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A network approach to measuring state preferences

Published online by Cambridge University Press:  20 January 2021

Max Gallop
Affiliation:
Departments of Political Science, University of Strathclyde, Glasgow, UK
Shahryar Minhas*
Affiliation:
Department of Political Science, Michigan State University, East Lansing, Michigan (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]

Abstract

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.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

Action Editor: Stanley Wasserman

**

We would like to thank comments from discussants and audience members at the International Studies Association and Midwest Political Science Association Conferences as well as participants from the Speaker Series at the University College London. We are also grateful for comments from Michael D. Ward, Scott de Marchi, and three anonymous reviewers who provided thoughtful insight to help us improve our paper.

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