Our Research

Effective response to and rapid and reliable detection of infectious disease outbreaks require successful coordination of countries’ border policies early on. As threats from diseases are highly salient to the public, researchers agree that a better understanding of domestic politics is crucial. This article investigates a key piece of this question: public demands for border closures. Our experiments in the United Kingdom and the United States show that a greater pandemic threat mildly increases support for border closures, but the World Health Organization's (WHO) guidance against border closures and reminders about international legal obligations to follow the guidance substantially weaken support for border closures. However, during the COVID-19 pandemic, many countries flouted WHO's recommendations and restricted their borders. Examining media attention suggests people's lack of knowledge of the WHO guidance as a crucial reason for those border closures. Our study produces insights into the design of effective global health governance.

Kobayashi, Y., Cilizoglu, M., Heinrich, T., & Christiansen, W. (2024). No entry in a pandemic: public support for border closures. American Journal of Political Science, 68(2), 372-389.

Substantive research in the Social Sciences regularly investigates signed networks, where edges between actors are positive or negative. One often-studied example within International Relations for this type of network consists of countries that can cooperate with or fight against each other. These analyses often build on structural balance theory, one of the earliest and most prominent network theories. While the theorization and description of signed networks have made significant progress, the inferential study of link formation within them remains limited in the absence of appropriate statistical models. We fill this gap by proposing the Signed Exponential Random Graph Model (SERGM), extending the well-known Exponential Random Graph Model (ERGM) to networks where ties are not binary but positive or negative if a tie exists. Since most networks are dynamically evolving systems, we specify the model for both cross-sectional and dynamic networks. Based on hypotheses derived from structural balance theory, we formulate interpretable signed network statistics, capturing dynamics such as “the enemy of my enemy is my friend”. In our empirical application, we use the SERGM to analyze cooperation and conflict between countries within the international state system. We find evidence for structural balance in International Relations.

Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025). Exponential random graph models for dynamic signed networks: An application to international relations. Political Analysis, 33(3), 211-230.