Decomposition of Inequality of Opportunity

Balwant Singh Mehta (), Ravi Srivastava () and Siddharth Dhote ()
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Balwant Singh Mehta: Institute for Human Development
Ravi Srivastava: Institute for Human Development
Siddharth Dhote: Institute for Human Development

Chapter Chapter 3 in Predicting Inequality of Opportunity and Poverty in India Using Machine Learning, 2025, pp 41-73 from Springer

Abstract: Abstract This chapter presents new ways to measure inequality of opportunity (IOp) in income for India, based on Roemer’s theory. It compares two methods: the ex-ante approach, which looks at outcomes before effort, and the ex-post approach, which considers outcomes after effort. Using machine learning tools such as conditional inference trees and transformation trees, the study finds that IOp explains 48–53% of income inequality in the ex-ante method, and about 34% in the ex-post method. Key factors driving IOp include parental education, region, rural-urban location, and parental occupation. The analysis shows that people from rural eastern and central India, marginalized social groups (like SCs and STs), and families with low education levels tend to earn the least. These patterns are consistent across both methods. The findings highlight the deep-rooted disadvantages some groups face. To reduce income IOp, the chapter calls for targeted regional policies and support for marginalized communities.

Keywords: Machine Learning; Inequality of Opportunity; Conditional Inference trees; Transformation Trees; Conditional Inference Forests (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isbchp:978-981-96-2544-4_3

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DOI: 10.1007/978-981-96-2544-4_3

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