Factor models have become useful tools for studying international business cycles. Block factor models [e.g., Kose, Otrok, and Whiteman (2003)] can be especially useful as the zero restrictions on the loadings of some factors may provide some economic interpretation of the factors. These models, however, require the econometrician to predefine the blocks, leading to potential misspecification. In Monte Carlo experiments, we show that even small misspecification can lead to substantial declines in t. We propose an alternative model in which the blocks are chosen endogenously. The model is estimated in a Bayesian framework using a hierarchical prior, which allows us to incorporate series-level covariates that may influence and explain how the series are grouped. Using similar international business cycle data as Kose, Otrok, and Whiteman, we find our country clusters differ in important ways from those identified by geography alone. In particular, we find that similarities in institutions (e.g., legal systems, language diversity) may be just as important as physical proximity for analyzing business cycle comovements.