Recent debates on distributive justice have started to prioritize inequality of opportunities, that is exclusively generated from circumstance factors beyond individual control. Using data from the National Sample Survey we estimate inequality of opportunity for India in consumption expenditure and wage earning, on the basis of caste, sex, region and parental backgrounds as our circumstances. Adopting the widely used methods of non-parametric and parametric analysis, we find that even in 2011-12, more than one-third of the total wage inequality can be attributed to the differences in the ascribed social positions of an individual. Inequality of opportunity in consumption on the other hand is relatively low. Furthermore, we used the regression tree algorithm to find the hierarchical order among the circumstances and construct the opportunity tree for India, that the previous methods are unable to provide. In the fashion of machine learning, the opportunity tree identifies parental background as one of the most important circumstance factor behind the underlying unequal opportunity in the country, for either outcomes. But the effect of casteism is prominent as well, that in interaction with region, affirms a forward caste premium for most part of the country, particularly for the regular salaried wage earners.
Caste, Inequality of opportunity, Mean Log Deviation; Multiple imputation, Parental background, Regression tree