if the mayor’s most recent place of work was in a given professional sphere.

First, we investigate whether a professional history in the security services protects mayors from arrest: such connections to the FSB or Interior Ministry might help shield mayors from persecution. Second, we investigate whether work connections in the federal government help protect from arrest. Third, we investigate whether a professional background in the regional administration helps protect mayors from arrest. If, as some observers aver, arrests are driven primarily by political conflict between mayors and regional administrations, then professional connections in regional government may help mayors avoid prosecution. Indeed, if a mayor’s most recent place of work is the regional administration, it is likely that the mayor is a direct client of the governor. Finally, we also investigate whether mayors whose background is outside the city (“outsiders”) are more likely to be arrested with a dummy variable equal to 1 if the mayor’s most recent place of work is outside the city.¹⁸ One narrative about arrests in Russia is that they occur when mayors get involved in conflicts with the local elite. Outsiders are more prone to such conflicts (Kynev, 2018). In all of our main model specifications, we include a set of control variables, including: a dummy variable indicating whether there was turnover of the regional governor in that year, a measure of the non-Russian ethnic composition of the region, and the natural logarithm of the city’s population. We also include a variable capturing the level of regional democracy (the Petrov-Titkov measure for the region in the most recent year available, Petrov & Titkov, 2013).¹⁹ For all models, we run logistic regressions and include mayor tenure in linear, quadratic, and cubic transformations to produce an equivalent to survival analysis (Carter & Signorino, 2010).

Results

Table 3 shows the main results from our analyses. First, we find that work experience in various government structures appears to have little effect on a mayor’s chances of being arrested. In Models 1 and 2, we can see that none of the indicators of government or security service connections are significant. Work experience in the private sector appears to increase the risk of arrest, but this result does not hold in Model 2, which looks only at the mayor’s most recent work experience.

Table 3.

Personal Connections, Regime Affiliation, and Mayor Arrest.

	DV: Mayor Arrest
	(1)	(2)	(3)	(4)	(5)
Ever worked: Regional Gov’t	.003 (0.006)
Ever worked: Security Services	.012 (0.008)
Ever worked: Federal Gov’t	−.009 (0.013)
Ever worked: Business	.013** (0.006)
Most recent work: Business		.001 (0.013)
Most recent work: Local admin		−.006 (0.012)
Most recent work: Regional admin		−0.005 (0.014)
Most recent work: Regional legis		.013 (0.013)
Most recent work: Federal govt		−.007 (0.019)
Outsider	.005 (0.006)	.002 (0.007)
Non-elected			−.017*** (0.006)		−.010 (0.007)
Regime affiliated				−.034*** (0.007)	−.030*** (0.008)
New governor	.011 (0.007)	.009 (0.007)	.009 (0.007)	.011 (0.007)	.011 (0.007)
Percent ethnic Russian (region)	.015 (0.014)	.015 (0.014)	−.0003 (0.015)	.010 (0.014)	.003 (0.016)
Log city population	.005* (0.003)	.005* (0.003)	.005* (0.003)	.006** (0.003)	.006** (0.003)
Petrov-Titkov democracy (region)	.001 (0.0004)	.001 (0.0004)	.001 (0.0004)	.00001 (0.0004)	.00002 (0.0004)
Num mayors	963	973	1019	910	910
Num cities	220	220	220	220	220
Num regions	81	81	81	81	81
N	4158	4200	4367	3803	3803

Coefficients shown are average marginal effects from logistic regression with HC0 standard errors clustered at the region level. * indicates p < .1; ** p < .05; *** p < .01.

There are several interesting results on the control variables. We find no evidence that insider mayors are more (or less) likely to be arrested, and we find no evidence that governor turnover is associated with more mayoral arrests. In some specifications, city size has a positive effect on arrest probability, with mayors in large cities being more vulnerable.²⁰ Mayors in large cities are more likely to enter into conflict and pose a threat to regional governors, which may explain this finding. Finally, we find that mayors are slightly more likely to be arrested in more democratic regions.

In Models 3–5, we examine how regime affiliation and being elected (as opposed to appointed) affect the probability of arrest. Model 3 indicates that appointed mayors are significantly less likely to be arrested. However, since almost all appointed mayors are affiliated with the regime, this variable is highly collinear with partisan affiliation. In Model 4, we look at the effect of regime affiliation on being arrested using a dummy variable equal to one if the mayor is regime affiliated and zero if he or she is not.²¹ Consistent with the descriptive results in Table 2, pro-regime mayors are significantly less likely to be arrested. The effect size is very large. The predicted probability of a pro-regime mayor being arrested in any given year is 2%. For oppositional mayors, the probability increases more than threefold to 7%.

In Model 5, we enter both Non-Elected and Regime Affiliated in the same model. The latter remains significant, while the former drops just below statistical significance. Thus, we are confident that regime affiliation has an effect on the probability of arrest, but we are less certain about the effect of being elected.

In Table 4, we examine how regime vote mobilization and mayor popularity affect the probability of arrest. In Model 1, we can see that getting out the vote for United Russia has little impact on a mayor’s chance of avoiding arrest. This result holds while controlling for regime affiliation. In Model 2, we find strong evidence that winning one’s election by a large margin helps protect a mayor from arrest. The coefficient on Mayor Margin of Victory is statistically significant and substantively large. A mayor who won his or her election by 77 percentage points (the 90th percentile on this variable) has just a 1.5% chance of being arrested in any given year. However, if a mayor won their election by 8 percentage points (the 10th percentile in this data), the predicted probability of arrest in any given year more than triples to 5.2%

Table 4.

Regime Service, Mayor Popularity, and Mayor Arrest.

	DV: Mayor Arrest
	(1)	(2)	(3)	(4)	(5)
Regime affiliated	−.016*** (0.006)	−.009 (0.007)	−.010 (0.008)
UR vote share in regional elections, by mayor	−.00003 (0.0003)		.0001 (0.0005)
Mayor margin of victory		−.001*** (0.0002)	−.001** (0.0003)	−.0002 (0.0002)	−.001*** (0.0003)
New governor	−.004 (0.011)	.018 (0.014)	.003 (0.018)	−.030 (0.030)	.054*** (0.020)
Percent ethnic Russian (region)	.006 (0.020)	−.021 (0.036)	−.046 (0.036)	−.036 (0.027)	0.122 (0.117)
Log city population	.003 (0.003)	.004 (0.004)	.004 (0.005)	.004 (0.006)	.0005 (0.007)
Petrov-Titkov democracy (region)	.0003 (0.001)	.0004 (0.001)	.001 (0.001)	.0004 (0.001)	.00001 (0.001)
Num mayors	624	321	272	186	180
Num cities	219	181	173	145	138
Num regions	81	67	64	61	57
N	2358	1729	1210	902	827

* indicates p < .1; ** p < .05; *** p < .01.

Coefficients shown are average marginal effects from logistic regression with HC0 standard errors clustered at the region level.

Model 3 enters both Mayor Margin of Victory and UR vote share in the same model. The former remains significant and negative, while the result on the latter remains unchanged. Both United Russia’s electoral performance (in the city) and the mayor’s own margin of victory are plausibly influenced by the strength of the mayor’s political machine. But the mayor’s own margin of victory is a much cleaner measure of the mayor’s personal popularity. The fact that we find results for Mayor Margin of Victory, but not for UR’s electoral performance suggests that the impact of Mayor Margin of Victory on arrest likelihood is driven more by the mayor’s popularity than it is by the ability of the mayor to deploy administrative resources.²²

In Models 4 and 5, we explore our main results further by splitting the sample according to regime affiliation. Model 4 reproduces Model 3, but only uses the subset of mayors who are regime affiliated. Model 5 shows mayors who are independent or oppositional. The coefficient is negative in both models, but is only significant for non-regime mayors. The fact that the result is significant for non-regime mayors suggests that fear of popular backlash drives at least some of the results on mayor popularity. If popular mayors were not arrested solely because the regime wanted to rely on their political capital (or if only those with access to administrative resources were less likely to be arrested), then we would expect that this result would only hold among pro-regime mayors. But the fact that it holds among mayors not aligned with the regime suggests that there is more to the story. Since the regime cannot reliably draw upon the political machines of these mayors, it stands to reason that the regime’s fear of popular backlash is what is driving this result.

The weak results among pro-regime mayors are peculiar. One interpretation is that a mayor’s margin of victory is a much noisier signal of regime popularity among United Russia mayors than it is among non-regime mayors. For pro-regime mayors, their margin of victory is jointly determined by their own popularity and the use of pro-regime administrative resources. For non-regime mayors, the margin of victory is a cleaner measure of their popularity.²³

In Table 5, we explore the robustness of our main result. Model 1 shows that Mayor Margin of Victory remains substantively and statistically significant in a minimal model with no controls. It is also unchanged when we control only for the regime affiliation of the mayor (Model 2). In Model 3, we drop Moscow Oblast from the models. Moscow Oblast contains many mid-size cities, but almost all of these cities are suburbs of Moscow—politics in the federal capital could easily spill over and overwhelm local considerations in these cases. Model 3 shows that the results are robust to dropping this region. Model 4 examines whether the Mayor Margin of Victory result is contingent on the size of the city. It could be that the Kremlin is less willing to arrest popular mayors in large cities, where the arrest would have special resonance. We find no evidence, however, that the result depends on the size of the city. Models 5 and 6 explore alternative operationalizations of the dependent variable. Model 5 uses a more expansive coding of arrests than in previous models. Here, the dependent variable counts all charges against mayors, irrespective of the outcome (conviction, exoneration, etc.), as arrests. It counts charges that occur while the mayor is in office or within 1 year of leaving office. Model 6 uses the most “restrictive” dependent variable, only treating charges against mayors who were in office at the time of the charge and that eventually result in conviction as arrests. Finally, in the supplemental appendix, we examine whether this result changes when we use alternative codings of arrests. We find that it does not. A full description of these models can also be found in the supplemental appendix.

Table 5.

Robustness.

	DV: Mayor Arrest
	(1)	(2)	(3)	(4)	(5)	(6)
Regime affiliated		−.010 (0.007)	−.011 (0.008)	−.010 (0.007)	−.010 (0.007)	−.008 (0.006)
City population × Mayor margin of victory				.0001 (0.0002)
Mayor margin of victory	−.001*** (0.0002)	−.001*** (0.0002)	−.001*** (0.0002)	−.001 (0.002)	−.001*** (0.0002)	−.001*** (0.0002)
Percent ethnic Russian (region)			−.010 (0.041)	−.021 (0.035)	−.013 (0.040)	.021 (0.043)
Log city population			.0001 (0.001)	.0004 (0.001)	.0004 (0.001)	.0004 (0.001)
Petrov-Titkov democracy (region)			.002 (0.004)	.002 (0.007)	.0002 (0.005)	.001 (0.004)
Num mayors	396	321	275	321	321	321
Num cities	191	181	156	181	181	181
Num regions	72	67	66	67	67	67
N	2145	1729	1441	1729	1700	1729

* indicates p < .1; ** p < .05; *** p < .01.

Coefficients shown are average marginal effects from logistic regression with HC0 standard errors clustered at the region level.

Finally, in Supplemental Appendix Table A3, we explore some extensions of our theoretical framework above. We have argued that because leaders in electoral autocracies must court public opinion in order to win elections, they would be more sensitive to the possible backlash from arresting popular mayors. One plausible extension of this logic would be to argue that when leaders—in any regime—are more popular they would be more emboldened to arrest officials. Popular autocrats have more political capital to spare and may have to less to fear from local protests or discontent. Might it then be the case that arrests in Russia go up when the regime is more popular?

This is a hard hypothesis to test with our data. For one, Putin’s popularity at the national level only varies in a narrow band between approximately 60% and 88%. It varies more at the regional level, but unfortunately, regionally representative data on Putin’s popularity is unavailable for the time period we are analyzing. Still, as Supplemental Figure A3 suggests, arrests go up when the regime is popular. Note that the highest peak in arrests comes in 2015 amidst the post-Crimea surge in regime popularity. Note also that the second highest peak in arrests comes in 2007, when Putin’s popularity reached its highest point in the 2000s. By contrast, arrests were relatively infrequent between 2011 and 2013, when regime popularity reached its nadir.

In Supplemental appendix Table A3, we probe this relationship more systematically and enter Putin’s national popularity as a predictor in our baseline model. Since our data is aggregated at the yearly level, our measurement of Putin’s popularity necessarily becomes a very coarse yearly aggregation (taken for January of each year). Despite this we find a positive—though weak—association between Putin’s yearly popularity and the prevalence of arrests (p < .1). In Column 2, we interact Mayor’s Margin of Victory with Putin’s yearly popularity and, interestingly, we find some evidence to suggest that effect of Mayor’s Margin of Victory is attenuated in years when Putin is less popular. When Putin’s popularity is 65% the marginal effect Mayor’s Margin of Victory is −0.007 and is statistically significant. However, when Putin’s margin was at its historical highs, the marginal effect of Mayor’s Margin of Victory drops to 0.000 5 and is no longer statistically significant. However, these findings are only indicative of a trend; with the limited sample size, the difference between these conditional coefficients is not statistically significant.

Conclusion

What protects subnational political elites in autocratic contexts from being repressed by their own regimes? Where scholarly literatures on purges and anti-corruption campaigns find that being connected to the wrong network can put one in the line of fire, we uncover a personal asset that can help shield from danger. Our analysis shows that being popular or having political capital to spare is the only factor that consistently protects Russian mayors from repression. At least in this context, popularity matters more than whom you know or what you have done.

In Russian city halls, neither pedigree, friends in high places, nor dutiful service done for the regime’s benefit are exculpatory when a target is drawn on your back. We muster evidence from a new dataset covering all mayors in large Russian cities and all cases of arrest or prosecution of these mayors to show that the only reasonably reliable protection a municipal leader has against repression is to be fundamentally politically strong. Strength may come in the form of personal brand, ability to lead your constituents in your defense, or highly valuable political capital that you can apply to your own reelection.

Our analyses paint a more complicated picture of elite repression than may be found in much of the work in this area. While purges and similar attacks on officialdom may be largely about intra-palace (or at least intra-regime) intrigue and informal ties in some contexts, this is not universal. Nor are autocracies solely concerned with their cadres performing well on regime-assigned tasks like getting out the vote. They also struggle with managing—selecting, controlling, and eventually perhaps eliminating—intrinsically powerful individuals within their ranks. In this way, we show how efforts to establish meritocracies around principles of either technocratic performance on the one hand or loyalty on the other can fall flat in the face of personal ambitions. Personal cachet matters—even relatively low down within autocratic regime structures and even if higher-ups may wish it were not so.

This is not to say that whom you know or what you have done for the regime is irrelevant. Nor is personal political capital all-powerful. Outright repression of one’s own political elites is a quite extreme step for a regime to take. Given that autocracies very often seem willing to take that step, the fact that they may hold back when their potential target is personally popular highlights the importance of that feature. Future work on elite repression can explore the mechanisms by which popularity acquires this power. Scholars can also work to identify heterogeneity in how connections, popularity, and relative “rank” within authoritarian regimes interact to determine who is promoted, who is pushed out, and who is ultimately punished.

Finally, future research might also broaden the scope of analysis in order to examine repression as a tool alongside other tactics, such as cooptation. In order to keep our analysis tractable, we have focused only on arrests. But a fuller analysis of this problem might profit by considering, both theoretically and empirically, how regime leaders manage the trade-off between repressing powerful elites and coopting them. The analyses in this paper suggest that even in a consolidated autocracy, such as Russia, popular local officials are, on average, less likely to be targets for arrest. But the extent to which regime leaders feel a need to go a step further and coopt and placate such officials (e.g., with rewards or promotions) remains an open question.

Supplemental Material

sj-pdf-1-cps-10.1177_00104140211047399 – Supplemental Material for Staying Out of Trouble: Criminal Cases Against Russian Mayors

Supplemental Material, sj-pdf-1-cps-10.1177_00104140211047399 for Staying Out of Trouble: Criminal Cases Against Russian Mayors by Noah Buckley, Ora J. Reuter, Michael Rochlitz and Anton Aisin in Comparative Political Studies

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5-100.”

ORCID iD

Michael Rochlitz

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Noah Buckley Noah Buckley is Assistant Professor in the Department of Political Science at Trinity College Dublin and Research Fellow at the International Center for the Study of Institutions and Development at the Higher School of Economics. His research is on authoritarian politics, Russian elites and public opinion, and corruption.

Ora John Reuter is Associate Professor in the Department of Political Science at the University of Wisconsin–Milwaukee and Research Fellow at the International Center for the Study of Institutions and Development at the Higher School of Economics. His research is in the areas of comparative political institutions, authoritarianism, elections, democratization, comparative political economy, and Russian politics.

Michael Rochlitz is Professor of Institutional Economics at the Faculty of Business Studies and Economics at the University of Bremen and Associate Fellow at the International Center for the Study of Institutions and Development at the Higher School of Economics. His research is on the interrelation between political institutions, economic development and societal change, with a regional focus on Russia and China.

Anton Aisin is an independent researcher and private consultant residing in Moscow, Russia. His research is on Russian elites, comparative political institutions and economic development.

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