SOCIETY AND SECURITY INSIGHTS
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СОЦИОЛОГИЯ БЕЗОПАСНОСТИ
SOCIOLOGY OF SECURITY
УДК 323.269.6(477.52/.6)
THE ONSET OF ARMED REBELLION
IN EASTERN UKRAINE: OPPORTUNITIES
FOR MULTIMETHOD RESEARCH
A.V. Protasov
University of Bristol, Bristol, UK,
e-mail: antonprotasov1993@gmail.com
The article examines the problem of studying the causes of the onset of the
armed rebellion in Eastern Ukraine in 2014. The author critically examines a pub-
lished scientic paper, in which, based on extensive statistical data on violence
and economic activity, several hypotheses are checked regarding the causes
of the uprising in Eastern Ukraine. The author points out that the research design
of the published paper relies on a statistical view of causality and shows a statis-
tical correlation between the variables. However, this paper does not explain why
the analyzed data demonstrate statistical correlation, i.e. why some or other factors
lead to a rebellion. Using the possibilities of a multimethod research design, the au-
thor demonstrates how, based on a cross-case statistical research, it is possible
to investigate casual mechanisms via process tracing and counterfactual analysis,
i.e. via case studies for establishing within-case inference.
Keywords: armed rebellion, Eastern Ukraine, research design, multimethod.
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НАЧАЛО ВООРУЖЕННОГО ВОССТАНИЯ
В ВОСТОЧНОЙ УКРАИНЕ:
ВОЗМОЖНОСТИ МУЛЬТИМЕТОДОЛОГИЧЕСКОГО
ИССЛЕДОВАНИЯ
А.В. Протасов
Бристольский университет, Бристоль, Великобритания,
e-mail: antonprotasov1993@gmail.com
Рассматривается проблема изучения причин начала вооруженного восста-
ния в Восточной Украине в 2014 г. Автор критически оценивает опубликован-
ную научную работу, в которой на основе обширных статистических данных
по насилию и экономической деятельности проверяется несколько гипотез
относительно причин возникновения вооруженного восстания в Восточной
Украине. Автор указывает на то, что исследовательский дизайн опубликован-
ной работы опирается на статистический взгляд на причинность и показыва-
ет статистическую корреляцию между переменными. Однако данная работа
не объясняет, почему проанализированные данные демонстрируют статисти-
ческую корреляцию, т.е. почему те или иные факторы ведут к возникновению
вооруженного восстания. Используя возможности мультиметодологического
дизайна, автор статьи показывает, как на основе поперечного статистического
исследования возможно изучить причинно-следственные механизмы посред-
ством отслеживания процессов и контрфакторного анализа, т.е. с помощью
кейс-исследований для последующего установления внтурикейсового заклю-
чения.
Ключевые слова: вооруженное восстание, Восточная Украина, исследо-
вательский дизайн, мультиметодология.
1. Introduction
The Revolution of Dignity, or a coup d’état if looking from high Kremlin towers,
took place in Ukraine almost four ago, in February 2014. Further events were developing
according to all necessary elements of a successful Hollywood movie and included Pres
-
ident’s gateway, revolutionary romanticism, annexation of territories, people’s uprisings,
counterterrorism operations, shooting and bombing, and information warfare. Military
actions were so severe that Hollande and Merkel put a lot of eort in order to seat the
warring parties to the negotiating table. These parties had two opposite views on the onset
of rebellion in Eastern Ukraine. Despite the fact that the pro-Russian protests took place
in 6 regions of Eastern and Southern Ukraine, the armed rebellion occurred only in two
of them, Donetsk and Luhansk.
Relying on extensive data that include 3,037 municipalities in the Donetsk and Lu
-
hansk regions, Yuri Zhukov, an Assistant Professor of Political Science at the University
of Michigan in Ann Arbor, attempts to empirically test “identity-based” and “economic”
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explanations of the onset of armed rebellion and its development (Zhukov, 2016). He also
clearly shows why the rebellion occurred and remained contained only in the Donetsk and
Luhansk regions by looking at identity and economic factors, as explanatory variables
(Xs), and rebel violence and rebel control, as outcome variables (Ys), in the period that in
-
cludes the data from (a) early protests in the regions after the ouster of the former president
Viktor Yanukovych in March 2014 and (b) until the day after the second Minsk ceasere
agreement was signed (February 15, 2015). Zhukov uses statistical methods to test the
impact of identity and economic factors on rebel violence and rebel control.
However, in my opinion, Zhukov’s statistical analysis, on the one hand, clearly
shows the correlation between the variables but, on the other hand, does not provide any
causal mechanism that would be able to explain why these relationships between Xs and
Ys actually hold. In other words, such a complex statistical model gives us convincing
evidence that Xs produce Ys, which is supported by a cross-case large-N research, but it
is still very unclear exactly how Xs cause Ys. In order to address this issue, one should
support Zhukov’s general ndings with case studies that can establish and conrm possi
-
ble causal mechanisms. Such a combination of statistical analysis and case studies means
conducting a multimethod research. The goal of this paper is to show how a scholar may
choose dierent cases to explore within-case inference that can supplement Zhukov’s
large-N, cross-case ndings on the onset of armed rebellion in the Donetsk and Luhansk
regions.
The paper proceeds as follows. First, I briey summarize Zhukov’s research, high
-
light the general ndings that are based on sophisticated statistical models, and discuss
remaining questions. Second, I analyze possible cases from Zhukov’s research that can be
used for establishing within-case inference and providing much stronger casual mecha
-
nisms. Lastly, I nish with some conclusions and suggest that even very complex statisti-
cal models cannot explain casual mechanisms; therefore, the use of case studies becomes
crucial for such research projects.
2. Yuri Zhukov’s statistical research: Main ndings and remaining questions
Yuri Zhukov’s research on the rebellion in Eastern Ukraine is, in my perspective,
the best one of this kind, because it is based on large-N data and statistically proves the nd
-
ings. The main goal of his research is to explain the drivers of the armed rebellion in Eastern
Ukraine. He addresses two main groups of explanations of the onset of rebellion widely
used in scholarly literature: (i) language and ethnicity as the main drivers of the conict that
help “local rebels to overcome collective actions problems” (Zhukov, 2016: 2) due to the
geographical concentration of ethnic groups (identity-based models); (ii) economic oppor
-
tunity cost models of political violence as the explanations of the onset of rebellion (“as real
income from less risky legal activities declines relative to income from criminal or rebellious
behavior, participation in the illicit activity is expected to rise”) (Zhukov, 2016: 3). Zhukov
tests both explanations in his research with the use of micro-level data on violence and eco
-
nomic activity, which is collected by himself and his assistance.
The whole data are consisted of four parts with respect to the outcome and explan
-
atory variables. First, it includes the violent event data for all 3,037 municipalities (cities,
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towns, and villages) in the Donetsk and Luhansk regions, which is based on the coding
of press releases, news reports, and blog posts in three languages (Ukrainian, Russian, and
English) for a relevant period of time, resulting in 10,567 unique violent events. In par
-
ticular, Zhukov codes these unique violent events as rebel attacks in connection with
a particular municipality, which allows him to show their spatial distribution. “For a re
-
port to be classied as a rebel attack, it must involve a specic act of organized violence
initiated by any anti-Kyiv organized group” (Zhukov, 2016: 6). Also, he ranges all rebel
attacks in a particular municipality on the daily basis, which permits him to measure the
intensity of violence. These data is collected in order to explain the rst outcome variable
called rebel violence.
Second, Zhukov is interested in whether a populated place was under rebel or gov
-
ernment control on a given day, which becomes his second outcome variable, namely
territorial control. In order to code each municipality under territorial control on a given
day, Zhukov decides whether a particular populated place falls inside the rebel control
polygon. On the basis of these two data sets, Zhukov makes two maps that spatially show
rebel attacks and rebel control in the municipalities in the period of March 2014 — Feb
-
ruary 2015 (see Appendix 2).
Third, the data on local languages (Ukrainian, Russian) is based on the 2001 Ukrain
-
ian Census. Zhukov measures the proportion of the Russian-speaking population for each
municipality (Appendix 3). And the forth part of the data is collected with the purpose to
calculate the proportion of the local labor force employed in machine-building, mining,
and metal industries (Appendix 4). These data is taken from the Bureau van Dijk’s Orbis
database that includes “records for 445,399 private and publicly owned rms in Donetsk
and Luhansk provinces” (Zhukov, 2016: 8).
Additionally, Zhukov controls for other variables: (i) population density; (ii) ele
-
vation: (iii) forest cover; (iv) distance to the nearest road; (v) distance to the Russian bor-
der; (vi) prewar political loyalties; and (vii) persistence and spatial spillover of violence
(Zhukov, 2016: 9). Zhukov uses Bayesian Model Averaging (BMA) in order to “evaluate
the relative explanatory power of ethnic and economic explanations of violence” in the
Donetsk and Luhansk regions.
I estimate four BMA ensembles of models: two on the determinants of rebel vio
-
lence and two on territorial control. First, I use an ensemble of logit models to explain the
incidence of any rebel violence across municipalities during the rst year of the conict.
Second, I model the intensity of rebel violence in a municipality-week, using an ensemble
of spatiotemporal autoregressive GLMs with quasi-Poisson links. Third, I model the dura
-
tion until a municipality falls under rebel control, using Cox proportional hazards (CPH)
models. Finally, I consider the duration until the loss of rebel control to pro-government
forces (Zhukov, 2016: 9–10).
By running the BMA model on the statistical data collected, Zhukov nds the fol
-
lowing strong correlations between the two outcome and two explanatory variables out-
lined above:
1. There are three main variables that predict rebel violence: (i) the proportion of the
local labor force employed in machine-building; (ii) the population density; and (iii)
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the distance to the nearest road (i.e. the military-geographic factor). The linguistic com-
position of a municipality and the language-economics interaction do not explain the
overall occurrence of rebel violence.
2. Those areas that were most vulnerable to economic shocks caused by the disruption
of economic ties with Russia fell under rebel control faster and remained longer, and
they also experienced rebel violence more frequently. In these municipalities, “the
population was employed in the machinery-building and mining industries” (Zhukov,
2016: 13) prior to the onset of rebellion. The Russian language, according to the model,
had no impact on the establishment and duration of rebel control there. Zhukov states
that these municipalities had low opportunity costs for rebellion. In contrast, “rebels
had much harder time establishing and maintaining control” in those municipalities
“where the opportunity costs of rebellion were higher, such as in centers dominated by
Ukrainian’s relatively competitive metals industry” (Zhukov, 2016: 16).
3. The Russian language was a signicant factor of rebel control only in municipalities
with a high geographical concentration of the Russian-speaking population, along with
smaller industrial labor force and lower exposition to economic shocks. As Zhukov
states, “… a non-industrial, but majority Russian-speaking town was highly likely to
fall under rebel control on a given day… higher than in a majority Ukrainian-speaking
non-industrial town” (Zhukov, 2016: 13).
4. Military-geographic are the only variables, despite economic ones, that strongly cor
-
relate with the loss of rebel control. “Pro-Kyiv forces were able to-re-establish gov-
ernment control much sooner in municipalities at relatively low elevation, with low
population density and farther away from the Russian border” (Zhukov, 2016: 14).
In general, Zhukov shows strong cross-case inference of the variables, providing
convincing evidence that such variables as language and employment have a particular
eect on the intensity of rebel violence and the scale of rebel control in the Donetsk and
Lugansk regions of Eastern Ukraine. However, Zhukov’s statistical model does not fully
explain casual mechanisms, or actually why Xs cause Ys. Therefore, there are many still
remaining questions about the onset of rebellion in Eastern Ukraine. For instance, the tran
-
sition from people’s uprising and protests, which took place in many regions of Ukraine,
into a full armed rebellion cannot be just explained by Zhukov’s cross-case ndings.
First, Zhukov provides evidence that the Russian language had a dierent eect on
the scale and intensity of rebel violence as well as on the establishment and duration of re
-
bel control in various municipalities of the Donetsk and Lugansk regions, largely depend-
ing on the employment in a particular industry. At the same time, he acknowledges that
“the Donbas conict has not been fought primarily along ethnic lines” (Zhukov, 2016: 4).
So, what factors explain the inuence of the language on rebel violence and rebel control?
A survey conducted in February 2014 by the Kyiv International Institute of Sociology
shows that the status of the Russian language in those regions was not a serious issue on
the eve of the onset of rebellion (Kiev International Institute of Sociology, 2014). In my
opinion, the correlation between the Russian language and rebel violence/control cannot
be solely explained by population’s support for rebellion that could lead to a higher re
-
cruitment among the Russian-speaking population (Sherbak, Komin and Sokolov, 2016).
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Second, the exposition to economic shocks in the economically Russian-depend-
ent municipalities of Ukraine (mining and machine-building industries) also is not a ful-
ly convincing argument to explain rebel’s ability to assert control in those parts of the
Donetsk and Lugansk regions. There are strong correlations, no doubts. But the cau
-
sality behind such correlations is not explained and cannot be explained by statistical
means. Both possible sources for the low costs of rebellion briey mentioned by Zhukov
in his article (few alternative sources of income for the population in those municipal
-
ities and rebels’ economic interests) are not developed enough to provide strong causal
explanations.
Third, Zhukov provides statistics on rebel attacks in all municipalities of the
Donetsk and Lugansk regions without any connection with the ghting between the gov
-
ernment and rebel forces. However, I am sure that a closer look at the stages of the military
conict may bring additional and vary valuable observations on the correlation between
rebel violence and rebel control, which eventually could lead even to alternative explana
-
tions of the rebellion in Eastern Ukraine. Also, Zhukov statistical model shows the high
importance for rebels of those locations that had high military-strategic value but, once
again, does not explain the underlying logic of such correlation. The same applies to the
ndings on rebel control. The correlation between local employment and rebel control
does not explain why it was possible to establish and maintain rebel control in the regions
exposed to economic threats from Russia.
In sum, despite very important and very strong cross-case ndings made by Zhukov
on the rebellion in Eastern Ukraine, his statistical model could not provide clear casual
mechanisms between the variable. We know that there is a correlation between X and Y,
but the research is unable to explain such a correlation only by statistical means. There
-
fore, one needs to do case studies for establishing within-case casual inference and shed-
ding light on deep casual mechanisms that could prove Zhukov’s ndings or even lead
to alternative explanations. In the next part of the paper, I am going to briey show what
cases can be used for such a research and how they can be selected on the basis of Zhuk
-
ov’s statistical model.
3. Within-case inference: The logic of selecting cases for exploring casual mechanisms
In my perspective, Zhukov demonstrates a statistical view of causation in the article
and shows the correlation between the variables, providing convincing evidence that X
causes Y on the basis of a cross-case and large-N research. However, his ndings do not
explain why this relationship holds, or, in other words, exactly how X causes Y, since it is
mostly impossible to estimate such a complex statistical model that could explain strong
casual mechanisms coming into play in the onset and development of rebellion in Eastern
Ukraine. As we have the cross-case evidence from Zhukov’s research, it is possible to in
-
vestigate casual mechanisms via process tracing and counterfactual analysis, i.e. via case
studies for establishing within-case inference (Goertz and Mahoney, 2012; Goertz, 2016;
Goertz, 2017).
The rst step in case selection is to dene the potential scope of the causal mecha
-
nism (Goertz and Mahoney, 2012). Since Zhukov’s model is based on extensive data on so
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many municipalities in the Donetsk and Lugansk regions of Ukraine, his cross-case dataset
is a good place to start. The scope of case studies should be also limited to the Donetsk and
Lugansk regions since we cannot generalize the ndings even to other regions of Ukraine
(mainly because they are dierent in terms of linguistic composition, employment, and
never experienced rebel violence or rebel control). One could expand the scope while
doing a comparative analysis of all six Ukrainian regions in which the pro-Russian unrest
and seizures of administrative buildings took place (i.e. to expand the scope by including
more cases). However, I think that one should focus, at least on this stage of the research,
on only those two regions that truly experienced rebellion, and individual characteristics
of which pose the limit to generalizability of the casual mechanism X.
The second step is to provide a list of all possible case studies or the criteria for
such a list. Since Zhukov includes data from all municipalities into his cross-case statisti
-
cal model, we could make four 2x2 tables: (i) Russian language (X) à rebel violence (Y);
(ii) Russian language (X) à rebel control (Y); (iii) local employment (X) à rebel violence
(Y); and (iv) local employment (X) à rebel control (Y), and ll these tables with particular
municipalities.
Because the goal is to explore casual mechanisms, it would be appropriate to look
for extreme cases in the (1; 1) cell of each table, in which two variables meet each other.
The goal is to do a within-case analysis of these extreme cases (since we are concerned
with exactly how X produces Y) and to see if cross-case observations would also fall into
this cell. These two factors would be able to conrm that the proposed casual mechanisms
actually works. It is also necessary to observe the regularity within the cases (if X = 1, how
often Y = 1), which is crucial for further generalization of the research ndings.
One also can use the (1; 1) cell to pick up only those cases that clearly show casual
mechanism and allow to avoid overdetermination. As a responsible scholar, one should
also look closely at the cases from the (1; 0) cell, i.e. at disconrming / falsifying cases
that show evidence against the casual mechanisms. It would have two scenarios: (i) one
nds ways to rene the theory and nd alternative explanations; or (ii) it is necessary to
change the scope of the casual mechanism.
Alternative paths to Ys can be explored with the cases from the (0; 1) cell, where X
is absent, but Y occurs. Actually, confounders are strongly built into Zhukov’s statistical
model. He controls for a number of variables that eventually could become clues for alter
-
native casual mechanisms within the cases. It is also worth noting that such cases are not
a threat to theory. The (0; 0) cell provides a researcher with counterfactuals, which should
be used for a counterfactual analysis (to observe Ys by making Xs = 1).
Since there is no opportunity to access Zhukov’s data, I use his published maps and
compare them with Google Maps in order to nd those municipalities that would t all
the cell of 2x2 tables. I am able to list extreme cases for each cell except only those that
include data on employment in metallurgy. In general, such 2x2 tables may be used by
a scholar who wants to explore within-case inference on the basis of cross-case dataset
made by Zhukov.
Thus, one can build a multimethod research design to look for solid causal mecha
-
nisms on the basis of the cross-case and large-N statistical data.
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Table 1
Russian language (X) and rebel violence (Y).
X = 0 X = 1
Y = 1
(0; 1): Equinality.
Correlation: Russian language
is not dominant; rebel violence
occurs.
Municipalities: Pokrovsk, Dobro-
pillya, Kramatorsk, Shabelkivka,
Druzhkivka, Noviserhiivka,
Zelene, Bakhmut, Krasnotorka,
Yasnohirka, Bilen’ke, Vasilivs’ka
Pustosh, Yusnohirka, Yasna Poly-
ana, Soivka
(1; 1): Casual mechanism.
Correlation: Russian language is dominant; rebel
violence occurs.
Municipalities: Donetsk, Lugansk. Makiivka, Khart-
syz’k, Yenakijeve, Horlivka, Torez, Alchevsk, Di-
kuchaevsk, Novyi svit, Ilovais’k, Zuhres, Shyroke,
Zaproshchens’ke, Hirne, Pelahiivka, Rozsypne, Sjev-
erne, Ol’khovatka, Nikishyne, Vuhlehirs’k, Nyzhnaya
Krynka, Zhdanivka, Stizhkivs’ke, Zaporoshensk’ke,
Mnohopillya, Shyroke, Nyzhnya Krynka, Zhdanivka,
Yasynuvata, Avdiivka, Pisky, Luhans’ke, Vuhlehirs’k,
Verkn’otorets’ke, Yasynuvata, Krynychna, Svitlo-
dars’k, Rozsadky, Mospyne, Svitle, Starobesheve,
Styla, Dokuchajevsk, Leb’yazhe, Panteleimonivka,
Puteprivid. Krynychna, Monakhove, Shchebenka, Kor-
sun’, Pyatykhatky, Gorlovskaya Pravda, Mikhailivka,
Karlo-Marksovove, Avilovka, Rozivka, Yunokomuna-
rivs’k, Bulavyns’ke, Hrozne, Vuhlehirs’k, Rozdadky,
Roty, Klynove, Zaitseve, Klynove, Midna Ruda, Myk-
olayevka, Stansiya Luhanskaya, Metalist, Teplychne,
Sabivka, Vesela Tarasivka, Lutuhyne, Fabrychne,
Khyashchuvate, Lobacheve, Bolotene
Y = 0
(0; 0): Counterfactuals. Russian
language is not dominant; rebel
violence does not occur.
Municipalities: Troits’ke,
Pokrovs’ke, Yamy, Man’kivka,
Solidarne, Pryvillya, Ozero,
Shapran, Novobila, Biloluts’k,
Osynove, Novopskov, Kam’yan-
ka, Krasne Pole, Pinivka, Taraba-
ny, Sadky, Lozove, Bondareve
(1; 0): Falsication/scope. Russian language is domi-
nant; rebel violence does not occur.
Municipalities: Babycheve, Lantrativka, Solontsi, and
Novoznam’yanka
Table 2
Russian language (X) and rebel control (Y)
X = 0 X = 1
Y = 1
(0; 1): Equinality. Russian lan-
guage is not present; rebel control
is present.
(1; 1): Casual mechanism. Russian language is present;
rebel control is present.
Municipalities: Donetsk, Luhansk, Makiivka, Horliv-
ka, Ilovais’k, Novyi Svit, Mospyne, Staribesheve,
Dokuchaevs’k, Olenivka, Volnovakha, Donskoye,
Myrne, Razdol’ne, Komsomomol’s’ke, Novozarivka,
Shyrokoe, Hilynka, Kumachove, Kuteinykove, Merez-
ky, Mnohopillya, Proletars’ke, Zuhres, Kumachove,
Shyroke, Hlynka, Kurnetsovo-Mykhailivka,
SOCIETY AND SECURITY INSIGHTS
106 № 3 2018
X = 0 X = 1
Y = 1
Municipalities: Astakhove,
Novoborovytsi, Lyubyme,
Dar’ino-Yermakivka, Zeleno-
pillya, Marynivka, Kozhevnya,
Hryhorivka, Kalinine, ternivka,
Svobodne, Chumak, Dersove,
Lukove, Prymors’ke, Sosnivs’ke,
Ukrains’ke, Kozats’ke, Porokhn-
ya, Roza
Zori, Svododne, Tel’manove, Lukove, Dmitrivka, Ver-
khnii Kut, Dibrivka, Kozhevnya, Dyakove, Bobryk-
ove, Vyshneve, Tatsyne, Orikhove, Nizhii Nahol’chyk,
Sadovnyi, Antratsyt, Rafailivka, Schotove, Lutuhyne,
Rozkishne, Krasne, Habun, Stansiya Luhanska, Schas-
tia, Lobacheve, Zhovte
Y = 0
(0; 0): Counterfactuals. Russian
language is not present; rebel
control is not present.
Municipalities: Lyman, Stavky,
Zarichne, Pryshyb, Sydorove,
Svyatohirs’k, Yarova, Socnove,
Bohorodyche, Oleksandrivka,
Serednje, Zelena Dolina, Nove,
Karpivka, Ridkodub, Karpivka,
Volchyi Yar, Lozove, Rubetsi,
Yats’kivka, Koroviy Yar, Dolyna,
Krasnopillya, Adamivka, My-
kil’s’ke, Khrestyshche, Pryvillya,
Maidan, Prelesne, Troits’ke
(1; 0): Falsication/scope. Russian language is present;
rebel control is not present.
Municipalities: Babycheve, Lantrativka, Solontsi, and
Novoznam’yanka, Shyrokyi, Kozachyi, Chuhunka,
Zolotarivka, Rozkvit, Krepy, Rozkvit, Vil’ne, Zolo-
tarivka, Verkhnii Minchenok, Nizhnii Minchenok,
Teple, Plotyna, Nyzhn’oteple, Artema, Petrivka
Table 3
Local employment (in machinery, mining, or metallurgy, X) and rebel violence (Y).
X = 0 X = 1
Y = 1
(0; 1): Equinality. There is no
employment in machinery, min-
ing, or metallurgy; rebel violence
is present.
Municipalities: Severodonetsk,
Lysychans’k, Blagodatne,
Zorynivka, Mykil’s’ke, Sheles-
tivka, Kabychivka, Velykots’k,
Kamykivka, Musiivka, Kolomy-
ichykha
(1; 1): Casual mechanism. There is employment in
machinery, mining, or metallurgy; rebel violence is
present.
Municipalities: (a) Machinery: Stakhanov, Bryanka,
Alchevs’k, Rerecal’sk’, Buhaivka, Seleznivka, Arte-
mivs’k, Zoryns’k, Yuzhna Lomuvatka, Yashchykove,
Krasna Zorya, Maloivanivka, Troits’ke. (b) Mining:
Krasnodon, Uralo-Kavkaz, Zakhidnyu, Izvaryne,
Vlasivka, Porichchya, Verkhn’oshevyrivka, Ordzhon-
ikidze, Myrne, Novoaleksandrovka, Hirne, Enhe’sove,
Talove. (c) Metallurgy: Too hard to nd on the map
Y = 0
(0; 0): Counterfactuals. There is
no employment in machinery,
mining, or metallurgy; rebel
violence is not present.
Municipalities: Zachativka,
Vil’ne, Peredove, Rivnopil’,
Krasna Polyana, Stepne
(1; 0): Falsication/scope. There is employment in
machinery, mining, or metallurgy; rebel violence is not
present.
Municipalities: Novopskov, Kamyanka, Osynove,
Pisky
СОЦИОЛОГИЯ БЕЗОПАСНОСТИ
107
№ 3 2018
Table 4
Local employment (in machinery, mining, or metallurgy, X) and rebel control (Y).
X = 0 X = 1
Y = 1
(0; 1): Equinality. There is no
employment in machinery, min-
ing, or metallurgy; rebel control
is present.
Municipalities: Svobodne,
Kalinine, Kon’kove, Chumak,
Dersovem Pervomais’ske, Zori,
Ternivke, Zaporozhets’
(1; 1): Casual mechanism. There is employment in ma-
chinery, mining, or metallurgy; rebel control is present.
(a) Machinery: Novokaterinka, Petrivs’ke, Artemida,
Novozariv’ka, Komsomol’s’ke, Stakhanov, Bryanka,
Alchevs’k, Rerecal’sk’, Buhaivka, Seleznivka, Arte-
mivs’k, Zoryns’k, Yuzhna Lomuvatka, Yashchykove,
Krasna Zorya, Maloivanivka, Troits’ke. (b) Mining:
Krasnodon, Uralo-Kavkaz, Zakhidnyu, Izvaryne,
Vlasivka, Porichchya, Verkhn’oshevyrivka, Ordzhon-
ikidze, Myrne, Novoaleksandrovka, Hirne, Enhe’sove,
Talove. (c) Metallurgy: It is hard to identify such
municipalities on the basis of the published map; one
needs the dat
Y = 0
(0; 0): Counterfactuals. There is
no employment in machinery,
mining, or metallurgy; rebel con-
trol is not present.
Municipalities: Zachativka,
Peredove, Novopetrykivka,
Yalyns’ke, Kluchove, Starom-
lynivka, Zavitne Bazhannya
(1; 0): Falsication/scope. There is employment in
machinery, mining, or metallurgy; rebel control is not
present.
Municipalities: (a) Machinery: Selidove, Mykhailivka,
Vyshneve, Ukrains’k, Tsukurne. (b) Mining: Zelenyi
Hai, Iskra, Tolstoi, Hrushivs’kem Yalta, Piddubne, Per-
ebudova, Schevchenko, Komyshuvakha, Novosilka,
Zelene Pole, Novopil’. (c) Metallurgy: Too hard to nd
on Zhukov’s maps, one needs his data
4. Conclusion
The onset of rebellion in Eastern Ukraine is a complex social phenomenon that
has many casual mechanisms in place. Zhukov’s statistical research nds relationships
between Xs and Ys, but it cannot explain why they hold. In order to do that, one should
connect a cross-case analysis with within-case analysis. Zhukov’s cross-case dataset pro
-
vides opportunities to select appropriate cases for exploring within-case inference. Such a
multimethod research could conrm that the proposed casual mechanism actually works
or leads to alternative explanations.
Appendix 1. Violent event locations in the Donetsk and Luhansk regions, by data source.