No abstract

States use repression to enforce obedience, but repression -especially if it is violent, massive, and indiscriminate -often incites opposition. Why does repression have such disparate effects? We address this question by studying the political legacy of Stalin's coercive agricultural policy and collective punishment campaign in Ukraine, which led to the death by starvation of over three million people in 1932-34. Using rich micro-level data on eight decades of local political behavior, we find that communities exposed to Stalin's "terror by hunger" behaved more loyally toward Moscow when the regime could credibly threaten retribution in response to opposition. In times when this threat of retribution abated, the famine-ridden communities showed more opposition to Moscow, both short and long-term. Thus, repression can both deter and inflame opposition, depending on the political opportunity structure in which post-repression behavior unfolds.

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.

This article offers the first disaggregated, quantitative comparison of Islamist and nationalist violence, using new data from Russia's North Caucasus. We find that violence by Islamist groups is less sensitive to government coercion than violence by nationalist groups. Selective counterinsurgency tactics outperform indiscriminate force in suppressing attacks by nationalists, but not Islamists. We attribute this finding to rebels’ support structure. Because Islamist insurgents rely less on local support than nationalists, they are able to maintain operations even where it is relatively costly for the local population to support them. These findings have potentially significant implications for other contemporary conflicts in which governments face both types of challenges to their authority and existing political order.

Zhukov, Yuri M. and Brandon M. Stewart. (2012) Choosing Your Neighbors: Networks of Diffusion in International Relations. International Studies Quarterly, doi: 10.1111/isqu.12008 
© 2012 International Studies Association In examining the diffusion of social and political phenomena like regime transition, conflict, and policy change, scholars routinely make choices about how proximity is defined and which neighbors should be considered more important than others. Since each specification offers an alternative view of the networks through which diffusion can take place, one’s decision can exert a significant influence on the magnitude and scope of estimated diffusion effects. This problem is widely recognized, but is rarely the subject of direct analysis. In international relations research, connectivity choices are usually ad hoc, driven more by data availability than by theoretically informed decision criteria. We take a closer look at the assumptions behind these choices, and propose a more systematic method to asses the structural similarity of two or more alternative networks, and select one that most plausibly relates theory to empirics. We apply this method to the spread of democratic regime change and offer an illustrative example of how neighbor choices might impact predictions and inferences in the case of the 2011 Arab Spring.