Decomposition of Inequality of Opportunity in India: An Application of Data-Driven Machine Learning Approach

This paper introduces a novel measure of inequality of opportunity (IOp) in India, by comparing both ex-ante and ex-post results, which aligns with Roemer’s (1998) equality of opportunity, theory. The study utilizes data-driven machine learning algorithms, namely conditional inference tree and conditional inference forest, to measure ex-ante IOp, and a transformation tree to estimate ex-post IOp. The findings indicate that, according to the ex-ante approach, approximately 58–61 percent of the overall income inequality can be attributed to variations in circumstances, while around 46 percent of the overall income inequality is explained by differences in the degree of efforts. The results from the tree-based analysis reveal that parents’ occupation, sector (rural–urban areas), and geographical regions are the primary circumstances contributing to IOp, which is further confirmed by the Shapley decomposition exercise. Specifically, individuals residing in rural areas in the eastern and central parts of the country, whose parents are employed in low-skilled and unskilled occupations, and have below secondary and no formal education, and who belong to marginalized social groups, exhibit significantly lower average income. Consequently, it is crucial to implement regional-level development policies that specifically target marginalized groups in order to foster a more equitable society and mitigate overall income inequality.