In this set of projects, I use advanced quanitative methods to tackle the problem of more accurately measuring concepts of interest in international relations. One uses Bayesian latent variable models to directly assess the strength of peace agreements in civil conflict rather than having to use agreement duration as a proxy for strength. Another harnesses advances in big data and develops new measures of economic interdependence and methods for detecting disruptions of regular economic exchange between states from a product-level trade dataset with over two billion observations.
Rob Williams, Daniel Gustafson, Stephen Gent, and Mark Crescenzi. “A Latent Variable Approach to Measuring and Explaining Peace Agreement Strength.” Political Science Research and Methods.
Much of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975-2005. We illustrate the measure’s utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.