Estimating between- and within-cluster covariate effects, with an application to models of international disputes

Students of international politics often use data in which the covariates vary both within and across units of observation. This is particularly true for dyadic data, which has come to dominate quantitative studies of international conflict, but is also a concern in any work involving a time-series cross-sectional component. Standard regression methods treat both types of covariates as equivalent with respect to their influence on the dependent variable, ignoring possible differences between cross-dyad and within-dyad effects. Here, I discuss the potential pitfalls of this approach, and show how between- and within-dyad effects can be separated and estimated. I then illustrate the approach in the context of a logistic regression, using data on international disputes.