The paper studies the effects and the determinants of interregional redistribution in a model of residential and political choice. We find that paradoxical consequences of interjurisdictional transfers arise if people are mobile: while self-sufficient regions are necessarily identical with respect to policies and average incomes in our model, interregional redistribution always leads to the divergence of regional policies and per capita incomes. Thus, interregional redistribution prevents inter regional equality. At the same time, however, transfers may allow for more inter personal equality among the inhabitants of each region. The voting population may therefore in a decision over the fiscal constitution deliberately implement such a transfer scheme to foster regional divergence. Empirical evidence from panel data from OECD countries and Canadian provinces is consistent with the theory.

This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in-sample likelihood criteria.