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

Balwant Mehta (), Siddharth Dhote and Ravi Srivastava
Additional contact information
Siddharth Dhote: Institute for Human Development
Ravi Srivastava: Institute for Human Development

The Indian Journal of Labour Economics, 2023, vol. 66, issue 2, No 5, 439-469

Abstract: Abstract 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.

Keywords: Inequality of opportunity; Machine learning algorithm; Conditional inference tree; Conditional inference forest; Transformation tree (search for similar items in EconPapers)
JEL-codes: C60 D30 D31 D63 G51 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s41027-023-00446-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijlaec:v:66:y:2023:i:2:d:10.1007_s41027-023-00446-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/41027

DOI: 10.1007/s41027-023-00446-5

Access Statistics for this article

The Indian Journal of Labour Economics is currently edited by Alakh Sharma

More articles in The Indian Journal of Labour Economics from Springer, The Indian Society of Labour Economics (ISLE) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().