18 countries. The resulting indices of inequality of opportunity and residual inequality were then included as explanatory variables in growth regressions that also included measures of male and female human capital investment and a measure of investment price distortion, following the specification in Forbes (2000). The same regressions were run for the overall income inequality measure (with no decomposition). The two country-level samples were unbalanced panels with a minimum time dimension of three periods and we relied on OLS, fixed effects, long-run OLS, and various Generalized Method of Moments specifications for estimation.

Our main findings are such that we cannot reject the null hypotheses that there is no relationship between initial inequality and subsequent growth. Using a data set of income and expenditure surveys and the mean log deviation of income (or expenditure) as our measure of overall inequality, there is some suggestive evidence of a negative association between overall income inequality and subsequent growth. While this weak finding is neither robust to changes in specification nor to switching to the DHS data set, it is nonetheless inconsistent with a positive association such as the one found by Forbes (2000) and Li and Zou (1998). Furthermore, we find no evidence in support of our original hypothesis, which found some support in a data set of 26 U.S. states (Marrero and Rodríguez, 2013): there is no apparent relationship between either component of inequality and growth in either of our two data sets.

What can we take away from these null results, if anything? It would be hard to argue that the data we use is much more problematic than other available data sets. Both the IES and the DHS data sets are the most comprehensive cross-country data sets put together specifically for this purpose – products of thousands of hours of very meticulous data work.<sup>33</sup> The only differences between our analysis and that in Forbes (2000) are in the coverage of countries and time periods (and in the specific inequality measure used as a dependent variable). As both studies are equally opportunistic in using the best data available at the time, it is hard to argue that the findings in one should be preferred to the other. The best explanation might be that any relationship between inequality and growth is not robust to the set of countries and/or the time period included in the analysis.

It is harder to argue that our data are ideal for the construction of types needed to build a measure of inequality of opportunity. While the numbers of variables we use to construct types in our data sets are large (see Tables A1 and A2 for details), circumstance variables that are consistently available within a country over time are limited. There is little doubt that the resulting estimates of inequality of opportunity must be substantially lower than the actual measures that remain unobserved. This in turn implies that the residual inequality term is contaminated with (unobserved) inequality of opportunity rather than being purely a measure of inequality due to variation in effort within types.

It is again hard to argue that the resulting measure is inferior or superior to that used in other studies. For example, in the only study examining the same question in the United States, Marrero and Rodríguez (2013) use only two circumstances: father’s education and race, which explain approximately 5% of overall inequality in their sample of 26 states. Our data sets include these circumstances, along with other circumstances, but not consistently for all countries in all years, causing them to be left out of

> 33 In fact, one tangible thing that can be taken away from this endeavor is the public data set. Our aim is to make these data sets available online as soon as possible, but interested researchers can request these data from the authors in the meantime.