12 earlier, we use two types of household surveys: income or expenditure households surveys (IES) such as labor force surveys, household budget surveys or Living Standard Measurement Surveys, to construct our first sample, and Demographic and Health Surveys (DHS) for the second sample.

The IES sample contains 42 countries, both developed and developing.<sup>20</sup> For a large proportion of the countries, we use three harmonized meta-databases that allow for the construction of comparable measures of household income or consumption. We use the Luxembourg Income Study (LIS) for 23 (mostly developed) countries, the Socioeconomic Database for Latin America and the Caribbean (SEDLAC) for six Latin American countries, and the International Income Distribution Database from the World Bank (I2D2) for another 10 developing economies. For the remaining three countries included in the sample, we use the respective national household surveys. The advantage variable used to compute total inequality and inequality of opportunity is always a measure of household wellbeing. For 32 countries, it is net household income per capita, while for another ten, where reliable income data are not available, it is household expenditure per capita. Definitions are always consistent across periods within countries and a dummy variable indicating whether the inequality measure is based on expenditure or income is included in the estimation.

We use a number of circumstance variables, referring to the characteristics of the household head, to partition the population into types. We classify circumstances into two sets. The first set is frequently used in the literature, and it is generally agreed that these variables satisfy the exogeneity requirement for circumstances. They include gender, race or ethnicity, the language spoken at home, religion, caste, nationality of origin, immigration status and region of birth.<sup>21</sup> In the second set, we add the current region of residence for those countries where the birth region was unavailable. While migration decisions are obviously very important, region of residence is strongly correlated with birth region, and might thus provide a proxy for the latter, which is unavailable in many surveys. Table A1 provides more detailed information on the source and years of the household survey, the welfare and circumstance variables and the number of types in the partition for each country. Once again, the circumstance variables and the number of categories for each variable are unchanged over time within countries.

Unlike most studies of inequality of opportunity undertaken within specific countries, we were unable to draw on a richer set of circumstance variables including father’s and/or mother’s education and occupation and region of birth, in addition to race or language spoken at home.<sup>22</sup> Since these family background variables have typically been found to account for a substantial share of the between-type inequality in other studies, we anticipate the cost of having to rely on a “lowest common denominator”

> 20 Note that we treat Germany before and after reunification as two separate countries to avoid any spurious change in inequality of opportunity, so the result tables report 43 country observations.

> 21 It is clear that not all of these characteristics satisfy the criteria to be considered ‘circumstances’. For example, gender of the head of the household could be a choice or a circumstance. However, the gender of the head of household does explain a non-negligible part of overall inequality in many countries and, hence, presents a tradeoff with respect to its exclusion. Given the limited number of circumstance variables available to us and to avoid further underestimation of inequality of opportunity, we chose to include gender among our set of circumstance variables. Immigration status is also clearly a choice variable, but its inclusion has little effect on our empirical analysis, as this information is only available in a few IES data sets (see Table A1).

22 When the advantage variable is individual earnings, rather than household income or expenditure, gender is typically also included. The resulting partitions typically contain larger numbers of types: 72 in Checchi and Peragine (2010) and Belhaj-Hassine (2012), 54 or 108 in Ferreira and Gignoux (2011), and so on.