Developing states lacking a monopoly over the use of force are commonly seen as having failed to live up to the ideal Weberian sovereign type. Yet rather than being a calling card of anarchy, the devolution of important state functions to subnational actors is a rational strategy for developing states to effectively provide important public goods. The case study of the Jewish Community of Palestine demonstrates one instance where subnational communities provided public goods. This study highlights the causal effect of property rights within institutions to drive behavior consistent with the provision of public and private goods. Analyzing temporal and institutional variation across two agricultural communities demonstrates a unique strategy of subnational governance and public goods provision in a developing state. Devolution of public goods provision to subnational actors may be an alternative strategy of governance for developing states that are not yet able to effectively provide important public goods.

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.

The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L 1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.

Studies of religious violence have established that when states restrict religious freedom, the probability of religious violence increases. Conventional wisdom holds that religious violence is primarily a result of religious grievances. When religious groups are denied religious freedom, they seek to revise the status quo in their favor though the use of violence. This study challenges this narrative. It finds, rather than being caused only by grievances, religious violence is also fueled by moments of opportunity. Utilizing cross-national data for the years 2008 and 2001–2005, it is found that religious violence occurs most frequently in anocratic regimes marked by weak and decaying state institutions. Hence, the current narrative is incomplete. Studies analyzing religious violence need to consider how various regimes provide or stifle the opportunity for religious actors to engage in violence as well as how those regimes fuel religious violence through restricting religious freedom and increasing religious grievances.