A novel borrowing strength measure and an overall borrowing index to characterize the strength of borrowing behaviors among subgroups are proposed for a given Bayesian hierarchical model. The constructions of the proposed indexes are based on the Mallow's distance and can be easily computed using MCMC samples for univariate or multivariate posterior distributions. Consequently, the proposed indexes can serve as meaningful and useful exploratory tools to better understand the roles played by the priors in a hierarchical model, including their influences on the posteriors that are used to make statistical inferences. These relationships are otherwise ambiguous. The proposed methods can be applied to both the continuous and binary outcome variables. Furthermore, the proposed approach can be easily adapted to various settings of clinical trials, where Bayesian hierarchical models are deem appropriate. The effectiveness of the proposed method is illustrated using extensive simulation studies and a real data example.