IDEAS home Printed from https://ideas.repec.org/h/spr/isbchp/978-981-96-2544-4_3.html
   My bibliography  Save this book chapter

Decomposition of Inequality of Opportunity

In: Predicting Inequality of Opportunity and Poverty in India Using Machine Learning

Author

Listed:
  • Balwant Singh Mehta

    (Institute for Human Development)

  • Ravi Srivastava

    (Institute for Human Development)

  • Siddharth Dhote

    (Institute for Human Development)

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.

Suggested Citation

  • Balwant Singh Mehta & Ravi Srivastava & Siddharth Dhote, 2025. "Decomposition of Inequality of Opportunity," India Studies in Business and Economics, in: Predicting Inequality of Opportunity and Poverty in India Using Machine Learning, chapter 0, pages 41-73, Springer.
    • Handle: RePEc:spr:isbchp:978-981-96-2544-4_3
    DOI: 10.1007/978-981-96-2544-4_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isbchp:978-981-96-2544-4_3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.