Ethnic movements continue to challenge state governments globally, with many ethnic conflicts revolving around the status of groups’ territories. Yet, politically mobilized ethnic groups vary considerably in their territorial demands: some press for increased autonomy or even outright secession, while others do not make such demands at all and prefer integration in the existing state. What explains this divergence in ethnic group demands with respect to the group's territorial status? We argue that the expected benefits of ethno-regional autonomy or secession compared to integration in a centralized state differ across distinct segments within the group as a function of three structural factors: heterogeneity in the group's income sources, cultural divisions, and territorial fragmentation, leading to disagreement over self-determination demands between different political organizations representing the same ethnic group. We test our argument using an expanded version of the Ethnic Power Relations–Organizations (EPR-O) dataset. Our pre-registered study finds support for one of our hypotheses: heterogeneity in groups’ income sources increases disagreement over self-determination demands. This finding sheds new light on the structural sources of internal divisions within ethno-political movements.

We study how an intervention combining youth intergroup contact and sports affects intergroup relations in the context of an active conflict. We first conduct a randomized controlled trial (RCT) of one-year program exposure in Israel. To track effects of a multiyear exposure, we then use machine-learning techniques to fuse the RCT with the observational data gathered on multiyear participants. This analytical approach can help overcome frequent limitations of RCTs, such as modest sample sizes and short observation periods. Our evidence cannot affirm a one-year effect on outgroup regard and ingroup regulation, although we estimate benefits of multiyear exposure among Jewish-Israeli youth, particularly boys. We discuss implications for interventions in contexts of active conflict and group status asymmetry.

Coercive institutions' internal structures remain poorly understood. Bureaucratic reorganizations within security institutions cause significant variation in their behavior, however. Intra-agency reforms interact with officers' careerist incentives to cause changes in coercive capacity or repression. In this paper, I test the effects of intra-agency reforms on surveillance capacity. I exploit a rare source of exogenous variation in the structure of the secret police in communist Poland. Difference-in-differences models find that when security headquarters were duplicated through an administrative reform, the proliferation of higher-level posts within the service caused a large and statistically significant increase in the number of informants it employed. Intra-agency reform substantially altered the agency's coercive capacity. Previously overlooked dynamics within coercive institutions have important effects on authoritarian repression.

Violent hostility between ordinary partisans is undermining American democracy. Social media is blamed for rhetoric threatening violence against political opponents and implicated in offline political violence. Focusing on Twitter, I propose a method to identify such rhetoric and investigate substantive patterns associated with it. Using a data set surrounding the 2020 Presidential Election, I demonstrate that violent tweets closely track contentious politics offline, peaking in the days preceding the Capitol Riot. Women and Republican politicians are targeted with such tweets more frequently than men and non-Republican politicians. Violent tweets, while rare, spread widely through communication networks, reaching those without direct ties to violent users on the fringe of the networks. This paper is the first to make sense of violent partisan hostility expressed online, contributing to the fields of partisanship, contentious politics, and political communication.

Can data on government coercion and violence be trusted when the data are generated by state itself? In this paper, we investigate the extent to which data from the California Department of Corrections and Rehabilitation (CDCR) regarding the use of force by corrections officers against prison inmates between 2008 and 2017 conform to Benford's Law. Following a growing data forensics literature, we expect misreporting of the use-of-force in California state prisons to cause the observed data to deviate from Benford's distribution. Statistical hypothesis tests and further investigation of CDCR data—which show both temporal and cross-sectional variance in conformity with Benford's Law—are consistent with misreporting of the use-of-force by the CDCR. Our results suggest that data on government coercion generated by the state should be inspected carefully before being used to test hypotheses or make policy.

High temperatures have been linked to aggression and different forms of conflict in humans. We consider whether exposure to heat waves increases discriminatory behavior toward outgroups. Using data from two large-scale field experiments in Germany, we find a direct causal effect of exposure to heat shocks on discrimination in helping behavior. As temperature rises, German natives faced with a choice to provide help to strangers in every-day interactions help Muslim immigrants less than they do other German natives, while help rates toward natives are unaffected by temperature. This finding suggests that there may be a physiological basis for discriminatory behavior toward outgroups.

Are individuals in violent contexts reluctant to tackle corruption for fear of future violence? Or does violence mobilize them to fight corruption? We investigate these questions looking at the effects of fear and violence stemming from the Mexican Drug War on attitudes toward corruption. We conducted two surveys before the 2012 Mexican presidential election. First, as part of a nationally representative survey, we find a positive correlation between fear of violence and willingness to accept corruption in exchange for lower levels of violence. To disentangle causal effects, we conducted a follow-up survey experiment in Greater Mexico City where we manipulated fear over the Drug War. We find that individuals within this context are not easily scared. Those who received a common fear-inducing manipulation do not report higher levels of fear and are less willing to tolerate corruption. Conversely, we find strong evidence that individuals who have been victims of crime are more likely to report both higher levels of fear and willingness to accept corruption if it lowers violence. Our findings suggest that voters are more strategic and resilient in the face of violence than many extant theories of political behavior suggest.

What is the effect of violence on political mobilization? Taking the repression-mobilization nexus debate as a starting point, we study the effects of police interventions on political participation, focusing on the Spanish police crackdown on Catalonia's independence referendum on 1 October 2017. We analyze the effect of police actions on turnout using detailed aggregate data, as well as a survey conducted a few days after the referendum. The two empirical approaches show that police interventions had both deterrent and inverse spatial spillover effects. Although police raids had a local negative impact on turnout, they induced positive spillover effects in the surrounding areas. Our findings also indicate heterogeneity in the spatial dynamics, with police actions encouraging people to go to vote in nearby areas, but also mobilizing residents in neighboring areas to participate, especially those individuals with fewer incentives to turn out to vote.

Interactions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.