Stalin’s Terror and the Long-Term Political Effects of Mass Repression
(with Yuri Zhukov)
Repression has a long-term negative effect on political participation. Using millions of arrest records from archival documents, and polling station-level election results, we examine how exposure to Stalin-era repression affects voter turnout in Putin’s Russia. To estimate the effect of repression on voting, we exploit exogenous variation in repression due to the structure of mid-century Soviet railroads, and travel distances to prison camps. We find that communities more heavily repressed under Stalin are less likely to vote today. The electoral legacy of Stalin’s terror – decades after the Soviet collapse, and across multiple election cycles (2003–12) – is systematically lower turnout. To show that our result is not unique to the Putin regime, we replicate our analysis in Ukraine (2004–14), and find similar patterns. These results highlight the negative consequences of repression for political behavior, and challenge the emerging view that exposure to violence increases political engagement. While past research has emphasized the short-term effects of repression over several months or years, we show that these effects may be durable over generations and even changes of political regime. Our findings also demonstrate that repression need not be collective or indiscriminate to have community-level effects.
Zhukov, Yuri M., and Roya Talibova. "Stalin’s terror and the long-term political effects of mass repression." Journal of Peace Research 55, no. 2 (2018): 267-283.
Terrorist Group Abilities, Ideology & Motives (TAIM): Introducing a New Global Database on Terrorist Groups and Activities (with Carly Wayne)
What are the factors that influence the patterns of terrorist violence? Terrorism is often a group-based phenomenon, perpetrated by individuals who claim membership in a larger organization. However, the unit of analysis for many existing terrorism datasets is at the event-level. In this paper, we address this issue, introducing a new cross-national dataset of characteristics of terrorist attacks and groups: the Terrorist Abilities, Ideologies & Motives (TAIM) Metadata. The TAIM Metadata contains a set of three comprehensive data sets of militant groups who have actively engaged in terrorism between 1970 to 2015, drawn from the population of terrorist attacks in the Global Terrorism Database. Designed hierarchically, the TAIM Metadata includes TAIM-event (66,675 obs and 142 variables), TAIM- group-year (4,405 obs and 69 variables), and TAIM-group (935 obs and 47 variables). TAIM is unique in both the breadth of organizations covered — 935 unique militant groups in 117 countries over a 45-year time-span — and the number of original variables coded for each organization, including organizational-level features such as motive, ideology, strength, and birth country, among several others, and dynamically changing event-level attributes such as lethality to date, targeted country, attack frequency to date, and more. The complexity and scope of this dataset open multiple new avenues for the study of terrorism, providing a unique opportunity for scholars to carry out research on multiple levels of analyses in order to uncover relationships between group- and event-level attributes and outcomes over time.
How hard will citizens fight to defend a state they see as tyrannical? Using a stylized model of soldier’s choice, we show that exposure to state repression should increase effort by lower-motivated soldiers, but decrease effort by higher-motivated ones. We test this claim by utilizing over 100 million declassified Red Army personnel records from World War II. Our empirical strategy exploits plausibly exogenous variation in the scale of Stalin’s repression prior to war due to explicit random targeting, logistical costs, and local administrative discretion. Consistent with our expectations, soldiers from places exposed to higher repression were more likely to fight until death and less likely to flee, but also received fewer decorations for personal bravery. Repression appears to have induced obedience at the expense of initiative and increased the human costs of war.
Political Violence and its Legacies
State Repression and War-Making: Raising an Army of Dissidents
Over the course of the past few centuries, authoritarian states have raised and trained strong armies that have waged deadly conflicts. Despite the ubiquity of such regimes in contemporary times, there is very little research on the role of state repression in shaping individuals' decisions to support the state in its war-making effort. I develop a novel theory of combat motivation that formulates the conditions under which different levels of repression can incite or depress combat motivation. The most common explanation in the literature for how citizens decide on a trade-off between supporting or fighting the state has rested on the logic of coercion, whereby compliance of the citizens can be achieved through only selective violence, which gives citizens an opportunity to avoid it by behaving in a desired manner. I present a model in which this mechanism plays out in the opposite direction, such that indiscriminate repression motivates individuals to support the regime in its war-making efforts to signal loyalty. The model illustrates how, in anticipation of international conflict, an authoritarian has an incentive to target population indiscriminately based on group-level characteristics. The intensity of such repression, however, is qualified by a trade-off between the imminence of external threat and the possibility of a domestic uprising.
Repression, Military Service and Insurrection
(Job Market Paper)
Why do some military veterans take up arms against the state, while others do not? Past research has identified the long-term effects of repression on political behavior and the crucial role of combat experience in advancing human capital, yet little is known about how combat veterans from marginalized backgrounds utilize these skills in a post-war society. Using multiple datasets containing millions of individual records on the Russian Imperial Army conscripts of WWI, soldiers of the revolutionary Red Army and state-backed Imperial White Guard of the Russian Civil War, I study whether WWI veterans from ethnic minority groups were more likely to rebel. The results provide strong evidence that soldiers from marginalized groups and inhabitants of ethnically diverse districts were more likely to join the revolutionary forces to fight against the crumbling empire, while ethnic Russians were more likely to join state forces. These long-term effects matter – in authoritarian settings, even more so – because the state resorts to its military to ensure regime survival when internal security agencies fail in the face of domestic unrest.
Do the Means Match the Ends? Exploring the Connection between Terrorist Tactics and Motives (with Carly Wayne)
What explains the tremendous variation in targeting strategies between different terrorist organizations? This article examines the relationship between terrorist group objectives and tactics using a newly created data set of terrorist organizations. We hypothesize that the relative scope of a terrorist group’s motives — whether they are limited or maximalist — will lead groups to choose different types of attack and targeting strategies. Specifically, we argue that groups with limited aims will utilize an attrition strategy, designed to inflict persistent pain that induces a government to concede policy objectives, while groups with maximalist goals will pursue a provocation strategy, designed to achieve important process goals for the group so that the group can eventually take what they want by force. Using a newly built cross-national dataset of 66,675 attacks by 935 different terrorist organizations, we find that, indeed, groups with limited goals are more likely to use conventional weapons to frequently attack mass public targets, while groups with maximalist goals are significantly more likely to utilize sensational weaponry and launch rarer attacks against symbolic government targets. Because of this emphasis on sensational weaponry, attacks by maximalist groups are more lethal, despite their focus on government (rather than mass public) targets. This research has important implications for our understanding of terrorist violence, demonstrating the crucial role motives play in structuring terrorist groups’ strategic in- incentives.
Data and Measurement
Decision Tree Guided Multi-Class Support Vector Machines with Dynamic Class Selections
I propose a new approach to the traditional classification task in machine learning that combines binary decision trees with multi-class SVM classifiers. In contrast to the existing studies that use binary tree architecture as a decision method to achieve classification given a fixed number of classes, I utilize it to train data on a dynamically changing number of classes conditional on a certain available feature. Each data point in the data space is mapped against a unique list of classes, pre-designed for it with the combination of prior knowledge of the researcher and the information derived from the underlying data. Given that the level of aggregation is much higher for the base attribute in the feature space, the number of SVMs to be trained is reduced to achieve high computational efficiency, while maximizing the accuracy of the classification task. I evaluate the accuracy of this new feature selection algorithm on a task aimed at correctly recovering the identities of unknown perpetrators of terrorist attacks, using one of the largest, publicly available terrorism databases.
Political Violence and its Legacies
Fealty Tested, Loyalty Forgotten: Does Military Service Exempt Veterans from Post-war Repression?
Officer Staffing: Political Loyalty and Combat Performance
Order and Violence: the Authoritarian Legacy of the Russian Imperial Police
(with Travis Curtice)
Data and Measurement
Modeling Uncertainty in Data Matching
Local Human Rights Monitoring in a Changing Human Rights Context: Evidence from Turkey
(with Christopher Fariss)
Field Selection in Probabilistic Record Linkage
(with Ted Enamorado)