Concept and Measurement of IOp

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 2 in Predicting Inequality of Opportunity and Poverty in India Using Machine Learning, 2025, pp 11-39 from Springer

Abstract: Abstract This chapter explores the growing interest among scholars and policymakers in measuring inequality of opportunity (IOp) in income. It is well documented that focusing solely on inequality based on outcomes does not fully explain the rising income disparities within and across countries. It first traces the evolution of IOp concept and later presents empirical findings for India using the data from National Sample Survey (NSSO). The study employs both traditional and machine learning techniques to estimate income IOp. The results show that about 26–27% of income inequality in India can be attributed to factors beyond an individual's control. Parental education and occupation emerge as the most significant contributors to income IOp. Among regular workers, these parental backgrounds play a key role. For self-employed, gender is the primary driver of IOp, while for casual workers, geographical has the greatest impact. These findings highlight the importance of addressing unequal circumstances to promote a fairer and more inclusive society.

Keywords: Mean Log Deviation; Regression; Inequality of Opportunity; Equality; Circumstances; Regression trees (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_2

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DOI: 10.1007/978-981-96-2544-4_2

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