The majority of studies on international conflict escalation use a variety of measures of hostility including the use of force, reciprocity, and the number of fatalities. The use of different measures, however, leads to different empirical results and creates difficulties when testing existing theories of interstate conflict. Furthermore, hostility measures currently used in the conflict literature are ill suited to the task of identifying consistent predictors of international conflict escalation. This article presents a new dyadic latent measure of interstate hostility, created using a Bayesian item-response theory model and conflict data from the Militarized Interstate Dispute (MID) and Phoenix political event datasets. This model (1) provides a more granular, conceptually precise, and validated measure of hostility, which incorporates the uncertainty inherent in the latent variable; and (2) solves the problem of temporal variation in event data using a varying-intercept structure and human-coded data as a benchmark against which biases in machine-coded data are corrected. In addition, this measurement model allows for the systematic evaluation of how existing measures relate to the construct of hostility. The presented model will therefore enhance the ability of researchers to understand factors affecting conflict dynamics, including escalation and de-escalation processes.
This was originally published on SAGE Publications Ltd: Journal of Peace Research: Table of Contents.