Are police agencies less likely to use torture in democracies than in non-democracies? Much empirical research has shown that democracies are less likely to engage in torture in general, but most of this research does not distinguish among victim types or state agencies. Using the Ill-Treatment and Torture (ITT) Data, we focus on police agencies and evaluate whether they are less likely to use torture against (separately) political dissidents, criminals, and marginalized communities. Using logistic regressions with random effects, we find that the well-established and relatively high level of democracies' respect for the rights of dissidents extends to police agencies as well. However, we find weaker statistical evidence that police agencies in democracies are less likely to use torture against criminals, and no evidence that they are less likely to torture marginalized communities. Our results suggest that one of the most robust empirical facts in the literature must be qualified. The protection from violence offered by democratic institutions does not seem to generalize beyond violence directly related to political competition and dissent.

Why do states accept refugees? While there are a number of factors that influence a state’s decision to accept refugees, interstate relations play an important yet understudied role in refugee flows. In this paper, we build on previous work that has suggested that states with an adversarial relationship will be more likely to accept refugees. We incorporate existing conceptualization and theory from the rivalry literature and extend this logic to state strategy of refugee acceptance to provide one of the first empirical evaluations of refugee acceptance by states. Specifically, we argue that the issues rivals are contending over will change the incentives and disincentives for admitting a rival’s refugees. We anticipate that rivals disputing over ideology will be more likely to accept their rival’s refugees than rivals contending over other rivalry types. We test and find evidence for our arguments using a data set of all directed dyads from 1960 to 2006.

Because existing issue classification schemes omit prominent issues (e.g., domestic armed conflict) or contain significant within-category heterogeneity, theorizing about the role of issues in international conflict processes has stagnated. Our project jump-starts it again, by independently—and systematically—reconceptualizing and gathering data on five issues connected to dyadic militarized interstate disputes (MIDs) during the period 1900–2010: land (borders), maritime (borders), islands, civil conflict, and coups. After conceptually introducing these issues and embedding them within a larger framework, we describe and apply our MID-Issue data. These efforts show that (i) the MID dataset’s issue classification scheme does not systematically capture our issues, (ii) events in 37.58% of dyadic MIDs connect to domestic armed conflict—a prevalence not on the field’s radar, (iii) some factors promote issue-based international conflict, but only via indirect channels, and (iv) significant value even derives from a further conceptualization of “territorial issues” (broadly defined).

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Spatial conditionally autoregressive (CAR) models in a hierarchical Bayesian framework can be informative for understanding state politics, or any similar population of border-defined observations. This article explains how a hierarchical CAR model is specified and estimated and then uses Monte Carlo analyses to show when the CAR model offers efficiency gains. We apply this model to data structures common to state politics: A cross-sectional example replicates Erikson, Wright and McIver’s (1993) Statehouse Democracy model and a multilevel panel model example replicates Margalit’s (2013) study of social welfare policy preferences. The CAR model fits better in each case and some inferences differ from models that ignore geographic correlation.