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Introduction

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 introductory chapter discusses two major global challenges: poverty and inequality, with a special focus on unfair inequality linked to justice and equal access to opportunities. It draws on Roemer’s concept of inequality of opportunity (IOp), which refers to differences in people’s outcomes caused by circumstances beyond their control. Measuring poverty and IOp is difficult, especially in countries like India, due to limited and outdated data. To overcome this, the chapter highlights the use of innovative machine learning methods that combine traditional, non-traditional, and geospatial data. These tools can improve how we measure poverty and IOp with greater accuracy and timeliness. The chapter also sets the stage for the rest of the book, explaining why these issues matter. It outlines the key research questions, the unique contributions of the study, and the methods used. Finally, it offers an overview of what each chapter covers and how the book is structured.

Suggested Citation

  • Balwant Singh Mehta & Ravi Srivastava & Siddharth Dhote, 2025. "Introduction," India Studies in Business and Economics, in: Predicting Inequality of Opportunity and Poverty in India Using Machine Learning, chapter 0, pages 1-10, Springer.
    • Handle: RePEc:spr:isbchp:978-981-96-2544-4_1
    DOI: 10.1007/978-981-96-2544-4_1
    as

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