11 year interval. Unlike in Forbes (2000) or most other studies in this literature, these inequality indices do not come from a compilation of scalar measures from earlier studies, such as the Deininger and Squire (1996) database, or the WIDER World Income Inequality Database. Instead, the inequality indices are computed from the original microdata for all surveys in all countries. Details on the household-level metadata set are provided below. Summary statistics for the growth and income variables, as well as the total inequality variable, are reported in Table 1 (Income and Expenditure Surveys) and Table 2 (Demographic and Health Surveys).

Female and male education data ( MEi,t−5 and FEi,t−5) come from Lutz et al. (2007, 2010), and are defined as the proportion of adult (male/female) population that attained at least one year of secondary education. Lutz and co-authors produced estimates for 120 countries from 1970 to 2010, on a quinquennial basis.<sup>18</sup> These data are in the spirit of Barro and Lee (2001), although the method used to complete missing data differs slightly.<sup>19</sup> Finally, as in Forbes, market distortions are proxied by the price level of investment from Penn World Tables (version 6.3), defined as the purchasing power parity of investment/exchange rate ( PPPIi,t−5 ). αi denotes country _i_ ’s fixed effect, ηt is a period dummy, and uit is the error term.

Equation (5) provides estimates for the effect of total inequality on growth à la Forbes (2000). However, we are interested in whether the two components of overall inequality – namely inequality between morally irrelevant groups and inequality within them, interpreted as proxies for inequality of opportunity and inequality due to effort – have heterogeneous effects on growth. Therefore, in equation (6), we re-estimate equation (5) but replacing I(y)i,t−5 with our measures of inequality of opportunity: I��μik�� in the IES sample, and V({w�}) in the DHS sample. For simplicity, we denote both of these as IOpi,t−5 in the generic specification. We also include the residual term, IRi,t−5 = I(y)i,t−5 − IOpi,t−5 , and estimate: We estimate equations (5) and (6) using a variety of different techniques, which are discussed in the next section before we present the results. All regressions for equation (6) include a quartic in the number of types used to estimate inequality of opportunity. In the remainder of this section, we briefly describe the microdata sets used to compute the inequality and inequality of opportunity variables. Tables 1 and 2 also show the percentage of total inequality accounted for by inequality of opportunity.

The availability of household survey micro-data with information on _both_ a reliable indicator of wellbeing (income, consumption, or wealth) _and_ circumstance variables – which are required for computing inequality of opportunity measures – is the key factor constraining our sample(s) of countries. The requirement is even more stringent since we need, for each country, at least two comparable surveys five years apart to construct the panel of countries – three when using GMM estimators. As noted

> 18 For the IES sample the five-year intervals align with the Lutz data. However, for the DHS sample, the five-year intervals are one year later (e.g. the end-year is 1991 or 1996). Therefore, we move the Lutz data forward by one year when matching to the DHS sample.

> 19 While Barro and Lee used the perpetual inventory method to complete their data set, and transform flux into stock of education, Lutz et al. used backward (2007) and forward (2010) projections from empirical observations given by UNESCO and UN data on population structure.