We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian Model Averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the  specifications that compose the model space. We apply the method to two datasets of potential  determinants of economic growth. Clustering the posterior covariate inclusion structure of the model pace  formed by linear regression models reveals interesting patterns of complementarity and substitutabiliy across economic growth determinants.