1.1 Background
Inequality and poverty are pressing global issues affecting millions of people worldwide. Addressing these issues is essential for creating a fair and just society. These two concepts are deeply intertwined, creating a vicious cycle that is difficult to break, impacting individuals, households, regions, and countries globally. Understanding and addressing these issues requires a multifaceted approach, involving both theoretical and empirical perspectives. Poverty, as defined by Sen's capability approach (1999), refers to the condition where people or households lack the resources necessary for full social participation and to meet their basic needs, such as food, shelter, and health care. Poverty can be absolute, where individuals cannot afford the minimum standard of living, or relative, which considers individuals’ standard of living compared to the broader society they reside in (Sen, 1999). Poverty is often measured by a poverty line, which defines the minimum income level required to meet basic needs. On the other hand, inequality refers to the uneven distribution of resources and opportunities among different groups of people. It can take many forms, including economic inequality, which focuses on differences in income and wealth, and social inequality, which includes disparities in education, health, and access to basic services (Atkinson, 2015).
High levels of inequality can exacerbate poverty, as resources and opportunities are concentrated in the hands of a few, leaving many without access to what they need to escape poverty. In this context, distributive justice in society, which addresses various outcomes of fair (justifiable) and unfair (unjustifiable) inequality, has been widely discussed by scholars such as Rawls (1971), Dworkin (1981), Arneson (1989), Cohen (1989), and Sen (1980, 1985, 1992). This concept is concerned with ensuring that everyone in society has access to what they need to live a decent life, regardless of their background or circumstances. It advocates policies that promote a more equitable distribution of resources (Rawls, 1971). Further, Roemer (1993, 1998, 2006) formalized this concept as ‘Inequality of Opportunity’ (IOp), which distinguishes between outcomes influenced by personal effort and those determined by circumstances. Roemer’s theory posits that inequalities arising from circumstances are unjust and should be addressed through compensatory measures (Hufe et al., 2018; Roemer & Trannoy, 2015). Circumstances refer to differences in life outcomes that are beyond an individual's control, such as family background, place of birth, and other inherited characteristics.
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Poverty and Inequality Trends: Over the past decades, substantial progress has been made in reducing poverty globally. The percentage of the world’s population living in extreme poverty declined from 29% in 2000 to about 9% in 2019 (World Bank, 2020). In particular, extreme poverty decreased much faster in South Asia, from 43% to around 10% in the same period. Despite substantial economic growth resulting in notable poverty reduction, challenges of inequality persist. The Gini coefficient, which measures income inequality, rose from 0.32 in 2000 to 0.35 in 2019 (Chancel et al., 2022). Similarly, India saw a decline in poverty from 37% to around 10% in the same period (World Bank, 2020), but inequality also increased from 0.32 to 0.35 (World Bank, 2020). However, these figures have lately changed somewhat. Recent data reflect that extreme poverty in India fell below 3% in 2022–23, with multidimensional poverty also declining sharply from 29.2% in 2005–06 to 11.3% in 2019–21 (NITI Aayog, 2024).
Challenges of Addressing IOp and Poverty: In developing countries such as India, where access to quality education, healthcare, and job opportunities is often unevenly distributed, IOp plays a significant role in perpetuating income inequality (Ferreira & Peragine, 2016). In India, IOp is a major driver of overall inequality, with factors such as social groups (caste), gender, and parental background significantly influencing individuals’ access to education, employment, and income opportunities. Studies suggest that IOp ranges from 30% to 50% in India (Azam & Bhatt, 2015; Kundu, 2020; Mehta et al., 2023; Motiram, 2018; Singh, 2012). In particular, the measurement of IOp has gained popularity among government policymakers, academics, and other stakeholders in recent years. However, traditional data sources have many limitations, lacking timely and granular level estimates. To address these challenges, innovative approaches are being explored. One such approach involves the application of machine learning (ML) techniques to integrate conventional sample survey or census data with non-conventional data sources such as administrative records and geospatial data, including satellite images or nightlight data, which are regularly updated and readily available.
In this context, the book aims to delve into the complexities of Inequality of Opportunity (IOp) and poverty, with a particular focus on India. It examines the underlying factors contributing to these issues and proposes innovative solutions to address them. By providing insights into these dynamics, the book seeks to assist policymakers and stakeholders in crafting more effective strategies for equitable resource distribution and ensuring access to essential opportunities for all individuals. Ultimately, this approach aspires to disrupt the cycle of poverty and inequality, fostering a more just and equitable society over the long term.
1.2 Relevance
The relevance of the topic lies in several critical aspects:
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Meeting Sustainable Development Goals (SDGs 2030): The United Nations’ SDG 1 aims to end poverty in all its forms, while SDG 10 focuses on reducing inequality within and among countries. Addressing inequality of opportunity (IOp) is crucial to achieving these goals, as it ensures that everyone, regardless of their background, has a fair chance to succeed.
Promoting Social Justice: Reducing inequality of opportunity is essential for building a just and equitable society. Equal access to opportunities fosters social cohesion and reduces social tensions, creating a more harmonious and stable social fabric.
Economic Growth: An equal society where everyone has the opportunity to succeed can lead to sustainable economic growth. When people have equal access to education and employment, they can contribute more effectively to the economy, enhancing overall economic productivity and innovation.
Human Capital Development: Investing in people’s education, health, and skills development enhances the overall human capital of a country. This boosts productivity and innovation, participation in decent and productive work, and leading to a more competitive and resilient economy.
Innovative Methodologies: Utilizing innovative data sources and methodologies can significantly improve the analysis of IOp and poverty. By integrating diverse datasets, including survey data and geospatial information, and applying advanced data analytics, such as machine learning (ML) techniques, this approach offers a more timely and comprehensive understanding of these complex issues.
1.3 Key Questions
This book addresses several critical questions to understand the interrelated issues of poverty and IOp, with a focus on India:
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What are the current levels of poverty and IOp in education, income, and health outcomes in India?
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What are the most important circumstances affecting individual outcomes in these areas?
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Which intersections of circumstances characterize the most disadvantaged groups?
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What is the spatial distribution of IOp and poverty across different regions?
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Are geographical areas with unequal opportunities also those with high poverty rates?
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What key areas need urgent attention to improve poverty and IOp in India and across its regions?
By exploring these questions, the book aims to provide a comprehensive understanding of the mechanisms driving poverty and IOp in India. It seeks to offer insights into effective interventions and policies that can foster a more equitable society. The findings are intended to guide policymakers in crafting targeted strategies to reduce both poverty and inequality, ensuring that all citizens have the opportunity to achieve their full potential.
1.4 Value Addition and Contribution
The book contributes to the field of IOp and poverty through several innovative approaches and methodologies. Some of the key value additions and contributions are discussed below:
Use of Innovative Approaches: Traditional measures of inequality and poverty often focus solely on outcomes, such as income and wealth, without considering the opportunities available to individuals. This approach is limited because it overlooks the underlying factors that create disparities. Traditional methods are also frequently costly and time-consuming, which can delay data availability. This book addresses these gaps by emphasizing the importance of understanding distributive justice and advocating the use of innovative data sources to predict IOp and poverty more effectively. By incorporating a broader perspective on opportunities, the book provides a more nuanced view of how disparities arise and persist.
Application of Advanced Data Analytics: A key innovation in this book is the application of advance data analytics, particularly machine learning (ML) techniques to analyse large and diverse datasets. ML algorithms can handle intricate patterns and provide granular insights that traditional methods often miss (Jean et al., 2016). This book uses ML to improve the understanding of IOp and poverty within country settings. Unsupervised learning algorithms are employed to identify latent groups and patterns, while supervised learning techniques determine key factors affecting IOp and poverty. This analytical approach improves accuracy of predictions, helps uncover hidden relationships, and offers fresh perspectives on socio-economic patterns (Donaldson & Storeygard, 2016; Wurm & Taubenböck, 2018).
Utilization of Unconventional Sources: The book also demonstrates the benefits of utilizing unconventional data sources, such as geospatial data including satellite images and night-time light data, to uncover socio-economic patterns and environmental conditions across different geographies. With advances in satellite technology, high-resolution images and ML algorithms can extract valuable socio-economic information, including data on nightlight emissions, the built environment, and pollution patterns. Utilizing free datasets, such as those from the Sentinel fleet of the European Copernicus program or night-time lights data from the SNPP-VIIRS1 sensor, allows for extensive geographic coverage and enhances the accuracy of socio-economic condition assessments. This approach fosters a more detailed understanding of socio-economic disparities across regions.
Integration of Conventional and Non-Conventional Data: The book integrates traditional survey data with unconventional sources, including administrative data and geospatial information. Recent research has shown that integrating diverse data sources can improve the reliability of inequality and poverty statistics and reveal new patterns (Alderman et al., 2002; Bricker et al., 2016; Dämmrich & Triventi, 2018). By combining conventional data with innovative sources, this book uncovers hidden patterns of IOp and poverty, and contributes to the understanding of ‘Resource Distribution and Inheritance’ and ‘Socio-Ecological Processes of Inequality’. This novel approach provides a standard operating procedure for applying ML to analyse socio-economic inequality across different regions, which is especially valuable for countries like India, where comprehensive and granular data is often lacking.
Multidisciplinary Approach: Incorporating insights from various disciplines—such as computer science, economics, geography, and philosophy—enriches the analysis of IOp and poverty. Computer science contributes through innovative data processing and modelling techniques, economics offers frameworks for understanding resource distribution and socio-economic behaviour, geography provides context on spatial disparities, and philosophy contributes ethical perspectives on justice and fairness. This interdisciplinary approach ensures a well-rounded and robust analysis.
Practical Insights for Policymakers: The practical insights derived from these methodologies are invaluable for policymakers and stakeholders. By providing timely and detailed information on the factors influencing IOp and poverty, this approach enables more informed decision-making. Policymakers can develop targeted interventions that address the root causes of disparities, improve resource allocation, and promote a more equitable society. The ability to act on precise, data-driven insights enhances the effectiveness of policy measures and fosters more equitable social outcomes.
1.5 Study Framework and Methodology
The following details the study framework, methodology, data sources, and analytical tools and techniques employed in the book to address the key questions discussed above.
Study Framework and Methodology: This book employs a robust analytical framework integrating the theoretical concepts of IOp and poverty as articulated by leading scholars such as John Roemer and Amartya Sen. Roemer’s theory distinguishes between outcomes resulting from personal efforts and those influenced by external circumstances, positing that individual outcomes should be analysed in light of both personal agency and external conditions (Roemer, 1998). Sen’s capabilities approach, on the other hand, focuses on what individuals are able to do and be, focusing on the role of personal capabilities in achieving well-being (Sen, 1999).
To measure income-related outcomes, the analysis uses data on household consumption and labour earnings. For education outcome, the primary indicator used is the number of years of schooling. Health outcomes are assessed by looking at access to maternal and child health services, including vaccinations, institutional delivery, antenatal care, prenatal care, and care by trained staff. These five factors are combined into a single indicator called “adequate care” to assess health outcomes. Poverty is measured using two main approaches: the international poverty line, set by the World Bank at $2.15 per day (2022 update) in purchasing power parity (PPP) terms, and the Multidimensional Poverty Index (MPI). The MPI, developed using the Oxford Poverty and Human Development Initiative (OPHI) methodology, evaluates poverty through three dimensions beyond income: education, health, and living standards. In-depth discussions of these methodologies are also provided in the relevant chapters of the book, elucidating how these frameworks contribute to a comprehensive understanding of poverty and IOp.
National Level Traditional Survey Data Sources: The Employment and Unemployment Surveys (EUS) were conducted roughly in every five years interval up to 2011–12, thereafter Periodic Labour Force Surveys (PLFS) were conducted annually from 2017 to 2018 by National Statistics Office (NSO), India. These surveys provide key data on demographic information of households, employment, and income (earnings and consumption). Household Consumption Surveys, also conducted by NSO, offer data on household consumption expenditure as a proxy of income and poverty measurement. National Family and Health Surveys (NFHS) conducted by International Institute for Population Studies, Mumbai, India. This survey provides data on demography, health, education, and living standards, and was last conducted in 2015–16 and 2019–21. In addition, the census of India, is conducted by the office of registrar general and census commissioner of India in every 10 years. The most recent census was conducted in 2011.
Non-Conventional Geospatial Satellite Data: The high-resolution satellite images, sourced from European Copernicus program and the SNPP-VIIRS sensor, provide data on nightlight emissions, built environment morphology, and pollution patterns. These images are utilized to infer geo-information about electricity access and living conditions. Recent advancements in remote sensing demonstrate that built environment characteristics can also serve as proxies for urban poverty (Taubenböck et al., 2018; Wurm & Taubenböck, 2018).
Cross-Country Data on Poverty, Inequality, and IOp: The comparative cross-country data on inequality is sourced from the World Inequality Database, while the poverty data is obtained from the World Bank, which is complemented by reports from the OECD, ADB, ILO, and other institutions. This cross-country data enables comparative analysis to identify how different socio-economic structures and cultural values impact IOp and poverty.
India-Focused and Comparative Analysis: Apart from country level analysis for India, regional comparative analysis has been done across state and district level to identify local level differences and craft targeted policy interventions. In addition, India’s situation in relation to other nations has also been compared wherever necessary to understand the influence of various factors on IOp and poverty, offering insights into potential solutions.
Temporal and Trend Analysis: To assess both the present conditions and long-term trends in poverty, and IOp. This approach helps in understanding the impact of economic growth and policy initiatives on these issues, identifying ongoing challenges, and suggesting short- and long-term solutions.
Machine Learning Algorithms: The advanced ML algorithms such as linear and logistic regression, decision trees, ensemble methods (e.g. random forest, gradient boosting), and confusion matrices have been used for the analysis. These techniques are implemented using R and Python software to analyse complex datasets and derive actionable insights.
Geospatial Analysis: The geemap library in python was used to extract geospatial data from google earth engine at the district and state level and the Q-GIS software has been used to create detailed maps at the state and district levels in India. This visualization of spatial data related to poverty and IOp, facilitate more nuanced regional analyses.
1.6 Chapterization
This book is divided into seven chapters providing an in-depth analysis of inequality of opportunity (IOp) and poverty in India. It explores various aspects of these issues using data from national surveys and unique sources like night-time satellite images and location data of points of interest. The book combines traditional econometric methods with newer machine learning techniques, such as regression trees and random forests, to analyse the data. It aims to offer practical insights for policymakers and researchers who want to address social and economic inequalities in India.
This introductory chapter sets the stage by outlining the importance of studying inequality of opportunity and poverty in India. It discusses the limitations of traditional measures of inequality, which often focus solely on outcomes rather than opportunities, and highlights the significance of distributive justice. The chapter emphasizes the potential of innovative approaches, such as using geospatial data and machine learning techniques, to analyse complex data and derive timely, granular insights.
The second chapter ‘Concept and Measurement’ explores the theoretical underpinnings of inequality of opportunity and poverty. It discusses the philosophical concept of distributive justice and its relation to IOp. The chapter provides an empirical framework for measuring IOp and poverty in India, using data from National Statistical Office (NSO) surveys and geospatial data, such as nightlights and points of interest. The analysis reveals that unequal circumstances, such as parental education and occupation, significantly contribute to income inequalities. It also highlights the critical role of gender and regional factors in shaping economic opportunities and poverty.
Chapter 3 ‘Decomposition of Inequality of Opportunity’ introduces innovative measures to decompose inequality of opportunity in India. It utilizes machine learning algorithms like conditional inference trees to distinguish between the effects of circumstances and effort on income inequality. The findings indicate that a significant portion of income inequality stems from circumstances beyond an individual’s control, such as parental occupation and regional disparities. The chapter emphasizes the need for targeted policies to address these structural inequalities and promote a more equitable society.
Chapter 4, ‘Predicting Poverty with Geospatial Data and Machine Learning’, explores the application of machine learning techniques to predict both income-based (consumption) poverty and multidimensional poverty in India. The chapter integrates consumption-based poverty data from the Periodic Labour Force Surveys with Multidimensional Poverty Index (MPI) data from the National Family Health Survey and geospatial sources such as satellite imagery to generate district-level poverty statistics. Various machine learning techniques are employed to estimate poverty levels, with tree-based methods demonstrating superior performance. The analysis identifies nightlights and the density of points of interest as key predictors of poverty. The spatial analysis reveals significant regional disparities, particularly high poverty levels in the eastern and central regions of India. The findings suggest that new data sources and advanced analytical techniques can improve the accuracy of poverty measurements and support more effective policy interventions. Additionally, the chapter advocates for the use of geospatial data as a cost-effective and timely method for predicting poverty at a granular level.
Chapter 5 ‘Inequality of Opportunity in Education’ explores the factors contributing to educational inequality in India. It uses large-scale data from the Periodic Labour Force Survey to predict years of formal education using machine learning techniques. The analysis reveals that parental educational levels and type of occupation they engaged in are the primary determinants of educational IOp. The chapter highlights the persistent educational disparities across different regions and social groups contributing inequality in education in the country.
Chapter 6, ‘Inequality of Opportunity in Health’ examines how unequal the opportunities are for accessing maternal and child health services. The study uses the Human Opportunity Index (HOI) and the Dissimilarity Index to analyse this inequality. It also employs hierarchical clustering, an unsupervised machine learning method, to identify regional patterns of access to these services at the district level across India. The analysis shows significant inequality in access to health services, with over three-fourths of the population lacking adequate or proper access, especially to prenatal care, immunization, and at least four antenatal visits. The main factors contributing to health inequality are geographical regions, wealth, parental education (particularly the mother's education), and social groups.
Chapter 7, the concluding chapter, summarizes the key findings and challenges discussed in various chapters of the book. It also provides policy recommendations and suggests areas for future research. It emphasizes the importance of addressing inequality of opportunity and poverty through targeted interventions that consider regional and social disparities. The chapter also outlines potential future research directions and the need for innovative data sources and analytical techniques to enhance our understanding of social and economic inequalities.
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