Many highly-disaggregated cross-country indicators of institutional quality and the business environment have been developed in recent years. The promise of these indicators is that they can be used to identify specific reform priorities that policymakers and aid donors can target in their efforts to improve institutional and regulatory quality outcomes. Doing so however requires evidence on the partial effects of these many very detailed variables on outcomes of interest, for example, investor perceptions of corruption or the quality of the regulatory environment. In this paper we use Bayesian Model Averaging (BMA) to systematically document the partial correlations between disaggregated indicators and several closely-related outcome variables of interest using two leading datasets: the Global Integrity Index and the Doing Business indicators. We find major instability across outcomes and across levels of disaggregation in the set of indicators identified by BMA as important determinants of outcomes. Disaggregated indicators that are important determinants of one outcome are on average not important determinants of other very similar outcomes. And for a given outcome variable, indicators that are important at one level of disaggregation are on average not important at other levels of disaggregation. These findings illustrate the difficulties in using highly-disaggregated indicators to identify reform priorities.