education is simply the correlation coefficient between the parents’ education and the child’s education, where both are measured by years of completed schooling, as reported by Hertz et al. (2007). Parental education is the average of mother’s and father’s attainment “wherever possible” (Hertz et al, 2007, p.11). The correlation we report is what the authors call a measure of “standardized persistence”.

The measures of intergenerational earnings elasticity reported in Table 1 come from eleven different studies published over the last ten years, namely Azevedo and Bouillon (2010); Cervini Pla (2009); Christofides et al. (2009); Corak (2006); D’Addio (2007); Dunn (2007); Ferreira and Veloso (2006); Grawe (2004); Hnatkovskay et al. (2012); Hugalde (2004); Nuñez and Miranda (2006); and Piraino (2007). Denoting parental earnings (or income) by yf , and the adult child’s earnings by ys , these elasticity estimates generally come from an equation of the form: An elasticity (β) of 0.4, for example, would mean that income differences of 100% between two fathers (say), would lead to a 40% gap between their sons (on average). As in the case of the IEO measures, the datasets and econometric methods used for estimating this elasticity are not homogeneous across studies. This comparative exercise is very much in the same spirit as Corak (2012), and the same caveats he discusses are applicable here. The values for the Human Opportunity Index reported in Table 1 come from Molinas et al. (2011) for Latin America, and World Bank (2012a, b) for Africa.

Table 1 should be read in close conjunction with Table 2, which provides some basic information on each of the eight studies used to construct the inequality of opportunity estimates in Table 1. Table 2 describes which countries are studied in each paper; the specific data sets (including survey year); the precise income and circumstance variables used; whether the estimation was parametric or otherwise, and the number of types included in each calculation. The table highlights a number of problems for comparability across these studies. First is the nature of the advantage variable ( _y_ ) itself: whereas Checchi et al. (2010), Pistolesi (2009), Singh (2011) and Belhaj-Hassine use labor earnings, Ferreira and Gignoux (2011) and Piraino (2012) use incomes, Cogneau and Mesple-Somps (2008) use consumption, and Ferreira et al. (2011) use imputed consumption. And the definitions of earnings and incomes are not exactly the same across each of these papers either.

These distinctions are not immaterial: in a comparison of six Latin American countries, Ferreira and Gignoux (2011) found substantially higher estimates of IEO-R for consumption expenditure than for income distributions, in the same countries.<sup>13</sup> They attributed this finding to the fact that income inequality measures are thought to contain greater amounts of measurement error, as well as transitory income components, which are less closely correlated with circumstances than permanent income or consumption might be. Bourguignon et al. (2007) also noted differences between estimates for individual earnings and for household per capita incomes, which they attributed to the fact that unequal opportunities affect the latter not only through earnings, but also through assortative mating, fertility decisions, and non-labor income sources.

> 13 Similarly, Singh (2010) finds a higher IEO-L for consumption than for earnings in India.

10