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.

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Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-n research initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.

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In a recent article, Gibler, Miller, and Little (2016) (GML) conduct an extensive review of the Militarized Interstate Dispute (MID) data between the years 1816 and 2001, highlighting possible inaccuracies and recommending a substantial number of changes to the data. They contend that, in several instances, analyses with their revised data lead to substantively different inferences. Here, we review GML's MID drop and merge recommendations and reevaluate the substantive impact of their changes. We are in agreement with about 76 percent of the recommended drops and merges. However, we find that some of the purported overturned findings in GML's replications are not due to their data, but rather to the strategies they employ for replication. We reexamine these findings and conclude that the remaining differences in inference stemming from the variations in the MID data are rare and modest in scope.

This article introduces the latest iteration of the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 5 dataset. We begin by outlining the data collection process used in the MID5 project. Next, we discuss some of the most challenging cases that we coded and some updates to the coding manual that resulted. Finally, we provide descriptive statistics for the new years of the MID data.

While the evolving nature and proliferation of UN peacekeeping operations in the post-Cold War period is well documented, we know less about how personnel are recruited for these missions. Furthermore, recent developments have rendered existing supply-side explanations for troop contributions less convincing. The increasing demand for personnel, along with stagnant UN reimbursement rates and the rising costs of participation that began during the 1990s, mean that it is less attractive than ever for developing countries to offer their own troops to what have become increasingly ambitious operations. Yet, we see a large pool of developing countries continuing to do so. To address this puzzle, we argue that UN member states with strong preferences for establishing peacekeeping missions have begun using foreign aid as an inducement to help potential contributors overcome the collective action problem inherent in multilateral peacekeeping operations. We uncover strong empirical evidence that these ‘pivotal states’ strategically allocate foreign aid to persuade contributing states to boost their contributions, and also to ensure that these missions continue to be staffed and maintained as costs rise, particularly during the post-1999 period. We also find that states are responsive to these financial inducements: foreign aid increases both the likelihood of contributing personnel and the size of a state’s contribution. Theoretically, this article advances the scholarly understanding of international organizations and cooperation by illuminating an informal, extra-organizational strategy by which IOs can facilitate cooperation.

Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly, it produces quality data. Machine coding is fast and inexpensive, but the data are noisy. To diminish the severity of this tradeoff, we introduce a method for analyzing news documents that uses crowdsourcing, supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes, and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding, and to cost less and be much faster than expert coding.