Political Machines and Regional Identities: Evidence from Post-Soviet Ukraine

Grigore Pop-Eleches

(Princeton University)

gpop@princeton.edu

and

Graeme B. Robertson

(University of North Carolina at Chapel Hill)

graeme@email.unc.edu

Note: Highly preliminary. Please contact authors about most recent version.

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Introduction

Ever since independence, a scholarly, and sometimes highly political, debate has raged around the question of political identities in Ukraine. This debate, which is now being literally fought on the streets of eastern Ukraine, centered around what kind of identity was possible or necessary in a state that brought together the borderlands of the Polish, Habsburg, Russian and Ottoman Empires (Szporluk 1997; 86). This debate has had a number of different aspects. One key issue was the extent to which the Ukrainian state would be able to build a sense of Ukrainian nationalism that would bind the country together for the long run (Kuzio 1998), or whether indeed such a sense of nationalism was necessary at all (Zimmerman 1998). Another element, driven in large part by the sharply divided electoral map of Ukraine, which repeatedly saw the eastern and western parts of the country supporting different candidates and parties in elections to national office (even if candidates, like Leonid Kuchma, could sometimes successfully switch sides), was about the source of this divide. It is to this latter discussion that we seek to contribute here.

In thinking about the underlying sources of the political divide in Ukraine, scholars and analysts have put forward a range of different theories, which might usefully be thought of in two broad categories. One category of argument sees the regional cleavage apparent on maps as being largely the product of unevenness in the distribution of traits across the country. Different scholars have emphasized different traits, with some seeing the key trait as being language, others ethnicity, and still others some combination of the two (Arel 2014, Kulyk 2012, Ryabchouk 1999). In a different vein, some have argued that the real difference is not ethnicity or language but policy preferences that derive from some other source (Frye 2014). However, whether the emphasis was on language or ethnicity or policy, the emphasis in this group of studies is on the individual level and the distribution of characteristics of individuals.

A second group of scholars, by contrast, while not rejecting the idea that there is lumpiness in the distribution of traits, sought deeper explanations of the cleavage in factors that were inherently spatial in nature. Some versions of this story emphasized historical boundaries and sought to show how the particular placement of the borders of the Russian and Austro-Hungarian empires and of the Polish state had important consequences for how language, ethnicity and political preferences are distributed (Katchanovski 2006a, Peisakhin 2013, Darden 2014). Others, in a style more akin to studies in political or economic geography emphasized the structures of economies and trade as being crucial in shaping the regional political identities, relegating ethnicity and language to a less important role (Barrington 2002).

In this paper, we develop a related argument about the importance of political geography in shaping cleavages in Ukraine. Drawing on work in the political geography of regions and regionalism in the European Union, we look at the development of regional political identities and the informal institutionalization of these identities. Focusing specifically on the Donbas region of eastern Ukraine, we make the case that Donbas has developed its own political identity

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that is separate from ethnicity and language and more consequential for patterns of political trust than either of these two factors. We also argue that this Donbas identity has different political effects than identities associated with other parts of eastern Ukraine. The process from which this identity emerged, we argue, has both historical (Soviet and pre-Soviet) roots and contemporary elements in the construction of the Donbas political clan as a force on the regional and national political stage in the late 1990s and 2000s, and these different phases help to explain the particular content of the identity as political and economic rather than cultural.

Our argument is clearly of interest to scholars and policy-makers focused on Ukraine, but in showing the political nature of cleavage formation and structure, we are also making an original contribution to the understanding of how “regions” become subjects of political life above and beyond the characteristics of the individuals inhabiting them. From Catalonia to Kyrgyzstan, and in dozens of countries around the world, regions have become important political identities that seem to independently shape voting patterns and political behavior beyond what underlying demographics or patterns of opinion would suggest. In British politics, for example, voting patterns in Scotland have diverged radically from the rest of the UK over the last 20 years, even while underlying distributions of attitudes to social services and other government functions remain remarkably similar across the island (Patterson 2014). In the US, scholars have demonstrated too that even controlling for demographic factors, there is a substantial impact on voting that can be put down to differences in state level “political culture” (Erikson et al. 1987).

A key question in this literature concerns the origins and nature of this “regional effect”. In much of the literature, the regional effect is clear but is treated as something of a black box. In this paper, we attempt to unpack different elements of the regional effect, and in doing so demonstrate the highly political nature of that effect, at least in Ukraine. It has already been amply demonstrated that the overall context of formal institutions is important in shaping political behavior (Posner 2004). In this paper, we build on work in political geography and argue that regional identities and behaviors are also shaped by less formal structures such as political machines that create economic incentives for political allegiances to exist independently of or in addition to demographic and cultural characteristics. To illustrate this, we show that only a small part of differences in political attitudes across Ukraine’s regions can be explained by ethnicity, language or levels of development. Moreover, we show that while there is some role for older historical legacies in explaining such differences, there is considerable variation within eastern Ukraine that cannot be explained by deep historical legacies, but that matches patterns of political machines and organization in the post-Soviet era. As a test of our argument, we report the findings of a survey of Ukrainian citizens undertaken following parliamentary elections in the fall of 2012. We use a combination of observational data and embedded survey experiments to evaluate the relative importance of regional, ethnic and linguistic factors.

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Regions and Politics

In this section, we start to unpack the meaning and origins of “regional effects” in politics. Drawing on literature in political geography, we argue that for a regional effect to be present, regions must be more than just an aggregation of individual level factors. Instead, regions emerge as important units in politics when they become part of the conceptual map of politics in the mind of citizens and when particular geographical terms connote specific political meanings that are either shared or contested by different actors in the political space.

The principal theoretical challenge in talking about regions in politics is to distinguish effects that are truly regional in nature from the compositional effects of things like ethnicity, language and socio-economic status that happen not to be uniformly distributed across space. Some sociological traditions have tended to see “regionalism” as being largely a function of fundamentally non-spatial processes based on class or ethnic status hierarchies that just happen to be geographically concentrated (Laitin 1978, Ragin 1977). Political geographers, by contrast, have resisted this collapsing of space and instead have argued that regional differences in the world are becoming more not less important under conditions of globalization and regions are “central” rather than “derivative of nonspatial process” Agnew (2000). For “region” to have an effect then, that effect must be more than simply a result of the particular distribution of other theoretically relevant traits across individuals.

Nevertheless, the concept of what a region might be has been “elusive” among geographers (Agnew 2000). Although there is wide agreement that “region” matters, just what about it matters is the subject of much debate. Much of the literature has focused on the European Union, whose integrative project and institutional reach directly to sub-national levels of government within member states has given rise to a whole host of new ways of thinking about political territory beyond the Westphalian model. One major strand in this literature is in economic sociology, where scholars have placed heavy emphasis on economic networks as driving the formation of regional identities. Thus, economically integrated spaces like Silicon Valley or Route 128 come to be considered as regions even when they may or may not map on to other previously existing political or cultural concepts of region (Keating 1998, Saxenian 1994).

For others there is a political or mobilizational element around notions of self-governance that seek to construct what Jones and MacLeod (2004) call “spaces of regionalism.” This idea is developed by Paasi, who argues that regions are the result of a “socio-spatial process during which some territorial unit emerges as part of the spatial structure of society and becomes established in different spheres of social action and social consciousness” (Paasi 1986; 121). The culmination of this process, he argues is institutionalization, when regions take a formal place in the politics of the state and beyond. However, institutionalization need not consist of formal institutions around spaces of regionalism. Instead the politics of region in Europe today “is characterized by multidimensionality, complexity, fluidity and non-conformity and by the fact

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that it involves a variety of state and non-state actors that often come together in rather informal ways” (Paasi 2009; 127).

In the rest of this paper, we take this general understanding of regions as a starting point for analyzing politics in contemporary Ukraine in general and the Don basin particular. We do this in two ways. First, we unpack the effect of “region” by looking at responses to different kinds of questions on a survey and examining the extent to which there is a “regional” effect over and above cultural and demographic characteristics of individuals. We look first at broad issues of political orientation such as preferences for close relations with the EU over Russia, attitudes towards language policy in Ukraine and attitudes toward democracy. We demonstrate that region has minimal impact on attitudes towards democracy and it plays an important role in explaining attitudes on language and foreign policy, even after we control for cultural and developmental variables. Using the same questions, we explore intra-regional differences within eastern Ukraine to see how patterns of political patronage machines influence patterns of opinion across the region. Second, we use survey experiments to demonstrate these effects in action, showing that region plays a more important role in shaping patterns of trust on economic promises than on promises related to ethnicity and inter-ethnic cooperation. Taken together, we argue that these results illustrate the importance of post-communist political organization in explaining patterns of opinion and cleavage formation in Ukraine.

Regions and Politics in Ukraine

There is little doubt in reading the literature on politics in Ukraine over the last 25 years that there is a regional dimension to politics. Study after study has focused on regional differences in Ukrainian politics. Nevertheless, the origins of those differences are the subject of some debate. In this section, we outline the existing arguments before proposing our own theory in the next section.

Most salient in journalistic and popular accounts of politics in the country are ethnic and language issues. Unpacking ethnicity and language in Ukraine is complicated. Language is clearly a major divide, with Ukrainian being dominant in the west and Russian in the east (Arel 2014), and language use has been shown to be a strong predictor of policy attitudes. Indeed, legislation on language use has been one of the hot-button wedge issues of post-Soviet Ukrainian politics. Similarly, ethnicity is a major issue that is related to language use, though is not necessarily closely tied to language practice, with many Russian speakers self-identifying as ethnically Ukrainian. Scholars have critiqued the notion that political identities in Ukraine are best understood either through the prism of language or ethnicity (Kulyk 2012). Instead, language and ethnicity intersect in ways that are not straightforward. Some Russian speakers identify as Ukrainian, and, as our data suggest, even some Ukrainian speakers identify as Russian. Consequently, as Pirie put it “Language usage is an important factor which informs national self-identification, and political attitudes, but it should not be regarded as the Alpha and Omega of national identity in Ukraine” (1996; 1081)]. Responding to these arguments, in this

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paper we treat language and ethnicity as intersecting in politically important ways. To do so, we divide respondents into four groups that we think are likely (and we find) to be politically consequential – Russian speaking people who identify themselves as ethnically Russian, Russian-speaking people who identify as ethnically Ukrainian, Ukrainian-speaking people who identify as Russian and others. To the extent that language and ethnicity explain differences between regions, then there is support for interpreting differences as compositional in nature rather than as being true regional effects.

More popular in scholarly accounts of regional differences in Ukraine are cultural factors with deep historical roots that go back to times when Ukraine was divided among different empires (Katchanovski 2006a, Peisakhin 2013, Darden 2014). Accounts differ somewhat on what the key mechanisms and important cleavages are, but differences in the degree to which people in different parts associate or do not associate with Russia, in the nature and intensity of religious practice and in attitudes toward the state are often explained in terms of legacies from the Habsburg, Polish or Russian empires. Such differences go beyond compositional effects, since they are thought to constitutive of how identities were constructed before the adoption of contemporary ideas about ethnicity and language. The political culture argument holds, broadly speaking, that differences in political history, most notably legacies of empire and incorporation into the USSR have led to clear differences in outlook that hold independently of economic and other factors (Katchanovski 2006a). For example, citizens from Galicia in the West will, controlling for other factors, feel themselves to be more European than citizens from the east for reasons that reach deep back into the historical trajectory of Ukraine.

A third explanation for regional differences in Ukraine focuses on economics. The economic version of the argument holds that the particular configuration of economic circumstances in the region will shape individual attitudes over and above the effect of individual level economic circumstances. A citizen of the east will be more likely to support intervention in the market even if he is small business owner than a citizen of the west because of the industrial structure of the economy in the east (Birch 2000). Birch exploits the fact that these two sets of factors do not overlap precisely in order to carefully assess the relative impact of political culture and economic context. She finds both effects at work, but also illustrates the dominance of economics in that relationship. Barrington (2002) uses a mixture of the two arguments, looking at nine regions including Crimea, the East (Donetsk, Luhansk and Kharkiv) and the northeast (Chenihivsk and Sumska). Barrington argued, based on 1998 public opinion data, that region was a better predictor of an index of support for the regime than the macro regions, though the construction of the regions based on proximity to Russia and dependence on industry or agriculture was not developed.

We adopt a different approach to regional effects. We do not deny the very substantial evidence for the importance of history, political culture and economic interests in shaping political and social cleavages in Ukraine. Nevertheless, as we demonstrate below, there is more to the story of the politics of regions in Ukraine than these large and hard to change structural

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factors. Alongside history and economics, and operating within the framework created by them, is a post-Soviet process of political construction of meaning that shapes how citizens understand regions, politics and politicians in contemporary Ukraine, and this process has been most marked in the Donbas region. Consequently, in defining regions in Ukraine, we partly follow the approach of Katchanovski (2006b) in looking at macro-regions defined as east, west, south and center. However, since we are particularly interested in the Donbas and differences between the Donbas regions of Donetsk and Luhansk, we disaggregate the macro-region “East” into smaller units, treating Donetsk, Luhansk and Crimea separately from the rest of the East (O’Loughlin 2001).1

The Political Construction of the Donbas

In this section, we outline the political process by which post-Soviet Donetsk has come to occupy the particular semantic space it holds in contemporary Ukrainian politics. We divide the process into two (chronologically quite unbalanced) periods – before and after Ukrainian independence. The mass political culture and economic structure of Donetsk specifically, and the Donbas more broadly were shaped in by industrialization in the Soviet era. However, the political meaning of those structures has changed over time, with the Donbas going from occupying a space as the most Soviet of places to playing a key role in securing Ukrainian independence from the USSR. In the post-independence era, Donetsk gradually moved from being a self-absorbed (and rather violent) zone that follow the mantra of “politics is done in Kyiv and business in the Donbas (Zon 2005; 79) to launching a take-over of Ukrainian national politics that ended in revolution.

While, to our knowledge, this is one of the first efforts to understand the politics of region in Ukraine in this way, the “socio-spatial” process (Paasi 1986) by which the Donbas has come to have a particular meaning in the “spatial structure” of politics in Ukraine is both multifaceted and widely known. The term Donbas refers to the coal mining area that comprises Donetsk and Luhansk oblasts in Ukraine and parts of Rostovskaya Obast’ in Russia. Historically, although the Donbas has never been a single administrative unit (Kuromiya 1998; 14), the Donbas has had a strong regional identity since its founding in the nineteenth century by Welsh coal magnate John Hughes.2 In the Soviet period, Donetsk developed into a major industrial center and became one of the jewels in the Soviet’s industrial crown, a testament to development and an avatar for the creation of a Soviet working class and industrial society. Between 1897 and 1959, the population of the Ukrainian side of the Donbas alone increased from 700 000 to nearly 7 million (Kuromiya

                                                            1 In the West macro-region we include the oblasts of Volyn, Zakarpatska, Ivano-Frankivsk, Lviv, Rivne, Ternopil and Chernivtsi. In the South, we include Mykolaiv, Odesa and Kherson oblasts, and in the Center, Vinnytsia, Zhyotmyr, Kyiv Oblast, Kyiv City, Kirovograd, Poltava, Sumy, Khmelnitsk, Cherkasy and Chernigiv. In the East, we separate out Donetsk, Lugansk and Crimea, leaving Dnipropetrovsk, Zaporijia and Kharkiv Oblasts as the rest of the East. 2 While by no means all of either Donetsk or Luhansk are industrial (Osipian and Osipian 2006), we are interested here more in the political perception of the Donbas and Donetsk rather than the underlying reality on the ground.

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1998; 14) and as it grew the city and region developed political clout to match its industrial might.

However, by the late Soviet period, Donetsk and the broader Donbas coal producing region were already starting to decline as the focus of coal production shifted to the Far East (Zimmer 2004; 2). Donbas coal miners subsequently were to play a key role in the collapse of the USSR as they threw their support behind Boris Yeltsin and Ukrainian nationalists in their struggle against Mikhail Gorbachev and those seeking to hold the Soviet state together. A key element of this decision was the belief prevalent among miners that the coal produced in the Donbas would find more lucrative markets and would generate higher incomes in private hands and outside of the Soviet framework. Unfortunately for the citizens of the Donbas, these hopes were quickly disappointed and the new era had catastrophic economic consequences for the region (Mandel 1993). As a result, by the mid-1990s, attitudes to the Soviet period had switched from being critical to being very strongly positive, with the Soviet era being associated with honest and reliable government and a more prosperous life (Hrytsak 1998).

Nevertheless, while the late 1980s and early 1990s were catastrophic for many in Donetsk (Seigelbaum and Walkowitz 1995), it was in this period that the specific form of political organization that has come to be associated with Donetsk started to form. With the passage under perestroika of the Law on Cooperatives and the Law on Enterprises, networks of small firms started to appear around the large industrial combines of the Donetsk region, often owned and run either by directors of the large state firms or often members of their families. These small firms were used as a device to squeeze money out of the state-owned enterprises and vast fortunes began to be made in Donetsk. These fortunes were in turn used build networks of economic and political power, as representatives of Donetsk firms slowly squeezed Communist incumbents out of power. The mid 1990s saw competition among the emergent financial industrial groups lead to dramatic instances of violence, with leading politicians and businessmen being assassinated. Slowly, however, a more unified group of Donetsk elites, which came to be known as the Donetsk clan, emerged in the name of regional autonomy, seeking to keep Kyiv-based and other oligarchs out of the economic and political affairs of the region (Zon 2005; 78-9). It was at this time – 1997 – that Viktor Yanukovych, a local businessman and protégé of the leading Donetsk oligarch, Rinat Akhmetov, was appointed Governor of Donetsk. It was at this time, according to Zon (2005; 79) that an agreement was reached with Kyiv that Donetsk would be left alone by the government in Kyiv, in return for support for President Kuchma (who in turn represented the Dnipropetrovsk clan, another Eastern power center in post-communist Ukraine).

While Donetsk may have been one of the most profitable business locales in Ukraine, the domination of its politics by financial industrial groups that subordinated state institutions to their interests was by no means unusual in Ukraine. Similar arrangements were in place across the country and some of the groups, such as the Dnipropetrovsk clan had an ever higher national profile than the Donetsk clan. A key change in the role of Donetsk in the spatial structure of

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Ukrainian politics, however, came with the movement of the Donetsk clan and Yanukovych out of Donetsk itself and into national politics. This process began with President Kuchma’s unexpected victory over his Communist opponent and Donetsk native in Donetsk oblast in the 1999 presidential election. The machine that delivered Kuchma’s victory was then formalized as the Party of Regions, founded by Yanukovych, which went on to establish itself as a key parliamentary bloc in the Rada elections of March 2002. Yanukovych was appointed Prime Minister and he brought along members of his own team to take key positions in the State Property Fund, the Ministry of the Economy and the Prosecutor General’s office (Zimmer 2004; 4-5). This move of the Donetsk clan into politics in the capital has been a key factor in shaping both how citizens of Donetsk view politics in Ukraine and how citizens of Ukraine perceived the role of Donetsk in Ukrainian politics.

In 2004, the takeover of power in the center by the Donetsk clan seemed to be complete as Kuchma supported Yanukovych as the president’s preferred successor. With the support of Kuchma and Moscow, Yanukovych looked likely to win the election and consolidate his power. What happened next -- the Orange Revolution in which opponents of Yanukovych used massed rallies to pressure the Supreme Court into calling for a re-run of the Presidential election which Yanukovych lost, is well known. The perceptions of what was a stake in the Orange Revolution were diametrically opposed between eastern and western Ukraine, but more particularly between Donetsk and the rest of Ukraine. In western Ukraine, Yanukovych was presented as a Donbas bandit with a criminal record bent on seizing control with Russian support , while in Donetsk newspapers, Viktor Yushchenko was portrayed as a criminal and a bandit out to lay waste to the Donbas on behalf of fascist and foreign forces (Osipian and Osipian 2006, Kuzio 2012). Although many assumed at the time that Yanukovych’s political career would be over when he resigned the prime ministership and conceded defeat in the presidential election, Yanukovych used his continuing power in Donetsk not only to stay on the political scene but to return to the office of Prime Minster in 2006 and to the Presidency in 2010.

The result of these political developments in the post-communist period is that the Donbas in general, and Donetsk in particular, has come to assume a distinctive meaning in Ukrainian politics and one that separates it out from the rest of eastern Ukraine, despite a largely shared linguistic, ethnic and even economic context. Citizens of Donetsk have maintained and even increased their sense of political separateness and uniqueness. As Osipian and Osipian (2006; 499) document, “Donbas positions itself not only as a separate part of Ukraine but also as equal to it”. Consequently, we would expect that even controlling for ethnic and linguistic factors, there are likely to be marked particularities in how citizens of Donetsk oblast related to politics in Ukraine that make them different from others even within the same macro-region of eastern Ukraine.

Just as Donetsk has been the home of a powerful and nationally known political machine, so a number of the other “clans” with which the Donetsk machine has competed also have a home in eastern Ukraine. Most notable amongst this group is the Dnipropetrovsk clan.

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Dnipropetrovsk traces its political power at least as far back as the Brezhnev era when large numbers of key players in the region followed Leonid Brezhnev into power in the Kremlin. In the post-Soviet era, both President Kuchma and Orange Revolution leader, Yulia Tymoshenko, were products of the Dnipropetrovsk machine. In power, Kuchma actively used his authority to appoint the prime minister as a tool for balancing between Dnipropetrovsk and Donetsk (Matsuzato 2005; 48). Yanukovych, by contrast, was feared by others in the east at least as early as 2003 because “he and the ‘Donetsk clan’ from which he came appeared to want to seize as much as they could get their hands on, rather than splitting the spoils with others” (Anieri 2005; 240). Living alongside the Donetsk and Dnipropetrovsk behemoths have been the smaller political clans from Ukraine’s second largest city, Kharkiv. For most of the post-Soviet period, in fact up to the Orange Revolution of 2004, business and political elites in Kharkiv acted much like those in Donetsk and other cities, creating a “cartel of elites”, largely unified around sharing control of politics and business in the city and resisting incursions from other regional clans.3 This cartel, however, was broken up by the instability in Kyiv around the Orange Revolution with sharp splits emerging between the “anti-Orange” mayor of the city and the “pro-Orange” governor of the oblast (Zhurzhenko 2011).

Given these divisions among political machines in eastern Ukraine, we would expect to see divisions emerging within the east, even taking into account variations in the number of Russian speakers and Russian ethnics. These divisions are most likely in cases where there are contradictions between the interests of “cartels of elites”, rather than on issues that are “public goods” for eastern elites. Hence, we should see contradictions where narrow partisan interests are at stake and see less evidence of this on issues that have a stronger ethnic or linguistic component.

If our political understanding of regional effects is correct, then we should observe a number of specific patterns in Ukraine with regard to attitudes and identities.

H1: Once we account for differences in ethno-linguistic composition and socio-economic development, “residual” regional effects should be stronger in the East and particularly in Donbas/Donetsk.

H1a: Residual regional effects in the Donbas should be stronger on issues closely related to the functioning of clans/political machines.

H2: There should be differences in partisan preferences but not in broad policy preferences between regions controlled by competing political machines in Eastern Ukraine.

                                                            3 The term cartel of elites is due to Gel’man 1998. 

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Empirical Set-up

To test our expectations we conducted a nationally representative survey in Ukraine in December 2012. The sample size for the survey was quite large, just over 1800, in order to allow us to implement a number of different treatment conditions and included respondents from all 24 oblasts, plus the Republic of Crimea and the capital, Kyiv. About 20 percent of respondents were in Western Ukraine, 32 percent in Central oblasts, 10 percent in the South, 15 percent in the Donbas (Donetsk and Luhansk) , 18 percent in the non-Donbas East and 5 percent in Crimea.

Through the first set of empirical tests we analyze observational data to identify the regional patterns in political attitudes on a number of key dimensions of Ukrainian politics to establish whether regional differences exist, and whether these differences can be explained by either different compositions of individual identity markers or by indicators of different socio-economic development patterns. In the second part of the empirical section we present the results of a series of survey experiments that vary the regional/linguistic background of fictional politicians to explore some of the mechanisms underlying the regional differences in political attitudes and partisan preferences identified in the first part.

Observational Data

While a wide variety of political attitudes may be the basis for regionally based cleavages, in this paper we focus on four questions that address fundamental questions about the nature of the Ukrainian polity. The first question addresses the debate that sparked the Euromaidan protests, namely whether Ukraine should seek closer integration with the European Union or with the Russian-dominated Customs Union and broadly pitted a pro-EU West and Center against a pro-Customs’ Union South-East. For the present analysis we combined two survey questions that asked respondents about their support towards joining the EU/ Customs Union and offered them three options (support/oppose/neutral).4 The second question, which was also highly politicized along regional lines both before and after the 2014 regime change, concerned giving Russian the status of state language, yielding a three-point scale (disagree/hard-to-say/agree).

The second set of questions addresses two fundamental questions about the nature of the Ukrainian regime and state. Thus, we asked respondents about the degree to which they agreed with the statement that “democracy might have its problems but it’s still the best form of government” using a five-point scale (ranging from strongly disagree to strongly agree.) Furthermore, given the ongoing debates about greater local/regional autonomy in Ukraine, we created a dichotomous indicator of whether a respondent favored greater autonomy either for her own oblast or for their own oblast together with neighboring oblasts.

                                                            4 The DV was calculated as the difference between the EU vs. the Customs Union support, thus yielding a 5-point scale. See appendix for question wording and summary statistics.

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Finally, in line with many studies of regional effects in politics, we looked at two indicators of electoral and partisan politics. First, we asked respondents to evaluate the presidency of Victor Yanukovych, who at the time of the survey had been in office for almost three years, on a four-point scale (ranging from very weak to very good). Second, given that the survey was fielded shortly after the October 2012 parliamentary elections, we coded a dichotomous indicator for whether a responded reported that he/she voted for the Party of Regions (POR), the largest parliamentary party which supported Mr. Yanukovych and drew much of its support from the South and East of the country.

In terms of explanatory variables, in addition to the regional indicators discussed earlier, we focus primarily on two sets of indicators. In the first category we include variables that capture aspects of an individual’s ethno-linguistic and religious background, which may account for cross-regional attitude differences simply because of compositional differences between regions. As discussed above, the primary indicators in this respect are based on the intersection of self-declared ethnicity (Ukrainian vs. Russian vs. other) and home language (Ukrainian vs. Russian vs. other). Given that other minorities (e.g. Crimean Tatars, Hungarians, Romanians, Bulgarians etc.) account for small proportions of the population and our survey sample and given that very few respondents claim to be ethnic Russians but to speak Ukrainian at home, we used three main ethno-linguistic categories: Ukrainian-speaking people who identify as Ukrainian, Russian-speaking people who identify as ethnically Ukrainian, and Russian speaking people who identify themselves as ethnically Russian with the excluded category being respondents who either reported speaking both Ukrainian and Russian at home or who identified with another ethnicity.

Another identity-based divide correlated with regional boundaries is of a religious nature: thus, Greek Catholics are heavily concentrated in the West (particularly in the former Habsburg areas), but even among the country’s Eastern Orthodox majority, the division between the Moscow and the Kyiv patriarchate follows regional lines, with the former much more prevalent among Crimean and Donbas residents, while the latter is more concentrated in the South and Center. Since Ukrainians are also regionally divided in terms of religiosity – with church density and attendance significantly higher in the West and noticeably lower in the South and Donbas – we also included indicators of whether respondents reported frequent church attendance (defined as monthly or weekly) or no church attendance.

The second set of indicators tries to capture key aspects of the developmental differences between Ukrainian regions, and particularly the heavier concentration of Soviet-era socio-economic development in the East and especially in the Donbas. To do so we included three oblast-level indicators (% employment in industry, % employment in agriculture and % of household living below the national poverty threshold) as well as a series of individual indicators from the survey, including employment status, major occupational and education categories, locality size. Since income measures are problematic and have high missing data problems, we

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created an “affordability index” that captures whether respondents were able to afford a number of key goods and services (see appendix for details.)

Finally, our regressions contain a number of additional demographic variables, including age and gender. Since many Ukrainians work abroad in both Russia and the West, and we expect political preferences to be affected by how policies (such as EU integration vs. closer ties to Russia) affect potential work opportunities and remittances, we also included a series of indicators capturing whether the respondent had personally worked in Russia, had friends/relatives in Russia/EU countries and whether they received remittances from Russia/EU countries.5

Observational Data Results

In presenting the statistical results, before turning to the actual regressions we start out with a simple scatter plot graph that illustrates the average responses by oblast for the relevant survey questions. These plots help us assess the distinctiveness and coherence of different regional clusters and also illustrate the magnitude of the regional differences we then test in a regression setting.

Figure 1 & Table 1 here

As illustrated in Figure 1, our surveys capture quite clearly the important regional differences on foreign policy and language policy questions even before the polarizing impact of the Euromaidan protests and the subsequent regime change. Thus, support for EU integration and opposition to having Russian as a state language was particularly intense in the Western oblasts (marked in Orange in Figure 1), while the two Donbas oblasts (Luhansk and especially Donetsk) as well as Crimea were at the pro-Russian end of the spectrum on both issues. Also in line with the conventional wisdom, other Eastern oblasts (marked in purple) as well as much of the South (marked in blue) were quite supportive of pro-Russian language and foreign policy positions, though their positions were somewhat more centrist, especially compared to Donetsk. As expected the oblasts in the Center region occupy an intermediate position on both issues, though there is a fair bit of heterogeneity within the region.

These patterns are confirmed by the baseline regression results in models 1 and 5 of Table 16, which highlight the significant differences between the geographic and political extremes of post-communist Ukraine: the West on the one hand and the Donbas and Crimea on the other. But even beyond these extremes, the baseline models confirm that Luhansk and

                                                            5 Arguments could be made for including such variable either in the identity-based category (because people may be more comfortable working in places with greater cultural/linguistic similarities) or in the developmental group (because the East may be more integrated with Russia as a legacy of Soviet developmental policies thereby promoting greater post-communist economic interdependence.) Alternatively, the choice of Russia vs. Central/Western Europe may simply be a function of geographic proximity. 6 Note that in all regressions the excluded category is the Central region, which means that all coefficients represent the difference between that particular region and the Central Ukrainian oblasts.

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especially Donetsk were significantly more pro-Russian than even their neighbors in Eastern and Southern Ukraine. Not only are the cross-regional differences statistically significant but they are also quite large in substantive terms: thus, the difference between the average Western and Donetsk resident is corresponds to 1.25 standard deviations in the DV in model 1 and to over 1.5 SDs in model 5, and judging by the adjusted R-squared statistic, the overall explanatory power of the two models is quite high, especially with respect to language policy.

As a next step we introduce – first separately and then jointly – the two blocs of indicators capturing compositional differences in individual ethno-linguistic and religious characteristics and developmental differences between different regions. As expected, given the nature of the questions, the regional effects are more sensitive to the inclusion of the ethno-linguistic and religious indicators in models 2 and 6 than to the inclusion of developmental indicators in models 3 and 7, even though the latter also contribute to the overall explanatory power of the regression models. Overall, judging by the comparison between the baseline models 1&5 and the full specifications in models 4&8, two main patterns are worth highlighting. First, even though there are some noticeable variations across issues and regions, for ethno-linguistically charged issues, such as foreign policy and language policy, a substantial part of the large cross-regional variation in preferences can be attributed to the compositional differences between the regions, i.e. to the fact that Russians and Russian-speakers are much larger proportions of the population in Crimea and the Donbas than in other regions, especially the West. Second, however, models 4&8 also suggest that even once we account for ethno-linguistic and developmental differences, most cross-regional differences in policy preferences continue to be statistically significant and substantively large. Thus, in model 4, the difference between Donetsk/Luhansk and the Western oblasts is still about 30% larger than the difference between an ethnic Russian and a Ukrainian-speaking ethnic Ukrainian, while in model 8 the two effects are roughly identical. Moreover, as predicted by Hypothesis 1, the magnitude of the residual regional effects in the two fully specified models was generally greater for Donetsk (and for Luhansk in model 4 and Crimea in model 8) than for other regions with weaker political machines such as the West and the South.

Figure 2 & Table 2 here

Next we turn to two fundamental questions about the nature of the regime and the state: normative support for democracy and preferences about local autonomy/decentralization. Judging by Figure 2, intra-regional variation in democratic preferences was quite large for most regions (including the Donbas), while cross-regional differences were fairly modest and failed to conform to a simple East-West pattern. This lack of a regionally based regime cleavage is also confirmed by the weak individual and collective explanatory power of the regional variables in models 1-4 of Table 2. Not surprisingly, the differences between different ethno-linguistic groups in models 2&4 were also inconclusive, which further confirms that at least prior to the 2013-14 crisis the political conflict in Ukraine cannot be interpreted in terms of disagreements about democracy vs. authoritarianism along regional or ethnic lines.

14  

On the other hand, Figure 2 and model 5 in Table 2 reveal significant regional differences in support for greater local/regional autonomy. The most striking difference is between Donetsk, where a plurality of respondents supported greater local autonomy even before the Euromaidan, and most of the rest of Ukraine, where autonomy demands were made by relatively small minorities. However, even beyond Donetsk, autonomy demands were stronger in the East and the South, though there was some important sub-regional variation in both regions. Another point worth noting is that unlike the West-East gradient we have seen for foreign and language policies, with respect to local autonomy there seems to be a greater center-periphery divide, as highlighted by the moderately sized but statistically significant positive effect of the Western region indicator in model 5 (compared to the baseline Center region.)

Judging by the statistical results in models 5-8 of Table 2, even though both identity-based and developmental indicators contributed to the explanatory power of the statistical models, neither set of variables had a significant effect on the magnitude of the regional effects. In other words, in line with Hypothesis 1, the greater support for local autonomy among Donetsk residents (and to a lesser extent of residents of several other Southern and Eastern oblasts, including Luhansk, Kharkiv, Dnipropetrovsk and Odessa) cannot be explained by their different ethno-linguistic composition and developmental profiles but rather seems to be driven by the logic of oblast-level politics. Given that this list contains the oblasts with the most prominent Ukrainian political clans – Donetsk, Kharkiv and Dnipropetrovsk – it seems plausible that in line with Hypothesis 1a these autonomy demands are rooted in the desire for greater maneuvering space among the beneficiaries of local patronage networks. However, this issue needs to be addressed more systematically in future research.

Figure 3 & Table 3 here

Finally, in Figure 3 and Table 3 we turn to the electoral and partisan preferences of Ukrainian citizens. In line with earlier work about the regional bases of voting in Ukraine, Figure 3 reveals a clear West-South/East gradient in partisan preferences, with support for Yanukovych and the Party of Regions significantly higher in the Donbas and Crimea and to a slightly lesser extent in other Southern and Eastern oblasts than in the Center and especially in the West. As both Figure 3 and model 1 of Table 3 illustrate, Donetsk once again stands out even compared to Luhansk and the rest of the South-East in its much greater support for Yanukovych, whereas its support for the Party of Regions is somewhat less of an outlier compared to other Eastern oblasts.

Once we add the controls for ethno-linguistic and developmental differences, the results in Table 3 provide strong empirical support for our theoretical predictions. In line with Hypothesis 1a, the residual importance of regional effects was the strongest on the issues that were most closely related to political machine politics in a given region: thus, the difference between Donetsk and the rest of the country was the strongest for the variable with the strongest “subregional clan logic”: the evaluation of the Yanukovych presidency in model 4. In line with

15  

the prediction in Hypothesis 2 about the clan-based divisions within Eastern Ukraine, on this issue respondents from other Eastern oblasts were actually closer in their views to respondents from the Central region (from which they were statistically indistinguishable) than to the much greater enthusiasm of Donetsk residents. By contrast, according to model 8, when it comes to voting for the Party of Regions, in which Eastern clans were on the same side of the partisan divide, the residual effect Donetsk residents was substantively similar and statistically indistinguishable from Luhansk and other Eastern oblasts, while the East overall differed significantly from the Western and Central regions (and to a lesser extent also from the South).

Thus, the observational data presented so far provides solid support for Hypotheses 1 in the sense that the residual regional effects were stronger in the East and particularly in Donetsk, i.e. in those areas with the most visible and influential post-communist sub-regional political clans. Furthermore, in line with Hypothesis 1a, these greater residual effects in the East and the Donbas appear to be stronger in areas that are more closely related to the functioning of political machines, such as support for local autonomy and patterns of electoral support. Finally, the contrast between the uniformly high residual Eastern support for the Party of Regions and the clear disagreements between Donetsk and the non-Donbas East in how to evaluate the Yanukovych Presidency highlights the double-edged nature of political clans in Eastern Ukraine, which may help mobilize East Ukrainian residents on certain issues, while dividing them on others (as predicted by Hypothesis 2).

Survey Experiments

The experimental setup was organized in such a way as to allow us to examine the interactions of region, language and issue areas, while minimizing the complexity of administering the survey on the ground. In this paper, we focus upon the issues of region and ethnicity (see Appendix for full list of treatments). Respondents were given one of four questionnaires at random in which they were asked two questions about the degree of trust that would place in promises to create jobs made by politicians from different regions. Specifically, respondents were asked:

Now suppose that a politician from Kharkiv/Donetsk/Kyiv/Lviv came to your village/neighborhood and promised to provide more government jobs in your community. How likely do you think that he will keep his promise?

Respondents were asked whether they felt this was very unlikely, rather unlikely, rather likely, very likely, or do not know.

Later in the survey was asked a similar question but this time about improving inter-ethnic relations and rotating the place of origin of the politician:

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In the last few months our country has experienced a great deal of debates about ethnic and language issues. Now suppose that a political party leader from Donetsk came to your village/neighborhood and said that his party’s main political goal was to work with politicians from all nationalities and all parts of the country to promote greater peace and stability in our country. How likely do you think that such a politician will keep his promise after the election?

Respondents were given the same five possible answers. In the next section we use responses to these questions and a battery of questions on ethnicity and language to analyze the relative effects of each in political trust in Ukraine.

Survey Experiment Results

As a first step we evaluate the relative importance of region, ethnicity and language in driving how respondents reacted to the regional priming experiment. To do so, we start out by illustrating the credibility differences of electoral promises of two fictional politicians – one from Lviv (in the West) and one from Donetsk – in two different issue areas: providing jobs (Figure 4) and improving ethnic relations (Figure 5). Each of these figures is based on a regression where we interact the treatment variable (Lviv vs. Donetsk politician) with a set of dummy variables for five different regions of Ukraine (West, Center, South, Donbas, and other East) and with indicators of the three crucial ethno-linguistic groups discussed above (Ukrainian-speaking ethnic Ukrainians, Russian-speaking Ukrainians and Russian-speaking Russians). 7 For all groups listed on the vertical axis, the horizontal axis indicates the difference in credibility between the politician from Lviv and his/her counterpart from Donetsk, where positive values indicate a preference for the Lviv politician and negative values indicate a preference for the Donetsk politician.

Figures 4&5 here

Judging by the effects in Figure 4, when it comes to evaluating the credibility of job promises, the magnitude and statistical significance of regional differences was considerably greater than for the ethno-linguistic categories. As expected, the difference was the largest for the two polar opposite regions of Ukraine’s political scene: thus, the difference between how Western and Donbas respondents evaluated the credibility of promises made by Lviv and Donetsk politicians was about 1.25, which corresponds to over 1.6 standard deviations of the 1-4 scale on which the credibility question was measured. Furthermore, both the West and Donbas were significantly different from the other three regions, which in turn were fairly similar to each other. Perhaps the most interesting finding that emerges from Figure 4, however, is that it

                                                            7 While the ordinal nature of the dependent variables would normally call for ordered logit/probit tests, their substantive interpretation (particularly in the context of multiple interaction effects) is much less intuitive, and since the results are very similar using ordered probit and OLS, we present the latter.

17  

questions the dichotomous East-West division often applied to Ukrainian politics: thus, we find substantively large and statistically significant differences between respondents from Central and Western Ukraine, and even between Donbas residents and their counterparts from other oblasts in East Ukraine. In other words, at least prior to the Euromaidan, broad East-West differences were not particularly stark when it comes to economic electoral appeals and Donbas residents stand out even compared to their predominantly Russophone brethren in South and East Ukraine.

By comparison, the results for the ethno-linguistic variables are more modest. Even though, in line with traditional expectations, ethnic Russians were significantly more likely to trust promises of a politician from Donetsk than Russian-speaking Ukrainians, the effects were noticeably weaker than the difference between Donbas and Western residents. Moreover, once we control for region, Ukrainian-speaking Ukrainians were actually more likely to trust the job promises of a Donetsk-based rather than a Lviv-based politician, and their preferences were statistically indistinguishable from those of ethnic Russians, which suggests that ethno-linguistic factors had a modest impact on political support on “bread-and-butter” political issues.

Figure 5, which is based on the survey experiment about the credibility of promises to improve interethnic relations, reveals a similar regional pattern as Figure 4. Once again, the difference between the two geographic and political extremes – Donbas and the West - is statistically significant and substantively large (equivalent to two thirds of a standard deviation in the dependent variable), while the differences between other regions were weaker and did not conform to a uniform North-West/South-East pattern because of the surprising receptivity of Southern respondents to the appeals of Lviv over Donetsk politicians. However, it should be noted that these effects are substantively smaller than for the jobs promise, and were affected more by the inclusion of ethnicity and language controls,8 and the difference between Donbas and other Eastern oblasts disappears almost completely. On the other hand, the difference between Russian and Ukrainian-speaking ethnic Ukrainians is also relatively small and only marginally significant (at .1), while ethnic Russians are still in an intermediate position, which reinforces the weak impact of ethnicity. Overall, even after controlling for ethno-linguistic differences does not eliminate regional credibility differences, which continue to be substantively large and statistically significant for Donbas residents compared to both Western and Southern Ukrainian respondents.

However, two regional pattern differences between Figures 4 and 5 are worth noting. First, in Figure 5, the difference between Donbas and other East Ukrainian residents is much less important than in Figure 4, which suggests that on ethnic issues it may be more justified to talk about a broader East Ukrainian credibility pattern that extends beyond the Donbas and is significantly different from how respondents react to political appeals in Western and even Southern Ukraine. On the other hand, the non-Donbas East is also statistically indistinguishable

                                                            8 The difference between Western and Donbas respondents is about 50% larger if we do not control for ethno-linguistic factors (results omitted).

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from Central Ukraine, and in substantive terms it is actually closer to the Center than the Donbas. Second, across all five regions, the estimates in Figure 5 have shifted to the right (i.e. away from the Donetsk politician and towards the Lviv politician), which suggests a fairly widespread agreement about issue ownership: whereas Donetsk politicians are more credible in offering jobs – a perception arguably linked to the dominance of Yanukovych and the Donetsk clan over national politics at the time of the survey - the greater credibility (outside of the East) of Lviv-based politicians in dealing with inter-ethnic relations is in line with the role of Western Ukraine (and Lviv in particular) as a cradle of Ukrainian nationalism.

While the discussion so far confirms that Ukrainian voters are more likely to trust the electoral promises of politicians from their own region or from geographically proximate regions, these effects could reflect either psychological attachments rooted in regionally based cultural identities or more pragmatic calculations about the role of regionally based patronage networks. In Figures 6&7 we offer some preliminary evidence that tries to disentangle these two different mechanisms by looking at the more fine-grained sub-regional variation in responses to the two types of electoral appeals. To the extent that regional cultural identities matter, then we should see similar cross-regional differences for both the jobs and the ethnic cooperation promises, as respondents should trust politicians from their own group to represent their interests. Moreover, within a given region, we should see few differences between respondents from the same oblast as the politician and respondents from other oblasts in the same region. If, however, regional effects are the result of regionally based patronage networks, then we should see stronger cross-regional differences for the jobs question and – to the extent that patronage network boundaries do not coincide with regional boundaries – larger differences across different oblasts from the same region.

In particular, we show results for respondents from the two Donbas oblasts (Donetsk and Luhansk) as well as their two most important (and populous) neighboring oblasts in East Ukraine (Kharkiv and Dnipropetrovsk), which had comparable shares of ethnic Russians and Russian speakers. On the other side of the Ukrainian regional divide we include residents of Western Ukraine, differentiating between residents of Lviv oblast and those of neighboring West Ukrainian oblasts, which have a similar ethno-linguistic and historical background but may not benefit as directly in case of narrowly based patronage networks. Finally, for comparative reference we also included residents of Kyiv city and oblast.

Figures 6&7 here

The most interesting finding that emerges from the two figures is the contrast between the support patterns among residents of Donetsk and Luhansk oblasts. For the jobs promise experiment respondents of Luhansk were almost identical to (and certainly statistically indistinguishable from) Donetsk oblast residents in their willingness to trust a Donetsk politician more than a politician from Lviv. Moreover, residents of both Donbas oblasts were significantly different not just from West Ukrainians and Kyiv residents but even from residents of the

19  

neighboring Eastern oblasts of Kharkiv and Dnipropetrovsk. By contrast, for the ethnic cooperation promise, Luhansk residents were significantly less likely to favor a Donetsk over a Lviv-based politician than their counterparts from Donetsk, while at the same time being statistically indistinguishable not only from other Eastern Ukrainian respondents but even from their supposed polar opposites in Western Ukraine. Taken together, these two findings suggest that while Donbas residents are tied together by shared economic interests, which are arguably rooted in their overlapping patronage networks, this commonality does not seem to extend to a broader Donbas-level cultural identity (at least prior to the Euromaidan and the subsequent separatist conflicts.)

The credibility patterns in West Ukraine present a very different picture. According to both Figures 6&7 residents of Lviv were statistically indistinguishable from the residents of other West Ukrainian oblasts. While the jobs-related results in Figure 6 could also be the result of belonging to a common West Ukrainian patronage network, the fact that we find similar patterns in Figure 6 suggests that West Ukrainians, unlike Donbas residents, are bound together by a stronger shared regional political identity. This finding reinforces the importance of the shared historical legacies of Western Ukraine.

Finally, the comparative responses of Kharkiv and Dnipropetrovsk residents also offer some interesting insights into the regional political dynamics of Ukrainian politics. Unlike for Luhansk residents, the differences between Kharkiv and Dnipropetrovsk residents on the one hand and Donetsk residents on the other were actually more pronounced on the jobs promise question than on the ethnic cooperation question. Thus, as mentioned above, in Figure 6, Kharkiv and Dnipropetrovsk residents were no more likely to believe the jobs promises of a Donetsk than of a Lviv-based politician, a finding that confirms the predictions about the divisive effects of the well-known rivalries between the Donetsk clan and patronage networks based in Kharkiv and particularly Dnipropetrovsk. On the other hand, with respect to ethnic cooperation promises in Figure 7, the differences between Donetsk and Kharkiv/Dnipropetrovsk residents are only marginally statistically significant and somewhat closer to Donetsk than to Lviv residents (though the results fall short of revealing a unified Eastern regional identity). In other words, whereas Donbas residents are largely bound to each other by their common economic patronage networks, these same networks undermine the cohesion between the Donbas and other East Ukrainian oblasts despite their similar ethno-linguistic profiles and shared historical experiences.

Discussion and Conclusion

In this paper we have proposed a framework for analyzing regional politics in Ukraine by differentiating between three main mechanisms through which residents of different regions could come to hold distinctive political attitudes and partisan preferences. The first mechanism, which is the most commonly referenced by observers of post-communist Ukrainian politics,

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emphasizes the different regional compositions in terms of the country’s key ethnic and linguistic groups, and particularly the greater concentration of ethnic Russians and Russian speakers in the Donbas, Crimea and other parts of Eastern and Southern Ukraine. The second mechanism focuses on the political repercussions of the socio-economic developmental differences across different regions, and especially the greater degree of communist-era modernization (including industrialization and urbanization), which may produce particular types of political institutions and preferences both during and after Communism.

The third mechanism, which we develop in this paper, goes beyond these two “compositional” approaches in the sense that certain regions engender among their residents political attitudes and partisan preferences that cannot be reduced to the individual demographic characteristics of their residents but reflect a particular regional identity with an independent “logic.” While there can be multiple sources of such distinctive regional identities, including (real or imagined) shared experiences rooted in different historical legacies – such as different imperial traditions in the Ukrainian case – in this paper we have highlighted the importance of particular informal political institutions in shaping the political expression of different regional identities. In particular, we have argued that a key factor in post-communist Ukraine was the impact of regionally based political “clans” – patronage networks controlled by a few prominent businessmen/politicians – that controlled regional politics in large parts of Ukraine (especially in the East) and competed with each other and with other political actors for controlling the national government. We predicted that such networks would exert greater influence on individual preferences in particular regions (especially Donetsk and a few other Eastern regions with strong political machines) and in particular policy areas that are more closely connected to the exercise of patronage politics.

Using a combination of observational data and survey experiments from a nationally representative public opinion survey fielded in December 2012, we find that regional differences matter for a range of important policy and partisan preferences, and that only a small part of these distinctive regional preference patterns can be explained by the different ethno-linguistic and developmental configurations of different Ukrainian regions. While some of these “residual” regional effects, especially in Western Ukraine, are undoubtedly rooted in older historical legacies (dating back to both the interwar period and the impact of different imperial occupations), such explanations are less helpful in explaining the distinctiveness of the Donbas compared to other parts of Eastern Ukraine (which have similar ethno-linguistic and developmental profiles and even historical legacies.) Instead, we show that the nature of patronage networks helps us explain both issue areas where a broader East Ukrainian consensus exists (e.g. support for the Party of Regions or for Russian language rights) and areas where Donbas and especially Donetsk residents have very different preferences than their East Ukrainian neighbors (e.g. on the demand for greater local autonomy or in evaluating the Yanukovych Presidency.) These divisions are further confirmed by the sharp differences

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between respondents from the Donbas and other East Ukrainian oblasts in how they responded to the region-priming experiments presented in the second part of the empirical analysis.

While the approach to studying region we have proposed here was developed to understand regional political dynamics in Ukraine – and particularly the puzzle about the Donbas regional identity – we believe that the framework can be applied fruitfully beyond the Ukrainian context. Most obviously, the approach is suited for understanding cases where regional differences in descent-based characteristics (like language, ethnicity, race or religion) or socio-economic development interact with political patronage networks. Such cases are frequent in many ethnically diverse developing countries in many parts of the world including Eurasia (e.g. Kyrgyzstan, Moldova, Tajikistan), Asia (e.g. Thailand, Pakistan, India, Sri Lanka), Latin America (e.g. Mexico, Bolivia, Brazil), the Middle East (e.g. Iraq, Syria) and Africa (e.g. Nigeria, Mali). More broadly, this framework may also be useful for analyzing regional politics in more advanced democracies, where patronage networks play a less important role, but where formal and/or informal political institutions (e.g. political parties and regional legislatures) shape political competition in ways that differ from political competition in other regions. In all of these cases we would expect regional effects in public opinion to be shaped not only by compositional differences in identity and demographic characteristics but also by whether and how economic and political elite interests are organized territorially and how this organization shapes the incentives for cooperation/competition along regional/subregional lines on any given political issue.

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Volyn

Zakarpatska

Ivano-Frankivsk

Lviv

Rivne

Ternopil

ChernivtsiVinnytsia

Zhytomyr

Kyiv

Kyiv city

Kirovograd

Poltava

Sumy

KhmelnitskCherkasy

Chernigiv

MykolaivOdesa

Kherson

Dnipropetrovsk

ZaporijiaKharkiv

DonetskLugansk

Crimea

-10

12

EU

_vs_

Ru

ssia

1 1.5 2 2.5 3Russian official language

Fig. 1: Regional Patterns of Foreign vs. Language Policy Preferences

Volyn

Zakarpatska

Ivano-FrankivskLviv

Rivne

Ternopil

Chernivtsi

Vinnytsia

Zhytomyr

Kyiv

Kyiv city

KirovogradPoltava

Sumy

Khmelnitsk

Cherkasy

Chernigiv Mykolaiv

Odesa

Kherson

Dnipropetrovsk

Zaporijia

Kharkiv

Donetsk

Lugansk

Crimea

22

.53

3.5

Dem

ocr

acy

best

0 .1 .2 .3 .4Support autonomy

Fig.2 Regional Patterns of Support for Democracy and Autonomy

26  

  

 

 

Volyn

Zakarpatska

Ivano-FrankivskLvivRivne

Ternopil

Chernivtsi

Vinnytsia

Zhytomyr Kyiv

Kyiv cityKirovogradPoltava

Sumy

Khmelnitsk

Cherkasy

Chernigiv

Mykolaiv Odesa

Kherson

Dnipropetrovsk

ZaporijiaKharkiv

Donetsk

LuganskCrimea

0.1

.2.3

.4.5

Pa

rty

of R

egio

ns V

ote

.6 .8 1 1.2 1.4 1.6Yanukovych_Presidency

Fig.3 Regional Patterns of POR vote vs. Yanukovych Support

Russian speaking Russian

Russian speaking Ukrainian

Ukrainian speaking Ukrainian

Donbas

East (other)

South

Center

West

-1 -.5 0 .5 1Credibility difference (Lviv-Donetsk)

Fig. 4: Regional Priming for Jobs Promise - Lviv vs. Donetsk Politician

Lviv Donetsk 

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Russian speaking Russian

Russian speaking Ukrainian

Ukrainian speaking Ukrainian

Donbas

East (other)

South

Center

West

-.5 0 .5 1Credibility difference (Lviv-Donetsk)

Fig. 5 Regional priming for ethnic cooperation promise: Lviv vs. Donetsk

Other West

Lviv

Kyiv

Dnipropetrovsk

Kharkiv

Lugansk

Donetsk

-1 -.5 0 .5 1Credibility difference (Lviv-Donetsk)

Fig. 6: Regional priming for jobs promise (Lviv vs. Donetsk) - Subregional Differences

28  

 

 

  

Other West

Lviv

Kyiv

Dnipropetrovsk

Kharkiv

Lugansk

Donetsk

-1 -.5 0 .5 1 1.5Credibility difference (Lviv-Donetsk)

Fig. 7: Regional priming for ethnic cooperation promise (Lviv vs. Donetsk) - Subregional Differences

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Table 1 Foreign Policy and Language Policy Preferences (1) (2) (3) (4) (5) (6) (7) (8) EU vs.

Russia  EU vs. Russia  

EU vs. Russia  

EU vs. Russia

Russian official

language

Russian official

language

Russian official

language

Russian official

language West .860** .516** .763** .387* -.187# .046 -.125 .046 (.218) (.180) (.215) (.160) (.097) (.132) (.093) (.122) South -.615* -.614** -.542* -.480** .606** .419** .589** .418** (.230) (.195) (.211) (.166) (.120) (.104) (.132) (.118) East -.496** -.375** -.593** -.384* .835** .589** .754** .525** (.121) (.125) (.180) (.151) (.081) (.090) (.134) (.116) Luhansk -.846** -.670** -.809** -.507** .937** .599** .876** .529** (.117) (.132) (.189) (.161) (.059) (.092) (.133) (.119) Donetsk -.977** -.664** -.907** -.519* 1.230** .853** 1.111** .770** (.117) (.134) (.264) (.203) (.059) (.098) (.174) (.144) Crimea -1.009** -.492** -.858** -.364* 1.557** 1.043** 1.572** 1.077** (.117) (.149) (.118) (.154) (.059) (.112) (.072) (.128) Identity indicators  Ukrainian speaking Ukrainian

.127 .208* -.356** -.367** (.112) (.095) (.083) (.081)

Russian speaking Ukrainian -.075 -.192 .198* .157# (.146) (.127) (.094) (.091) Ethnic Russian -.383** -.390** .457** .435** (.094) (.107) (.104) (.111) Orthodox Moscow -.444** -.283** .000 .008 (.110) (.097) (.065) (.066) Orthodox Kiev -.071 .024 -.071 -.063 (.111) (.101) (.051) (.048) Greek Catholic .408* .474** -.203# -.161 (.166) (.170) (.103) (.108) Church regularly .067 .179* .011 .025 (.079) (.079) (.050) (.051) Church never -.216 -.152 .070 .071 (.127) (.120) (.064) (.058) Developmental indicators Union member -.482* -.462* -.046 -.070 (.189) (.203) (.073) (.079) % agric employment -1.971* -1.629# .613 .514 (.893) (.812) (.606) (.570) % indl employment .455 -.104 .397 .926 (1.519) (1.164) (1.130) (.935) %HH below poverty line .024 .017 -.004 .003 (.015) (.012) (.008) (.007) Vocational education .190# .198# -.011 -.015 (.103) (.103) (.063) (.062) Secondary education .139 .163 -.001 -.027 (.110) (.099) (.065) (.065) Higher education .149 .185# -.135* -.179** (.107) (.098) (.059) (.059) City resident .109 .184 .203* .113 (.111) (.113) (.084) (.075) Village resident .157 .094 -.169* -.124# (.110) (.109) (.080) (.071) State employment -.041 -.080 .024 .038

30  

(.089) (.091) (.069) (.058) White collar employee .216 .182 -.116 -.064 (.146) (.138) (.119) (.105) Industrial worker -.011 -.024 -.065 -.039 (.115) (.109) (.091) (.077) Agricultural employee -.206 -.232 -.052 .027 (.200) (.184) (.126) (.096) Service employee .134 .087 -.114 -.074 (.153) (.154) (.077) (.070) Unemployed -.062 -.068 -.023 .016 (.140) (.133) (.093) (.086) Retired -.190 -.240* -.076 -.016 (.125) (.111) (.086) (.083) Student .209 .176 -.149 -.109 (.141) (.141) (.091) (.082) Affordability index .132 .198 .008 -.064 (.198) (.191) (.077) (.081) Work in Russia (self) -.119 -.107 .073 .039 (.092) (.087) (.066) (.057) Work in Russia (family) -.068 -.029 .074 .061 (.097) (.088) (.044) (.041) Remittances (Russia) -.130 -.131 -.042 -.030 (.105) (.099) (.057) (.049) Work in West (family) .207* .165# -.103 -.101 (.100) (.093) (.066) (.060) Remittances (West) .060 .029 -.046 -.048 (.161) (.152) (.082) (.079) Female -.132* -.166* .026 .033 (.053) (.071) (.029) (.026) Age -.009** -.008** .000 -.000 (.003) (.003) (.002) (.002) Married -.073 -.093 -.094# -.060 (.085) (.088) (.046) (.047) Constant .194 .347* .399 .479 1.388** 1.546** 1.367** 1.433** (.117) (.163) (.378) (.330) (.059) (.108) (.241) (.196) Observations 1,766 1,766 1,766 1,766 1,740 1,740 1,740 1,740 R-squared .195 .231 .265 .302 .381 .453 .414 .473 Adj R^2 .192 .225 .252 .286 .379 .448 .403 .460

OLS coefficients with robust standard errors in parentheses (** p<.01, * p<.05, # p<.1).

31  

Table 2 Political system choices

(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Democracy

best Democracy

best Democracy

best Democracy

best Pro

autonomyPro

Autonomy

Pro Autonom

y

Pro autonomy

West .220 .119 .216 .071 .146** .107* .147** .097# (.140) (.125) (.166) (.153) (.048) (.054) (.052) (.054) South -.026 -.106 .010 -.061 .292** .298** .324** .348** (.235) (.240) (.244) (.237) (.055) (.064) (.068) (.076) East -.325 -.331 -.282 -.241 .265** .242** .247** .247** (.262) (.243) (.388) (.375) (.055) (.055) (.087) (.081) Luhansk .166 .178# .223 .285 .405** .369** .422** .420** (.109) (.104) (.270) (.274) (.044) (.048) (.102) (.099) Donetsk -.190# -.135 -.216 -.093 .548** .511** .598** .588** (.109) (.111) (.357) (.341) (.040) (.043) (.100) (.098) Crimea .335** .455** .371* .479** (.109) (.121) (.163) (.162) Identity indicators Ukrainian speaking Ukrainian

-.256* -.237# .030 .034# (.122) (.132) (.019) (.019)

Russian speaking Ukrainian

-.209 -.218 .055* .023 (.149) (.130) (.023) (.020)

Ethnic Russian -.135 -.133 .057# .032 (.115) (.105) (.033) (.029) Orthodox Moscow -.129 -.072 -.001 .007 (.088) (.088) (.018) (.019) Orthodox Kiev .073 .117 -.029 -.031 (.137) (.135) (.024) (.021) Greek Catholic .354* .411* .035 .039 (.157) (.160) (.046) (.048) Church regularly -.067 -.012 .038* .043* (.072) (.067) (.017) (.018) Church never -.278** -.287** .052* .039# (.088) (.082) (.022) (.022) Developmental indicators Union member -.036 -.031 -.027 -.029 (.252) (.236) (.023) (.022) % agric employment -.371 -.159 -.214 -.238 (1.297) (1.168) (.190) (.188) % indl employment -.941 -1.164 -.010 -.046 (2.470) (2.327) (.311) (.288) %HH below poverty line -.004 -.003 .003 .003 (.023) (.021) (.002) (.002) Vocational education .038 .045 .054* .050* (.079) (.075) (.023) (.021) Secondary education .129 .129 .035 .032 (.112) (.102) (.025) (.023) Higher education .033 .045 .055# .045 (.108) (.109) (.030) (.029) City resident -.092 -.067 .054# .053# (.177) (.162) (.029) (.030) Village resident .031 .022 -.005 -.011 (.109) (.106) (.027) (.026)

32  

State employment .053 .039 .032 .034 (.059) (.053) (.029) (.029) White collar employee .114 .096 -.044# -.044* (.103) (.096) (.023) (.022) Industrial worker -.035 -.034 -.013 -.018 (.098) (.098) (.023) (.021) Agricultural employee -.093 -.088 -.006 -.010 (.207) (.208) (.045) (.042) Service employee .048 .038 -.014 -.016 (.124) (.122) (.029) (.026) Unemployed .122 .106 .029 .033 (.167) (.156) (.030) (.030) Retired -.095 -.079 -.008 -.012 (.089) (.088) (.026) (.025) Student .157 .159 -.056* -.053* (.148) (.143) (.027) (.026) Affordability index .473** .490** .012 .017 (.145) (.133) (.035) (.034) Work in Russia (self) -.039 -.040 .012 .010 (.085) (.084) (.014) (.012) Work in Russia (family) -.115 -.090 .003 .004 (.082) (.089) (.027) (.026) Remittances (Russia) .115 .135 .024 .021 (.078) (.085) (.023) (.022) Work in West (family) .132# .113 .009 .004 (.068) (.068) (.024) (.023) Remittances (West) -.173# -.240* -.012 -.005 (.090) (.094) (.034) (.037) Female -.094* -.140** -.020* -.022# (.037) (.043) (.010) (.012) Age .000 -.000 -.000 -.000 (.002) (.002) (.000) (.000) Married -.027 -.038 -.037* -.032* (.058) (.059) (.016) (.016) Constant 2.610** 2.863** 2.604** 2.804** (.109) (.114) (.536) (.516) Observations 1,595 1,595 1,595 1,595 1,681 1,681 1,681 1,681 Adj R^2 .0305 .0502 .0554 .0764 Pseudo R^2 .144 .161 .176 .190

OLS coefficients (models 1-4); Marginal effects (models 5-8). Robust standard errors in parentheses ** p<.01, * p<.05, # p<.1    

33  

Table 3 Partisan and electoral preferences (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Evaluatio

Yanukov. Presid.

Evaluatio Yanukov.

Presid.

Evaluatio Yanukov.

Presid.

Evaluatio Yanukov.

Presid.

Voted POR

Voted POR

Voted POR

Voted POR

West -.320** -.234* -.362** -.301** -.127* -.069 -.144** -.096* (.091) (.107) (.088) (.097) (.056) (.059) (.045) (.044) South .150 .141 .189# .195* .148** .141** .119** .118** (.094) (.105) (.099) (.092) (.039) (.036) (.042) (.037) East .162# .100 .179 .121 .225** .180** .375** .326** (.086) (.096) (.166) (.156) (.050) (.045) (.064) (.055) Luhansk .368** .285** .388* .303* .288** .220** .455** .381** (.078) (.087) (.143) (.140) (.039) (.041) (.066) (.058) Donetsk .631** .508** .624** .505** .354** .255** .510** .421** (.078) (.092) (.176) (.167) (.038) (.043) (.093) (.085) Crimea .357** .149 .419** .240* .300** .121** .294** .142** (.078) (.099) (.088) (.106) (.039) (.041) (.047) (.048) Identity indicators Ukrainian speaking Ukrainian

.012 .009 -.023 -.022 (.082) (.071) (.031) (.031)

Russian speaking Ukrainian .141* .116* .059# .053* (.059) (.050) (.031) (.026) Ethnic Russian .226** .198** .145** .119** (.073) (.062) (.037) (.043) Orthodox Moscow .144# .162* .090** .084** (.072) (.062) (.031) (.029) Orthod_kiev .024 .054 .007 .011 (.047) (.040) (.025) (.025) Greek Catholic -.137# -.077 -.147** -.136** (.079) (.081) (.035) (.029) Church regularly .016 .025 .031 .021 (.063) (.061) (.027) (.028) Church never -.058 -.055 -.009 -.005 (.090) (.081) (.037) (.031) Developmental indicators Union member .102 .080 -.029 -.040 (.138) (.139) (.070) (.069) % agric employment .481 .426 .536* .465** (.594) (.542) (.218) (.177) % indl employment -.272 -.078 -1.109** -.941** (1.212) (1.071) (.397) (.314) %HH below poverty line .006 .007 -.004 -.002 (.009) (.008) (.005) (.004) Vocational education .080 .074 .006 .002 (.055) (.052) (.030) (.028) Secondary education .024 .012 .038 .026 (.072) (.068) (.045) (.042) Higher education .025 .003 .045 .028 (.064) (.061) (.048) (.047) City resident .031 .007 -.003 -.018 (.095) (.089) (.027) (.022) Village resident -.061 -.058 -.026 -.021 (.062) (.059) (.031) (.030) State employment -.045 -.036 -.021 -.011

34  

(.068) (.072) (.027) (.029) White collar employee -.032 -.039 .002 -.001 (.071) (.075) (.045) (.043) Industrial worker .003 -.009 -.010 -.017 (.058) (.056) (.035) (.032) Agricultural employee .053 .060 .026 .029 (.153) (.146) (.073) (.070) Service employee -.066 -.056 -.061 -.056 (.078) (.079) (.045) (.046) Unemployed -.023 -.031 -.074# -.072# (.066) (.066) (.038) (.038) Retired .045 .054 -.043 -.039 (.088) (.093) (.035) (.036) Student .157 .168 -.062 -.057 (.150) (.146) (.064) (.063) Affordability index .694** .688** .211** .200** (.138) (.133) (.071) (.070) Work in Russia (self) -.123# -.118# -.041 -.043 (.067) (.064) (.031) (.028) Work in Russia (family) .030 .017 .030 .025 (.048) (.049) (.030) (.029) Remittances (Russia) .112 .120# .007 .009 (.068) (.068) (.046) (.047) Work in West (family) -.076 -.066 -.014 -.011 (.066) (.066) (.039) (.039) Remittances (West) .011 .024 .026 .039 (.088) (.082) (.064) (.059) Female .062# .028 .025 .008 (.034) (.037) (.020) (.017) Age .003# .003 .004** .003** (.002) (.002) (.001) (.001) Married -.051 -.056 -.002 -.001 (.047) (.045) (.025) (.025) Constant 1.010** .917** .296 .222 (.078) (.092) (.263) (.231) Observations 1,529 1,529 1,529 1,529 1,773 1,773 1,773 1,773 Adj R^2 .119 .133 .175 .186 Pseudo R^2 .100 .125 .142 .160

OLS coefficients (models 1-4); Marginal effects (models 5-8). Robust standard errors in parentheses ** p<.01, * p<.05, # p<.1