A Dynamic Additive and Multiplicative Effects Model with Application to the United Nations Voting Behaviors

TL;DR

This paper introduces a dynamic network regression model that captures temporal dependencies and missing data in international voting networks, providing insights into foreign policy and alliances.

Contribution

It extends the additive and multiplicative effects network model to handle dynamic, time-varying networks with missing data, applied to UN voting behavior analysis.

Findings

Identified key factors influencing UN voting patterns

Revealed meaningful foreign policy positions and alliances

Demonstrated model's effectiveness through simulations

Abstract

Motivated by a study of United Nations voting behaviors, we introduce a regression model for a series of networks that are correlated over time. Our model is a dynamic extension of the additive and multiplicative effects network model (AMEN) of Hoff (2019). In addition to incorporating a temporal structure, the model accommodates two types of missing data thus allows the size of the network to vary over time. We demonstrate via simulations the necessity of various components of the model. We apply the model to the United Nations General Assembly voting data from 1983 to 2014 (Voeten (2013)) to answer interesting research questions regarding international voting behaviors. In addition to finding important factors that could explain the voting behaviors, the model-estimated additive effects, multiplicative effects, and their movements reveal meaningful foreign policy positions and alliances of various countries.