Introduction

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 1 in Predicting Inequality of Opportunity and Poverty in India Using Machine Learning, 2025, pp 1-10 from Springer

Abstract: 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 Measuring poverty and IOp is difficult, especially in countries like India, due to limited and outdated data.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.

Keywords: Poverty; Inequality; Inequality of Opportunity (IOp); Machine Learning Algorithm; Geospatial Data (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_1

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DOI: 10.1007/978-981-96-2544-4_1

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