A network approach to measuring state preferences

State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.

Forty Years of United Nations General Assembly Voting

Studies of bloc voting in the United Nations have appeared periodically since the early 1960s. This article examines the evolution of UN voting in its first four decades using multidimensional scaling, which is compared to factor analysis and found to be superior for representation and interpretation. UN voting is important for showing how world politics is reflected in that body, hence the frequent use of UN votes as a dependent variable in the analysis of foreign policy behaviour.