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 ( _x_ ) itself: whereas Pistolesi (2009), Singh (2011) and Belhaj-Hassine (2012) use labor earnings, Checchi et al. (2010), Ferreira and Gignoux (2011) and Piraino (2012) use incomes, Cogneau and Mesple-Somps (2008) use consumption, and Ferreira et al. (2011) use imputed consumption. In addition, because of the usual differences across survey questionnaires and data collection protocols, the definitions of earnings and incomes cannot be assumed to be exactly the same across each of these papers either.

# [PLEASE PLACE TABLE 8 HERE]

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>24</sup> They attributed this finding to the fact that income 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 found 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.

Second, the studies differ in the number of types used for the decomposition and, naturally, in the exact set of circumstances used in each case. At one extreme, the Cogneau and MespleSomps study has a mere three types for Uganda, based on father’s occupation and education levels, while on the other Pistolesi has 7,680 types, constructed on the basis of information on age (20 levels), parental education (4 levels for the mother and 4 for the father), occupational group of the father (6 categories), individual ethnic group (2 categories), individual region of birth (2 categories). Fortunately, there is a core set of studies - which account for most countries in the sample - with 72 to 108 types each. Nevertheless, results for Africa and the US should certainly be interpreted with caution, in light of the number of types used in each case. Finally, a third comparability caveat, on which we have already dwelled, is the fact that some studies use nonparametric estimates while others use parametric ones.

Bearing these caveats in mind, Table 7 nevertheless illustrates the substantial variation in inequality levels – both in advantages and in opportunities - across countries. The mean log deviation for incomes (or the corresponding advantage indicator) ranges from 0.083 in Denmark to 0.675 in South Africa. Norway, Slovenia and Sweden also have comparatively low levels of overall inequality, while Brazil and Guatemala stand out at the upper end. Inequality of opportunity levels (IEOL) range from 0.003 in Norway and 0.005 in Slovenia to 0.199 in Guatemala and 0.223 in Brazil. In other words, the (lower-bound) level of inequality in the distribution of values of

> 24 Similarly, Singh (2011) finds a higher IEO-L for consumption than for earnings in India.

**_25_**