from Latin America where it was developed originally to other Bank regions), and through that channel to the broader policy-oriented literature. Its popularity no doubt owes much to its simplicity, being relatively straightforward to apply with readily available household survey data. In part, though, our interest in the work stems from the fact that in empirically operationalizing the equality of opportunity approach researchers are forced to confront a whole host of tough questions that are more easily overlooked in conceptual discussions. How satisfactorily these questions can be answered at the empirical level determines ultimately the practical usefulness of the whole equality of opportunity approach.

There are, in fact, two somewhat separate approaches in de Barros et al. (2009) and we will take these up in turn in this section and in the next. In the first approach the primary outcome variable is the usual variable used to calculate standard inequality and poverty measures – earnings, income or consumption. The conceptual basis and implementation method is stated succinctly by de Barros et al. (2009) as follows: “ _To measure inequality of opportunity for a certain outcome, total inequality in the outcome can be decomposed into two parts: one resulting from circumstances beyond individual control and a second part resulting from unequal individual effort and luck....First, six variables related to circumstances exogenous to the individual were identified from the most comprehensive data sets available: gender, race or ethnicity, birthplace, the educational attainment of the mother, the educational attainment of the father, and the main occupation of the father.... Then the sample was partitioned (in each country) into groups or “cells,” such that all individuals in any given cell have exactly the same combination of circumstances. The resulting subgroups are known in the literature as “types.” These cells are then compared with one another. The difference in outcomes between cells can be attributed to inequality of opportunity, while the differences within cells can be considered the result of effort or luck_ .” (pp. 125-126) A decomposable measure of inequality, the Theil-L index, is used in a non-parametric method of quantifying the extent of variation in income accounted for by within cell variation and between cell variation, the latter the being identified as “inequality of opportunity” (IEO).

It is easy to see why this method has caught on. It is relatively simple and intuitive. Indeed, it is nothing more than standard inequality decomposition analysis which has been conducted in economics and development economics for a long time (Kanbur, 2007). For example, the percentage of inequality accounted for by caste has been a staple of the Indian discourse, as has the contribution of ethnic differences to inequality in Malaysia, as has the north-south divide in Ghana, and so on. What is new is twofold. First is its systematic application in a consistent manner across a range of countries as done for Latin America in de Barros et al. (2009). Second, crucially, is the interpretation and labeling of the between group component in the inequality decomposition as inequality of opportunity, thereby linking to the philosophical literature discussed the last section, and also thereby linking to 7