Jointness in Bayesian variable selection with applications to growth regression

We present a measure of jointness to explore dependence among regressors, in the context of Bayesian model selection. The jointness measure proposed here equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. We illustrate its application in cross-country growth regressions using two datasets from the model-averaging growth literature.