Coalition research increasingly emphasizes party-level explanations of coalition outcomes. However, this work does not account for the complex multilevel structure between parties and governments: many parties participate in multiple governments and governments often comprise multiple parties. In this paper, I show that this crisscrossing structure creates dependencies among observations both across and within governments. If ignored, these dependencies produce downward-biased uncertainty estimates that cluster-robust standard errors fail to fully correct. To address this issue, I then introduce a model that extends the Multiple Membership Multilevel Model to represent the multilevel structure of coalition government data. The model accounts for party-level dependencies across governments through party-specific effects in each coalition they join, and for dependencies within governments by representing the total party effect on a government as a weighted sum of its members’ contributions. By allowing party weights to vary with covariates describing their interrelationships, the model enables researchers to examine the interdependent nature of coalition outcomes. I validate the model through simulation and an empirical application to coalition government survival, showing that ignoring party-level dependencies can produce misleading conclusions at all levels of analysis. The model is estimated via Bayesian MCMC and implemented in the accompanying R package ‘bmlm’.
- R&R at Political Analysis.