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

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”

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

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.

crucial for such research projects.

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

nomic activity, which is collected by himself and his assistance.

atory variables. First, it includes the violent event data for all 3,037 municipalities (cities,

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.

ruary 2015 (see Appendix 2).

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).

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

models. Finally, I consider the duration until the loss of rebel control to pro-government

forces (Zhukov, 2016: 9–10).

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)

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

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.

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-

cruitment among the Russian-speaking population (Sherbak, Komin and Sokolov, 2016).

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

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

ities and rebels’ economic interests) are not developed enough to provide strong causal

tions of the rebellion in Eastern Ukraine. Also, Zhukov statistical model shows the high

to alternative explanations. In the next part of the paper, I am going to briey show what

vestigate casual mechanisms via process tracing and counterfactual analysis, i.e. via case

nism (Goertz and Mahoney, 2012). Since Zhukov’s model is based on extensive data on so

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

cal model, we could make four 2x2 tables: (i) Russian language (X) à rebel violence (Y);

native casual mechanisms within the cases. It is also worth noting that such cases are not

nisms on the basis of the cross-case and large-N statistical data.