the (much smaller) set of 70 countries for which all seven outcome variables are available.<sup>21</sup> In this dataset, we find the same pattern of strong instability across outcomes. The p‐values for the null hypothesis that the event of inclusion in the set of "important" regressors is indepdendent across outcomes is 0.96. This suggests to us that our instability across outcomes finding is not driven by such compositional effects.<sup>22</sup> Based on all this we argue that our instability finding is indeed a robust feature of the data: disaggregated indicators that are significant determinants of one outcome are on average not significant determinants of other very similar outcomes, and for a given outcome variable, indicators that are significant at one level of disaggregation are on average not significant at other levels of disaggregation. Given that this finding is so robust, we now ask how it should be interpreted. One possibility is simply that the world is in fact complicated, in the sense that different outcome variables truly do respond very differently to the many indicators in DB and GII. For example, it might truly be the case that the notions of transparency and accountability reflected in the World Bank’s CPIA assessments do indeed require a very different set of policy interventions from those required to improve the notions of clean government and absence of corruption monitored by commercial risk rating agencies such as DRI or EIU. However, this interpretation is hard to reconcile with the very strong correlations across outcome variables, which limit how different can be the notions of corruption, or regulatory quality, that these different outcome variables are measuring.

Another interpretation is that all of the disaggregated indicators, and all of the outcome variables, are just imperfect proxies for some broader notion of good governance. Under this interpretation, the most disaggregated indicators could be thought of as the noisiest proxies for governance, and averaging them together at successively higher levels of aggregation smooths out measurement error in the individual indicators, resulting in better and better proxies for governance at higher levels of aggregation. This interpretation can account for the fact that there is less instability

> 21    We do not attempt a similar exercise for the GII data simply because the resulting dataset would contain only 44 countries.

> 22 A median of pairwise correlation coefficients across seven outcome variables for these 70 countries is reduced to 0.59 (from 0.68). One may argue that this might contribute to instable bma results across seven outcome variables. When comparing bma outputs across DRI, PRS, WMO and CPIA, four of these are correlated as much as 7 outcome variables are in our analysis above, the similar instability across these 4 outcome variable emerges. More specifically, only three (Tax Total, EmployFiringCost, and BorderTimeImp) out of 41 variables appear in top 25 percent of indicators for these four outcome variables. This suggests that the slightly smaller correlation across seven y variables under 70 sample countries is not driving the “instability” outcome.

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