Can an Eye for an Eye Turn the Whole World Sanctioned?

• In Event: What We Can Know about the Future of War and Its Humanitarian Impacts?

Abstract

International sanctions have been an increasingly common tool for enforcing international norms of behavior, among other goals. There is continuing debate on what drives sanctioning behavior: while IR theories of sanctioning behavior have a long empirical history, a few recent studies claim that common IR theories often fail to account for the endogenous nature of state sanction networks. Using a combination of network and IR-based insights, we aim to build a theory-driven, interpretable model of international sanctions that has high predictive utility in forecasting. Using dynamic network logistic regression with L1 regularization, we task network theories of transitivity, “Matthew effects,” reciprocity, cumulative disadvantage, previous state-level sanctioning activity, and state-pair (dyadic) sanctioning history alongside traditional IR theories regarding the democratic peace, cultural or institutional similarity, power imbalance, and trade, in a sweeping model of dynamic sanctions networks over seventy years. In addition to key interpretable insights in sanctioning behavior, our model has high predictive utility out of sample, primarily gained from network-based theories of state behavior.

Authors

• Khrystyna Holynska, Pardee RAND Graduate School

• C. Ben Gibson

• Renato Corbetta, University of Alabama, Birmingham