Understanding the causes of interstate conflict continues to be a primary goal of the field of international relations. To that end, scholars continue to rely on large datasets of conflict in the international system. This paper introduces the latest iteration in the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 4 data. In this paper we first outline the updated data-collection process for the MID4 data. Second, we present some minor changes and clarifications to the coding rules for the MID4 datasets, as well as pointing out how the MID coding procedures affect several notable “close call” cases. Third, we introduce updates to the existing MID datasets for the years 2002–2010 and provide descriptive statistics that allow comparisons of the newer MID data to prior versions. We also offer some best practices and point out several ways in which the new MID data can contribute to research in international conflict.

Pandemics are imbued with the politics of bordering. For centuries, border closures and restrictions on foreign travelers have been the most persistent and pervasive means by which states have responded to global health crises. The ubiquity of these policies is not driven by any clear scientific consensus about their utility in the face of myriad pandemic threats. Instead, we show they are influenced by public opinion and preexisting commitments to invest in the symbols and structures of state efforts to control their borders, a concept we call border orientation. Prior to the COVID-19 pandemic, border orientation was already generally on the rise worldwide. This trend has made it convenient for governments to “contain” the virus by externalizing it, rather than taking costly but ultimately more effective domestic mitigation measures. We argue that the pervasive use of external border controls in the face of the coronavirus reflects growing anxieties about border security in the modern international system. To a great extent, fears relating to border security have become a resource in domestic politics—a finding that does not bode well for designing and implementing effective public health policy.

Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event. We demonstrate the utility of the model by focusing on a dataset that defines ‘one-sided-killing’ as government-caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths for each country-year in the dataset. These new event-based, count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time. Our modeling framework also provides a principled method for inferring unobserved count variables based on conceptually related categorical information.

How does the passage of time contribute to the establishment of civilian control of the State? We argue that civilian dominance of politics is achieved once civilianized institutions are adopted and sufficient time has passed to permit: (1) the development of a shared norm of civilian control within the military and (2) learning among military elites that fosters a belief that civilian rule is robust to military challenges. As a result, civilian control is self-reinforcing. We evaluate these claims by developing and validating a latent variable model of self-reinforcing institutional dynamics. We generate estimates of civilian control for all countries, 1945–2010, and find strong evidence that civilian control self-reinforces, but incrementally and over the course of several decades.

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