Political Machines and Regional Identities: Evidence from Post-Soviet Ukraine
Grigore Pop-Eleches
(Princeton University)
[email protected]
and
Graeme B. Robertson
(University of North Carolina at Chapel Hill)
[email protected]
Note: Highly preliminary. Please contact authors about most recent version.
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 AustroHungarian 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
1 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.
2 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
3 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
4 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
5 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.
6 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 postcommunist 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
7 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.
8 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. 9 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 socioeconomic 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.
10 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 socioeconomic 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
11 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.
12 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 ethnolinguistic 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 ethnolinguistically 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.
13 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 identitybased 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
14 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 interethnic relations and rotating the place of origin of the politician:
15 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.
16 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 ethnolinguistic factors (results omitted).
17 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 Lvivbased 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
18 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,
19 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
20 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 socioeconomic 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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24 2
Fig. 1: Regional Patterns of Foreign vs. Language Policy Preferences
Ivano-Frankivsk
EU_vs_Russia
0
1
Ternopil
Lviv
Zakarpatska
Kyiv
Rivne
Kyiv city
Chernivtsi
Volyn
Poltava
Vinnytsia
Kherson
Zhytomyr
Sumy
Cherkasy
Khmelnitsk
Kirovograd
Chernigiv
Mykolaiv
Zaporijia
Kharkiv
Dnipropetrovsk
Odesa Lugansk
-1
Donetsk
Crimea
1
1.5
2
Russian official language
2.5
3
3.5
Fig.2 Regional Patterns of Support for Democracy and Autonomy
Democracy best
2.5
3
Ternopil
Zakarpatska
Zaporijia
Kyiv city
Crimea
Kyiv
Ivano-Frankivsk
Lviv
Kirovograd
Poltava
Kherson
Chernivtsi
Lugansk
Cherkasy
Volyn
Mykolaiv
ChernigivKhmelnitsk
Sumy
Rivne
Vinnytsia
Donetsk
Odesa
2
Kharkiv
Dnipropetrovsk
Zhytomyr
0
.1
.2
Support autonomy
.3
.4
25 .5
Fig.3 Regional Patterns of POR vote vs. Yanukovych Support
Donetsk
Crimea
Party of Regions Vote
.2
.3
.4
Kharkiv
Vinnytsia
Dnipropetrovsk
Mykolaiv
Odesa
Khmelnitsk
Kherson
Zakarpatska
Cherkasy
Chernivtsi
Kyiv city
Poltava Kirovograd
Kyiv
Zhytomyr
Sumy
Volyn
Chernigiv
.1
0
Lugansk
Zaporijia
Rivne
Ivano-Frankivsk
Lviv
Ternopil
.6
.8
1
1.2
Yanukovych_Presidency
1.4
1.6
Fig. 4: Regional Priming for Jobs Promise - Lviv vs. Donetsk Politician
West
Center
South
East (other)
Donbas
Ukrainian speaking Ukrainian
Russian speaking Ukrainian
Russian speaking Russian
-1
Donetsk -.5
0
.5
Credibility difference (Lviv-Donetsk)
1
Lviv 26 Fig. 5 Regional priming for ethnic cooperation promise: Lviv vs. Donetsk
West
Center
South
East (other)
Donbas
Ukrainian speaking Ukrainian
Russian speaking Ukrainian
Russian speaking Russian
-.5
0
.5
1
Credibility difference (Lviv-Donetsk)
Fig. 6: Regional priming for jobs promise (Lviv vs. Donetsk) - Subregional Differences
Donetsk
Lugansk
Kharkiv
Dnipropetrovsk
Kyiv
Lviv
Other West
-1
-.5
0
.5
1
Credibility difference (Lviv-Donetsk)
27 Fig. 7: Regional priming for ethnic cooperation promise (Lviv vs. Donetsk) - Subregional Differences
Donetsk
Lugansk
Kharkiv
Dnipropetrovsk
Kyiv
Lviv
Other West
-1
-.5
0
.5
1
1.5
Credibility difference (Lviv-Donetsk)
28 Table 1 Foreign Policy and Language Policy Preferences
(1)
EU vs.
Russia West
South
East
Luhansk
Donetsk
Crimea
Identity indicators Ukrainian speaking
Ukrainian
Russian speaking Ukrainian
Ethnic Russian
Orthodox Moscow
Orthodox Kiev
Greek Catholic
Church regularly
Church never
Developmental indicators
Union member
% agric employment
% indl employment
%HH below poverty line
Vocational education
Secondary education
Higher education
City resident
Village resident
State employment
.860**
(.218)
-.615*
(.230)
-.496**
(.121)
-.846**
(.117)
-.977**
(.117)
-1.009**
(.117)
(2)
EU vs.
Russia .516**
(.180)
-.614**
(.195)
-.375**
(.125)
-.670**
(.132)
-.664**
(.134)
-.492**
(.149)
(3)
EU vs.
Russia .763**
(.215)
-.542*
(.211)
-.593**
(.180)
-.809**
(.189)
-.907**
(.264)
-.858**
(.118)
.127
(.112)
-.075
(.146)
-.383**
(.094)
-.444**
(.110)
-.071
(.111)
.408*
(.166)
.067
(.079)
-.216
(.127)
(4)
EU vs.
Russia
(5)
Russian
official
language
(6)
Russian
official
language
(7)
Russian
official
language
(8)
Russian
official
language
.387*
(.160)
-.480**
(.166)
-.384*
(.151)
-.507**
(.161)
-.519*
(.203)
-.364*
(.154)
-.187#
(.097)
.606**
(.120)
.835**
(.081)
.937**
(.059)
1.230**
(.059)
1.557**
(.059)
.046
(.132)
.419**
(.104)
.589**
(.090)
.599**
(.092)
.853**
(.098)
1.043**
(.112)
-.125
(.093)
.589**
(.132)
.754**
(.134)
.876**
(.133)
1.111**
(.174)
1.572**
(.072)
.046
(.122)
.418**
(.118)
.525**
(.116)
.529**
(.119)
.770**
(.144)
1.077**
(.128)
.208*
(.095)
-.192
(.127)
-.390**
(.107)
-.283**
(.097)
.024
(.101)
.474**
(.170)
.179*
(.079)
-.152
(.120)
-.482*
(.189)
-1.971*
(.893)
.455
(1.519)
.024
(.015)
.190#
(.103)
.139
(.110)
.149
(.107)
.109
(.111)
.157
(.110)
-.041
-.462*
(.203)
-1.629#
(.812)
-.104
(1.164)
.017
(.012)
.198#
(.103)
.163
(.099)
.185#
(.098)
.184
(.113)
.094
(.109)
-.080
-.356**
(.083)
.198*
(.094)
.457**
(.104)
.000
(.065)
-.071
(.051)
-.203#
(.103)
.011
(.050)
.070
(.064)
-.367**
(.081)
.157#
(.091)
.435**
(.111)
.008
(.066)
-.063
(.048)
-.161
(.108)
.025
(.051)
.071
(.058)
-.046
(.073)
.613
(.606)
.397
(1.130)
-.004
(.008)
-.011
(.063)
-.001
(.065)
-.135*
(.059)
.203*
(.084)
-.169*
(.080)
.024
-.070
(.079)
.514
(.570)
.926
(.935)
.003
(.007)
-.015
(.062)
-.027
(.065)
-.179**
(.059)
.113
(.075)
-.124#
(.071)
.038
29 (.089)
(.091)
.216
.182
(.146)
(.138)
Industrial worker
-.011
-.024
(.115)
(.109)
Agricultural employee
-.206
-.232
(.200)
(.184)
Service employee
.134
.087
(.153)
(.154)
Unemployed
-.062
-.068
(.140)
(.133)
Retired
-.190
-.240*
(.125)
(.111)
Student
.209
.176
(.141)
(.141)
Affordability index
.132
.198
(.198)
(.191)
Work in Russia (self)
-.119
-.107
(.092)
(.087)
Work in Russia (family)
-.068
-.029
(.097)
(.088)
Remittances (Russia)
-.130
-.131
(.105)
(.099)
Work in West (family)
.207*
.165#
(.100)
(.093)
Remittances (West)
.060
.029
(.161)
(.152)
Female
-.132*
-.166*
(.053)
(.071)
Age
-.009**
-.008**
(.003)
(.003)
Married
-.073
-.093
(.085)
(.088)
Constant
.194
.347*
.399
.479
1.388** 1.546**
(.117)
(.163)
(.378)
(.330)
(.059)
(.108)
Observations
1,766
1,766
1,766
1,766
1,740
1,740
R-squared
.195
.231
.265
.302
.381
.453
Adj R^2
.192
.225
.252
.286
.379
.448
OLS coefficients with robust standard errors in parentheses (** p<.01, * p<.05, # p<.1).
White collar employee
(.069)
-.116
(.119)
-.065
(.091)
-.052
(.126)
-.114
(.077)
-.023
(.093)
-.076
(.086)
-.149
(.091)
.008
(.077)
.073
(.066)
.074
(.044)
-.042
(.057)
-.103
(.066)
-.046
(.082)
.026
(.029)
.000
(.002)
-.094#
(.046)
1.367**
(.241)
1,740
.414
.403
(.058)
-.064
(.105)
-.039
(.077)
.027
(.096)
-.074
(.070)
.016
(.086)
-.016
(.083)
-.109
(.082)
-.064
(.081)
.039
(.057)
.061
(.041)
-.030
(.049)
-.101
(.060)
-.048
(.079)
.033
(.026)
-.000
(.002)
-.060
(.047)
1.433**
(.196)
1,740
.473
.460
30 Table 2 Political system choices
VARIABLES
West
South
East
Luhansk
Donetsk
Crimea
Identity indicators
Ukrainian speaking
Ukrainian
Russian speaking
Ukrainian
Ethnic Russian
Orthodox Moscow
Orthodox Kiev
Greek Catholic
Church regularly
Church never
Developmental indicators
Union member
% agric employment
% indl employment
%HH below poverty line
Vocational education
Secondary education
Higher education
City resident
Village resident
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Democracy Democracy Democracy Democracy
Pro
Pro
Pro
Pro
best
best
best
best
autonomy Autonom Autonom autonomy
y
y
.220
(.140)
-.026
(.235)
-.325
(.262)
.166
(.109)
-.190#
(.109)
.335**
(.109)
.119
(.125)
-.106
(.240)
-.331
(.243)
.178#
(.104)
-.135
(.111)
.455**
(.121)
.216
(.166)
.010
(.244)
-.282
(.388)
.223
(.270)
-.216
(.357)
.371*
(.163)
-.256*
(.122)
-.209
(.149)
-.135
(.115)
-.129
(.088)
.073
(.137)
.354*
(.157)
-.067
(.072)
-.278**
(.088)
.071
(.153)
-.061
(.237)
-.241
(.375)
.285
(.274)
-.093
(.341)
.479**
(.162)
-.237#
(.132)
-.218
(.130)
-.133
(.105)
-.072
(.088)
.117
(.135)
.411*
(.160)
-.012
(.067)
-.287**
(.082)
-.036
(.252)
-.371
(1.297)
-.941
(2.470)
-.004
(.023)
.038
(.079)
.129
(.112)
.033
(.108)
-.092
(.177)
.031
(.109)
-.031
(.236)
-.159
(1.168)
-1.164
(2.327)
-.003
(.021)
.045
(.075)
.129
(.102)
.045
(.109)
-.067
(.162)
.022
(.106)
.146**
(.048)
.292**
(.055)
.265**
(.055)
.405**
(.044)
.548**
(.040)
.107*
(.054)
.298**
(.064)
.242**
(.055)
.369**
(.048)
.511**
(.043)
.147**
(.052)
.324**
(.068)
.247**
(.087)
.422**
(.102)
.598**
(.100)
.030
(.019)
.055*
(.023)
.057#
(.033)
-.001
(.018)
-.029
(.024)
.035
(.046)
.038*
(.017)
.052*
(.022)
.097#
(.054)
.348**
(.076)
.247**
(.081)
.420**
(.099)
.588**
(.098)
.034#
(.019)
.023
(.020)
.032
(.029)
.007
(.019)
-.031
(.021)
.039
(.048)
.043*
(.018)
.039#
(.022)
-.027
(.023)
-.214
(.190)
-.010
(.311)
.003
(.002)
.054*
(.023)
.035
(.025)
.055#
(.030)
.054#
(.029)
-.005
(.027)
-.029
(.022)
-.238
(.188)
-.046
(.288)
.003
(.002)
.050*
(.021)
.032
(.023)
.045
(.029)
.053#
(.030)
-.011
(.026)
31 State employment
White collar employee
Industrial worker
Agricultural employee
Service employee
Unemployed
Retired
Student
Affordability index
Work in Russia (self)
Work in Russia (family)
Remittances (Russia)
Work in West (family)
Remittances (West)
Female
Age
Married
Constant
2.610**
(.109)
2.863**
(.114)
.053
(.059)
.114
(.103)
-.035
(.098)
-.093
(.207)
.048
(.124)
.122
(.167)
-.095
(.089)
.157
(.148)
.473**
(.145)
-.039
(.085)
-.115
(.082)
.115
(.078)
.132#
(.068)
-.173#
(.090)
-.094*
(.037)
.000
(.002)
-.027
(.058)
2.604**
(.536)
.039
(.053)
.096
(.096)
-.034
(.098)
-.088
(.208)
.038
(.122)
.106
(.156)
-.079
(.088)
.159
(.143)
.490**
(.133)
-.040
(.084)
-.090
(.089)
.135
(.085)
.113
(.068)
-.240*
(.094)
-.140**
(.043)
-.000
(.002)
-.038
(.059)
2.804**
(.516)
.032
(.029)
-.044#
(.023)
-.013
(.023)
-.006
(.045)
-.014
(.029)
.029
(.030)
-.008
(.026)
-.056*
(.027)
.012
(.035)
.012
(.014)
.003
(.027)
.024
(.023)
.009
(.024)
-.012
(.034)
-.020*
(.010)
-.000
(.000)
-.037*
(.016)
.034
(.029)
-.044*
(.022)
-.018
(.021)
-.010
(.042)
-.016
(.026)
.033
(.030)
-.012
(.025)
-.053*
(.026)
.017
(.034)
.010
(.012)
.004
(.026)
.021
(.022)
.004
(.023)
-.005
(.037)
-.022#
(.012)
-.000
(.000)
-.032*
(.016)
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
32 Table 3 Partisan and electoral preferences
VARIABLES
West
South
East
Luhansk
Donetsk
Crimea
Identity indicators
Ukrainian speaking
Ukrainian
Russian speaking Ukrainian
Ethnic Russian
Orthodox Moscow
Orthod_kiev
Greek Catholic
Church regularly
Church never
Developmental indicators
Union member
% agric employment
% indl employment
%HH below poverty line
Vocational education
Secondary education
Higher education
City resident
Village resident
State employment
(1)
Evaluatio
Yanukov.
Presid.
(2)
Evaluatio
Yanukov.
Presid.
(3)
Evaluatio
Yanukov.
Presid.
(4)
Evaluatio
Yanukov.
Presid.
(5)
Voted
POR
(6)
Voted
POR
(7)
Voted
POR
(8)
Voted
POR
-.320**
(.091)
.150
(.094)
.162#
(.086)
.368**
(.078)
.631**
(.078)
.357**
(.078)
-.234*
(.107)
.141
(.105)
.100
(.096)
.285**
(.087)
.508**
(.092)
.149
(.099)
-.362**
(.088)
.189#
(.099)
.179
(.166)
.388*
(.143)
.624**
(.176)
.419**
(.088)
-.301**
(.097)
.195*
(.092)
.121
(.156)
.303*
(.140)
.505**
(.167)
.240*
(.106)
-.127*
(.056)
.148**
(.039)
.225**
(.050)
.288**
(.039)
.354**
(.038)
.300**
(.039)
-.069
(.059)
.141**
(.036)
.180**
(.045)
.220**
(.041)
.255**
(.043)
.121**
(.041)
-.144**
(.045)
.119**
(.042)
.375**
(.064)
.455**
(.066)
.510**
(.093)
.294**
(.047)
-.096*
(.044)
.118**
(.037)
.326**
(.055)
.381**
(.058)
.421**
(.085)
.142**
(.048)
.012
(.082)
.141*
(.059)
.226**
(.073)
.144#
(.072)
.024
(.047)
-.137#
(.079)
.016
(.063)
-.058
(.090)
.009
(.071)
.116*
(.050)
.198**
(.062)
.162*
(.062)
.054
(.040)
-.077
(.081)
.025
(.061)
-.055
(.081)
.102
(.138)
.481
(.594)
-.272
(1.212)
.006
(.009)
.080
(.055)
.024
(.072)
.025
(.064)
.031
(.095)
-.061
(.062)
-.045
.080
(.139)
.426
(.542)
-.078
(1.071)
.007
(.008)
.074
(.052)
.012
(.068)
.003
(.061)
.007
(.089)
-.058
(.059)
-.036
-.023
(.031)
.059#
(.031)
.145**
(.037)
.090**
(.031)
.007
(.025)
-.147**
(.035)
.031
(.027)
-.009
(.037)
-.022
(.031)
.053*
(.026)
.119**
(.043)
.084**
(.029)
.011
(.025)
-.136**
(.029)
.021
(.028)
-.005
(.031)
-.029
(.070)
.536*
(.218)
-1.109**
(.397)
-.004
(.005)
.006
(.030)
.038
(.045)
.045
(.048)
-.003
(.027)
-.026
(.031)
-.021
-.040
(.069)
.465**
(.177)
-.941**
(.314)
-.002
(.004)
.002
(.028)
.026
(.042)
.028
(.047)
-.018
(.022)
-.021
(.030)
-.011
33 White collar employee
Industrial worker
Agricultural employee
Service employee
Unemployed
Retired
Student
Affordability index
Work in Russia (self)
Work in Russia (family)
Remittances (Russia)
Work in West (family)
Remittances (West)
Female
Age
Married
Constant
1.010**
(.078)
.917**
(.092)
(.068)
-.032
(.071)
.003
(.058)
.053
(.153)
-.066
(.078)
-.023
(.066)
.045
(.088)
.157
(.150)
.694**
(.138)
-.123#
(.067)
.030
(.048)
.112
(.068)
-.076
(.066)
.011
(.088)
.062#
(.034)
.003#
(.002)
-.051
(.047)
.296
(.263)
(.072)
-.039
(.075)
-.009
(.056)
.060
(.146)
-.056
(.079)
-.031
(.066)
.054
(.093)
.168
(.146)
.688**
(.133)
-.118#
(.064)
.017
(.049)
.120#
(.068)
-.066
(.066)
.024
(.082)
.028
(.037)
.003
(.002)
-.056
(.045)
.222
(.231)
(.027)
.002
(.045)
-.010
(.035)
.026
(.073)
-.061
(.045)
-.074#
(.038)
-.043
(.035)
-.062
(.064)
.211**
(.071)
-.041
(.031)
.030
(.030)
.007
(.046)
-.014
(.039)
.026
(.064)
.025
(.020)
.004**
(.001)
-.002
(.025)
Observations
1,529
1,529
1,529
1,529
1,773
1,773
1,773
Adj R^2
.119
.133
.175
.186
Pseudo R^2
.100
.125
.142
OLS coefficients (models 1-4); Marginal effects (models 5-8). Robust standard errors in parentheses ** p<.01, *
p<.05, # p<.1
(.029)
-.001
(.043)
-.017
(.032)
.029
(.070)
-.056
(.046)
-.072#
(.038)
-.039
(.036)
-.057
(.063)
.200**
(.070)
-.043
(.028)
.025
(.029)
.009
(.047)
-.011
(.039)
.039
(.059)
.008
(.017)
.003**
(.001)
-.001
(.025)
1,773
.160
34