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, forthcoming at Political Science Research and Methods, uses a Bayesian latent variable model to directly assess the strength of peace agreements in civil conflict rather than having to use agreement duration as a proxy for strength. The second harnesses advances in big data and develops a measure of trade substitutability from a product-level trade dataset with over a billion observations.
Rob Williams, Daniel Gustafson, Stephen Gent, and Mark Crescenzi. “A Latent Variable Approach to Measuring and Explaining Peace Agreement Strength.” Forthcoming at Political Science Research and Methods. [Article], [Preprint], [Supplemental Information], [Replication Archive], [GitHub Repo]
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.
Rob Williams. “PASS: Peace Agreement Strength Scores.” [Working Paper]
This paper presents the Peace Agreement Strength Scores (PASS) of peace agreements in civil conflicts. The scores capture the strength of peace agreements at the time of signing and can be used to avoid relying on the duration of agreement survival as a proxy for agreement strength. The scores are used to show descriptively that stronger peace agreements tend to be signed in more intractable conflicts, suggesting that a selection effect may be at play in the process of agreement signing and duration. The scores are available for all peace agreements signed in UCDP/PRIO civil conflicts from 1975-2018.
Bailee Donahue, Rob Williams, and Mark Crescenzi. “Unsettled Borders in a Market Context.” Presented at the Annual Meeting of the Peace Science Society (International), Manhattan, KS, November 2019 and the International Studies Association Midwest Annual Conference, St. Louis, MO, November 2019.
Border disputes between states can be very costly and disruptive, including major disruptions in trade. From an aggregate perspective, scholars traditionally expect these costs and disruptions to place pressure on states to avoid or resolve these disputes quickly. This view, however, risks oversimplification of the quality of trade and the economic actors driving that trade. We investigate the consequences of complex trade relations on border disputes. Variation in the composition of trade, whether characterized by comparative advantage trade, inter-industry trade, or intra-industry trade, generates variation in the presence and intensity of domestic pressure to avoid or resolve border disputes. We examine the effects of this variation on dispute behavior using an original dataset that combines product-level trade data (spanning from 1962-2001) with ICOW territorial claims data. The use of product-level trade data allows for the analysis of substitutability options which may reduce exit costs and make it easier to escalate border disputes. This analysis helps us better understand the choice to forego trade due to border disputes, and furthers our understanding of the economic impact of unsettled borders.
Ryan Denniston, Howard Liu, Juan Tellez, and Rob Williams. “Extracting Road Networks from Spatial Data.” Presented at the Annual Conference of the American Political Science Association, Washington, D.C., August, 2019.
Roads connect cities and determine the movement of people, goods, and armies across a country’s territory. As a result, there is growing interest among researchers in understanding how roads, rail, and other transportation infrastructure shapes a variety of development and security-related outcomes. Within this agenda, researchers often conceptualize road systems as networks, and ask how a city’s connections or its position within the network influences its political and economic trajectory. In spite of this interest and the growing availability of data, however, there are no systematic tools available to extract networks from spatial data. This project makes three methodological contributions. First, it delineates the conceptual and computational challenges associated with creating road networks out of spatial data. Second, it proposes a flexible method for extracting road networks from spatial data and outlines its assumptions and potential limitations. Third, it presents both R and ArcGIS software to implement this method for others to use. Finally, We look at the country of Colombia to illustrate the methodology.