Gender Inequality and its Implications on Education and Health: A Global Perspective 1837531811, 9781837531813

Table of contents :
Dedication
Table of Contents
List of Figures and Tables
About the Contributors
List of Contributors
Foreword
Acknowledgements
Introduction • Chandrima Chakraborty and Dipyaman Pal
Section I: Implications of Gender Inequality on Education and Health
1 Adverse Child Sex Ratio in India: The Role of Women’s Agency, an Empirical Analysis • Antara Bhattacharyya and Sushil Kr. Haldar
2 Do Government Expenditures on Education and Health Reduce Gender Inequality? The Case of the Least Developed and Developing Countries • Gizem Kaya Aydin
3 Is There Any Relationship Between Gender Inequality and Nutrition? Experience From India • Kavitha Kasala, Rudra Prosad Roy and Abhishek Das
4 Gender Discrimination in Education Expenditure in Public Primary Schools in Rural India Among Religious Groups: An Oaxaca–Blinder Decomposition Analysis • Puja Biswas and Amit Kundu
5 Can Gender Inequality in School Enrollment Hinder the Efficiency of the Education Sector? • Sangita Choudhury and Arpita Ghose
6 Understanding Gender Through an Educational Construct • Manisha Subba
7 Gender Inequality in India Intertwined Between Education and Employment • Dyuti Chatterjee and Pallabi Banerjee
8 A Critique of Gender Inequality: Study of Education and Health in the North Bengal Region • Bishal Rai
9 Gender Bias in Child Deprivation: A Study in the Context of West Bengal, India • Satyanarayan Kumbhakar and Pinaki Das
10 Is Dropout in Schools Related to Gender and Birth Order? • Chayanika Mitra and Indrani Sengupta
11 Investigating the Role of Air Quality and the Nexus Between Female Health Status and Their Labor Force Participation Rate: Evidence From Rural India • Kaushiki Banerjee and Arpita Ghose
Section II: Gender Inequality and Its Implications to Other SDGs
12 Only Development or Gender Norm? Explaining Gender Inequality in Emerging Market Economies • Amrita Chatterjee
13 Women Empowerment as a Key to Support Achievement of the Sustainable Development Goals and Global Sustainable Development • Begum Sertyesilisik
14 Linkage Between Women Empowerment and Gender-Based Violence in India: Evidence From NFHS-5 Data • Susobhan Maiti, Tanushree Gupta and Govind Singh Rajpal
15 Public Social Expenditures and Outcomes in Nigeria: ALook Through the Gender Lens • Nkechinyere Rose Uwajumogu, Ebele Stella Nwokoye, Kingsley Chike Okoli and Mgbodichimma K. Okoro
16 Equitable Pathways for a Sustainable Future: The Case for Mainstreaming Gender Across Sustainable Development Goals (SDGs) • Ananya Chakraborty and Sreerupa Sengupta
17 Sustainable Environment and Urbanization Policies to Enhance Gender Equality and Women Empowerment • Egemen Sertyesilisik
18 Understanding Gender, Poverty, and Social Justice: A New Look From the Perspectives of Indian Experience • Asim K. Karmakar, Sebak K. Jana and Sovik Mukherjee
19 Twitter Imparting and Reinforcing Gender-Based Identities of the Aboriginal Australia Women • Ali Saha
20 The Impact of the Pandemic on the Female Unorganized Sector Workers: A Study in the Rural Backdrop of West Bengal • Srimoyee Datta and Tarak Nath Sahu
21 A Gender Sustainable Development Index for Italian Regions • Marianna Bartiromo and Enrico Ivaldi
22 Association Between Crime Against Women and Income Inequality: A Study on Indian States • Debarati Nandigrami and Ramesh Chandra Das
23 Gender Responsive Budgeting Approach to Combating Climate Change • Gamze Yildiz Şeren
Index

Citation preview

Gender Inequality and its Implications on Education and Health

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Gender Inequality and its Implications on Education and Health: A Global Perspective EDITED BY CHANDRIMA CHAKRABORTY Vidyasagar University, India

And DIPYAMAN PAL Bethune College, India

United Kingdom – North America – Japan – India – Malaysia – China

Emerald Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2023 Editorial matter and selection © 2023 Chandrima Chakraborty and Dipyaman Pal. Individual chapters © 2023 The authors. Published under exclusive licence by Emerald Publishing Limited. Reprints and permissions service Contact: www.copyright.com No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-83753-181-3 (Print) ISBN: 978-1-83753-180-6 (Online) ISBN: 978-1-83753-182-0 (Epub)

This book is dedicated to our daughter SHRINIKA

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Table of Contents

List of Figures and Tables

xi

About the Contributors

xv

List of Contributors Foreword Acknowledgements

xxi xxiii xxv

1

Introduction Chandrima Chakraborty and Dipyaman Pal

Section I: Implications of Gender Inequality on Education and Health Chapter 1 Adverse Child Sex Ratio in India: The Role of Women’s Agency, an Empirical Analysis Antara Bhattacharyya and Sushil Kr. Haldar Chapter 2 Do Government Expenditures on Education and Health Reduce Gender Inequality? The Case of the Least Developed and Developing Countries Gizem Kaya Aydin Chapter 3 Is There Any Relationship Between Gender Inequality and Nutrition? Experience From India Kavitha Kasala, Rudra Prosad Roy and Abhishek Das

11

23

31

viii

Table of Contents

Chapter 4 Gender Discrimination in Education Expenditure in Public Primary Schools in Rural India Among Religious Groups: An Oaxaca–Blinder Decomposition Analysis Puja Biswas and Amit Kundu

43

Chapter 5 Can Gender Inequality in School Enrollment Hinder the Efficiency of the Education Sector? Sangita Choudhury and Arpita Ghose

55

Chapter 6 Understanding Gender Through an Educational Construct Manisha Subba

69

Chapter 7 Gender Inequality in India Intertwined Between Education and Employment Dyuti Chatterjee and Pallabi Banerjee

79

Chapter 8 A Critique of Gender Inequality: Study of Education and Health in the North Bengal Region Bishal Rai

91

Chapter 9 Gender Bias in Child Deprivation: A Study in the Context of West Bengal, India Satyanarayan Kumbhakar and Pinaki Das

101

Chapter 10 Is Dropout in Schools Related to Gender and Birth Order? Chayanika Mitra and Indrani Sengupta

113

Chapter 11 Investigating the Role of Air Quality and the Nexus Between Female Health Status and Their Labor Force Participation Rate: Evidence From Rural India 125 Kaushiki Banerjee and Arpita Ghose

Section II: Gender Inequality and Its Implications to Other SDGs Chapter 12 Only Development or Gender Norm? Explaining Gender Inequality in Emerging Market Economies 141 Amrita Chatterjee

Table of Contents

Chapter 13 Women Empowerment as a Key to Support Achievement of the Sustainable Development Goals and Global Sustainable Development Begum Sertyesilisik Chapter 14 Linkage Between Women Empowerment and Gender-Based Violence in India: Evidence From NFHS-5 Data Susobhan Maiti, Tanushree Gupta and Govind Singh Rajpal

ix

153

165

Chapter 15 Public Social Expenditures and Outcomes in Nigeria: A Look Through the Gender Lens 177 Nkechinyere Rose Uwajumogu, Ebele Stella Nwokoye, Kingsley Chike Okoli and Mgbodichimma K. Okoro Chapter 16 Equitable Pathways for a Sustainable Future: The Case for Mainstreaming Gender Across Sustainable Development Goals (SDGs) 191 Ananya Chakraborty and Sreerupa Sengupta Chapter 17 Sustainable Environment and Urbanization Policies to Enhance Gender Equality and Women Empowerment Egemen Sertyesilisik

203

Chapter 18 Understanding Gender, Poverty, and Social Justice: A New Look From the Perspectives of Indian Experience Asim K. Karmakar, Sebak K. Jana and Sovik Mukherjee

213

Chapter 19 Twitter Imparting and Reinforcing Gender-Based Identities of the Aboriginal Australia Women Ali Saha

223

Chapter 20 The Impact of the Pandemic on the Female Unorganized Sector Workers: A Study in the Rural Backdrop of West Bengal 235 Srimoyee Datta and Tarak Nath Sahu Chapter 21 A Gender Sustainable Development Index for Italian Regions Marianna Bartiromo and Enrico Ivaldi

247

x

Table of Contents

Chapter 22 Association Between Crime Against Women and Income Inequality: A Study on Indian States Debarati Nandigrami and Ramesh Chandra Das

259

Chapter 23 Gender Responsive Budgeting Approach to Combating Climate Change Gamze Yildiz S¸ eren

273

Index

285

List of Figures and Tables

Chapter 1 Figure 1.1. Chapter 3 Figure 3.1.

Chapter 7 Figure 7.1.

Trend of OSR and CSR in India: 1951–2011.

13

Trend of GII and % of Women Under OW Category in India. (A) Trend of GII in India. (B) Trend of % of Women Under the OW Category in India.

34

Gender Parity Index in Different Levels of Education Over Time.

85

Chapter 10 Figure 10.1. The Frequency Distribution of the Dropped-Out Child Considering the Birth Order and Gender.

118

Chapter 12 Figure 12.1. Average GDP (on Log Scale) and Average RLPR and Average Sex-Ratio and Average Secondary School Enrollment of Female for 25 Countries Over 2007–2020.

145

Chapter 13 Figure 13.1. Gender Equality, WE, and Achievement of Global SD and SDGs. Figure 13.2. Gender Equality Principle Based WE Enabled Gender Equality Policies and SD Policies.

156 159

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List of Figures and Tables

Chapter 16 Figure 16.1. Gender Data Availability Across SDGs at the Global Level. Figure 16.2. Gender-Wise SDG Indicator Mapping and Availability Globally and at India Level.

198

Chapter 17 Figure 17.1. Sustainable Environment and Urbanization Policies and Women Empowerment.

205

Chapter 21 Figure 21.1. GSDIs Radar Chart.

254

Chapter 22 Figure 22.1. Yearly Trends of Crime Rate and Per Capita NSDP (Current Price) of the States.

265

Chapter 1 Table 1.1.

Table 1.2.

Chapter 2 Table 2.1. Table 2.2. Chapter 3 Table 3.1.

Chapter 4 Table 4.1. Table 4.2.

Variable Definitions, Sample Means, and Range (Standard Deviation in Parentheses), 1991, 2001, and 2011. Role of KSY, SHG, RMK, and Other Socio Economic Variables on CSR: Pooled Regression; Dependent Variable: lnCSR.

197

17

18

Descriptive Statistics. Regression Results.

26 27

Analysis of GII and OW Using Time Series Modeling.

36

Regression Result for Different Religious Groups. The Decomposition Result.

49 51

List of Figures and Tables

Chapter 5 Table 5.1. Table 5.2.

Chapter 7 Table 7.1. Table 7.2A. Table 7.2B.

Chapter 8 Table 8.1. Table 8.2.

Chapter 9 Table 9.1.

Table 9.2.

OUTTE Scores of Higher Secondary Level for General Category States (GCS). Significant Variables Determining OUTTE of Higher Secondary Stage, Considering GCS.

Dropout Rates of Male and Female Students at Different Educational Levels. Female and Male WPR in Different NSSO Sample Survey Rounds. Occupational Classification of Female Workers in the Tertiary Sector.

Educational Level of Male and Female in North Bengal (%). ANOVA Table of BMI for all the Six Districts of North Bengal.

MCDI, CDR, and ICD Among Male and Female Child Estimation of West Bengal, 2005–2006 and 2015–2016. Logit Econometrics Analysis of Multidimensionally Male and Female Child Deprivation in West Bengal.

xiii

62 64

83 86 86

96 97

106

109

Chapter 10 Table 10.1.

The Frequency Distribution of Dropouts.

119

Chapter 11 Table 11.1.

Estimated Result of Rural FLE and FLFPR.

131

Chapter 12 Table 12.1.

Impact of Development on Ratio of Female to Male Labor Force Participation Rate. 147

xiv

List of Figures and Tables

Chapter 14 Table 14.1. Table 14.2.

Descriptive Statistics of the Variables and Result of State Level Analysis. Regression Analysis – Gender-Based Violence and Women Empowerment in India (2019–2020).

171 172

Chapter 15 Table 15.1. Table 15.2.

Global Gender Gap Index 2006–2013. 180 Error Correction and Long-Run Models Coefficients. 186

Chapter 19 Table 19.1.

Result of Online Ethnography.

Chapter 20 Table 20.1. Table 20.2. Table 20.3.

Chapter 21 Table 21.1. Chapter 22 Table 22.1.

230

Pair-Wise Correlation Matrix With Variance Inflation Factor. 240 Results of Multiple Regression Analysis Considering Household Expenditure as Dependent Variable. 241 Result of F-Test and t-Test for Savings Level Changes of the Borrowers. 242

GSDIs and Related Ranks.

253

Correlation and Regression Coefficients and GINI Values in Crime Rates and PCNSDP.

267

About the Contributors

Kaushiki Banerjee is Assistant Professor (Economics), Barasat Government College, and Research Scholar, Department of Economics, Jadavpur University, India. She was a UGC NET Junior Research Fellow. Research interest includes gender and social sector/econometrics. She has made paper presentations in national and international conferences, and publications in journals and edited volumes. Pallabi Banerjee is currently serving at the Directorate of Census Operations, WB, as a Junior Consultant. She also had experience of academic content writing. She has an interest in academic research and presented papers in different seminars. Marianna Bartiromo holds an MSC in Administration and Public Policies with honors and press dignity from the University of Genoa. She collaborates with the Channel & Retail Lab at SDA Bocconi School of Management in Milan. She works on data analysis from both quantitative and qualitative perspectives, and her research interests focus on sustainability, corporate social responsibility, gender differences, and political participation. She also works on marketing and in particular on the sectors of retail, healthcare, and digital payments. Antara Bhattacharyya was awarded a Doctoral Degree from Jadavpur University, India. Now she is working as a Lecturer in Humanities at Arambagh Government Polytechnic, India. Puja Biswas is currently employed as an Assistant Professor in Economics, Prasanta Chandra Mahalanobis Mahavidyalaya under West Bengal State University and is also a Research Scholar, Department of Economics, Jadavpur University, India. Ananya Chakraborty is Senior Research Analyst with Climate Resilient Practice, World Resources Institute, India. She has a doctorate in Development Studies from Tata Institute of Social Science, Mumbai, and is an alumnus of the German Institute of Development and Sustainability. Her current research looks at gender and climate smart agriculture. Amrita Chatterjee is an Assistant Professor at the Madras School of Economics since 2016. She obtained her PhD from Jadavpur University, India. Her primary research interest is Development Economics, Financial Inclusion, and the

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About the Contributors

Economics of Gender. She has both contributed to national and international peer-reviewed journal publications. Dyuti Chatterjee is currently serving as Assistant Professor of Economics at The Heritage College, India. She has academic experience of nearly 15 years. She has taught Economics both at the graduate and postgraduate level in several reputed colleges in Kolkata. Sangita Choudhury is an Assistant Professor of Applied Economics at Maulana Abul Kalam Azad University of Technology (MAKAUT, WB) and a PhD scholar in the Economics Department, Jadavpur University. She published in reputed book and journals. Her interest area includes econometrics, development economics, and the social sector. Abhishek Das is an Agricultural Economist at ICRISAT, India. His research focuses on Quantitative and Economic policy-related research in Africa and South Asia. Presently he is working on developing system dynamic models for different agricommodities to identify and leverage opportunities for the policies and institutions targeting market-led innovations. Pinaki Das is a Professor, Department of Economics, Vidyasagar University. He was awarded with a Gold Medal in MSc in Economics. He has conducted three MRPs funded by DST, UGC, and ICSSR. He has published six books and 50 research papers. He has guided 14 research scholars. He has research interest in Labor Economics, Social Protection, Food Security, Multidimensional Poverty, and Women Empowerment. Ramesh Chandra Das, PhD, is presently a Professor at the Department of Economics of Vidyasagar University, India. Dr Das has teaching and research experience of 25 years and a list of articles published in internationally reputed journals and books. Srimoyee Datta is currently working as an Assistant Professor in the Department of Business Administration, Sidho Kanho Birsha University. Her research area incorporates Financial Inclusion, Microfinance Institutions, and Women Empowerment. She has published various research articles in various reputed national and international journals including Taylor & Francis, Inderscience, Sage, Springer, etc. Arpita Ghose is Professor of Economics, Jadavpur University, and Chair Professor (Honorary), Planning and Development Unit, JU(NITI-Aayog), Government of India. She was former Dean (Arts) and Head (Economics), JUA PhD of ISI. She authored and edited books and published many journal papers from renowned International publishers, completed renowned national and international Institution’s funded projects, supervised PhDs, with research interest in Econometrics, Macroeconomics, Productivity, Efficiency, Empirical Studies on International Trade, Applied Development Economics, and the Social Sector. Tanushree Gupta is currently working as an Assistant Professor in the School of Commerce and Management Studies, Sandip University, Maharashtra, India.

About the Contributors

xvii

She is the review member of the International Journal of Research and Analytical Reviews (Peer-reviewed, Referred Journal) and also the member of an editorial board in the International Journal in Management and Social Science (international peer-reviewed journal). She has in her credit many research papers/ publications of national and international repute. Sushil Kr. Haldar is a Professor of Economics at the Department of Economics, Jadavpur University. He has numerous publications in different reputed national and international journals. He has published many papers in different reputed international and national journals of Economics. Enrico Ivaldi is a researcher in Social Statistics at the Department of Political Science, University of Genoa. He is on the Editorial Board of the “Revista de Estudios Andaluces” (REA), the Centro de Investigaciones en Econometr´ıa of the Universidad de Buenos Aires, Argentina, and the Pontificia Accademia Mariana Internazionale, Vatican City. Sebak K. Jana is currently Professor of Economics at the Department of Economics, Vidyasagar University, Midnapore, West Bengal, India. Asim K. Karmakar is Assistant Professor in Economics, School of Professional Studies, Netaji Subhas Open University, Kalyani, Nadia, West Bengal, India. He is currently Joint Secretary of the Indian Economic Association. Kavitha Kasala is a Nutrition and Gender Researcher at the International Crops Research Institute for the Semi-Arid Tropics. She has academic training in Nutrition and has two decades of experience in interdisciplinary research related to human nutrition and gender, project management, and training experience both at field and program level. Gizem Kaya Aydin was a Research Assistant; she was a member of Istanbul Technical University. She has an MSc in Economics from Istanbul Technical University and a PhD in Econometrics from Marmara University. Her research area includes income distribution and consumer economics. Satyanarayan Kumbhakar is an ICSSR doctoral fellow pursuing his PhD from the Department of Economics, Vidyasagar University. He is actively publishing in national and international peer-reviewed journals. He has expertise in field investigation and data processing. His research interests embrace Heath Economics, Development Economics, and Social welfare Economics. Amit Kundu is working as Professor in the Department of Economics, Jadavpur University, India. His area of research interests includes Economics of Education, Economics of Public Policy, Labor Economics, Economics for Rural Development, and Agricultural Economics. He has already published more than 55 papers in different international and national journals of Economics. Susobhan Maiti is an Assistant Professor in the Department of Economics, School of Humanities and Social Sciences (SHSS), Jain (Deemed-to-be University),

xviii

About the Contributors

Bangalore, India. He has published many research papers in the area of industry, efficiency, and productivity in both national and international journals. Chayanika Mitra is Assistant Professor of Economics in St. Xavier’s University, Kolkata. She completed her PhD in Economics from ISI, India. She has published in several reputed national and international journals. Her areas of research include Economics of education, Time series forecasting, Gender Economics, and Family Economics. Sovik Mukherjee is Assistant Professor in Economics at the Department of Commerce (Morning Section) under the Faculty of Commerce and Management at St. Xavier’s University, India. Debarati Nandigrami has an MSc in Economics from Vidyasagar University, India. Her research interests lie in social sectors, women studies, agricultural economics, etc. Ebele Stella Nwokoye teaches Economics at Nnamdi Azikiwe University, Nigeria. She has membership in the Nigerian Economic Society and Young Scholars Initiative of the Institute of New Economic Thinking. Kingsley Chike Okoli is a doctoral student of Development Economics. He teaches Economics at Nnamdi Azikiwe University, Nigeria, and has a master’s degree in Human Resource Economics. Mgbodichimma K. Okoro is a graduate assistant as well as a master’s degree candidate in Alex-Ekwueme Federal University, Nigeria. She obtained her bachelor’s degree in Economics from Ebonyi State University. Bishal Rai is currently pursuing his PhD from the University of North Bengal. Presently he is teaching in the Department of Economics, St. Joseph’s College, Darjeeling. He has published papers in journals and edited volumes. His teaching and research interest includes Development Economics, Regional Economics, Education, Human Capital, and Econometric Analysis. Govind Singh Rajpal is an eminent legal scholar and researcher. He completed his postgraduation from the University of Bikaner and a Postgraduate Diploma in Criminal Law. He earned his PhD from the University of Bikaner. He is currently employed as a Professor at School of Law, Sandip University, Nashik. He supervises a number of PhD research scholars, and four of them have already been awarded doctorates. He has more than 20 research papers published in national and international journals of repute. Rudra Prosad Roy is a Doctoral Research Scholar at the Department of Economics, Jadavpur University, India. His research interests lie in the areas of macroeconomics, financial economics, and applied econometrics. He has published in journals of international repute such as Economic Modeling, International Review of Economics and Finance, Journal of Economic Asymmetries, and several edited volumes.

About the Contributors

xix

Ali Saha is a Researcher and Sessional Lecturer in the field of Media Studies and Social Science at Monash University, Australia. She also teaches at the University of Melbourne and Swinburne University, and frequently serves as a guest lecturer at other universities. Her research interests and expertise are in the field of media sociology, and she frequently works with interdisciplinary subjects such as sociology, communication design, and journalism. Tarak Nath Sahu is currently working as an Associate Professor and Head, Department of Business Administration, Vidyasagar University, Midnapore. His research publications are in the areas of corporate finance, corporate governance, CSR, corporate sustainability, stock markets, etc. Presently he is working as Director in a research program sponsored by the ICSSR. Under his supervision seven researchers have been awarded their PhDs. Dr Sahu has published more than 100 research articles in reputed national and international journals published by different reputed publishers, including Springer, Wiley-Blackwell, Emerald, Taylor & Francis, SAGE, Palgrave MacMillan, Inderscience, etc. Indrani Sengupta is Assistant Professor of Economics in Xavier Law School, St. Xavier’s University, India. She completed her MA in Economics from Jawaharlal Nehru University, New Delhi, and MPhil in Social Sciences from the Centre for Studies in Social sciences, India. Her research interests include Economics of education, Sociology of education, Gender, and Economic Sociology. Sreerupa Sengupta is Assistant Professor in Healthcare Management at the Goa Institute of Management. Her areas of interest include public policy, health communication, and gender and development. She holds a PhD in Women’s Studies, a Master’s in Sociology, and is an alumnus of the German Institute of Development and Sustainability. Gamze Yildiz S¸eren is an Associate Professor at Namık Kemal University, Public Finance Department Tekirdag, Turkey. She holds a master’s degree in Public Economics from Marmara University and a PhD in Public Finance from Marmara University. Her main research fields focus on public finance, taxation, budgeting, and gender. Begum Sertyesilisik is a Professor. She has been awarded her PhD at the Middle East Technical University and her MSc, MBA, and BSc degrees at the Istanbul Technical University. She has been specialized in the fields of sustainable construction project management, construction project management, sustainability, and sustainable built environment. Egemen Sertyesilisik has been awarded an undergraduate degree from the Bilkent University. He has been awarded an MA degree from the University of Liverpool, an MBA degree from the Yıldız Technical University, and a PhD from the Marmara University. He has many publications especially on sustainability, political economy, and sustainability policies. Manisha Subba has a Masters in History and Education, and Doctorate from the University of Delhi. She is presently with the Department of Elementary

xx

About the Contributors

Education, Mata Sundri College for Women, University of Delhi. Her teaching and research interests include Pedagogy of Social Science, Contemporary India, and History of Education. She has contributed as a consultant in various projects and was a member of Joint Review Mission, 2014, in Teacher Education, Tamil Nadu. Nkechinyere Rose Uwajumogu teaches Economics at Alex-Ekwueme Federal University, Nigeria. She was a recipient of United Nations University Institute for Natural Resources in an Africa Home-based Scholar Programme.

List of Contributors

Gizem Kaya Aydin Kaushiki Banerjee Pallabi Banerjee Marianna Bartiromo Antara Bhattacharyya Puja Biswas Ananya Chakraborty Chandrima Chakraborty Amrita Chatterjee Dyuti Chatterjee Sangita Choudhury Abhishek Das Pinaki Das Ramesh Chandra Das Srimoyee Datta Arpita Ghose Tanushree Gupta Sushil Kr. Haldar Enrico Ivaldi Sebak K. Jana Asim K. Karmakar Kavitha Kasala

Istanbul Technical University, Turkey Barasat Government College, India Independent Researcher, Former Junior Consultant at Census Operations, Kolkata SDA Bocconi School of Management, Italy Arambagh Government Polytechnic, India Prasanta Chandra Mahalanobis Mahavidyalaya, India World Resources Institute, India Vidyasagar University, India Madras School of Economics, India The Heritage College, India Maulana Abul Kalam Azad University of Technology, India International Crops Research Institute for the Semi-Arid Tropics, India Vidyasagar University, India Vidyasagar University, India Sidho Kanho Birsha University, India Jadavpur University, India Sandip University Nashik, India Jadavpur University, India University of Genova, Italy Vidyasagar University, India Netaji Subhash Open University, India International Crops Research Institute for the Semi-Arid Tropics, India

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List of Contributors

Satyanarayan Kumbhakar Amit Kundu Susobhan Maiti Chayanika Mitra Sovik Mukherjee Debarati Nandigrami Ebele Stella Nwokoye Kingsley Chike Okoli Mgbodichimma K. Okoro Dipyaman Pal Bishal Rai Govind Singh Rajpal Rudra Prosad Roy Ali Saha Tarak Nath Sahu Indrani Sengupta Sreerupa Sengupta Begum Sertyesilisik Egemen Sertyesilisik Manisha Subba Nkechinyere Rose Uwajumogu Gamze Yildiz S¸ eren

Vidyasagar University, India Jadavpur University, India Jain (Deemed-to-be Univetsity), India St. Xavier’s University, India St. Xavier’s University, India Vidyasagar University, India Nnamdi Azikiwe University, Nigeria Nnamdi Azikiwe University, Nigeria Alex Ekwueme Federal University, Nigeria Bethune College, India St Joseph’s College, India Sandip University Nashik, India Jadavpur University, India Monash University, Australia Vidyasagar University, India St. Xavier’s University, India Goa Institute of Management, India Izmir Democracy University, Turkey Gozuyilmaz Engineering and Marine Indus¨ tries Ltd, Turkiye University of Delhi, India Alex Ekwueme Federal University, Nigeria Tekirdag Namik Kemal University, Turkey

Foreword

There is a well-known African proverb which says: “If you educate a man, you educate an individual. But if you educate a woman, you educate a nation.” This can be extended to areas beyond education, such as healthcare, among others. Unfortunately, due to discrimination and other social factors, huge inequalities in gender exist in many countries including India in education, in healthcare, in the labor market, and so on. Purely from an economic point of view, it is highly inefficient as women constitute nearly half of the total population: gross domestic product can be significantly increased if such inequalities are eliminated. The ethical dimension of the problem is even more of concern. The consequences of such inequalities also often have devastating effects on women and young girls in terms of malnutrition and morbidity. A clear manifestation of that can be found in the adverse female-to-male ratios in many South Asian countries. In the present book, Dr Chandrima Chakraborty, a faculty member in the Department of Economics at Vidyasagar University, India, and Dr Dipyaman Pal, a faculty member at Bethune College, India, have collected 23 research articles on this topic. Most of the contributors are established academics, but there are also some doctoral students among the contributors. The geographic coverage of the contributions is also impressive with contributions from Asia, Australia, Africa, and Europe. The studies included also cover the above continents. The geographic coverage would allow researchers and policymakers to compare different country experiences, and then to see if success in one country can help to formulate policies in other countries. As one would expect, most of the contributions are on gender inequalities in education and in healthcare, but other areas of concern such as gender ratio in population are also present. This book project must have been painstakingly arduous and challenging because of the complex nature of the broad problem. I congratulate the editors for being successful in the project. They have done a great service to the profession by completing this project. To summarize, the book is on an extremely important subject, which should be attractive to any scholar and policymaker interested in the issue of gender inequality in emerging countries, and it should be a supplementary reading in any undergraduate and graduate course in development economics. Sajal Lahiri Vandeveer Chair Professor in Economics and Distinguished Scholar Southern Illinois University Carbondale, USA

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Acknowledgements

With extensive hard work on the stages from submission of the book proposal to final submission of the proposed book titled Gender Inequality and Its Implications on Education and Health: A Global Perspective, it is an abundant pleasure for the editors as well as the chapter contributors that the book is now published. In carrying out the entire project, the help and support of different organizations, academicians, and other members of the society who are directly or indirectly associated to the project cannot be forgotten. First and foremost, we must acknowledge the cooperation and support of the Emerald Publishing Ltd. Team for approving the proposal and continuously guiding us at all stages of developments of the book. Secondly, we are highly grateful to all the contributing authors for their valuable chapter contribution and adding to the existing literature through this volume. I would like to express my special thanks to the contributors for helping me to complete the project and effectively on time. Thirdly, we are indebted to our little daughter for her support and sacrifice in carrying out this lengthy project. At last but not in least, we would like to thank everyone who helped and motivated us to work on this project. Although all care has been taken, no one other than us, as the editor, discloses to remain entirely responsible for any errors that still stay behind this book. Chandrima Chakraborty Dipyaman Pal The Editors

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Introduction Chandrima Chakraborty and Dipyaman Pal Every child deserves to reach her or his full potential, but gender inequalities in their lives and in the lives of those who care for them hinder this reality. Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous, and sustainable world. Gender equality and empowerment, Goal 5, is one of the Sustainable Development Goals (SDGs), and all the SDGs depend on the achievement of Goal 5. Women across the world continue to suffer from gender inequality, including child and forced marriage, gender-based violence, health care, as well as barriers to participation in education and employment. Achieving gender equity globally is crucial to meeting development goals and reducing human suffering. Education is universally acknowledged to benefit individuals and promote national development. However, educating girls produces many additional socioeconomic gains that benefit entire societies. These benefits include increased economic productivity, higher family incomes, delayed marriages, reduced fertility rates, and improved health and survival rates for infants and children. But women and girls are not regarded as equal in status as compared to their male counterparts. Due to gender disparities, girls are discouraged from acquisition of education and participation in various tasks and activities. Their daily routine activities are based on the rules set by the male members of the household. Education would render an effective contribution in causing a reduction in gender disparities and providing equal rights and opportunities to both men and women. Women report worse health than men, despite the fact that they live longer. Even excluding reproductive conditions, women have more health problems than men. Health problems that women experience may be minor from a medical viewpoint, but they are not so minor in women’s daily lives. Compared with men, women may experience more day-to-day stress associated with socioeconomic disadvantage, such as poorly paying jobs, economic hardship, routine and oppressive work, and low household income, all of which contribute to poor health. There are several examples of gender inequality existing in the world today, e.g., women are forbidden to drive in Saudi Arabia, despite numerous protests, and must rely on their fathers or husbands to get from place to place. While allowed to participate in the army, women are still not permitted to serve in frontline combat in Turkey and Slovakia; this gender inequality persisted in the Gender Inequality and its Implications on Education and Health, 1–8 Copyright © 2023 Chandrima Chakraborty and Dipyaman Pal Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231001

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Chandrima Chakraborty and Dipyaman Pal

United Kingdom as well. India’s recent ruling that rape laws do not apply to married couples clearly illustrates the sexual subjugation and violence to which women remain exposed. Access to education is especially a problem in Afghanistan where groups that oppose female education attack many schools. This deserves analysis of the gender inequality and its various implications in the perspective of the abovementioned issues across a range of sectors and development concerns such as health, education, governance, Industry, urbanization, climate change, and social justice. In view of the above perspective, this book tries to touch upon a range of issues which may be considered as important as well as critical in the sphere of gender inequality and its various implications in the global context. There are 23 chapters which are broadly classified into two parts. Part I covers the implications of gender inequality on education and health (Chapters 1–11) and Part II (Chapters 12–23) discusses gender inequality and its implications to other SDGs. Brief sketch of all the chapters is presented below:

1. Section I: Implications of Gender Inequality on Education and Health Chapter 1 tries to evaluate the impact of women’s agency along with some affirmative actions on child sex ratio (CSR) in India. The role of women’s agency is assumed to be significant toward correcting the adverse CSR. However, it is confined to two variables like female literacy rate (FLR) and female work force participation rate (FWFPR). Women agency should take into account women’s ability to make effective choices and to transform those choices into desired outcomes. Therefore, in order to explore the effect of some affirmative actions in explaining the variations of CSR across the states in India, three popular schemes, namely, Self Help Group (SHG), Rashtriya Mahila Kosh (RMK), and Kishori Shakti Yojona (KSY), are used in the present analysis. Pooled regression shows that FWFPR has a positive impact on CSR but FLR has a nonlinear relationship with the CSR. It is found that the SHG has positive but the KSY has negative effect on CSR; the other variable like RMK does not play any significant role toward variations of CSR. States showing higher concentration of ST population are found to be conducive to favorable CSR compared to SC population. Per capita net state domestic product (PCNSDP) has a same effect like FLR. This study also finds significant discriminating role (against female child) of major states compared to minor states and UTs. Therefore, the role of women’s agency toward improving CSR needs to be highlighted more profoundly in Indian context. In Chapter 2 the effect of government’s health and education expenditures on gender inequality has been examined in the least developed and developing countries. The study covers 24 countries for the period 2010–2017. It has been observed that the government’s health expenditures reduce gender inequality, while education expenditures increase gender inequality. This finding indicates

Introduction

3

that education expenditures of governments do not reach girls in the least developed and developing countries. However, GDP per capita is the most important factor in reducing gender inequality. In Chapter 3 the relationship between gender inequalities and percentage of female’s overweight and obesity have been estimated. The results show that there is a long-run relationship between these variables. Moreover, it is also found that a decrease in gender inequality influences the increase in the number of females under overweight and obese. In conclusion, the findings of this study reveal that, while elevating the position of women in society may be an important step toward combating the epidemic of overweight and obesity, strategies must also tackle unhealthy habits that promote obesity. Chapter 4 tries to capture the disparity in expenditure on primary education based on gender among the religious groups (Hindu, Muslim, and Christian) in rural India. The gender gap in education expenditure for a certain demographic group is calculated using the Oaxaca–Blinder decomposition approach. Further, the various household-related factors which might influence the decision of spending on a child’s education is tried to be identified using the 75th-level National Sample Survey Office (NSSO) unit-level dataset of July 2017–June 2018 (one academic year) to obtain data on education expenditure and other household factors which play a manifesting role in the gender gap in expenditure on education. The finding suggests that the total differential (log mean boys education expenditure-log mean girls education expenditure) is positive among all religious groups signifying the gender bias in education expenditure. It is also found that the magnitude of the “Unexplained Effect” component is higher compared to the “Explained Effect” component signifying that the treatment of characteristics by students differs by their sex at elementary education. Household size and if household members are employed on a casual basis, then their expenditure on education falls on the other hand income of the household, a household with computer availability and household member engaged in regular wage/salary earning plays a positive role in expenditure on primary education in rural India. Chapter 5 contributes to the literature by estimating output oriented Technical Efficiency (TE) of Indian higher secondary education for the period 2010–2011 to 2015–2016, using nonparametric Data Envelopment Analysis, for general category states and in the second stage, using the estimated TE scores from the first stage, and the regression analysis establishing the positive impact of the girls’ enrollment relative to boys’ on the resulting TE and hence the positive role of gender equality in enrollment on enhancing TE. The favorable role of government expenditures on education (as a ratio to aggregate expenditure for the state), proportion of para teachers’ and the adverse role of “percentage of schools without girl’s toilet,” and “percentage of schools without building,” in determining TE of Indian higher secondary education are evident. Various feminist theories that have contributed to the understanding of gender are presented in Chapter 6. The important role of the schools and in particular the textbooks in socializing and building learners’ understanding of the sociopolitical contexts cannot be negated. Hence, the chapter concludes by analyzing how

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Chandrima Chakraborty and Dipyaman Pal

gender content and issues are experienced and gets represented in the school curriculum and the textbooks. Many researchers have emphasized the need for gender inclusion to achieve holistic and sustainable development goals. This is important because only with the achievement of social equality can we work toward economic equality. Chapter 7 highlights two dominant factors leading to gender inequality in country education and employment. Empirical evidence suggests that the gross enrollment of females decreases from the upper primary level of schooling onwards. Moreover, higher education for women has not translated to higher employment post liberalization. India continues to be a country with one of the poorest female work participation ratios. Employment along with education is a key tool to improve the condition of women in our society. The chapter concludes that an integrated approach linking education of women and employment is essential for the reduction of gender inequality. Gender inequality in terms of education and health in the North Bengal region is examined in Chapter 8 as it can have adverse effects on the overall development in the region. The study relies on the available secondary data on education and health. It is imperative that the authors realize the need to narrow the gender gap for development to be inclusive as investing in women’s education and health can contribute to holistic economic growth and development. Analyses of the status of child health in West Bengal from a multidimensional perspective and disaggregates it on the basis of their gender in order to catch the effect of discrimination persisting in the society is done in Chapter 9. In order to do so, the NFHS unit level data of the latest two rounds are considered. The present study contributes to the existing literature from methodological perspective as well as by formulating a child deprivation index using a multidimensional approach. Together with that, this chapter unearthed the factors influencing the health status of the children based on their gender. Chapter 10 concludes that the factors pushing a child to dropout become more effective for the eldest sibling. The major reason is the family structure of India as the eldest sibling is expected to be more responsible and look after other younger siblings. Consequently, a certain number of the younger siblings try to follow the elder siblings and discontinue going to school. Chapter 11 contributes to the literature by establishing the simultaneous dependence between female labor force participation rate (FLFPR) and female health status as measured by female life expectancy (FLE), the negative impact of outdoor air pollution as measured by prevalence of SPM, SO2, NO2 on FLE, and the interaction among different demographic factors in determining both FLFPR and FLE. The interaction effect of air pollution with economic growth and poverty on FLE is negative implying that the partial effect of a change in growth (poverty) depends on air pollution level. Thus reduction in air pollution will increase FLE and hence FLFPR, as the simultaneous positive dependence between FLFPR and FLE is supported. The interaction effect of women’s political power and education on rural FLFPR is significant and nonlinear with

Introduction

5

positive marginal effect. Thus the partial effect of a change in women’s political power on FLFPR will in turn depend on level of education and vice versa. The positive impact of other demographic factors like education, female leader, poverty, and urbanization, on FLFPR, and education, female household head, female leader, sex ratio, and growth on FLE are apparent. However, the household size significantly and negatively affects FLFPR.

2. Section II: Gender Inequality and Its Implications to Other SDGs Chapter 12 chooses a panel of 25 EEs for a period of 2007–2020 to investigate how gender norms can affect the Female labor force participation (FLFP) and development relationship. Results suggest that EEs are in a stage of development where even if countries are growing at a reasonable rate, FLFP is falling. Further investigation reveals that skewed sex ratio can dampen the impact of development, whereas secondary school enrollment and legislation to protect women from sexual harassment in workplace may foster the effect of development. Thus, policies to encourage parents to invest more on the girl child and providing legal support to women at the workplace can be effective policies to reduce gender inequality. Chapter 13 underlines that the gender inequality hinders and obstructs global sustainable development and achievement of SDGs. Furthermore, this chapter examines causes of gender inequality as they need to be identified and eliminated to achieve global sustainable development. Women empowerment plays a significant role in solving gender inequality caused problems (e.g., health problems, education inequality, discrimination, crime, violence). Women empowerment achieved through supported gender equality can act as multiplier factor in achieving synergy creation and influence sustainable future. It highlights the influence of women empowerment and gender equality on all three pillars of sustainability. Furthermore, it underlines the importance of women empowerment in all industries and politics for addressing the gender inequality problems. It provides recommendations on how to enhance women empowerment to support achievement of all SDGs. Women empowerment based sustainable development policies can influence achievement of the SDGs. This chapter is expected to be useful to the academics and policymakers focusing on achievement of SDGs, sustainability, and sustainable development. Chapter 14 analyzes the direction of the relationship between women’s empowerment and gender-based violence, or if women’s empowerment influences gender-based violence or vice versa. Analysis shows a link between women’s empowerment and gender-based violence and a large disparity among states. The differential effects of social expenditures on males and females are assessed in Chapter 15 by establishing the impact of public expenditures on education and health on gender parity in primary and secondary enrollment and on gender

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Chandrima Chakraborty and Dipyaman Pal

parity in life expectancy for Nigeria given age dependency ratio, annual population growth rate, and GDP per capita growth rate. It is found that increased social spending on health and education increased female education enrollment which was hitherto lower than male enrollment. Again, increased social expenditure on health and education improved male life expectancy which was hitherto lower than female life expectancy. The importance of increased social expenditure on health and education; gender budgeting and gender-sensitive budgets; and implementation of inclusive growth policies in engendering gender parity in Nigeria were established. Gender data are the foundation for ensuring gender equality and promoting evidence-based policymaking. It therefore makes a case for gender-related indicators in SDGs 6, 7, 9, 12, 14, 15, and 17 along with expanding the gendered understanding of people-related goals in the areas of education, health, and employment. Moreover, it reiterates the need for gender data collection to move beyond the binary construct of male and female and integrate an intersectional lens. The whole thing is discussed in Chapter 16. Chapter 17 examined the role of environment and urbanization policies in enhancing gender equality and women empowerment. Furthermore, it investigates causes and consequences of failure in environment and urbanization policies in addressing gender equality and women empowerment. It emphasizes impacts of environment and urbanization policies on health especially on women health and well-being. It further highlights the role of gender equality in achieving healthy and sustainable environment and urbanization policies. Furthermore, this chapter provides recommendations on how to enhance environment and urbanization policies so that they can further support gender equality and women empowerment effectively. The position and status of women in India in the realm of gender equality, poverty reduction, and social justice as well as the public actions viewed from India’s perspectives are focused in Chapter 18. At the same time, it highlights the importance of global actions in an endeavor to establish gender equality, breaking the chain of poverty trap and establishing social justice along with their fallouts in the subsequent years. Chapter 19 highlights how the narratives of Aboriginal Australian women’s discriminatory issues are conveyed and discussed on twitter. In attempting so, this chapter highlighted whether the digital space has contributed to the equality in the society or is it attempting to reassert the existing hegemonic discourses and status in the Australian Community. The materialization and continuation of pandemic have a big toll on everyone’s life. Female workers specifically from the unorganized sector faced diversified financial crises during the pandemic phase. These households go through multiple changes in terms of expenditure, loan burden, job uncertainty, etc. Chapter 20 attempts to understand the changes that take place in terms of health, expenditure, and other associated evolved behavior in lockdown and post lockdown phases in selected rural-based areas of West Bengal. By applying

Introduction

7

different statistical tools like regression, f-test, and t-test, various influencing factors for household expenditure along with the changes in savings behavior have been observed in the chapter. A sudden crisis like Covid-19 has made the selected respondents responsive toward vivid positive lifestyle and attitude changes like financial literacy, savings, crisis management, and so on. Chapter 21 identifies gender differences by analyzing the levels of sustainable development achieved by Italian regions. The Italian case in fact is very peculiar due to its historical territorial gap between the regions of the North (among the most developed) and those of the Center-South, which still show high gender inequalities. A Gender Sustainable Development Index (GSDI) is constructed through the use of 50 indicators from the Benessere Equo e Sostenibile survey of Istat. The technique used is the stacking method (Landi et al., 2017; Norman, 2010), which was chosen for its high replicability of results. The results show that only 40% of Italian regions have higher levels of female sustainable development than male sustainable development. Moreover, the regions with the worst levels of both female and male sustainable development are located in the Center-South of the country, confirming the strong territorial gap present within the Italian Peninsula. Whether income inequality has any sort of associations with the crime against women in the states of India have been examined in Chapter 22. The study has observed rising trends of crime rates and per capita incomes across the states in India for the period 2000–2019, and crime rates in the states are positively and significantly correlated with rising inequality in income. There, thus, needs to be policies related to reduction of crime against women and reduction of income inequality. The study thus suggests the interventions of the legislative system, government tax policies toward the rich , public awareness programs, etc. to reduce violence against women. Chapter 23 focusses on gender inequalities and climate change which are global problems that concern the whole world. Disruptions in natural life, usually due to human activities, lead to climate change over time. Climate change, on the other hand, deepens the already existing gender inequalities. Problems such as water scarcity, natural disasters, lack of access to clean water, and energy shortages are gender-responsive issues that affect women and men in different ways. All these factors cause women to be in an even more disadvantageous position against climate change. One of the policy tools of states in the face of this problem is fiscal solutions. As a fiscal policy tool, government budgets can be used to eliminate the negative effects of climate change on women. This is called gender responsive climate budgeting (GRCB) in the literature. In order to apply GRCB, firstly sex-disaggregated data are required. In addition, institutional structures should be strengthened and strategic plans should be designed in a way that establishes the link between gender and climate change. This process should be carried out in a multistakeholder manner and the resources allocated for the financing of the problems should gain a gender-responsive structure. The purpose of the book is to raise awareness among stakeholders and institutions, reaching gender equality and inclusiveness, and ensure equal rights for all.

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We hope that the book will provide an immense knowledge base to undergraduate and postgraduate students, researchers, and faculty members around the globe related to various subjects such as Economics, Business & Management, Education, Sociology, and Gender Studies, among others. The volume will also be helpful for policymakers, multinationals, and government officials as well as the general readers interested in the field. Chandrima Chakraborty Dipyaman Pal The Editors

Section I Implications of Gender Inequality on Education and Health

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Chapter 1

Adverse Child Sex Ratio in India: The Role of Women’s Agency, an Empirical Analysis Antara Bhattacharyya and Sushil Kr. Haldar

Abstract Over the decades, the child sex ratio (CSR) is found to be declining in India. Declining CSR has been one of the biggest social problems in India; the problem is assumed to be deep-rooted because economic growth or social progress fails to correct the adverse CSR in India. The proposed research tries to evaluate the impact of women’s agency along with some affirmative actions (toward empowering women) on CSR in India. The role of women’s agency is assumed to be significant toward correcting the adverse CSR. However, it is confined to two variables like female literacy rate (FLR) and female work force participation rate (FWFPR). Women agency should take into account women’s ability to make effective choices and to transform those choices into desired outcomes. Therefore, in order to explore the effect of some affirmative actions in explaining the variations of CSR across the states in India, three popular schemes, namely, Self Help Group (SHG), Rashtriya Mahila Kosh (RMK), and Kishori Shakti Yojana (KSY), are used in the present analysis. Pooled regression shows that FWFPR has a positive impact on CSR but FLR has a nonlinear relationship with the CSR. It is found that the SHG has positive but the KSY has negative effect on CSR; the other variable like RMK does not play any significant role toward variations of CSR. States showing higher concentration of ST population are found to be conducive to favorable CSR compared to SC population. Per capita net state domestic product (PCNSDP) has a similar effect like FLR. This study also finds significant discriminating role (against female child) of major states compared to minor states and UTs. Therefore, the role of women’s agency toward improving CSR needs to be highlighted more profoundly in Indian context.

Gender Inequality and its Implications on Education and Health, 11–22 Copyright © 2023 Antara Bhattacharyya and Sushil Kr. Haldar Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231002

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Antara Bhattacharyya and Sushil Kr. Haldar Keywords: Child sex ratio; gender; discrimination; female literacy rate; female work force participation rate; women’s agency JEL Classification: D63; J16; J71

1. Background Human beings are the means and ends in all development process. Initially in the human development report (HDR) published by the United Nations Development Programme (UNDP), the issue of gender was not incorporated, but in 1993–1994 the gender issues are included, as a result the successive HDRs published the Gender Development Index (GDI) and Gender Empowerment Measure (GEM). How far and to what extent is the position of females in different spheres of life studied in GDI and GEM? In this related field, the most inhuman tragedy in social life is the “missing female” observed among the developing as well as underdeveloped nations (Sen, 1990). Physiologically, female survival rate is found to be higher than the male; as a result, in some of the developed countries where gender bias is low and even nil, the child sex ratio (CSR) of female is found to be higher than that of male (Klasen & Wink, 2003). Recent Census Reports in India give us that the overall sex ratio (OSR) is low and it is found to be higher than the CSR. Therefore, the discrimination between male and female starts at a very early stage of life and it is reflected by the declining trend of CSR in India. This is socially generated which goes against the physiological norms. The trend of OSR and CSR is shown in Fig. 1.1 which clearly manifests the missing female argument at an early stage of life. It is evident from the fact that India overtime did experience a spectacular economic growth as well as growth of social development. But the emerging issue is that, this kind of growth could not bring equality between male and female in respect of survival, literacy rate, enrollment rate at primary, secondary and tertiary level, labor force participation rate, etc. Moreover, it is noticed that India at the aggregate level has been witnessing a continuous decline of CSR, and this decline is space dependent (Bhattacharyya & Haldar, 2019). The Government of India has launched various developmental programs especially for women during the last two decades, but such affirmative actions toward development of female could not improve gender position in India; for example, if we look into the CSR for two time points, it is noticed that CSR has declined from 927 (in 2001) to 914 (in 2011). Indeed it’s a great social problem which is deep-rooted where simple economic growth or social progress like reduction of mortality, increase in life expectancy, increase in female literacy rate (FLR), or enrollment etc. could not improve the CSR from 2001 to 2011. Even over the decades crimes against women have increased, and it is also spatial autocorrelated (Bhattacharyya, Haldar, & Banerjee, 2021) In order to improve the position of female in our patriarchal society, the Government of India has launched various development programs. Here, some of the programs and policies are mentioned, and it is expected that these programs could be an effective instrument toward female

Child Sex Ratio in India

Fig. 1.1.

13

Trend of OSR and CSR in India: 1951–2011. Source: Author’s Estimation, Basic Data Census.

empowerment. Even decline in wage gap between both the sexes increased economic opportunity for female (Bhattacharyya & Haldar, 2020). Among the various schemes, Kishori Shakti Yojana (KSY) is one of the important schemes. In this development program, the problem of nutritional and health of adolescent girls is addressed. This program is also very crucial to make these girls aware about their social strata and environment to become self-sufficient. It is evident that the early marriage causes to increase the risk of both maternal and child death. This program is also a part of the ICDS program; hence, KSY is a very important scheme. It may have some positive impact on CSR. The Government of India had promoted a novel and most innovative institutional setup, namely, Self Help Group (SHG), in order to empower women. This organizational setup mainly focused on providing training and other developmental skills to uplift women to build leadership, management, and administrative quality. So it may be possible that, enhancement of empowerment of women do have any positive influence on CSR. The Government of India established the RMK in 1993, to serve the needs of poor and asset-less women belonging to the informal sector. It has taken into consideration for popularizing thrift and credit as well as microfinance. This also helps to stabilize SHG by providing adequate required fund. This may help to improve CSR.

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Antara Bhattacharyya and Sushil Kr. Haldar

2. Review of Literature The empirical literature related to declining child (0–6) sex ratio and its determinants and some findings are as follows:

2.1 Effect of Female Literacy Rate on Variation of CSR Dasgupta (1995) and Das Gupta (1987) concluded that there were families who apply death by neglect before the fourth born daughter and found a positive bivariate association between antifemale bias and maternal education. Dasgupta and Mari Bhat (1995) explained that educated mothers have lower fertility, which tends to be accompanied by higher gender bias. Bourne and Walker (1991) attempted to assess the relationship between maternal education and gender bias in child survival generally negative, but possibly positive in South India. Amin (1990) argued the relationship between maternal education and gender bias in child survival may be negative in the case of first daughter but positive for higher-parity daughter. Simmons, Smucker, Bernstein, and Jensen (1982) showed that the relationship between maternal education and gender bias in child survival is basically negative. Chen, Huq, and D’Souza (1981), Sen and Sengupta (1983), and Caldwell, Reddy, and Caldwell (1989) contextualized no simple relationship between the maternal education and gender bias. Simmons et al. (1982) showed that the relationship between maternal education and gender bias in child survival is basically negative. Chen et al. (1981), Sen and Sengupta (1983), and Caldwell et al. (1989) contextualized no simple relationship between the maternal education and gender bias. Citing Census Reports of 2001, Patel (2002) had observed the deterioration in the CSR in Kerala; low levels of fertility could mean that proportion of childless women or that of couples with an only child increases and with higher levels of education and access to technology, it is possible that such couples would want to ensure that the one child they have is male. Higher female literacy reduces child mortality and antifemale bias in child survival independently of male literacy (Dreze & Murthi, 2001). Chakraborty and Sinha (2006) have examined the monotonic decline in CSR over the past four decades and they assess that improving literacy is necessary but not sufficient for improving the CSR. Female literacy rate has no significant effect on CSR in the random effect estimate of panel data regression (Bhattacharya & Saxena, 2015).

2.2 Effect of Female Work Force Participation Rate on Variation of CSR Rosenzweig and Schultz (1982) and Kishor (1993) have confirmed the hypothesis that FWFPR tends to be associated with lower levels of female disadvantage in child survival. FWFPR reduces the extent of gender bias in child survival and this is statistically significant (Dreze & Murthi, 2001). But Bhattacharyya et al. (2015) could not find any significant effect on CSR in the random effect model.

Child Sex Ratio in India

15

3. Research Gap and Scope of the Present Study • In the State level panel study, only 15 major states were undertaken while the

rest of other minor states and UTs are not included. • None of the studies consider the role of affirmative actions toward improve-

ment of female child advantage. • Only two variables, namely, FLR and female work force participation rate

(FWFPR) were introduced as Women’s Agency in different regression models but the impact of SHG, KSY, and Rashtriya Mahila Kosh (RMK) on CSR were not studied earlier. • None of the studies consider the impact of smaller/minor states and UTs in pooling data regression. Major Research Questions: (1) How do affirmative actions and policies toward development of women across states influence CSR? (2) How far and to what extent the variations of CSR are explained by the economic, demographic, social, and cultural factors?

4. Data, Data Source, and Research Methods 4.1 Data and Data Source In our present study, data are collected from secondary sources. This is mentioned as follows: Data on CSR, OSR, urbanization (URB), proportion of Scheduled Caste (SC) and Scheduled Tribe (ST) population, FLR, male literacy rate (MLR), FWFPR are drawn from various Census Reports. Data of SHG, RMK, and KSY are drawn from the Ministry of Women and Child Development, Government of India. Per capita net state domestic products (PCNSDP) are drawn from the website of the Reserve Bank of India (RBI). In order to capture the incidence of poverty, we have considered head count ratio (HCR) from different survey periods of NSSO. It is to be noted here that in 2009–2010, we have used Tendulkar’s estimation of poverty. We do not have data pertaining to HCR for the following states/UT: Sikkim, Mizoram, Daman and Diu. We have used the HCR values of North Eastern States in order to estimate HCR of Sikkim and Mizoram as it was done by the Planning Commission, Government of India.

4.2 Research Methods In order to study the effect of affirmative actions toward improvement of women, we consider pooled regression. Since data on SHG and RMK are limited only for two time points (viz. 2001 and 2011) across states, a pooled regression is

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Antara Bhattacharyya and Sushil Kr. Haldar

performed; in the same way, data on KSY are only available for two time points (viz. 1991 and 2001), hence another pooled regression is to be estimated.

4.3 Descriptive Statistics of the Variables Like KSY, SHG, and RMK The mean, range, and standard deviation of the variables like KSY, SHG, and RMK are shown in Table 1.1. The mean KSY has increased from 14.9 in 1991 to 38 in 2001; but the data on KSY were not available in 2011. This is why we are not in a position to comment about the recent growth of KSY in India across states. The standard deviation of KSY has increased from 21.1 in 1991 to 26.8 in 2001. The mean number of SHGs per 10,000 females has declined from 12.4 in 2001 to 10.56 in 2011, but its standard deviation is more or less fixed. The mean of disbursement of funds per 1,000 females has increased from Rs. 322.2 to Rs. 7,218.1 in 2011. The standard deviation of RMK has increased from 712.7 in 2001 to 7,381.6 in 2011. This means that there exists a lot of diversity among the states in respect of RMK.

5. Econometric Estimation Most of the studies consider two variables like FLR and FWFPR in regression models. But many studies have revealed the fact that only making the women more literate does not necessarily empower them in respect of decision-making, especially female advantage in child survival. We have examined here the effect of some social schemes launched by India on women empowerment which ensure they take any kind of positive decision toward the improvement of CSR. By the term “women agency” we mean woman’s ability to make effective choices and to transform those choices into desired outcomes. Agency can be understood as the process through which women use their endowments and take advantage of economic opportunities to achieve desired outcomes. Thus, agency is the key to understanding how gender outcomes emerge and why they are equal or unequal. Across all states women and men differ in their ability to make effective choices in a range of spheres, with women typically at a disadvantageous position. In order to find out the determinants of CSR, the following pooling regression model is considered: CSRit ¼ b9 Xit 1 hi 1 «it

(1.1)

Xit 5 vector of the explanatory variables, hi 5 captures the space effect, «it stands for white noise term. Here, i 5 1, 2, 3,. . .. . ., 32 and t 5 1, 2. Due to multicollinearity of the explanatory variables, we have formulated different models. Moreover, in order to overcome the problem of heteroscedasticity, robust standard error is taken in all the models.

Table 1.1. Variable Definitions, Sample Means, and Range (Standard Deviation in Parentheses), 1991, 2001, and 2011. 1991

2001

Range Variable

KSY SHG

Mean Max Min

Percentage of number of blocks coverage

14.9 100 (21.1) (number of SHG/total no. of females) 3 10,000 – –

Total disbursement of funds per 1,000 number of females

–

–

Range Mean

Max Min

Range Mean

Max

Min

1.44 38 100 1.68 – – – (26.8) – 12.4 68.48 0 10.56 63.7 0 (14.1) (14.9) – 322.2 3,079 0 7,218.1 22,653 7,381.6 (712.7) (7,381.6)

Source: Author’s representation, Basic data, Ministry of Women and Child Development.

Child Sex Ratio in India

RMK

Definition

2011

17

Explanatory Variables

lnSC lnST lnPCNSDP (lnPCNSDP)2

Model 2

Model 3

Model 4

Model 5

Model 6

20.0039*** (0.001) 0.0041*** (0.0012) 20.1696** (0.0735) 0.0103** (0.0045)

20.0039*** (0.001) 0.0041*** (0.0012)

20.0047*** (0.0018) 0.006*** (0.0013) 20.0871* (0.0492) 0.0052* (0.002)

20.0047*** (0.0018) 0.006*** (0.0013)

20.0057 (0.0039) 0.005** (0.002) 20.198* (0.1141) 0.012* (0.007)

20.0057 (0.0039) 0.005** (0.002)

20.0332 (0.0281) 0.0108** (0.005)

(lnPOV)2 0.0271*** (0.0080)

lnFLR (lnFLR)2 lnKSY

RMK

Model 1

lnPOV

lnFWFPR

SHG

20.0115*** (0.0038)

20.0044 (0.0181) 0.0056 (0.004) 0.0504*** (0.0169)

20.1034 (0.1937) 0.0148 (0.0255) 20.0085*** (0.002)

20.0081 (0.039) 0.0068 (0.0083) 0.0524* (0.033)

22.011*** (0.8668) 0.252** (0.1080)

22.197** (0.966) 0.277** (0.119)

Antara Bhattacharyya and Sushil Kr. Haldar

KSY

18

Table 1.2. Role of KSY, SHG, RMK, and Other SocioEconomic Variables on CSR: Pooled Regression; Dependent Variable: lnCSR.

lnSHG

0.0041** (0.0019)

0.0044*** (0.001)

lnRMK D1

20.0163 (0.0219)

20.0214 (0.0152)

20.0334 (0.024) 11.11*** (1.93) 42 2.62 0.024 0.46 0.0415

7.0419*** (0.3690) 64 8.90 0.000 0.6449 0.02836

7.0205*** (0.2153) 64 3.25 0.0082 0.3555 0.0467

10.791*** (1.721) 64 9.84 0.000 0.5735 0.03868

Source: Author’s estimation; Robust standard errors are within the parentheses; (***), (**) and (*) represent 1%, 5% and 10% level of significance respectively.

Child Sex Ratio in India

No. of obs.5 F(6,57)5 F(8,55)5 PROB . F5 R-SQ5 Root MSE5

0.013 (0.015) 7.43*** (0.3866) 42 1.61 0.1735 0.2708 0.05095

20.04369*** (0.001)

D3 7.47*** (0.0308) 64 7.65 0.000 0.4238 0.0354

0.0008 (0.001)

20.0355*** (0.009)

D2

Constant

0.007 (0.001)

19

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Antara Bhattacharyya and Sushil Kr. Haldar

5.1 Effect of KSY, SHG, and RMK on CSR Here 32 states and UTs are being considered for two time points: 1991 and 2001. In order to capture the effect of KSY on CSR, we have considered two models as shown in Models 1 and 2. The variables like percentage of SC, ST population, PCNSDP, POV, URB, FLR, FWFPR, and dummy variable are considered. Here we consider nonlinear form of PCNSDP, Poverty, and FLR. Dummy variable (D1) is used for 15 major states. In Table 1.2, the role of KSY and other socioeconomic variables on CSR have been reported. In Model 1, ST and FWFPR have a positive impact on CSR; SC and KSY have a negative impact on CSR. Income measured by PCNSDP has a nonlinear U-shaped relationship with CSR. It is surprising to note that the FLR does not appear to be significant. Here 32 states and UTs are being considered for two time points: 2001 and 2011. In order to capture the effect of SHG on CSR, we have considered two models like Models 3 and 4 (as shown in Table 1.2). It is really an interesting fact to be noted that the SHG has been exerted as a powerful tool toward empowering women. The SHG helps toward improvement of CSR in the regression model (as shown in Models 3 and 4). Here major states have a negative impact on CSR. Due to unavailability of data on RMK, here only 21 states and UTs are being considered for two time points: 2001 and 2011. To capture the effect the RMK on CSR, we have considered two models as shown in Table 1.2. In Table 1.2, the role of RMK and other socioeconomic variables on CSR have been reported. In both the models (Models 5 and 6), RMK does not appear significant.

6. Conclusion and Policy Implication The analysis clearly reveals that a continuous decline in CSR is observed over time. This has been a serious social concern, and it also reveals that gender discrimination is acute in Northern States. Severity of imbalance of CSR is noticed among the six states, namely, Punjab, Haryana, Gujarat, Rajasthan, Uttar Pradesh, and Maharashtra. Therefore, profemale development programs such as SHG etc. are suggested to be strengthened among these states especially. Sociocultural discriminating attitude against women is deep-rooted and it varies across regions, thus increase in income or FLR are unable to rectify these social imbalance but women’s agencies like SHG and FWFPR play a significant role toward improving CSR. It clearly provides evidence of using medical technology in the recent past in determining the sex of the fetus and its removal if detected as female. It is a violation of the Pre-Natal Diagnostic Test (PNDT) Act, 1996, in various parts of the country. This practice is to be stopped and some stringent measure is suggested to be implemented along with elimination of dowry. Therefore, at one front, the law enforcement agencies and medical fraternity are required to work together to abolish such evil choices, which are criminal offenses. While on the other front, the society as a whole should work for women education, employment, empowerment, their rights and representation within and beyond the household activity. The role of SHG and other Government actions like Save the

Child Sex Ratio in India

21

Girl Child and Balika Samridhi Yoyana should be encouraged and implemented properly. In addition, provisions of incentives in case of bearing a girl child would not influence positively unless such incentives are substantial and long term. In brief, preference for a son must be eliminated completely from our patriarchal society; otherwise, current declining CSR may create several sociocultural and demographic challenges.

References Amin, S. (1990). The effect of women’s status on sex differentials in infant and child mortality in South Asia. Genus, 46(3–4), 55–69. Bhattacharya, P. C., & Saxena, V. (2015). Socio-economic determinants of child and child sex ratios in India: A longitudinal analysis with district-level data. Working Paper, No. 2015-03, January 2015. Heriot-Watt University, Department of Economics, Edinburgh, EH14 4AS. Bhattacharyya, A., & Haldar, S. K. (2019). Socio-economic development and child sex ratio in India: Revisiting the debate using spatial panel data regression. Journal of Social and Economic Development. doi:10.1007/s40847-019-00089-7 Bhattacharyya, A., & Haldar, S. K. (2020). Does U feminisation work in female labour force participation rate? India: A case study. Indian Journal of Labour Economics, 63, 143–160. doi:10.1007/s41027-019-00202-8 Bhattacharyya, A., Haldar, S., & Banerjee, S. (2021). Determinants of crime against women in India: A spatial panel data regression analysis. Millennial Asia, 13, 1–31. doi:10.1177/09763996211003379 Bourne, K., & Walker, G. M. (1991). The differential effect of mothers’ education on mortality of boys and girls in India. Population Studies, 45(2), 203–219. Caldwell, J. C., Reddy, P. H., & Caldwell, P. (1989). The causes of demographic change. Madison, WI: University of Wisconsin Press. Census Reports. (2001). Registrar General–Government of India, New Delhi. Chakraborty, L. S., & Sinha, D. (2006). Determinants of declining child sex ratio in India: An empirical investigation. MPRA Paper 7602. University of Munich. Chen, L. C., Huq, E., & D’Souza, S. (1981). Sex bias in the family allocation of food and health care in rural Bangladesh. Population and Development Review, 7(1), 55–70. Das Gupta, M. (1987). Selective discrimination against female children in rural Punjab, India. Population and Development Review, 13, 77–100. Dasgupta, M. (1995). Life course perspectives on women’s autonomy and health outcomes. American Anththropologist, 97(3), 481–491. Dasgupta, M., & Mari Bhat, P. N. (1995). Intensified gender bias in India: A consequence of fertility decline. Working paper No. 95.02. Harvard Center for Population and Development Studies. Dreze, J., & Murthi, M. (2001, March). Fertility, education, and development: Evidence from India. Population and Development Review, 27(1), 33–63. Kishor, S. (1993). “May God give sons to all”: Gender and child mortality in India. American Sociological Review, 58, 247–265. Klasen, S., & Wink, C. (2003). Missing women: Revisiting the debate. Feminist Economics, 9(2–3), 263–299.

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Patel, V. (2002). Women´ıs challenges of the new millennium (p. 70). Delhi: Gyan Books. Rosenzweig, M. R., & Schultz, T. P. (1982). Market opportunities, genetic endowments, and intrafamily resource distribution: Child survival in rural India. The American Economic Review, 72, 803–815. Sen, A. (1990). More than 100 million women are missing. New York Review of Books, 37(20). Sen, A., & Sengupta, S. (1983). Malnutrition of rural Indian children and the sex bias. Economic and Political Weekly, 18. Annual Number. Simmons, G. B., Smucker, C., Bernstein, S., & Jensen, E. (1982). Post-neonatal mortality in rural India: Implications of an economic model. Demography, 19(3), 371–389.

Chapter 2

Do Government Expenditures on Education and Health Reduce Gender Inequality? The Case of the Least Developed and Developing Countries Gizem Kaya Aydin

Abstract Despite the rapid progress and developments in education and health all over the world, gender inequality is still an important issue in many parts of the world. Girls benefit less from education opportunities than boys, and it causes gender inequality. The same situation is also valid for health. While gender inequality is still an issue even in developed countries, it is more serious in the least developed and developing countries. Therefore, there is a need to reduce gender inequality through government intervention. The aim of this study is to examine the effect of government’s health and education expenditures on gender inequality in the least developed and developing countries with panel data analysis. The study covers 24 countries for the 2010–2017 period. As a result of the analysis, it has been observed that the government’s health expenditures reduce gender inequality, while education expenditures increase gender inequality. This finding indicates that education expenditures of governments do not reach girls in the least developed and developing countries. However, GDP per capita is the most important factor in reducing gender inequality. Keywords: Education expenditures; health expenditures; gender inequality; government expenditures; least developed countries; developing countries

1. Introduction Health and education expenditures are very important for the welfare and development of individuals, as they are human investments. With this way, the Gender Inequality and its Implications on Education and Health, 23–30 Copyright © 2023 Gizem Kaya Aydin Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231003

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welfare and development of individuals play an important role in the development process of any country. In some of the developing countries and most of the least developed countries, certain problems may be experienced in the field of education and health. In this case, people living in these countries cannot access education and health opportunities. These countries usually are not able to allocate sufficient expenditure on health and education due to the lack of resources. Low health expenditures cause difficulty in accessing basic nutrients and cause individuals to be unhealthy. On the other hand, low education expenditures can reduce human development in any country. These two situations cause a decrease in the qualified workforce of the countries in the long run and affects the development of the countries. The education and health system has an important role in overcoming gender stereotypes. It is observed that equality of opportunity cannot be provided for women in terms of participation in education, working life, politics, and decision-making mechanisms, which are the basic areas of life in developing and the least developed countries. Existing ideas of traditional gender roles also pose a significant barrier. Traditional norms and values regarding women’s roles keep girls out of school. Another obstacle is the low level of income. Households with low income face difficulties in sending their children to school, and the preference for education is for boys. Eliminating this gender gap in the field of education and strengthening the education level of girls contribute to the development and welfare of women (Kızıgol, 2012). In this study, the effect of education and health expenditures on gender inequality in the period of 2010–2017 for 24 developing and least developed countries was examined by using panel data methodologies. In the second section, the literature review of the studies on the impact of education and health expenditures on development is given. In the following sections, data and analysis results are presented by using panel data methodology. Lastly, according to the results of econometric analysis, the findings were evaluated, and suggestions were presented.

2. Literature Review According to many studies in the field of health, the main factors that determine the level of benefiting from health services are employment status, income, education level, and whether being insured or not (Payne, 2006). However, the effect of these factors is not independent from gender. S¸avran (2014) states that, in underdeveloped countries where health services are based on out-of-pocket expenses, women have less access to health services than men. This low level of access is due to limited access to household economic resources, and, on the other hand, gendered barriers such as the prohibition of women on public transport alone in some developing countries or the requirement for women to obtain permission from men in their households to be examined or treated. Using a longitudinal survey of rural cancer patients in India, Batra, Gupta, and Mukhopadhyay (2014) found that expenditure on women adults was significantly less than on men. Other variables, especially when controlling for cancer type, emphasized that 73% of the difference was attributed to gender

Expenditures on Education and Health

25

discrimination. In addition, they attribute the biggest reason for the difference in expenditure to gender discrimination in seeking treatment and medical expenditures. Mondal and Dubey (2020) determined how much of the gender difference between age groups is explained by characteristic of the patients (endowment effect) and how much is not explained by factors that can be seen (coefficient effect) by using the data of the Health Survey of India which is conducted in 2014. The coefficient effect, which is responsible for over 50% of the total gender disparity in hospitalization costs across all age groups, suggests that there is a strong gender bias in favor of women. In terms of education, Wongmonta and Glewwe (2017) examined the causes and effects of gender inequality in Thailand’s household allocation of education spending. The findings indicated that households spend more on girls’ schooling than on boys’; girls also provide their parents a larger share of their income than do boys because parents view girls as the foundation of their later years. By using the household data of Indonesia, Salam, Majid, Dawood, and Suriani (2021) found a gender inequality in education spending, which tends to be more toward girls more than boys. According to Stash and Hannum (2001), there is a sizable gender gap in elementary school enrollment rates. They discover, using information from the 1991 Nepal Fertility, Family Planning, and Health Survey, that the educational level of household heads and rural–urban households had no bearing on the percentages of girls who attended school. They draw the conclusion that discriminatory educational outcomes were rarely affected by traditional metrics of development. According to the results of three decomposition techniques, by using the data of Nepal Living Standards Survey, Khanal (2018) indicated that gender discrimination in education expenditure has increased over time, and households in the lowest and highest income quintiles are the households most exposed to discrimination between boys and girls. In terms of high-income countries, Khan et al. (2017) examined the effect of gender discrimination in health and education on economic growth by using the 20 high-income OECD countries for the period 1980–2015. They suggested that the health and labor markets need considerable policy changes to minimize health and labor market inequities to ensure long-term economic growth, since the gender equality index for educational success greatly helps economic growth.

3. Data and Methodology This study aims to examine the effect of government’s health and education expenditures on gender inequality in the least developed and developing countries with panel data analysis. It covers 24 countries1 for the 2010–2017 period. Government’s education expenditure in GDP, government’s health expenditure in GDP, and GDP per capita (2017 PPP $) are determined as independent variables. The dependent variable is the gender inequality index (GII). All these variables are drawn from the World Bank’s World Development Indicators database. The government’s spending on education (% of GDP) includes all current, capital, and transfer expenses on education. However, health expenditure (% of GDP) reveals

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Gizem Kaya Aydin

the explicit amount spent on healthcare products and services. It does not include capital health investments in things like infrastructure, equipment, IT, and emergency vaccination supplies. Last but not least, GDP per capita (2017 PPP $) compares a period’s GDP to that period’s total population. The dependent variable, the GII, is a composite metric that captures the accomplishment gap between men and women in three areas: labor market participation, empowerment, and reproductive health (HDR, 2019). It demonstrates the lost opportunity for human development brought on by the disparity between male and female accomplishments in various fields. The scale goes from 0, when men and women perform equally to 1, where one gender performs as poorly as possible across the board. The regression equation used in the study is shown below: c0 1 b c1 LnðEducÞ 1 b c2 LnðHealthÞ 1 b c3 LnðGDPÞ 1 uit LnðGGIÞit ¼ b it it it

(2.1)

Here, Ln(GGI) represents the dependent variable, logarithm form of the GII. LnðEducÞ shows logarithm form of the government’s spending on education (% of GDP), LnðGDPÞ shows logarithm form of the GDP per capita, and LnðHealthÞ shows the logarithm form of government health expenditure (% of GDP). Since the data are panel data and the time dimension is short (T 5 8), it is preferred to use basic panel data models to estimate Eq. (2.1). Whether the effects are two-way or one-way, or whether these effects are individual-specific or time-specific effects were examined with the help of appropriate statistical tests.

4. Results In Table 2.1, descriptive statistics of the variables used in the modeling are given. The average value of the GII is obtained as 0.46. This value varies between 0.13 and 0.75 in countries. The government expenditures on education to GDP is around 4.31% on average. The maximum value of it is equal to 7.40%. The rate of health expenditures in GDP is 6.27 on average. The maximum value of it is equal to 11.80. GDP per capita is around $8,065 on average. The lowest is around $774, whereas the highest is around $21,415. As expected, these results show that there is a heterogeneity in the dataset which covers least developed and developing countries. After this step, the regression model is estimated by using panel data methodologies. First of all, models with individual-specific fixed and random effects

Table 2.1. Descriptive Statistics. Mean

GII Education Health GDP N 5 192

0.46 4.31 6.27 8,085.79 n 5 24

Source: Author’s calculation.

Standard Deviation

Min

Max

0.12 1.39 1.93 5,631.15 T58

0.13 1.50 2.30 774

0.75 7.40 11.80 21,415

27

Expenditures on Education and Health

are estimated. Afterward, fixed time-specific effects are added into these models. According to F test results, the coefficients of year dummies are found as significant (p: 0.00 , 0.05). On the other hand, as a result of the Hausman test, it is found appropriate to use the model with random effect (chi2(10): 2.79, p: 0.99 . 0.05). Diagnostic tests are also applied to check if there is any problem in the model or not. Levene (1960) and Brown and Forsythe (1974) test shows that there is a heteroscedasticity problem in the model (p: 0.00 , 0.05). Moreover, according to the results of the LM test, the autocorrelation problem is found as significant (p: 0.00 , 0.05). Contrary to these, Friedman test result shows that there is no cross-sectional dependency (p: 1.00 . 0.05). For this reason, Arellano (1987), Froot (1989), and Rogers (1994) robust standard error estimator is used against heteroscedasticity and autocorrelation problems in the model. Model results are presented in Table 2.2.

Table 2.2. Regression Results. Random-Effects GLS Regression

R2 within: 0.55 between: 0.36 overall: 0.36 corr(ui, X): 0 (assumed)

Dependent Variable: Ln(GII)

Ln(Educ) Ln(Health) Ln(GDP) 2011 2012 2013 2014 2015 2016 2017 Constant Source: Author’s estimation.

Coefficients Robust Standard Errors

0.070 20.090 20.188 20.007 20.022 20.033 20.035 20.034 20.052 20.062 0.910

0.027 0.039 0.078 0.004 0.008 0.007 0.008 0.011 0.008 0.010 0.701

Number of Obs: Number of groups:

192

Min: Avg: Max: Wald chi2(10): Prob . chi2:

8 8 8 203.3

Z

P> jzj

2.64 22.29 22.42 21.87 22.94 24.65 24.19 23.17 26.19 26.25 1.30

0.008 0.022 0.015 0.061 0.003 0.000 0.000 0.002 0.000 0.000 0.194

24

0.00

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According to the results of Table 2.2, when the share of the governments education expenditures in GDP increases, the GII increases significantly (p: 0.008 , 0.05). As the share of health expenditures in GDP increases, the GII decreases (p: 0.022 , 0.05). Similarly, the increase in GDP per capita reduces the GII (p: 0.015 , 0.05). When the coefficients of the year dummy variables are examined, it can be observed that the GII decreases on average compared to 2010. This shows a decrease in gender inequality over the years. These findings indicate that education expenditures of governments do not reach girls in the least developed and developing countries. However, GDP per capita and health expenditure in GDP are the important factors in reducing gender inequality.

5. Conclusion Today, although the resources and possibilities of learning and acquiring knowledge are increasingly diversified with the opportunities offered by technology, the school still maintains its feature as the most important institution that transforms knowledge into power and education obtained through school is one of the important indicators of development in the long run. However, socioeconomic factors such as poverty and unemployment, and cultural factors such as early marriage and pregnancy, violence against women, and traditional attitudes about the role and status of women create a disadvantaged situation. Globally, women make up two-thirds of the 758 million illiterate world population aged 15 and over and are underrepresented at all levels of education (UNESCO, 2016). In addition, the roles and standards that society places on “women” because of their gender ultimately prevent them from exercising certain rights. For example, women are more susceptible to sickness and poor health, while living longer (EU, 2022). Moreover, according to studies, women with college education live longer than those with less education (WHO, 2016). For instance, Masterson (2012) found a promale bias in Paraguayan household education spending. Significant gender differences in household educational spending also were reported in rural China by Li and Tsang (2003). For these reasons, education and health policies should not create more gender inequalities. To check this, this study examines the gender inequality effects of education and health expenditures by using data of 24 least developed and developing countries. As a result of the study, an increase in health expenditures reduces gender inequality, but an increase in education expenditures increases gender inequality. This finding indicates that education expenditures of governments do not reach girls in the least developed and developing countries. However, GDP per capita one of the most important factors in reducing gender inequality. For these reasons, governments should give importance to education expenditures that ensure gender equality in these countries, and inspections regarding this should be tightened. Girls should be encouraged to read. This study can be repeated by adding other variables or with more country data for future studies.

Expenditures on Education and Health

29

Note 1. The data cover these countries: Afghanistan, Armenia, Belarus, Burundi, ˆ d’Ivoire, El Salvador, Guatemala, Iran Cameroon, Colombia, Costa Rica, Cote (Islamic Republic of), Jamaica, Kenya, Kyrgyzstan, Mauritius, Mongolia, Pakistan, Peru, Rwanda, Senegal, South Africa, Sri Lanka, Togo, and Uganda.

References Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics & Statistics, 49(4), 431–434. Batra, A., Gupta, I., & Mukhopadhyay, A. (2014). Does discrimination drive gender differences in health expenditure on adults: Evidence from cancer patients in rural India. Indian Statistical Institute. Discussion Paper, (14-03). Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69(346), 364–367. EU. (2022). Gender Equality Index 2019. Retrieved from https://eige.europa.eu/ publications/gender-equality-index-2019-report/women-live-longer-poorer-health. Accessed on November 6, 2022. Froot, K. A. (1989). Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data. Journal of Financial and Quantitative Analysis, 24(3), 333–355. HDR. (2019). HDR technical notes. Retrieved from https://hdr.undp.org/sites/default/ files/data/2020/hdr2019_technical_notes.pdf. Accessed on November 6, 2022. Khanal, S. (2018). Gender discrimination in education expenditure in Nepal: Evidence from living standards surveys. Asian Development Review, 35(1), 155–174. Khan, H. U. R., Khan, A., Zaman, K., Nabi, A. A., Hishan, S. S., & Islam, T. (2017). Gender discrimination in education, health, and labour market: A voice for equality. Quality and Quantity, 51(5), 2245–2266. ¨ A. (2012). Turkiye’de ¨ ¨ Kızıgol, O. e˘gitimde cinsiyet es¸itsizli˘ginin yoksulluk uzerindeki etkisi. Y¨onetim ve Ekonomi Dergisi, 19(1), 179–191. Levene, H. (1960). Robust tests for equality of variances. In I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, & H. B. Mann (Eds.), Contributions to probability and statistics. Essays in honor of Harold Hotelling (pp. 278–292). Stanford, CA: Stanford University Press. Li, D., & Tsang, M. C. (2003). Household decisions and gender inequality in education in rural China. An International Journal, 1(02), 224–248. Masterson, T. (2012). An empirical analysis of gender bias in education spending in Paraguay. World Development, 40(3), 583–593. Mondal, B., & Dubey, J. D. (2020). Gender discrimination in health-care expenditure: An analysis across the age-groups with special focus on the elderly. Social Science & Medicine, 258, 113089. Payne, S. (2006). The health of men and women (p. 55). Cambridge: Polity Press. Rogers, W. (1994). Regression standard errors in clustered samples. Stata Technical Bulletin, 3(13). Salam, T. D. O., Majid, M. S. A., Dawood, T. C., & Suriani, S. (2021). The effect of gender and household education expenditure in Indonesia. International Journal of Quantitative Research and Modeling, 2(4), 184–192.

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S¸avran, T. G. (2014). Sa˘glıkta toplumsal cinsiyet es¸itsizlikleri: Eskis¸ehir’de kırsal ve kentsel alanlarda kadın sa˘glı˘gı. Fe Dergi, 6(1), 96–115. Stash, S., & Hannum, E. (2001). Who goes to school? Educational stratification by gender, caste, and ethnicity in Nepal. Comparative Education Review, 45(3), 354–378. UNESCO. (2016). Women’s and girls’ education. Retrieved from http://en.unesco.org/ themes/women-andgirls-education. Accessed on November 13, 2022. WHO. (2016). Women’s health and well-being in Europe: Beyond the mortality advantage. Denmark: World Health Organization. Regional Office for Europe. Wongmonta, S., & Glewwe, P. (2017). An analysis of gender differences in household education expenditure: The case of Thailand. Education Economics, 25(2), 183–204.

Chapter 3

Is There Any Relationship Between Gender Inequality and Nutrition? Experience From India Kavitha Kasala, Rudra Prosad Roy and Abhishek Das

Abstract Presently gender inequality and women’s nutrition are the most concerning area of any development policy. Recent empirical evidence emphasizes that gender inequality decreases over time and the on the other hand percentage of overweight (OW) and obesity for women, especially in developing countries, increases over time. However, the relationship between these two phenomena (gender inequality and obesity) has rarely been investigated. Using time series yearly data (1990–2016) from the Nutrition Landscape Information Systems (NLiS) database of World Health Organisation (WHO) for India, we apply standard time series analysis including break test, stationarity test, cointegration test, and vector error correction model (VECM) to estimate the relationship between gender inequalities and percentage of females OW and obese. Our results show that there is a long-run relationship between these variables. Moreover, we also find that a decrease in gender inequality influences the increase in the number of females under OW and obese. In conclusion, the findings of this study reveal that, while elevating the position of women in society may be an important step toward combating the epidemic of OW and obesity, strategies must also tackle unhealthy habits that promote obesity. Keywords: India; gender inequality; female overweight; female obesity; nutrition; cointegration

1. Introduction Women’s health and gender inequality are currently the two primary outcomes taken into consideration while designing and implementing development policy. It is impossible to accomplish SDG 2 and SDG 3—ensuring health and well-being Gender Inequality and its Implications on Education and Health, 31–41 Copyright © 2023 Kavitha Kasala, Rudra Prosad Roy and Abhishek Das Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231004

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for all—without addressing the unique obstacles and difficulties that women, men, girls, boys, and gender-diverse people experience. Globally, there are clear patterns of women having less access to opportunities and resources. Women are also consistently underrepresented in decision-making processes that influence their societies and their personal lives. Women and girls are disproportionately impacted by gender inequality. They are more likely to be subjected to aggression, coercion, and harmful practices because they typically have lesser status and less influence over decisions involving their bodies in their intimate relationships, families, and communities. The lack of decision-making autonomy, restricted access to finances, lower literacy rates, and discriminatory attitudes toward healthcare professionals are just a few of the barriers that gender inequality creates for women and girls in terms of their ability to access health information and essential services. The primary and possibly most important factor influencing nutrition outcomes was gender (Bener´ıa, 2012). The health and nutrition of every family member are significantly impacted by the nutritional condition of women and girls, particularly on their future generations. The dynamics of gender in families and communities frequently have a role in the factors that contribute to malnutrition. Therefore, gender dynamics must be considered and improved to alleviate malnutrition. Even though it is challenging, creating "gender-equity-sensitive” indicators are crucial to better understanding the causes of gender inequalities, evaluating policies, and tracking the development of nations (Beneria & Permanyer, 2010). Research has mostly concentrated on the usage of composite indices with this objective in mind as well as to capture a complicated multidimensional phenomenon. Researchers and policymakers now have access to several composite indicators of gender gaps as a result of extensive collective efforts (Van, 2013). Recently the United Nations Development Programme (UNDP) introduced the Gender Inequality Index (GII) in the 20th-anniversary edition of the Human Development Report in 2010 (Klugman, 2010). The GII is a composite index that reflects the disparity between men’s and women’s accomplishments in three areas: labor market participation, empowerment, and reproductive health. Based on this index, worse achievements are indicated by higher GII levels and vice versa. The double burden of malnutrition (DBM) has been identified as one of the most important contemporary global health challenges (Popkin, Adair, & Ng, 2012). The paradox is characterized by the coexistence of undernutrition along with overweight (OW) and obesity within individuals, households, and populations, across the life course. In recent decades, the prevalence of underweight among women has declined considerably in most of the low- and middle-income countries (LMICs), with many of these countries experiencing a more rapidly increasing prevalence of OW and obesity in women than previously thought (Mamun & Finlay, 2015). OW or obesity is usually measured as follows: (1) OW is a Body Mass Index (BMI) greater than or equal to 25 and (2) obesity is a BMI greater than or equal to 30.1 Despite extensive awareness about gender inequality and women’s OW and obesity over the past decade, the relation between these is still not yet fully explored. This is likely due to the complexity of these research areas with its

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various dimensions and the lack of composite outcome measures that may help researchers better understand how gender affects people who live with obesity (Ferretti & Mariani, 2017). Additionally, a complicated interplay between genetic predisposition, psychological makeup, and environmental factors determines female OW and obesity (Akabas, Lederman, & Moore, 2012; Kapoor et al., 2019). India, the second-most populous nation in the world, makes for an intriguing case study for examining gender disparities and nutritional outcomes. The purpose of this study is to measure gender inequality and its relationship to OW and obesity prevalence among women in India. Following this brief introduction, the subsequent section describes the methodology underpinning our analysis, followed by the next section on the results of the empirical estimations; in the next section we discuss our results. Finally, conclusions and policy recommendations are presented in the last section.

2. Database and Methodology The objective of the study is to check the existence of any long-term and short-term relationship between gender inequity and women’s malnutrition with a focus on OW and obesity. Therefore, in this present study, we use two time series, namely: the GII and the percentage of women obesity population. The GII data of India have been accessed from the Nutrition Landscape Information Systems (NLiS) database of WHO2 which is used as a composite measure, reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment, and the labor market. The GII varies between 0 (when women and men fare equally) and 1 (when men or women fare poorly compared to the other in all dimensions). Another indicator used was malnutrition in women which included the percentage of adult women under OW and obese (BMI .25 kg/m2 in women above 18 years) from the same data source as GII. The yearly data for GII and the percentage of OW women used in the analysis were between 1990 and 2016. Since the objective of the study is to check the existence of any long-run and short-run relationship between GII and the percentage of women obesity population, we choose to do a standard time series econometric analysis including a structural break test (Bai & Perron, 2003), stationarity test (Perron, 1997), cointegration test (Johansen, 1991), and vector error correction model (VECM) (Johansen, 1991).

3. Results Gender is the basic, and perhaps the most critical determinant of nutrition outcomes (Bener´ıa, 2012). Therefore, it is imperative to use the gender inequality lens to understand the trends of nutritional outcomes. In this section, we will present and discuss the results in two subparts: basic trends of gender inequality and OW women and the long-run relationship between the GII and OW women in India.

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3.1 The Trend in GII and the Population of Overweight Women The trend in GII (Panel A Fig. 3.1) has decreased over the years. This decrease in GII can be attributed to several factors such as increased income levels, enhancement in literacy, safety net programs, and women empowerment (Tisdell, 2019, 2021). Although India’s GII declined continuously, relative female participation in its labor force did not, but the participation declined overall. Also, gender inequality has many different dimensions such as distorted male–female ratios, the extent of gender inequality within families, the relative threat power of wives, which in turn is affected by their comparative entitlements, the controlling behaviors of men, and violence against women, especially intimate partner violence which cannot be captured by a single index such as GII (Tisdell, 2021). However, GII continues to be the global index in depicting the status of women and is thereby used in the present study for exploring the relationship between GII and obesity prevalence among Indian women. The trend of the percentage of women who have been OW (Panel B in Fig. 3.1) has been increasing indicating the shift toward the other side of malnutrition and has surpassed the global average (Luhar et al., 2020). This leads to noncommunicable diseases and hence affects the quality of life. Around 25% of the women population in India in the age group 15–49 is OW or obese, which has increased from the past survey estimates by almost two times. Although obesity or OW has been a problem in developed countries, the epidemic has also started to pose challenges in developing countries. In a developing country like India where the double burden of malnutrition and hunger persists, the increasing burden of OW or obesity may be questionable (Kennedy, 2006).

3.2 Long-Run Relationship Since the objective of the study is to investigate the existence of any long-run and short-run relationship between gender inequality and women’s nutritional status

Fig. 3.1. Trend of GII and % of Women Under OW Category in India. (A) Trend of GII in India. (B) Trend of % of Women Under the OW Category in India. Source: Author’s calculation.

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(percentage of women who are OW and obese—BMI .25 kg/m2), we chose to do the cointegration analysis. The cointegration analysis checks the presence of any long-run relationship between two economic variables. The error correction models, as a by-product of the cointegration analysis, examine the presence of any short-run relationship between the two variables. In other words, when the two time series variables move together and fluctuate around a long-run equilibrium, the existence of that long-run equilibrium is tested using the cointegration analysis. The VECM on the other hand describes the dynamic mechanism or how the variables adjust when disequilibrium occurs or when the variables deviate from the long-run equilibrium. The first step in the cointegration analysis is to check whether the variables are stationary. To check whether the variables are stationary or not, the unit root tests have been done. Perron (1990) argues that in the presence of a structural break, the standard stationarity test (ADF tests) is biased toward the nonrejection of the null hypothesis. Therefore, it is necessary to check whether there is any structural break in the series. We conducted the structural break test (Zivot & Andrews, 1992) and found that both of our series have multiple structural breaks. The GII has four break points in the years 1997, 2003, 2008, and 2012, whereas for OW we found five break points in the years 1995, 2000, 2004, 2008, and 2012. All the breakpoints for GII and OW are presented in Fig. 3.1. As there are breakpoints in both series, we used a unit root test with a breakpoint (Dickey–Fuller Min-t test). The null hypothesis of the test assumes that a unit root is present in the time series variable. For both the variables at levels, that is GII and OW, the null hypothesis is accepted. Whereas the same is found to be rejected when the first difference of the variables is considered. This indicates that the variables are integrated of order one or I(1). Results are shown in panel A of Table 3.1. Next, we move on to the cointegration tests. In literature, the two most common cointegration tests are the Engle–Granger Cointegration test and the Johansen Cointegration test (Johansen, 1991). The former considers that there is only one cointegrating vector between the variables considered. In other words, the procedure assumes that when the variables considered are cointegrated, the residual obtained from the cointegrating regression is stationary. On the contrary, the Johansen test methodology depends on the VECM to examine the existence of any cointegrating relationship between the variables. For our analysis, we have adopted the Johansen test procedure to determine the number of cointegrating relationships between the two variables considered, viz., GII and OW. The Johansen test methodology considers the trace test and the maximal eigenvalue test that assumes that in the VECM, the rank of the long-run impact matrix determines whether the variables considered in the VAR model are cointegrated or not. From panel B of Table 3.1, it can be seen that both the trace statistics and maximum eigenvalue statistics of the unrestricted cointegration rank test are suggestive of the existence of two cointegrating equations at the 5% statistical significance level. Therefore, it can be safely concluded that there exists a long-run relationship between GII and OW.

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Table 3.1. Analysis of GII and OW Using Time Series Modeling. Panel A: Test of Non-Stationarity

Panel C: VECM

Variable ADF Test Statistics GII at level -4.274353 GII at First Difference -6.096628*** OW at level -1.719301 OW at First Difference -8.517347*** Panel B: Johansen Cointegration test

INDIA_GII

INDIA_OW

INDIA_GII(-1)

0.524744 (0.27306) [1.92175]

-2.961370 (1.12485) [-2.63268]

INDIA_GII(-2)

-0.284568 (0.40845) [-0.69670]

-3.332244 (1.68260) [-1.98042]

INDIA_GII(-3)

-0.766849 (0.45988) [-1.66748]

-0.562022 (1.89448) [-0.29666]

INDIA_GII(-4)

0.221916 (0.43908) [0.50541]

1.174227 (1.80877) [0.64918]

INDIA_GII(-5)

0.071558 (0.31115) [0.22998]

-1.039944 (1.28177) [-0.81133]

INDIA_OW(-1)

-0.098620 (0.05445) [-1.81109]

-0.517981 (0.22432) [-2.30910]

INDIA_OW(-2)

0.006652 (0.04183) [0.15901]

-0.245380 (0.17234) [-1.42385]

INDIA_OW(-3)

-0.015335 (0.03594) [-0.42666]

0.384128 (0.14806) [2.59436]

Unrestricted Cointegration Rank Test (Trace) Hypothesized No. of CE(s)

Eigenvalue

None * At most 1 *

0.712306 0.385244

Trace Statistic 36.38016 10.21712

0.05 Critical Value

Prob.**

15.49471 3.841465

0.0000 0.0014

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized No. of CE(s)

Eigenvalue

Max-Eigen Statistic

0.05 Critical Value

Prob.**

INDIA_OW(-4)

None * At most 1 *

0.712306 0.385244

26.16304 10.21712

14.26460 3.841465

0.0004 0.0014

-3.50E-05 (0.04210) [-0.00083]

0.947417 (0.17344) [5.46252]

INDIA_OW(-5)

0.092574 (0.05848) [1.58305]

0.447839 (0.24090) [1.85902]

C

1.155989 (0.46513) [2.48531]

6.682152 (1.91609) [3.48739]

0.994183 0.988896 0.000362 0.005739 188.0133 89.94012 -7.176375 -6.630853 0.618727 0.054457

0.999967 0.999937 0.006147 0.023640 33261.33 58.79410 -4.344918 -3.799397 16.42727 2.975164

Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

R-squared Adj. R-squared Sum sq. resids S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent

Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion Number of coefficients

1.65E-08 4.13E-09 149.9118 -11.62835 -10.53731 22

Source: Author’s estimation.

Next, we turn to the VECM analysis mainly to check the nature of the short-run relationship between the two variables in consideration. The VECM differs from simple VAR as the former imposes an additional restriction on account of the existence of a nonstationary and cointegrated relationship between the two variables. The VECM utilizes the cointegration information obtained from the cointegration likelihood ratio tests into its specification. All the results are presented in panel C of Table 3.1. The error correction term of the first equation is found to be statistically significant and negative in sign, which implies that the relationship between GII and OW is stable. In other words, if a disequilibrium situation occurs in the short run, then the model can go back to the

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long-run equilibrium. However, considering up to the fourth lag of the variables, we do not find any short-run relationship between the two variables GII and OW as all the coefficients of lag values of variables are found to be statistically insignificant. Our results clearly show that the increase in the OW population is influenced by the decrease in GII.

4. Discussion The present study has thrown light into the long-run relationship between decreasing levels of GII and rising levels of OW and obesity among women in India. This indicates that an increase in women’s status in society and autonomy for any individual does not necessarily mean that their actions and decisions will be healthier; rather, improvement in GII negatively influences OW and obesity among women. Several factors trigger the female population toward OW which precisely comes with the improvement in the social status of women, especially in developing nations like India. We discuss our results with the help of the framework developed by Haddad, Cameron, and Barnett (2015). The framework provides three levels—immediate (health and biological factors); underlying (social and environmental factors); and basic (economic and political). The combination of these factors determines overnutrition. Interestingly most of the factors are the outcome of the increase in women’s status in society and autonomy. High intake of food energy (e.g., large consumption of sugar, salt, and saturated fats) coupled with low levels of physical activity and high amounts of sedentary behavior can result in a positive energy balance and excess weight (Swinburn et al., 2011). In this context, a higher social standing for women in urban areas leads to a bigger and more significant effect on weight increase. This is because urbanization is concurrently accompanied by the adoption of westernized habits and diets, such as eating more outside, greater availability of high-calorie processed foods, and increased attendance at fast-food restaurants and bars, all of which contribute to OW and obesity (Bishwajit, 2015; Popkin, 2006; Wells, Marphatia, Cole, & McCoy, 2012). On an underlying level, environments in which processed, inexpensive, energy-dense foods are widely available and promoted (such as through fast-food restaurants and retail chains) can lead to overconsumption. Social circumstances that encourage bad eating habits (such as high-calorie snacking) and sedentary job habits may also raise the risk of obesity (Popkin, 2006; Ramachandran, McCusker, Connors, Zuwallack, & Lahiri, 2008; Ramachandran, Snehalatha, & Vijay, 2002). Women are more involved in less labor-intensive activities that include sedentary lifestyles and work stress as their status in society and autonomy rises over time (Agrawal & Mishra, 2004; Kain, Vio, & Albala, 2003; Kibria et al., 2019). Empowerment of women resulting in more purchasing ability allows for getting everything to their doorstep without much movement and unhealthy dietary patterns which is reflected in the study result as well where the likelihood of becoming OW or obese is high among those who eat more fast foods (Bhurosy & Jeewon, 2014; Lear et al., 2014). Higher levels of empowerment would mitigate the effects of being

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exposed to an obesogenic environment by increasing the likelihood that women would exercise. However, in the case of India, gender norms are still deeply ingrained when it comes to women engaging in physical activity (Mathews, Lakshmi, Ravindran, Pratt, & Thankappan, 2016). As a consequence of this, Indian women are not encouraged by their culture to participate in any form of physical activity health-care services, and this leads women to neglect their bodies and aspirations; hence, empowering women in obesogenic (urban) situations may encourage the adoption of a sedentary style of living as well as unhealthy consumptions that are connected with weight gain. On the basic level, different system-level factors can act as directional forces for the underlying and immediate factors and ultimately overnutrition. For instance, over the period women’s presence in higher education has increased due to women’s empowerment as a result they enjoy a much better life with income which again brings in the factor of sedentarism and unhealthy lifestyle practices (Bishwajit, 2017; Chowdhury, Adnan, & Hassan, 2018; Gouda & Prusty, 2014). Further investigation is needed to understand several factors such as socioeconomic conditions (McLaren, 2007) and gender norms, effecting the increasing trends in obesity prevalence. Any benefits of policies aimed at improving women’s status might emerge cumulatively over generations and would therefore be difficult to study over the short term. However, such multigenerational effects are likely to prove a valuable approach for addressing the global obesity crisis, as well as being beneficial in many other ways, and therefore this concern merits further exploration (Wells et al., 2012).

5. Conclusion and Policy Recommendations The present study depicts the long-run relation between decreasing levels of GII and rising levels of OW and obesity among women in India. Different studies show that among the important determinants, higher education, residence, and higher economic status are the important factors contributing to the prevalence of OW and obesity among women in the reproductive age groups. The findings of this study indicate that to effectively battle the epidemic of OW and obesity, strategies must not only focus on elevating the position of women in society but also address the unhealthy habits that cause obesity. Therefore, having a better understanding of how the advancement of gender equality might affect the prevalence of obesity in India has the potential to contribute to the formulation of more effective policies as well as programs to reduce obesity among women. It is imperative that attention be paid to the growing epidemic of OW and obesity among urban women. Doing so will help further prevent the burden of chronic disorders such as diabetes, cardiovascular diseases, hypertension, and infertility in India. Development of nutrition-specific, as well as nutrition sensitive, interventions can be planned for mobilization of local resources that addresses issue of OW and obesity among women. The involvement of policy makers is key to better implement the targeted health programs that can promote healthy diets and physical exercises by introducing policies while reducing gender inequalities.

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Notes 1. https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/body-massindex 2. https://www.who.int/data/nutrition/nlis/info/gender-inequality-index-(gii). • The health dimension is measured by the maternal mortality ratio and the adolescent fertility rate. • The empowerment dimension is measured by the share of parliamentary seats held by each gender, and by secondary and higher education attainment levels. • The labor dimension is measured by women’s participation in the workforce.

References Agrawal, P., & Mishra, V. K. (2004). Covariates of overweight and obesity among women in North India. Honolulu: East West Center Working Papers; Population and Health Series, 116. Akabas, S. R., Lederman, S. A., & Moore, B. J. (Eds.). (2012). Textbook of obesity: Biological, psychological and cultural influences. Hoboken, NJ: John Wiley & Sons. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. Bener´ıa, L. (2012). The World Bank and gender inequality. Global Social Policy, 12(2), 175–178. Beneria, L., & Permanyer, I. (2010). The measurement of socio-economic gender inequality revisited. Development and Change, 41(3), 375–399. Bhurosy, T., & Jeewon, R. (2014). Overweight and obesity epidemic in developing countries: A problem with diet, physical activity, or socioeconomic status? The Scientific World Journal, 2014. Bishwajit, G. (2015). Nutrition transition in South Asia: The emergence of non-communicable chronic diseases. F1000Research, 4. Bishwajit, G. (2017). Household wealth status and overweight and obesity among adult women in Bangladesh and Nepal. Obesity Science & Practice, 3(2), 185–192. Chowdhury, M. A. B., Adnan, M. M., & Hassan, M. Z. (2018). Trends, prevalence and risk factors of overweight and obesity among women of reproductive age in Bangladesh: A pooled analysis of five national cross-sectional surveys. BMJ Open, 8(7), e018468. Ferretti, F., & Mariani, M. (2017). Gender discrimination, gender disparities in obesity and human development. Heliyon, 3(3), e00263. Gouda, J., & Prusty, R. K. (2014). Overweight and obesity among women by economic stratum in urban India. Journal of Health, Population and Nutrition, 32(1), 79. Haddad, L., Cameron, L., & Barnett, I. (2015). The double burden of malnutrition in SE Asia and the Pacific: Priorities, policies and politics. Health Policy and Planning, 30(9), 1193–1206. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 1551–1580. Kain, J., Vio, F., & Albala, C. (2003). Obesity trends and determinant factors in Latin America. Cadernos de Sa´ude P´ublica, 19, S77–S86.

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Kapoor, N., Chapla, A., Furler, J., Paul, T. V., Harrap, S., Oldenburg, B., & Thomas, N. (2019). Genetics of obesity in consanguineous populations—A road map to provide novel insights in the molecular basis and management of obesity. EBioMedicine, 40, 33. Kennedy, E. T. (2006). Evidence for nutritional benefits in prolonging wellness. The American Journal of Clinical Nutrition, 83(2), 410S–414S. Kibria, A., Muhammed, G., Swasey, K., Hasan, M. Z., Sharmeen, A., & Day, B. (2019). Prevalence and factors associated with underweight, overweight and obesity among women of reproductive age in India. Global Health Research and Policy, 4(1), 1–12. Klugman, J. (2010). Human development report 2010–20th Anniversary edition. The real wealth of nations: Pathways to human development. Lear, S. A., Teo, K., Gasevic, D., Zhang, X., Poirier, P. P., Rangarajan, S., . . . Yusuf, S. (2014). The association between ownership of common household devices and obesity and diabetes in high, middle and low income countries. Canadian Medical Association Journal, 186(4), 258–266. Luhar, S., Timæus, I. M., Jones, R., Cunningham, S., Patel, S. A., Kinra, S., . . . Houben, R. (2020). Forecasting the prevalence of overweight and obesity in India to 2040. PLoS One, 15(2), e0229438. Mamun, A. A., & Finlay, J. E. (2015). Shifting of undernutrition to overnutrition and its determinants among women of reproductive ages in the 36 low to medium income countries. Obesity Research & Clinical Practice, 9(1), 75–86. Mathews, E., Lakshmi, J. K., Ravindran, T. S., Pratt, M., & Thankappan, K. R. (2016). Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India. Global Health Promotion, 23(4), 27–36. McLaren, L. (2007). Socioeconomic status and obesity. Epidemiologic Reviews, 29(1), 29–48. Perron, P. (1990). Testing for a unit root in a time series with a changing mean. Journal of Business & Economic Statistics, 8(2), 153–162. Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80(2), 355–385. Popkin, B. M. (2006). Global nutrition dynamics: The world is shifting rapidly toward a diet linked with noncommunicable diseases. The American Journal of Clinical Nutrition, 84(2), 289–298. Popkin, B. M., Adair, L. S., & Ng, S. W. (2012). Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews, 70(1), 3–21. Ramachandran, K., McCusker, C., Connors, M., Zuwallack, R., & Lahiri, B. (2008). The influence of obesity on pulmonary rehabilitation outcomes in patients with COPD. Chronic Respiratory Disease, 5(4), 205–209. Ramachandran, A., Snehalatha, C., & Vijay, V. (2002). Temporal changes in prevalence of type 2 diabetes and impaired glucose tolerance in urban southern India. Diabetes Research and Clinical Practice, 58(1), 55–60. Swinburn, B. A., Sacks, G., Hall, K. D., McPherson, K., Finegood, D. T., Moodie, M. L., & Gortmaker, S. L. (2011). The global obesity pandemic: Shaped by global drivers and local environments. The Lancet, 378(9793), 804–814. Tisdell, C. A. (2019). Gender inequality: Socioeconomic analysis and developing country case studies. Hackensack, NJ: World Scientific.

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Tisdell, C. A. (2021). How has India’s economic growth and development affected its gender inequality? Journal of the Asia Pacific Economy, 26(2), 209–229. Van Staveren, I. (2013). To measure is to know? A comparative analysis of gender indices. Review of Social Economy, 71(3), 339–372. Wells, J. C., Marphatia, A. A., Cole, T. J., & McCoy, D. (2012). Associations of economic and gender inequality with global obesity prevalence: Understanding the female excess. Social Science & Medicine, 75(3), 482–490. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 20(1), 25–44.

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Chapter 4

Gender Discrimination in Education Expenditure in Public Primary Schools in Rural India Among Religious Groups: An Oaxaca–Blinder Decomposition Analysis Puja Biswas and Amit Kundu

Abstract This chapter tries to capture the disparity in expenditure on primary education based on gender among the religious groups (Hindu, Muslim, and Christian) in rural India. The gender gap in education expenditure for a certain demographic group is calculated using the Oaxaca–Blinder decomposition approach. Further, we tried to identify the various household-related factors which might influence the decision of spending on a child’s education. We used the 75th-level National Sample Survey Office (NSSO) unit-level dataset of July 2017 to June 2018 (one academic year) to obtain data on education expenditure and other household factors which play a manifesting role in the gender gap in expenditure on education. Our finding suggests that the total differential (log mean boys education expenditure-log mean girls education expenditure) is positive among all religious groups signifying the gender bias in education expenditure. We also found that the magnitude of the “Unexplained Effect” component is higher compared to the “Explained Effect” component signifying that the treatment of characteristics by students differs by their sex at elementary education. Household size and if household members are employed on a casual basis, then their expenditure on education falls on the other hand income of the household, a household with computer availability and household member engaged in regular wage/salary earning plays a positive role in expenditure on primary education in rural India. Keywords: Gender bias; religious groups; education expenditure; Oaxaca– Blinder decomposition; NSSO dataset; rural India

Gender Inequality and its Implications on Education and Health, 43–54 Copyright © 2023 Puja Biswas and Amit Kundu Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231005

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1. Introduction Several works of literature portray the picture of within-household preferential feeding toward the male household members in the intra-household allocation of resources in Indian patriarchal society (Azam & Kingdon, 2013; Subramanian & Deaton, 1991; Dreze & Kingdon, 2001; Kambhampati, 2008; Kingdon, 1998, 2005; Lancaster, Maitra, & Ranjan, 2008; Pal, 2004; Tilak, 2002; Zimmerman, 2012). The within-household gender bias toward male household members is even more predominant in rural areas than in urban areas (Muralidharan & Sheth, 2013). Parents prefer to enroll their male children in fee-charging private schools to receive better education, and on the other hand, enroll their female children in fee-free public primary schools (Datta & Kingdon, 2019; Murlidharan, 2013). The government has initiated various policies to bridge the gender gap in educational attainment like Ladli Scheme (Delhi Government), Balika Samriddhi Yojana (Gujrat), Kanyashree Prakalpa (West Bengal), and many others. Some policies are also launched to give assistance focusing on girls living below the poverty line like Bhagyalaksmi Scheme (Karnataka), Kanya Jagriti Jyoti Scheme (Punjab), Bangaru Thali (Andhra Pradesh), etc. But despite all these schemes, there is a gap in primary education attainment in rural India based on gender (DISE, 2014).

2. Literature Review and Research Hypothesis India is the second largest populous country in the world comprising 1.4 billion population with inhabitants of various religions who lived peacefully and practice their religion freely. The dominant religion strata of India’s population are Hindu (81%) followed by Muslim (12.9%), Christian (2.4%), Sikh (1.9%), Buddhist (0.7%), and other religions (Census, 2011). Thus, the massive population is diverse as well as devout. Indian society is still the victim of dowry and child marriage and the figure is more depressing in rural areas (DLHS 4). Investing on girls’ education is considered an additional monetary burden over dowry for poor families. Boys are thought to provide age-old support and provide bread and butter to the family, and the benefits of the girl’s education are reaped by her in-laws and not by the investing parents. Muslim families score lower on the dimension of woman autonomy (Morgan, Stash, Smith, & Mason, 2002). This restricted autonomy prevails in terms of taking household-related decisions like the choice of schooling for their children, expenditure on education, and health, etc. Jejeebhoy and Sathar (2001) pointed out the scenario of female-constrained autonomy and access to information across different religious groups in India. Existing literature has portrayed the picture of differentiated treatment based on gender bias in primary education expenditure in the Indian context (Saha, 2013; Zimmerman, 2012). Government data also portray that more boys are enrolled in private-unaided schools compared to girls, and this gap is even more in rural areas (NSSO, 75th Round Data). This study tries to verify the hypothesis that there exists unequal treatment with respect to gender in primary education expenditure in rural India within the various religious groups. Prior studies suggest that parents prefer to enroll their male child in a fee-charging private school and enroll

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their girl child in a free-fee public school. And this scenario is even denser in rural society (Biswas & Kundu, 2022; Datta & Kingdon, 2019; Kingdon & Pal, 2014; Murlidharan, 2013). Thus, there is a difference in primary education expenditure based on gender, and though with time this gender discrimination in education expenditure has reduced (Kingdon, 2007), it is still a perennial problem in patriarchal Indian society. This chapter aims to study the magnitude of the gender gap in primary education expenditure in rural India among the various religious groups using the Oaxaca–Blinder decomposition model. Still, now there is no work focusing on differentiated treatment based on gender in primary school education expenditure focusing on rural India. This chapter will try to bridge this gap in the existing literature.

3. Data India is a socioeconomic diverse country with a majority of people (68.84%) living in rural areas (Census, 2011), and the pace of urbanization is still slow (Bhagat, 2011). Rural workers mainly engaged in informal service without proper job security. The gender gap in educational achievement is higher in rural India in comparison to its urban counterparts (Kingdon, 2007). The main motive of our study is to capture the disparity in expenditure on primary education based on gender among the religious groups (Hindu, Muslim, and Christian) in rural India. Data used in the study are based on the 75th-level National Sample Survey Office (NSSO) unit-level dataset of July 2017 to June 2018 (one academic year). The survey covers 29 major Indian states and six Union territories. From the total dataset of 513,366 samples, we have taken only the rural sample of 305,904 (59.59%) as our focus is on rural India. For this study, the sample is limited to children within the age group 6–10 years1 (i.e., 39,014 samples) in rural India. Individuals within the age group of 6–10 years residing in rural India but not enrolled in primary school or studying above the primary level were also excluded from our sample study.2 Our eligible sample consists of 5,655 boys and 4,723 girls (n 5 10,378) as the main focus of our study is limited to primary school level children of rural India.

4. Model: Oaxaca–Blinder (1973) Decomposition Analysis The main objective of this chapter is to capture the disparity in expenditure on primary education based on gender across the religious groups (Hindu, Muslim, & Christian) in rural India. This objective cannot be estimated with the technique of interactive dummy as this differential could only be estimated through the statistical significance of specific coefficients of the interactive. The Blinder (1973) and Oaxaca (1973), decomposition analysis enables us to segregate the contribution of the explained factors (the difference between the average characteristics of the boys and girls) and the unexplained factors (differential treatment based on gender) in explaining the education expenditure gap in attaining basic primary education in rural India.

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Two separate equations for the ith boy and ith girl belonging to zth religious groups are given by: YMz ¼ a 1 xbMz 1 « ðFor BoysÞ

(4.1)

YFz ¼ a 1 xbFz 1 « ðFor GirlsÞ

(4.2)

M and F are indexes of Boy and Girl, respectively. The regression equation in Eqs. (4.1) and (4.2) is estimated separately for boys and girls for each zth religious group. Here Y, the dependent variable, is the expenditure incurred by the household on primary education expenditure items. X is the vector of independent variables like Log Income, Household Size, and Household Occupation and a dummy d1, with a value of 1 if the child is enrolled to public school and 0 otherwise, and another dummy d2, with a value of 1 if a member of the household has ownership of computer and 0 otherwise. These household characteristics might influence the household’s expenditure decision on their children’s primary school attainment. b’s are the vectors of the coefficient. «ðscalarÞ is a random error term capturing unmeasured and immeasurable effects on the dependent variable, i.e., log expenditure on education. The response vector is measured on an ordinal scale. x (matrix) contains the household-related variables which might influence the expenditure decisions of households on primary education. Thus, the explanatory variables considered in our model are: log(income), [log(income)]2, household size, enrollment in public school, ownership of a computer, and occupation of the household. We have Log Expenditure on Education as our outcome variable, and we have considered three religious groups (Hindu, Muslim, Christian). The parameter of Eqs. (4.1) and (4.2) is estimated separately for boys and girls on the considered household characteristics. The early model prior to 1973 considered the difference between intercepts of two regressions for measuring the discrimination between the groups, but Blinder (1973) and Oaxaca (1973) consider the slope coefficient as it also contains information regarding the discrimination. In this model, we have considered the expected differences method by Oaxaca and Blinder where the concept of the composite mean is used. In general Oaxaca–Blinder approach can be used to study group differences in any continuous outcome variable. Let N ¼ N M 1 N F

(4.3)

Here, N denotes the total number of observations and NM denotes the number of male(boys) observations and NF denotes the total number of female(girls) observations. As the difference between the gender is statistically significant the decomposition is performed. The difference between the overall mean for boys and girls among the different religious groups can be decomposed in the following ways:

Gender Discrimination in Education ln Y M 2 ln Y F

47

F M F ¼ Sz5H;M;C aM Z lnYZ 2 Sz5H;M;C aZ lnYZ M F F 9 bF M9 b ¼ Sz5H;M;C aM Z XZ bz 2 Sz5H;M;C aZ XZ bz  9   9 F bF M F b M 1 Sz5H;M;C X F aM b bM b ¼ Sz5H;M;C aM Z XZ 2 XZ Z z 2 aZ bz Z z

¼ Explained Effect 1 Unexplained Effect

(4.4)

Eq. (4.4) explains the components of the decomposition results for the major F religious groups in India. Here aM Z and aZ denotes the fraction of boys and girls observations belonging to the zth religious groups. For identifying the religious groups with highest gender discrimination, we have done a similar decomposition separately for each zth religious group in the following ways: lnYzM 2 lnYzF

9 M b 2 X F9 b bF ¼ XZM b z Z z ¼

 XZM 2 XZF

9

  bF 1 X F 9 b bM 2 b bF b z z z Z

 9   bM 2 b b F ¼ Endowment EffectðEÞ 1 XZM 2 XZF b z z 1 Coefficient EffectðCÞ 1 Interaction EffectðIÞ

(4.5)

Eq. (4.5) tries to identify the religious group where the magnitude of gender discrimination is highest comparing the components of all religious groups. The endowment Effect(E) is the expenditure gap due to the average characteristic gap between the gender. If the discriminated group(girls) have the same endowment as the favored groups (boys), then this term will be zero. This is the explained part. Coefficient Effect(C) quantifies the change in discriminating groups, i.e., unequal treatment of characteristics based on gender. It is the unexplained part. The interaction effect(I) measures the simultaneous effect of endowment and coefficient effect between the discriminating group and the favored group.

5. Factors Influencing Expenditure Decisions in Primary Schools in Rural India Outcome Variable: Log Expenditure on Education: It provides the details of expenditure (Rs.) on basic education during the current academic year by the religious group “z”. This expenditure on education includes expenditure on course fees (including tuition fees, examination fees, development fees, and other compulsory payments), books, stationary and uniform fees, transport fees, private coaching fees, and other educational expenditures. Household factors which play a manifesting role in the gender gap in expenditure on education: (1) Log Income: Log of the household’s usual monthly expenditure (in Rs.) is taken as a proxy of household income. (2) Household Size: Distribution among the household members in terms of age is not given. So, it is not possible to calculate the household size on the adult

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equivalence scale. So total household members of a particular rural household represent the household size. As household size raises household spending per child expenditure on education falls as the household has to bear the education expense from the limited income of the household. (3) Type of School: In this study, the primary schools are classified as public schools3 and private-unaided schools. Public schools are mainly owned, funded, and managed by Central, State, or Local governments or NGOs or foreign funds so education is free in public schools. On the other hand, private-unaided schools are owned and managed by private organizations but receive no grants or aid from the government. Enrollment in public school plays a major role while taking decisions regarding their children’s education expenditure in the household as education is free in public school. The value of it is assigned “1” if the ith child surveyed from the zth religion the household belongs to is enrolled in public schools and “0” otherwise. (4) Ownership of Computer: Owning a computer is considered as a proxy for the parental education level and economic solvency of the household.4 The value of it is assigned “1” if the ith child surveyed from the zth religion the household belongs has ownership of a computer and “0” otherwise (5) Occupation: The occupation of the household plays a major role while taking decisions regarding their children’s education expenditure by the household. The occupation of the household is clubbed into four categories, they are self-employed (agriculture and nonagriculture sector), regular wage/ salary earners (agriculture and nonagriculture sector), and casual labor (agriculture and nonagriculture sector) and other workers. Though parental education is an important variable affecting the choice of schooling and education expenditure decision of the household, but as we have only included individuals within the age group of 6–10 years residing in rural India and enrolled in primary education for our sample study, we cannot incorporate individuals above primary education or parents with only primary education but above the age group of 6–12 years. The occupation of the household members is considered a proxy of parental education in our study5 (Kingdon, 1999).6

6. Results and Discussion Before Regression, we will test whether the independent variable of interest is correlated with the remaining independent variable included in the multiple regression analysis. Test of Multicollinearity among the exogenous variables is done by VIF estimates. VIF ¼

Tolerance is estimated7 as 1 2 R2

1 Tolerance

Table 4.1. Regression Result for Different Religious Groups. Hindu Explanatory Variable

Household size log(income) [log(income)]2 Access to computer

Regular/salary workers Casual workers Other workers R-squared _cons No of observation

Christian

Boys

Girls

Boys

Girls

Boys

Girls

20.0797*** (0.0066) 0.8108 (0.5399) 20.0781** (0.0305) 0.2641*** (0.0676) 21.733*** (0.0348) 0.2374*** (0.0498) 20.1047** (0.0336) 0.1446 (0.0895) 0.4818*** 10.1946*** (2.3967) 4,199

20.0995*** (0.0084) 21.826** (0.6341) 0.1332*** (0.0355) 0.1985* (0.0762) 21.6557***

20.0854*** (0.0180) 3.4035** (1.4423) 20.2132*** (0.0808) 0.7319*** (0.1839) 21.2028*** (0.0865) 0.1499 (0.1176) 0.0577 (0.0827) 0.1834 (0.1534) 0.3126*** 22.151*** (6.4552) 719

20.1074*** (0.0212) 21.4509 (1.8379) 0.1092 (0.1015) 0.3962* (0.2379) 21.3947*** (0.0948) 20.2818** (0.1450) 20.0907 (0.0951) 20.1355 (0.1807) 0.3577*** 13.3455* (8.3428) 557

20.0463*** (0.0277) 22.6964** (1.3005) 0.1636* (0.0730) 0.0214 (0.1566) 21.447*** (0.0895) 0.0765 (0.1209) 20.3575*** (0.1431) 0.3069 (0.3303) 0.4293*** 19.931*** (5.8132) 468

20.1329*** (0.0270) 24.6787*** (1.5300) 0.26973*** (0.0858) 0.1326 (0.1720) 21.2458*** (0.0994) 0.0990 (0.1276) 20.4547*** (0.1459) 0.9643*** (0.3852) 0.4248*** 29.469*** (6.8380) 396

Note: ***p , 0.01, **p , 0.05 and *p , 0.10.

49

Source: Author’s estimation.

0.1637*** (0.0535) 20.1629*** (0.0376) 0.0596 (0.0935) 0.4206*** 14.8613*** (2.8439) 3,549

Gender Discrimination in Education

Enrolled in public school

Muslim

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Puja Biswas and Amit Kundu

If the value of VIF .10, then it indicates multicollinearity among the dependent variables. We only observe collinearity between the occupation of the household so we had to drop those household who are engaged as self-employed (agricultural and nonagricultural sector) for our study. In Table 4.1, the regression result for boy and girl children belonging to Hindu, Muslim, and Christian communities are presented separately. The coefficient of household size is statistically significant and is negative for both genders implying that with an increase in the member of the household, there is a cut-down in education expenditure of the children in that household. The coefficient of log income and (log income)2 is statistically significant for Hindu(girl) and Christian and Muslim(boy) communities. We find that the coefficient of log income is positive for son and negative for daughter belonging to the Hindu community and Muslim communities portraying the picture of gender-based discrimination on elementary education expenditure in rural India. This implies that with an increase in income the expenditure on education increases for boys but falls for girl child denoting the intra-household disparity in education expenditure based on gender for the Hindu and Muslim communities. As per NCPCR Report, most of the household that corresponds to the Christian community belongs to the lower strata of society. Among the total minority group, the Christian community comprises 11.54% of the total minority population, but there is 71.96% of Christian missionary schools among total minority schools (NCPCR Report, 2016). It is found that among the total 2,977(8%) household belonging to the Christian community, most of the children are enrolled in private-aided schools8 followed by public schools followed by private schools (NSSO, 75th Round data). Here our results show a negative relationship between household income and household belonging to Christian community expenditure on primary education.9 It is noticed that a household with regular or salary earners spends more on education expenditure for household belonging to the Hindu community but incurs negative expenditure on a girl child belonging to the Muslim community denoting the picture of gender discrimination. Household belonging to Hindu and Christian community involved in casual labor tends to spend less on primary education expenditure of their children. The coefficient of the dummy variable (d1 5 1, if a child is enrolled in public schools) is negatively significant denoting that if education is free, then education expenditure on primary education falls in rural India. Another dummy variable (d2 5 1, if any member of the household owns a computer) is positive for children belonging to Hindu and Muslim communities. It implies that if a household member owns a computer, then it induces the parents to spend more on their child’s primary school education expenditure even in rural areas. In Table 4.2a, the components of the differential based on religion group are noted. Oaxaca–Blinder’s Decomposition shows that difference in primary education expenditure between the gender is statistically significant denoting the presence of gender bias within households when deciding on expenditure on primary education in rural India. The magnitude of the “Unexplained Effect” component is higher in comparison to the “Explained Effect” component. The “Unexplained Part” captures all potential effects of differences in unobserved

Table 4.2. The Decomposition Result. (a) The Decomposition Result Based on the Religious Groups Total Difference ðln Y M 2 ln Y F Þ ¼ 0:1089773 Explained Effect  9 ! M bM ¼ 0:0298 Sz5H;M;C aZ XZM 2 XZF b z

Hindu

0.0439 40.26%

Christian

0.0364 33.40%

Hindu

Muslim

0.0823 75.52%

0.0579 53.13%

Christian

0.0576 52.8%

(b) The Decomposition Result within the Religious Groups Hindu Total Difference 5 0.129 Endowment Effect

0.05129 39.82%

Coefficient Effect

0.08233 63.93%

Source: Author’s estimation.

Muslim Total Difference 5 0.101 Interaction Effect

0.00484 3.75%

Endowment Effect

0.0588 58%

Coefficient Effect

0.06749 66.58%

Interaction Effect

0.01494 14.74%

Christian Total Difference 5 0.094 Endowment Coefficient Interaction Effect Effect Effect

0.07713 81%

0.04713 50.11%

0.03022 32.13%

Gender Discrimination in Education

0.0464 42.58%

Muslim

Unexplained Effect   9 M bM F bF F Sz5H;M;C XZ aZ bz 2 aZ bz ¼ 0:0791

51

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variables. The contribution of “Unexplained Part” is highest for households belonging to the Hindu community (75.52%) followed by the Muslim community (53.13%) and the Christian community (52.8%). It implies that discrimination based on gender is more severe in the Hindu community in comparison to Muslim and Christian communities. The contribution of the “Unexplained part” is lowest for households belonging to the Christian group denoting that the gender bias in primary education expenditure is least for them in comparison to other religious groups. In Table 4.2b, the components of the decomposition for each religious group are presented. The total differential between the boy and girl child expenditure on primary education is positive for all religious groups. This difference is highest for the Hindu community followed by the Muslim and Christian communities. The contribution of the coefficient effect is higher than the endowment effect for the Hindu and Muslim group. The coefficient effect is positive for all religious groups implying the pro-male bias in education expenditure and this component denotes different treatment based on gender. The contribution of the coefficient effect is highest for Hindus followed by Muslim and Christian communities. It portrays a higher degree of gender bias in this community concerning primary school education expenditure in rural India.

7. Conclusion In this chapter the gender gap among the religious groups is examined using the NSSO 75th round unit level dataset. We found that the mean log expenditure difference between boys and girls is positive denoting the presence of discrimination in primary school education expenditure between the gender. Differences in potential discrimination explained by the “unexplained part” are higher compared to the “explained part” for all the three dominant religious groups of India. The gender gap in education expenditure as highlighted by the unexplained effect is highest among the household belonging to the Hindu community followed by the household belonging to the Muslim community and least in households belonging to the Christian community. The education expenditure of Christian community children are mainly financed by Christian missionaries which receives foreign fundings (mostly developed countries) so the gender-based discrimination is least among the Christian communities children in receiving basic education in rural India.

Notes 1. Mainly between the age group of 6 and 10 years children’s studies in primary school. 2. From our sample study we have dropped the transgender sample as they are less than 0.5 percentage of our sample. 3. We consider the government school and private-aided school under the umbrella of public schools.

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4. Data are extracted on the basis of age group between 6 and 10 years. So, we failed to incorporate parental education in this study. 5. Better education results in better occupation. There is a positive relation between education achievement and occupation. 6. There are many other factors which influence rural household to spend on child’s education like perception of parents, etc. but due to unavailability of data we cannot incorporate it in our study. 7. Where R2 is calculated by regressing the independent variable of interest onto the remaining independent variable included in the Multiple Regression Analysis. 8. So households belonging to the Christian community mainly enrolled their children in aided Christian schools (NSS, 75th Round). 9. With time Christian missionary schools are raising which attracts households mainly belonging to the Christian community to admit to these aided schools which are mainly funded by Christian missionaries (NCPCR Report, 2016). There are also special reservations for children belonging to the Christian community in these schools.

References Azam, M., & Kingdon, G. G. (2013). Are girls the fairer sex in India? Revisiting intra-household allocation of education expenditure. World Development, 42, 143–164. Bhagat, R. B. (2011). Emerging pattern of urbanization in India. Economic and Political Weekly, XLVI. Biswas, P., & Kundu, A. (2022). Determinants of enrolment of girl children in primary education in rural India: A region-based analysis. Indian Journal of Human Development, 16(2), 1–21. Blinder, A. S. (1973). Wage discrimination: Reduced form and structural estimates. Journal of Human Resources, 8(4), 436–455. Census. (2011). Government of India. Retrieved from https://www.census2011.co.in/ district.phpCensus,2011 Datta, S., & Kingdon, G. G. (2019). Gender bias in intra-household allocation of education in India: Has it fallen over time?. IZA Discussion Paper No. 12671. Retrieved from https://ssrn.com/abstract=3468619. doi:10.2139/ssrn.3468619 Dreze, J., & Kingdon, G. G. (2001). School participation in rural India. Review of Development Economics, 5(1), 1–24. Jejeebhoy, S. J., & Sathar, Z. A. (2001). Women’s autonomy in India and Pakistan: The influence of religion and region. Population and Development Review, 24(4), 687–712. Kambhampati, U. S. (2008). Does household expenditure on education in India depend upon the returns to education? Henley Business School University. Kingdon, G. G. (1998). The quality and efficiency of public and private schools: A case study of urban India. Oxford Bulletin of Economics & Statistics, 58(1), 57–82. Kingdon, G. G. (2005). Where has all the bias gone? Detecting gender bias in the intra-household allocation of educational expenditure. Economic Development and Cultural Change, 53(2), 409–451.

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Kingdon, G. G. (2007). The progress of school education in India. Oxford Review of Economic Policy, 23(2), 168–195. Kingdon, G. G., & Pal, S. (2014). Can private school growth foster ‘education for all’: Tracing the aggregate effects at the district-level. SSRN. Lancaster, G., Maitra, P., & Ranjan, R. (2008). Household expenditure patterns and gender bias: Evidence from selected Indian States. Oxford Development Studies, 36(2), 133–157. Morgan, S., Stash, S., Smith, H. L., & Mason, K. (2002). Muslim and non-Muslim differences in female autonomy and fertility: Evidence from four Asian countries. Population and Development Review, 28(3), 515–537. Muralidharan, K. (2013). Priorities for primary education policy in India’s 12th five-year plan. India Policy Forum, 9(1), 1–61. Muralidharan, K., & Sheth, K. (2013). Bridging education gender gaps in developing countries: The role of female teachers. Working Papers id:5481, eSocial Sciences. NCPCR Report. (2016). National Commission for Protection of Child Rights: Understanding child rights. NSS Report. (75th Round). Household social consumption on education. Ministry of Statistics and Programme Implementation, Government of India. Oaxaca, R. (1973). Male–female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709. Pal, S. (2004). How much of the gender difference in child enrolment can be explained? Evidence from rural India. Bulletin of Economic Research, 56(2), 133–158. Saha, A. (2013). An assessment of gender discrimination in household expenditure on education in India. Oxford Development Studies, 41(2), 220–238. Subramanian, S., & Deaton, A. (1991). Gender effects in Indian consumption patterns. Princeton, NJ: Woodrow Wilson School-Development Studies. Tilak, J. B. (2002). Education poverty in India. Review of Development and Change, 7(1), 1–44. U-DISE. (2014). Unified-District Information System for Education report. MHRD. Retrieved from https://schoolreportcards.in/DISE.InResponsive/District ReportCards/DistrictReportCards.aspx Zimmermann, L. (2012). Reconsidering gender bias in intrahousehold allocation in India. Journal of Development Studies, 48(1), 151–163.

Chapter 5

Can Gender Inequality in School Enrollment Hinder the Efficiency of the Education Sector? Sangita Choudhury and Arpita Ghose

Abstract India depicts the picture of severe social stringencies keeping girls away from attending school education due to the harsh reality of early child marriage and denial of aspirations of girl students in Indian society. The gender disparity in school educational attainment is evident as the figures of girls’ enrollment in comparison to boys’ enrollment at higher secondary stage of education in India always turn lower. In this context, measurement of technical efficiency (TE) is important because existence of technical inefficiency implies that one cannot produce maximum amount of output, given the resources, which can be interpreted as the penalty that the system is paying, and there is also the need to find out the relation between TE and gender inequality. The chapter contributes to the literature by (i) in the first stage estimating output-oriented TE of Indian higher secondary education for the period 2010–2011 to 2015–2016, using nonparametric Data Envelopment Analysis, for general category states and (ii) in the second stage, using the estimated TE scores from the first stage, and the regression analysis establishing the positive impact of the girls’ enrollment relative to boys’ on the resulting TE and hence the positive role of gender equality in enrollment on enhancing TE. The favorable role of (1) “government expenditures on education (as a ratio to aggregate expenditure for the state),” “proportion of para teachers” and the adverse role of (2) “percentage of schools without girl’s toilet” and “percentage of schools without building,” in determining TE of Indian higher secondary education are evident. Keywords: Education; technical efficiency; gender inequality; school enrollment; Data Envelopment Analysis; India

Gender Inequality and its Implications on Education and Health, 55–67 Copyright © 2023 Sangita Choudhury and Arpita Ghose Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231006

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1. Introduction All children should have the right to access proper opportunities that enable them to perform at their potential level throughout their lifetime. In a developing nation like India, this fact is mostly denied by providing unequal opportunities in favor of only the boy child. Conventional gender-biased culture and social customs in Indian society are the root causes of early-age girl child marriage, domestic exploitation, and domestic barriers of girls in the name of household responsibilities, external threat of verbal and physical abuse, and deprivation in basic facilities (like health and nutrition) impeding high aspirations of girls which basically reflect the reality of gender disparity in Indian society. Gender inequality is although present in every possible sphere of activities; however, it is highly observed in a crucial sector of economy like the education sector in India. The fact is reflected by the enrollment of girl students turning lower consistently as compared to the boys over the period 2010–2011 to 2015–2016 at higher level of school education which is a crucial stage of education, being a stepping stone toward higher education in India. In the year 2010–2011, girl students’ enrollment in India considering higher secondary (H.S.) stage appeared as 6919888, whereas the figure for boys’ enrollment shows 8282666. Again, over 2012–2013 to 2015–2016, the number of girls enrolled at H.S. level in the country as a whole was 9267168 (2012–2013), 10485285 (2013–2014), 11061022 (2014–2015), and 11733280 (2015–2016), and the number of boys enrolled for the same consecutive years was 10656614, 11829029, 12440776, and 13002117, respectively. This basically reveals the issue of gender inequality in formal H.S. educational participation for the nation as for each year girls’ enrollment is found to be lower than that of the boys’, evident from the above figures (Source: District Information System for Education). According to UNICEF (https://www.unicef.org/ india/what-we-do/gender-equality),1 “India will not fully develop unless both girls and boys are equally supported to reach their full potential”. For achieving full potential, the major support functions through providing the opportunity for education, irrespective of gender. The significance of education for any nation is already established in existing literatures. Some studies on endogenous growth models established the linkage between human capital (formation of this rests on development of education sector) and growth aspect of the nation (Lucas, 1988; Romer, 1986, 1990, among others). Mankiw, Romer, and Weil (1992) on human capital augmented the Solow model established that the higher level of per capita income associates with the country possessing higher amount of human capital, under the condition of other things being equal. Barro (1991) recognized the creation of human capita along with education as the basis of differences in labor productivity as well as overall technology. The immense importance therefore induces the Indian government to allocate around four percent of gross domestic product in education every year. Hence, gender disparity on educational ground, specifically in enrollment, may threaten sustainable development of the nation. In some existing studies, a significantly positive correlation is established between economic growth development and gender equality in education, using historical data (Hill & King, 1993; Klasen, 2002; Klasen & Lamanna, 2009; Knowles,

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Lorgelly, & Owen, 2002). Recognizing the importance of gender aspect in education sector, the present study tries to find out whether the existence of such gender disparity in enrollment at H.S. level of education is hindering the efficiency performance of H.S. education in India; as the inefficient performance of the education sector may be responsible for lower growth of the country (Hulten, 1996). In light of this, the objective of this chapter is to evaluate the extent of efficiency of H.S. education for all the General Category States (GCS) in India over the period 2010–2011 to 2015–2016 and also to investigate the impact of gender inequality in enrollment, as represented by girls’ enrollment relative to boys’ at H.S. level on the resulting efficiency of H.S. education for these states. In India, 28 states and seven union territories are considered together for classifying these into – General Category States and Special Category States. This classification is crucial as the ratio of grants and loans, being the parts of overall assistance received from Central government, are different for these two categories of states (Planning Commission Government of India, Report of the Working Group on State’s Financial Resources for the twelfth five-year plan, 2012). As General Category States receive only 30 percent of overall assistance in the form of grants (remaining larger percentage, i.e., seventy percent in the form of loans), therefore the major concern of the present study has been conducting entire analysis with particularly a focus on this category of states. However, though performance of education sector can be represented either by gross enrollment ratio or by literacy rate, the present study represents ‘Technical Efficiency’ (TE) as an indicator of performance of H.S. education in this chapter. Accordingly, an essential purpose of this chapter is answering to the question, “whether gender inequality in Higher Secondary level school enrollment is hindering TE of Higher Secondary education in GCS?” This chapter develops perception in this direction. Efficiency estimation of decision-making units (DMUs) using linear programming method based “Data Envelopment Analysis” (DEA) is introduced by Charnes, Cooper, and Rhodes (CCR, 1978, 1981) for not-for-profit entities like schools, courts, and hospitals, producing quantifiable multiple outputs using quantifiable multiple inputs, but without the prevalence of market prices of outputs and a benchmark production function is constructed under constant returns to scale (CRS) assumption. Later, the CCR model (1978, 1981) is extended by Banker, Charnes, and Cooper (1984) to a more generalized assumption of variable returns to scale (VRS). The considerable advantage of using nonparametric DEA over the parametric approach is no requirement of any prior specification of functional form of the criterion function. Existing literature that applied DEA for estimating TE of school education are: Ray (1991), Arshad (2014), Gavurova, Kocisova, Belas, and Krajcik (2017), Haug and Blackburn (2017), Ciro and Torres (2018), Masci, De Witte, and Agasisti (2018), Sotiriadis, Menexes, and Tsamadias (2018) for other countries; and Tyagi, Yadav, and Singh (2009), Sankar (2007), Sengupta and Pal (2010, 2012), Mohapatra (2015), Ghose (2017), Singh, Pant, and Goel (2018), Choudhury and Ghose (2022) for India. Dearth in the literature is found in the line of work considering H.S. stage of school education in India, except Singh et al. (2018) which estimated efficiency

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using nonparametric estimation technique and treating state boards of H.S. education as DMUs for six years (2009–2014). However, the attempt of investigating the determinants of TE of H.E. stage for examining the role of gender inequality in enrollment, i.e., girls’ enrollment relative to boys’ at H.E. level in determining TE of this stage of education is lacking. Major contributions made through this chapter are: first of all, evaluating the performance, measured in terms of TE of H.S. education focusing on those states that are considered having relatively lesser dependence on the central governmental grants, i.e., general category states in India over the consecutive years 2010–2011 to 2015–2016 (In contrast to single-year specific study, the exploration over the years is crucial for developing the idea regarding the pattern of change in TE); secondly, using the estimated TE scores from the first stage, and the regression analysis, establishing the positive impact of the girls’ enrollment relative to boys’ on the resulting TE. While examining the role of gender inequality in enrollment on TE of H.S. education under the determinant analysis segment, no composite index is formed (using the variables under a particular determinant category) contrasting Sengupta and Pal (2010, 2012), as investigating the individual effect of all the possible socioeconomic determinants including “girls” enrollment relative to boys at H.S. level’ is the aim of this chapter. Accordingly, policies are prescribed for improving TE of H.S. level. The rest of the chapter is formatted as follows: Section 2 discusses methodology and possible socioeconomic determinants of TE and data sources; Section 3 reveals the results of the analysis; Section 4 concludes and suggests policies for improving TE.

2. Methodology of TE Estimation, Possible Determinants of TE, and Data Sources 2.1 Methodology of TE Estimation The methodology of DEA as discussed in Ray (2004) and explained in Choudhury and Ghose (2022) is followed for efficiency estimation of this chapter. TE and allocative efficiency (AE) are the two components of efficiency. TE of a DMU is measured by either (i) output-oriented technical efficiency (OUTTE) approach showing the maximum output quantities that can be proportionately increased without altering input quantities or (ii) input-oriented technical efficiency (INPTE) approach representing the maximum amount of input quantities, which can be proportionately reduced without changing quantities produced as output. The ability of a DMU to use the inputs in optimal proportions, given their respective price, is reflected by AE. This chapter is concerned with the measurement and finding out the determinants of TE, in particular OUTTE. A two-step problem is resorted to the measurement of TE. First, a benchmark production function, known as frontier, is constructed which is supposed to be perfectly efficient. Second, the observed performance of DMU is compared with the benchmark for measuring TE.

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Let input be denoted by x and output by y and let the actual input–output bundle of a DMU be ðx0 ; y0 Þ. Then y 5 f ðx0 Þ, y* is the maximum output producible from input x0 . OUTTE of DMU yy0 which is the ratio showing the comparison of actual output with the maximum producible quantity from the observed input. For the same output bundle y0 , the input quantity can be reduced proportionately till the frontier is reached. So, y0 can be produced from minimum  input x*. Thus INPTE for DMU xx0 which is the ratio showing the comparison of the minimum input required to produce given output y0 and the actual input used. The TE score of a DMU takes a value between 0 and 1. A value 1 indicates DMU is fully technically efficient. The extent of TE of the DMU also depends on returns to scale; CRS or VRS. Given the actual input–output bundle, OUTTE is estimated in this chapter by constructing the frontier under VRS using nonparametric DEA following Banker, Charnes, and Cooper (BCC) (1984), after making a number of fairly general assumptions about the nature of the underlying production technology, namely: (i) all actually observed input–output combinations are feasible; (ii) the production possibility set is convex; (iii) inputs are freely disposable; and (iv) outputs are freely disposable.

2.1.1 Methodology for Finding Output-Oriented TE Score Suppose that there are N DMUs. Each of them is producing “g” outputs using “h” inputs. The DMU t uses input bundle xt 5 ðx1t; x2t;... xht Þ and produces the output bundle yt 5 ðy1t; y2t;... ygt Þ. This study assumes VRS. The specific production possibility set under VRS is given by ( T

VRS

¼

N

N

)

N

ðx; yÞ : x $ + lj x ; y # + lj y ; + lj ¼ 1; lj $ 0; ðj ¼ 1; 2 . . . NÞ j

j¼1

j

j¼1

(5.1)

j¼1

the output oriented measure of TE of any DMU t under VRS technology requires the solution of the following LP problem max f N

Subject to + lj yrj $ fyrt ; ðr 5 1; 2; . . . gÞ; j 51

N

+ lj xij # xit ; ði ¼ 1; 2; . . . hÞ;

j¼1

f free lj $ 0; ðj 5 1; 2 . . . NÞ N

+ lj ¼ 1

(5.2)

j¼1

OUTTE of DMU t can be determined by using Eq. (5.3). TEot ¼ TEot ðxt ; yt Þ ¼

1 f

(5.3)

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Where f is the solution of Eq. (5.2) showing the maximum value of f. The maximum output bundle producible from input bundle xt is y* and is defined as y 5 f yt . Some characteristics of educational production function make it different from standard microeconomics production function. First of all, intangible output production is observed in education sector, and therefore it gives rise to the necessity of proper representation of educational output. Two outputs have been taken into account and measured by (i) Retention rate at H.S. level and (ii) Passing percentage of students in H.S. exam, reflecting the quality of output. Again, four inputs are considered and measured by (i) Number of existing H.S. schools for each one lakh population, (ii) Classroom–student ratio at H.S. level, (iii) Teacher–pupil ratio at H.S. level, and (iv) Percentage of postgraduate or further qualified teachers at H.S. level reflecting the quality of teacher input. Secondly, due to the absence of market prices of both the outputs and inputs used in educational production function, computation of shadow prices for inputs as well as outputs is required. Since the secondary source data availability allows the state to be considered as unit of observation, OUTTE of GCS is estimated using the average values for inputs as well as outputs at state level for the relevant state, considering all the schools taken together within the states; admitting the possibility of school-wise variation of the values of input and output variables. For identification of the determinants of resulting OUTTE of 17 GCS over the years 2010–2011 to 2015–2016, using regression analysis, the following determinants are considered.

2.2 Determinants of Technical Efficiency (OUTTE) (1) Poor Infrastructural indicators: Financial stringencies restrict the Government of India to provide sufficient infrastructural facilities to schools and due to that reason many schools are compelled to function under extremely poor infrastructural condition. Therefore, the existence of negative impact of poor infrastructure on OUTTE scores of GCS at HS level is examined in this chapter considering the following variables for H.S. level: (i) Percentage of H.S. schools operating without building, (ii) Proportion of contractual teachers, (iii) Proportion of single classroom school, (iv) Percentage of classrooms in “bad” condition, (v) Proportion of single teacher school, (vi) Percentage of H.S. schools functioning without any drinking water facility, (vii) Percentage of H.S. schools operating without electricity, (viii) Percentage of H.S. schools functioning without any girls toilet, and (ix) Percentage of H.S. schools operating without computer and internet connection. (2) Social indicators: Gender biasness (favoring the boy students) in creation of opportunity, in an underdeveloped country like India, is the reason behind less participation of girl students in educational institutions. For improving the reality of gender disparity in formal education participation in India, the

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government attempts to design incentive generating policy, targeting the girl students for including them in formal education system. Besides, the government also attempts to bring other socially disadvantageous groups like Scheduled Tribe (ST) and Scheduled Caste (SC) in formal education for enlightening them with basic skills and knowledge. For investigating the presence of any positive and significant effect of social inclusion policy in favor of the mentioned disadvantageous groups on OUTTE at H.S. level of education, the following variables are considered: (i) Proportion of girls’ to boys’ enrollment at H.S. level, (ii) Percentage of ST enrollment at H.S. level, (iii) Percentage of SC enrollment at H.S. level, and (iv) Proportion of female to male teachers at H.S. level. (3) Policy Indicator: To check whether the policy indicator has any significant influence on OUTTE considering H.S. level, the role of government education expenditure (as a ratio to aggregate expenditure for the state) in determining OUTTE is assessed. (4) Macro indicator: For examining whether OUTTE of H.S. is affected by general environment of the state, the impact of per capita net state domestic product (PCNSDP) (at constant 2011–2012 prices) is examined. In addition to the above indicators, the effect of “Percentage of H.S. Schools organizing Parent-Teacher Association” is tested on OUTTE of H.S. stage as well.

2.3 Data Set and Its Sources Seventeen Indian General Category States are: Andhra Pradesh (AP), Bihar (BI), Chhattisgarh (CHHT), Goa (GO), Gujarat (GU), Haryana (HA), Jharkhand (JH), Karnataka (KA), Kerala (KE), Madhya Pradesh (MP), Maharashtra (MH), Orissa (OR), Punjab (PU), Rajasthan (RA), Tamil Nadu (TN), Uttar Pradesh (UP), and West Bengal (WB). The secondary data source, “District Information System for Education” (DISE), is the main source of data for this analysis along with the data from Central Statistics Office, Budget documents of the state government, and Ministry of Statistics and Programme Implementation, Government of India.

3. Empirical Findings 3.1 Result of OUTTE Estimation For the purpose of this study, OUTTE is estimated for H.S. level constructing VRS frontier for each time period between the time frame 2010–2011 and 2015–2016. The average OUTTE scores for respective GCS, calculated using estimated OUTTE for each sample year considering different GCS, are reported in Table 5.1. The grand average value of OUTTE, taking into consideration all the GCS and all sample years, is also presented in Table 5.1.

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Table 5.1. OUTTE Scores of Higher Secondary Level for General Category States (GCS).

Andhra Pradesh Bihar Chhattisgarh Goa Gujarat Haryana Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Grand average

2010–2011

2011–2012

2012–2013

2013–2014

2014–2015

2015–2016

Average (A.M.)

1.000 1.000 0.983 0.943 0.995 0.998 1.000 0.992 1.000 0.953 1.000 1.000 0.986 1.000 1.000 1.000 1.000

0.946 1.000 0.982 0.980 0.975 0.979 1.000 0.992 1.000 0.948 1.000 1.000 0.980 1.000 0.986 1.000 1.000

0.940 1.000 0.980 1.000 0.973 0.980 1.000 0.994 1.000 0.952 1.000 1.000 0.985 0.961 0.995 1.000 0.963

0.938 1.000 0.981 0.960 0.971 0.971 1.000 1.000 1.000 0.955 1.000 1.000 0.981 1.000 0.983 1.000 0.964

0.981 1.000 0.981 0.977 1.000 0.971 1.000 1.000 1.000 0.928 1.000 1.000 0.993 1.000 0.985 1.000 0.960

1.000 1.000 0.981 0.965 1.000 0.967 1.000 1.000 1.000 0.928 1.000 1.000 0.980 1.000 0.986 1.000 0.964

0.97 1.000 0.981 0.97 0.99 0.98 1.000 0.996 1.000 0.94 1.000 1.000 0.98 0.994 0.989 1.000 0.96 0.985

Source: Author’s computation with DISE data.

Sangita Choudhury and Arpita Ghose

States

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The results of the initial estimation suggest that all the GCS are not turning fully technically efficient for each of the sample years and OUTTE differs within and between GCS. The results of Table 5.1 show the average OUTTE scores of 10 GCS namely, KE, KA, MH, RA, TN, OR, BI, JH, GU, and UP, are higher than that of the grand average OUTTE score 0.985. Table 5.1 also reveals the following result for H.S. education considering GCS. (i) KE, MH, JH, OR, UP and BI achieved full OUTTE all through the sample years, (ii) MP, CHHT, HA and PU are the states showing failure to achieve efficiency all through the time frame, (iii) Though initially KA and GU show inefficiency, gradually the states show perfect efficiency, (iv) Although started with efficiency, TN and WB states failed to maintain efficiency in the subsequent years, (v) GU and KA show an increasing pattern of OUTTE over the sample years though the efficiency of GU falls in intermediate year, (vi) Declining pattern of OUTTE scores over the sample years have been observed for WB, MP, HA, TN, CHHT and PU.

3.2 Factors Determining OUTTE As the dataset consists of the information on 17 GCS over six sample years, i.e., 2010–2011 to 2015–2016, conducting a Breusch–Pagan Lagrange multiplier test seems necessary for selection between pooled model and panel model for finding out the determinants of TE. Result of the test confirms appropriateness of using pooled model over panel regression model for the analysis. Further, simple pooled model is estimated here because of nonpresence of cross-sectional dependence in residuals, confirmed by Pesaran’s cross-sectional dependence (CD) test. The best fitted model among different alternative specifications is described in Table 5.2. The result of Table 5.2 identifies the proportion of girls’ to boys’ enrollment at H.S. level as a positively affecting significant social indicator variable, at 10% level of significance, in determining OUTTE of H.S. stage for GCS and confirms the fact that reducing gender disparity by increasing H.S. enrollment of girl students (relative to boys) can lead to an increase in OUTTE of H.S. level. Apart from the social indicator category, Table 5.2 also confirms poor infrastructure and policy indicator as crucial determinant categories of OUTTE of H.S. stage for GCS. Particularly, OUTTE of H.S. level is significantly affected by government education expenditure (as a ratio to total expenditure for the state) at 1% level and “proportion of para teachers” at 5% level, showing positive role of these variables. “Proportion of para teachers at H.S. level” is considered as a possible determinant of OUTTE as lack of availability of full-time teachers in the school is managed by employing para teachers, and thus its influence on OUTTE is important to examine. OUTTE at the H.S. level is also negatively and significantly influenced by “percentage of H.S. schools without building” (at 10% level) and “percentage

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Table 5.2. Significant Variables Determining OUTTE of Higher Secondary Stage, Considering GCS. Variables

Co-efficient

t Stat

P Value

Percentage of H.S. schools with ‘no building’ Proportion of para teachers in H.S. schools Percentage of H.S. schools with ‘no girls’ toilet’ Proportion of girls’ to boys’ enrollment at H.S. Government education expenditure (as a ratio to total expenditure for the state) Constant F(14, 87) 5 3.73

20.0005499

21.83

0.070

2.00

0.048

22.41

0.018

0.0002691

1.79

0.077

0.0033951

3.68

0.000

0.9412368

44.70 0.000 Prob . F 5 0.0001

0.0004828 20.0004066

Source: Author’s estimation with DISE data.

of H.S. schools without girls toilet” (at 5% level) showing negative roles of poor infrastructure.

4. Conclusion In this chapter nonparametric DEA is applied on the state level data of 17 GCS for each year between 2010–2011 and 2015–2016 under VRS assumption for estimation of OUTTE of H.S. education in India and also to find out determinants of such estimated OUTTE scores with the objective of testing the role of gender disparity, as reflected by the ratio of girls’ enrollment to boys’ at H.S. education, along with the roles of other socioeconomic variables in determining OUTTE by conducting regression analysis. Since the result of this chapter does not show perfect efficiency for all GCS, suggesting that output of H.S. level of these states can be further increased using existing inputs. Considering these states, variations of OUTTE scores are also observed. Application of pooled model is confirmed by the Breusch–Pagan Lagrange multiplier test for GCS. Thus, simple pooled regression model is opted for these states because of the absence of cross-sectional dependence in residuals as confirmed by Pesaran’s CD test. The result of this chapter showing a positive role of proportion of girls to boys enrollment at H.S. level, in determining OUTTE of H.S. for GCS confirms an increase in enrollment of girl students (relative to boys) at H.S. level will improve OUTTE. Apart from the role of gender in education enrollment, OUTTE of H.S.

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level is significantly and favorably affected by government education expenditure (as a ratio to total expenditure for the state) and “proportion of para teachers at H.S. level” and negatively affected by “percentage of H.S. schools without building” and “percentage of H.S. schools without girls toilet”. For GCS, the government should design more incentivizing policies targeting adolescent girls for enrolling them in formal education system to improve OUTTE at H.S. level. Besides, constructing school buildings and girls’ toilets for providing necessary facilities inside the school, increasing government education expenditure, and employing sufficient H.S. level teachers will also improve OUTTE of H.S. education in these states.

Note 1. https://www.unicef.org/india/what-we-do/gender-equality.

References Arshad, M. N. M. (2014). Efficiency of secondary education of a selected OIC countries. Global Education Review, 1(4), 53–75. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092. Barro, R. J. (1991). Economic growth in a cross section of countries. Quarterly Journal of Economics, 106(2), 407–443. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through. Management Science, 27(6), 668–697. Choudhury, S., & Ghose, A. (2022). Does an increase in enrollment of girls relative to boys stimulate technical efficiency of secondary education? Empirical evidence using non-parametric data envelopment analysis with Indian state level data. In C. Chakraborty & D. Pal (Eds.), Environmental sustainability, growth trajectory and gender: Contemporary issues of developing economies (pp. 207–218). Bingley: Emerald Publishing Limited. Ciro, A. J., & Torres Garc´ıa, A. (2018). Economic efficiency of public secondary education expenditure: How different are developed and developing countries? Desarrollo y Sociedad, (80), 119–154. Gavurova, B., Kocisova, K., Belas, L., & Krajcik, V. (2017). Relative efficiency of government expenditure on secondary education. Journal of International Studies, 10(2), 329–343. Ghose, A. (2017). Efficiency of elementary education in India: Empirical evidence using a nonparametric data envelopment approach. New Delhi: Springer (India) Pvt. Ltd. Haug, A. A., & Blackburn, V. C. (2017). Government secondary school finances in New South Wales: Accounting for students’ prior achievements in a two-stage DEA at the school level. Journal of Productivity Analysis, 48(1), 69–83.

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Hill, M. A., & King, E. M. (1993). Women’s education in developing countries: An overview. In E. M. King & M. A. Hill (Eds.), Women’s education in developing countries: Barriers, benefits and policies (pp. 1–50). Baltimore, MD: John Hopkins University Press. Hulten, C. R. (1996). Infrastructure Capital and Economic Growth: How well you use it may be more important than how much you have. NBER Working Paper Series, Working Paper 5847. Klasen, S. (2002). Low schooling for girls, slower growth for all? Cross-country evidence on the effect of gender inequality in education on economic development. The World Bank Economic Review, 16(3), 345–373. Klasen, S., & Lamanna, F. (2009). The impact of gender inequality in education and employment on economic growth: New evidence for a panel of countries. Feminist Economics, 15(3), 91–132. Knowles, S., Lorgelly, P. K., & Owen, P. D. (2002). Are educational gender gaps a brake on economic development? Some cross-country empirical evidence. Oxford Economic Papers, 54(1), 118–149. Lucas, R. E., Jr. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407–437. Masci, C., De Witte, K., & Agasisti, T. (2018). The influence of school size, principal characteristics and school management practices on educational performance: An efficiency analysis of Italian students attending middle schools. Socio-Economic Planning Sciences, 61, 52–69. Mohapatra, R. (2015). Ranking of efficient states of India on the basis of performances in secondary education: An application of super efficiency models. Asian Journal of Research in Social Sciences and Humanities, 5(12), 1–15. Planning Commission Government of India. (2012). Report of the working group on state’s financial resources for the twelfth five year plan. Retrieved from https://niti. gov.in/planningcommission.gov.in/docs/aboutus/committee/wrkgrp12/wg_state_ finan0106.pdf Ray, S. C. (1991). Resource-use efficiency in public schools: A study of Connecticut data. Management Science, 37(12), 1620–1628. Ray, S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. New York, NY: Cambridge University Press. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71–S102. Sankar, D. (2007). Education system performance among Indian states: A public expenditure efficiency analysis using linear programming methods. Washington, DC: South Asia Human Development Unit (SASHD) of the World Bank. Sengupta, A., & Pal, N. P. (2010). Primary education in India: Delivery and outcome—A district level analysis based on DISE Data. Journal of Educational Planning and Administration, 24(1), 5–21. Sengupta, A., & Pal, N. P. (2012). Assessing the primary schools—A multi-dimensional approach: A school level analysis based on Indian data. International Journal of Educational Development, 32(2), 264–272.

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Singh, N., Pant, M., & Goel, A. (2018). ANN embedded data envelopment analysis approach for measuring the efficiency of state boards in India. International Journal of System Assurance Engineering and Management, 9(5), 1092–1106. Springer; The Society for Reliability, Engineering Quality and Operations Management (SREQOM), India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, October. Sotiriadis, D., Menexes, G., & Tsamadias, C. (2018). Investigating the efficiency of senior secondary schools: Evidence from schools in the Greek region of Central Macedonia. International Journal of Business and Economic Sciences Applied Research, 11(2), 36–43. Tyagi, P., Yadav, S. P., & Singh, S. P. (2009). Efficiency analysis of schools using DEA: A case study of Uttar Pradesh state in India. Department of Mathematics, IIT. Retrieved from https://www.researchgate.net/.../254871435 UNICEF India. Retrieved from https://www.unicef.org/india/what-we-do/genderequality. Accessed on May 5, 2020.

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Chapter 6

Understanding Gender Through an Educational Construct Manisha Subba

Abstract Gender studies have become an important area of study in recent times. It is being accorded an important place in the curriculum, both at the school and university levels. The many misconceptions that exist regarding the term “gender” need to be addressed, the most common being that gender has solely to do with women and their issues only. The basic important idea that gender studies is inclusive of female, male, and third gender and their issues isn’t understood nor made aware to many. The role of education has become all the more important so that we are able to break the prevalent societal stereotypes and address the existing gender inequality. This chapter attempts to present various feminist theories that have contributed to the understanding of gender. The important role of the schools and in particular the textbooks in socializing and building learners’ understanding of the sociopolitical contexts cannot be negated. Hence, the chapter will conclude by analyzing how gender content and issues are experienced and get represented in the school curriculum and the textbooks. Many researchers have emphasized the need for gender inclusion to achieve holistic and sustainable development goals. This is important because only with the achievement of social equality can we work toward economic equality. Keywords: Gender; feminist theories; education; curriculum; textbooks; representation

1. Introduction Gender studies have become an important area of study in recent times. It is being accorded an important place in the curriculum. The many misconceptions that exist regarding the term “gender” need to be addressed, the most common being that gender has solely to do with women and their issues only. The basic Gender Inequality and its Implications on Education and Health, 69–78 Copyright © 2023 Manisha Subba Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231007

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important idea is that gender studies are inclusive of female, male, and third gender. The role of education is important to break the prevalent societal stereotypes and address gender inequality. This is important because only with the achievement of social equality can we work toward economic equality. The chapter will focus on various feminist theories that have contributed to the understanding of gender and highlight how gender practices get reflected in the school curriculum and the textbooks.

2. Feminist Theories In building our understanding of gender, it is important we become familiar with different feminist perspectives. These may seem to be overlapping at places, but it is important to acknowledge these various theories in our quest for understanding the struggle for equality throughout history. Besides giving us an insight into human society, they open us to the possibilities to the larger goals of emancipation and social change. These debates help bring to the forefront the questions of subordination and exclusion that people continue to experience worldwide. It is only when theories confirm existing grievances and problems, then activists, educators, and researchers can legitimize their resistance and further work toward developing perspectives to theorize their experiences and struggles and march ahead toward the goal of social equality. Five feminist theories would be discussed—Liberal, Marxist, Radical, Psychoanalytic, and Socialist.

2.1 Liberal Liberal feminists were influenced by the school of liberal political thought which upholds our uniqueness as human beings due to our rationality. This rationality could be defined in moral or prudential terms, but what is agreed upon is the importance of individual autonomy and toward self-fulfillment, provided we don’t do injustice to others. They were influenced by the liberal democratic slogan of the French Revolution—Liberty Equality Fraternity. They argued that women too were rational human beings with the potential to be responsible and fully capable on their own. Basically, the fight was to rectify the laws that then existed which excluded females from the public sphere, and to gradually replace these with new laws that would provide equality of opportunity to all. The practice of following different norms for girls and boys is a prerequisite for the inequality to exist, as the process of socialization ensures forced difference in subject preference and profession selection. The struggle has been to remove the hindrances that prevent girls from achieving their educational goals. Unless this is achieved, they won’t be able to understand the process of socialization and the sex stereotyping that exists in society. As the society already labels which subject and profession are fit for girls and others for boys, so for most their future gets limited to sex-stereotyped professions and family roles. This would impact both females and males negatively and without the freedom to decide for themselves. On one hand, females would think of themselves as being in a disadvantaged position

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where there is dependency and things forced onto them; on the other hand, males would also be forced to suppress their actual interests and pursue only those professions which are deemed acceptable. Both of them would have to restrain their emotional needs and “act” the stereotypical roles society demands of them. This further leads us to the issue of discrimination, rights and fairness as subjects, and eventually specialized professions would not be accessible to all. For example, science and technology hasn’t been accessible to females due to societal pressure or lack of institutional infrastructure, and this would lead to fewer females in the area of science and technology. The main objective has been to provide equal opportunities for both sexes. Most importantly, we shouldn’t harbor preconceived notions regarding which subjects and professions to be pursued on the basis of one’s sex.

2.2 Marxist Marx and Engels’ theory has influenced the Marxist feminists and the women question of subjugation. They provided a systematic explanation which stated that, “women’s subordination began with the development of private property, i.e., both the division of classes and the subordination of women developed historically” (as cited in Bhasin, 1993, p. 23). With the coming of settled agriculture and domestication of animals, humans began to have secure lives and also began to accumulate production. This control of production and animals led to the idea of private property and eventually power. But to retain this property and power and to pass it to their own children, women had to be “domesticated” and turned into a private property so that their sexuality could be controlled. To quote Engels, this was “the world historic defeat of the female sex” (as cited in Hamilton, 1993, pp. 12–13). Though the historical context and relations were the same, Marxists suggested that it impacted the females and males differently. With the coming of the capitalist economy and goods production in the public space, the role and contribution of women were termed “unproductive” whereas the work done by men was termed “productive”. While women became subordinated beings associated with procreation, rearing and doing unpaid, unappreciated household works, men became powerful beings who controlled the economy and owned properties. The studies by Marxist feminists have yielded better understanding on the work-related concerns of women. They have highlighted the concerns of the working-class women, who share the same concerns as any other working-class men such as issues of wages, benefits, working hours, and safety procedures. They may have exclusive concerns too such as issues of reproductive control, maternity leave, childcare, unequal pay for same work, sexual harassment, discrimination, and legal rights. Though the contribution of Marxist theory is appreciated, it has been critiqued for not acknowledging the important contribution made by women in the development of agriculture or in the economic spheres. It surely gets underrepresented and hence never discussed. This restricted basis of women subjugation on the basis of economy doesn’t give a clear picture in understanding

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the larger underlying factors leading to subordination of women. It is no guarantee that the change in the public sphere will bring about a change in the private sphere. There are innumerable instances wherein women who are economically emancipated or in working-class families with no material basis have yielded no concrete changes to their position as women in the society. They continue to reel under the practice of patriarchy and being subjugated to male dominance even in contemporary times.

2.3 Radical Radical feminists believed that patriarchy came way before private property. In contrast to Marxist feminists, they proposed that the basic struggle is between the sexes rather than between classes. There is control and ownership of men over women’s reproductive capabilities, which is the basis for the subordination of women. They stand up against the ideological and material dominance of men over women, and demand for a fundamental change in the social structure with the “abolition of gender as an oppressive cultural reality” (Acker, 1994, p. 50). They have been instrumental in highlighting women’s issues and real narratives of women who have experienced such extreme cases of violence in everyday conversation, which used to be generally ignored nor acknowledged earlier. They have highlighted the monopolization of culture and knowledge by males, with a complete denial of knowledge, resources, self-esteem, and freedom from fear and harassment of women. This could be reflected through the domination of men in the decision-making process in all contexts, be it the family or any organization; or the language and visuals used to depict females; all which control the way how women conceptualize themselves and the world. The other aspect which they highlighted is the issue of sexual politics, wherein it has been shown “how women’s bodies can be used by men against women” (Tong, 1989, p. 138). In fact, they have contributed to theorizing violence and patriarchy revealing how men control women’s bodies and eventually procreation. This enables men “to rape, to intimidate and control women” (Bhasin, 1993, p. 26). In recent years, radical feminists have helped make connections of pornography, prostitution, sexual abuse, rape, and physical violence against women as symptoms and symbols of controlling female sexuality by the males. There have been studies which highlighted the problems faced by females, both students and teachers, in schools. These could be in the form of harassment such as verbal abuse, physical abuse, and visual abuse such as graffiti and pornographic drawings. This could be a practice in any public setup as well. These abovementioned incidents may get further multiplied by racist, casteist, and homosexual cases, affecting both sexes equally.

2.4 Psychoanalytic Psychoanalytic feminists have taken forward developing Freud’s Psychoanalysis in feminist directions. The first attempt has been to reject the biological basis of

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determinism and the second attempt has been to analyze the pre-Oedipal stage rather than the detailed discussion of Oedipus complex, and to reinterpret the Oedipus complex by using nonpatriarchal lenses. The first group of theorists stressed the experiential and cultural influences as important factors for the growth of a person, rather than the biological determinism. It is the social ideas and the lack or presence of social constraint that determines any person’s holistic development, and not the unchanging male and female biological organs they possess. Though Biology was used to predetermine roles and expected roles of males and females, there are no scientific bases for the same. So, there is an immense need to transform “the legal, political, economic and social structures that contain culture” as a necessary step in transforming of women’s psychology (Tong, 1989, p. 148). The second group of theorists stresses that the oppression of women is the result of parenting being monopolized by females. Our in-depth interaction of being mothered may lead us to the process of rejecting our mother and giving less importance to all things female, blaming them for all our rejections. Both girls and boys may distance themselves from their mothers eventually as they look for power and independence in their father figure. The need for dual parenting has been suggested by these sets of theorists because the equal participation of the father and the mother would help lessen the intensity of the mother–child relationship. The stereotypical gender roles would not be percolated to the next generation as the fluid gender roles of their parents would set precedence of things. For example, girls and boys would group up with the understanding that both women and males can be caring as well as independent. Girls need not assume feminine roles as definite nor do boys reject feminine roles as unworthy of a man. Overall, the psychoanalytic feminists have been instrumental in addressing the social and symbolic attributes of gender differences which they believe is deep-rooted in our psyches. They have tried to reinterpret various gender stereotypical notions about sexual behavior and mothering that exists in the society. This reflection into the inner self is an alternative approach in understanding of self and the others, keeping in consideration each one of us is unique and equal.

2.5 Socialist Socialist feminists combined the positions of Marxist, Radical, and Psychoanalytic feminists and have developed two different approaches to explain women’s oppression (Tong, 1989, p. 175). These are the dual-systems theory and the unified-systems theory. Dual-systems theorists agreed that economic class and sex class are two different struggles and the need to understand the relationship between the two to fully understand oppression. They do not agree that the oppression of women would disappear with the overthrow of capitalism because patriarchy is not a consequence of the rise of private property. There is a need to analyze and understand both the social relations separately and then dialectically in relation to

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each other. The struggle is dual, both against patriarchy as well as capitalism. They are influenced by the Reproduction Theory and argue that instead of capitalist and biological relations, the relations of reproductions are “cultural reproductions which are carried over from one historical period to another” (Bhasin, 1993, p. 28). They rejected the biological basis of differentiation and instead emphasized on the ideological and political bases of differentiation. The question is not of “when” but rather “how” and “why” did the division of labor become a hierarchical relationship of power and subjugation. Whether it is a capitalist country or a socialist country, women continue to experience patriarchy in various ways such as unequal wages, sexual harassment, and unpaid household work. Unified-systems theorists attempted to analyze and understand both the social relations of capitalism and patriarchy together, clubbing these as one concept. Instead of a Marxist analysis which gives a general “gender-neutral understanding” of the capitalist system, or a Radical and Psychoanalytic analysis which gives a “gender-biased” understanding of capitalism, these theorists called for a division-of-labor analysis as a substitute for a nuanced explanation of a “genderbiased” understanding of capitalism (Tong, 1989, p. 184). They emphasized that workers aren’t interchangeable but are influenced by gender, race, and ethnicity. Schools are seen as places of socialization where there is reproduction of sexual as well as social division of labor in the family and workplace (Acker, 1994, p. 98). Females are restricted to take up clich´ed subjects and eventually professions deemed fit for them such as Humanities, Home Science, Teaching, and Nursing, to name a few. Hence, it is seen that the education sector influences the economy by determining the roles for females and creating a large number of females for low-paid sectors or taking up professions having a low social acceptance.

3. School Practices and Representation in Textbooks Many researchers have shown how dominant ideas of gender roles influence what students get to experience and learn in educational institutions. These may get transmitted through the various curricular practices which reflect male domination in the representation of ideology, access to schooling and resources, teachers’ representation, games played, examples undertaken during discussions, representation or underrepresentation in textbooks, selection of subjects and professions, and so on. All these would be in stark contrast to the objectives of equal access, giving space to discussions on the ideas of equality, and providing opportunities of representation across gender in the curriculum. This section attempts to analyze existing curriculum practices and representations in the textbooks to understand the gaps. These may be important to identify the challenges to gender construct and find ways to address the same.

3.1 Curriculum Practices The “hidden” curriculum in educational institutions may contribute toward learners’ construction of gender. The social processes in the schools are an

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extension of the gender socialization experienced within the family. Norms, values, attitudes, expectations, behaviors, and experiences are encouraged and internalized by young learners. Nambissan (1995) highlighted the fact that, “teacher attitudes and pedagogic practices play a crucial role in reinforcing gender inequality in education” (p. 197). It may be used as an “organizational category” such as sex-based seating arrangements, formation of groups, and allocation of tasks identified as “masculine” given to boys and “feminine” tasks given to girls. Bhattacharjee (1999) in one study of a primary school in Gujarat also reiterated the sex-based organizational arrangements during the morning assembly, in classrooms and on the playground, sexual division of labor in the classroom with “boys minding boys, running errands outside the school, carrying furniture, serving lunch during mid-day meal; and girls minding girls, cleaning and sweeping, entrusted with keys, carrying registers and teacher’s tea cups, teaching in teacher’s absence or when she’s busy, writing questions and answers on the board” (p. 341). The girls were given tasks which projected them as dependable while boys had tasks which required strength and also moved outside the school campus. Bassi (2003) in an exploratory study done in a Kendriya Vidyalaya, and Narang (2014) in a study of a government school in Delhi have stated similar observations. In addition, teachers supported sex-differentiated cocurricular activities, with girls expected to take up dance, music, and knitting and the boys encouraged to take up sports and debating. Rather than encouraging the students to take up activities based on interest and abilities, they were expected to take activities based on biological determinism. Ghosh (2012) stated the introduction of domestic science or home science in the 1920s as a step-in feminization of the content of science education. It was to impart “simplified and fragmented knowledge of different branches of science and social science” to give females a sense of receiving education within the social boundaries and making them efficient for domesticity (p. 86). She further discusses the issue of female underrepresentation in science education. Females continue to constitute the “minority in science-based courses like agriculture, veterinary science, engineering/technology” (p. 84). Exception is seen in the field of medicine, wherein more numbers of women are visible as it’s a profession deemed as appropriate for women and adhering to “the socially prescribed conventional roles of women as nurturers” (Ghosh, 2012). Manjrekar (2013) in her study with students from migrant families in a municipal school in the city of Baroda, Gujarat, tried to understand the process of how students and teachers construct what is “work” in a language class. She discusses how various intersections like gender, class, caste, and nation interweave and influence this understanding, further linking it with the larger issue of work as a means to nation-building. Though girls and boys contributed to domestic work and assisted their family in their trade, the work done was differentiated and evaluated differently by them. She found how boys were encouraged by the teacher for choosing professions such as police, scientist, engineer, doctor, farmer, and leader which was seen as contributing to the nation’s development process; whereas a majority of the girls chose a profession of teaching which was seen as

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noble with the teacher emphasizing the role of women as moral agents in building good citizens for the nation’s progress. Kumar (2009) discusses the impact of total segregation of boys and girls in their growing up years. This along with the influence of media and society leads to how boys learn to relegate girls as objects rather than as humans. Rather than viewing schools as a place where the process of gender socialization continues, he emphasizes the “need to perceive school in conflict with the community’s code of socialization” (p. 87). This can be possible by making important changes in teacher training programs wherein teachers are able “to deal with boys and girls in an undiscriminating way” (Kumar, 2009, p. 84).

3.2 Representation in Textbooks Textbooks provide the major resource at the school levels and are a medium of gender socialization of school children. There is an attempt to discuss a few studies which address the representation or underrepresentation of gender in textbooks. Mirza (2004) in the analysis of 194 textbooks across grades I-X of various subjects in Pakistan has found that the textbooks depict stereotypical gender differential attributes. Males were depicted as brave, truthful, wise, and kind, whereas females were shown as modest, helpless, pious, and beautiful. As the grades progressed there was gradual withdrawal of female characters from the textbooks, which was reflective of the social norm wherein females are expected to withdraw from the public sphere as they grow older. Kerkhoven, Russo, Land-Zandstra, Saxena, and Rodenburg (2016) analyzed the visual content of two online primary science education resources, Scientix, a Europe-wide website; and OER commons, an international website. It was found that adult males and females were depicted in stereotypical ways whereas there was no significant difference in depiction of boys and girls. More males were shown in science professions whereas more women were shown as teachers. In both their studies, Dawar and Anand (2017a, 2017b) have highlighted the male biases as illustrated in texts, illustrations, and language of international as well as national level of textbooks. The notion that males are superior and females are inferior has been communicated through the stereotypical depictions as well of invisibility of women both in content and visuals. Bhog (2007) addresses the attempt as part of a Delhi government project to humanize middle school “civics” textbooks by looking at gender “as a space of social inequality” (p. 69). Rather than increasing the number of women or depicting role reversals in the textbooks in the name of innovative textbooks, the interventions were done through depictions of situations which would be relatable to the students through narratives and personal experiences, presenting issues and problematizing them within “other structures of power like class and caste” with examples of women with various identities (Bhog, 2007, p. 70). Most importantly, the objective was to aid learners to imagine women in the public space. Post the

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National Curriculum Framework 2005, NCERT has attempted gender inclusion in different subjects at the elementary stages. In its analysis of textbooks of 10 Indian states, it has highlighted the need to include experiences of ordinary women and their everyday struggles, aspirations, coping strategies, and success (Department of Gender Studies, NCERT, 2013–2016). There is a need to represent people who are impacting in leading and unconventional roles.

4. Conclusion Many researchers have emphasized the need for gender inclusion to achieve holistic development goals. Studies have shown that work participation of both genders lead to higher productivity, financial progress, and increase of Gross Domestic Product (Lagarde & Ostry, 2018). These outcomes would further escalate income, welfare, and overall growth. Thus, there is a need for gender equity which would help contribute to development. But, unless the larger goal of social equality is achieved, we cannot work toward economic equality. The struggle for the rights to equality and education, entry into various professions, the right to own property, hold public offices, and most importantly become active citizens is still a distant reality for many females and third gender. The prejudices and stereotyping related to gender needs to be addressed by building learners who can think critically on issues and contemporary debates both within and outside the institutional setup. For this, we need to have an education that includes gender construct as an important concept as part of its curricular and textbooks engagement right from the primary to the university levels. The stark difference in experiences of male and female students; the minimal discussion of female perspectives and their issues; and the complete negation of experiences of third gender in the curriculum point to an unequal representation. As educators, it is imperative that we reflect on our curriculum, which includes the syllabi, textbooks, and various resources to be used in the classrooms. The curriculum must focus on more theoretical and academic oriented content, with emphasis on strategies of resistance to the existing hegemony. There is a need for building critical perspectives by bringing in real issues and challenges, and problematizing these in the classrooms highlighting human rights, women’s rights, and LGBTQIA1 (lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual, and more) rights. There is a need to include discussions on sexuality and sexual harassment in the forefront within educational institutions and in the society. Real narratives of resistance and change within educational setup and outside would be inspiration for the students to stand up to the challenges in the real world. In addition, we also need to look into the existing preservice and in-service teacher training programs and prepare our teachers to critically address the rampant sexism that exists around us. Besides curricula revisions and a participatory pedagogy, there is a need to instill the belief that change is possible and it is us, the teacher and student community, who can make it happen.

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References Acker, S. (Ed.). (1994). Gendered education: Sociological reflections on women, teaching and feminism. England: Open University Press. Bassi, T. (2003). Gender in school: Observations from an exploratory study. Journal of Indian Education, XXIX(3), 135–145. Bhasin, K. (1993). What is patriarchy? New Delhi: Kali for Women. Bhattacharjee, N. (1999). Through the looking glass: Gender socialization in a primary school. In T. S. Saraswathi (Ed.), Culture, socialization and human development: Theory, research and applications in India (pp. 336–355). New Delhi: SAGE. Bhog, D. (2007). Textbooks and gender sensitivity. In S. C. Jakka (Ed.), Textbooks & The School Curriculum: A Report of the Sir Ratan Tata Trust Colloquium on Education, 2004 (pp. 68–77). Mumbai: Comet Media Foundation. Dawar, T., & Anand, S. (2017a). Gender bias in textbooks across the world. International Journal of Applied Home Science, 4(3 & 4), 224–235. Dawar, T., & Anand, S. (2017b). Gender roles representation and portrayal: An analysis of primary level NCERT textbooks. The National Life Skills, Value Education and & School Wellness Program, 3(2), 7–15. Department of Gender Studies, NCERT. (2013–2016). Analysis of the textbooks of Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Odisha, Maharashtra, Manipur and Rajasthan: An Overall Report. New Delhi: NCERT. Ghosh, P. (2012). Women and science education in India: A saga of marginalization. Science and Culture, 78(1–2), 84–89. Hamilton, R. (1993). Feminist theories. Left History: An Interdisciplinary Journal of Historical Inquiry & Debate, 1(1), 9–33. Kerkhoven, A. H., Russo, P., Land-Zandstra, A. M., Saxena, A., & Rodenburg, F. J. (2016). Gender stereotypes in science education resources: A visual content analysis. PLoS One, 11(11), e0165037. Kumar, K. (2009). What is worth teaching? (4th ed.). New Delhi: Orient Longman. Lagarde, C., & Ostry, J. D. (2018). Economic gains from gender inclusion: Even greater than you thought. Retrieved from Economic Gains from Gender Inclusion: Even Greater than You Thought (imf.org). Manjrekar, N. (2013). Gender, childhood, and work in the nation: Voices and encounters in an Indian school. In G. B. Nambissan & S. Srinivasa Rao (Eds.), Sociology of education in India: Changing contours and emerging concerns (pp. 157–181). New Delhi: Oxford University Press. Mirza, M. (2004). Gender analysis of school curriculum and textbooks. Islamabad: UNESCO. Nambissan, G. B. (1995). Gender and education: The social context of schooling girl children in India. Perspectives in Education, 11(3 & 4), 197–207. Narang, N. (2014). Exploring gender relations in the context of school practices. IMPACT: International Journal of Research in Humanities, Arts and Literature, 2(2), 73–82. Tong, R. (1989). Feminist thought: A comprehensive introduction. Boulder, CO: Westview Press.

Chapter 7

Gender Inequality in India Intertwined Between Education and Employment Dyuti Chatterjee and Pallabi Banerjee

Abstract Gender inequality is one of the most concerning issues for a developing country like India. Gender inequality has many dimensions which are intricately related to the socioeconomic structure of the country. The chapter highlights two dominant factors leading to gender inequality in the country – education and employment. Empirical evidence suggests that the gross enrollment of females decreases from the upper primary level of schooling onwards. Moreover, higher education for women has not translated to higher employment post liberalization. India continues to be a country with one of the poorest female work participation ratios. Employment along with education is a key tool to improve the condition of women in our society. The chapter concludes that an integrated approach linking education of women and employment is essential for the reduction of gender inequality. Keywords: Gender inequality; education; gross enrollment ratio; socioeconomic structure; work participation ratio; India

1. Introduction The term “Gender Inequality” refers to discrimination between men and women. Gender inequality is universal and a common phenomenon to all countries across the globe. Evidence shows that throughout the world, women receive lower wages than men. Gender inequality is most reflected in the spheres of education and employment. This adversely affects the quality of life of women and the economic growth of the country (Moheyuddin, 2005). Needless to say that the magnitude of gender inequality is far greater in developing countries like India than the developed countries. The United Nations has announced the Sustainable Development Goals to be achieved by 2030, which separately includes the Agenda of Gender Equality. It has given special importance to establish equality for women Gender Inequality and its Implications on Education and Health, 79–90 Copyright © 2023 Dyuti Chatterjee and Pallabi Banerjee Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231008

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among all sections and age groups worldwide, by eliminating the effect of poverty, food insecurity, and other economic adversities (World Bank Report, 2000). According to the United Nations’ Human Development Report (2013), India ranks 132 out of 187 countries on the Gender Inequality Index despite the fact that the Constitution of India guarantees equality among every citizen as one of their rights. In India, men and women in general receive different treatment right from their birth starting from access to resources to opportunities at their workplace.

2. Literature Review A review on the existing literature on gender inequality can be divided into two strands – gender inequality in education and gender inequality in employment. Kingdom and Unni (1998) claim that women in India “acquire substantially less education than men.” However, education by itself has a significant relationship with wages for both men and women. In fact, there is an increased return to education with increased higher education. Women’s return to education is much higher than that of men. Each extra year of schooling raises the wages of women by 2% more than that of men. Sen (2001) has summed up some of the important types of gender inequalities present in the Indian society as: Natality Inequality: The strong desire to have a male child often leads to the abortion of the female fetus that has worsened the child sex ratio for India. Male children, regarded as the main wage earner of the family, have always been the preference to the parents for a long time. Ownership Inequality: For a long time, the traditional property rights were in favor of men debarring women to any kind of claim over the land of their fathers. This automatically reduced the availability of capital for women and made it harder for them to venture into new territories. However, with the Equal Inheritance Right (2005), married as well as unmarried daughters now have equal claim to their father’s property along with the son. Employment Inequality: Women have fewer opportunities than men when it comes to work. Balancing work and household pressure itself is a big challenge for women. Discriminations are faced with regards to promotion, salary, etc. Chanana (2006) points out that the gender disparity in education in India could stem from the fact that women had access to higher education much later than men. It was only after independence when higher education became state funded and fully subsidized those women started entering colleges and universities on a large scale. However, women mainly clustered in nonprofessional and nonmarket courses in general education. Women started entering the so-called male-dominated professions post globalization. Globalization saw the rise of private institutions which catered to this need of new professional courses like Computers, Management, etc. Chanana states that a hefty tuition fee charged by the private tuitions does not allow women from all fields to join them thus increasing gender inequality.

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Ghai (2018) is of the opinion that education in India has been used to “domesticate rather than empower women.” Thus, higher education levels have not translated into higher work participation for women despite a reduction in gender education gap. Regarding gender gaps in employment, there are a number of closely related arguments. According to Esteve-Volart (2004), it imposes a distortion on the economy as do gender gaps in education. It artificially reduces the pool of talent from which employers can draw upon, thereby reducing the average ability of the workforce. Klasen (2006) reinforce the fact that gender inequality plays a crucial role in lowering the economic performance of a nation as human development is severely impacted by gender inequality. Women constituting almost half the population receive a lower level of skills and education in the presence of gender biasness. This automatically restricts the pool of talent excluding qualified girls who can participate in economic activities. Klasen found evidence for the U-shaped curve between education and female labor force participation. Women who are illiterates or with very poor education generally tend to join the workforce to support the family. However, when their education level increases, they tend to be choosy about their jobs and tend to remain at home. Thus, female labor force participation drops at this stage. It is only when women achieve a high level of education that they again choose to join the job market where the labor force participation rate increases again. This leads to the rise of the famous U-shaped curve between education and female labor force participation. The views of society, customs, and cultural practices play a big role in keeping women at lower places in the society – refusing them opportunities, which are generally provided to men. Ravindranath and Viswanathan (2021) analyzes the role of social capital affecting gender parity in higher education. Higher education, however, not being a compulsion like secondary level education, becomes a personal choice. In this case, social capital in the form of social networks, occupation, or education of family members play a crucial role for girls getting into different fields of their choices. This gives rise to higher levels of gender disparity for female students enrolling in higher studies, especially in the non-arts stream.

3. Research Gap and Objective Gender inequality is a complex multifaceted problem in a developing country like India. Gender inequality overall has not received the attention that it requires. There are different research papers which have worked on different dimensions of gender inequality separately. For example, research papers highlighting gender inequality in education focus on how women are clustered in general subjects, such as arts, humanities, etc. However, there is not enough work on gender inequality in employment. Here only a handful of researchers have worked on occupational segregation across gender in the sphere of employment. The fact that there is a strong linkage between gender inequality in education and that of

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employment is not strongly established in these papers. The chapter tries to address the issue of gender inequality in education and employment. The main objectives of the chapter are thus twofold. (1) To study the levels of education across men and women and analyze the factors leading to gender inequality in education. (2) To discuss occupational segregation between men and women in the sphere of employment.

4. Data Source and Methodology The chapter uses secondary data from various sources. Census 2001 and 2011 data have been used to compare sex ratios and employment figures. The data related to education have been taken from the All-India Survey of Higher Education (2015–2016) published by the Ministry of Human Resource Development, Department of Higher Education. The hierarchy of the Indian education system is divided into four different levels, elementary, secondary, senior secondary, and higher education. In this chapter attempts have been made to analyze the instances of gender inequality at different levels of education. A comparison between male and female enrollment ratios at different levels of education shows the basic reflection of gender disparity. Further dropout rates at these levels and gender parity index over a time period (2010–2015) have been examined to address the levels of inequality in the field of education over time. NSSO sample survey reports on employment and unemployment situations for different rounds between the time period 1993 and 2011 have been used to compare the rural– urban Work Participation Rate of males and females. The Census 2011 identifies nine occupational divisions at the one-digit level. The one-digit level occupations are classified as: i. ii. iii. iv. v. vi. vii. viii. ix.

Legislators, senior officials, and managers Professionals Technicians and associate professional Clerks Service workers and shop and market sales workers Skilled agricultural and fishery workers Craft and related trades workers Plant and machine operators and assemblers Elementary occupations

The percentage of female workers calculated on the basis of the Census 2011 data show how women in both rural and urban areas are clustered around certain occupations.

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5. Data Analysis and Interpretation One of the earliest manifestations of gender inequality is the sex ratio of the country. There is a huge regional disparity among the states, regarding gender inequality. The Census 2011 shows that overall sex ratio of the country is 940 in 2011. Child sex ratio (0–6) age group is 914 which is the lowest level since independence. Among the states, Kerala (1,084), Tamil Nadu (996), and Andhra Pradesh (993) have the highest sex ratios, while states like Haryana (879) and Punjab (895) have the lowest sex ratios. The Census (2011) shows that the literacy rate of females is 65.46% as compared to male literacy rate which is 82.14%. The female literacy rate is particularly low in states like Rajasthan (52.7%), Jharkhand (56.2%), and Uttar Pradesh (59.3%), and the gap between female and male literacy rate in these states are significantly wide (Census, 2011). The enrollment of girls in different levels of education has always been lower than boys. While enrollment of female students at primary level of education is around 48%, in the higher education category the enrollment falls to 45%. From the table, it can be observed clearly that though the dropout rate is quite similar in girls and boys at the primary level, it is much higher for girls at the upper-primary level (Table 7.1). Evidence also suggests that girls are less likely to take up professional courses. Professional courses being costlier, parents lack interest to invest in higher studies of girls. STEM subjects (Science, technology, engineering, and mathematics) particularly have a record of lower enrollment of girls since a long time. It can be inferred that, education for women is considered more like an optional facility and their talent as skilled labor receives less access to education or employment facilities. Gender inequality in education can be attributed to several causes, some of which have been discussed below.

5.1 Poverty The data from Census 2011–2012 show 29.5% of the people are still below the poverty line. In particular, 30.9% of rural people are still below the poverty line. In such poor households, the choice of expenditure for the children’s growth and care is usually biased toward male children (Rammohan & Vu, 2018). Parents

Table 7.1. Dropout Rates of Male and Female Students at Different Educational Levels. Primary

Upper Primary

Year

Boys

Girls

Total

Boys

Girls

Total

2011–2012 2012–2013 2013–2014

5.89 4.68 4.53

5.34 4.66 4.14

5.62 4.67 4.34

2.13 2.3 3.09

3.2 4.01 4.49

2.65 3.13 3.77

Source: Author’s representation based on https://educationindia.gov.in/.

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choose to educate and protect their sons to secure financial as well as social securities in future. The nutrition of girls and their proper growth and education cannot be prioritized due to acute poverty. High population growth in past decades has made the situation even more complicated. Thus, a gender-based choice becomes obvious.

5.2 Patriarchal Society Indian society is patriarchal by nature. Historically, the male members have been expected to be the main breadwinners of the family. The females in the family on the other hand are expected to stay at home and take care of the family (Jayachandran, 2015). Since the female members are economically dependent on the male members, they have little or no bargaining power at home.

5.3 Social Customs, Beliefs, and Practices Our social customs and practices often deprive women from the opportunities which are provided to men. In many households and communities, women are prevented from pursuing higher education or doing a job to protect family honor (Swain & Purty, 2018). It is also a common belief that for highly educated girls, it would be difficult to find a matching groom. Early marriage and dowry have a major role to play behind such disparity. The fertility rate is higher in case of early marriages. After marriage, a very low percentage of girls are thus able to carry on with their education. Due to the pressure of dowry, parents do not find it worthy to invest in the human capital of the girl child; rather they tend to save more for their marriage. The reality is for lower and middle-income families, it becomes too difficult to invest for both purposes and the common trend is to choose any of them. Parents feel it unwise to invest too much on the education of girls since once married, a girl moves out of her parents’ house.

5.4 Lack of Awareness and Willingness Among Women Women are often not aware of their social rights. The main reason behind this is that women consider men to be more superior to them and are therefore content playing second fiddle to them. Moreover, it has been found out that women themselves are often not willing to pursue higher studies or seek employment in the job market. Apart from these factors, lesser number of educational institutions and poor teacher–student ratio traveling to distant places for school and college education often forces girls to drop out. Education for girls somehow was never viewed as an impetus to development of skilled labor in the economy. Thus, education for females has an intrinsic constraint in creating a significant impact in female labor force participation. From Fig. 7.1, it can be inferred that gender parity in higher education has always been lower compared to primary and secondary levels of

Gender Inequality in India

Fig. 7.1.

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Gender Parity Index in Different Levels of Education Over Time. Source: Author’s representation.

education. The value of learning for a girl child is considered less or sometimes “not necessary,” which is also reflected in the gender differential in learning outcomes as well (Bandyopadhyay & Subrahmanian, 2008). Inequality in education causes further disparity in women’s social condition. Females are found to lag behind men in major development parameters. Women are also less likely to achieve prestigious positions in organizational hierarchies. The differences in education can partly explain the wage difference between male and female workers.

5.5 Gender Gap in Employment The female work force participation rate in India is not only one of the lowest but has also remained near stagnant over the past several decades. As per the estimates of the World Bank, the female labor participation rate (LFR) in India fell to 20.3% in 2019 from above 26% in 2005 and 31.9% in 1983. This decline in female LFPR can be attributed to various factors like obligations toward the performance of domestic duties and the lack of flexible work models. In the financial year 2020, while the male workforce participation rate stood around 57%, female workforce participation rate was merely 22.2%. Moreover, there still exists a large difference between the work participation rates (WPRs) of males and females.

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Table 7.2A. Female and Male WPR in Different NSSO Sample Survey Rounds. Female WPR NSSO Round

50th 55th 61st 66th 68th

Male WPR

Urban

Rural

Urban

Rural

15.5 13.9 16.6 13.8 14.7

32.8 29.9 32.7 26.1 24.8

52.1 51.8 54.9 54.3 54.6

55.3 53.1 54.6 54.7 54.3

Source: Employment and Unemployment surveys of NSSO.

Table 7.2B. Occupational Classification of Female Workers in the Tertiary Sector. 2011 Occupation

Rural

Urban

Legislators, senior officials, and managers Professionals Technicians and associate professionals Clerks Service workers and shop and market sales workers Skilled agricultural and fishery workers Craft and related trades workers Plant and machine operators and assemblers Elementary occupations Workers not classified by occupations

1.86 5.56 26.89 1.91 19.11 0.70 0.85 0.72 6.70 35.70

3.93 12.05 21.54 6.37 15.26 0.22 1.20 1.01 15.03 23.40

Source: Economic tables, B series, Table no 23 A, Census 2011.

The Tables 7.2A and 7.2B shows the large difference between male and female WPRs. It is also a very crucial observation that, whereas female WPRs in rural and urban areas has a large disparity, the same is not reflected in male WPRs. Besides, differences in the nature of work performed also bear evidence to gender inequality. Following the one-digit classification of occupation (Census, 2011), women in the urban areas mainly cluster around three occupations – Technicians and Associate Professionals, Service workers and shop and market sales workers and Elementary occupations while for the women in the rural areas, clustering occurs mainly in the first two occupations. All these occupations require no

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specialized skill and are therefore suited for women with little education. Around 1% women are absorbed in occupations like Craft and related trade and Plant and machine operator and assemblers in both rural and urban areas owing to the lack of technical education among women. Overall, women are therefore largely confined to unpaid work (at home or in the field) or in the informal sector while men concentrate more on valued forms of remunerative work. Lower investment in human capital development for women has constrained their opportunities in organized sectors. Thus, women participation is more in the unorganized sector where gender oppression is already more prevalent. Moreover, across all the sectors, they also face significant wage differentials vis-a` -vis their male colleagues. Female representation in senior positions and entrepreneurship remains low. Women’s ability to participate in the labor market is constrained by their higher allocation of time to unpaid work. Women spend more time (hours) than men on unpaid work each day. Women tend to choose part-time work since they can balance household responsibilities with such type of work. The lower earnings of women in many cases lower their importance in decision-making in the household. Occupational segregation and reduced working hours, in combination with differentials in work experience, explain around 30% of the wage gap on average. The wage gap increases steeply during childbearing and childrearing years. Women become more engaged with household responsibilities around the age of 30, which sometimes compels them to leave the labor market too early. Lack of creches, rigidities at the workplace, etc. discourage the participation of mothers in the workforce. Women in unorganized sectors are more vulnerable to malpractices that promote gender inequality. They are often denied securities, insurance, and additional support/allowances, etc. For informal sectors, women also face the risk of violence and sexual harassment. Women and girls are particularly vulnerable to the effects of any economic crisis.

5.6 Analysis of Policy Measures Taken by the Government to Reduce Gender Inequality The government has tried to reduce the prevalent gender inequality in the country through certain measures. Most of these measures have been in the field of education. The Sarva Shiksha Abhiyan was launched in 2001 and aims to make education free and compulsory for all children aged 6–14. In 2002, the Right to Education Act was passed ensuring education for every child, preventing the evil practice of not sending the girls to school. The Sarva Shiksha Abhiyan has a special focus on girl’s education and the education of children with special needs. The program intends to open new schools in regions which do not have schooling facilities and equip the existing schools with more teachers and other learning-related facilities. The Kasturba Gandhi Balika Vidyalaya (KGBV) provides access and quality education to girls for the age group of 10–18 years belonging to minority

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communities and those belonging to families below the poverty line. KGBV provides at least one residential school for girls from Classes VI to XII in every educationally backward block. The Mahatma Gandhi National Rural Guarantee Act (MNREGA) was introduced in 2005 with the objective of providing 100 days of wage employment in rural areas. Though this scheme was not exclusively for women, many women from rural areas have benefitted from this. The Act ensures that 30% of the beneficiaries are women. The Beti Bachao Beti Padhao (BBBP) Yojana is one of the most discussed schemes on behalf of the Central government and is a joint initiative by the Ministry of Woman and Child Development, Household Welfare, and Education. The main agenda of this scheme aims to reduce sexism against girls in general and to create a safe environment for the growth of the girl child. It mainly targets to improve the child sex ratio as well as child mortality of female children through increase in institutional delivery, registration of Antenatal Care centers, successful implementation of Integrated Child Development Services (ICDS) program, etc. It also tries to ensure nutrition and hygiene for girls through maintaining functional toilets in schools, monitoring girl’s participation in ICDS care centers, etc. The principal target of the scheme is to address the social evils of female feticide, poor child sex ratio as reflected in the decadal Census, as well as gender oppression and harassment against the girl child. The Sukanya Samriddhi Yojana was declared under the BBBP scheme that gives the parents the opportunity to save for the girl child and secure the funds for her future, higher studies, marriage, etc. The evils of the dowry system often increase the uncertainty of future financial status of parents. As a result, the majority of them consider a girl as a burden and are reluctant to invest in them. This scheme tries to address the problem of income uncertainty as the parents can earn financial benefits by investing in the scheme for the first 15 years. The fund grows thereafter from the accumulated compound interest and the girl can access the fund when she becomes an adult. Self help groups (SHGs) are informal collateral bodies formed by women at the local level. SHGs have an immense impact on women empowerment by helping women to achieve economic stability and improving their financial inclusion. Apart from these primary objectives, the associated impact of these groups makes them very unique and significant for the development of women. SHGs have dedicatedly worked to reduce gender inequality by protecting women, engaging them in mainstream economic activity, and preventing social oppression, etc.

6. Conclusion Gender inequality is a very prominent phenomenon in developing countries, like India, and is reflected strongly in different spheres like education. The inequality in education gets transmitted to the field of employment. Many social factors

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contribute to this problem such as patriarchal society, poverty, lack of infrastructure, etc. The government has been able to bridge the gender inequality in education at the primary level to some extent through its various measures like the Sarva Shiksha Abhiyan. However, given the limited fund that is devoted to education, issues like dropouts at upper primary level and lack of colleges in the rural areas have not been addressed properly. Unfortunately, the government has failed to meet the needs of the job market through the education sector. The measures of the government to promote employment opportunities among the youth have not met with the same success as its other measures on education. Given the position of the country in the Gender Parity Index, it is clear that adequate policy measures need to be taken up to remove the evils of gender inequality. The government needs to focus more on secondary and higher levels of education including technical education so that there is a smooth transition from colleges to the job market.

References Bandyopadhyay, M., & Subrahmanian, R. (2008). Gender equity in education: A review of trends and factors. CREATE pathways to access. Research Monograph No. 18. Census. (2011). Primary census abstracts, Registrar General of India, Ministry of Home Affairs, Government of India. Chanana, K. (2006). Gender and disciplinary choices: Women in higher education in India. Knowledge, power and dissent, 267. Esteve-Volart, B. (2004). Gender discrimination and growth: Theory and evidence from India. LSE STICERD Research Paper No. DEDPS42. Ghai, S. (2018). The anomaly of women’s work and education in India. (No. 368). Working Paper. Jayachandran, S. (2015). The roots of gender inequality in developing countries. Annual review of economics, 7(1), 63–88. Kingdom, G. G., & Unni, J. (1998). Education and women’s labour market outcomes in India: An analysis using NSS household data. University of Oxford, Department of Economics, Economics Series Working Papers. Klasen, S. (2006). Pro-poor growth and gender inequality (No. 151). IAI Discussion Papers. Moheyuddin, G. (2005). Gender inequality in education: Impact on income, growth and development. MPRA Paper No. 685. Retrieved from https://mpra.ub.unimuenchen.de/685/ National Sample Survey Organization. Employment-Unemployment Situation in India, Various rounds. Ministry of Statistics and Programme Implementation, Government of India, New Delhi. Rammohan, A., & Vu, P. (2018). Gender inequality in education and kinship norms in India. Feminist Economics, 24(1), 142–167. Ravindranath, K., & Viswanathan, B. (2021). Gender parity in higher education enrolment: Role of family networks. Sen, A. (2001). The many faces of gender inequality. New Republic, 225(4522), 35–40.

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Swain, R. N., & Purty, L. (2018). Gender inequality in India. The Researchers’ International Research Journal, 4(3), 7–13. World Bank. (2000). World development report 2000/2001: Attacking poverty. The World Bank. https://censusindia.gov.in/census.website/ https://educationindia.gov.in/ https://hdr.undp.org/

Chapter 8

A Critique of Gender Inequality: Study of Education and Health in the North Bengal Region Bishal Rai

Abstract Human development encompasses not only income, other factors of life such as education and health are equally important. Investments in education and health positively impact the development of any region. Therefore, development in general and human development in particular of a region highlights not only the application of income but also the (actual) living conditions of people. It should also focus on the living conditions of women. This can somehow be assessed by looking at the Gender Development Index (GDI), introduced by the UNDP in 1995 as the intital Human Development Index (HDI) did not address gender-related issues. The present study thus tries to examine gender inequality in terms of education and health in the North Bengal region as it can have adverse effects on the overall development in the region. The study relies on the available secondary data on education and health. It is imperative that we realize the need to narrow the gender gap for development to be inclusive as investing in women’s education and health can contribute to holistic economic growth and development. Keywords: Human development; education; health; gender inequality; GDI; development

1. Introduction Development is supposed to be for all irrespective of one’s sociocultural and socioeconomic identities. Accordingly, the benefits of development are to be guaranteed for all. Any divergence from this notion can have an adverse affect on development where only some section of the population could be benefitted by development. Ferrant (2015) agrees that of all other determinants of economic Gender Inequality and its Implications on Education and Health, 91–100 Copyright © 2023 Bishal Rai Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231009

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growth and human development, gender inequality, a socioculturally created concept, may act as one explanation for development gaps. Accordingly, higher inequality obstructs economic development. This in turn leads to even more inequality (p. 313). Thus, the objective “development for all” remains merely a dream. Henceforth, the inclusion of gender as part of the discourse on development is indispensable in contemporary times. It is for this reason that of the 17 sustainable development goals adopted at the UN Sustainable Development Summit in September 2015, goal 5 precisely focuses on gender aspect which is “achieve gender equality and empower all women and girls1.” Furthermore, human development, a concept that emerged in the late 1980s and a process of enlarging people’s choices (HDR, 1990, p. 10), has shown much improvement across the globe. Between 1990 and 2015, the developing regions have shown considerable improvement not only in income but also on education and health though it has slowed down since 2010 (HDR, 2016, p. 26). However, enlarging people’s choices as narrated in Human Development Reports should not be limited to only men. According to the World Development Report (2019, p. 50), the world has drastically improved in terms of health and education than ever, but gaps still persist between developing countries and rich countries. The gap is also visible within the developing nations. “Equal access to education and health is far from realized. Despite progress, girls continue to face greater challenges. Child marriage, household chores and responsibilities, teenage pregnancies, and gender based violence in schools pose challenges to keeping girls enrolled, especially, but not only, in low-income settings” (World Bank, 2021, p. 34). Also as initial Human Development Index (HDI) did not address gender-related issues, it was necessary to have another measure which would focus on the living conditions of women. This can be assessed by looking at the Gender Development Index (GDI), introduced by the UNDP in 1995. Accordingly, the Human Development Report (1995) incorporated gender-related development index (GDI) to access the disparity between men and women. The index considers the same indicators as that of HDI but takes into account the inequality in achievement between men and women (HDR, 1995, p. 73). Therefore, the reduction of the disparity or narrowing the gap is important for not only contemporary development but also future productivity and hence development. Appleton and Teal (1998) state that apart from just considering the components of human capital, education and health are also the contributors of human welfare. They argue that although education reduces poverty through high wage employment, education and nutrition can also raise productivity in farm and nonfarm self-employment, activities in which mostly the poor are engaged. Furthermore, the distribution of health and education may be very unequal like income and wealth, but improvements in these components of human capital can help families trapped in vicious circles of poverty to overcome it (Todaro & Smith, 2012, pp. 360–361). Therefore, the notion that women too have an equal strength and responsibility of overcoming poverty needs to be expanded in the development discourse. The policy only needs to bridge the gaps which are vital indicators of development or for that matter overcoming poverty such as education and health. Furthermore, investing in individuals’ education and health or human capital is important for

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increasing well-being as well as sustainable economic growth (Pocas, 2014, p. 1). It is hence considered important to emphasize on education and health of the population in general and women in particular especially in regions where gaps still is prevalent. Several studies on gender inequality, i.e., a concept of disparities based on gender, has not only defined it differently but also viewed it differently. The disparities can be seen in terms of income, which again is based on disparities in education and health. This brings us to understand that gender inequality is prevalent everywhere, i.e., from the workplace to (education and health) institutions to households. According to Sen (2001) gender inequality is “not one homogeneous phenomenon, but a collection of disparate and interlinked problems.” These include mortality inequality, natality inequality, basic facility and special opportunity inequality, such as unequal access to schooling and higher education for girls as compared to boys, professional inequality, household inequality, etc. (pp. 466–468). Reeves and Baden (2000) define gender equality as “women having the same opportunities in life as men, including the ability to participate in the public sphere” (p. 2). In cases where the opportunities are unequal, gender inequality persists. According to the World Bank (2022) the generation of large economic gains are possible if gender equality is accelerated. This will allow both men and women to shape not only their own lives but also contribute in shaping their families, communities, and countries2. The persistence of gender inequality is visible across the globe and India is no exception to this form of inequality, amid other forms of inequality. India ranked 99 with the score of 0.401 out of 130 countries in GDI in the Human Development Report (1995) published by the United Nations Development Program (UNDP). This reflected not only greater gender disparity but also low levels of achievement. In 2020, India achieved a score of 0.82 in GDI but was placed in group 5 out of five groups3. India also ranked 123 with a score of 0.488 out of 189 countries on the gender inequality index according to the Human Development Report (2020) published by UNDP. India’s rank in the index lies below Sri Lanka and Bhutan, which ranked 90 and 99, respectively, in the index. This reflects that countries like Sri Lanka and Bhutan in South Asia show much gender parity than India. Jha and Nagar (2015) state that factors like economic, social, cultural, political, and legal contribute much to gender inequality in India. These factors not only lead to gender inequality but also make its existence much prevalent in India. It is much so in the rural areas. While education empowers women at the grassroots level and makes her confident, illiterate women in rural areas correlate with low nutritional status and high fertility rates (Rani, 2019). The need is to reduce this gender inequality in India and for which much efforts and progress is to be made. Furthermore, Sumanjeet (2017) argues that despite various policies, programs, and schemes undertaken by India like the National Mission for Empowerment of Women (NMEW), Development of Women and Children in Rural Areas (DWCRA), Gender Sub Plan (under the 8th Five-Year Plan), Women Component Plan (under the 9th Five-Year Plan), Condensed Course of Education for Adult Women, National Programme of Nutritional Support to Primary Education, National Programme for Education of Girls at Elementary

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Level, etc., gender gap still persists. The consequence of gender inequality is that it not only constrains women’s access to resources and opportunities but also endangers future prospects (pp. 141–142). In addition to it, the lack of socioeconomic equality for women in India would limit the realization of any development efforts. The need is also the socioeconomic development which can empower women and also raise the Indian economy status (Razvi & Roth, 2004, p. 174). Hooda (2021) also reports that among the South Asian countries, India accounts for the highest female child mortality rate (37.3) as compared to the male child (35.8). In terms of education, a large section of female children, especially from a poor economic background remain outside the periphery of school. In addition to it, females also report high dropouts in India. Factors like early marriages, household work burden, inability to access schools within their surroundings, etc. stand in the way of a girl’s education in India (pp. 1055–1062).

2. Objectives of the Study Under this backdrop, the objectives of the study in North Bengal region are as follows: i. To examine the literacy rate of male and female and hence gender gap. ii. To examine gender disparity in education. iii. To examine gender inequality in health and nutrition.

3. Area of Study The research work is mainly delineated along the developmental aspect as education, health, and development are intertwined. The study related to the secondary source is based on the North Bengal region of West Bengal in India. North Bengal as compared to other parts of West Bengal also has been lagging behind in various aspects of developmental initiatives such as infrastructure, civic amenities, and indicators of human development (Basu, 2012, pp. 28–29). The study is thus an attempt to understand the status of education and health from a gender perspective in the region. North Bengal comprises eight districts, viz., Alipurduar, Cooch Behar, Dakshin Dinajpur, Darjeeling, Jalpaiguri, Kalimpong, Malda, and Uttar Dinajpur. Of these the Alipurduar and Kalimpong districts are newly formed districts. Kalimpong is carved out of the Darjeeling district. Hence no separate data are available for these districts, and henceforth the analysis of the region is based on only the six districts which exclude the newly formed districts.

4. Data Source and Methodology The study is based on secondary data. Hence, the study has made use of the secondary sources and literatures available on gender. The secondary sources include census reports, district census handbook, district statistical handbook,

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National Family Health Survey (NFHS), etc. The study has also relied much on reports both national and international by institutions and organizations. The reports include HDI reports, World Development Reports, Human Capital Index, etc. The data collected through secondary sources have been analyzed with the help of simple statistical tools like tables, ratios, percentages, and ANOVA (Analysis of Variance). In case of ANOVA, men and women (15–49 years) whose Body Mass Index (BMI) is below normal is considered for all the six districts of North Bengal (NFHS, 2015–2016). The sum of squares for variance between the samples (SS) is then calculated. Thereafter, the sum of squares for variance within samples for all the six districts is calculated. The result obtained is divided by respective degrees of freedom4 (d.f) to find mean square (MS) between and within samples. Once mean squares are found, the F – value which is the ratio of mean square between samples upon mean square within samples is calculated. This is compared with F – critical value at 5% level of significance with d.f. (1, 10). The comparison of F –value with F – critical value is drawn to conclude the acceptance or rejection of the null hypothesis of no difference in sample means. The same is detailed in the results and discussion section.

5. Results and Discussion 5.1 Literacy Rate As far as the development of the community is concerned, educational advancement represented by literacy rate can be taken to be the best single indicator (Gupta, 1994, p. 199). In this regard, effort is made to find the literacy rate from gender perspective in the region. As far as literacy rate in the districts of North Bengal is concerned, Darjeeling district ranks number one in North Bengal with a literacy rate of 79.56% against the state average of 76.3%. All the districts in North Bengal show literacy rate lower than that of the state except Darjeeling. The least literate district is Uttar Dinajpur with a literacy rate of 59.07% (Census, 2011). However, according to the census (2011) all the districts show a higher male literacy rate than female. The highest gap between male and female is found in the Jalpaiguri district with male literacy rate being 79.95 and female 66.23, thereby creating a gap of 13.72%. Though the literacy rate of Malda is only 61.73%, it is the district in North Bengal with the least gender gap. The male literacy rate in Malda is 66.24 while that of female is 56.96, thereby creating a gap of only 9.28%. Furthermore, the gap is more pronounced in the rural regions than urban in North Bengal. According to census (2011), the overall literacy rate of men in the region is around 76% while that of women is around 64%. The gender gap in literacy levels accounts to 12% in the region which is marginally higher than that of state average which is 11.15%. The census data (2011) thus reveal that educational development reflected by literacy rate shows bias toward men in the North Bengal region. The same is also seen in case of illiteracy. The percentage of women illiteracy is more than that of men in the North Bengal region. Around

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36% of women are illiterate compared to men, which are only around 24%. The illiteracy gap between men and women thus accounts to 12%. This could possibly be one reason among many for underdevelopment of the region.

5.2 Educational Level of Male-Female Though literacy can be considered as one of the indicators of development, it does not provide the actual educational status. Education not only enhances human capital accumulation but also has an effect on individual human development. The need is thus to see the level of education5 of male and female. In the North Bengal region, the total number of male with certain level of education is 1,940,831. This accounts for approximately 22% of the total male population in the North Bengal region. For female, the total number with certain level of education is 1,901,850. This is 22.69% of the total female population. However, the actual picture becomes lucid once the segregation of the educational level is done. The highest level of education for males was higher secondary with 36.22% followed by primary level of education (34.80). For females, it was primary education, i.e., 34.58%, followed by higher secondary (33.50). This reflects that female education has always received less priority than their male counterpart (See Table 8.1). Data provided in Table 8.1 highlight the fact that the educational base needs to be made stronger in the North Bengal region in general and of women in particular.

Table 8.1. Educational Level of Male and Female in North Bengal (%). Educational Level

Male

Total

Female

Total

Primary Middle High school Higher secondary Higher education Total

675,587 129,499 334,284 702,970 98,491 1,940,831

34.80916 6.672348 17.22376 36.22005 5.074682 100

657,778 142,422 382,745 637,167 81,738 1,901,850

34.58622 7.488,603 20.12488 33.50248 4.297,815 100

Source: Author’s calculation based on Census, 2011; Department of Statistics & Programme Implementation, District Statistical Handbook, West Bengal, 2013.

5.3 Sex Ratio Sex ratio is the number of females per 1,000 males. Apart from the number, it also suffices as a composite indicator of women’s health, nutrition, and survival status (Rustagi, 2000, p. 4276). Accordingly, an effort is made to understand the gap between male and female in the region. According to the report by State Bureau of Health Intelligence (2012–2013), and also census (2011), all the districts in North Bengal highlight lesser number of females per 1,000 males. While the

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highest sex ratio is in the Darjeeling district (970), the least is found in the Uttar Dinajpur district (939). The average sex ratio in the North Bengal region is approximately 951, which is slightly higher than the average sex ratio of both the state (950) and the nation as a whole (943). The average sex ratio of the region in 2001 was approximately 944. Comparatively, the region seems to be doing fairly well than the state and the nation as a whole, but the figures show no trend or sluggish improvement in sex ratio when compared to 2001, except for the Darjeeling district which has improved its sex ratio from 937 in 2001 to 970 in 2011. For instance, according to census (2011), the number of males in the least sex ratio district, i.e, Uttar Dinajpur reflects that there are approximately 1,065 males per 1,000 females. The reasons, though inconclusive, for such demographic imbalance can be many like poverty, malnourishment, preference for boys, high prevalence of (girl) child deaths, etc. But over the course of time and global development, the gap between numbers of females available per 1,000 males is not getting closed. This definitely posits social and economic problems and is reflective of gender bias. The same pattern is also reflective if one considers 0–6 population by sex6. This would also mean that in the future too, the number of females per 1,000 males will be less.

6. Health and Nutrition Better health is the outcome of income spent on food and nutrition. Better health in turn enhances productivity and hence income. However, due to data limitation on income, effort is made to understand the health status of men and women considering indicators such as nutritional status, anemia, and blood sugar level. The analysis is based on National Family Health Survey (NFHS-4, 2015–2016) to bring in the parity with the study. Hence, NFHS-5 (2019–2020) is not considered for the study due to certain data limitation. First of all, Body Mass Index (BMI) for men and women aged 15–49 years (2015–2016) is considered in all the districts of North Bengal. According to NFHS-4 (2015–2016), BMI with 18.5 kg/m2 is considered as normal. The data reveal that in all the districts of North Bengal, women have BMI below normal, i.e., below 18.5 kg/m2 than men. Approximately 23.46% of women have BMI below normal as against state average of 21.3% while for men it is only 14.73%, the state average for men being 19.9%. The variation in BMI between men and women is thus examined with the help of Analysis of Variance (ANOVA). The result is shown in Table 8.2.

Table 8.2. ANOVA Table of BMI for all the Six Districts of North Bengal. Source of Variation

Between groups Within groups Total

SS

Df

MS

F

P Value

F Crit.

228.8133 1 228.8133 12.48254 0.005419 4.964,603 183.3067 10 18.33067 412.12 11

Source: Author’s calculation based on NFHS-4, 2015–2016.

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The ANOVA table clearly shows that with respect to BMI, there is significant difference between men and women in the mean values of BMI in the districts of North Bengal as the observed F-value is greater than F crit. value. We therefore reject the null hypothesis that there is no significant difference in the mean values of men and women (15–49 years) in the six districts of North Bengal and conclude that the mean values of BMI for men and women in North Bengal are not equal. Though not conclusive, below normal BMI for women can be the consequence of several factors such as malnourishment or diseases that affects absorption of nutrients. But whatever be the reason, it may lead to development of some serious health problems. This highlights that women are more prone to serious health problems than men in the region. Furthermore, it was also seen that for the same age group as mentioned above, women are more vulnerable than men in terms of anemia. While it was found that approximately 33% of men are anemic, for women it was approximately 64%. The State stands slightly better in this aspect with men and women who are anemic being 31.9 and 64.4%, respectively. In terms of overweight or obese also, women are seen to be more overweight or obese than men in all the districts of North Bengal7. It was only in case of blood sugar level (both high and very high) that women’s position was better than men. It was men who had both high and very high blood sugar level than women in the region, identical with the state result. The results, however, highlight that in case of health vis-`a-vis nutrition, women are more vulnerable to health problems than men. This can be due to higher illiteracy among women than men, less attention paid to the health of women than men, lesser amount of resources available for women, etc. Whatever the reason be, considerable amount of gap between men and women exists in the region as in terms of health too.

7. Conclusion Despite the discussion of development across the globe and regions, SDGs, human development, etc. regions have suffered due to persistence of gender inequality. This form of inequality mostly in education and health has retarded any growth and development taking place. North Bengal finds no exception to this form of inequality. Women’s education remains an area which has received low priority than men. This is evident from the discussion on literacy rate and education level in the region. Women have taken a back seat in terms of literacy rate and educational level than men in the region. The demographic imbalance between male and female speaks about less survival of women than men. From social and economic perspectives, this certainly is not a healthy sign and can have development repercussions. In terms of health and nutrition also, women finds itself on the negative side. As a consequence, women are exposed more to health problems than men. Thus, it is imperative that we realize the need to narrow the gender gap for development to be inclusive as investing in women’s education and health can contribute to holistic economic growth and development. This also does not mean neglecting men’s education and health. It only means enlarging

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choices for both men and women, choices which can shift the gear of development in the region. Enlarging choices is again a result of the policies which are thought to be relevant for further improving human development and overall development of the region.

Notes 1. https://sdgs.un.org/goals. 2. https://www.worldbank.org/en/topic/gender/overview. 3. In 2020, GDI was divided into five groups. Countries belonging to group 1 resembling high equality in HDI achievements between women and men while group 5 comprises countries with low equality in HDI achievements between women and men. 4. Degrees of freedom for sum of squares between samples and within samples are 1 and 10, respectively. 5. Education between class I and IV is the primary education, class V and VIII is the middle school, class IX and X is the high secondary, class XI and XII is the higher secondary and above is considered as higher education. 6. Please refer to Directorate of Health Services, Government of West Bengal, 2012–2013. 7. For further details on overweight or obese, please refer to NFHS-4 (2015–2016) data.

References Appleton, S., & Teal, F. (1998). Human capital and economic development. African Development Bank Group. Basu, S. (2012). Gorkhas, adivasis and others in North Bengal. Economic and Political Weekly, 47(35), 28–29. Department of Statistics & Programme Implementation, Government of West Bengal. (2013). District statistical handbook. Retrieved from http://wbpspm.gov.in/ publications/DistrictStatisticalHandbook Directorate of Census Operations, West Bengal. (2011). District census handbook. West Bengal, Series – 20, Part XII – B. Ferrant, G. (2015). How do gender inequalities hinder development? Cross-country evidence. Annals of Economics and Statistics, 117/118, 313–352. Gupta, L. P. (1994). Tribal development administration: A study in Darjeeling district of West Bengal. Doctoral dissertation. University of North Bengal, West Bengal, India. Retrieved from https://shodhganga.inflibnet.ac.in Hooda, D. S. (2021). Gender inequality in India: Status and determinants. International Journal of Social Science and Economic Research, 6(3), 1054–1070. doi:10.46609/IJSSER.2021.v06i03.020 Jha, P., & Nagar, N. (2015). A study of gender inequality in India. The International Journal of Indian Psychology, 2(3), 46–53. Ministry of Health and Family Welfare, Government of India (2015–16). National family health survey – 4. Retrieved from http://rchiips.org/nfhs

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Pocas, A. I. (2014). Human capital dimensions – Education and health – And economic growth. Rani, R. (2019). Gender inequality among rural women: A sociological study of Dadri block in Gautam Buddh nagar, Uttar Pradesh. Doctoral dissertation, H.N.B. Garhwal University Srinagar Garhwal, Uttarakhand, India. Retrieved from https://shodhganga.inflibnet.ac.in Razvi, M., & Roth, G. L. (2004). Socio-economic development and gender inequality in India. Retrieved from https://eric.ed.gov/?id5ED492144 Reeves, H., & Baden, S. (2000). Gender and development: Concepts and definitions: Prepared for the Department for International Development (DFID) for its gender mainstreaming intranet resource. Bridge (development gender). Institute of Development Studies. Rustagi, P. (2000). Identifying gender backward districts using selected indicators. Economic and Political Weekly, 35(48), 4276–4286. Sen, A. (2001). The many faces of gender inequality. New Republic, 35–39. State Bureau of Health Intelligence. (2012–2013). Health on the March 2012–2013, Directorate of Health Services Government of West Bengal. Retrieved from www. wbhealth.gov.in Sumanjeet, S. (2017). The state of gender inequality in India. Gender Studies, 15(1), 139–157. doi:10.1515/genst-2017-0009 Todaro, M. P., & Smith, S. C. (2012). Economic development (11th ed.). United Nations Development Programme. (1990). Human development report. Retrieved from https://hdr.undp.org/content/human-development-report-1990 United Nations Development Programme. (1995). Human development report. Retrieved from https://hdr.undp.org/content/human-development-report-1995 United Nations Development Programme. (2016). Human development report: Human development for everyone. Retrieved from https://hdr.undp.org/content/humandevelopment-report-2016 World Bank. (2019). World development report 2019: The changing nature of work. Washington, DC: World Bank. doi:10.1596/978-1-4648-1328-3 World Bank. (2021). The human capital index 2020 update: Human capital in the time of COVID-19. Washington, DC: World Bank. doi:10.1596/978-1-4648-1552-2 World Bank. (2022). The World Bank in gender. Retrieved from https://www. worldbank.org/en/topic/gender/overview

Chapter 9

Gender Bias in Child Deprivation: A Study in the Context of West Bengal, India Satyanarayan Kumbhakar and Pinaki Das

Abstract The early childhood years are a period of great opportunity but are also of great vulnerability. Responsive caring is important for children to live, learn, grow, and develop to their full potential. Being healthy at childhood is the crucial requirement for a nation to be healthy throughout. But children in developing countries are earmarked to be vulnerable to the adverse socioeconomic conditions. Without the availability of proper nutritional diet and immunization, children remain underweight, stunted, and wasted. In order to capture their condition, analyzing their health status is inevitable. Further, since discrimination based on gender in every sphere of life be it at home or outside is quite evident in a patriarchal nation like India, therefore, an analysis of the child health based on their gender gains momentum. Thus, the present study analyzes the status of child health in West Bengal from a multidimensional perspective and disaggregates it on the basis of their gender in order to catch the effect of discrimination persisting in society. In order to do so, we have considered the NFHS unit level data of the latest two rounds. The present study contributes to the existing literature from methodological perspective as well as by formulating a child deprivation index using a multidimensional approach. Together with that, we have unearthed the factors influencing the health status of the children based on their gender. Keywords: Gender bias; child health; multidimensional deprivation; NFHS unit level data; West Bengal; India JEL Classification: I00; I12; I18

Gender Inequality and its Implications on Education and Health, 101–112 Copyright © 2023 Satyanarayan Kumbhakar and Pinaki Das Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231010

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1. Introduction Gender is a socially constructed characteristic of women, men, girls, and boys; therefore, gender varies from society to society and can change over time. Every child deserves to reach his or her full potential, but gender inequalities in their lives and in the lives of those who care for them hinder this reality. Gender inequality results in unequal opportunities, although it imposes severe impact on the lives of both genders but statistically in India it is the girl who is the most disadvantaged (UNICEF, 2022). The gender bias or gender inequality is associated with social norms, customs, and culture. Historically there has been evidence of gender bias in the society. No doubt, boys and girls (or men and women) are different due to biological factors. But the area of particular concern is the disproportionate number of women compared to men in developing countries. Gender variety in children requires specific thought and concern. Although the gender bias has reduced over time, it is still observed in India and in West Bengal. In West Bengal, the multidimensional child deprivation gap is still comparatively high for the female child than the male child. Further, health is an elementary component of human development and hence controls society’s welfare. Without the availability of proper nutritional diet and immunization, children remain underweight, stunted, and wasted. Healthy grown-up children are expected to lead to a healthy future. It is a means to empower the deprived sections of society and thus an important element in the strategy of poverty alleviation. In the recent period, there has been an increasing focus on the issues that affect children and on the improvement of their health. As girls and boys see gender inequality in their homes and communities every day in spite of their place of residence in a patriarchal nation like India. Therefore, an analysis of the child health based on their gender gains momentum. However, the concept of child health is multidimensional in nature. To capture the gender bias experienced by children, we have measured child deprivation among male and female child from a multidimensional perspective by considering three dimensions, specifically health care, nutritional status, and hygiene. Thus, the chapter aims to analyze the status of multidimensional deprivation for male children compared to female children in West Bengal by their socioeconomic background. It further explores the change in the status of multidimensional deprivation during 2005–2006 to 2015–2016. Furthermore, it investigates the factors that influence child deprivation among male child and female child in West Bengal, which will be useful for formulating policy.

2. Literature Review of Gender Bias Dr`eze and Khera (2012), Murthi, Guio, and Dr`eze (1995) have enlightened the issue of well-being among the children under six years and explored the structural causes of child deprivation. Panda, Kumar, and Awasthi (2020) found the gap between coverage, maternal, neonatal, and child health indicators using the composition index, namely, Coverage Gap Index (CGI) in 640 districts of India. Patra (2014) revealed that women’s empowerment and literacy may have a role in

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India’s attempts to narrow the gender gap in child health. This study uses the NFHS-1, NFHS-2, and NFHS-3 data as its foundation in order to identify the factors that influence various health outcomes for girl children, including socioeconomic status and demographics. Sharma (2005) observed the gender differences patterns for children health outcomes and found the status of health encasing the treatment for immunizations and nutrition of gender bias. Dutta (2021) examined the persistent problems of multifaceted child deprivation and poverty in Bangladesh and India. She used the NFHS-IV, 2015–2016, data for India and the Demographic and Health Survey, 2014, for Bangladesh in order to carry out this analysis. High levels of literacy and maternal emancipation were found to reduce the multidimensional deprivation of children. Kakwani (1984) presented a relative deprivation curve which can represent the size distribution of income and wealth. Trani, Biggeri, and Mauro (2013) analyzed multidimensional poverty among children in Afghanistan using the Alkire–Foster methodology and discussed the relevant dimensions used. For this purpose, they used the data from a survey carried out by Handicap International. Based on capability approach they concluded that younger children, those living in rural areas, girls and disabled children are the most deprived. Mishra and Ray (2013) examined the stunted children and found that the health dimension to be the most significant source of deprivation in both rural and urban areas in India.

3. Data Base and Methodology 3.1 Data Base For the analysis and estimation of the status of child health deprivation among male child and female child from a multidimensional perspective, we have taken the National Family and Health Surveys unit level data of the latest two: NFHS-3 (2005–2006) and NFHS-4 (2015–2016). We have considered 2,368 and 5,328 children from 2005–2006 and 2015–2016, respectively, for the analysis. Sample size distribution of individual children is from the National Family Health Survey of West Bengal.

3.2 Methodology (1) Alkire–Foster Methodology We have considered the Alkire–Foster methodology for measuring the Multidimensional Child Deprivation Index (MCDI), which can be expressed as the product of CDR and ICD. This method has been widely used to measure Multidimensional Poverty Index (Alkire & Santos, 2010; Das & Paria, 2020; Das, Ghosh, & Paria, 2021; Das, Paria, & Firdaush, 2021; Kumbhakar, Firdaush, & Das, 2022; UNDP, 2013). Here, Child Deprivation Ratio (CDR) is the proportion of the child population who are multidimensionally deprived and CDR 5 c/n, whereas Intensity of child deprivation (ICD) reflects the proportion of the weighted component indicators in

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which, on an average, multidimensionally deprived children are deprived in different c indicators. ICD 5 1c+i51 si ðkÞ where si is the score based on the deprivation experienced by the children in all the considered indicators. MCDI can be expressed as the product of MCDI 5 CDR 3 ICD. (2) Binary logit Model To analyze the impact of gender biasness along with socioeconomic characteristics of the households on multidimensional child deprivation, we have used the binary logit model. For this purpose, pooled data for two years (2005–2006 and 2015–2016) have been considered. Here, Yit is the dependent variable which is dichotomous in nature and takes the value 1 if the child is multidimensionally deprived and 0 otherwise. For individual “i” in time “t”, the model is specified as follows: K

Yit ¼ bo 1 b1 Genderit 1 + bk Xit;k 1 «it

(9.i)

k¼2

Where i 5 number of children (2,368 in 2005–2006 and 5,328 in 2015–2016), t 5 2 (2005–2006 and 2015–2016), Xit are the regressors including time dummy (0 for 2005–2006 and 1 for 2015–2016), b is the vector of coefficients, and «it is the vector of error term. Here, we have considered another logit model to analyze the impact of socioeconomic characteristics of the households on the deprived male and female children. Thus, Dit is considered as the dependent variable which takes the value 1 for the female derived child and 0 for male deprived child. Here also we have considered the pooled data for two years (2005–2006 and 2015–2016). For individual “i” in time “t”, the model is specified as follows: K

Dit ¼ bo 1 + bk Xit;k 1 «it

(9.ii)

k¼1

Where i 5 number of deprived children (1,145 in 2005–2006 and 2,180 in 2015–2016), t 5 2 (2005–2006 and 2015–2016), Xit are the regressors including time dummy (0 for 2005–2006 and 1 for 2015–2016), b is the vector of coefficients, and «it is the vector of error term. To analyze the status of child derivation, we have considered several explanatory variables. These variables are as follows: (1) Location of the households (rural 5 1, 0 5 otherwise), (2) Social caste of the household ST (Yes 5 1, 0 5 No), SC (Yes 5 1, 0 5 No), (3) Religion of the household are Hindu (Yes 5 1, 0 5 No), Muslim (Yes 5 1, 0 5 No), or Christian (Yes 5 1, 0 5 No), (4) Wealth index of the household are high wealth (Yes 5 1, 0 5 No), (5) Household security is captured on the basis of the household had insurance or not (Yes 5 1, 0 5 No), (6) Demography status of the household like size of household (HHSIZE) and year of education (YOFEDU) of that household, and (7) Social protection benefits: like SNFANC (received supplementary nutrition from the Anganwadi center during pregnancy SNFANC 5 1, 0 5 otherwise), and ICDSW (ICDS worker available 5 1, 0 5 Otherwise). Here time dummy is considered by 0 for 2005–2006 and 1 for 2015–2016.

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4. Results 4.1 Status of Child Deprivation Across Indicators and Level of Deprivation Between Male Child and Female Child To measure multidimensional child deprivation between male child and female child, we have considered three dimensions, namely, health care, nutrition status, and hygiene. A. Health Care dimension consists of four indicators, namely, antenatal care, number of antenatal visits during pregnancy, child health check-up before and after discharge, and immunizations. B. Nutritional Status dimension is organized by three indicators, which are stunted, underweight, and wasted child which will indicate undernutrition level of children. C. Hygiene domain of deprivation consists of two indicators, namely, sanitation facility and safe drinking water. If the household has not acquired safe intake water facility and improved sanitation system, then the child of that household is considered to be deprived. The dimensions and indicators are measured in the availability of NFHS information. The percentage of deprived children across indicators in West Bengal is shown in Table 9.1, and if the male female gap is positive, it implies that the male is more deprived and if the male female gap is negative, it implies that the female child is more deprived. In 2005–2006, the male female deprivation gap is positively associated with sanitation (3.1 percentage point) and safe drinking water (4.2 percentage point), whereas it is negatively associated with antenatal care (24.2 percentage point) followed by ANC visits during pregnancy (23.8 percentage point), underweight (23.3 percentage point), and breastfeeding (21.5 percentage point). In 2015–2016 the male and female deprivation gap is negative mostly in antenatal care (23.9 percentage point) followed by underweight (22.1 percentage point), child health check-ups (22.0 percentage point), and stunted (21.4 percentage point). The change in percentage share of male child deprivation is negative and higher in ANC visit during pregnancy (220.2 percentage point) followed by sanitation (13.4 percentage point) and stunted (29.5 percentage point) although the change deprivation shares are positive as in immunization (6.9 percentage point), CHC (6.7 percentage point), and wasted (1.5 percentage point). In case of female child, the changes are positive or increases of CHC (8.4 percentage point) and immunization (6.9 percentage point) during the period of 2005–2006 to 2015–2016.

4.2 Distribution of Male Child and Female Child by Level of Deprivation in West Bengal The distribution of children under the age of five who are male and female according to West Bengal’s multidimensional deprivation status for the years 2005–2006 and 2015–2016. Between 2005–2006 and 2015–2016, the percentage of nondeprived male children went from 69.81 to 81.92, and the percentage of nondeprived female children increased from 70 to 81. During 2005–2006 and 2015–2016, there was a significant difference in the percentage of

Male child West Bengal Location Urban Rural Castes SC ST OBC GEN Religion Hindu Muslims Christian Others Wealth LWC MWC HWC

2015–2016

Change in Percentage Points

MCDI

CDR

ICD

MCDI

CDR

ICD

MCDI

CDR

ICD

0.21

48.03

44.46

0.13

32.12

41.47

20.08

215.91***

22.99***

0.08 0.25

16.75 56.36

39.13 44.88

0.07 0.15

21.72 36.30

41.70 41.42

20.01 20.10

4.96 220.06***

2.56 23.46**

0.21 0.32 0.13 0.19

50.76 68.43 29.67 43.75

45.14 46.38 42.86 43.50

0.14 0.24 0.11 0.12

34.94 54.71 25.15 28.23

40.68 43.83 43.18 40.75

20.07 20.08 20.02 20.08

215.82*** 213.71** 24.51** 215.51**

24.47** 22.55* 0.33 22.76**

0.19 0.25 0.23 0.24

42.92 55.81 47.77 47.32

44.72 44.03 48.33 50.00

0.12 0.14 0.40 0.15

30.15 34.93 86.70 36.49

41.33 41.70 45.72 40.82

20.07 20.11 0.17 20.09

212.77** 220.89*** 38.93* 210.83**

23.39* 22.34** 22.61** 29.18**

0.29 0.13 0.04

65.82 30.16 9.36

44.86 43.49 38.60

0.19 0.08 0.03

44.59 20.03 7.25

42.19 38.18 37.67

20.10 20.05 20.01

221.23*** 210.13** 22.11**

22.67*** 25.32*** 20.93**

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Table 9.1. MCDI, CDR, and ICD Among Male and Female Child Estimation of West Bengal, 2005–2006 and 2015–2016.

0.22

48.79

44.24

0.14

33.95

41.85

20.07

214.84**

22.38***

0.08 0.26

18.84 56.48

41.24 44.49

0.09 0.16

21.50 38.54

41.07 42.01

0.01 20.10

2.67 217.94**

20.18* 22.48**

0.23 0.37 0.10 0.20

48.79 82.32 26.79 44.60

44.00 44.74 38.95 44.51

0.15 0.27 0.14 0.12

35.03 59.36 32.82 30.34

42.22 45.75 41.21 40.60

20.09 20.10 0.03 20.08

213.76** 222.96* 6.03* 214.26***

21.78*** 1.01* 2.26* 23.91***

0.19 0.26 0.48 0.20

43.02 57.85 95.38 47.43

43.96 44.43 50.00 41.67

0.14 0.15 0.15 0.21

32.13 35.52 34.40 50.90

42.60 40.59 43.66 41.35

20.05 20.10 20.33 0.01

210.89** 222.33* 260.99*** 3.47*

21.36** 23.85** 26.34** 20.32*

0.30 0.15 0.06

65.44 36.99 13.14

44.66 41.82 44.04

0.20 0.09 0.02

47.35 22.48 6.06

42.51 38.69 38.20

20.09 20.07 20.03

218.09** 214.50* 27.08*

22.15** 23.12* 25.84*

Source: Authors Estimation, NFHS Unit level data of the third (2005–2006) and fourth (2015–2016) rounds. Note: Significant test of change (percentage points) by the means of t-test, where *** implies significant at 1% level, ** implies significant at 5% level and * implies significant at 10% level.

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Female child West Bengal Sector Urban Rural Cast SC ST OBC GEN Religion Hindu Muslims Christian Others Wealth LWC MWC HWC

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multidimensionally deprived children (i.e., the 33.33% to 50% cut-off), with the male share falling from 22.12% to 14.59% while the female share increased from 20.78% to 15.09%. At the same period, the percentage of multidimensionally severely deprived children (defined as those who meet the 50% cut-off) has decreased from 8.07 to 3.49% of male children and 8.80% to 3.91% of female child over the decade.

4.3 MCDI, CDR, and ICD Among Male Child and Female Child in West Bengal Table 9.1 represent the status of multidimensional child deprivation index (MCDI) which is the component of child deprivation ratio and intensity of child deprivation across household characteristics between male child and female child for the year of 2005–2006 and 2015–2016 in West Bengal. All the components of the measurement of multidimensional deprivation declined of male as well as female child during 2005–2006 to 2015–2016. The multidimensional CDR reduces in both male (15.91 percentage point) and female (14.84 percentage point) child. This reduction was found to be statistically significant. ICD also significantly declined for male (2.99 percentage point) child as well as female (2.38 percentage point) child during the same period. Therefore, there has been a reduction in MCDI for both male (0.21–0.13) and female (0.22–0.14) child for the year 2005–2006 to 2015–2016. The CDR among male and female was significantly higher in the rural areas than that of the urban areas. Here, it is observed that the female CDR are higher compared to the male child in West Bengal. It is observed that in rural areas, MCDI reduced by 0.10 percentage point for male and female child during 2005–2006 to 2015–2016. While in case of urban areas, MCDI increased by 0.01 percentage point for both male and female child during the same time. Across social castes, the MCDI for male children decreased for ST, SC, OBC, and GEN, but for a female child, the MCDI increased for OBCs by 0.03 percentage point. It is evident that both CDR and ICD have significantly reduced across religious groups during 2005–2006 to 2015–2016. It is observed that reduction of CDR was highest in case of a Muslim male child (220.89 percentage point) followed by Hindu (212.77 percentage point). Whereas, in case of a female child, the CDR reduction was the highest for Christian (260.99 percentage point) followed by Muslim (222.33 percentage point).

4.4 Econometric Analysis of Multidimensional Child Deprivation in West Bengal Logit models specifically model 1 and model 2 is used to analyze the impact of socioeconomic characteristics of the households on the deprived male and female children in West Bengal and is given in Table 9.2. Here, Yit and Dit are the dependent variables. There are a number of explanatory variables like GENDER (Female 5 1, 0 5 Male) children, location of the household (RURAL), scheduled

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Table 9.2. Logit Econometrics Analysis of Multidimensionally Male and Female Child Deprivation in West Bengal. Model-1: All Children Coef.

GENDER RURAL SC ST HINDU MUSLIM HWC HHINS EDU INMZ NBICDS TD _cons

Model-2: Deprived Children z

P>z

Odd Ratio

0.09 1.69 0.091 1.09 0.58 8.41 0.000 1.79 0.28 4.1 0.000 1.32 0.94 8.84 0.000 2.56 20.31 22.16 0.031 0.73 0.08 0.52 0.603 1.08 21.88 220.64 0.000 0.15 20.30 24.6 0.000 0.74 20.08 26.67 0.000 0.92 20.39 26.13 0.000 0.68 20.19 23.41 0.001 0.82 20.49 27.37 0.000 0.61 0.62 3.69 0.000 1.87 Number of Obs. 5 7,696 LR chi2(12) 5 1,580.41 Prob . chi2 5 0.0000 Log likelihood 5 24,472.9535 Pseudo R2 5 0.1501

z

Coef.

–

P>z

Odds Ratio

–

– – 0.20 1.88 0.060 1.22 20.04 20.42 0.672 0.96 0.09 0.71 0.479 1.10 0.16 0.8 0.424 1.17 0.06 0.28 0.777 1.06 0.12 0.74 0.458 1.13 0.13 1.32 0.186 1.14 20.03 21.91 0.056 0.97 20.09 21.05 0.292 0.92 20.28 23.52 0.000 0.76 0.20 2.16 0.031 1.22 20.18 20.81 0.417 0.83 Number of Obs. 5 3,325 LR chi2(12) 5 24.53 Prob . chi2 5 0.0172 Log likelihood 5 22,292.4379 Pseudo R2 5 0.0053

Sources: Authors Analysis from NFHS Unit level data of the third (2005–2006) and fourth (2015–2016) rounds.

cast (SC) and scheduled tribe (ST), HINDU, MUSLIM, higher wealth class of the household (HWC), household insurance (HHINS), year of household head education (EDU), immunization (INMZ, Yes 5 1, 0 5 No), received supplementary nutrition from the Anganwadi center during pregnancy (NBICDS), and time dummy (TD). The pooled logistic regression models are specified as follows: Yit ¼ a0 1 b1 GENDERit 1 b2 RURALit 1 b3 SCit 1 b4 STit 1 b5 HINDUit 1 b6 MUSLIMit 1 b7 HWCit 1 b8 HHINSit 1 1 b9 EDUit 1 b10 INMZit 1 b11 NBICDSit 1 b12 TDit 1 «it Dit ¼ a0 1 b1 RURALit 1 b2 SCit 1 b3 STit 1 b4 HINDUit 1 b5 MUSLIMit 1 b6 HWCit 1 b7 HHINSit 1 1 b8 EDUit 1 b9 INMZit 1 b10 NBICDSit 1 b11 TDit 1 «it

(9.1)

(9.2)

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Model 1: The estimated logit result of model 1 as given by Eq. (9.i) reveals that the LR x2 statistic is significant at less than 1% level implying that multidimensionally child deprivation is significantly explained by GENDER, RURAL, SC, ST, HINDU, HWC, EDU INMZ, NBICDS, and TD variable. Here, GENDER is positive and statistically significant indicating that female children are more likely to be deprived as compared to the male children. Children living in the rural than in the urban areas are more likely to be deprived. Children belonging to the lower strata of the society (SC and ST) are more likely to be deprived. Children belonging to the Hindu community are less likely to be multidimensionally deprived. Children belonging to the higher wealth class family (HWC) as well as having educated head (EDU) are less likely to be deprived. Children of the households having health insurance (HHINS) and the children of the women getting immunization (INMZ) and supplementary nutritional benefits from ICDS (NBICDS) are less likely to be deprived. Significant TD indicates that the multidimensional child deprivation reduced during 2005–2006 to 2015–2016. Model 2: In this model 2 as given by Eq (9.ii) RURAL is found to be significant which indicates that female children residing in the rural areas are more likely to be deprived as compared to the male children. With the increase in the year of education of the household head, the female children are less likely to be deprived as compared to the male children. Similarly, in the household where women receive supplementary nutritional benefits from ICDS, female children are less likely to be deprived as compared to the male children. However, TD is positive and statistically significant that indicates that the female children are more likely to be deprived as compared to male child in 2015–2016 as compared to 2005–2006.

5. Conclusion The multidimensional approach to assess child deprivation by sex reveals that multidimensional child deprivation is significantly reduced in West Bengal during 2005–2006 and 2015–2016. The reduction in the percentage share of deprivation in “health care” and “nutritional status” is higher than the reduction in the “hygiene” dimension. These shares varied considerably across household characteristics. There has been a significant reduction in the percentage share of deprived children across each social caste, religion, and wealth group, but the female children were more deprived in more indicators of health care, nutrition status, and hygiene dimension in both 2005–2006 and 2015–2016. The present estimation of multidimensional child deprivation share implies that vulnerability was considerably higher among female children across major indicators. Gender disparity is higher in rural areas, among STs and Muslims, and low wealth class families. Further, it has been found that female children are less likely to be multidimensionally deprived in health in those families having higher educated head and health insurance, and whose mother received supplemental nutrition from ICDS in West Bengal. Thus, the government must take necessary steps to safeguard children and advance national development, focusing on the

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widespread and successful implementation of the ICDS program and running awareness campaigns to educate the public and advance the notion of an egalitarian society.

Acknowledgements Satyanarayan Kumbhakar is a recipient of the Indian Council of Social Science Research Doctoral Fellowship. The responsibility for the facts stated, opinions expressed, and the conclusions drawn is entirely that of the authors.

References Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. Working Paper No. 38. Oxford Poverty and Human Development Initiative, University of Oxford. Das, P., Ghosh, S., & Paria, B. (2021). Multidimensional poverty in India: A study on regional disparities. GeoJournal, 1–20. Das, P., & Paria, B. (2020). Multidimensional poverty in India: An analysis based on NSSO unit level data. Vidyasagar University Journal of Economics, 79–95. Retrieved from http://inet.vidyasagar.ac.in:8080/jspui/bitstream/123456789/5534/1/6% 20Pinaki%20Das%2C%20Multidimensional%20Poverty%20in%20India.pdf Das, P., Paria, B., & Firdaush, S. (2021). Juxtaposing consumption poverty and multidimensional poverty: A study in Indian context. Social Indicators Research, 153, 469–501. Dr`eze, J., & Khera, R. (2012). Regional patterns of human and child deprivation in India. Economic and Political Weekly, 42–49. Dutta, S. (2021, January). Multidimensional deprivation among children in India and Bangladesh. Child Indicators Research Springer Nature B.V. 2021. Electronic ISSN 1874-8988, Print ISSN: 1874-897X. doi:10.1007/s12187-020-09787-9 Kakwani, N. (1984). The relative deprivation curve and its applications. Journal of Business & Economic Statistics, 2(4), 384–394. Kumbhakar, S., Firdaush, S., & Das, P. (2022, July). Status of Child Health Deprivation in West Bengal during 2005–06 to 2015–16: A multidimensional analysis. Productivity: A Quarterly Journal of The National Productivity Council, 62(4), 377–386. ISSN: 0032-9924. doi:10.32381/PROD.2022.62.04.4 Mishra, A., & Ray, R. (2013). Multi-dimensional deprivation in India during and after the reforms: Do the household expenditure and the family health surveys present consistent evidence? Social Indicators Research, 110(2), 791–818. Murthi, M., Guio, A. C., & Dr`eze, J. (1995). Mortality, fertility, and gender bias in India: A district-level analysis. Population and Development Review, 745–782. Panda, B. K., Kumar, G., & Awasthi, A. (2020). District level inequality in reproductive, maternal, neonatal and child health coverage in India. BMC Public Health, 20(1), 1–10. Patra, N. (2014). Patterns and determinants of gender bias in child health in India. Health and Human Development Aspects, 55.

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Sharma, S. (2005). Child health and nutritional status of children: The role of sex differentials. North Campus: Institute of Economic Growth, University of Delhi Enclave. Trani, J. F., Biggeri, M., & Mauro, V. (2013). The multidimensionality of child poverty: Evidence from Afghanistan. Social Indicators Research, 112(2), 391–416. UNDP. (2013). Human development report 2013. New York, NY: Oxford University Press, Retrieved from http://hdr.undp.org/en/2013-report https://www.unicef.org/india/what-we-do/gender-equality

Chapter 10

Is Dropout in Schools Related to Gender and Birth Order? Chayanika Mitra and Indrani Sengupta

Abstract The issue of dropout looms large in the context of school education in India despite various flagship programs that have been initiated in school education. According to U-DISE report (2019–2020), girls drop out more than boys at the upper primary level. An analysis of the dropout problem demands probing deeper into intrahousehold dynamics that involves bargaining at the household level on investment decision. These decisions are often influenced by the social context in which the girl child in the family faces discrimination which gets reflected in terms of dropout of girl children. Apart from the issue of gender, birth order also determines which child is more likely to drop out. Using NSSO data (2017–2018), we observe that not all children of a household are equally susceptible to the dropout problem. Moreover, the eldest sibling is found to be more susceptible to the dropout problem and dropout rate goes down with the other younger siblings in the same household. First-born girl children drop out more than their male counterparts showing gender bias. The chapter concludes that the factors pushing a child to drop out become more effective for the eldest sibling. The major reason is the family structure of India as the eldest sibling is expected to be more responsible and look after other younger siblings. Consequently, a certain number of the younger siblings try to follow the elder siblings and discontinue going to school. Keywords: Dropout; schools; gender; birth order; eldest sibling; NSSO data

1. Introduction Education has an emancipatory effect on the learner apart from the instrumental value which entails linkages between labor market participation and human capital investment. In light of this, education of women is paramount toward Gender Inequality and its Implications on Education and Health, 113–123 Copyright © 2023 Chayanika Mitra and Indrani Sengupta Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231011

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bringing a transformation in the existing social structure that discriminates against women in the social, economic, and political domain. However, the road to achieving these goals is challenging for women as the differential investment in human capital between girls and boys begins at an early stage of schooling. Decision-making at the household level involves several considerations which are specific to the family background and demography and at the same time are influenced by the social and cultural milieu. In India, the biasness can be observed in school choice where the boy child in the family is enrolled in a private school and the girl child is enrolled in a government school with the prevalent perception that private schools perform better (Kumar & Choudhury, 2021; Sahoo, 2017). Despite the realization and drive for female literacy that began in India during pre-independence India for varied reasons of which one is social reform (Chanana, 1996), the issue is still a cause of concern in policy debates. According to NSSO data (2017–2018), percentage of females within the age group 3–35 years who never enrolled in any educational institute is higher (16.6%) than their male counterparts (11%) in India. The same report on educational attainment and educational expenditure captures that 11.6% and 11.8% of male and female students belonging to the age group 14–15 years, respectively, are not attending school. Although the difference between male and female children appears marginal in the above figures, the percentage of dropout among ever enrolled persons is more for females up to upper primary level (18.3%) than for males (16.9%) according to the NSSO (2017–2018). A closer examination on the reasons for dropout among males and females significantly differ. The same data source reveals that 30.2% females cite engagement in domestic activities as a reason for not continuing education, whereas only 4% males cite the same reason. The division of labor tied to one’s gender often surfaces as a major impediment in the completion of education among girls as they are expected to engage in household chores or look after their younger siblings. According to the “Educational statistics at a glance” report (2016) by the Ministry of Human Resources, India, the dropout rate among boys and girls are 4.53% and 4.14%, respectively. Although Sarva Shiksha Abhiyan (2010) and Right to Education Act (RTE) (2009) have played major roles in the decline of dropout rate in India, still a huge number of children do not complete their education in schools. In spite of various flagship programs in India such as Beti Bachao Beti Padhao, Sukanya Samriddhi Yojana and schemes specific to some states (such as Ladli scheme in Haryana, Kanyasree in West Bengal), the ASER report (2017) shows dropout in rural India among girls is rising. While there are supply-specific factors that push children out of schools such as poor infrastructure, lack of teachers, etc., household-specific factors have got ample attention in the literature. Chugh (2011) traced out that the family background (lack of financial resources and lack of interest in studies) are major reasons for dropout of children living in the slum areas in Delhi. In light of this, examining dropout of girls necessitates that we situate it in the context of family decision-making, i.e., whether dropout of girl children from schools after a certain stage of schooling is due to reasons specific to factors related to the family such as engaging in household chores or raising younger siblings. In this context,

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consideration of birth order becomes pertinent as the first child often becomes a means of earning as child labor in low-income families. Dammert (2010) finds that a boy child with younger siblings has a comparative advantage in terms of child labor, i.e., he is likely to remain in the labor market, whereas a girl child is likely to perform domestic works. In both ways, the higher birth order children are likely to drop out of schools. These considerations are to be understood in terms of financial conditions of the family. On the other hand, first-born children in developed countries are found to attract more resources in terms of money and time (de Haan, Plug, & Rosaro, 2014). The aspect of gender gives an interesting entry point to these theories of birth order especially in the context of developing countries because of rigid social norms governing women. The purpose of the chapter is to briefly outline the issue of dropout by interacting gender with birth order to discern whether drop out of girl children from school is associated with being the first-born. We restrict our analysis till secondary level (below class 10 level). The chapter is organized into three sections. The first section deals with the literature on dropouts. The second section brings out the importance of birth order in analyzing dropout. The last section presents evidences on dropout from NSSO data (2017–2018) by concluding the importance of looking at birth order to understand as to why the girl children drop out, i.e., whether the family obligations of first-born girl child push the girl children out of educational trajectory. Our preliminary findings are expected to motivate further research in this area. We observe that the higher percentage of dropout is the first-born children in India. Besides, for female first-born, the dropout rate is higher than their male counterparts, indicating how girls are trapped more in household chores than boys. In other words, the findings reflect that first-born girls have a greater tendency to face obstacles to continue studies due to the expected responsibilities that the family demands. We also find state-level variation in dropout rate among first-born boys and girls indicating some state-level intervention to bring girl children to school.

2. Who Drops Out and Why? There are ample scholarships examining dropout in schools that address the dropout problem of India from different aspects (Choudhury, 2006; Chugh, 2011; Ghosh, 2011; Gouda & Sekher, 2014; Hati & Majumder, 2012; Patel, Singh, Chandra, Khanna, & Mehra, 2018; Sengupta & Guha, 2002). Hati and Majumder (2012) found that 19.6% dropout rate in primary schools for boys and 15.4% for girls are higher than the overall dropout rate in India (6.4%) in 2009–2010. Gouda and Sekher (2014) observed that the dropout was high among the children belonging to Muslim, Scheduled Caste, and Scheduled Tribe families. They also noted that dropouts among the children belonging to illiterate parents were four times higher than that of the literate parents and that, if parents were not working the possibility of dropout among their children was relatively high. Sengupta and Guha (2002) have found out that parental educational attainment

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significantly increases the probability of a girl child to be enrolled through a primary survey in West Bengal. They have also pointed out that the mother’s workforce participation reduces the probability of a girl child being enrolled. Choudhury (2006) pointed out that the problem of dropout is acute in households where the number of siblings is high compared to other households in Assam. Ghosh (2011) pointed out that almost 10% of the children drop out in order to look after younger siblings and household chores in India. Patel et al. (2018) noted that a large number of siblings increases the probability of a girl child to drop out in India. However, Chernichovsky (1985) has shown that a larger number of school-going siblings will more likely enhance the probability of a child to continue the studies in schools. It has been argued that a large number of children in a household reduces the indirect cost of education. There are certain items (books, stationery items) that can be shared among the younger siblings. However, the story seems to be opposite in India, and it is evident that an elder child drops out and starts to work in order to financially help the younger siblings and sometimes to simply look after them in a household (Chugh, 2011). Chugh (2011) also points out that 31.7% of dropout children are first in the birth order and 43.8% are second in the birth order. This phenomenon is very prominent among socioeconomically backward households in order to increase the household income. Further, Mukherjee (2012) has noted that the parents send their child to the workplace to use their earning as a supplementary source of income. In line with this, Pal (2004) indicates that boys with older brothers (as opposed to older sisters) were more likely to go to schools. This perhaps suggests that older brothers can relieve younger siblings of some family responsibility, say supplementing family earnings. In this context, it should be noted that the RTE Act (Right to Education) enables the children belonging to the age group 6–14 years will receive free education from the schools. Hence, it is expected that the child schooling outcome (dropout or enrollment) would be more of a demand side problem till below class 10 level. In other words, the dropout problem is more dependent on the parents of a household than any other external factors. Rajaram and Sunil (2003) using NFHS (National Family Health Survey) data point out that the social economic background of a child including the decisions and resources of the parents/family and community-level characteristics such as availability and accessibility are more important factors in determining a child’s schooling outcome. In most of the empirical works, the incidence of dropout is analyzed taking the dependent variable to be a dichotomous variable. It might be possible that a younger child drops out if the elder child has already dropped out. So, the tendency of a child to drop out might be dependent on the educational activities of the older siblings in a household. Further, it can be argued that the elder siblings are more susceptible to the dropout problem as they are considered to be more responsible as compared to younger siblings in a household in terms of income generation and taking care of other household chores. However, the effect of siblings’ activity on the dropout tendency of a child has been addressed in the existing literature by only considering the number of siblings in household and sometimes this number is divided into male and female categories to capture the

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gender aspect. Hence, the impacts of siblings are only considered traditionally by using them as the explanatory variables. This approach fails to address the question of how far the elder siblings are susceptible to the dropout problem.

3. Why the Consideration of Birth Order Is Important? To begin with this problem, let us start with the seminal work of Becker and Lewis (1973). In this research paper, they have highlighted the trade-off between the “quantity” (related to the fertility) and the “quality” that households face for each new-born child. Further, it has been mentioned that an increase in quality is more expensive if there are more children because the increase must apply to more units; similarly, an increase in quantity is more expensive if the children are of higher quality because higher-quality children cost more. Hauser and Sewell (1985) have tried to trace out the relationship between the allocation of resources among the children and their respective birth orders. Using a composite birth order index, it has been observed that the children get unequal access to the resources of a household. Further, the allocation of resources is decreasing with the birth order of a child. Later-born children are more likely to get lesser resources. Jayachandran and Pande (2017) reveal that the elder-son preference of the Indian households leads to unequal distribution of resources across other children. The situation becomes graver when the first-born child is female. The parents will continue to increase the number of children until a boy child is born in the family. Congdon and Lindskog (2017) point out that there are negative birth order effects on human capital in developed countries. First-born children tend to perform better on measures of educational outcomes. However, the research works from developing countries suggests the opposite relationship. Later-born children tend to have better educational outcomes. The suggested explanation is more binding resource constraints combined with increasing family income over time, in particular if older siblings can contribute to household income. ¨ Ejrnæs and Portner (2004) point out that parents choose to reinforce rather than compensate differences between children via investments in human capital which leads to positive birth order effects, as last-born children will be the children with the highest endowments and thus receive the most human capital investment. Seid and Gurmu (2015) in their study on Ethiopia reveal that the probability to participate in the labor market decreases as the birth order of the child increases. Further, it has also been highlighted that the children of higher birth order are more likely to spend longer hours in studies/school. It could be reexplained in the way that the children with higher birth order (later-born) are less susceptible to the dropout problem. Rammohan and Dancer (2008) in their study on Egyptian children highlight that the first-born male child is adversely affected due to the presence of younger siblings.

80

(b)

118

(a)

Percent 40 20

21.52

4.578

0

.9859

0

1

2 3 Birth Order of the dropped out child

4

Fig. 10.1. The Frequency Distribution of the Dropped-Out Child Considering the Birth Order and Gender. Source: Authors’ calculation from NSSO (2017–2018). Note: (a) in Fig. 10.1 represents percentage of dropout by birth order whereas (b) compares percentage of male dropouts and female dropouts by birth order.

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60

72.92

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4. Findings From the NSSO Data A total of 29,145 households (18,354 in the rural sector and 10,791 in the urban sector) having at least one dropped-out child in the below class 10 level. A total of 43,891 children (31,154 in the rural sector and 12,665 in the urban sector) have dropped out in the below class 10 level. Fig. 10.1a represents the frequency distribution of the dropped-out children considering the birth order in the below class 10 level. It can be observed that 72.92% of the dropped children are first born, followed by 21.52% of the second born. First, the first-born child of any household is

Table 10.1. The Frequency Distribution of Dropouts. Panel a: The Frequency Distribution of Dropouts by Birth Order for Different Income Levels of a Household Low Income

High Income

Birth orders

Rural

Urban

Rural

Urban

First born Second born Third born Fourth born

74.62 21.37 3.35 0.67

76.33 19.65 3.40 0.63

67.21 23.71 7.39 1.70

71.55 20.75 6.17 1.54

Panel b: The Frequency Distribution of Dropouts by Birth Order and Gender for Low-Income Levels of a Household Urban Birth Order

Male

Female

First born Second born Third born Fourth born

49.02 61.86 64.94 52.00

50.94 38.14 35.06 48.00

Rural Total

100 100 100 100

Male

Female

Total

46.35 63.72 61.94 60.36

53.64 36.28 38.06 39.64

100 100 100 100

Panel c: The Frequency Distribution of Dropouts by Birth Order and Gender for High-Income Levels of a Household Urban Birth order

Male

Female

First born Second born Third born Fourth born

43.85 57.82 58.13 52.78

56.15 42.18 41.87 47.22

Rural Total

100 100 100 100

Source: Authors’ calculation from NSSO (2017–2018).

Male

Female

Total

43.43 57.45 61.72 58.94

56.56 42.55 38.28 41.06

100 100 100 100

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considered to be more responsible and as a result, there is a tendency among the parents to send them to look after household chores or to the labor market to earn money. Desai and Jain (1994) indicate that the family highly depends on the older siblings. This tendency is also evident in the existing literature and the reason might be that households provide more opportunities for the younger siblings to study further. Also, the recent NSSO report (2017) points out that almost 31% of the dropped-out children has specified engagement in household chores and labor market as the major reason to drop out. Second, the children of higher age are more likely to drop out due to the difficulties faced in the studies, and this leads to lack of interest and discontinuity in going to school. Fig. 10.1b represents the frequency distribution of the dropped-out male and female children separately, considering the birth order in the below class 10 level. It can be seen that 65.77% of male dropouts are first-born, followed by 27.46% of the second-born. However, for the female dropouts, the percentage for the first-born is 78.19%, followed by 17.3% for the second-born. Hence, the obstacles faced by a child to continue study are more stringent and binding for the first-born. The percentage of dropouts gradually decreases with the increase in the birth order reflecting the fact that the younger siblings have a larger chance to continue study. Considering the aspect of the gender of the child, we can see that the percentage of dropping out is larger when the first-born child is female. Hence, this indicates that the household scenario becomes more stringent if the child is female and wishes to continue the study. It is interesting to note that the percentage of male dropouts is more as compared to female starting with the second-born child. While trying to further understand the pattern of dropout among various states, the data show some anomalies. It suggests that the first-born male and female difference in dropout rate is not uniform across states. In the states of Jammu and Kashmir, Uttarakhand, Himachal Pradesh, and Bihar dropout rate is higher among the first-born females in the household (NSS 2017–2018). On the other hand, in states like Haryana and Punjab with skewed sex ratio in favor of male, we find that first-born male dropout rate is more than females. This might be due to some flagship programs for girl children in these states. For instance, Haryana launched its ladli scheme to decrease female infanticide and to provide quality education. Also, higher dropout rate among first-born male suggests incidence of child labor where elder male children are sent to work to earn additional income for the family. In order to understand the impact of income, we further disintegrated our analysis by considering gender and birth order intersection between low-income group and high-income group. Table 10.1a depicts that the first-born dropout rate is more than the younger siblings in both rural and the urban. The dropout is higher among the low-income group due to financial constraints that they face. As we follow the birth order across various regions, we find a particular pattern wherein the dropout rate decreases irrespective of income groups for younger siblings. The higher birth order children (later born) dropout less than elder ones. Within the low-income group, the dropout figure varies in rural and urban indicating low-income individuals in the rural are more likely to discontinue

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studies than in the urban. This implies social norms are more rigid in the rural than in the urban. Therefore, dropout cannot be only explained by financial constraints. Table 10.1b and Table 10.1c analyze the interplay of gender and birth order among male and female in low-income and high-income households, respectively. Table 10.1b highlights an important result. For the low-income household, dropout for first-born female is higher than male, but there is a reversal of pattern from second-born onwards in both rural and urban. It implies that the household with second-born or third-born female children do not have bias against female children. It might also be the case that responsibilities of the household are bestowed upon the first-born female child. Therefore, the chances of discontinuing education are less among the later-born female children. On the other hand, for higher birth order, dropout figures for male are higher than female indicating that later-born male children in low-income household are sent for work more than female children. It might be because the ease with which parents send male children to work is absent for female children due to safety issues. Therefore, in low-income households, the first-born female child takes care of the households and the later-born female children continue with studies. Another interesting aspect is that first-born males’ dropout rate is less than the later born males’ which implies that the first-born male is privileged as compared to the later-born. This might be because resource investment is more on male child, and it is likely that in families with son preference, number of siblings is less if the first child is male. Therefore, resource allocation goes in his favor. On the other hand, when there is more than one child, resources compete and hence there later-born drop out of schools. The scenario is similar for high-income households (Table 10.1b). A comparison of Table 10.1b and Table 10.1c shows that for the high-income household, dropout figure of first-born male is less than the dropout figure for the low-income household. On the contrary, first-born female drops out less for low-income households than high-income household indicating that income effect is stronger for low-income households.

5. Conclusion The chapter is an attempt to understand the pattern of dropout among male and female children using NSSO data (2017–18). An important finding of the chapter is the difference between the first-born male and female children in India in terms of human capital investment. This shows gender discrimination follows a certain trend, i.e., first-born females are more discriminated against than later-born females. We find that the dropout figures for first-born female is more than later-born females irrespective of income levels. However, this pattern is reversed when we examine the difference in percentage of dropouts between male and female from the second-born child onwards. This needs a deeper scrutiny by looking at intrahousehold bargaining decisions. The first female borns dropout more than their male counterparts due to various reasons. The elder daughters are expected to look after their siblings and other household responsibilities. Apart

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from this, there are safety and marriage concerns coupling with the access and availability of infrastructure. The preliminary findings of the chapter call for further scrutiny into the aspect of gender and birth order to understand why later-borns drop out less than the first-borns.

References Abhiyan, S. S. (2010). Ministry of human resource development, Government of India. Becker, G. S., & Lewis, H. G. (1973). On the interaction between the quantity and quality of children. Journal of Political Economy, 81(2, Part 2), S279–S288. Chanana, K. (1996). Education attainment, status production and women’s autonomy: A study of two generations of Punjabi women in New Delhi. In Girls’ schooling, women’s autonomy, and fertility change in South Asia (pp. 107–132). New Delhi: Sage Publications. Chernichovsky, D. (1985). Socioeconomic and demographic aspects of school enrollment and attendance in rural Botswana. Economic Development and Cultural Change, 33(2), 319–332. Choudhury, A. (2006). Revisiting dropouts: Old issues, fresh perspectives. Economic and Political Weekly, 5257–5263. Chugh, S. (2011). Dropout in secondary education: A study of children living in slums of Delhi. NUEPA Occasional paper 3. National University of Educational Planning and Administration, New Delhi (pp. 1–53). Congdon Fors, H., & Lindskog, A. (2017). Within-family inequalities in human capital accumulation in India: Birth order and gender effects. Dammert, A. C. (2010). Siblings, child labor, and schooling in Nicaragua and Guatemala. Journal of Population Economics, 23, 199–224. de Haan, M., Plug, E., & Rosero, J. (2014). Birth order and human capital development evidence from Ecuador. The Journal of Human Resources, 49(2), 359–392. Desai, S., & Jain, D. (1994). Maternal employment and changes in family dynamics: The social context of women’s work in rural South India. Population and Development Review, 115–136. ¨ Ejrnæs, M., & Portner, C. C. (2004). Birth order and the intrahousehold allocation of time and education. The Review of Economics and Statistics, 86(4), 1008–1019. Ghose, M. (2011). Gender bias in education in India. Journal of Economics and Sustainable Development, 7(2), 118–128. Gouda, M. S., & Sekher, T. V. (2014). Factors leading to school dropouts in India: An analysis of national family health survey-3 data. e-ISSN: 2320–7388, p-ISSN: 2320–737X. IOSR Journal of Research & Method in Education, 4(6), 75–83. Ver. III (Nov–Dec. 2014). Hati, K. K., & Majumder, R. (2012). Proximate determinants of school dropout: A study on Rural West Bengal. Hauser, R. M., & Sewell, W. H. (1985). Birth order and educational attainment in full sibships. American Educational Research Journal, 22(1), 1–23. Jayachandran, S., & Pande, R. (2017). Why are Indian children so short? The role of birth order and son preference. The American Economic Review, 107(9), 2600–2629.

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Kumar, D., & Choudhury, P. K. (2021). Determinants of private school choice in India: All about the family backgrounds?. Journal of School Choice, 15(4), 576–602. Mukherjee, D. (2012). Schooling, child labor, and reserve army evidences from India. Journal of Developing Societies, 28(1), 1–29. Pal, S. (2004). How much of the gender difference in child school enrolment can be explained? Evidence from rural India. Bulletin of Economic Research, 56(2), 133–158. Patel, R., Singh, A. K., Chandra, M., Khanna, T., & Mehra, S. (2018). Is mother’s education or household poverty a better predictor for girl’s school dropout? Evidence from aggregated community effects in rural India. Educational Research International. Rajaram, S., & Sunil, T. S. (2003). Child schooling in India: A multilevel approach. Educational Research for Policy and Practice, 2(2), 123–141. Rammohan, A., & Dancer, D. (2008). Gender differences in intrahousehold schooling outcomes: The role of sibling characteristics and birth-order effects. Education Economics, 16(2), 111–126. Sahoo, S. (2017). Intra-household gender disparity in school choice: Evidence from private schooling in India. The Journal of Development Studies, 53(10), 1714–1730. Seid, Y., & Gurmu, S. (2015). The role of birth order in child labour and schooling. Applied Economics, 47(49), 5262–5281. Sengupta, P., & Guha, J. (2002). Enrolment, dropout and grade completion of girl children in West Bengal. Economic and Political Weekly, 1621–1637.

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Chapter 11

Investigating the Role of Air Quality and the Nexus Between Female Health Status and Their Labor Force Participation Rate: Evidence From Rural India Kaushiki Banerjee and Arpita Ghose

Abstract Using 13 major Indian state-level data of the rural sector, covering the period 2004–2005 to 2011–2012 and by estimating a simultaneous-panel model employing Baltagi’s Instrumental-Variable EC2SLS estimation method, this chapter contributes to the literature by establishing: (i) the simultaneous dependence between female labor force participation rate (FLFPR) and female health status as measured by female life expectancy (FLE), (ii) the negative impact of outdoor air pollution as measured by prevalence of SPM, SO2, and NO2 on FLE, and (iii) the interaction among different demographic factors in determining both FLFPR and FLE. The interaction effect of air pollution with (i) economic growth and (ii) poverty (POV) on FLE is negative implying that the partial effect of a change in growth (POV) depends on air pollution level. Thus reduction in air pollution will increase FLE and hence FLFPR, as the simultaneous positive dependence between FLFPR and FLE is supported. The interaction effect of women’s political power and education on rural FLFPR is significant and nonlinear with positive marginal effect. Thus the partial effect of a change in women’s political power on FLFPR will in turn depend on level of education and vice versa. The positive impact of other demographic factors like (i) education, (ii) female leader, (iii) POV, and (iv) urbanization on FLFPR and (a) education, (b) female household head, (c) female leader, (d) sex ratio, and (e) growth on FLE are apparent. However, the household size significantly and negatively affects FLFPR.

Gender Inequality and its Implications on Education and Health, 125–137 Copyright © 2023 Kaushiki Banerjee and Arpita Ghose Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231012

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Keywords: Female labor force participation; life expectancy; air pollution; India; simultaneous panel model; rural JEL Classification: J21; I19; Q59; O53; C33

1. Introduction Participation rates of females have been found to be absurdly low in South Asian nations, especially in India. Though India is experiencing rapid growth rate in the post liberalization era unfortunately, female labor force participation rate (FLFPR) is low and declining consistently in the face of unswerving economic growth. The literature has identified health, which is a human capital positively affecting FLFPR (Barro, 1996; Bridges & Lawson, 2008; Grossman, 1972; Novignon, Nonvignon, & Arthur, 2015). In fact, simultaneous dependence between health status and FLFPR can be justified. First of all, improved health status leads to more labor supply as leisure becomes less valued (Barro, 1996; Grossman, 1972). Soares and Falcao (2008) found that poor health of females imposes even more adverse impact on FLFPR in terms of early exit from the labor force. Secondly, increased FLFPR on the other hand promotes better health outcomes through higher wages and enhanced living standard. Saravi, Navidian, Rigi, and Montazeri (2012) observed health problems are severe among females who are not engaged in market activity compared to working women. It is also evident that tremendous spurt in economic activities around the globe in the recent decades has been accompanied by persistent deterioration of air quality. A considerable body of literature has provided evidence of the detrimental effects of air pollution on human health where females are found to be disproportionately affected (Gupta et al., 2006; Mishra, Malhotra, & Gupta, 1990; Padmavati & Arora, 1976). The consequences of rural air pollution are much less discussed, whereas there are studies estimating the urban health damages due to air pollution (Banerjee & Ghose, 2022; Black & Henderson, 1999; Moore, Gould, & Keary, 2003). But deterioration of rural air quality is also wrecking havoc. The majority of the premature deaths in India resulting from atmospheric pollution are in rural as opposed to urban regions (Karambelas et al., 2018). Therefore, given the two-way interaction between FLFPR and their health, reduction in rural air pollution can generate greater FLFPR by producing superior female health status. The chapter tries to contribute to the literature in the following way given this scenario: (i) it establishes simultaneous positive dependence between health status, as measured by life expectancy at birth (FLE) and FLFPR by estimating a simultaneous panel model employing Instrumental Variable EC2SLS estimation method (Baltagi, 2008) focusing only on rural areas of 13 major states, spanning over 2004–2005 to 2011–2012, considering the role of the socioeconomic variables like education, economic growth, poverty (POV), urbanization, gross fixed capital

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formation, women’s political power etc., (ii) adverse impact of outdoor air pollution on FLE, and (iii) the interaction among different demographic factors and with that of pollutants in determining both FLFPR and FLE. The joint interaction effect of (a) economic growth and (b) rate of POV with air pollution on rural FLE is negative, implying that the partial effect of a change in growth (POV) depends on air pollution. Given the level of air pollution growth lowers FLE in rural India. POV shortens FLE significantly, where rise in POV along with rising pollution further deteriorates rural FLE. The interaction effects of some other explanatory variables on FLFPR and FLE are apparent. The estimated results will be relevant in formulating strategy regarding air pollution abatement to improve rural FLE and thus promote rural FLFPR. The remainder of the chapter is organized as follows: Section 2 contains brief literature review discussing factors affecting FLE and FLFPR and describes the variables used. Section 3 explains the data sources and model specification. Section 4 discusses the results. Section 5 concludes with policy implications.

2. The Brief Literature Review The existing literature has identified the role of the following socioeconomic variables in explaining FLFPR and FLE. These are summarized below.

2.1 Factors Affecting FLE The following explanatory variables are having positive impact on FLE. Economic growth: Higher the economic growth, better the life expectancy of the masses (Miladinov, 2020; Monsef & Mehrjardi, 2015). The relationship is tested here considering the logarithm of net per capita state domestic product (Lnnsdp) at constant price as a measure of economic growth following Lahoti and Swaminathan (2016). Poverty (POV): POV generates poor health outcomes and reduces life expectancy (Tafran, Tumin, & Osman, 2020) Women’s health are worst affected because of increasing feminization of POV (Cohen, 1994). POV is measured in this chapter by the percentage of people below the POV line. Education: The existing literature documents that education promotes better life expectancy (Cervellati & Sunde, 2002; Lin, Chen, Chien, & Chan, 2012). The present study considers two indicators of education, viz., the percentage of women completing (i) primary education (Priedu) and (ii) class 12 and above education level (12_abv). The possibilities of nonlinear effects are tested using square of the indicators. Women’s Political Power (WP): Increasing women’s participation in politics is found to have a negative impact on mortality, for both mother and child (Bhalotra, Clarke, Gomes, & Venkataramani, 2021). Also, positive impact of women leaders in India on neonatal and child health by improving child health services is evident (Bhalotra & Clots-Figueras, 2014). This chapter measures WP

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by the statewise percentage of women-elected candidate out of total seats in Parliament. Female as Head of the Household (FHH): Better female health outcome is evident in FHH due to better household resource allocation toward their well-being and good health (Richards et al., 2013; Unisa & Datta, 2005). However, the literature has also documented the negative impact of FHH on FLE. In a patriarchal society, a woman becomes the household head not by choice but rather by compulsion, and they mostly suffer from poor health due to low income (Veiseni, Delpisheh, & Sayehmiri, 2015). In this chapter, statewise percentage of female household head (FHH) has been considered. Sex ratio (SR): Adverse SR produces worst health outcomes for girls due to discriminatory health-care activities for girls leading to higher female morbidity, mortality, and “missing women” phenomenon (Milazzo, 2018; Sen, 1990). However, the existing literature has identified the negative effect of air pollution on health. Level of Air Pollution: Prolonged exposure to polluted air causes myriad respiratory diseases and increases risk of heart diseases, thereby raising mortality among females. Even air pollution is also hampering cognitive ability, mental growth of children, and loss of life expectancy among them (Chauhan & Johnston, 2003; Chay & Greenstone, 2003; Currie & Neidell, 2005). This chapter estimates the impact of level of rural air pollution (measured by the presence of different pollutants in the air, such as SO2, NO2, SPM, and RSPM concentrations) on rural FLE. Further, there is a dearth in the literature in considering the interaction effect of: i. Growth in income and air pollution: Given the level of air pollution, rise in growth in income may further affect FLE. ii. Air pollution and poverty: FLE is affected by poverty and if FLE is guided by air pollution, given the level of poverty, an increase in air pollution will further affect FLE.

2.2 Factors Affecting FLFPR The literature has cited following socioeconomic variables having positive impact on FLFPR. Education: The literature has identified a positive impact of education on labor supply of women (Chatterjee, Desai, & Vanneman, 2018; Chaudhary & Verick, 2014; Szulga, 2014). However the uniform positive relationship between female educational attainment and FLFPR is not always evident. Rise in education level of girls may not always increase FLFPR (Das & Desai, 2003; Dasgupta & Goldar, 2005). Instead of a linear relationship, Das (2006) and Klasen and Pieters (2015) have obtained existence of a U-shaped relationship between education and FLFPR. Women’s Political Power (WP): There is a growing body of literature documenting positive impact of women leaders on FLFPR (Afridi, Iversen, &

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Sharan, 2013; Chaudhary & Verick, 2014). Since women politicians are more devoted in improving female health outcome, providing decent standard of living, and reducing crime against women which encourages more FLFP (Chattopadhyay & Duflo, 2004; Ghani, Mani, & O’Connell, 2013; Iyer, Mani, Mishra, & Topalova, 2012). Poverty (POV): The literature identifies POV a crucial indicator affecting FLFPR since abject POV compels females to join the workforce (Bhalla & Kaur, 2011; Klasen & Pieters, 2015). Economic Growth: The popular feminization-U-hypothesis documents that FLFPR first declines and then ultimately increases with development (Mammen & Paxson, 2000; Tam, 2011) though it has little empirical evidence (Gaddis & Klasen, 2014; Lechman & Kaur, 2015). In India, such a pattern is found to be nonexistent by studies both at the individual and state level (Bhalla & Kaur, 2011; Bhattacharyya & Haldar, 2020; Lahoti & Swaminathan, 2016). Rate of Urbanization (Urb): Empirically positive influence of urbanization on rural FLFPR is evident, where rapid urbanization causing huge male labor migration from rural to towns has created shortage of labor in rural area which facilitates FLFPR (Shrestha, 2017). The percentage of urban population in total population is used as a measure of Urb in this chapter. Gross Fixed-Capital Formation (GFCF): Greater gross fixed-capital formation leads to better FLFPR (Meier & Sansui, 2019). Female as Head of the Household (FHH): With rise in FFH, they are more likely to be in the labor force (Ghosh & Mukhopadhyay, 1984; Khan & Khan, 2009). Sex ratio (SR): The literature cites greater SR facilitates higher FLFPR (South, 1988). There is evidence of negative impact of the following factor on FLFPR. Household Size: Large family size causes poor FLFPR as women act as a primary caregiver in the family (Khan & Khan, 2009). For the present study, average household size (Avg_hhsz) for each state is considered. In addition, the following joint interaction effects of the explanatory variables are used here to test the phenomenon that the effect of one explanatory variable depends on the magnitude of the other. There is dearth of literature measuring such interaction effects. i. WP and (12_abv) ii. WP and Priedu

3. Data Sources and the Estimation Methodology 3.1 Data Sources The NSS-EUS rounds data for 2004–2005, 2009–2010 and 2011–2012 are used. FLFPR in the rural sector is based on usual (principal and subsidiary) status activities. The necessary data are collected from different NSS reports1, the National Family Health Survey2, reports of the Election Commission of India3,

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the Reserve Bank of India,4 and the Central Pollution Control Board5. The Indian states like Andhra Pradesh, Assam, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal are considered.

3.2 Estimation Methodology Simultaneously determined equations representing FLFPR and FLE for panel model containing air pollution and other abovementioned explanatory variables are estimated for 13 Indian state-level for rural sector, spanning over 2004–2005 to 2011–2012, employing IV (EC2SLS) estimators (Baltagi, 2008), providing a wider range of instruments to achieve small-sample efficiency gain (Baltagi & Liu, 2009), and account for potential endogeneity. To create a time-invariant dummy, as required by IV-EC2SLS estimation, the states are classified according to the Gender Inequality Index (GII) scores of the states for 2017–20186. Based on all-India GII score (50.462), the dummy (DS) takes the value “1,” for the states where state-GII score . 0.462 (i.e., higher gender disparity than Indian average) and “0,” otherwise.

4. Results Breusch and Pagan Lagrange Multiplier test (1980) and Hausman test (1978) confirm panel regression and endogeneity of FLE. Since correlation between (i) Lnnsdp and POV and (ii) Priedu and (12_abv) are found, these variables are kept in different specifications. Both for FLFPR and FLE different combinations of the explanatory variables are estimated and the best fitted are reported in Table 11.1. Some of the included explanatory variables are not statistically significant, but keeping them in the model has improved overall fit and also significance level of other explanatory variables. Those significant variables are considered which are significant either at 10% level or less.

4.1 Results of FLE Equation Table 11.1 suggests that rural FLE is affected positively and significantly by rural FLFPR. Thus greater rural FLFPR improves their LE. The detrimental impact of air pollution on rural FLE is evident. The sole impact of pollution (SPM) is significant and nonlinear (inverted-U shaped) with critical value 5 215.69(,mean 5 216.62) indicates that since the sample mean is already ahead of the cutoff level, thus further rise in SPM will worsen rural FLE, marginal effect (ME) being negative. Also, the interaction effects of pollution (NO2) and Lnnsdp is nonlinear (inverted-U shaped) with negative ME implying rise in NO2 beyond critical value, reducing rural FLE. The critical value being 20.95(,mean 5 24.8), i.e., the sample mean is greater than the critical value, thus further rise in NO2 will shorten rural FLE. Though the sole impact of Lnnsdp is positive and significant, but its interaction with pollution is negative on FLE, implying that given the level of

Table 11.1. Estimated Result of Rural FLE and FLFPR. Dependent Variable: FLFPR EC2SLS Estimates

Dependent Variable: FLE EC2SLS Estimates Explanatory Variables

FLFPR Lnnsdp WP (12_abv)

SPM SPM2 RSPM RSPM2 NO2 (NO2)2

0.11001 (2.72)*** 4.026459 (5.61)*** 0.1158348 (1.89)*

Specification 2

0.0756118 (2.05)**

Explanatory Variables

FLE Lnnsdp (Lnnsdp)2

20.0001665 (21.91)* 0.043946 (0.45) 20.0000706 (20.18) 0.237932 (1.91)* 20.0021573 (21.32)

Avg_hhsz

(12_abv) (12_abv)2

3.362486 (2.34)** 165.6667 (1.20) 28.429495 (21.25) 3.585583 (0.80) 20.4362868 (21.25)

POV

Urb

24.817085 (21.17) 1.579003 (4.61)***

(Priedu 3 WP) (Priedu 3 WP)2 (12_abv 3 WP)

20.0360095 (22.43)**

Specification 4

2.796396 (2.16)**

0.4000697 (1.99)** 29.952363 (22.19)** 0.5239364 (1.54) 0.0011948 (0.71) 20.0000533 (21.15)

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0.1873112 (2.25)** 1.481278 (4.05)*** 20.1350632 (23.20)*** 0.07312 (1.57)

Specification 3

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POV

Specification 1

Table 11.1. (Continued)

Lnnsdp 3 NO2 (Lnnsdp 3 NO2)2

Specification 1

(12_abv 3 WP)2

Wald chi2 Prob . chi2 R-sq: Within Between Overall

cons

Specification 3

Specification 4

0.0074944 (1.69)* 6.985242 (1.25) 21,106.402 (21.56)

24.777745 (20.68) 2175.4186 (20.75)

143.43 (0.0000) 0.7911 0.8897 0.8515

70.38 (0.0000) 0.7309 0.7054 0.6865

20.0025719 (20.88) 0.0003652 (0.60)

FHH 3 SR Cons

DS 0.0198269 (2.64)*** 20.0000199 (22.06)**

(POV 3 SO2)2

Explanatory Variables

(12_abv 3 WP)2

0.00773086 (2.47)*** 20.000018 (22.04)**

(POV 3 SO2)

(12_abv 3 WP)

Specification 2

23.98331 (0.81) 104.52 (0.0000) 0.8448 0.7413 0.7323

0.0001769 (1.75)* 48.15105 (1.05) 311.67 (0.0000) 0.7044 0.9705 0.9310

Wald chi2 Prob . chi2 R-sq: Within Between Overall

Source: Authors’ estimation; Figure in the parentheses indicates the t-value. ***, **, * represent significance at 1, 5, 10% levels, respectively.

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Explanatory Variables

Dependent Variable: FLFPR EC2SLS Estimates

132

Dependent Variable: FLE EC2SLS Estimates

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pollution, rise in growth will reduce rural FLE. Similarly, the sole as well as interaction effect of POV with pollution (SO2) is having an adverse impact on rural FLE. Though the interaction effect is inverted-U shaped, i.e., nonlinear and significant indicating given the level of SO2, rise in POV will further reduce rural FLE after a critical level of SO2 (59.47 , sample mean 5 9.7). Since ME being negative shows a rise in SO2, it will lower rural FLE. Among the covariates, the positive impact on rural FLE is obtained by (i) WP, (ii) (12_abv), and (iii) interaction of FHH and SR. Thus (i) more female leaders, (ii) higher percentage of women completing higher education, can raise ruralFLE. Also, given the SR, higher FHH can improve FLE. However, the sole impact of (i) RSPM, (ii) NO2, and (iii) the interaction of (12_abv) and WP is found to be statistically insignificant.

4.2 Result of Estimation of FLFPR Equation Table 11.1 also states that better rural FLE can significantly raise rural FLFPR. The interaction effect of (12_abv) with WP bears a nonlinear (U-shaped) relationship with positive ME. The ME of WP on FLFPR is positive with threshold value 5 0.08 , sample mean 5 9.8. Thus, the threshold is achieved ensuring positive ME, where further increase in WP will foster greater rural FLFPR. The ME of (12_abv) on FLFPR is also positive having threshold value 525.71,mean 5 30.48, as (12_abv) cannot be negative hence rise in (12_abv) always raises rural FLFPR. POV has a significant positive influence on rural FLFPR, reinstating women’s work due to sheer necessity. Rise in Avg_hhsz discourages rural FLFPR as the impact obtained is negative and significant. Urb positively and significantly affects rural FLFPR which is in tandem with the literature (Shrestha, 2017). Though the variables (i) Lnnsdp, (ii) the joint effect of WP and Priedu, (iii) DS, and (iv) (12_abv) were included in the model, the results were insignificant. The variables like GFCF, FHH, and SR have been included as explanatory variables in the model, but the estimated coefficients are not significant with appropriate sign hence not reported here.

5. Conclusion The contributions of the chapter are to show (a) two-way interaction between FLFPR and female health status as measured by FLE, for 13 Indian states covering the period 2004–2005 to 2011–2012 focusing on rural areas, using Baltagi’s IV-EC2SLS method and (b) the impact of (i) level of outdoor air pollution on FLE and (ii) the interaction effects of the pollutants with socioeconomic variables. Since the rural air quality is worsening day by day, the present analysis therefore produces evidence of the unfavorable impact of outdoor air pollution on rural FLE. Nonlinear relationship of pollutants on FLE, for either sole effect (like SPM) or in interaction with (i) Lnnsdp (and NO2) and (ii) POV (and SO2), is obtained, suggesting that increase in growth will fail to improve FLE, given the

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level of air pollution and rise in POV given air pollution will further deteriorate rural FLE. Growth cannot improve rural FLE unless air pollution is curbed. Hence, with income growth, lessening air toxicity is essential for achieving longer rural-FLE. Also measures to reduce POV are not enough unless action to mitigate harmful impacts of air pollution is undertaken to foster rural FLFPR by improving their health. Besides these, policies to promote female education, urbanization, and rise in women political leader can further increase FLFPR through their impacts on FLE. Thus it is evident that rural air pollution, if left unchecked, will produce a harsh impact on FLFPR by sinking FLE, as FLE positively affects FLFPR. Urban air pollution mitigation is not only the thrust but improving rural air quality is urgently needed for better health and greater rural FLFPR.

Notes 1. Ministry of Statistics and Program Implementation – MOSPI, National Sample Survey Reports. 2. National Family Health Survey (NFHS-4). 2015–2016. Ministry of Health and Family Welfare, Government of India. 3. Election Commission of India, statistical reports. https://eci.gov.in/files/category/ 64-statistical-report/. 4. Reserve Bank of India, Handbook of Statistics on the Indian Economy, different issues. 5. Central Pollution Control Board, different reports. http://www.cpcbenvis.nic.in/ air_quality_data.html#. 6. Social Statistics Division, National Statistical Office (2021): “Gendering Human Development. A Working Paper for Computing HDI, GDI, and GII for States of India.” Ministry of Statistics and Programme Implementation, Government of India. http://mospi.nic.in/sites/default/files/publication_reports/Report%20on% 20Gendering%20Human%20Development.pdf.

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Section II Gender Inequality and Its Implications to Other SDGs

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Chapter 12

Only Development or Gender Norm? Explaining Gender Inequality in Emerging Market Economies Amrita Chatterjee

Abstract Emerging Economies (EEs) are characterized by sustained growth performance, but they suffer from inequality as well, especially the Gender Inequality. Literature points out a number of gender norms which play a significant role in aggravating the gender disparity. The chapter chooses a panel of 25 EEs for the period of 2007–2020 to investigate how gender norms can affect the female labor force participation (FLFP) and development relationship. Results suggest that EEs are in a stage of development where even if countries are growing at a reasonable rate, FLFP is falling. Further investigation reveals that skewed sex ratio can dampen the impact of development, whereas secondary school enrollment and legislation to protect women from sexual harassment in the workplace may foster the effect of development. Thus, policies to encourage parents to invest more on the girl child and providing legal support to women at the workplace can be effective policies to reduce gender inequality. Keywords: Development; gender norm; gender inequality; sex ratio; female labor force participation; emerging market economies

1. Introduction Emerging market economies play a crucial role in the world economy, as they together represent almost one-fifth of the world GDP and half of the world population. Though they represent a heterogeneous group of countries in terms of population size, per capita income, and growth performance, there are few common features such as their sustained growth performance (even better than OECD countries) before the great depression of 2008–2009 and better resilience Gender Inequality and its Implications on Education and Health, 141–152 Copyright © 2023 Amrita Chatterjee Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231013

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than OECD countries during the global crisis (OECD, 2011). However, they suffer from a higher income inequality than the OECD countries, and a key source of inequality is barrier to employment and education to women. Duflo (2012) argues that there is a two-way relationship between women empowerment and development; in one direction, development helps to reduce the gender inequality and on the other, women empowerment has the potential to improve development. However, Jayachandran (2015) rightly point out that even if development helps to narrow down the gender inequality, some society-specific factors such as gender norms reflected in skewed sex ratio may also play some significant role in explaining the GDP–gender inequality relationship. The chapter investigates the role of such societal factors, such as sex ratio, school enrollment, or protection in work space from sexual harassments, in explaining the relationship between development and gender inequality for 25 emerging market economies (EEs) using a dynamic panel data framework taking into account the potential endogeneity issue.

2. Literature Review Jayachandran (2015) examines various indicators of gender inequality such as ratio of the male and female college enrollment rates, age-adjusted mortality of women relative to men, ratio of the male and female labor force participation rate, difference in attitude between men and women toward violence against women, decision-making power of women within household, freedom of choice and control felt by married women, and satisfaction from life. She concludes that women from developing countries perform worse than the women from developed countries with respect to all the above indicators. In this subsection we focus on the existing studies explaining the Female Labor Force Participation (henceforth, FLFP) and development relationship especially in the developing societies.

2.1 Economic Factors Macro-level studies propose a feminization-U hypothesis which posits that, at low level of income FLFP reduces with development but as countries become richer, there emerges a positive relationship (Boserup, 1970; Goldin, 1990, 1995; Mammen & Paxson, 2000). The mechanism suggested is the structural change from agriculture to industry and gradually to services, declining fertility and the changing scenario of balancing work with childrearing, and more importantly emergence and persistence of social norms and formal restrictions for women’s employment in certain sectors and occupations (Elson, 1999; Kabeer, 1997). However, there is evidence of contradiction of U-shaped hypothesis from other cross-country as well as country-specific studies at the macro level (Besamusca, Tijdens, Keune, & Steinmetz, 2015; Gaddis & Klasen, 2014). Moreover, economic growth, rising education, and falling fertility seem to be ineffective to foster FLFP at the macrolevel literature (Aaronson et al., 2017; Besamusca et al., 2015; Ganguli, Hausmann, & Viarengo, 2014). Recent literature proposes societal and

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cultural norms such as women being homemakers, lack of education and job-oriented courses, lack of mobility and discrimination at the workplace do act as deterrents for women labor force participation in India (Banerjee, 2019). Costagliola (2021) argues in the Indian context that it is not the U-shaped hypothesis, rather the existing gendered notions of labor and age-old patriarchal and traditional values that prevent women from reentering the labor force in the industrial and service sectors.

2.2 Cultural Factors Apart from the difference in income, there are several cultural factors which can affect the gender inequality–GDP relationship and make a difference between poor and rich countries. Poverty accelerates the cultural bias toward men. Patrilocality, i.e., coresidence of men with their parents after marriage and women with their in-laws, demotivates the parents in Asia, the Middle East, and North Africa to invest more on health and education of their daughters rather than the sons, thereby widening the gender gap. This has a positive correlation with skewed sex ratio (Ebenstein, 2014). Several studies confirm the same in the Indian context (Chakraborty & Kim, 2010; Ramakrishnan et al., 2011). Support to older members of the family coming from the male counterparts again motivates the parents to have son preference and have similar impact on gender gap, especially in countries like India and China (Ebenstein & Leung, 2010). Dowry is another financial cost which the parents have to bear in South Asia for their daughter’s marriage which is a strong reason for son preference (Arnold, Choe, & Roy, 1998; Das Gupta et al., 2003). Though historically in Europe, dowry was supposed to provide property rights to the women’s premarriage inheritance from her parents, in countries like India it is controlled by the groom and even leads to domestic violence. Patrilineality which means that the property rights for land ownership will be inherited by sons after the father’s death plays an important role in setting a bias toward sons among mothers. However, after reform of this law in 1980s and 1990s in four states in India, women gain that right of succession and that gave them some household bargaining power and financial independence leading to higher school enrollment and delaying marriage accompanied by some male backlash from marital conflict as well (Anderson & Genicot, 2014; Deininger, Goyal, & Nagarajan, 2013; Roy, 2013). In order to maintain the purity and chastity of unmarried women in countries like India, China, Indonesia, Taiwan, and Iran, women are secluded from men by controlling their movement outside home, by not sending to too far away schools, restricting to gender-segregated schools, leading to low school enrollment and early marriage resulting in school dropouts (Adukia, 2014; Burde & Linden, 2013; Buss, 1989; Kim, Alderman, & Orazem, 1999; Muralidharan & Prakash, 2013). In rural areas, women’s role is traditionally restricted to domestic chores only and therefore face spousal control on participation of women in labor force (Bernal, 2008; Bernhardt et al., 2018; Eswaran, Ramaswami, & Wadhwa, 2013). Study on urban women of eight lowand middle-income countries shows that rising educational level and falling

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fertility rate do improve FLFP, but with rising household income women withdraw themselves from the workforce (Klasen, Le, Pieters, & Santos Silva, 2021).

2.3 Research Gap and Objective of the Study The perusal of the literature suggests that economic factors are not alone to affect FLFP or gender inequality and development relationship, and there are country-specific factors as well. However, in developing countries several cultural and deep-rooted societal norms persist over time and across generations (Dhar, Jain, & Jayachandran, 2019) which change very slowly (Kandpal & Baylis, 2019). There are studies on selected emerging market economies where a declining trend of FLFP is observed in spite of good performance of the development indicators. However, there is a dearth of study that intends to find the role of gender norms in explaining this relationship for the EEs. The current study uses a panel data of 25 EEs1 for the period of 2007–2020 to investigate whether some societal factors such sex ratio or school enrollment or protection from sexual harassment at the workplace provide some mechanism through which development affects gender inequality which is measured in terms of ratio of female to male labor force participation rate.

3. Data and Methodology 3.1 Data The current study uses ratio of female to male labor force participation rate as the indicator of gender inequality and therefore is the main dependent variable. Ratio of female to male labor force participation rate is calculated by dividing FLFP rate by male labor force participation rate and multiplying by 100. Labor force participation rate is the proportion of the population aged 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. The main explanatory variable is per capita gross domestic product (constant 2015 US$) in log scale which represents the development of any country. The other control variables are Population growth (annual %), Trade (% of GDP), Life expectancy at birth (years), Industry (including construction) value added (% of GDP), General government final consumption expenditure (% of GDP), School enrollment, secondary, female (% gross), and Sex ratio at birth (male births per female births). The other variable used to explain the mechanism is whether there is legislation on sexual harassment in employment (1 5 yes; 0 5 no) in a country. The indicator measures whether there is a legal provision or legislation that specifically protects women against sexual harassment in employment, including unwelcome verbal or physical conduct of a sexual nature. The sources of data are world development indicators, especially the gender statistics. Data exploration gives us the following bar graph (Fig. 12.1a), which indicates that there is not much variation across the EEs in terms of GDP over the chosen period of time. However, the ratio of female to male labor force participation rate

RLPR

lnGDP

panel a

sex_ratio

South…

Philip…

Roma…

Pakist…

Mexico

India

Israel

Egypt,…

Chile

Colom…

150 100 50 0

Brazil

Russia…

Ukraine

Philipp…

Israel

Nigeria

Colom…

Hungary

Argent…

0

Cambo…

50

Argen…

100

Ukraine

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Only Development or Gender Norm?

schoolenrollsecondfemale

panel b

Fig. 12.1. Average GDP (on Log Scale) and Average RLPR and Average Sex-Ratio and Average Secondary School Enrollment of Female for 25 Countries Over 2007–2020. Source: Author’s own calculation from WDI data.

(RLPR) varies across countries if average data on both variables are considered. Countries like Bangladesh, India, Egypt, Pakistan, and South Africa are way below the rest of the countries, even if their growth performances are more or less the same. Fig. 12.1b provides insights into the societal perspective of these countries toward female. We can observe that countries like China, India, and Pakistan have a very skewed sex ratio in favor of boys. As far as female enrollment in secondary school is concerned, a lot of countries are showing poor performance such as Pakistan, China, Peru, Cambodia, and Bangladesh. Providing protection from sexual harassment at the workplace for women is very important to motivate them to overcome the societal stigma against joining the labor force. Most of the countries (Indonesia and Romania) have such legal protection, but not for the entire period of study.

3.2 Methodology To fulfill the objectives of this chapter, a dynamic panel data of the abovementioned 25 EEs over 14 years, from 2007 to 2020, is considered and used a system GMM method to estimate the models. The fixed-effect panel data model may provide biased estimations, if the number of periods is small, and if the lagged value of the dependent variable is correlated with the individual effects leading to a problem of endogeneity. Endogenous explanatory variables may be correlated to the idiosyncratic error term by means of reverse causality or measurement errors. Due to the failure to address the problem of endogeneity, the fixed-effects estimator produces inconsistent estimates of results that are expected to have a downward bias (Bonnefond, 2014). Moreover, omitted variables can also lead to biased estimates. In such a situation Holtz-Eakin, Newey, and Rosen (1988) and Arellano and Bond (1991) recommended estimating dynamic panel data models using the generalized method of moments (GMM). GMM compensates for endogeneity through the inclusion of the lagged value of the dependent variable, as an explanatory variable. Arellano and Bond (1991) have

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suggested the use of instrumental variables technique (for t $ 2) where lagged levels of the lagged endogenous variable and the lagged levels of the explanatory variables are used as instruments. This is called the first-differentiated GMM estimator. To deal with the potential bias and imprecision of the first-differenced GMM estimators, additional moment conditions are proposed for an equation expressed in levels (Arellano & Bover, 1995; Blundell & Bond, 1998) known as system GMM estimator. The current study uses System GMM technique to estimate the following equations: ðRLPRÞi;t ¼ b0 1 b1 ðRLPRÞi;t 2 1 1 b2 ðlnGDPÞi;t 1 b3 ðsex ratioÞi;t 1 b4 ðsex  ratio  lnGDPÞi;t 1 b5 Xit 1 ai 1 «it

(12.1)

ðRLPRÞi;t ¼ b0 1 b1 ðRLPRÞi;t 2 1 1 b2 ðlnGDPÞi;t 1 b3 ðfemale secondary school enrolmentÞi;t 1 b4 ðfemale secondary school enrolment  lnGDPÞi;t 1 b5 Xit 1 ai 1 «it (12.2) ðRLPRÞi;t ¼ b0 1 b1 ðRLPRÞi;t 2 1 1 b2 ðlnGDPÞi;t 1 b3 ðsexual harassment protection lawÞi;t 1 b4 ðsexual harassment protection law  lnGDPÞi;t 1 b5 Xit 1 ai 1 «it

(12.3)

where Xit represents the set of control variables, ai is the country-specific effect and «it is the idiosyncratic shock. We have considered lnGDP as an endogenous regressor.2

4. Results and Findings The chapter aims to investigate whether development is enough to reduce gender disparity in EEs using a dynamic panel data framework. Table 12.1 reports the results of System GMM estimation. We use ratio of female to male labor force participation rate (RLPR) to measure gender disparity. A higher value of the ratio implies higher participation of women. Column 1 represents model 1 where one year lagged value of GDP has a significant impact on RLPR; however, the effect is negative. That means with developments the FLFP relative to men falls. Thus, EEs are currently in that stage of development where the downward sloping segment of the U-shaped curve is still persistent. This result finds support from Kapsos, Silberman, and Bourmpoula (2014) for India and Acevedo, Devoto, Morales, and Rodriguez (2021) in the context of Morocco. Women not being able to cope up with technological transformation and mechanization of agriculture can be some possible reasons behind this decline (Mahapatro, 2013; Sanghi, Srija, & Vijay, 2015) apart from gender norms prevailing in developing countries. This calls for further investigation into the reasons for this. One significant observation from model 1 is that sex ratio, which is defined as the number of males for every 100 females in a population is statistically significantly reducing the RLPR. Most of the EEs are suffering from sex ratio being skewed in favor of men. Therefore, skewed sex ratio can be a possible cause for low FLFP. Thus, in model (2) we introduce an interaction term between the sex ratio and lnGDP.

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Table 12.1. Impact of Development on Ratio of Female to Male Labor Force Participation Rate. (1) RLPR

(2) RLPR

(3) RLPR

(4) RLPR

0.990*** (0.0416) 23.073** (1.354) 20.322* (0.194)

1.026*** (0.0422) 3.942** (1.933)

0.931*** (0.0924) 23.879* (2.100)

0.990*** (0.0285) 22.445** (1.032) 20.278** (0.118)

0.00448* (0.00268)

0.133*

Variables

L.RLPR L.lnGDP Sex ratio

20.0418* (0.0234)

L.lnGDP*sex ratio L.Secondary school enrollment for female*lnGDP L.GDP*legislature in sexual harassment Government final con expd. 0.545** (0.245) Industry 0.155** (0.0779) Inflation 20.00125 (0.0334) Population growth 0.941 (0.594) School enrollment 0.0130 secondary female (0.00913) Trade 0.00432 (0.00416) Life expectation at birth Constant Observations Number of countries

46.73* (24.62) 325 25

(0.0780) 0.103 (0.105) 0.0524 (0.0514) 20.00226 (0.00951) 0.233 (0.251) 20.00674 (0.00625) 20.00147 (0.00484)

0.110 (1.841) 325 25

Source: Author’s estimation; Robust standard errors in parentheses. Note: ***p , 0.01, **p , 0.05, *p , 0.1.

0.506* (0.261) 0.113* (0.0679) 20.00224 (0.0379) 1.067 (0.676)

0.382*** (0.148) 0.122** (0.0530) 0.0101 (0.0199) 0.620* (0.364)

0.00814 (0.00867) 0.155 (0.166) 11.65* (6.727) 325 25

0.00256 (0.00317) 0.0822 (0.0554) 34.70** (14.05) 325 25

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Results suggest that with the introduction of one year lagged value of this interaction term, GDP per capita of previous year has a significant positive impact on RLPR, but the lagged interaction term is negative. Moreover, the marginal effect of lnGDP at the mean value of sex ratio is coming out to be negative. Thus, sex ratio which reflects the gender norm is a negative factor that drags down the positive impact of development in EEs. Especially countries like China and India suffer from highly skewed sex ratio which reflects the missing women theory of Sen (1990), which indicates favoritism toward a boy child by the parents. This son preference leads to low motivation of parents to invest in education and health of girl child leading to low educational attainment of the women. Thus, they are not ready for the job market. To investigate the role of educational attainment of women on RLPR, in model (3), we introduce lagged interaction of percentage of female in secondary school enrollment. The results suggest that the lagged interaction of the term with lnGDP is having a positive and significant impact on RLPR though lnGDP is significant and negative. The overall marginal effect of lnGDP at the mean percentage of female enrollment is coming out to be negative though. Thus, female enrollment at the secondary school level is a positive mediating factor that can help the EEs to reduce the gender disparity. Educated women with higher productivity draw higher wage and that encourages women to join labor force by reducing leisure. However, for highly educated women, income from other sources (husband’s income) may motivate to withdraw themselves from the labor force (Chatterjee, Desai, & Vanneman, 2018) explaining an overall negative effect of development. Another important factor which can affect the motivation of women as well as their families to join the labor force is the different kinds of harassments women face from their male counterparts in the workplace. If there is a law or legislation in place to protect women from sexual harassment or some implicit verbal misbehavior in the workplace, then women may feel more confident about their rights and their hesitation to join the labor force may also reduce. Not all EEs have this kind of law in place for the entire period of study; but most of the countries in the sample have introduced such laws in due course to protect the rights of women. We introduce a lagged interaction of this dummy which take a value 1 in case such a law is present with lnGDP in model 4. Results in column 4 suggest that the lagged interaction term is positive and significant, whereas lnGDP is still negative significant. Thus, we can conclude that this kind of law that provides a better work environment to the women can have a positive impact on RLPR and motivate more women to join the labor force. Here, we note that in all the models we have introduced lagged lnGDP as an endogenous regressor as there is possible reverse causality from RLPR to lnGDP. Interaction terms are used as exogenous regressor only but we have taken the lagged values to take care of possible endogeneity issue. As far as the control variables are concerned, government final consumption expenditure and industrial value added are coming out to be significant.

5. Conclusion Economic literature suggests that development and FLFP have a two-way relationship. However, at a different stage of development we observe a different

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nature of this relationship thereby giving rise to a U-shaped curve. Literature also accepts that development may not be always sufficient to push the FLFP as there are some deep-rooted gender norms, mostly prevalent in developing countries, which can create significant bottlenecks. Emerging market economies are on a high growth path, but they also suffer from gender inequality. Moreover, they suffer from skewed sex ratio and poor education and health of women. The chapter contributes to the development-FLFP literature by bringing in the role of gender norms in a dynamic panel data framework with 25 EEs. Results suggest that skewed sex ratio in developing countries which reflects the son preference of parents can dampen the positive role of development. However, educational attainment of woman can play a significant role to foster the effect of development. Another interesting policy that can be a motivation for women to join the labor force is a law to protect them from any kind of sexual harassment at workplace. This policy accompanied by development can be effective in reducing the gender disparity. However, in overall analysis, marginal effect of development still remains negative. Thus, even if EEs grow well, unless the societal perspective toward women changes, they will not be able to reduce the gender inequality. These results point toward some policies which the Government may take to encourage parents to change their perspective toward girl child. Firstly, conditional cash transfer schemes to get some financial benefits for educating the girl children may be effective to increase educational attainment among women and reduce school dropouts. Few such policies are in place in India. Secondly, increasing political representation of women at the local government may lead to better utilization of resources in the rural areas, where the awareness level is very low. Thirdly, change in attitude is necessary in not only among men but also among women. Then only they will resist against unreasonable son preference and crime against women. Lastly, legislations to strengthen women’s position in household in terms of property rights as well as protection from domestic violence, dowry, and crime in workplace must be in place to bring women in equal footing as men.

Notes 1. Countries considered: Argentina, Bangladesh, Brazil, China, Chile, Cambodia, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Israel, Malaysia, Mexico, Nigeria, Pakistan, Peru, Philippines, Poland, Romania, Russia, South Africa, Turkey, and Ukraine. 2. User-written Xtabond2 command is used in STATA 17. We have used collapse command to control for too many instruments.

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Acevedo, G. L.-, Devoto, F., Morales, M., & Rodriguez, J. R. (2021). Trends and determinants of female labor force participation in Morocco: An initial exploratory analysis. IZA DP No. 14218. Adukia, A. (2014). Sanitation and education. Working Paper, University of Chicago. Anderson, S., & Genicot, G. (2014). Suicide and property rights in India. NBER Work Paper 19978. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: MonteCarlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. Arnold, F., Choe, M. K., & Roy, T. (1998). Son preference, the family-building process and child mortality in India. Population Studies, 52, 301–315. Banerjee, M. (2019). Gender equality and labour force participation: Mind the gap. Indian Journal of Women and Social Change, 4(1), 113–123. Bernal, R. (2008). The effect of maternal employment and child care on children’s cognitive development. International Economic Review, 49(4), 1173–1209. Bernhardt, A., Field, E., Pande, R., Rigol, N., Schaner, S., & Troyer-Moore, C. (2018). Male social status and women’s work. AEA Papers and Proceedings, 108, 363–367. Besamusca, J., Tijdens, K., Keune, M., & Steinmetz, S. (2015). Working women worldwide. Age effects in female labor force participation in 117 countries. World Development, 74, 123–141. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 97(1), 115–143. Bonnefond, C. (2014). Growth dynamics and conditional convergence among Chinese provinces: A panel data investigation using system GMM estimator. Journal of Economic Development, 39(4), 1. Boserup, E. (1970). Woman’s role in economic development. London: George Allen and Unwin Ltd. Burde, D., & Linden, L. L. (2013). Bringing education to Afghan girls: A randomized controlled trial of village-based schools. American Economic Journal: Applied Economics, 5(3), 27–40. Buss, D. M. (1989). Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures. Behavioral and Brain Sciences, 12, 1–14. Chakraborty, T., & Kim, S. (2010). Kinship institutions and sex ratios in India. Demography, 47, 989–1012. Chatterjee, E., Desai, S., & Vanneman, R. (2018, January–June). Indian paradox: Rising education, declining womens’ employment. Demographic Research, 38, 855–878. Costagliola, A. (2021). Labor participation and gender inequalities in India: Traditional gender norms in India and the decline in the Labor Force Participation Rate (LFPR). The Indian Journal of Labour Economics, 64, 531–542. doi:10.1007/ s41027-021-00329-7 Das Gupta, M., Zhenghua, J., Bohua, L., Zhenming, X., Chung, W., & Hwa-Ok, B. (2003). Why is son preference sopersistent in East and South Asia? Across-country study of China, India and the Republic of Korea. Journal of Development Studies, 40, 153–187.

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Deininger, K., Goyal, A., & Nagarajan, H. (2013). Women’s inheritance rights and intergenerational transmission of resources in India. Journal of Human Resources, 48(1), 114–141. Project MUSE, doi:10.1353/jhr.2013.0005 Dhar, D., Jain, T., & Jayachandran, S. (2019). Intergenerational transmission of gender attitudes: Evidence from India. Journal of Development Studies, 55(12), 2572–2592. Duflo, E. (2012). Women empowerment and economic development. Journal of Economic Literature, 50(4), 1051–1079. Ebenstein, A. (2014). Patrilocality and missing women. Working Paper from the Department of Econnomics, Hebrew University, Jerusalem. Ebenstein, A., & Leung, S. (2010). Son preference and access to social insurance: Evidence from China’s rural pension program. Population and Development Review, 36, 47–70. Elson, D. (1999). Labor markets as gendered institutions: Equality, efficiency and empowerment issues. World Development, 27(3), 611–627. Eswaran, M., Ramaswami, B., & Wadhwa, W. (2013). Status, caste, and the time allocation of women in rural India. Economic Development and Cultural Change, 61(2), 311–333. Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labor force participation. Journal of Population Economics, 27(3), 639–681. Ganguli, I., Hausmann, R., & Viarengo, M. (2014). Closing the gender gap in education: What is the state of gaps in labor force participation for women, wives and mothers? International Labour Review, 153(2), 173–207. Goldin, C. (1990). Understanding the gender gap: An economic history of American women. New York, NY: Oxford University Press. Goldin, C. D. (1995). The U-shaped female labour force function in economic development and economic history. In T. P. Schultz (Ed.), Investment in women’s human capital and economic development (pp. 61–90). Chicago, IL: University of Chicago Press. Holtz-Eakin, D., Newey, W., & Rosen, H. (1988). Estimating vector auto regressions with panel data. Econometrica, 56(6), 1371–1395. Jayachandran, S. (2015). The roots of gender inequality in developing countries. Annual Review of Economics, 7, 63–88. Jayachandran, S., & Lleras-Muney, A. (2009). Life expectancy and human capital investments: Evidence from maternal mortality declines. Quarterly Journal of Economics, 124, 349–397. Kabeer, N. (1997). Women, wages and intra-household power relations in urban Bangladesh. Development and Change, 28(2), 261–302. Kandpal, E., & Baylis, K. (2019). The social lives of married women: Peer effects in female autonomy and investments in children. Journal of Development Economics, 140, 26–43. Kapsos, S., Silberman, A., & Bourmpoula, E. (2014). Why is female labour force participation declining so sharply in India? ILO Research Paper No. 10. Kim, J., Alderman, H., & Orazem, P. F. (1999). Can private school subsidies increase enrollment for the poor? The Quetta Urban Fellowship Program. The World Bank Economic Review, 13, 443–465.

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Klasen, S. (2019). What explains uneven female labor force participation levels and trends in developing countries? The World Bank Research Observer, 34(2), 161–197. Klasen, S., Le, T. T. N., Pieters, J., & Santos Silva, M. (2021). What drives female labour force participation? Comparable micro-level evidence from eight developing and emerging economies. Journal of Development Studies, 57(3), 417–442. doi:10. 1080/00220388.2020.1790533 Mahapatro, S. (2013). Declining trends in female labour force participation in India: Evidence from NSSO. MPRA Paper No. 44373. Mammen, K., & Paxson, C. (2000). Women’s work and economic development. The Journal of Economic Perspectives, 14(4), 141–164. Muralidharan, K., & Prakash, N. (2013). Cycling to school: Increasing secondary school enrollment for girls in India. NBER Working Paper 19305. OECD. (2011). Special focus: Inequality in Emerging Economies (EEs). Retrieved from https://www.oecd.org/els/soc/49170475.pdf Ramakrishnan, S., Khera, R., Jain, S., Saxena, A., Kailash, S., Karthikeyan, G., . . . Airan, B. (2011). Gender differences in the utilisation of surgery for congenital heart disease in India. Heart, 97, 1920–1925. Roy, S. (2013). Empowering women: Inheritance rights and female education in India. Unpublished manuscript, Department of Economics, Warwick University, Coventry, UK. Sanghi, S., Srija, A., & Vijay, S. (2015). Decline in rural female labour force participation in India: A relook into the causes. Vikalpa, 40(3), 255–268. Sen, A. (1990, December 20). More than 100 million women are missing (pp. 61–66). New York Review Books.

Chapter 13

Women Empowerment as a Key to Support Achievement of the Sustainable Development Goals and Global Sustainable Development Begum Sertyesilisik

Abstract Humanity experiences challenges caused by gender inequality which further obstructs achievement of global sustainable development. Considering gender equality as human equality, this chapter emphasizes women empowerment’s role in supporting global sustainable development. Based on literature review, this chapter aims to examine women empowerment’s role in and contribution to UN SDGs (sustainable development goals). This chapter underlines that the gender inequality hinders and obstructs global sustainable development and achievement of SDGs. Furthermore, this chapter examines causes of gender inequality as they need to be identified and eliminated to achieve global sustainable development. Women empowerment plays a significant role in solving gender inequality related problems (e.g., health problems, education inequality, discrimination, crime, violence). Women empowerment achieved through supported gender equality can act as a multiplier factor in achieving synergy creation and influencing a sustainable future. This chapter highlights the influence of women empowerment and gender equality on all three pillars of sustainability. Furthermore, this chapter underlines the importance of women empowerment in all industries and politics for addressing the gender inequality problems. This chapter provides recommendations on how to enhance women empowerment to support achievement of all SDGs. Women empowerment based sustainable development policies can influence achievement of the SDGs. This chapter is expected to be useful to the academics and policymakers focusing on achievement of SDGs, sustainability, and sustainable development.

Gender Inequality and its Implications on Education and Health, 153–163 Copyright © 2023 Begum Sertyesilisik Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231014

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Keywords: Gender equality; sustainability; sustainable development goals; sustainable development policies; women empowerment; industries and politics

1. Introduction Humanity experiences challenges caused by gender inequality which further obstructs achievement of global sustainable development (SD). Women empowerment (WE)’s support is reflected and given importance in the United Nations (UN) 2030 SD agenda as the fifth sustainable development goal (SDG) and as a gender equality topic related with all SDGs (United Nations, n.d. as cited in Gressel et al., 2020). SDGs, however, are not on track of being reached and accomplished (Haas & Ivanovskis, 2022). Countries’ implementation of SDGs is voluntary (Haas & Ivanovskis, 2022). Additionally, pandemic has further affected women adversely. Even if progress is achieved on gender equality in all SDGs, there are pandemic’s adverse impacts on the progress (UN Women and UN Department of Economic and Social Affairs (DESA), 2021). Furthermore, UN Women and UN DESA (2021) emphasizes that the statistics reveal failure in achieving gender equality across SDGs. Gender equality and WE are, however, important for accomplishment of SDGs and their importance is increasingly recognized (Kazembe, 2020). Gender equality can foster economic growth, welfare, and health covered within the scope of SDGs (Kazembe, 2020). Gender equality is equality of human. Each human being has the right and responsibility for supporting SD and sustainability. Each human being can contribute to the SDGs and can be affected by the success in SDGs’ achievement. For this reason, human being’s effective contribution to the SDGs’ achievement is important in humanity’s welfare, well-being, and success in SD, and in achieving all pillars of sustainability. As women have potential and capacity to support global SD, their empowerment can support effectiveness and magnitude of their contribution to SD. Based on literature review, this chapter aims to examine WE’s role in and contribution to SDGs of the UN’s 2030 agenda for SD.

2. Causes of Gender Inequality Inhibiting Global SD and Achievement of SDGS As the gender inequality hinders and obstructs global SD, the gender inequality acts as one of the important inhibiting factors challenging achievement of SDGs. Identifying and examining causes of gender inequality are important as these cause obstacles and challenge achievement of global SD. There are policy shortcomings regarding gender equality targets (Yakovleva, Vazquez-Brust, Arthur-Holmes, & Busia, 2022). More than 150 million females could be rescued from poverty if governments implemented strategies enabling enhanced access to education and family planning, and fair and equal wages (UN Women and UN DESA, 2021).

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Failure in providing equal opportunity for education can challenge WE and gender equality. As equal opportunity for education needs to be provided to all, everyone needs to have access to relevant resources equally. Girls leave the education at different school ages as the population of girls leaving schools is higher in upper levels (Unicef, n.d.). Furthermore, population of girls leaving school is higher in countries experiencing conflict compared to countries not experiencing conflict (Unicef, n.d.). Childhood marriage can obstruct WE and gender equality. Population of girls married in childhood is still high even if progress has been made to reduce child marriage, and this progress in reducing childhood marriage needs to be accelerated as annually 12 million girls still get married in their childhood (Unicef, 2022). Financial aspects can influence WE. For example, household gender wage gap can obstruct WE as Danquah, Iddrisu, Boakye, and Owusu (2021)’s research on Ghana reveals that reduced household gender wage gap can contribute to WE. COVID-19 affected WE adversely. COVID-19 changed governments’ budget allocation priority to deal with it obstructing SD (Qadeer et al., 2022). Population of women and girls living in poverty increased in the 2019–2021 period (UN Women and UN DESA, 2021). Furthermore, during the pandemic many schools remained closed and even if 128 million girls left school in 2018, the pandemic increased the number of students leaving school (UN Women and UN DESA, 2021). Additionally, the pandemic affected food security adversely (UN Women and UN DESA, 2021). The global gender gap in food insecurity increased in the 2019–2020 period (UN Women and UN DESA, 2021). Women are affected by the climate change. For example, women who rely on fishing and the sea to get food for their household and to earn their income in the Pacific islands and coastal areas need to spend more effort and time to get the food due to adverse impacts of climate change on the ecosystem (UN Women, 2012; UN the Economic and Social Commission for Asia and the Pacific (ESCAP), 2017). Furthermore, natural disasters can affect WE adversely. In Bangladesh, floods, which occur repeatedly nearly each year, affect properties and livelihood adversely (Shahin, Khanam, Aktar, Siddiqua, & Sharif, 2022). In 2019, organizations provided cash incentives to support WE and recovery phase of the flood in the Lalmonirhat District (Shahin et al., 2022). As UN Women and International Union for Conservation of Nature (IUCN) (2022) emphasized women encounter challenges in playing a role in climate change related actions and organizations. Even if women and girls contribute to the actions against climate change, their rights organizations and women environmental human rights defenders encounter challenges in participating in climate action (UN Women and IUCN, 2022).

3. Recommendations for Enhancing WE to Support Achievement of All SDGs WE plays an important role in achievement of SDGs. Dannevig, Hanssen Korsbrekke, and Hovelsrud (2022) highlighted the importance of covering

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SDGs 5 and 10 in the adaptation and coproduction of SDGs as participatory localizing processes can contribute to empowerment. As SDG 5 focuses on “gender equality” and SDG 10 focuses on “reduced inequalities” (UN website), these SDGs complement each other supporting women empowerment. WE achieved through supported gender equality can act as a multiplier factor in achievement of synergy creation and influence on sustainable future (Fig. 13.1). WE and gender equality can influence all three pillars of sustainability. WE plays an important role in future generation’s profile and contribution to sustainability and SD. Measurement/calculation of WE can support strategic and effective action/ policies/investments based on identification of areas/aspects to be improved. Based on Demographic and Health Surveys data on WE in different countries ¨ (e.g., Ethiopia), Miedema, Haardorfer, Girard, and Yount (2018)’s research provided a measure of WE. Furthermore, Gressel et al. (2020) provided a vulnerability mapping and assessment to support improvements related with WE. Women participation in politics can be encouraged to support gender equality in politics. Women are underrepresented in politics nearly all around the world. This is in compliance with Awoa, Ondoa, and Tabi (2022) who emphasized that even if political rights of women are given importance in modern states, women constitute relatively low shares in parliaments globally (Awoa et al., 2022). For example, in the Middle East and North Africa (MENA) region, even if improvement in reduction in gender gap in education and in ensuring economic opportunities and governmental empowerment, women are underrepresented in parliaments, cabinets, and boardrooms (Kairouz & Goodman, 2022). Encouraging and enabling WE and participation are important for achieving gender equality in the climate change actions as gender equality in this field can further support effectiveness and success of actions, policies, and precautions taken against climate change. For example, regarding the water diplomacy,

Gender equality

Women empowerment at the industry level

Enhanced capacity building, welfare and wellbeing and contribution of women contributing strategically to the SD and humanity’s uplifting in the Maslow’s hierarchy of needs

Achievement of long-term global SD and UN SDGs

at the country level

Fig. 13.1.

Gender Equality, WE, and Achievement of Global SD and SDGs. Source: Sketched by the Author.

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Carmi, Alsayegh, and Zoubi (2019) focused on challenges encountered by women wishing to attain high level positions in water diplomacy, qualifications and skills they need to have, and provided recommendations on how to empower women in decision-making positions. Similarly, WE can contribute to sustainable management and conservation of fish resources, gender equality, and WE (UN ESCAP, 2017). As these examples reveal, women can contribute to the climate-sensitive industries and policies. Their contribution can support and can be enabled and enhanced by their empowerment based on the gender equality principle. In other words, there is a potential for a two-way synergy in achievement of SD and SDGs as their contribution to industries can support industries and they can be empowered through their contribution to these industries. In compliance with the importance of achieving gender equality for dealing with and preventing the climate change, UN Women and IUCN (2022) indicated that recently gender-climate issues in policies and programs tend to be paid more attention internationally. Women can support the labor market, economic development and resilience, and reduction in income inequality (IMF, n.d.). WE to support and enable gender equality is important for all industries and policies for addressing the gender inequality problems. Women engagement in income-generating activities can contribute to poverty management (Haley & Marsh, 2021). As gender equality can contribute to the diversity of goods produced in the country, gender-friendly policies can have potential to contribute to diversification of countries’ economies (IMF, n.d.; Kazandjian, Kolovich, Kochhar, & Newiak, 2016). Women’s participation in economic activities can influence countries’ economies. Focusing on the MENA countries, Kairouz and Goodman (2022) emphasized that unless women participate in economic activities, competitiveness level of MENA countries’ economies can be adversely affected (Kairouz & Goodman, 2022). As even if, in the MENA region, women’s education level is high, their participation in economics and politics is low, which reveals countries’ failure in using their full capabilities (Kairouz & Goodman, 2022). Similarly, Danquah et al. (2021), focusing on sub-Saharan Africa, indicated that policies need to support availability and quality of employment opportunities for women overcoming gender barriers and enabling them to access these employment opportunities in sub-Saharan Africa. As gender inequality can be observed in many industries, policies need to focus on how to enhance women participation in the economy considering industry-specific needs so that WE enabled gender equality enabled synergy creation can further contribute to the industries’ development. Gender inequality is experienced in many industries (e.g., Abou-Shouk, Mannaa, & Elbaz, 2021; Elshaer, Moustafa, Sobaih, Aliedan, & Azazz, 2021; Maas, 2020; Waid, Wendt, Sinharoy, Kader, & Gabrysch, 2022; Yakovleva et al., 2022) all over the world. For example, gender inequality is observed in the artisanal and small-scale mining sector (Yakovleva et al., 2022). Effective, successful, and industry-specific sustainability policies and programs can influence and contribute to WE and gender equality. For example, Waid et al. (2022) indicated that nutrition-sensitive agricultural programs can contribute to WE. For this reason, industry-specific needs and characteristics need to be considered in development policies. For example,

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regarding the health sector, Maas (2020) recommended for more leading positions for women in this sector to prioritize women’s health as the main focus of medical science and patient care has been on males. Another example can be given focus on tourism industry based on Elshaer et al. (2021)’s and Abou-Shouk et al. (2021)’s research studies. Integrating WE into planning and implementation phases in the tourism industry can contribute to gender equality and WE, and sustainable tourism development (Elshaer et al., 2021). Regarding the tourism development, focusing on three countries, WE has affected tourism countries in Egypt, the United Arab Emirates, and Oman differently as among these countries the United Arab Emirates ranked first regarding WE’s influence on tourism development (Abou-Shouk et al., 2021). Financial aspects need to be considered in gender equality based development policies. For example, renewable energy development (SDG7) can affect WE which can be supported by green microfinance institutions (Atahau, Sakti, Huruta, & Kim, 2021). Rural microfinance institutions can support WE through energy management (Atahau et al., 2021). Regarding political empowerment of women and financial inclusion, policies addressing gender gap are needed to support financial inclusion (Ghosh, 2022). For example, Sedai, Vasudevan, and Pena (2021) highlighted rotating savings and credit associations contribution to WE and their socioeconomic development in India. WE can be supported by their enhanced capabilities (Barrios, Prowse, & Vargas, 2020). Investing in girls’ education is important, and their education can contribute to fulfilment of their potential, economy, stable and resilient societies, and reduction in inequality (Unicef, n.d.). Gender inequality, low access level to education, and difficult family situations can obstacle empowerment and foster disempowerment (Barrios et al., 2020). For this reason, education policies need to be strategically considered and developed as an important construct of and complying with SD and WE policies. Regarding off-farm employment opportunities, Maligalig, Demont, Umberger, and Peralta (2019) indicated the need for integrated thinking of education and training programs and investments. WE can support gender equality which can further act as a driver for achievement of long-term global SD and UN SDGs (Fig. 13.1). WE based SD policies can influence achievement of the SDGs. WE based and enabled gender equality policies need to be at the industry and country levels. At the industry level, these policies need to cover industry-specific aspects and detailed based on industry-specific requirements, SWOT analysis, and challenges complying with industry-specific strategic management and SD plans. This recommendation is in compliance with the literature as many industry-specific research studies, which have focused on different industries from the WE perspective (e.g., Abou-Shouk et al., 2021; Elshaer et al., 2021; Maas, 2020; Waid et al., 2022; Yakovleva et al., 2022) reveal a need for industry-specific research studies for addressing industry-specific challenges obstructing WE and for enhancing WE taking industry-specific actions. At the country level, WE based and enabled gender equality policies need to comply with and provide input to the country’s SD. At the country level, these policies need to cover industry level policies and country’s SD related fields covering all topics (e.g., education, economy, environment).

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Effective WE based and enabled gender equality policies, both at the industry and country levels, can contribute to women’s capacity building especially through education and employment opportunities which can further support their empowerment, welfare, and well-being as well as their contribution to economics, education, and policies (Fig. 13.1). In this way, long-term global SD and SDG achievement can be supported through effective and successful WE based and enabled gender equality policies which can support uplifting of entire humanity in the Maslow’s hierarchy of needs (Fig. 13.1). These policies for SD, at the industry and country levels, need to be coherent with and based on the gender equality/the human being’s equality principle. Achievement of global SD and UN SDGs can further contribute to women empowerment supporting gender equality which can further support global SD (Fig. 13.1). For this reason, elimination of causes fostering gender inequality can be considered as an integral part of policies at industry and country levels supporting achievement of SD and UN SDGs. Furthermore, as effective WE based and enabled gender equality policies, both at the industry and country levels, can contribute to WE, women’s welfare, and well-being as well as education, these policies can contribute to future generation’s profile contributing further to sustainability and SD and SDGs. Gender equality principle, which is humanity equality principle, needs to be at the core of the WE based and enabled gender equality policies as input to and integrated to the SD policies (Fig. 13.2). These policies can influence a country’s success in achieving its SD (Fig. 13.2). Each country’s success in its SD can contribute to the global SD. For this reason, gender equality principle is at the core of global SD. Each country’s success in achieving gender equality through enhanced WE can contribute to the global SDGs as WE achieved through supported and enabled gender equality can act as a multiplier factor in achieving synergy creation and influencing a sustainable future. WE and gender equality can influence and support all three pillars of sustainability.

gender equality principle = humanity equality principle

WE based and enabled gender equality policies as input to and integrated to the SD policies

Country’s success in its SD

Fig. 13.2. Gender Equality Principle Based WE Enabled Gender Equality Policies and SD Policies. Source: Sketched by the Author.

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4. Conclusion This chapter emphasized and examined WE’s role in and contribution to global SD and all SDGs of the UN’s 2030 agenda for SD. Based on the gender equality principle, which means human being’s equality, each human being needs to be provided opportunities and resources vital for accomplishment of his/her full capacity enabling his/her self-accomplishment (in compliance with the fifth step of Maslow’s hierarchy of needs) so that humanity and each human being can fully contribute to the SD and SDGs. SD politics need to encourage WE supported gender equality in all SDGs and industries and they need to address solution to the gender inequality caused problems as gender inequality acts as an inhibitor factor which obstructs achievement of SD and SDGs. Effective WE based and enabled gender equality policies, at the industry and country levels, can support women’s capacity building, welfare, well-being, and empowerment, which can further support gender equality and SD and achievement of SDGs (Fig. 13.1). Gender equality principle, as humanity equality principle, needs to be at the core of the WE based and enabled gender equality policies which can provide input to and which can be integrated to the SD policies (Fig. 13.2). Effective and successful WE based and enabled gender equality policies can contribute to a country’s SD which can further contribute to achievement of global SD and SDGs. WE based and enabled gender equality policies need to eliminate causes of gender inequality. There are many causes of gender inequality. Policy shortcomings (Yakovleva et al., 2022); girls leaving the education (Unicef, n.d.); child marriage (Unicef, 2022); household gender wage gap (Danquah et al., 2021); food insecurity (UN Women and UN DESA, 2021); and climate change (UN ESCAP, 2017; UN Women, 2012) affect WE adversely. WE based and enabled gender equality policies need to encourage and enable women involvement in SD. Women participation to politics (e.g., Awoa et al., 2022; Kairouz & Goodman, 2022), economy, and employment (e.g., Danquah et al., 2021; IMF, n.d.; Kairouz & Goodman, 2022) is important for WE. As gender inequality is experienced in many industries (e.g., Abou-Shouk et al., 2021; Elshaer et al., 2021; Maas, 2020; Waid et al., 2022; Yakovleva et al., 2022), industry-specific actions and policies need to be taken to encourage WE based gender equality. Financial aspects and supports [e.g., green microfinance institutions, rural microfinance institutions (Atahau et al., 2021); rotating savings and credit associations (Sedai et al., 2021)] of WE need to be considered so that gender equality based development policies can be supported. Education needs to be considered as a key for WE and important construct of WE based and enabled gender equality policies. Importance of education has been emphasized by the researchers (e.g., Barrios et al., 2020; Maligalig et al., 2019; Unicef, n.d.). WE based and enabled gender equality policies which can have potential for supporting long-term SD and UN SDGs can focus on the industry and country levels to support WE based and enabled gender equality. WE enabled gender equality can act as an important multiplier factor in achieving creation of synergy in the sustainable future and SD. Economic, social, and environmental pillars of SD concept and their requirements need to be

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considered in SD policies supporting SDGs’ achievement. Regarding the economic pillar of the SD, WE in economics need to be supported through their enhanced contribution to economic activities addressing industry-specific requirements and industry-specific development plans and programs. Regarding the social pillar of the SD, women participation in education and economics can support their empowerment and role in their social life and future generations. This situation can further support women’s well-being and welfare. Regarding the environmental pillar of the SD, WE in environmental sustainability actions, policies, and environment-friendly industries can support environmental sustainability which can further enhance WE. WE enabled gender equality based SD policies can influence achievement of the SDGs. WE can influence future generation’s profile and contribute to sustainability, SDGs, and SD. As gender inequality hinders and obstructs global SD and achievement of SDGs, causes of gender inequality need to be eliminated so that achievement of UN SDGs and global SD can be supported. WE can contribute to gender equality and solution to problems caused by gender inequality. This chapter can be useful to the academics, researchers, politicians, and policymakers focusing on achievement of SDGs, sustainability, and SD.

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Chapter 14

Linkage Between Women Empowerment and Gender-Based Violence in India: Evidence From NFHS-5 Data Susobhan Maiti, Tanushree Gupta and Govind Singh Rajpal

Abstract Women Empowerment means boosting the social, economic, political, and legal strength of women to secure equal right and make them confident to claim their rights. India has focused on women’s empowerment to reduce domestic abuse and gender violence in recent decades. The study analyzes the relationship between women’s empowerment and gender-based violence, employing a non-linear regression model using the National Family Health Survey (NFHS) 5, 2019–20 data. In the present study gender-based violence is measured on the basis of spousal violence and women’s empowerment is represented by women who are currently married and usually take part in three household decisions, women who worked in the past year and were paid in cash, women who own a house or land (alone or with others), women who use their own bank or savings account, and women who use their own cell phone for each state. Analysis of the result shows a link between women’s empowerment and gender-based violence and a large disparity among states. Keywords: Women empowerment; gender violence; domestic abuse; household decision; non-linear regression; India

1. Introduction and an Exhaustive Review of Literature One in three women are assaulted, usually by a partner (World Health Organization, 2021). Long-term and immediate effects are on women’s health, sexuality, mental health, and death. Women are harmed by violence. It affects families, neighborhoods, and the nation (Kaur & Garg, 2008). It’s expensive, straining the health-care system, and costing legal expenses and productivity. Stop Gender Inequality and its Implications on Education and Health, 165–175 Copyright © 2023 Susobhan Maiti, Tanushree Gupta and Govind Singh Rajpal Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231015

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gender violence (GV) (Kaur & Garg, 2008). Academics and legislators are increasingly concerned about women’s rights, leading to much discussion and planning on national and global levels in affluent and poor countries. The UN General Assembly has called for research on the causes, effects, and causes of violence against women, which violates women’s human rights (United Nations, 1993). Like men, women belong to several groups. They belong to particular economical, cultural, and religious communities, each with its own familial patterns and acceptable behavior. Each group of women has unique qualities that shape their behavior and views (Patrikar, 2014). Academics, political philosophers, and social scientists from wealthy and emerging countries have studied gender inequality and domestic violence because a society constructed on gender disparity wastes human resources no country can replace (Patrikar, 2014). According to the EU’s charter of fundamental rights, violence against women violates fundamental freedoms and rights (European Union, 2000). Women can be abused in many ways. Fear of violence prevents women from fully participating in society. Traumas stay in their minds post violence treatment (Shakti, 2017). Gender norms subjugate women to men contributing to domestic violence against women (Moreno & Mayer, 1999). Violence against women is more common in countries with tight gender roles (Shane & Ellsberg, 2002). Globally, gender-based violence violates human rights. No place is safer for women than their own (Klugman et al., 2014). Globally, women are abused. All women are affected. A woman’s life is at risk, and all socioeconomic and educational groups are affected (World Health Organization, 2021). Gender-based violence is averted through integrating women’s political, economic, and psychological sovereignty (Rivera, 2018). Besides women’s empowerment is also the process of protecting them from all sorts of abuse. Encouraging the empowerment of women requires the creation of a cultural and political climate where women may live without the fear of oppression and the general feeling of persecution that comes with being a woman in a historically male-dominated organization. To be empowered, one must be able to exert greater control over one’s environment and one’s own ideology. Autonomy, power, position, and agency have all been used to describe it. As a result, the Indian constitution has made it crystal clear that women have an equal opportunity to compete on an equal footing with men (Singh & Singh, 2020). The decade of the 1990s may be viewed as an impertinent time for women’s empowerment throughout the world. In fact, among the eight United Nations Millennium Development Goals (MDGs) created in 2000, the third was to promote gender equality and empower women by the year 2015. Women empowerment (WE) is defined as the change in the framework of a woman’s life, which permits her enhanced potential for leading a fulfilling human existence (Singh & Singh, 2020). It gets expressed both in exterior qualities like health, mobility, education, awareness, etc., and internal qualities (Singh & Singh, 2020). However, while women make up over half of the world’s populace, the gender ratio in India has remained disproportional, with a smaller number of women than men. They are not treated equally to males everywhere in terms of social status. In Western societies, women have obtained equal rights and positions with males in all sectors

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of life. But gender disadvantages and discriminations are evident in India even today (Shettar, 2015). The paradoxical circumstance was such that she was sometimes worried as Goddess and at other times only as a slave. The empowerment of women flows with the power. Women’s empowerment would imply equipping women to be economically self-sufficient and have positive self-esteem in order to handle any challenging scenario, as well as the ability to participate in development activities. Women who are empowered should be allowed to participate in the decision-making process. Women’s rights and legal entitlements have been safeguarded in India by the Ministry of Human Resource Development and the National Commission for Women. The government of India has ratified various international conventions and human rights instruments committing to secure equal rights for women (Perera, Nayak, & Long, 2019). The most positive development the last few years has been the growing involvement of women in the Panchayati Raj institutions. There are many elected women representatives at the village council level. At the central and state levels women are progressively making a difference. Studies have shown that empowering women reduces violence against women. Domestic violence is caused by society’s power, empowerment, and gender inequality (Kiani et al., 2021). Economic interventions alongside communicationand community-based interventions reduce domestic violence more effectively. Women’s empowerment is crucial to gender equality and eliminating gender-based violence (Moser, 2012) also talked about how important women’s empowerment is for achieving gender equality and reducing gender-based violence (Moser, 2012). Recent studies have shown that women’s empowerment reduces domestic violence (Kim et al., 2007; Kishor & Gupta, 2004; Malhotra, Schuler, & Boender, 2002; Moser, 2012). Empowering women is important, but does it increase gender-based violence? (Shettar, 2015). Many research studies have demonstrated that women’s empowerment and gender-based violence clash. Some believe economic and social empowerment increases the danger of gender-based violence (Abramsky et al., 2019; Anik, Towhid, & Haque, 2021; Dore et al., 2022; Schuler, Hashemi, Riley, & Akhter, 1996; Tripathi & Azhar, 2022). The reason being, men often use violence to enforce their dominance and reassert inegalitarian gender norms when patriarchal norms are challenged (Schuler et al., 1996). Understanding why these results differ can help women’s empowerment and antiviolence research. Women’s empowerment may have increased violence against women. This study seeks to explain how women’s empowerment affects GV. This study uses the National Family Health Survey 2019–20 (NFHS-5), the fifth in the series. It provides demographic, health, and nutrition data for India and each state/union territory (UT). Here are five women’s empowerment indicators. First, analyze women’s role in home decision-making, work, property ownership, bank accounts, and cell phones. This study used three GV indicators. Domestic violence, psychological violence, and sexual abuse follow.

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2. Research Gap and Objectives Several international studies on WE and gender-based violence have been undertaken in Zambia, Africa, Bangladesh, Tanzania, the United States, Sri Lanka, and others (Abramsky et al., 2019; Dore et al., 2022; Porter, 2013; Tripathi & Azhar, 2022). Their connection in India is poorly researched (Kishor & Gupta, 2004; Perera et al., 2019; Rocca, Rathod, Falle, & Pande, 2009; Sinha, Gupta, & Singh, 2017). State-specific research is scarce in India. Women’s empowerment and GV are examined in this study. For a conclusive discussion, research must extend the problem. Given the circumstances, this study tries to assess whether GV empowers women or vice versa.

3. Methodology and Data Source The National Family Health Survey (NFHS)-5 is a large-scale, multiround survey conducted in a representative sample of households throughout India published by the Ministry of Health and Family Welfare (MOHFW), Government of India, which is being used in this study to see if the erratic levels of women’s empowerment influence violence against women or not. Here gender-based violence is studied on the basis of spousal violence (SV) and WE is represented by women who are currently married and usually take part in three household decisions (WPHD), women who worked in the past year and were paid in cash (WW), women who own a house or land (alone or with others) (WHO), women who use their own bank or savings account (WHBA), and women who use their own cell phone (WHMP) for each state. There are many ways to measure the empowerment of women. However, for this study, only five indicators – WPHD, WW, WHO, WHBA, and WHMP – have been employed. So, it is equally important to investigate the justification for utilizing these variables to investigate whether there is a connection between SV and WE. SV is one of the most common types of violence against women, which continues despite an international commitment to achieving gender equality (Zegenhagen, Ranganathan, & Buller, 2019). Understanding the potential causes of SV is crucial. Nevertheless, there are significant relational elements that have gotten less attention in the literature, such as household decision-making, a sign of power, and control in partnerships (Zegenhagen et al., 2019). Making decisions reflects a conversation taking place in the home and enables an insight into relationship dynamics. Comprehension of relationship power dynamics through decision-making improves understanding of spousal abuse because it is based on control and power that gender-based violence is perpetrated (Zegenhagen et al., 2019). On the other side, there is a complete lack of studies that claim that women who participated in household decision-making were less likely to experience marital abuse. The goal of the current study is to determine whether women who are currently married and usually take part in three household decisions (WPHD) are experiencing spousal abuse or not. The second indicator, i.e., women who worked in the past year and were paid in cash (WW), has been taken to analyze the relationship between WE and SV. According to John Locke, where there is a

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right, there is a responsibility and vice versa. This belief holds that in addition to providing for his wife’s maintenance, a husband should try to manage her daily activities. Because of this, when a woman tries to get involved in home difficulties, it typically wounds the male ego and leads to SV. Given the foregoing context, it is essential to include WW as one of the indicators in the current study. Other than that, the third indicator for the present study is women who own a house or land (alone or with others) (WHO). The presence or absence of female property ownership may be associated with SV. Particularly, if there is no SV, a married woman’s property ownership may limit the amount of household services she provides to her spouse. The husband might use force and coerce her to demand a greater quantity of home services. By boosting her financial security and lowering her tolerance for violence, these would in turn lower her likelihood of becoming violent. The crucial factor, however, is not whether a woman really makes use of the escape route that immovable property offers, but rather the possibility that the very fact that the route exists could stop the husband’s abuse. Thus, it makes sense to investigate how women’s property ownership affects the prevalence of SV. In addition, the fourth indicator, Women who use their own bank or savings account (WHBA), is used to examine the connection between SV and WE. The family may be content and healthy if the husband is earning a sufficient living and the woman has added financial assistance from her earlier earnings and bank savings. However, it frequently wounds the husband’s ego and can lead to SV when he is unemployed or earning less than his wife. The purpose of this analysis is to evaluate the connection between SV and women’s financial inclusion (McDougal, Klugman, Dehingia, Trivedi, & Raj, 2019). A family’s daily existence is impacted by technology as well. Uncontrolled use of mobile devices can lead to an increase in hostility and a breakdown in communication, both of which may be factors in SV. On the contrary, mobile phones may lower women’s chances of experiencing SV by promoting communication, lowering their sense of isolation from family and nonfamily members, enabling them to discuss personal issues with other women, fostering community outreach and participation, enhancing their ability to make decisions in the home, and enabling them to access reproductive health services remotely. In the above context, the fifth indicator is women who use their own cell phone (WHMP). After calculating descriptive statistics and state-specific analysis, in the next stage, a nonlinear regression has been carried out to find out the nexus between women’s empowerment and gender violence. GV ¼ f WW; WPHD; WHO; WHMP; WHBA; WW2




The calculated equation is discovered to be nonlinear, it should be noted. Therefore, the sign of marginal effects will aid in determining whether a relationship is positive or negative for those factors that have a nonlinear relationship to the dependent variable. The Wald test has been used to determine the statistical significance of these variables.

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4. Analysis of Result and Discussion Table 14.1 shows that the minimum and highest values of WPHD are 82.70 and 99.20, respectively. WPHD has a mean of 90.48 and a standard deviation of 0.04. 51.85% of states, or a significant portion of them, have WPHD rates that are higher than average. With a mean of 27.39 and a coefficient of variation of 0.33, the minimum and maximum values for WW are 12.60 and 45.10, respectively. Despite the fact that 59.26% of the states, or the majority, fall below the national average. The lowest value for WHO is 17.20, while the highest value is 70.20. The average value is 42.07, and the coefficient of variation is 0.40. Despite the fact that the vast majority of states, or 55.56% of all states, are above the mean. It can be observed that the lowest possible value of WHBA is 63.70, while the highest possible value is 92.20. The WHBA has a mean value of 79.00 and a coefficient of variation of 0.080. A greater proportion of states, or 51.85% of all states, is below the national average. The WHMP ranges from 38.50 to 91.20, with a mean of 61.76 and a coefficient of variation of 0.25. The lowest value is 38.50, while the highest value is 91.20. Even though most states, i.e., 62.96%, are below the average. When looking at GV, we can see that the lowest is 6.40, and the highest is 44.40. These two numbers represent the extremes of the range. The average value of GV comes in at 23.81, with a coefficient of variation of 0.48. A greater proportion of states, namely, 55.56% of all states, is above the average at the state level. The case study of state-level analysis that is shown in Table 14.1 reveals a unique kind of feature for each variable. According to Table 14.1, the WPHD is the lowest in the state of Karnataka and the highest in the state of Nagaland. In the instance of Bihar, the WW is at its lowest, but in Telangana, it is at its highest. In terms of WHO, Tripura is considered to be in the lowest possible state, whereas Arunachal Pradesh receives the highest possible score. Both WHBA and GV are at their lowest points in Nagaland, while at their highest points in Tamil Nadu and Karnataka, respectively. Goa has the highest WHMP levels, while Madhya Pradesh has the lowest. One variable women who worked in the past year and were paid in cash have a U-shaped relationship with GV, while other variables like women who are currently married and usually take part in three household decisions, women who own a house or land (alone or with others), women who use their own bank or savings account, and women who use their own cell phone are linearly related. Table 14.2 shows Wald test results for women who worked in cash last year. The U-shaped relationship is found between women who worked in the past year and were paid in cash and GV. This indicates that with an increase in women who worked in the past year and were paid in cash, SV may decrease but after some threshold level SV may increase. The result perhaps is due to when a woman tries to get involved in home difficulties, it typically wounds the male ego and leads to SV. The value of the marginal effect of WW on GV is found to be positive as is revealed in Table 14.2 which implies that the net effect of WW on GV is positive. GV is found to be positively related with WPHD may be due to comprehension of relationship power dynamics through decision-making

Table 14.1. Descriptive Statistics of the Variables and Result of State Level Analysis. Descriptive Statistics of the Variables

Result of State Level Analysis Percentage of States Below the Mean

Percentage of States Above the Mean

WPHD 82.70 99.20 90.48 0.04 WW 12.60 45.10 27.39 0.33 WHO 17.20 70.20 42.07 0.40

48.15 59.26 44.44

51.85 40.74 55.56

Karnataka Bihar Tripura

WHBA 63.70 92.20 79.00 0.08 WHMP 38.50 91.20 61.76 0.25

51.85 62.96

48.15 37.04

GV

44.44

55.56

Nagaland Madhya Pradesh Nagaland

6.40 44.40 23.81 0.48

Source: Author’s calculation.

Min.

Max.

Nagaland Telangana Arunachal Pradesh Tamil Nadu Goa Karnataka

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Table 14.2. Regression Analysis – Gender-Based Violence and Women Empowerment in India (2019–2020). Explanatory Variable

Coefficient

t-Statistic

p Value

C WW

41.667 23.066**

1.589 22.454

0.13 0.02

WPHD WHO WHMP WHBA WW*WW Adjusted R-squared F-statistic Prob (F-statistic)

0.001** 0.199** 20.250** 0.350 0.057** 0.574 7.996 0

2.581 2.010 22.469 1.350 2.581

0.02 0.05 0.02 0.19 0.02

Marginal Effect

0.051 (7.266**)

Source: Author’s calculation; ***, ** and *Significant at 1%, 5% and 10% level respectively; Chi-square value is present in first bracket ().

improves the spousal abuse because it is based on control and power (Zegenhagen et al., 2019). There is a positive relationship between WHO and GV as it frequently wounds the husband’s ego and can lead to SV when he is unemployed or earning less than his wife. GV is also found to be positively related with WHBA though it is insignificant though the result shows a significant negative relationship between GV and WHMP. Mobile phones provide two-way communication, which may affect domestic violence in many ways. Mobile phones may reduce women’s risk of SV by promoting communication, lowering their sense of isolation from family and nonfamily members, allowing them to discuss personal issues with other women, fostering community outreach and participation, and improving their home decision-making abilities.

5. Conclusion and Recommendation Gender-based violence violates human rights worldwide. Their country is the safest for women. All ethnic, racial, social, and national women are affected. It’s a serious issue that threatens one woman’s life and impacts all socioeconomic and educational levels. This strategy empowers women and protects them from abuse. To empower women, society and politics must eliminate the fear and persecution that comes with being a woman in a male-dominated enterprise. Women’s empowerment is good, but does it increase gender-based violence? This study uses five indicators to answer this question (WPHD, WW, WHO, WHBA, & WHMP). The four indicators – women who are currently married and usually take part in three household decisions (WPHD), women who own a house or land (alone or with others) (WHO), women who use their own bank or savings account

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(WBHA), and women who use their own cell phone (WHMP – have a positive and linear relationship with GV. Our society is based on the idea that men make all decisions and women must follow them. Even while more women are making household decisions, some males think it’s unethical. They believed women couldn’t make home decisions because they lacked basic understanding. Thus, female decision-making undermines the male ego and promotes GV. GV is linked to home and landowner women (either alone or jointly with others; WHO). If the husband earns well and the wife has bank savings and prior employment, the family may be happy and healthy. If a husband is unemployed or earns less than his wife, his ego may be damaged and lead to domestic violence. WHBA women also have a linear connection with GV. Financial inclusion empowers first. If given more control, women may become autonomous and self-confident. Believing that strengthening women will make them independent and self-assured promotes GV. Another clue is women who own cell phones (WHMP). This positively impacts GV. Technology affects family life. The investigation shows that. Cell phones can distract women from home responsibilities and services, which can lead to gender abuse (GV). Finally, GV is nonlinearly associated with one of the five variables, women who worked in the past year and were paid in cash (WW), but only to a limited extent. This illustrates that GV may reduce with an increase in women who worked in the last year and earned monetary remuneration, but it may increase after a certain threshold. Finally, women’s empowerment increases GV.

6. Limitation The study includes substantial drawbacks. First, the study used secondary data from the National Family Health Survey (NFHS)-5, which limits its findings. Second, the analysis included five indicators, despite having additional indicators to analyze the problem.

References ¨ Abramsky, T., Lees, S., Stockl, H., Harvey, S., Kapinga, I., Ranganathan, M., . . . Kapiga, S. (2019). Women’s income and risk of intimate partner violence: Secondary findings from the MAISHA cluster randomised trial in North-Western Tanzania. BMC Public Health, 19(1), 1–15. doi:10.1186/S12889-019-7454-1 Anik, A. I., Towhid, M., & Haque, M. (2021). Association of spousal violence and women’s empowerment status among the rural women of sub-Saharan Africa. Journal of Biosocial Science, 1–19. doi:10.1017/S0021932021000602 Dore, E. C., Hennink, M., Naved, R. T., Miedema, S. S., Talukder, A., Hoover, A., & Yount, K. M. (2022). Men’s use of economic Coercion against women in rural Bangladesh. Psychology of Violence, 12(3), 183–193. doi:10.1037/VIO0000417 European Union. (2000). History of the EU. Retrieved from https://european-union. europa.eu/principles-countries-history/history-eu_en. Accessed on July 26, 2022. Kaur, R., & Garg, S. (2008). Addressing domestic violence against women: An unfinished agenda. Indian Journal of Community Medicine: Official Publication of

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Indian Association of Preventive & Social Medicine, 33(2), 73–76. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784629/ Kiani, Z., Simbar, M., Fakari, F. R., Kazemi, S., Ghasemi, V., Azimi, N., . . . Bazzazian, S. (2021). A systematic review: Empowerment interventions to reduce domestic violence? Aggression and Violent Behavior, 58, 1–9. doi:10.1016/J.AVB. 2021.101585 Kim, J. C., Watts, C. H., Hargreaves, J. R., Ndhlovu, L. X., Phetla, G., Morison, L. A., . . . Pronyk, P. (2007). Understanding the impact of a microfinance-based intervention on women’s empowerment and the reduction of intimate partner violence in South Africa. American Journal of Public Health, 97(10), 1794–1802. doi:10.2105/AJPH.2006.095521 Kishor, S., & Gupta, K. (2004). Women’s empowerment in India and its states: Evidence from the NFHS. Economic and Political Weekly, 39(7), 694–712. Retrieved from https://www.jstor.org/stable/4414645 Klugman, J., Hanmer, L., Twigg, S., Hasan, T., Jennifer, M. S., & Santamaria, J. (2014). Voice and agency: Empowering women and girls for shared prosperity. In World Bank Group (Ed.), Voice and agency: Empowering women and girls for shared prosperity. The World Bank. doi:10.1596/978-1-4648-0359-8 Malhotra, A., Schuler, S. R., & Boender, C. (2002). Measuring women’s empowerment as a variable in international development Related papers Individual and Households Determinants of Women Empowerment: Application to the case of. . . World Bank Conference (Ed.), pp. 1–60. McDougal, L., Klugman, J., Dehingia, N., Trivedi, A., & Raj, A. (2019). Financial inclusion and intimate partner violence: What does the evidence suggest? PLoS One, 14(10). doi:10.1371/JOURNAL.PONE.0223721 Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368. doi:10.1037/0022-0663.91.2.358 Moser, C. (2012). Gender planning and development: Theory, practice and training. In Routledge (Ed.), Gender planning and development. Routledge. doi:10.4324/9780 203411940/GENDER-PLANNING-DEVELOPMENT-CAROLINE-MOSER Patrikar, S. R. (2014). Study in domestic violence against women in India: Determinants and consequences (A). Gokhale Institute of Politics and Economics (GIPE). Retrieved from https://dspace.gipe.ac.in/xmlui/bitstream/handle/10973/34588/ Seema-Contents.pdf?sequence52&isAllowed5y Perera, C. H., Nayak, R., & Long, N. V. T. (2019). The impact of electronic-word-of mouth on e-loyalty and consumers’ e-purchase decision making process: A social media perspective. International Journal of Trade, Economics and Finance, 10(4), 85–91. doi:10.18178/ijtef.2019.10.4.642 Porter, E. (2013). Rethinking women’s empowerment. Journal of Peacebuilding and Development, 8(1), 1–14. doi:10.1080/15423166.2013.785657 Rivera, C. (2018, November). Volunteerism to empower women and prevent gender-based violence in Guatemala j UNV. UN Volunteers. Retrieved from https:// www.unv.org/Success-stories/volunteerism-empower-women-and-prevent-genderbased-violence-guatemala

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Rocca, C. H., Rathod, S., Falle, T., & Pande, R. P. (2009). Challenging assumptions about women’s empowerment: Social and economic resources and domestic violence among young married women in urban South India. International Journal of Epidemiology, 38(2), 577–585. Retrieved from https://academic.oup.com/ije/ article-abstract/38/2/577/655132 Schuler, S. R., Hashemi, S. M., Riley, A. P., & Akhter, S. (1996). Credit programs, patriarchy and men’s violence against women in rural Bangladesh. Social Science & Medicine, 43(2), 1729–1742. Retrieved from https://www.sciencedirect.com/science/ article/pii/S0277953696000688 Shakti, B. S. (2017). Tackling violence against women: A study of state intervention measures (A comparative study of impact of new laws, crime rate and reporting rate, change in awareness level). Retrieved from https://wcd.nic.in/sites/default/files/Final Draft report BSS_0.pdf Shane, B., & Ellsberg, M. (2002). Violence against women: Effects on reproductive health. Political Science. Shettar, R. M. (2015). A study on issues and challenges of women empowerment in India. IOSR Journal of Business and Management, 17(4), 13–19. doi:10.9790/487X17411319 Singh, S., & Singh, A. (2020). Women empowerment in India: A critical analysis. Tathapi, 19(44), 227–253. Retrieved from https://graduatewomen.org/wp-content/ uploads/2020/08/Article-written-by-Seema-Singh-BRPID-project-findings.pdf Sinha, P., Gupta, U., & Singh, J. (2017). Structural violence on women: An impediment to women empowerment. Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine, 42(3), 134–137. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5561688/ Tripathi, S., & Azhar, S. (2022). A systematic review of intimate partner violence interventions impacting South Asian Women in the United States. Trauma, Violence, & Abuse, 23(2), 523–540. doi:10.1177/1524838020957987 United Nations. (1993). Declaration on the elimination of violence against women j OHCHR. Retrieved from https://www.ohchr.org/en/instruments-mechanisms/ instruments/declaration-elimination-violence-against-women World Health Organization. (2021). Violence against Women Prevalence Estimates, 2018 Global, regional and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women WHO, on behalf of the United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED). Zegenhagen, S., Ranganathan, M., & Buller, A. M. (2019). Household decision-making and its association with intimate partner violence: Examining differences in men’s and women’s perceptions in Uganda. SSM – Population Health, 8, 1–13. doi:10.1016/J.SSMPH.2019.100442

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Chapter 15

Public Social Expenditures and Outcomes in Nigeria: A Look Through the Gender Lens Nkechinyere Rose Uwajumogu, Ebele Stella Nwokoye, Kingsley Chike Okoli and Mgbodichimma K. Okoro

Abstract We assessed the differential effects of social expenditures on males and females by establishing the impact of public expenditures on education and health on gender parity in primary and secondary enrollment and on gender parity in life expectancy for Nigeria given age dependency ratio, annual population growth rate, and GDP per capita growth rate. We found that increased social spending on health and education increased female education enrollment which was hitherto lower than male enrollment. Again, increased social expenditure on health and education improved male life expectancy which was hitherto lower than female life expectancy. We established the importance of increased social expenditure on health and education; gender budgeting and gender-sensitive budgets; and implementation of inclusive growth policies in engendering gender parity in Nigeria. Keywords: Education; gender inequality; gender parity; health; social expenditures; Nigeria JEL: D63; H5; J16

1. Introduction Gender expresses peculiar cultural and social attributes of males and females. It draws awareness to socially constructed facets of disparities between the two. Appreciating gender implies appreciating the prospects, limitations, and effects of Gender Inequality and its Implications on Education and Health, 177–190 Copyright © 2023 Nkechinyere Rose Uwajumogu, Ebele Stella Nwokoye, Kingsley Chike Okoli and Mgbodichimma K. Okoro Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231016

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alterations as they affect both males and females (George & Chukwuedozie, 2014). Gender equality implies a condition where males and females have equal chances for engaging their maximum civil rights: It is conditions in which everyone has equal chances of contributing to sociocultural, economic and political development of their country as well as enjoying equal benefits that their collective efforts yielded. Gender equality necessitates that basic causes of inequity and biases are recognized and removed in order to provide equivalent opportunities. Equality negates a zero-sum-game where one gender must lose for the other to gain. It requires that both males and females win simultaneously in life’s schemes. Existence of inequality along gender lines in distribution and control of economic, productive, and financial resources has kept females in disadvantageous and vulnerable positions relative to males. Some sociocultural and religious belief systems limit females’ capacities to participate, contribute, and benefit from Nigeria’s economic progress. Gender inequality could be addressed through legislation, organizational processes, and information gathering (Payne, 2009). Legislations could address and protect individual rights, ensure equal treatment, and protect all, irrespective of gender. Organizational approach encompasses gender-budgeting to assess gender-specific impacts of government expenditures. Information approach recognizes the importance of data, information, and knowledge-sharing in reducing gender inequality. We focused on the organizational approach, through instrumentality of government spending on health and education. We view education and health as the bedrock for materialization of other economic, social, and political opportunities whereon income of better educated, better trained, and healthier persons are usually higher than the average. Human capital development contributes to economic progress by impacting positively on people’s general attitudes and specific skills, reducing fertility and improving living standard. To females, effects of education include delayed marriage, increased contraceptive usage, access to quality health care, and positive manipulations of the mind. An educated female is more likely to know her rights, recognize when they are being infringed upon, and develop self-confidence to act appropriately. She also has requisite knowledge and capacity to access quality health-care services (Coora & Potrafke, 2011; Glewee & Michael, 2006). Government expenditure, resultant of market imperfections, performs allocative, distributive (re-distributive), regulatory, and stabilization roles that are important for human development in general and gender equality in particular. It stimulates gender equality and higher productivity, reduces fertility, improves physical environment, reduces poverty, and ensures social stability (Chakraborty, Elson, & Chowdhury, 2004). Empirical studies on growth impacts of government spending are mixed with some confirming positive impacts while others show negative impacts. Impacts of government expenditure also depend on expenditure type. While government transfers, consumptions, and total outlays had negative impacts on private productivity growth, educational expenditure had positive impacts, and government investments had no impacts (Hansson & Henrekson, 1994). Our study estimates these gender differential impacts by establishing the impacts of public social capital and recurrent expenditures on gender parity in

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literacy rate and on gender parity in gross enrollment gap from 1981 to 2017. To this end, following this introduction are the literature review, method of study, results and discussion of findings, conclusion, and policy recommendations.

2. Literature Review 2.1 Stylized Facts Gender gap report for Nigeria shows gender disparity in educational attainment and health survival with average scores of 0.815 and 0.926, respectively, over eight-year review periods. Gender parity score for economic participation and opportunity maintained an average of 0.626 while gender parity score for political empowerment was an average of 0.071. Despite lower scores for females, data show slight improvements in females’ position relative to males’ especially in terms of political empowerment. In 2006, female political empowerment scored 0.049 but increased over period to 0.086 in 2008/2008, fell to 0.038 in 2010/2011 and increased to 0.119 in 2012/2013. Health survival score was 0.66 in 2006, increased to an all-time high of 0.969 in 2007 and 2008, with a reduction to 0.960 in 2012/2013 (Table 15.1). Educational attainment had the highest parity level compared with others, with 0.816 in 2006, an increase in 2009 (0.832), and fall in 2013 (0.811). Gender parity score for economic participation and opportunity was 0.612 in 2006, its lowest being in 2011 (0.596), and an increase in 2013 (0.696). High disparity in political empowerment compressed the overall Gender Gap Index score for Nigeria. This shows that Nigerian females face disadvantages in human development. Social expenditures are therefore important and key in closing and eliminating gender inequality. There has been steady progress in females’ access to education with the sharpest rise in the Middle East and North Africa (MENA) countries due to increased gender budgeting and funding (Ostby, Urdal, & Rudolfsen, 2016). The UNESCO 2015 global monitoring report shows that less than half the countries (which prominently excludes sub-Saharan countries) achieved gender parity. Patriarchy is the major cause of gender inequality in Nigeria. Patriarchy encourages discriminatory cultural practices that place males above females in life’s projections. In addition, poor legal frameworks, which indirectly support patriarchy, and political systems that subtly and overtly discourage females from participating in active decision-making processes, cause gender biases against females. Patriarchic system breeds parochial sharing of authority, assets, advantages, and pay opportunities between males and females. Gender inequality in wealth and asset distribution is also evidenced in unemployment and underemployment disparity between genders. Female unemployment exacerbates national poverty because women breed the labor force (Adenike, 2014). Females have higher likelihood of being poorer even because they take on great deals of duties and hazards, earn less than their male counterparts, and do more unpaid house and care works which are incidentally not captured in the gross domestic product.

180

Overall

Economic Participation & Opportunity

Educational Attainment

Health Survival

Political Empowerment

Year

Rank

Score

Rank

Score

Rank

Score

Rank

Score

Rank

Score

2006 2007 2008 2009 2010 2011 2012 2013

94 out of 115 107 out of 128 102 out of 130 108 out of 134 118 out of 134 120 out of 135 110 out of 135 106 out of 136

0.610 0.612 0.634 0.628 0.606 0.601 0.631 0.6469

59 72 64 83 86 93 81 54

0.612 0.621 0.646 0.616 0.604 0.596 0.629 0.696

104 118 120 123 124 125 124 126

0.816 0.808 0.825 0.832 0.807 0.809 0.815 0.811

99 100 101 109 120 121 121 122

0.66 0.969 0.969 0.968 0.961 0.961 0.960 0.960

99 106 84 89 121 121 83 83

0.049 0.052 0.096 0.096 0.038 0.038 0.119 0.119

Source: Author’s presentation based on Global Gender Gap Report, Various Years.

Nkechinyere Rose Uwajumogu et al.

Table 15.1. Global Gender Gap Index 2006–2013.

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Consequently, decisions on tax and public expenditure should affect them differently. The Nation Bureau of Statistics (NBS) report a higher occurrence of unemployment and underemployment for females, with their accesses to quality job opportunities declining in recent times (Uwajumogu et al., 2022). In the third quarter of 2017, female unemployment was 4.7% higher than male’s and 2.4% higher than total unemployment (18%). This represents a 2.6%-point increase in unemployment from 2017Q2 and was remarkably the highest increase in female unemployment for past 11 quarters. Again, 21.8% of females in Nigeria were underemployed in 2017Q3. This means a 0.2%-point decrease in female underemployment from the previous quarter. A critical look suggests that this may account for fluctuations in number of females that have moved from underemployment to unemployment. In 2018Q4, 26.6% of females were unemployed. This is a 6.3%-point increase from male unemployment and represented a 5.4%-point increase in female unemployment for 2018Q3. In addition, 25.9% of females were underemployed representing a 4.1%-point increase in female underemployment from previous quarter. The rising underemployment rate suggests that female job-seekers are willing and ready to engage in low-pay, low-skill, and part-time jobs as long as they are able to meet their basic needs in the absence of nonwelfarist practices like social safety nets and unemployment benefits which the Nigerian State negates. Unfortunately, there is a 6.3% gap in unemployment rate between females and males. Unemployment rate imbalance along the gender divide may mean that more females depend on others for their continued existence. Income inequality, thus created, may have daring welfare consequences. Gender inequality has huge costs on sustainable growth. Gender parity in economic participation can add $250bn, $1,750bn, $550bn, $320bn, $310bn, and $2.5tr to gross domestic products of the United Kingdom, the United States, Japan, France, Germany, and China, respectively (World Economic Forum, 2017). A shrink in gender inequality translates to enhanced productivity of current generation, sustained improvements in development outcomes, and economic stability for the next generation (Newiak, 2018; Ostby et al., 2016). Nigeria, with a population of about 190,886,311 (2017 estimates) and with females constituting 49% of its population, had a per capita income of ₦708840 in 2019. We note that Nigeria’s budget ascended steadily from ₦299bn in 1999 to ₦8.6tr in 2018 except for 2015 when there was an 11.29% reduction ₦4.4tr. In order to develop and improve quality of human capital, our budgets provide for direct public expenditure on social services that include health and education and expectedly, low budgetary allocations have implications for gender inequality, inclusive growth, and poverty alleviation. Central Bank of Nigeria (2019) reports that government expenditures increased over the years with recurrent expenditures on education and health averaging ₦206.99bn and ₦122.88bn, respectively. The highest allocation to education recurrent expenditures of ₦465.30bn was in 2018 and this was 10% of total recurrent expenditures. In 2000, a total of ₦57.96bn, which is 13% of total recurrent expenditure, was spent on education recurrent expenditures. This was the highest allocation that education got out of total recurrent expenditures.

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The highest allocation to health recurrent expenditures of ₦296.44bn was in 2018 and was 4% of total expenditures. On the average, 5% of total recurrent expenditures was spent on health between 1999 and 2018. A total of ₦1945.70bn capital expenditures was made on social and community services between 1999 and 2018, and average of ₦97.29bn per year which was an average of 13% of total capital expenditures. Poor budgetary allocations manifests in poor access to education and health, lower literacy rates, higher mortality rates, among other human capital development impediments. Poor access to education and health not only incapacitates an individual but also stops them from effective labor force participation by robbing them of numerous opportunities. Human capital investment, through education and health development, builds up expertise and skills and improves efficiency in production; it is a facilitator for gender equity (Adenike, 2014). Adequate public social expenditures are thus appropriate mechanisms for putting Nigeria on a pedestal of gender parity laced with improved living standards and sustained GDP growth.

2.2 Theoretical Framework We located four dominant explanations which define female’s societal status and are likely correlates of various aspects of gender equality. First, classical modernization perspective focuses on economic development and establishes that rising economic growth associates with broad-based allocation of educational and occupational resources. Growth in economic indicators such as GDP, per capita income, and female labor force participation positively influence greater access to educational and occupational resources. They also boost females’ chances for professional advancement and further create a larger pool of women qualified for economic activities (Downes, von Trapp, & Nicol, 2017; Klasen, 2016; Siaroff, 2000). More importantly, gender budgeting and public expenditure reduce gender inequality gap. Second, human development perspective focuses on cultural modernity and advocates that rising emancipative values through changes in existential constraints lead to increases in female empowerment (Bissera, 2010; Durojaye, Okeke, & Adebanjo, 2014). It highlights changes in modern societies particularly conducive to women’s empowerment and therefore establishes a link between cultural modernity and practices that value greater equality between genders. Third, historical legacies perspective focuses on cultural and institutional path dependency and presents historical legacies as potentially capable of affecting advancements which modernization brings to women’s sociopolitical status. It asserts that as countries transit, traditional units restrictive of women’s development, such as family and church, lose their authority as individuals place greater emphasis on rationality and individualism thereby empowering women (Inglehart & Welzel, 2005; Klingorova & Havlicek, 2015; UN Women, n.d.). Other indices

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central to societies’ progress in gender equality and women’s full social inclusion are: suffragist policy (where dominant political groups open the system of political representation to promote female suffrage (Kenworthy & Malami, 1999)) and provision of state-financed welfarist policy (which expands State’s role in caregiving and domestic responsibilities by providing welfare packages and allowances (Bennett & Sung, 2013; Costa, 2014; Hirschmann, 2001; McGarthy, 2015)). As nations advance, political parties mediate for females, encouraging them to seek and win elective office (Kunovich, 2003; Klasen, O’Neill, & Vargas-Garcia, 2017; Kunovich & Paxton, 2005). Fourth, political institutions perspective focuses on supplying women with more rights and channels for making their voices heard. Inglehart and Welzel (2005) show that strengths of countries’ democratic traditions empower their females. Thus, measurement of female empowerment shifts from the gender empowerment index to percentage of females in parliament. We adopted the classical modernization perspective which sees public social expenditures as explanatory to gender parity.

2.3 Empirical Literature Empirical studies have verified the importance of government social expenditures on social outcomes. However, studies on gender-differential impacts of government spending in general and social spending in particular are scarce and to the best of our knowledge, nonexistence for Nigeria. But we were able to find some baseline studies. Most studies found that government expenditures impacted positively on females and reduced inequality. For instance, Chakraborty et al. (2004) examined the Asia Pacific region from 1992 to 1995 and from 1997 to 2000 and found that government expenditures on education and health, in per capita terms, has significant positive effects on Gender Development Index. Mart´ınez-Vazquez, Vulovic, and Moreno-Dodson (2012) engaged a sample of 150 developed, developing, and transition countries between 1970 and 2009 and found that income inequality reduced by 0.14%, 0.7% and 0.8% when there were percentage increases in expenditures for social protection, health, and housing, respectively. Using a panel dataset of 31 Indian states and union territories from 1983 to 1984 and 2011 to 2012, Barenberg, Basu, and Soylu (2015) found that increasing government expenditure on health care by 1% of state-level GDP reduced infant mortality rate by about eight infant deaths per 1,000 live births. Using a panel of 97 countries from 1990 to 2010, Detraz and Peksen (2018) found that public expenditures improved female’s economic and political empowerment. Using panel least square with regional dummies for 54 developing countries between 1990 and 2014, Emara and Hegazy (2019) found that government spending on education, among other variables, increased school enrollments, and had positive statistically significant impact on closing the gender gap for the three tiers of education.

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3. Method of Study We adopted the autoregressive distributed lag (ARDL) model bounds test for cointegration. Unlike other cointegration tests, the bound test is appropriate for small sample size and is applied when regressors are integrated either of order zero, one, or mutually cointegrated Pesaran, Shin, and Smith (2001). Our empirical model follows a modified version of Mart´ınez-Vazquez et al. (2012) income inequality model which explains gender parity in Nigeria with age dependency ratio, annual population growth rate, and GDP per capita growth rate as control variables. Age dependency ratio is a measure of productive population: a low dependency ratio means that the working population is not under pressure and that the working population can support its dependents (zero to 14 years and above 64 years). The burden of catering for very young and old persons usually falls on females. We expected a negative impact of dependency ratio for gender equality to be achieved. Similarly, we expected a rapidly growing population would undermine gender inequality and that GDP per capita growth rate would improve gender equality. Our model is: GD ¼ F ðSOCCAP; SOCCU; CONTÞ

(15.1)

Where GD is gender parity index for gross enrollment in primary and secondary school (GPIEDU) as well as life expectancy (GPILIF); SOCCAP, SOCCU, and CONT are capital social expenditure, recurrent social expenditure, and control variables, respectively. Eq. (15.1) was separated into two equations to capture disaggregated gender parity index into gender parity index for gross enrolment in primary school and secondary school (GDIEDU) and gender parity index for life expectancy (GDILIF) and these equations were stated in econometric form as: GDIEDU ¼ a0 1 a1 SOCCAP 1 a2 SOCCU 1 a3 GDP 1 a4 POP 1 a5 AGE 1 ∂

(15.2)

In Eq. (15.2) is: GDILIF ¼ b0 1 b1 SOCA 1 b2 SOCU 1 b3 GDP 1 b4 POP 1 b5 AGE 1 «

(15.3)

a0 2 5 and b0 2 5 are estimated parameters, ∂ and « are error terms for Eqs. (15.2) and (15.3), respectively. Data for social expenditures were sourced from Central Bank of Nigeria Statistical Bulletin (various issues). Data for other variables were sourced from World Development Indicators. Data analyses were conducted using Econometrics View (10).

4. Results and Discussions 4.1 Descriptive Statistics and Unit Root Test The descriptive statistics show that capital social expenditures ranged from ₦0.238bn to ₦154.707bn while recurrent social expenditures ranged from ₦0.289bn to ₦905.500bn. The GPI is the ratio of female to male values of a given indicator. A value of 1 indicates parity between the genders. A value below 1

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indicates gender inequality against females. A value above 1 indicates gender inequality against males. GPI in gross primary and secondary school enrollment ranged from 0.6765 to 0.9568. This means that between 68 and 96 females for every 100 males were enrolled in primary and secondary schools between 1981 and 2017. Furthermore, females had higher life expectancy rate than males. GPI for life expectancy ranged between 1.028 and 1.057. This means that between 103 and 105 females for every 100 males had higher life expectancy between 1981 and 2017. GDP per capita growth rate ranged from 215.45% to 12.46% while annual population growth rate ranged from 2.48% to 2.71%. The data feature high dependency ratio between 86.6% and 92.7%. Unit root results show GPI in education enrollment; GPI in life expectancy; GDP; age dependency ratio, and population growth rate were stationary at level while social capital expenditures and social recurrent expenditures were stationary at first difference. No variable was integrated of order two so ARDL bound test was appropriate for the cointegration test.

4.2 Cointegration Tests and Estimates of the Parameters The F-statistic for Eq. (15.2) was 19.40803 which was higher than the upper critical bounds for all significance levels, indicating the presence of long-run relationship between gender parity in educational enrollment and its explanatory variables. The F-statistic for Eq. (15.3) was 3.3014, which was significant at 2.5% level of significance. Therefore, we accepted that there was a long-run relationship between gender parity in life expectancy and its explanatory variables. The error term was properly signed, signifying that the speed of adjustment back to equilibrium was high. Human capital development is a long-run process hence we focused on the long-run results. A unit increase in capital and recurrent social expenditures significantly increased GPI in education by 0.000305 and 0.000157 units, respectively. In like manner, increase in GDP per capita growth rate raised GPI in education by 0.000694 units. However, a unit increase in age dependency ratio and population growth rate reduced GPI in education by 0.001976 and 0.157534 units, respectively. Table 15.2 shows that the error term was properly signed with a high speed of adjustment. In the long run, a unit increase in capital and recurrent social expenditures reduced GPI in life expectancy by 0.000070 and 0.000011 units, respectively. In like manner, GDP per capita also reduced GPI in life expectancy by 0.000171 units. Age dependency ratio and population growth rate were found to increase GPI in life expectancy by 0.003059 and 0.028674 units, respectively. Government expenditures on social services (education, health, and other welfare services) are important for increasing long-run earning ability and productive capacity of the population especially for the poor and vulnerable. With low capability to earn more and low productivity, out-of-pocket expenditures on health and education are usually inadequate thus stimulating poverty. Thus, public expenditures remain critical via its distributional impacts. For gender parity in education, results show that less Nigerian females than males enroll into primary and secondary schools. Therefore, a rise in education

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Table 15.2. Error Correction and Long-Run Models Coefficients.

Variable

Panel a: Gross enrollment D(GPIEDU(21)) D(SOCCAP) D(SOCCU) D(GDP) D(GDP(21)) D(AGE) D(POP) D(POP(21)) CointEq(21) Long-run coefficients Variable SOCCAP SOCCU GDP AGE POP C

t-Statistic

Prob.

0.096665 0.000087 0.000038 0.000574 0.000545 0.002411 0.348413 0.245115 0.163568

1.999291 3.030063 1.356738 23.492856 24.097809 21.279443 26.483502 8.174328 29.546389

0.0587 0.0064 0.1893 0.0022 0.0005 0.2147 0.0000 0.0000 0.0000

Std. Error 0.000069 0.000008 0.000525 0.001460 0.054401 0.077458

t-Statistic 4.396320 18.877127 1.320771 21.353005 22.895785 17.639466

Prob. 0.0003 0.0000 0.2008 0.1904 0.0086 0.0000

Coefficient

Std. Error

0.193261 0.000265 0.000051 20.002006 20.002233 20.003085 22.258937 2.003651 21.561483 Coefficient 0.000305 0.000157 0.000694 20.001976 20.157534 1.366315

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Cointegrating Form

Source: Author’s estimation.

0.596515 20.000008 20.000001 0.000012 0.000024 0.000337 20.001874 20.110316

0.122327 0.000002 0.000000 0.000010 0.000008 0.000170 0.003858 0.039822

4.876399 24.879876 22.921981 1.189965 2.909311 1.989244 20.485822 22.770243

0.0001 0.0001 0.0075 0.2457 0.0077 0.0582 0.6315 0.0106

Coefficient 20.000070 20.000011 20.000171 0.003059 0.028674 0.699597

Std. Error 0.000024 0.000003 0.000129 0.000604 0.033942 0.066822

t-Statistic 22.935097 23.931348 21.326119 5.062753 0.844802 10.469640

Prob. 0.0072 0.0006 0.1973 0.0000 0.4066 0.0000

A Look Through the Gender Lens

Panel b: Life expectancy D(GPILIF(21)) D(SOCCAP) D(SOCCU) D(GDP) D(GDP(21)) D(AGE) D(POP) CointEq(21) Long-run coefficients Variable SOCCAP SOCCU GDP AGE POP C

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GPI signifies increased female enrollment thus assuring parity between men and women. Our study found that increased social expenditures on health and education increased female enrollment into primary and secondary schools thereby improving female access to education. This finding supports the predictions that social spending improves gender equality in access to education. We provide evidence that capital expenditures equalize access to education thus supporting campaigns for free and universal basic education for all. Increased income per capita is important in achieving gender equality in access to education. Conversely, high dependency rate and population growth rate worsens gender inequality situation in Nigeria because they reduce savings, investments, growth and per capita income in view of persistent unemployment and underemployment. Results for GPI in life expectancy show females lived longer than males in Nigeria. Public expenditures on health and education also increased male access to health, thus male longevity. Capital expenditure was also important in bringing about parity in access to health. We summarize that increased expenditures in education of females and health of males, without neglecting the other gender, achieves more gender balance. The effectiveness of government social expenditures on inequality is also a function of the size of the economy. Our study shows that increased income per capita increased female access to education and male access to health.

5. Conclusion and Policy Recommendations In conclusion, targeting potential beneficiaries of government social spending is key to maintain gender balance but increased focus on maternal health should not be detrimental to paternal health. We recommend increased social expenditures which must be gender-targeting and gender-sensitive as well as increased capital expenditures. Gender budgeting should have well-described gender-specific outcomes. In addition, gender budgeting should analyze and document paid and unpaid contributions of women to economic progress and the differential impacts that government expenditures have on the genders. We advocate for policies that drive strong, resilient, and inclusive growth and exclusion of policies that reduce population growth rate and high dependency ratio. Gender inequality in economic opportunities and participation limits Nigeria’s drive toward economic diversification by reducing its pool of potential human capital and by limiting expansion of new ideas. These reduce effectiveness, efficiency, and efficacy of Nigeria’s labor force since the pool of talents (entrepreneurs and employees) available are not completely engaged.

References Adenike, E. T. (2014). Poverty and unemployment paradox in Nigeria. IOSR Journal of Humanities and Social Science, 19(5), 106–116.

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Barenberg, A., Basu, D., & Soylu, C. (2015). The effect of public health expenditure on infant mortality: Evidence from a panel of Indian States, 1983–84 to 2011–12. Economics Department Working Paper 199. Retrieved from https://scholarworks. umass.edu/econ_workingpaper/199. Accessed on June 15, 2020. Bennett, F., & Sung, S. (2013). Gender implications for UK welfare reform and government equality duties: Evidence from qualitative studies. Onati Socio-Legal Series, 3(7), 1202–1221. Bissera, K. (2010). How culture impacts development and gender equality. In Culture in development. Retrieved from www.cultureindevelopment.nl/News/Discussing_ Culture_and_Development/439/How_culture-impacts_development-and_gender_ equality. Accessed on December 15, 2018. Central Bank of Nigeria. (2019). Statistical bulletin. Nigeria: CBN. Chakraborty, L. S., Elson, D., & Chowdhury, S. (2004). Fiscal policy stance and gender equality in Asia Pacific: An empirical analysis. MPRA Paper No. 85402. Retrieved from https://mpra.ub.uni-muenchen.de/85402/. Accessed on May 3, 2020. Coora, A., & Potrafke, N. (2011). Gender inequality in education: Political institutions or culture or religion? European Journal of Political Economy, 27(2), 268–280. Costa, E. (2014). The welfare state and gender equality: Work-family reconciliation policies in Southern Europe. Paper presented at the ECPR Graduate Conference, Innsbruck, 3 to 5 July. Retrieved from https://www.researchgate.net/publication/ 273945418_The_welfare_state_and_gender_equality_work-family_reconciliation_ policies_in_Southern_Europe. Accessed on December 15, 2018. Detraz, N., & Peksen, D. (2018). Women friendly spending? Welfare spending and women’s participation in the economy and politics. Gender and Politics, 14(2), 137–161. Downes, R., von Trapp, L., & Nicol, S. (2017). Gender budgeting in OECD countries. OECD Journal on Budgeting, 16(3), 71–107. Durojaye, E., Okeke, B., & Adebanjo, A. (2014). Harmful culture practices and gender equality in Nigeria. Gender and Behaviour, 12(1). Retrieved from www. questia.com/library/journal/IP33606168891/harmful_culture_practices_and_ gender_equality_in_Nigeria. Accessed on December 15, 2018. Emara, N., & Hegazy, N. (2019). Government spending on education and closing the gender gap: The case of developing economies. Journal of Economics and Development Studies, 7(2), 1–10. George, T. O., & Chukwuedozie, O. N. (2014). Gender inequality and its effect on industrial development: Lessons for and from Nigeria. In D. Imohonopi & U. M. Urim (Eds.), Trajectory to industrial development in Nigeria (pp. 47–61). Otta: Covenant University. Glewee, P., & Michael, K. (2006). Schools, teachers and education outcome in developing countries. In E. Hanushek & F. Welch (Eds.), Handbook of economics of education 2 (pp. 945–1012). Amsterdam: Elsevier. Hansson, P., & Henrekson, M. (1994). A new framework for testing the effect of government spending on growth and productivity. Public Choice, 81, 381–401. doi: 10.1007/BF01053239. Accessed on May 3, 2020. Hirschmann, N. (2001). A question of freedom: A question of rights? Women and welfare. In N. Hirschmann & U. Liebert (Eds.), Women and welfare. New Brunswick, NJ: Rutgers University Press.

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Inglehart, R., & Welzel, C. (2005). Modernization, cultural change and democracy: The human development sequence. Cambridge: Cambridge University Press. Kenworthy, L., & Malami, M. (1999). Gender inequality in political representation: A worldwide comparative analysis. Social Forces, 78, 235–268. Klasen, S. (2016). Gender, institutions, and economic development: Finding and open research policy issues. Discussion paper 211 for Courant Research Center Georg-August- Universitat Gottingen. Retrieved from https://hdl.handle.net/ 10419/144760. Accessed on November 13, 2019. Klasen, S., O’Neill, M., & Vargas-Garcia, A. (2017). Increasing women’s support for democracy in Africa. Policy Brief, Growth and Economic Opportunities for Women Ottawa, Canada. Retrieved from https://hdl.handle.net/10625/56360. Accessed on November 13, 2019. Klingorova, K., & Havlicek, T. (2015). Religion and gender inequality: The status of women in the societies of world religions. Moravian Geographical Reports, 2(23). doi:10.1515/mgr-2015-0006 Kunovich, S. (2003). The representation of Polish and Czech women in national parliaments. Comparative Politics, 35, 273–291. Kunovich, S., & Paxton, P. (2005). Pathways to power: The role of political parties in women’s national political representation. American Journal of Sociology, 3(2), 505–552. Mart´ınez-Vazquez, J., Vulovic, V., & Moreno-Dodson, B. (2012). The impact of tax and expenditure policies on income distribution: Evidence from a large panel of countries. Review of Public Economics, 200(4), 95–130. McGarthy, L. (2015). Gender equality in the welfare state? People, Place, and Policy, 9(3), 207–211. Newiak, M. (2018). Good for women good for growth: Closing Nigeria’s gender gap. IMF Finance & Development Magazine. Retrieved from https://www.imf.org/en/ News/Podcast/All-Podcasts/. Accessed on May 14, 2018. Ostby, G., Urdal, H., & Rudolfsen, I. (2016). What is driving gender equality in secondary education? Evidence from 57 developing countries 1970–2010. Educational Research International. doi:10.1155/2016/4587194. Accessed on May 14, 2018. Payne, S. (2009). How can gender equity be addressed through health systems? Health Systems and Policy Analysis Policy Brief 12. World Health Organization on behalf of the European Observatory on Health Systems and Policies. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326. Siaroff, A. (2000). Women’s representation in legislatures and cabinets in industrial democracies. International Political Science Review, 21, 197–215. UN Women. (n.d.). The role of faith-based organizations, institutions and actors in achieving gender equality through the implementation of Agenda 2030. Policy Brief on Religion and gender equality. Uwajumogu, N. R., Nwokoye, E. S., Ojike, R. O., Okere, K. I., Ugwu, J. N., & Ogbuagu, A. R. (2022). Globalization and the proportion of women in vulnerable employment in sub-Saharan Africa: The role of economic, social, and political conditions. African Development Review, 34(3), 356–369. World Economic Forum (WEF). (2017). The global gender gap report. World Economic Forum, Geneva, Switzerland.

Chapter 16

Equitable Pathways for a Sustainable Future: The Case for Mainstreaming Gender Across Sustainable Development Goals (SDGs) Ananya Chakraborty and Sreerupa Sengupta

Abstract Countries across the world have committed to the attainment of Agenda 2030 by implementing policies to achieve all the 17 Sustainable Development Goals (SDGs). Development experience during the Millennium Development Goals (MDGs) suggests that ensuring equity is one of the basic pillars required to achieve SDGs. Unfortunately, gender is a major fault line across which development gets unequally distributed. While SDG 5 enshrines the need for achieving gender equality, its global progress has been staggered and saw a further decline during the COVID-19 pandemic. Gender equality is poorly integrated with all the SDGs as only 104 out of 246 SDG indicators identify gender-based issues. There continues to remain a widespread data gap even for the goals which have gender-related indicators as merely 35 out of the 104 gender-related indicators (9 of the 17 SDGs) had robust data systems and methodologies in place until recently. Consequently, countries with entrenched patriarchal and unequal societies have consistently lagged in the attainment of gender related SDGs and have struggled to mainstream gender. This chapter argues that gender data is the foundation for ensuring gender equality and promoting evidence-based policymaking. It therefore makes a case for mainstreaming gender-related indicators in SDGs 6, 7, 9, 12, 14, 15, and 17 along with expanding the gendered understanding of people-related goals in the areas of education, health, and employment. Moreover, it reiterates the need for gender data collection to move beyond the binary construct of male and female to integrate an intersectional lens.

Gender Inequality and its Implications on Education and Health, 191–201 Copyright © 2023 Ananya Chakraborty and Sreerupa Sengupta Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231017

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Keywords: Sustainable Development Goals; gender; sex disaggregated data; gender data; gender policy; intersectional lens

1. Introduction Nearly half a century after the first World Conference on Women in Mexico City highlighted the need to integrate women into national development and reduce the double burden of exploitation, the recent Global Gender Gap Report 2022 estimates that the world will take over 132 years to achieve gender parity at the current rate of progress (Jain, 2005; World Economic Forum, 2022). The fundamental purpose of human development is to ensure that people have a range of choices and the potential to realize their choices in order to have the freedom to lead and enjoy their lives (UNDP, 2015). Therefore, the true essence of human development can only happen when all people across the entire spectrum of gender have equal opportunities in all the spheres. Gender can be understood as the “social attributes and opportunities associated with being male and female and the relationships between women and men and girls and boys, as well as the relations between women and those between men.” These attributes, opportunities, and relationships are generally socially constructed and are taught and learned through the process of socialization. Therefore, gender equality means that people from any gender should have equal rights, responsibilities, opportunities, and are able to achieve equal outcomes irrespective of their sex (United Nations, 2002). To put it succinctly, gender equality is integral to human development. In 1995, the promise of gender equality and women’s empowerment was accelerated during the Fourth World Conference on Women organized in Beijing. The Beijing Declaration and Platform for Action (PfA) recognized the need for addressing structural inequalities and was adopted by 189 countries. The Beijing Declaration observed that the “status of women has advanced in some important respects in the past decade, but that progress has been uneven, inequalities between women and men have persisted and major obstacles remain, with serious consequences for the well-being of all people” (United Nations, 1995, p. 2). One of the main ways to overcome and address gender inequalities is by ensuring that development policy is informed by evidence on the myriad ways in which gender differences continue to exist. The Platform for Action established gender mainstreaming as a major goal for achieving gender equality which was defined as (United Nations, 2002, p. 5): . . .the process of assessing the implications for women and men of any planned action, including legislation, policies or programmes, in all areas and at all levels. It is a strategy for making women’s as well as men’s concerns and experiences an integral dimension of the design, implementation, monitoring and evaluation of policies and programmes in all political, economic and societal spheres so

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that women and men benefit equally, and inequality is not perpetuated. Temin and Roca (2016) observe that data are not neutral or passive but rather reflect the beliefs, attitudes, and values of people who create and collect data. Therefore, “data have the potential to reflect prevailing power structures and norms” (p. 264). This is particularly true for gender-related data that invisiblize, ignore, or misrepresent issues relating to women, girls, and other marginalized communities despite ongoing global efforts that call for stronger action to mainstream gender into development. Against this background, this chapter explores the importance and need for integrating gender-specific indicators across all the Sustainable Development Goals (SDGs). It also deliberates on the need to go beyond the process of collecting sex-disaggregated data and take cognizance of gender roles and relationships, and the embedded inequalities therein. Finally, the chapter also reflects on the ways in which gender data are captured and reflects on the need to integrate different methodologies for ensuring gender responsive and equitable outcomes for the SDGs.

2. Methodology and Data This review depends on literature review and secondary data analysis. The literature review was conducted by the authors using standard academic search engines like Google Scholar. Both academic and grey literature sources such as reports, blog articles, and published resources were utilized for analysis. Secondary data, on which the SDG analysis is compiled, have been taken from the Global Indicator Framework, E/CN.3/2022/2 (Annex I), and decision (53/101) by the 53rd United Nations Statistical Commission (E/2022/24-E/CN.3/2022/41), the Gender Snapshot, 2022, & UN Women Gender Data Gaps and Country Performance – India, 2022.

3. Gender Disaggregated Data for Policy Action In all societies, entrenched patriarchal norms create different life worlds for men and women. There exist discrepancies between what is expected and what is valued in men and women which impacts them through all stages of their lives and determine their identities. Differential social norms shape access to resources, participation in public domain, decision-making capabilities, and individual agencies for men and women in the society. These identities interact with other social categories such as income, class, caste, ethnicity, and race which further exacerbates inequalities which is termed as “intersectionality.” An intersectional approach would expand the scope of gender data to include the collection of data disaggregated by sex as well as by gender orientations. In addition, it effectively breaks down the homogenous categories of

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“women” and “men” and helps policymakers to address embedded inequalities across the entire spectrum of gender identities. Gender data are one of the main ways in which researchers and policymakers can support evidence-based policies to understand and mitigate the differences between the sexes. Gender data or gender statistics are data disaggregated by sex as well as data that affect women and girls exclusively or primarily. However, Buvinic and Levine (2016) point out that biases in data collection is often used to highlight or hide crucial differences in the society. An apt example of hidden biases in gender data collection is seen in the SDGs as there are no tracking mechanisms that collect and monitor progress through a gender lens across all the SDGs. This obliviates the scope of understanding gender differences as biases get engrained in the process of measurement itself. Since 1975, international women conferences have been a driving force for mobilizing action for reporting on the status of women. The United Nations Statistics Division (UNSD) initiated a program on gender statistics in the United Nations Decade for Women: Equality, Development and Peace (1976–1985) as a response to the need to assess the situation of men and women for designing gender-sensitive policies. The Beijing Declaration made it evident that the only way to achieve gender equality will be to bring about changes in policies and programs that enable women and men to influence, participate, and benefit from the process of development. In fact, Beijing Platform for Action (BPfA) set the agenda for all governments, international and national nongovernmental organizations, and civil society to adopt gender perspective in their goals and strategies in order to integrate gender into the model of development (United Nations, 2002). Moreover, BPfA emphasized the role of collection of appropriate gender data for designing effective policies and programs. The section on Institutional Mechanisms for the Advancement of Women in BPfA pointed out the need to generate and disseminate gender disaggregated data and information for planning and evaluation (United Nations, 1995). It also motivated international organizations to develop indicators and indices which were sensitive to the development and empowerment needs of women such as the Gender Development Index (GDI) introduced by the Human Development Report 1995. GDI measured aspects of women’s participation, economic empowerment, and decision-making roles to indicate women’s relative disadvantages (or advantages) in the human development process (Prabhu & Iyer, 2019). Some of the other gender responsive indices were the Global Gender Gap Index (2006); Gender Equality Index (2010); Gender Inequality Index (2010); Social Institutions and Gender Index (2010); and Women’s Economic Opportunities Index (2010). Since the declaration of SDGs in 2015, several new gender-specific big data projects have been launched to foster a more gender-responsive data revolution for sustainable development. Several organizations have focused on improving the availability and quality of sex disaggregated data to reduce gender inequalities. For instance, the Data2x platform was launched in 2014 with the support from global philanthropies like the Bill and Melinda Gates Foundation (BMGF).

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Additionally, multilateral organizations such as the World Bank and United Nations have also been proactive in addressing and investing for strengthening gender-related data projects. The Little Data Book on Gender was launched by the World Bank in 2016 and the Evidence and Data for Gender Equality Project (EDGE) was initiated by the United Nations Statistics Division and UN Women. India has had a long history of engaging with women’s issues represented by a strong women’s movement which has consistently raised concerns about the status of women in the country. The Towards Equality report published in 1974 by the Committee on Status of Women in India was pathbreaking on several counts. It was pioneering in drawing the country’s attention towards embedded discriminatory sociocultural practices which disempowered women. It also highlighted the power of gender data and established the necessity of collecting comprehensive sex disaggregated data for designing effective policies for women and girls. The report paved way for the development of gender-sensitive policy making in the country. Other gender-related committees and reports at the national level such as the Shram Shakti report of 1988 upheld the conditions of poor women in India and argued for an intersectional understanding based on caste, class, and employment activities (SEWA, 1988). The report also brought to light the issues of women-headed poor households which was once again raised by the final country report of India on Millennium Development Goal (2015) and called for newer approaches to survey and data collection along with expansion of data sources (usage of mobile phones and global positioning system data) to identify more vulnerable groups of women (for example, single women) whose lived realities have not been adequately covered by the existing mechanisms of data collection. The current methods of gender data collection have also been critiqued for the overt reliance on quantitative indicators at the cost of ignoring qualitative methodologies which may be more suited to capture people’s experiences, opinions, attitudes, and feelings. Qualitative and participatory methods like the “Another World is Possible” exercise carried out in Andhra Pradesh where rural women drew pictures to depict existing gender inequalities faced by them and recreated images that imagine a more equal world where girls go to school, women work with bullocks on the field, and men perform household work while women attend community meetings are a powerful way of capturing gender-based inequalities (Menon-Sen, 2006). Integrating qualitative and quantitative methods to promote an understanding of gender data is critical because evidence-based data are often more likely to influence policymaking and can support in furthering promoting policies that reduce gender inequalities. They are also critical for enabling planning for policy action and hold massive potential to drive in social changes.

4. Integration of Gender in Global Goals: From MDGs to SDGs A global goal for gender equality and empowerment of women is undoubtedly needed to address the pervasive gender-based inequalities existing, globally

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(Denny, 2015). In this light, Millennium Development Goal (MDG) 3, promote gender equality and empower women, and subsequently SDG 5 which aims to “achieve gender equality and empower all women and girls” have been a positive measure, catalyzing government and other organizations to mainstream gender in their policies and programs. MDGs called for a transformative commitment towards women’s rights and gender equality. For the first time in the history of development cooperation, there was a stand-alone goal on gender equality (Goal 3) focusing on education, economic empowerment, and political participation of women. There were gender-related targets in Goal 2: Achieve Universal Primary Education, Goal 4: Reduce Child Mortality, and Goal 5: Improve Maternal Health, which focused on improving health-care facilities for pregnant women and increasing access to contraceptives and family planning methods. MDGs also gave a push to all countries to collect data across various aspects of human development, including gender statistics. Despite global momentum on collecting gender data, Buvinic, Nicholas, and Koolwal (2014) found that severe gender data gaps continued to remain across several crucial domains such as health, education, economic opportunities, political participation, and human security during the MDG era. The progress on the political goal of reducing gender inequality (between 2000 and 2015) had been a very slow process. In fact, the MDG’s approach toward gender equality has been critiqued. Firstly, MDG 3 adopts an overt “economic case” approach toward women empowerment (Kabeer, 2005). Undeniably, economic and educational empowerment are necessary, but they are not sufficient to overcome the entrenched structural impediments such as discriminatory patriarchal norms, lack of legal protection, limited mobility, and decision-making capacity. An instrumentalist approach toward gender equality glosses over these lived realities of women. Therefore, governments and international community need to move beyond the idea of empowering women as a means to achieve economic gains. Another analysis of the MDGs highlights that it did not include any target for ending violence against women, the care work of women, or mentioned about women’s autonomy or agency (Dhar, 2018). The SDGs launched in 2015 showcased an improvement on the previous framework of global development cooperation. The 2030 Agenda goes beyond poverty eradication and focuses on three pillars of development: social, economic, and environmental. Unlike the MDGs, the SDGs are universal in nature, take a holistic approach to development, and apply to all countries rather than to developing countries only (Odera & Mulusa, 2020). A salient characteristic of SDGs is that it not only has a standalone goal on gender equality (SDG 5) but also included more gender-specific targets across other goals, thereby holding a larger potential for the realization of gender equality and promotion of women’s rights. Unlike MDG 3, SDG 5 expanded the scope of discussion on gender-related issues to include in its ambit specific targets on ending all forms of violence against women and girls, recognizing the value of unpaid work and ensuring access of women to ownership of land and other crucial assets. Consequently, the SDGs have moved the discussion beyond the

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instrumentalist approach toward women empowerment and included nuances of everyday discriminations faced by women and girls globally. However, gender data gaps continue to plague the SDGs. Out of the 246 SDG indicators, barely 104 identify gender-based issues. Merely 35 out of the 104 indicators (spanning across 9 of the 17 SDGs) have robust data collection systems and methodologies in place. Until recently, most of the gender-related indicators in SDGs were under the Tier II classification1 of the SDGs indicator framework. The successful implementation of SDGs depends on the ability to collect, compile, and utilize sex disaggregated data. As methodologies for measuring SDGs progress, there has been significant improvement in furthering an understanding of how women and girls are impacted across critical arenas such as poverty, education, and health. Data availability for the gender-specific dimensions of SDGs went up from 26% in 2016 to 42% in June 2022 (UN Women, 2022). Women Count, the UN Women’s global program to support sex disaggregated data, observes that at the current rate, it will take almost 22 years for countries to close the SDG gender data gap. Fig. 16.1 shows the present global status on availability of gender data for SDGs. Not surprisingly, most of the gender-related indicators are clustered around SDG 5 on “Gender Equality.” The presence of gender-related indicators

Fig. 16.1.

Gender Data Availability Across SDGs at the Global Level. Source: Authors’ representation.

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implies that these indicators have a gender-related term such as men, women, girls, or boys and are more prominent in the people-focused goals such as SDG 4 on education (66.67%), SDG 1 on eliminating poverty (38.46%), SDG 8 on decent work and economic growth (37.50%), SDG 16 on peace, justice, and strong institutions (25%), and SDG 3 on good health and well-being (21.43%) gender related indicators. However, gender-related goals remain conspicuous by their absence in the goals relating to planetary boundaries and environment such as SDGs 12, 14, and 15 on responsible consumption and production, life below water, and life on land, respectively. SDG 13 on climate action has merely one gender-related indicator out of the 8 indicators. Gender-related goals also have been missing from the goals for critical infrastructure provisioning such as SDG 6 (clean water and sanitation), SDG 7 (affordable and clean energy), and SDG 9 (industry, innovation, and infrastructure). It is unfortunate to observe that gender-related indicators are also missing from SDG 17 which focuses on partnerships for goals and between countries. Data availability at the country level has been a further impediment to advance gender equality at the national level. An assessment by Women Count estimates that Mexico, Armenia, Belarus, Guatemala, Ecuador, Peru, Costa Rice, Albania, Panama, and Zimbabwe have progressed most significantly in reducing the gender data gap for SDGs. Fig. 16.2 indicates the gender data availability for India which remains at 48%, as compared to 68% for Mexico. India continues to lag in the collection of gender

Fig. 16.2. Gender-Wise SDG Indicator Mapping and Availability Globally and at India Level2. Source: Author’s representation.

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data across most of the SDG indicators except SDG 3 on good health and well-being, where it has a robust system for collection of sex disaggregated data. It is worrisome that India does not collect or report gender-specific data for some of the SDGs which already have existing methodologies such as SDGs relating to zero hunger, reduced inequalities, climate change, and sustainable cities and communities. Ignoring the collection of gender-specific data has severe consequences in terms of addressing gender equality. For instance, although data are not collected about the sex disaggregated impacts of SDG 6 on clean water and sanitation, it has been globally recognized that women and girls are primarily responsible for the collection of water for domestic uses in about 80% of the households which do not have running water. Limited access to water, hygiene, and sanitation facilities place a bigger burden of unpaid work such as walking over longer distances and spending more time to collect water on women and young girls and also make them susceptible to the risks of higher dropout rates, health issues, and increased their vulnerability to sexual violence. A study in India indicated that young girls enrolled in schools significantly improved their performance in mathematics and reading skills when the time taken to fetch water was reduced (Hamlet, Chakrabarti, & Kaminsky, 2021). This example demonstrates the value of integrating gender data across all the SDGs where such data invisiblize women and girls and other marginalized people.

5. Discussion and Way Forward It is ironical that while 2030 Agenda promises to “leave no one behind,” the understanding of gender in the SDGs remains confined to the hegemonic gender binaries of men and women. In 1990, Judith Butler in her seminal work Gender Trouble: Feminism and the Subversion of Identity had called upon the society to disrupt the binary sex, gender, and sexuality. Butler had argued “rather than being women or men, individuals act as women and men, thereby creating the categories of women and men” (Morgenroth & Ryan, 2018). It is indeed disheartening to observe that after more than two decades, the diversified nature of gender has not been captured in the current framework of development. There are no targets in SDG 5 to address the concerns of the gender queer community. This narrow understanding of gender in SDGs has several salient implications – (a) it invisibilizes the gender queer community from the collection of gender-specific data thereby excluding them from the development process, (b) it reinforces their marginalized position in the society and denies them equal involvement in decision-making process, and (c) it excludes them from discussions on violence and harassment. If we want to effectively respond to SDG 10 (Reducing Inequalities), it is essential to articulate a broader understanding of gender which goes beyond the gender binaries. Unless the spectrum of gender diversity is acknowledged in national and international policies, gender data landscape cannot be transformed. Gender data collection has to be more inclusive, as it forms the bedrock for

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evidence-based policymaking. In 2022, the National Institution for Transforming India (NITI Aayog, 2022) took a positive step in this direction. In its thematic paper on Gender Mainstreaming in Governance published, NITI Aayog has adopted “inclusion of transgender” as a category for reviewing the performance of various government schemes and policies. The current expansion in the parameters for evaluating gender mainstreaming in India presents a positive nudge for the policymakers to revisit their process of gender data collection in future. There is a need to integrate stronger methodologies that include women’s contribution and issues. For example, better collection of time use data can further the understanding of rural women’s work in sectors like agriculture and allied activities which is often informal, short term, and infrequent in nature and therefore often missed out in larger employment surveys (Chakraborty, Ravula, Seymour, & Slavchevska, 2021). Country level investments in data collection combined with stronger methodological tools and innovations is critical for capturing wide-ranging data that will ensure equitable pathways for gender mainstreaming across all SDGs. Since gender equality is at the heart of human development, fulfilling the promise of Agenda 2030 will hinge on our collective ability to end all forms of gender inequalities and ensure that no one is left behind.

Notes 1. SDG indicators are classified on the basis of the levels of development of methodologies and availability of global data to monitor them. Tier I indicators are conceptually clear and have well-established methodology and standards and data are regularly collected. Tier II indicators are conceptually clear and have established methodologies, but data are not regularly collected for them, while Tier III indicators have no internationally established methodology or standards. As per the 51st Session of the UN DESA, there are no Tier III SDG indicators. 2. Fig. 16.2 shows that out of the 14 indicators for SDG 5, all collect gender-specific data. However, for SDG 4 only 8 out of 12 indicators have a gender focus, while critical indicators like information technology and communication education and skills among youth and adults do not have sex disaggregated data. Similarly, data on information on mortality, fatality, and missing individuals in disasters (SDG 1) are not disaggregated by gender but bear important implications on gender policy during disasters. Several such indicators across various SDGs such as on the impacts of climate change, usage of water and sanitation facilities, access to energy, and on peace and justice can benefit from sex disaggregation for better policy action.

References Buvinic, M., & Levine, R. (2016). Closing the gender data gap. The Royal Statistical Society. Retrieved from https://data2x.org/wp-content/uploads/2019/05/Closing-theGender-Data-Gap-Mayra-Buvinic-and-Ruth-Levine.pdf. Accessed on October 12, 2022.

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Buvinic, M., Nicholas, R. F., & Koolwal, G. (2014). Mapping gender data gaps. Data2X. Retrieved from https://www.fsnnetwork.org/sites/default/files/Data2X_ MappingGenderDataGaps_FullReport_0.pdf. Accessed on October 12, 2022. Chakraborty, A., Ravula, P., Seymour, G., & Slavchevska, V. (2021). Gendered patterns of work and time use: A review of methods and innovations. CGIAR GENDER Platform Working Paper #001. CGIAR GENDER Platform, Nairobi, Kenya. Denny, J. M. (2015). Gender and the Sustainable Development Goals: Moving beyond women as a ‘quick fix’ for development. Governance and Sustainability Issue Brief Series: Brief 11. Centre for Governance and Sustainability. University of Massachusetts Boston. Retrieved from https://genderandsecurity.org/sites/default/files/ Denney_-_Gender_and_the_Sustainable_Development_Goals-_Moving_Beyond. pdf. Accessed on October 10, 2022. Dhar, S. (2018). Gender and Sustainable Development Goals (SDGs). Indian Journal of Gender Studies, 25(1), 47–78. Hamlet, L. C., Chakrabarti, S., & Kaminsky, J. (2021). Reduced water collection time improves learning achievement among primary school children in India. Water Research, 203, 117527. Jain, D. (2005). Women, development, and the UN: A sixty-year quest for equality and justice. Bloomington, IN: Indiana University Press. Kabeer, N. (2005). Gender equality and women’s empowerment: A critical analysis of the third millennium development goal. Gender and Development, 13(1), 13–24. Menon-Sen, K. (2006). Another world is possible: An exercise to define change goals and work out ways to track the change process. Unpublished paper. Morgenroth, T., & Ryan, M. K. (2018). Gender trouble in social psychology: How can Butler’s work inform experimental social psychologists’ conceptualization of gender? Frontiers in Psychology, 9, 1320. doi:10.3389/fpsyg.2018.01320 NITI Aayog. (2022). Thematic report: Gender mainstreaming in governance. Retrieved from https://dmeo.gov.in/sites/default/files/2022-06/Thematic_Paper_Gender_ Mainstreaming_220622.pdf. Accessed on November 22, 2022. Odera, J. A., & Mulusa, J. (2020). SDGs, gender equality and women’s empowerment: What prospects for delivery? In M. Kaltenborn, M. Krajewski, & H. Kuhn (Eds.), Sustainable Development Goals and Human Rights (pp. 95–118). Cham: Springer. Prabhu, K. S., & Iyer, S. S. (2019). Human development in an unequal world. New Delhi: Oxford University Press. SEWA. (1988). Shram Shakti: A summary report of the National Commission on Self Employed Women and Women in the Informal Sector. SEWA, Ahmedabad. Temin, M., & Roca, E. (2016). Filling the gender data gap. Studies in Family Planning, 47(3), 264–269. UN Women. (2022). It will take 22 years to close SDG gender data gaps. Retrieved from https://www.unwomen.org/en/news-stories/feature-story/2022/09/it-will-take22-years-to-close-sdg-gender-data-gaps. Accessed on October 15, 2022. United Nations. (1995). Beijing declaration and platform for action. In The Fourth World Conference on Women, Beijing. Beijing. United Nations. United Nations. (2002). Gender mainstreaming: An overview. Retrieved from https:// www.un.org/womenwatch/osagi/pdf/e65237.pdf. Accessed on November 22, 2022. United Nations Development Programme. (2015). Work for human development. Oxford, MA: Oxford University Press. World Economic Forum. (2022). Global gender gap report. Retrieved from https:// www3.weforum.org/docs/WEF_GGGR_2022.pdf. Accessed on October 12, 2022.

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Chapter 17

Sustainable Environment and Urbanization Policies to Enhance Gender Equality and Women Empowerment Egemen Sertyesilisik

Abstract Failure in environment and urbanization policies can cause many challenges (e.g., widespread of informal settlements which generally lack proper infrastructure and sanitation) on human beings affecting their well-being and welfare adversely fostering gender inequality and gender inequality caused problems globally. Furthermore, environmental degradation caused epidemics due to animal-to-human zoonosis can further challenge health and well-being. Women are relatively more vulnerable and experience relatively more difficulties compared to men due to environmental degradation, urbanization problems, and informal settlements having poor infrastructure. Sustainable environment and urbanization policies and their integrated thinking with health policies are vital to enhance gender equality. Based on the literature review, this chapter aims to examine the role of environment and urbanization policies in enhancing gender equality and women empowerment. Furthermore, this chapter investigates causes and consequences of failure in environment and urbanization policies in addressing gender equality and women empowerment. This chapter emphasizes impacts of environment and urbanization policies on health especially on women health and well-being. This chapter further highlights the role of gender equality in achieving healthy and sustainable environment and urbanization policies. Furthermore, this chapter provides recommendations on how to enhance environment and urbanization policies so that they can further support gender equality and women empowerment effectively. Keywords: Environment policies; gender equality; informal settlements; urbanization policies; women empowerment; women health

Gender Inequality and its Implications on Education and Health, 203–212 Copyright © 2023 Egemen Sertyesilisik Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231018

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1. Introduction Urbanization is perceived to be essential for economic growth as countries can achieve high-income level with the help of urbanization (Rabenhorst & Bean, 2011). Urbanization is related with women’s independence due to its many opportunities (e.g., employment, better access to services) (Tacoli, 2013). Even if urbanization can play an important role in women’s freedom, environment problems can occur due to accelerated urbanization not paying attention to and/ or not giving priority to the sustainability, sustainable development, and sustainable urbanization. Rapid urbanization can influence dynamics (i.e., social, economic and political) which can affect women empowerment (Boyer, 2019). For this reason, urban planners should give importance to sustainable establishment, development, and expansion of cities, as there can be need for more infrastructure due to increase in the population in cities. According to the UN Habitat’s estimation, population living in cities in developing countries can reach to 3.9 billion in 2030 (Rabenhorst & Bean, 2011). Cities experience difficulty in responding the needs of their inhabitants due to migration to cities, population density, and environmental challenges (Boyer, 2019). Furthermore, urbanization can result in poverty intensified in slum areas (Rabenhorst & Bean, 2011). For example, “urbanisation of poverty” has been observed in South Asia due to the rapid and uncontrolled growth of urban areas and absolute poverty resulting in large areas of slums where people especially women have become vulnerable to disasters (Saad, 2021). These living conditions of women led to “feminization of poverty” reflecting the highest risk profile for disasters (Saad, 2021). As in slum areas women are affected more from natural disasters, establishment of adequate infrastructure in cities can help to save human life. Inclusive infrastructure planning, delivery and management is vital for empowerment of women and girls as noninclusive infrastructure affects especially women and girls adversely (Morgan et al., 2020). This is in compliance with the United Nations (UN) Sustainable Development Goals (SDGs). UN SDGs’ aim is to “. . . ‘leave no one behind’ in realizing sustainable, diverse and inclusive societies” (IGES, 2018, p. iv). It is important to understand vulnerabilities and risks of women who are living especially in slum areas (Saad, 2021). From the disaster-risk point of view, gender vulnerability is related with the preexisting vulnerabilities in society as outputs of many factors (e.g., skewed development process) (Saad, 2021). UN SDGs (e.g., SDG 5) focus on gender equality (Strumskyte, 2019). Based on the literature review, this chapter aims to examine environment and urbanization policies’ role in enhancing gender equality and women empowerment.

1.1 Importance of and Need for Sustainable Environment Focused Urbanization Policies and Women Empowerment There is interaction between sustainable environment and urbanization policies and gender equality as failure in sustainable environment and urbanization can obstruct gender equality whereas success in achieving gender equality can support sustainable environment and urbanization (Fig. 17.1). Factors (e.g., insufficient

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gender equality

Fig. 17.1. Sustainable Environment and Urbanization Policies and Women Empowerment. Source: Sketched by the author.

infrastructure, inadequate governance structures) adversely affecting accomplishment of urban opportunities affect women, children, and the poor (Boyer, 2019). Gender considerations need to be integrated into urban programming to provide benefits and opportunities to women and men (Boyer, 2019). Failure in sustainable urbanization addressing all pillars of sustainability can adversely affect well-being and welfare of citizens. Environmental degradation threatens right to life of generations (UNEP and UNHR, n.d.). Poor planning and management of urban settings (e.g., lack of sustainable transport systems, public, and green areas) can result in deteriorated environment (e.g., air pollution), life and health, reduced opportunities for physical activity, jobs, and education (WHO, 2020). Furthermore, as environmental degradation and biodiversity loss cause viral epidemics (due to animal-to-human zoonosis) and human’s vulnerability to viral infections (due to illnesses such as asthma), humanity’s health and resilience necessitate safe, clean, and healthy environment (e.g., water and sanitation) (UNEP and UNHR, n.d.). Sustainable environment focused urbanization policies can influence well-being which can further influence women empowerment. Urban infrastructure is important to mitigate the effects of pandemics like COVID-19. In slum areas women and girls can be affected more from the pandemic. For example, women in slum areas in Rio de Janeiro have been affected by the COVID-19 infection and its deathly consequences more than their neighborhoods in nonslum areas (UN Women and UN DESA, 2021). Furthermore, reduced well-being can adversely affect welfare. For example, COVID-19 affected gender equality adversely as women consists of 39% of global employment whereas experienced 54% of job losses as they are working in vulnerable jobs (Madgavkar, White, Krishnan, Mahajan, & Azcue, 2020). In the EU, working hours of women fell more than the men during the pandemic (Eige, n.d.). Poor housing and slum areas adversely affect women and girls (UN Women and UN DESA, 2021). For example, in Liberia, women and girls living in slum-like areas are less likely to attend to secondary or higher degree education compared to males (UN Women and UN DESA, 2021). For this reason, establishment of urban infrastructure can be useful/essential to empower women and increase their educational level. Urban poverty and rural poverty go hand in hand as even if rural poverty fosters women’s migration to cities, in cities in developing countries, women consist of 66% of labor force earning 10% of income (Rabenhorst & Bean, 2011).

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For this reason, women can encounter housing problems in cities and experience inadequate infrastructure in slum areas preventing their development (Rabenhorst & Bean, 2011). Furthermore, even if natural hazards can pose risk to the cities, urban setting is not exposed to these risks homogeneously (Saad, 2021). Policymakers cannot treat the slum areas as undifferentiated entities in cities (Saad, 2021). Inadequate infrastructure in cities can prevent and challenge girls’ development. Unsafe and inaccessible improper urban services adversely affect (e.g., displacement, violence, illness, malnutrition, and loss of livelihoods) especially women and their families living in slum areas (Boyer, 2019). For example, sanitation facilities inadequacy can adversely affect teenage girls’ attendance to schools (Strumskyte, 2019). Water infrastructure is important for safe access to enough water as otherwise water scarcity challenges communities from adequate sanitation (Interagency Task Force on Gender and Water, 2005). Similarly, poor communities in urban areas can fail to have access to the energy affecting women’s health, household tasks, education, and income-generating works (Boyer, 2019). As women are affected by poor infrastructure, their view should be taken into consideration in urban planning. Women involvement, however, tend to be ignored by city officials, urban planners, and development practitioners (UN Human Settlements Programme, 2013).

1.2 Recommendations for Enhancing Environment and Urbanization Policies to Support Gender Equality and Women Empowerment Urbanization policies need to cover effective infrastructure related investments strategically for supporting women empowerment. Infrastructure (e.g., health) can influence SDGs (Strumskyte, 2019). There is need for integrated policy approach to support sustainable infrastructure development having gender lens considering societal goals (e.g., employment) (Strumskyte, 2019). The benefits of cities need to be equally allocated through gender equity (UN-Habitat, 2013). Women’s rights protection and their access to cities’ material resources and their participation to social, political, and cultural environment need to be ensured (UN-Habitat, 2013). Equity in power and rights is related with gender and urban prosperity (UN-Habitat, 2013). Infrastructure in urbanization can influence women empowerment. Fabrizio et al. (2020) emphasized the role of infrastructure in women empowerment indicating that infrastructure and education investments in low-income countries can make large economic and social impact and contribute to female labor productivity. Infrastructure consists of hard and soft infrastructures (Strumskyte, 2019). Infrastructure can support sustainable development, growth, well-being, women empowerment, and their access to services and opportunities (Morgan et al., 2020). Women’s contribution to infrastructure development related decision-making process is important to empower them (Morgan et al., 2020). Interaction of different infrastructure types and their effects on well-being and environmental sustainability need to be understood and considered as both hard and soft infrastructure that are interrelated and

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influenced by development policies and urban and rural design (Strumskyte, 2019). Cities’ soft infrastructure is important for the education and self-development of the women. Women living in low and middle-income countries have relatively less access to resources (e.g., healthcare) than men (Azad, Charles, Ding, Trickey, & Wren, 2020). Hard infrastructure construction can provide advantages to women as especially, water, energy, and school infrastructure can support women’s development (Strumskyte, 2019). For example, the School Sanitation and Hygiene Education campaign was launched to provide facilities to improve health of girls and their attendance to school (Interagency Task Force on Gender and Water, 2005). Furthermore, digital infrastructure is playing an important role in empowering women in business life. Access to digital communications technology can support women’s employment opportunities and women-owned businesses (International Chamber of Commerce, 2017; as cited in Morgan et al., 2020). Information access and control is related with empowerment; digital communications infrastructure is important for knowledge sharing and creation of inclusive and empowered societies (Morgan et al., 2020). Urbanization policies need to be considered and prepared as effective tools for enhancing and supporting economic development and relevant infrastructure needed for fostering women empowerment. Economic development can be described by enhanced physical infrastructure, technology, and household income (Jayachandran, 2014). Technological development is important for women empowerment due to technological development’s potential contributions to women empowerment. Electrification can be given as an example for an innovation which can reduce home labor and enable women to have more free time (Jayachandran, 2014). Women in Africa spend much of their time to cook and to collect firewood for cooking (Njoh et al., 2018, as cited in Njoh et al., 2022, p. 8). They spent sometimes one hour in Zimbabwe and 5 hours in Sierra Leone to collect firewood (Njoh et al., 2018, as cited in Njoh et al., 2022). Effective and efficient energy infrastructure in cities can facilitate women’s lives and contribute to their empowerment. Increased electricity consumption and women empowerment have connection reflecting economic development indirectly (Njoh et al., 2022). This link becomes more visible in the gender-based inequalities in economically less developed societies (Njoh et al., 2022). Policy needs to focus on transition to clean energy. Clean energy is important as lack of clean energy can affect health adversely (UN ESCAP, 2017). For example, in the Asia-Pacific region, approximately two billion people use solid fuels for cooking and this situation causes health problems (UN ESCAP, 2017). Furthermore, household air pollution can cause deaths and premature deaths in this region (UN ESCAP, 2017). From this aspect, women employment in renewable energy sector is also important for women empowerment. Even if in renewable energy sector, women constitute 32% of the workforce, they are mainly employed in nontechnical positions (UN Women and UN DESA, 2021). For this reason, energy infrastructure policies need to be considered together with the relevant education and investments. For example, energy access challenges in Africa require a local skilled workforce due to the distributed and renewable energy’s role in meeting SDG 7 (Pailman & Groot, 2022). Gender equality is important in the degree and

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master programs which can be provided by universities to educate local skilled professionals to respond to SDG 7 (Pailman & Groot, 2022). Effective and sustainable public transportation needs to be paid attention in cities. Women tend to use public transportation more than men (e.g., in Buenos Aires) (IADB, 2017, as cited in Libertun de Duren et al., 2020). Transportation services need to answer the needs of women (e.g., baby trolleys) and women need to contribute to decision-making processes to solve transportation problems (Libertun de Duren et al., 2020). Women’s access to transportation can support their active participation to life. Sustainable transportation system needs to be given importance so that natural ecosystem is not harmed due to urbanization. Air pollution and climate change need to be considered in an integrated way to enhance transport, energy, and food systems having low environmental impact (WHO, 2020). Urbanization policies need to ensure safe cities and infrastructure. Safety aspect needs to be considered in the urban design (Strumskyte, 2019). Adequate and safe public spaces are important for women’s safety supporting elimination of insecurity problems to women (Libertun de Duren et al., 2020). Areas (e.g., dark areas, improper and inadequate street lightings) can cause security deficiency (Libertun de Duren et al., 2020). Furthermore, gender equality needs to be achieved in urban design at all levels. For example, in Vienna urbanization fostered girls to use the parks more through park design influencing gender equality (Gardner & Begault, 2019). In the 1990s public officials noticed in a park in Vienna that boys were dominating the park (Gardner & Begault, 2019). As a solution, park designers created spaces (e.g., volleyball courts, more private spaces, establishment of benches) to enable the girls to spend more time in the park (Gardner & Begault, 2019). Urbanization policies need to support cities’ capacity to support women empowerment and gender equality through supporting achievement of education policies. Cities can support women empowerment and gender equality through education policies and infrastructure enabling gender equality in the access of information, enhancing women’s capacity in information and communication technologies (ICT), supporting women with respect to education opportunities and their safety in schools (Libertun de Duren et al., 2020). As women’s skills development is important, in cities high quality and enough number of schools should be built to raise education level of women and for their self-development. Complying with SDGs (SDG5 Target 5.B), and in the New Urban Agenda equal access to ICT and education is important for women empowerment, and employment and achievement of cohesive, inclusive, and sustainable cities gender equity in public life (Libertun de Duren et al., 2020). Providing education is important to fight against poverty. Causes preventing girls to have access to education need to be overcome. Many girls quit school globally due to many reasons (e.g., poverty, child marriage) (Unicef, n.d.). Furthermore, education infrastructure in cities is important for women empowerment as education can enable women to become equipped with skills necessary to enable them to get higher income. Fair salary is important for gender equality in cities. Even if women can get benefit from employment opportunities in cities, they generally earn relatively less than men as they work in low-paid

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jobs and/or are paid less compared to men (Rabenhorst & Bean, 2011). Furthermore, investment in education is a key factor influencing future labor’s productivity (Fabrizio et al., 2020). Urbanization policies need to pay attention that infrastructure necessary to enable women to have more time for their empowerment need to be provided in cities. For example, childcare facilities can be important. Similarly, Fabrizio et al. (2020) emphasized the importance of childcare subsidies and paid maternity leave for enabling women participation in economic activities. Urbanization policies to support women empowerment need to be designed and established through women’s contribution and feedback to these processes. Women’s involvement in city related decision-making processes can contribute to urban development and their empowerment. Kitakyushu is an example for effective contribution of women’s participation to the governance of urban infrastructure resulting in city’s industrial transformation to environment-friendly economy (Strumskyte, 2019). Complying with the gender equality, Kitakyushu City Basic Plan for Gender Equality focused on development of a society enabling all people to achieve their ability (IGES, Institute for Global Environmental Strategies, 2018). Women participation and empowerment in environmental issues can help to save and protect natural environment. For example, despite deforestation in the Asia Pacific region, women empowerment in India and Nepal enabling women participation in decision-making related with forests and fisheries protection supports resource efficiency and sustainable management of forest resources (UN ESCAP, 2017).

2. Conclusion This chapter examined and emphasized the role of environment and urbanization policies in enhancing gender equality and women empowerment by emphasizing the relationship between and interaction of environment and urbanization policies, gender equality, and women empowerment. This chapter highlighted causes and consequences of failure in environment and urbanization policies in addressing women empowerment, health and well-being, and gender equality as well as recommendations on how to enhance those policies so that they can further support gender equality and women empowerment effectively. Failure in these policies can result in urbanization affecting life quality, well-being, and welfare adversely obstructing women empowerment. Slum areas adversely affect women and girls (UN Women and UN DESA, 2021). Urban poverty and rural poverty go hand in hand (Rabenhorst & Bean, 2011). Unsafe, inaccessible improper urban services can influence adversely women and their families in slum areas (Boyer, 2019). Planned urbanization and constructing the urban infrastructure is important in empowering women. In slum areas women and children are affected more than men from the pandemic, as the COVID-19 pandemic affected women living in slum areas in Rio de Janeiro more than those in their neighborhoods living in the developed part of the city (UN Women and UN DESA, 2021).

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Urbanization policies need to be prepared as effective tools for enhancing economic development and relevant effective and safe infrastructure and their investments so that women empowerment can be supported. Environment and urbanization policies can be established, enhanced, and improved to support gender equality and women empowerment covering many aspects. These policies need to give priority to the establishment of necessary infrastructure in cities. Infrastructure can contribute to women empowerment (Morgan et al., 2020). Infrastructure can influence SDGs (Strumskyte, 2019). Infrastructure plays an important role in women empowerment (Fabrizio et al., 2020). Integrated policy approach is needed to support sustainable infrastructure development based on the gender lens (Strumskyte, 2019). Gender equality needs to be considered in all aspects and levels of urban design and infrastructure. For example, in Vienna changes in a park design influenced gender equality and enabled more girls to use the park (Gardner & Begault, 2019). Electrification (Jayachandran, 2014) and clean energy (UN ESCAP, 2017) are examples for infrastructures supporting women empowerment. Furthermore, effective and sustainable public transportation and sustainable transportation system need to be paid attention in cities. Women tend to use public transportation more compared to men (IADB 2017, as cited in Libertun de Duren et al., 2020). It is important to consider safety aspect in the urban design (Strumskyte, 2019). Safe public spaces are important for solving insecurity problems for women (Libertun de Duren et al., 2020). Furthermore, education policies and infrastructure (Libertun de Duren et al., 2020) need to be considered. For this reason, urbanization policies can be designed to support cities’ capacity to support women empowerment through supporting achievement of education policies. Women participation in governance of urban infrastructure plays an important role in empowering women. Urbanization policies need to be designed and established through women’s contribution and their feedback to these processes so that women empowerment can be supported. Infrastructures should be established paying attention to the needs of women and entire society. For example, the industrial transformation of Kitakyushu city into anenvironment-friendly economy has been accomplished by effective contribution of women’s participation to urban infrastructure governance (Strumskyte, 2019). Women participation, involvement, and empowerment in city-related decision-making processes and environmental issues can help to save and protect natural environment and can contribute to urban development and their empowerment. For example, women empowerment in India and Nepal contributed to forest resources’ management (UN ESCAP, 2017). Even if all disciplines are key to contribute to mainstream/integrate gender considerations in infrastructure lifecycle, especially, infrastructure designers are the vital key to achieve inclusive design of infrastructure considering the needs of entire society covering needs of women, girls, and disadvantaged groups as currently infrastructures fail to be designed considering these needs (Morgan et al., 2020). This chapter can be useful for researchers and politicians focusing on enhancing sustainable urbanization supporting women empowerment and sustainability.

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Chapter 18

Understanding Gender, Poverty, and Social Justice: A New Look From the Perspectives of Indian Experience Asim K. Karmakar, Sebak K. Jana and Sovik Mukherjee

Abstract Feminist contributions to debates on gender, poverty, and social justice have deepened our understanding of the ways gender as a structuring principle of social life and an embedded hierarchy of values produces different concepts and experience of poverty as well as adds new meaning to the idea of “human flourishing.” Gender inequality remains a major barrier to human development; the disadvantages facing women and girls are a major source of inequality; since women and girls are discriminated against in health, education, political representation, and labor market, which has negative repercussions for development of their capabilities and their freedom of choice, remaining far away from social justice (Nussbaum, 1995). Recent statistics show just how far societies are from achieving gender equality. In the above backdrop, the chapter focuses on the position and status of women in India in the realm of gender equality, poverty reduction, and social justice as well as the public actions viewed from India’s perspectives. At the same time it highlights the importance of global actions in an endeavor to establish gender equality, breaking the chain of poverty trap and establishing social justice along with their fallouts in the subsequent years. Keywords: The Addis Ababa Action Agenda; BPA; capability and wellbeing; gender equality; poverty trap; social justice JEL Code: D63; J08; J16; J71; K31; K38

Gender Inequality and its Implications on Education and Health, 213–222 Copyright © 2023 Asim K. Karmakar, Sebak K. Jana and Sovik Mukherjee Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231019

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1. Introduction Feminist contributions to debates on gender, poverty, and social justice have deepened our understanding of the ways gender as a structuring principle of social life and an embedded hierarchy of values produces different concepts and experience of poverty as well as adds new meaning to the idea of “human flourishing.” Neoliberal globalization has triggered multiple processes of differentiation and complex intersections of social relations within and across societies in ways that links between gender, poverty, and social justice. And that cannot be understood without the addition of a gender lens. A gender analysis of the discourse, politics, and policies for reducing poverty forms an important component of a broader framework required to address the intersections of emerging forms of exclusion and vulnerability with class, cast, ethnicity, race, as well as local and transnational pressure and dynamics of power. A. K. Sen’s capability approach replaced the concept of welfare with well-being, permitting a shift of the analytical lens from an individual’s income and expenditure to their capability to function in historically shaped contexts of entitlements and rights. From the perspective of well-being thus defined, poverty signifies more than a welfare deficit. It is also a capability deficit linked ultimately with the ways of functioning of a given society. Sen’s intervention has revolutionized thinking on poverty by opening up for investigation of epistemological and ethical domain regarding consequences of poverty for justice. Linking poverty/inequality/democracy/freedom/and social justice, this perspective emphasized the importance of processes and outcomes. Hence poverty is defined as the absence of capabilities and development as the process of expansion of real freedoms and social justice. Sen’s view on the capability and well-being nexus has inspired large teams of social scientists in the United Nations Development Program (UNDP) and the European Commission. In this way came the social indicators for poverty assessment. Over the last three decades, the UNDP has incrementally developed a sensitive measure of human development index (the HDI) and a new aggregate measure – the capability measure (CPM) which was a forerunner of human poverty index (HPI) developed in 1997 to assess the average state of the people’s essential capabilities in a society. Additional measures developed by the UN include the Gender-related Development Index (GDI) and Gender Empowerment Measure (GEM), which together aim to show the inequalities between and women and men in relation to longevity, knowledge, a decent standard of living, and the extent to which women and men are able to participate in economic and political life. GDI focuses on

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some capabilities while GEM is concerned with the use of those capabilities to take advantage of the opportunities of life. While these indices are useful, they do not take into account multiple dimensions of gender and power. So a more profound definition of well-being – which includes the qualitative dimension of a social base of dignity and self-respect – was in fact narrowed down. Gender inequality remains a major barrier to human development; the disadvantages facing women and girls are a major source of inequality; since women and girls are discriminated against in health, education, political representation, and the labor market, which has negative repercussions for development of their capabilities and their freedom of choice, remaining far away from social justice. Recent statistics show just how far societies are from achieving gender equality.

2. Objective of the Study The objective of the chapter is to give specific focus to show the interrelationship between poverty, gender, and social justice from a theoretical standpoint and to show how women are placed distinctly at a disadvantaged position and underrepresented at the most prestigious ones, etc. in the global as well as Indian perspectives. In addition, the chapter also addresses (hitherto neglected) the issue onto gender dimension of poverty [for example, how to rescue gender from the “poverty trap” through a reformulation of poverty beyond a materialistic understanding, defining women not merely as especially poor but much more), alongside gender asymmetry in access to resources, care, and opportunity for voice, as well as other aspects crucial to well-being such as a sense of belonging and self-respect, dignity, empowerment, social justice, participation, and the awareness of the right.

3. Conferences for Women’s Emancipation and the Outcome Thereof The September 1995 Fourth World conference on Women was held at Beijing, where 189 governments across the world attended. This was the aftermath of the three previous Women’s Conferences held in Mexico City (1975), Copenhagen (1980), and Nairobi (1985). Thereafter post-Beijing international and regional meetings were held. At these conferences and meetings, different priorities emerged among women within the North and South and across borders, with the North prioritizing equality issues versus economic and policy issues (Moghadam, 2005, p. 6). The purpose of these UN-sponsored Women’s Conferences is to bring together diverse systems, cultures, and traditions and seek consensus on women’s issues (U.N. 1995). The final draft reaffirms gender equality as a fundamental prerequisite for social justice (Talukdar, 2022, pp. 163–164). The Beijing Declaration and Platform for Action (BPA) has been one of the major sources for gender mainstreaming. Each Conference created a progressive body of norms and standards. The BPA has policy recommendations for achieving GEWE (Gender Equality and Women’s empowerment) focused on 12

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Critical Areas of concern, viz., women and poverty, women, and health, training for women, violence against women, and women and armed conflict, women and the economy, institutional mechanisms for the advancement of women’s human rights, women and the media, women and the environment, and the girl child (Beijing Declaration, 1995). BPA still continues a feminist gold standard and a blueprint for government and stockholders to follow in the national and international policies for gender equality (The Addis Ababa Action Agenda, 2015). After the 1995 Beijing Women’s Conference, gender mainstreaming was soon adopted by a large number of organizations in the international arena, including organizations closely associated with the United nations such as UNDP and UNESCO, as well as the World Bank, ILO, and the WHO. Multilateral and Bilateral development agencies such as CIDA and IDB as well as other regional organizations such as the OSCE and the Commonwealth also endorsed mainstreaming. The European Union itself adopted gender mainstreaming in 1996. Therefore only a decade after Beijing Women’s Conference, mainstream as a doctrine had been widely endorsed at the international, regional, and even national levels (Rai, 2003). In this context, it is worth noting Symington’s (2002, p. 11) articulation: . . . women of the world is poorer and more marginalized, they continue to victims of gender-based violence, and hence are excluded from public decision-making positions. May be gender has been mainstreamed and women are now included in discussions with the power players, but can we really say that we are making progress? A gender mainstreamed 2030 Agenda and SDG 5 and its 9 targets are also pathbreaking. The Gender Equality goal is a promise to all women and girls that they will be enabled to realize their full potential no matter their multiple intersecting identities and status. Its nine targets address physical integrity, voice, and choice issues. Gender equality goals and targets have been international SDGs for the international community. Local has become global, and global cascades down to the local. In this context governments are now committed to pursuing these targets nationally and internationally. But the plight is that there are major implementation gaps in almost every area. Progress in this respects are slow and uneven. At this rate, gender equality and poverty among women, not to say about social justice will not be achieved let alone by 2030 (Puri, 2022). Priority must therefore be given to the gender-responsive implementation of the SDGs and the Gender Equality Compact is a holistic and integrated way through promoting an all-of-government, multisectoral and multilevel approach. Localization of global norms through adoption and reforms of laws, policies, and measures including special measures and actions, the removal of discriminatory laws and policies, and ensuring their full, effective, and accelerated implementation is necessary. Already the Human Development Report’s Gender Inequality Index – a measure of women’s empowerment in health, education and economic status – shows that overall progress in gender inequality has been slowing in recent years (UNDP, 2019). These inequalities in human development are a roadblock to achieving the 2030 Agenda for Sustainable Development. A recent speculative study by McKinsey (2015) suggests that advancing gender equality in the workplace could add as much as $12 trillion to global GDP by

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2025. While the advanced economies have the most to gain, developing countries and regions could expect to benefit from significant increases in income by 2025 including India ($0.7 trillion or 11% of GDP), Latin America ($1.1 trillion or 14% of GDP), China ($2.5 trillion or 12% of GDP), sub-Saharan Africa ($0.3 trillion or 12% of GDP), and the Middle East and North Africa ($0.6 trillion or 11% of GDP).

4. India Story of the Position and Status of Women The Human Development Report’s Gender Inequality Index – a measure of women’s empowerment in health, education, and economic status – shows that overall progress in gender inequality has been slowing in recent years (UNDP, 2019). These inequalities in human development are a roadblock to achieving the 2030 Agenda for Sustainable Development. The increasing vulnerable situations of India’s women, particularly of its women’s workers, is confirmed by the Global Gender gap Report published by the World Economic Forum (WEF) in 2017. The report looks at the four components: economic participation, educational attainment, health status, and political empowerment. In three of the four indicators, India ranks in the bottom half of the table. The only indicator where it ranked in the top-20 was political empowerment. Because of this, the overall position of India improved slightly, but the country still ranks a poor as far as gender gap was concerned (World Economic Forum, 2017).

4.1 Employment Trend: A Paint of Depressing Picture of Employment Prospects for Women There has been a declining trend in female labor force participation rates (FLFPR) since the 1980s due to changes in the overall macroeconomic policy regime from the dirigiste to the neoliberalization. The structural changes brought about in the latter era have led to a significant decline in the female employment and contributed to the already overwhelming informality which have impacted the world of Indian women’s work even more adversely (Pawar, 2020). FLFPR had declined massively. From the Indian data one can easily show that women are highly concentrated and overrepresented in casual work and unpaid self-employment (Papola, 2013). Thus, despite the recognition of gender equality in law, the progress toward gender equality as outcomes in the labor market remains quite insignificant. Besides, there exists a whole range of dense, diverse, and often overlapping categories of exclusions of women, particularly from the marginalized groups, from just conditions of work. Of late, the labor market is also characterized by a high level of segmentation in terms of gender, rural–urban and regional locations, as well as social groups. The female workforce participation rates in the country are also one of the lowest in the world, just above Pakistan and Afghanistan. The PLFS (Periodic Labor Force Surveys, 2018–2019) shows that 50% of regular women workers hold regular jobs with no written contract, paid leaves, and social security benefits.

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Such figures suggest the informalization of even the regular jobs among women in India (Mitra & Pandey, 2022). Although the gender gap in the labor market has been slowly declining, still in several spheres considerable gap exists. Further, labor productivity is also low, though it has risen significantly during the last three decades, more so in the nonagricultural sector. Lastly, the development path in India has been different from the experiences of developed countries, where both the GDP and workforce showed a rapid transit from agriculture to the manufacturing and finally to the services sectors. In India, on the other hand, the growth path has followed the trajectory from agriculture to services in terms of the structure of GDP, and the manufacturing sector has not been the engine of growth. In terms of employment share, there has been very slow transfer from agriculture to nonagricultural sector and only to construction and services. It has been convincingly argued that this distorted pattern of growth to a large extent is responsible for slow structural transformation and low employment growth in India, especially for women (Ghose, 2016). From the discussion, it is quite evident that the question of feminization of work is a complex one, and can only be grasped within the complex framework of the interplay between different forms of paid and unpaid workers on one hand, and the policy and institutional frameworks that regulate this work on the other (Jha, Kumar, & Mishra, 2020).

4.2 India’s Need for Universal Social Protection Public intervention and policies toward facilitating decent work, social security provisions, etc., have seen marked shifts, in general, in the neoliberal era. In fact, apart from disparity between men and women’s ability to bargain for their choice of work outside home, women, due to the complex relationship between patriarchy, caste, and class, are usually found working in unjust conditions. For example, in India, about 93% of the women workers work under conditions of informality without any social protection. The ILO flagship report on World Social Protection (2017) illustrates that only 28% of the world’s population receives social protection. One of the reasons for such underprotection is the inadequate investment in social areas, especially in countries of Asia, Africa, and Arab states. India has only ratified Convention 118 for providing maternity benefits, where about 41% of the mothers are said to be covered by maternity benefits. But in overall terms, only 19% of the population of the country is covered by at least one scheme of social protection. Along with this, the share of public expenditure on social security remained a meager 0.53% of the total expenditure and 0.07% of the GDP (Centre of Budget Governance and Accountability 2018).

5. Gender Inequality, Social Justice, and the Public Action in India It is an irony that women’s contribution to the economy has remained invisible to policymakers and even to scholars, knowing it very well that 94% of women in the

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workforce are in the informal sector. History is the evidence to the fact that in spite of the active participation of the women in the economy, due to pervasive gender ideologies, women’s identity as workers has always been less established, though women enter the labor market as bearer of gender identity, though taking wages less than male wages, despite the Equal Remuneration Act of 1976. There are no states in India where women and men earn equal wages in agriculture. The only public employment program like MGNREGS displays no wage differential due to legislative backing. In the context of present-day job opportunities, we can see a direct relationship with women as secondary earner and their vulnerability to poverty. Recent empirical analyses on the effects of the contemporary trade-induced changes in the economy suggest that consequences of trade liberalization for men and women can be looked at in terms of gender patterns of rights to resources, female labor participation rates, and educational levels and gaps by gender, etc. The principle of gender equality is enshrined in the Indian Constitution in its preamble and in sections on “fundamental rights,” “fundamental duties,” and “directive principles of state policy.” The Constitution not only guarantees equality to women but also empowers the state to adopt measures of positive discrimination in favor of women. The movement and struggles during the emergency rule between June 26, 1975 and March 21, 1977, and also in the post-emergency period, led to further debates on women’s issues and renewed activity in favor of women (Mathur, 2014, p. 203). The civil liberties movement was the product of emergency rule, no doubt. Post emergency, there was a sharper focus on women in development in Five Year Plans VI and VII, the national Perspective Plan (NTP) for women, and the alternative perspective plan offered by the women’s movement. The National Commission for Women was set up by an act of Parliament in 1990 to protect and safeguard the rights and legal entitlements of women. India also ratified various international conventions and human rights instruments committed to securing equal rights of women. Of key importance was the ratification of the Convention of the Elimination of All Forms of Discrimination Against Women (CEDAW) in 1993. In an effort to ensure equal representation, laws have also been instituted in India for a quota-based representation of women in political and public bodies. The 73rd Constitutional amendment Act of 1992 aimed to empower women in rural local governments (panchayats) and the 74th amendment reserved seats for women in urban local bodies (Karmakar & Bagchi, 2022, p. 96). Decentralized governance – through the Panchayat Raj system – and quotas for women, implemented since April 24, 1993, have brought over a million elected women into position of responsibility, and has been hailed as a “silent revolution” (GoI, 2011). Further, various social legislations such as Anti-dowry Act, Child Marriage Restraint Act, Indian Succession Act, and Equal Remuneration Act have been enacted to protect the women from gender bias operating against their interest. The approach of the government here to abolish gender inequality has been so far one of the ad hoc basis leading to marginal benefits only. Marginal benefits will not add to positive results. Women’s needs have not been in commensurate

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with official programs. Governmental implementery machinery has been found to be very weak, indifferent, and ineffective to reach the women; it is now well accepted that the poorest families are most dependent on women’s earnings (Sen, 1992). Yet despite this which suggests that women play a crucial role in eradicating poverty of the country’s nearly 60 million households living below the poverty line, the Government invests far less in women workers in terms of education, health, and productive assets compared to male workers. At present barely 6% of the economically active women are in the formal sector. Majority of the workers are in the informal sector and any plan to improve women’s economic conditions will have to focus in this area (Karmakar & Mukherjee, 2020). It is ironic that while on the one hand, India has one of the lowest labor force participation rates for women in the world, on the other hand, the unemployment rates among the young women who are joining the labor force is extremely high. The Indian state has responded to the unemployment challenge largely by creating a number of livelihood and employment generation programs for men and women as well as other working-age people. Imparting skills training to youth has been an important agenda in recent years, which is also evident from the creation of a new Ministry of “Entrepreneurship and Skill Development.” Some of the important programs for employment generation and skill development that have recently been initiated include the Prime Minister Employment Generation Program (PMEGP), National Rural Livelihood Mission, National Urban Livelihood Mission, MUDRA, National Skills Mission, the National Apprenticeship Promotion, Pradhan Mantri Kaushal Vikas Yojana (PMKVY), Deen Dayal Upadhyay Grameen Kaushal Yojana (DDU-GKY), Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), setting up of a Rural Development and Self-employment Training Institute (RUDSETI), and other innovative initiatives like Hunar Se Rozgar Tak, Skill India, Digital India, Startup India, among many others like several women empowerment schemes like Beti Bachao Beti Padhao, Women Helpline, as well as Mahila E-Haat, STEP, SWADHAR Greh, Mahila Shakti Kendra, etc. While all these schemes and programs have made their own contributions in the generation of livelihoods, empowerment, and employment opportunities, they are still not adequate to meet the current as well as future challenges (Sharma, 2022, pp. 38–39).

6. Conclusion and Policy Pointers Few Years back in September 2015, though the first-ever Global Leaders’ Meeting on Gender Equality and Women’s Empowerment to reduce their poverty and attaining social justice: A Commitment to Action with 165 countries making commitment to implement the Global gender Equality Compact with the presence of prime minister Narendra Modi and Bangladesh’s prime minister Seikh Hasina resulted in unprecedented political support, the progress in later years in this respect is very slow. That is why we need giant leaps, not faltering steps. This requires the international feminist order, and Global Gender Compact marshaled

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by the UN to work hand in hand with gender-responsive political leadership and feminist movement in countries. Planet 50–50 is humanity’s most important transformative mission and goal of international cooperation too. It needs to be addressed with the sense of priority and urgency it deserves as 2030 is just around the corner. A new era of international cooperation among governments and peoples toward the achievement of SDGs by 2030 requires a full and equal partnership of all men and women, being equipped with men with equality and social justice (Karmakar & Jana, 2020). Last but not the least, solutions to the disadvantaged position of Indian women in development, bereft of gender equality, removing of their poverty and social justice range from the passage of antidiscrimination laws, calls for greater integration of Indian women, in particular, in the development process. Strategies to improve women’s position include making economists and policy planners more gender-aware; calling on government’s and employers to spread women’s reproductive responsibilities equitably recommended in the different global Conferences including The Beijing Platform for Action and other ones; and calling for better investments in women’s education, employment, and access to credit and loans.

References Ghose, A. K. (2016). India employment report 2016. Institute for Human Development and Oxford University Press, New Delhi. Government of India. (2011, February). Road map for Panchayati Raj (2011–2016): All India perspective. February, Ministry of Panchayati Raj, Government of India, New Delhi. Jha, P., Kumar, A., & Mishra, Y. (2020). Labouring women: Issues and challenges in contemporary India. New Delhi: Orient Blackswan. Karmakar, A. K., & Bagchi, P. (2022). Gender inequality, women empowerment and public action in India. In S. Maiti, T. Gupta., & D. Sharma (Eds.), Economic growth and development and social changes in the new normal. New Delhi: Kunal Books. Karmakar, A. K., & Jana, S. K. (2020). Women in BRICS—Are they moulders and builders in a Great transformation for Future World ? In T. Rahman (Ed.), Women empowerment—Awakening of a new era. Empyreal Publishing House. UAE, Nigeria Uzbekistan, and Montenegro. Karmakar, A. K., & Mukherjee, S. (2020). Looms large “A Broken women” in ‘Booming India’: Recognition of women’s rights and political empowerment. In A. Bhowmick & P. K. Singh (Eds.), Indian women: A March towards modernity. New Delhi: Mittal Publications. Mathur, K. (2014). Crafting political pathways through the exclusionary mesh in India. In M. Tadros (Ed.), Women in politics: Gender, power and development. London: Zed Books. McKinsey Global Institute. (2015, September). The power of parity: How advancing women’s equality can add $12 trillion to global growth.

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Mitra, S., & Pandey, N. G. (2022). Women’s work in India: What needs to be monitored. In N. K. Mahawar & A. Dutta (Eds.), Women and power: Gender within international relations and diplomacy. Macmillan: Indian Council of World Affairs. Moghadam, V. M. (2005). Globalizing women: Transnational feminist networks. Baltimore, MD: John Hopkins University Press. Nussbaum, M. (1995). Human capabilities, female human beings. In M. Nussbaum & J. Glover (Eds.), Women, culture and development. Oxford: Oxford Clarendon Press. Papola, T. S. (2013). Role of labour regulation and reforms in India: Case study of labour market segmentation. Working Paper No.147. Geneva: ILO Employment. Pawar, A. (2020). Agony of Indian women: A study on prostitution, gender discrimination, and domestic violence. Nagpur: Dattsons Publishers. Puri, L. (2022). The United Nations: The wellspring for gender mainstreaming of international relations and foreign policy. In N. K. Mahawar & A. Dutta (Eds.), Women and power: Gender within international relations and diplomacy. Macmillan: Indian Council of World Affairs. Rai, S. M. (2003). Mainstreaming gender, democratizing the state? Institutional mechanisms for the advancement of women. Manchester: Manchester University Press. Sen, R. K. (1992). Gender in economic theory and practice. In IEA 75th Annual Conference Volume, Indore. Sharma, A. N. (2022). Youth employment and unemployment in India: Issues and challenges. In Presidential Address at The Indian Society for Labour Economics, 62nd Labour Economics Conference, IIT, Roorkee, 11th April. Symington, A. (2002). Globalization on our times: A time for radical action. In Re-inventing Globalization: Highlights of AWID’s 9th International Forum on Women’s Rights in Development. Guadalajara, Mexico, 3–6 October 2002, Toronto, Canada, Association for Women’s Rights in development (pp. 10–11). Talukdar, S. (2022). Right to health in India: Law, policy and practice. New Delhi: Sage Publications. The Addis Ababa Action Agenda. (2015). Third International Conference on Financing for Development. Addis Ababa, Ehiopia, 13–16 July 2015. U. N. (1995). Beijing declaration and platform for action-Beijing1 5 political declaration and outcome. New York, NY: UN Women. March 2015. UNDP. (2019). Human development report 2019: Beyond income, beyond averages, beyond today: Inequalities in human development in the 21st century. New York. United Nations. (2015). Report of the Fourth World Conference on Women, Beijing. 4–15 September 1995. United Nations Publication, Sales No. E.96.IV.13, chap. I, resolution 1, annex II, para. 1. World Economic Forum. (2017). The global gender gap report. WEF, Geneva.

Chapter 19

Twitter Imparting and Reinforcing Gender-Based Identities of the Aboriginal Australia Women Ali Saha

Abstract Digital media platforms on one hand are tools of communication. On the other hand, it is a site where narratives are shared among members of various communities. Thus providing a space to create identities and educate audiences about behaviors toward “them” from “us.” This chapter will highlight how the narratives of Aboriginal Australian women discriminatory issues are conveyed and discussed on Twitter. In attempting so, this chapter will highlight whether the digital space has contributed to equality in the society or is it attempting to reassert the existing hegemonic discourses and status in the Australian Community. Accordingly, this research suggests policies that could help create emancipatory pedagogy. Keywords: Aboriginal women; gender; twitter; hashtag; agenda setting; Australia women; digital practices

1. Introduction Gender inequality takes many different forms and can depend on the socioeconomic structure, political and cultural beliefs of the society. Irrespective of the culture of a specific geographical area location, it is women who are at the receiving end of negative consequences of gender inequality (Lorber, 2001). Such that the women face severe forms of discrimination and an ongoing subordination. These differences often manifest in education as well. For example, Jacob (1996) reflects that already existing gender inequality influence (1) access to higher education; (2) college experiences; and (3) postcollegiate outcomes. Whereas Cooray and Potrafke (2010) state that the primary influence on gender inequality in education is through culture and religion. Gender Inequality and its Implications on Education and Health, 223–234 Copyright © 2023 Ali Saha Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231020

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In this current networked society, media and communication networks play an important role in constructing identities and societies (Curran & Leibes, 2002). Such that we can argue that these media representation influence the way populations are educated about a particular community or gender and accordingly construct their identities. While such influence on the audience by media representations have been going on since long, these have intensified in this era of social media. Especially because social media has allowed for a space to the audience to take control of their own narratives and negotiate their identities. Which then plays the educative function not just among the ones who are making the post but the readers as well. Hence, social media representations influence how the audiences contextualize identities and educate themselves about cultures and genders. While the gendered discourses are quite common in context to the white women or women from the hegemonic backgrounds, previous research reflect an ongoing lack of understanding of minority women’s practices and identities. On a similar note, it could be stated that while several studies discuss about the mainstream Australian population, especially the white Australian women and their experiences, the literatures lack an understanding of Aboriginal women experiences on online and offline spaces. Or an understanding on how media representations, especially social media representations, educate audiences about the Aboriginal Australian women. Hence, in this chapter, I look at how gendered discourses on Aboriginal women occur on Twitter. This study analyzes 100 posts that appears with the #aboriginalwomen. These posts are conceptualized along the lines of framing theory to understand what voices are being put out to the audiences and further understanding what agenda such communications shape in the society can.

2. Aboriginal Women Aboriginal women or Native women are those women who belong to tribal or aboriginal communities. While a woman is discriminated across the intersectional issues of gender, religion, class, patriarchy, and many others, Aboriginal women are often the victims of very serious human rights violation, and suffer from a more intense discrimination where the aspect of Aboriginality acts as an additional intersectional factor that leads to Aboriginal women discrimination and subordination (Williams, 2008). Harper (2006) comments that extreme racialized violence against Aboriginal women leads to their disappearances and even murder. In a way it could be argued that the Aboriginal women continue to be oppressed because of the cultural understanding that tends to continue to label Aboriginal women as once who could be discriminated against (Frederick, 2010). Although identity of Aboriginal women is often generalized as similar, these identities can be different and often varies based on the socio, cultural, and political situations of a country. Considering that differences between the Aboriginal women located in various geographical locations could be vast. As discussed elsewhere, this chapter focused on Aboriginal women of Australia

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(Goeman, 2013). The Aboriginal people are considered as the traditional owners of the Australian land. However, the arrival of the colonists led to the invasion of Aboriginal lands and the subsequent colonization of Australia. And had a disastrous effect on Aboriginal women who suffered from dispossession and disempowerment. The system of colonization deprived Aboriginal women of land and personal autonomy and restricted the economic, political, social, spiritual, and ceremonial domains that had existed prior to colonization. It also involved the implementation of overriding patriarchal systems (Andrews, 1996; Bolger, 1991). In an era where one’s identity depends on the media system, it could be noticed that the Aboriginal women’s identities were represented in a negative manner. For example, one of the earliest reports published in 1996 by the Royal Commission of Aboriginal people stated that mainstream media altogether ignore and/or neglects the indigenous and hence does not report about their issues. If the media ever represent Aboriginal people, the images are often portrayed in terms of the common stereotypes of (1) helpless victims, (2) angry fighters, and (3) environmentalists. Eirtola (2014) in their study of Aboriginal representations in the two main Australian newspapers – The Sydney Morning Herald and the Australian demonstrate a lack of Aboriginal voices in the media. The scholar’s further ad that when the voices appear, they are either “outnumbered by the voice of the mainstream/non-indigenous Australian” or “mediated by white voices that appear on behalf of, and instead of, Aboriginal voices.” Thus, creating a cultural understanding where the Aboriginal people are reflected to be powerless and dependent on the white Australians for their representations. These scholars argue the reason for such representation to be because of the continuing presence of racial discrimination in the Australian media houses and the journalists. A similar study by Proudfoot and Habibis (2015) argued that the mainstream media were negative in their portrayal of the Aboriginal people and silent about their resistance. The way Aboriginal people have been represented in Australian history plays an important part in how Aboriginal people, including Aboriginal women, are perceived in today’s Australian society and elsewhere (Andrews, 1996; Bolger, 1991; Proudfoot & Habibis, 2015). As a result of these repeated negative constructions of Aboriginal women in mainstream media, nowadays these women have taken up a more active agentic role both online and offline to advocate for themselves, their rights, and to reflect on a more positive identity. In these times of rapid technological change, digital spaces especially social media spaces have helped women put out their narratives and advocate for change. We all live in a society where digital media connects the various populations to each other. Such that the information flowing through these networks influence the way different ideas and ideologies are constructed, both on media and mainstream discourses and hence on the way behaviors are enacted. For example, previous studies on women’s participation on digital media have repeatedly reflected that in the absence of stories that vouch for women rights and equality, the narratives within the mainstream discourses are skewed or devoid of the women issues as well (Saha, 2022). Where the women continue to serve as the

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subordinate and less powerful compared to that of men. For example, Aslam’s (2009) study states that the way women are represented in literature and mass media reinforces their lower status in society. Similar conversations have been common across the western population as well where Blair (2018) states “the absence of women in technology impacts our understanding not only of the role of women in the history of information technology but also their identities.” Overall, these studies reflect the relevance of putting out women-related discourses on digital media spaces. For example, Guta and Karolak (2015) stated that Saudi Arabian young women use social media for negotiating and expressing their identity. Moreover, these platforms with its protection of individual privacy provided the participants a space to negotiate the boundaries imposed on them by cultural and societal rules. Bonilla and Rosa (2015) further add that hashtag usage on social media platforms have become powerful sites for documenting and challenging episodes of brutality and the misrepresentation of racialized bodies in mainstream media. Overall, social media spaces provide arenas for educating about the alternative ideas about genders and cultures. However, not much study located about Aboriginal women’s practise of social media to represent their identity. Hence, this chapter will investigate how social media narratives about aboriginal women are constructed and accordingly delineate the agenda setting function of the contents.

3. Media’s Agenda Setting Theory In this section, I elaborate on Agenda setting theory to discuss how media could create impressions and educate the audiences about aboriginal women. Later the results have been conceptualized along the lines of agenda setting theory to corelate Twitter discourses and the resulting impressions and identities. Agenda setting was developed in 1968 by McCombs and Shaw following their study on 1968 presidential election. Their study demonstrated a strong correlation between media reports of the elections and the public perceptions. Agenda setting theory hence came to be defined as the ability of media to influence the audiences especially on topics related to public agenda. That is, media influences the audiences by telling them what they should think about (Shaw & Martin, 1992). This media influence is a result of the mass media selecting certain issues and portraying them frequently and prominently, which leads people to perceive those issues as important than any other. Since, McCombs and Shaw many other theorists have been studying agenda setting to understand the educative potential that media has on the audiences. According to Scheufele and Tewksbury, agenda setting theory is defined as the “idea that there is a strong correlation between the emphasis that media place on certain issues and the importance attributed to these issues by mass audiences” (2007, p. 11). That is, they emphasize that audience’s perceptions are heavily impacted by the amount of importance an issue receives in media, here the news. As various studies progressed, the scholars identified two levels of agenda setting theory. While level 1 agenda setting theory focuses on

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“relative salience of issues or objects,” the second level agenda setting theory focuses on “relative salience of the attributes of the issues or objects” (Weaver, 2007, p. 144). Here it is important to note that, the first level agenda setting helps identify the issues that are dominant within the media representations. Whereas the second level agenda setting theory is concerned with the properties and the qualities and characteristics associated with the person, issue, or object. For example, the second level helps understand whether a person, culture, or community is represented in a positive, negative, or neutral manner. From an educational perspective, the media images not only educate audiences on what issues, people, or object are important/not important but also elaborate on what aspects make them relevant/not relevant. For example, Goldstein, Macrine, and Chesky (2011) in their study Welcome to the new normal, in context to media and neoliberalism claim that media often lacks narratives that depart from the neoliberal interpretations. Rather, the media represents the ideologies that fit the dominant interpretive and ideological framework that supports neoliberalism. As a result, neoliberalism is now the common-sense way on which most of interpret, live in, and understand the world (Harvey, 2005, p. 3). Similarly, other researchers have repeatedly pointed out media’s agenda setting role in context to other topics and issues. For example, Inoue and Patterson (2007) explored that the people created a negative perception of the Japan as a result of the biased representation of Japan in context to American–Japan relations in the American media. Similarly, Fortunato (2008) and explained that people became fan of certain sports and sportsman because of the media’s agenda setting effects. Such is not limited to news, but dominant in the entertainment industry as well (Hill and Hallbroork, 20,181). Overall, what agenda setting reflects is that media repeatedly plays a role in shaping perceptions. John Dewey, an advocate of experiential learning, states that one understands the world through a process of manipulation of contents. Whereas Conkey and Green (2018) reflect those literacies, especially about place, place ecologies, and place-based pedagogy occur because of integration of knowledge from various sources and contents. Onyemechi and Ojoma (2022; cited in Ashraf & Hayat, 2022) argue new media portrayal as an important tool to the reconstruction of gendered roles and identities in Nigeria. Similarly, Dasgupta (2018) highlighted that social networking sites such as social media role has helped influence, although subtly, the stereotypical portrayal of gender in mainstream discourses and media content. Considering that media in today’s world is the major source through people acquire information, it could thus be conceptualized that media contents provide opportunities for active manipulation and education about of the world, genders, and communities within which one lives in. That is the representations within the media discourses through its agenda setting property magnificently influences public opinions, ideologies, and models that shape cultural and gendered perceptions. Overall, it could be stated that media as a powerful tool to reconstructing social system plays an inevitable role in creating a person’s understanding about people, gender, societies, and identities. The next sections of this chapter highlight the method untaken and the outcome of the

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analysis of 100 Twitter posts to answer: How does the Twitter discourses and representations educate about the Aboriginal women identity.

4. Method and Results A mixed-method approach combining online ethnography comprising of qualitative and quantitative analysis of the digital contents is undertaken. Lane and Lingel (2022) in their study argue digital ethnographic or online ethnography method as a core methodological approach that helps us read the “the digital” and answer important sociological questions related to community, culture, violence, activism, identity, and sociality. This study analysis more than 100 posts derived from Twitter with the #aboriginalwomen and #australia over a period of three months from first May to November 1, 2022. While several conversations took place, only 100 posts were selected to be able to give us a targeted understanding of social media’s educative purposes. The results reflected a variety of topics that were discussed under the #aboriginalwomen and #australia. Some of the main themes were: (1) Advocating against Aboriginal women discrimination: Approximately 38% of the posts which used the hashtags above were done to advocate against the Aboriginal women discrimination. Interestingly most of the posts came from organizations which work toward the Aboriginal women rights. Only a few posts were made by Aboriginal women themselves. Instead, the participation of many white Australian women was evident through the hashtag analysis. Media researchers have claimed that the media influence which can also be termed as “the educative purpose” of a post or any media content depends on the identity of person who made the post and the audiences who follow the person (Al-Rahmi, Othman, & Musa, 2014). Hence, considering that most of the posts that were against discrimination were made by white Australian women and major Australian organizations, these would have been played an important role in setting an agenda of understanding a society free of discrimination and atrocities. (2) Issues that need change: More than 23% of the posts focused on the issues that require change. While this theme coincides with the previous theme, this theme was more descriptive while the one “Advocating against Aboriginal women discrimination” was provocative. The various posts recognized under this theme brought forth the various aspects that oppress the Aboriginal women and accordingly vouched for a change. Asogwa (2015) in his study argued that social media posts played an inevitable role in sensitizing Nigerian youths for election and create awareness for social change. Conceptualizing this representation along the lines of agenda setting we can argue that this discussion of issues that require attention could similarly arouse reflection on the issues that require attention. (3) Highlighting the issues of equality such as equal pay and equal job: Another type of post which was quite common to that of the previous theme is the

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post that highlighted the issues of equality (20%). Apart from the various issues or discriminatory brutality that the Aboriginal women endure, it is equally important to bring on narratives of equality. While scholars believed that fearful or emotionally arousing messages attract audiences more (Skurka, Niederdeppe, Romero-Canyas, & Acup, 2018), positive messages are equally important in giving the audience a sense of direction in which changes should be taking place (Asigwa, 2015). In this context, the posts that highlight the issues of equality were equally important in arousing audience’s action. Precisely, these messages are important in educating audiences about the actions that need to be undertaken to ensure gender inequality and voice against the intersectional aspects such as patriarchy and white hegemony which limits Aboriginal women participation. (4) Voicing against the posts that deem the Aboriginal women as subordinate and secondary: 6% of the posts where voicing against the online shame that took place against an aboriginal woman. Such posts reflected an ongoing support for Aboriginal women’s dignity on an online space. (5) Promoting the various activism moments for the Aboriginal women: Another 11% posts mostly were dedicated to sharing the already existing discourses or activism moments that were conducted online. Such posts also disseminate an attempt to circulate voices that has been missing from the mainstream media and society. (6) Digital participation: This study reflects that most of the posts were made by women themselves. However, it’s not clear from the Twitter profiles whether those women were Aboriginals, white Australians, or half castes. A study of the Twitter profiled reflect more involvement from the non-Aboriginal women and organization. Irrespective, the women seemed to come from varied backgrounds. Shareability of a post and its reach plays an important role in which it impacts people or creates an awareness among the audience about the issue being discussed (Szymanski, Orellana-Rodriguez, & Keane, 2017). Hence, in this context conceptualizing along the lines of agenda setting theory, it could be argued that the increased participation of women from different backgrounds could influence the extent to which the messages were shared and the way it could have educated the media users about the same. Table 19.1 reflects on the distribution of the posts across Twitter. Here it is important to note that the data demonstrate that at times a similar type of qualitative content can be present in more than one post. Hence the total quantitative data do not add to 100%.

5. Discussions Discourses on gender and gender-based inequality have occupied an important and ongoing space in the society, making women as one of the vulnerable and subordinate among men and women. Traditional sociological analysis of media,

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Table 19.1. Result of Online Ethnography. Qualitative Data

Advocating against Aboriginal women discrimination Issues that need change Highlighting the issues of equality such as equal pay and equal job Voicing against the posts that deem the Aboriginal women as subordinate and secondary: Promoting the various activism moments for the Aboriginal women Anonymous Digital participation

Quantitative Data

38% 23% 20% 6%

11% 2% 35% percent from the non-Aboriginal women. 12% from Aboriginal women and the remaining 53% cannot be located.

according to De-Fluer and Ball-Roeach (1982), tend to treat media as a social institution as well, which is a key theoretical construct for the social life. That is, media, as a powerful social system influence plays a strong role in reinforcing the beliefs, values, and norms that influence the various identities and beliefs (Gergen, 2015). The mediascape has grown exponentially in the era of digital technologies. Similarly, digital media as a site of construction of gendered understanding has acquired an important space. Of the various forms of media, social media took over the educational aspect of books, especially in context to learning about cultures and identities (Seaman & Tinti, 2013). In spite of the growing relevance of media to gender studies, there has been an ongoing debate on how media representations about the gendered influence the audiences. Hence, this chapter investigates the depiction of Aboriginal Australian women in twitter through the #aboriginalwomen hashtag. Bringing together the representations of the Aboriginal women identities, twitter reflects: (1) support for aboriginal women rights, (2) dominance of non-Aboriginal women’s discourse about the Aboriginal Australian women, (3) presence of narratives that discuss the changes that were required, and (4) advocacy against Aboriginal women discrimination. Overall, the posts reflect an ongoing emphasis on negative images than the positive cultural experiences of the aboriginals. Secondly while the posts repeatedly reflected and advocated against the Aboriginal women discrimination and fought for the same, these posts lack the narratives of strength that the aboriginal women have endured so far. The social media depictions lack the historical understanding of Aboriginal life and fail to highlight the aspects that have

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been responsible for oppressing the Aboriginal women. History repeatedly states the deliberate attempts of the Europeans to control the Australian land and their control over the Aboriginal women bodies and livelihood which have been responsible for the current identities that the Aboriginal women and overall population carry. Wexler (2009) states that collective/cultural memory provides positive guidance to Indigenous and Aboriginal people in constructing their identities by referring to their past. However, the social media posts lacked mention of the historicity thus resulting in incomplete understanding of the gendered aspects. Several times the social media images of the Aboriginal were covered in images of miser, subordination, and poverty. Nonetheless, it could be argued that media depicted the Aboriginal resilience in the posts and the approach to get back to their life in spite of the tremendous oppression. Considering that new media portrayal as an important tool to the reconstruction of gendered roles and identities (Onyemechi & Ojoma, 2022; cited in Ashraf & Hayat, 2022; Dasgupta, 2018). Overall, the social media/twitter posts bring to life and educate the audiences about the oppression of Indigenous women in the Aboriginal communities. However, the posts fail to reflect on the not-so-distant past reality of racial segregation and the intrusion of white management and authority in defining women identities. While the posts do bring back scenes of Aboriginal women suffering and oppression and educates the audiences of a lifetime of struggle and refusal to sit down in the face of injustice. Conceptualizing these findings along the lines of the agenda setting potential of media, it could be stated that the social media posts efficiently created a clearer understanding of the Aboriginal women rights. But in doing so, these posts require to be situated in the historical contexts predating the 2000s and hence, creating an increased understanding of what constitutes or makes up the Australian Aboriginal women identity. These outcomes and results of the analysis are partially in line with the various academic studies (Graydon, 2008; Moraca & Nuntjis, 2022) which claim biased and negative representation of minorities in the media. While the social media images studied as a part of this research do not produce a complete image/identity of the Aboriginal Australian women, it does advocate against the ongoing discrimination. Following the data, it could be stated that the reason behind these images is a result of growing participation of Aboriginal people in the social media and of other non-Aboriginal people who support aboriginal women rights. Thus, when conceptualizing along the lines of agenda setting theory, it could be stated that the agenda that the media puts forth is an outcome of not just the hegemonic narratives but also the ones that come from the grassroots audiences and social media participants. And while media does not only reinforce these narratives but also educate the citizens on the cultural identities. While such social constructs provide a clearer overview of the cultures and “power in the society” which was evident in the enactment of interactions, the consequences of certain actions and most importantly the meanings that were grounded in the time, place, and manner of action. Such media images, which featured and acknowledged certain kind of identities, according to the agenda setting theory, influenced how people learned about everyday Aboriginal life and culture and their identity as an Australian. These agenda set by the media are considered by have several impacts on audiences. For example, on one hand these biased, anticommunity narratives

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impact the self-esteem of those population (Green, Kaufman, Flanagan, & Fitzgerald, 2017). On the other hand, the already existing awareness about equality and human rights forces these underprivileged and oppressed population to advocate for change (Hoffman, Granger, Vallejos, & Moats, 2016). Hence, in context to Aboriginal people as well, it could be argued that such cultural education which has become easily accessible through social media allows for greater Aboriginal mobilization against the ongoing oppression against the women from the Aboriginal communities and forces these population to bring forth the hidden narratives that has been suppressed since long. One of the major examples has been the growth of #aboriginallivesmatter movement which gained momentum on social media platforms pertaining to people’s awareness of Aboriginal suppression. Hull and Stornaiuolo (2014) state that such mobilization and cultural education imparted through digital platforms which has a global reach not only brings together people from the communities who have been suppressed but also reflects solidarity among other population who fight for the rights of the oppressed population. This can be applied to the movies and its representation of Aboriginal culture. That is, considering that twitter posts which depict Aboriginal suppressions can educate citizens across the world about the Aboriginal Australians. Thus, bringing together voices who could voice for the rights of these population. For example, in context to the #Aboriginallivesmatter movement, people from across the world (with and without Aboriginal identity) participated in the social media movement. Overall, here it could be stated that media as an educator helps in educating about gendered identities and rights. The importance of producing and receiving information through social media is vital for the development of any minority community and the nation consequently it’s inevitable to understand and connect the use of social media. This study suggests introduction of policies that support the use of digital devices and social media pages among the minority communities. As social identities are a result of appropriate use of social media pages, which includes using correct words and hashtags to reach the designated audiences (Saha, 2022). It is, therefore, equally important to have policies that support the development of minority’s digital literacy skills among minorities. Lastly, this study suggests that by linking education with the nexus of social media networks, this chapter aims to offer possible ways to achieve an alternative, emancipatory pedagogy.

References Al-Rahmi, W. M., Othman, M. S., & Musa, M. A. (2014). The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education. Asian Social Science, 10(8), 210. Andrews, P. (1996). Violence against Aboriginal women in Australia: Possibilities for redress within the international human rights framework. Albany Law Review, 60, 917. Ashraf, C. A., & Hayat, N. (2022). The impact of Facebook on the framing of gender roles: An application of agenda setting theory. Journal of Positive School Psychology, 6(11), 1681–1691.

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Chapter 20

The Impact of the Pandemic on the Female Unorganized Sector Workers: A Study in the Rural Backdrop of West Bengal Srimoyee Datta and Tarak Nath Sahu

Abstract The materialization and continuation of the pandemic have a big toll on everyone’s life. Female workers specifically from the unorganized sector faced diversified financial crises during the pandemic. These households went through multiple changes in terms of expenditure, loan burden, job uncertainty, etc. A selected sample of 149 has been considered to understand the changes that had taken place in terms of health, expenditure, and other associated evolved behavior in lockdown and post-lockdown phase in a selected rural-based area of West Bengal. By applying different statistical tools like regression, f-test, and t-test, various influencing factors for household expenditure along with the changes in savings behavior have been observed in the chapter. A sudden crisis like COVID-19 has made the selected respondents responsive toward vivid positive lifestyle and attitude changes like financial literacy, savings, crisis management, and so on. Keywords: COVID-19; unorganized sector; rural economy; household expenditure; savings; India

1. Introduction COVID-19 and its consequences has stretched across a longer period of time worldwide. It influenced our life from varied dimensions and also did not spare multidimensional spheres like society, economy, market, business, health, etc. (Kumar & Nayar, 2020). Despite of all the efforts taken, the uncertainty to fight against this critical situation is still vague. COVID-19 and its affect disturbed the mental and physical health of the world population intensely. It had been projected by the World Bank that advanced economies would shrink by 7% by 2020 Gender Inequality and its Implications on Education and Health, 235–245 Copyright © 2023 Srimoyee Datta and Tarak Nath Sahu Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231021

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due to the pandemic. Different sectors like international trade, commodity exports, and hospitality industry were affected badly in the post-COVID-19 era. The pandemic brought in one of the biggest global recessions since World War II. The circumstances compel to adapt different restricts in the forms of economy-wide lockdowns, border closures, restrictions on mobility and commodity movement, sharp fall in transportation, fall in global oil demand, generate unemployment, fall in retail market, pause in investment, turmoil in financial market, food insecurity, etc. The number of people losing their job, specifically those who were engaged in informal and unorganized sectors, increased by leaps and bounds. They struggled for the basic necessities of life to survive. This situation created a lot of panic, anxiety, depression, self-harm, domestic violence, fall of nutrition, and so on. As a whole, the COVID-19 pandemic created a disrupted livelihood all over, leading to a collapse in worldwide activities. World Bank Report, 2019, reveals that India outfits to a large number of workers (second largest after China globally). Majority of the workforce in India belongs to the unorganized sector and nearly about 70% of them are located in rural areas (Census Report, 2011). According to the report of 2011–2012 by National Sample Survey Office (NSSO), 82.7% workforce of India is working in the unorganized sector. This unorganized sector is the foundation of economic progress and the backbone for the organized sector. But this sector suffers from uncertainty, deprivation, exploitation regarding payment, working environment, safety measures, work profile, etc. The current situation takes a toll on different perspective of unorganized workers in our country. It has an adverse effect even on the basic necessities of life. The pandemic situation has a direct effect on food security in rural India. “50 per cent of over 5,000 households in rural India have reduced the number of meals ever since the lockdown was imposed” (The Indian Express, 14th May, 20). The situation can get worse as a large number of migrant workers are on their way home. The intrusion of COVID-19 creates a halt in the entire nation. The economic implication of COVID-19 on rural India is drastic and wide-ranging (Datta & Sahu, 2020). Though the rural population of India is the core target for most of the relief measures due to lockdown, the real scenario is not so hopeful. As the wage largely influenced by the seasonal supply and demand of the work and scattered location, the current situation of unorganized sector workers becomes more traumatic. COVID-19 has extreme consequences. Potential availability of labor will increase as the migrant labors are returned from other districts, states, or urban places. It means the current equilibrium of supply and demand related to manpower and job supply will be distressed badly. In rural areas where the job opportunity is limited, this status creates a big difference in livelihood. This turmoil creates vivid changes in the daily chores of life. Income and expenditure are the two key tools of lifestyle management, especially for the unorganized sector worker in the rural backdrop. So, this study includes these two parameters (income and expenditure) to understand the effect of COVID-19 on the life of rural unorganized workers. Further, this situation is able to bring down some major changes. So behavioral changes of informal workers in this new normal economy also is an important area to study. In this backdrop another important

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element called attitude toward savings has been observed as it has direct linkage with the income–expenditure paradigm. It mostly covers labors, construction workers, housemaids, workers in retail shops, road work, etc. Moreover, to comprehend the effect of COVID-19 on rural households the rural-dominated district of West Bengal needs to be considered. Purulia, an extreme southern fragment of West Bengal, includes Jharkhand at one end. Most of the district possesses rural areas (90% population lives in rural settings) with a good number of informal workers. Moreover, as movement was restricted, Purulia, being the home district, has been ideally positioned to fulfil all the criteria for this study.

2. Literature Review and Hypotheses Development Studies on household spending have encompassed several aspects. It mainly addresses monthly food expenses, high-valued quality food and protein consumption, elasticity of food spending, and demand for specific food (Huang & Gale, 2009). Also, the budgetary settings and expenditure-savings linkage plays a vital role in household spending (Cl´ement, 2011). The lifestyle management changes with the level and certainty of income (Chan, Ofstedal, & Hermalin, 2002; Hubacek, Guan, & Barua, 2007). The scope of income or work is very limited in rural settings (Rahman & Kabir, 2019) that is the reason a large number of people every year migrate to advance urban settings to ensure income, family stability, and prosperity (Chandrasekhar & Sharma, 2015; Imbert & Papp, 2020). Also with the urbanization, the possibilities of getting work enhance in cities (Li, Fan, Zhang, Diao, & Cui, 2019) and developed areas every time. The unorganized sector dominates a big chunk in the work population (Census Report, 2011). The basic problem of this sector lies in the nature. It has uncertainty about every aspect, be it job payment, duration, condition, future, advancement, etc. (Harriss-White & Gooptu, 2001). But with this uncertainty, a majority of the workforce are working in this sector. Due to urbanization, less focus on agriculture, temptation of quick money, illiteracy, nature of job, social obligation etc. attracts the population toward the informal sector (Singh, 2005). In fact, informal enterprises help to shape up the growth of the developed economy (Kulshrestha, 2011). So, literatures discussing unorganized sector workers, their household management, and different schemes and policies affecting their lifestyle have been there. But the sudden outbreak of the pandemic and the effect on the livelihood of the rural unorganized sector is the key focus of this study. This research contributes to existing literature as enumerated: this study can act as a ready reference for the effect of COVID-19 on local workers. It can be a document on the changing lifestyle of rural workers due to the pandemic; a reckoner on the effect of public distribution and other support services on the rural unorganized workers of India; and an affirmation toward the different determinants especially considering the lockdown scenario. A lot of talk has been going on about the migrant laborers and their future, but the local workers are getting neglected,

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facing threats of losing their job, etc. So, this study can act as a documentation of the journey of the local rural informal worker during the lockdown.

2.1 Household Expenditure During Lockdown There are numerous factors that influence the household expenditure. Generally, monthly expenditure of a household composed of food, clothing, health, education, communication, entertainment, and loan repayment (if any). Household income coming from the unorganized sector is used a lot on the basic necessities of life. During the pandemic, the general income-savings-expenditure pattern adopted, changed, and modified a lot as per the environmental conditioning. Both demographic and socioeconomic factors influence the household monthly expenditure in this commotion. So, the hypothesis for this study is as follows: H1. There is no significance influence of changed demographic and socioeconomic factors on the household expenditure behavior especially during lockdown. H01. H1 is not true. Moreover, the current pandemic situation pushes a lot toward the general practice of lifestyle. One of the major elements, i.e., savings, has been considered an important criterion toward lifestyle management. But workers especially who are engaged in the unorganized sector generally do not feel the urge of savings to combat future crisis. But this uncertain situation creates a lot of difference to every dynamic of livelihood practices. So, in this regard the following hypothesis has been considered for analysis: H2. There is no significant difference in the level of savings due to the pandemic situation. H02. H1 is not true.

3. Methodology 3.1 Study Design A cross-sectional study has been adopted to understand and analyze the above stated hypotheses. The survey has been conducted by maintaining proper social distance or by using telephonic discussion, wherever possible, during April– August 2020.

3.2 Questionnaire Design After searching the digital and online platform, 32 questions have been sorted out to understand the current scenario in the rural backdrop of unorganized worker. A pilot study has been conducted with 40 randomly chosen sample for reliability test in SPSS. Based on Cronbach’s alpha score 0.625 questions were finally retained.

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3.3 Sampling Design The sample has been derived from those who work in the informal and unorganized sector. In this study, we consider basically three categories of informal workers which are house help, workers in local retail/dealer/distributor/stockiest points, and local labors who work in local construction sites. In the initial phase of lockdown, use of mobile phone and available sample references were considered due to the extremeness of COVID-19 uncertainty in the air. In the later stage, by maintaining proper social distancing other categories of sample feedback of 149 respondents have been collected. Convenience sampling technique has been adopted here as it helps to gather information as the samples hesitated to give their personal information initially. The respondents are from the Purulia and Bankura districts.

3.4 Data 3.4.1 Primary Data The well-structured questionnaire consists of 25 questions to collect information about the respondents’ demographic profile and about the details of spending habits and perception about the current situations and its effect on their family spending. The entire questionnaire is composed of both close- and open-ended questions. 3.4.2 Variables This predictor of this study is determinants of spending. The determinants have been further divided into demographic factor (number of earners in the family) and socioeconomic factors (effect of change in income, loan burden, due clear, savings, and social distance influence) (Hopkins, Levin, & Haddad, 1994; Ismail & Bakar, 2012; Seng, 2018; Shkodra, Ymeri, & Ibishi, 2021; etc.). The dependent variable considered in this study is household spending during the lockdown period.

3.5 Statistical Tool Used For analyzing the impact of COVID-19 on the informal or unorganized sector workers’ inferential statistics like t-test and F-test have been applied to observe the significant difference, if any, between the pre- and post-lockdown period of the savings level of the respondents. Further, to identify the impact of pandemic on the unorganized sector workers in rural settings (considering their household expenditure and associated changes), multiple regression analysis has been carried out.

4. Results and Discussion 4.1 Descriptive Statistics In the first phase, the study reports the demographic profile of the respondents. The respondents are both men and women from the selected districts in West

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Bengal who are engaged in any of the unorganized sectors for income purposes. According to the respondents’ profile of the selected 149 respondents, the majority are men (72.13%), mostly belonging to 26–40 years of age (69%), engaged in household work (29%), construction-based works (54%), workers in various distribution channel setups (17%), mostly residing in rural areas, and practicing Hinduism.

4.2 Influence of Lockdown on Household Expenditure In this study, we check the influence or impact of lockdown on household expenditure among the respondents.

4.2.1 Diagnostics Tests Multicollinearity epitomizes a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to inappropriate outcomes of regression analyses (Kim, 2019). In order to avoid such occurrence, the study introduces Variance Inflationary Factor (VIF) and pairwise correlation matrix for the explanatory variables (Table 20.1). The pairwise correlation matrix and VIF values interprets that the explanatory variables in the study are free from multicollinearity issue. The correlation between the variables is found to be very low and the highest value of VIF is found to be 1.452 (Table 20.1). In the nonappearance of any agreed criterion for tolerance level in the case of VIF, a level of 10 can be observed as the bottom line to regulate the existence of multicollinearity issue. According to Hair, Black, Babin, Anderson, and Tatham

Table 20.1. Pair-Wise Correlation Matrix With Variance Inflation Factor. Independent Variables

Effect of Change in Income

Effect of change in income Loan burden Due clear

1

Savings

20.154

0.384** 20.033

Social distance 20.007 influence Source: Author’s own calculation. **Denotes 1% level of significance.

Loan Due Savings Burden Clear

Social Distance Influence

VIF

1.176

1 2 1 0.055 0.028 1 2 0.455** 0.100 0.023 2 0.121

1.452 1.004 1.270 1

1.020

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Table 20.2. Results of Multiple Regression Analysis Considering Household Expenditure as Dependent Variable. Independent Variable

Coefficient

Effect of change in income 379.485 Loan burden 918.691 Due clear 72.370 Savings 254.249 Social distance influence 84.587 Constant 60.245 Value of R 5 0.662, Adjusted R2 5 0.419

t Values

3.598209848 7.692740281 0.495689578 2.320562048 0.780669135 0.488 F-value 522.366

p Values

0.001 0.000 0.560 0.019 0.506 0.626 0.000

Source: Estimated by Authors.

(1998), if the VIF values are less than 10, it indicates the lack of multicollinearity property between the said independent variables. After checking the nonexistence of multicollinearity, multiple regression analysis has been executed with selective explanatory variables and outcome variables. The result of the multiple regression analysis presented in Table 20.2 shows that three independent variables, i.e., effect of change in income, loan burden, and savings, are significant at 5% level, which indicates that these variables have a significant positive impact on the household expenditure due to lockdown of the unorganized sector worker. Moreover, two independent variables due clear (clearance of previous due payment) and social distance influence (impact of maintaining social distance norms) have no significant impact on household expenditure. It may be due to the fact that whatever may be the situation, awareness about the upcoming circumstances may not be clear among the borrowers. They were unable to comprehend the necessity to prepare themselves and their respective households by stocking on with basic food and nonfood items. Also, the sudden lockdown won’t let them clarify or ensure their job status (present and future) or block the money flow or clearance of dues or not allowing them to ponder an alternative income option. Initially, they are somehow unable to understand the true meaning of lockdown. The scenario is evenly applicable to the job provider too. That is the reason in the first phases, the household expenditure is not influenced by these variables as the lockdown and associated changes framed as temporary to the selected respondents. The Regression equation is: Household Expenditure ¼ 60:245 1 379:485Effect of change in income 1 918:691loan burden 1 72:370Due clear 1 254:249Savings 1 84:587Social distance influence 1 e

Where, effect of change in income, loan burden, due clear, savings, and social distance influence are the independent variables considered in the study. Further, the R2 value of 0.419 significant at 1% level of significance indicates 41.9% of the

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variance of the dependent variable is explained by the independent variables. The statistically significant coefficient of determination (R2) also indicates the proper fitness of the model used in the study.

4.3 Impact of COVID-19 on the Need of Enhancing Savings Behavior Level In this study, the pre- and during lockdown period changes in lifestyle of informal sector rural worker have been considered. Savings is one of the key influencers in the daily lifestyle especially for the informal worker where job uncertainty dominates a lot. The habit of saving helps to make a more secure future, and the formation of this behavior is time bound. So, in this study we approached the same respondents two times (once in the initial phase of lockdown and another in the last phase of survey) consisting of three months gap to understand whether this habit of savings has taken place among them or not. So initially an F-test had been considered to understand the changed situation on savings perspective. The F-test result reflects unequal variance. Keeping it in mind, a further t-test has been applied to check the existence of mean difference, if any, in the income level of both phases. Table 20.3 indicates that the calculated value of t statistics is greater than the critical value of t (p 5 0.0000) at 5% significance level and the mean of post-lockdown phase savings level is higher than the pre-lockdown phase. It indicates an increasing trend toward the savings status of the borrowers in the study and thus incurs a series of lifestyle changes.

5. Summary and Conclusion This study is focusing on the changes that had taken place due to the recent pandemic. The overall effect of COVID-19 caused severe financial crisis which ultimately led to a global recession beyond public health disaster. The fallout of COVID-19 is severe in countries where the unorganized sector is responsible for the functioning of the formal sector and hold a major proportion of employment. However, today most of the discussions are about the effect of COVID-19 on the globe, nation, or the urban perspective. Rural areas are somehow getting limited exposure as they are regarded as a comparatively safer zone. But as most of the unorganized workers of rural areas face challenges to execute their daily livelihood due to this lockdown, an overall stopgap emerged in the backward areas. As

Table 20.3. Result of F-Test and t-Test for Savings Level Changes of the Borrowers.

Pre-lockdown phase Post-lockdown phase

Mean

Variance

F-Value

t-Value

4,565.101 12,549.66

8,974,044 5,979,544

0.666 (0.007)

25.204 (1.650)

Source: Calculated by Authors. Note: ( ) indicate the probability values.

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India is mostly dominated by rural areas, so the effect of this pandemic gradually turned out to be a nightmare and a matter of concern as well. For testing the hypothesis, multiple regression analysis has been used in this study. The statistically significant results have confirmed the likely rejection of H1 and the alternate research hypothesis is found to be true. It means that certain predictors influence the household spending during lockdown and those factors evolve due to the pandemic circumstances. Among the considered variables, effects on changes in income, burden of loan, and savings act as significant while deciding the household expenditure. The reason behind this is the sudden slash of income and uncertainty influence the consumption pattern of the selected sample vividly. In that phase, the pressure of loan repayment also creates a great impact to alternate the domestic expenditure. As the situation is uncertain, the possibility of getting back the job assignment, the future of job market, the opportunity of income generation, etc. all of a sudden take a backseat due to the spread of the coronavirus. Though most of the household runs on the instant income of the earners, in most cases the savings behavior is minimum. The samples are unable to comprehend the necessity of savings as lending facilities, specifically the informal ones readily available in their community. Further, they are financially illiterate so the burden of informal or formal loan cycle is something which they are mostly unaware of. The general tendency of this group is to consider the present, not judging the future need and crisis. So, the savings practices are somehow minimum among the respondents. But as the pandemic situation brings major changes in the lifestyle practices so the dynamic of income-savingsexpenditure need to be observed. So, for this purpose the sample has been asked to mention their tentative savings amount in the initial lockdown phases and at the time of survey. A time frame of five months (April-August) has been considered to see whether any changes have been made or any positive attitude has been built toward saving practices in this respondent group. In this chapter, in order to comprehend the trends of savings level due to COVID-19 on unorganized sector workers, t test has been applied. The result indicates an increasing trend of savings of the borrowers which compel them to incur major lifestyle changes. So, in this regard, the alternate hypothesis is found to be true. The current study is primarily confined to understand the mental setup of rural informal workers, the associated environments and its effect on them, the extent of awareness, outreach of the institutional support both from government and nongovernment sectors, the acceleration of anxiety with the passage of time regarding income and support the livelihood, the comprehension of social distancing, the necessity of better financial planning, disaster management, necessity of loan repayment within time, perception about the priorities of food and nonfood items, the urge of economic independence, distribution of financial burden irrespective of gender, household budget management of informal workers, preparedness for the future, creation and exploration of more job opportunities, the effect of public distribution system on the home front, health consciousness, hygiene management and modifications, the aggravation of

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joblessness, stress and mental health issues, eagerness toward alternative sources of income, considering self-employment and entrepreneurship, adopting changed lifestyle, and so on.

6. Policy Recommendations Future research endeavors should be integrated with data for assessing the sudden pandemic situation. As a part of primary initiatives, fiscal resources need to be engaged to serve public health services and support the informal workers’ livelihoods. In the face of economic slowdown, the financial institutions follow the unconventional monetary policies to combat the situation. The nature of job, population, and dependency profile of the informal workers will catch the attention of policymakers as it holds a big chunk of the workgroup in India. So, empirical and qualitative research may be attempted to counter the same. Comparative studies considering different setups like urban-rural, family profile, statewise, etc. may be attempted to assess the lifestyle changes and related affects of the economy at different points of time. In the present time, building up health infrastructure, employment protection, social safety, etc. issues need to be taken care with utmost urgency. Research may be considered on the household budgetary changes and decision, personal finance behavior, alternative and self-employment projections, and the findings would likely be very useful for stakeholders, especially for policymakers, and financial service providers in the emerging economies.

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Chapter 21

A Gender Sustainable Development Index for Italian Regions Marianna Bartiromo and Enrico Ivaldi

Abstract The COVID-19 pandemic, in addition to causing a very serious economic crisis, has slowed the path taken toward achieving gender equality. For example, the closure of third sector activities by governments has meant the loss of many jobs in this female-dominated sector (ILO, 2020; UN, 2020) slowing and hindering the professional careers of many women (Carli, 2020). The objective of this work is to identify gender differences by analyzing the levels of sustainable development achieved by Italian regions. The Italian case in fact is very peculiar due to its historical territorial gap between the regions of the North (among the most developed) and those of the Center-South, which still show high gender inequalities. A Gender Sustainable Development Index (GSDI) will be constructed through the use of 50 indicators from the Benessere Equo e Sostenibile survey of Istat. The technique used is the stacking method (Landi, Ivaldi, & Testi, 2017; Norman, 2010), which was chosen for its high replicability of results. The results show that only 40% of Italian regions have higher levels of female sustainable development than male sustainable development. Moreover, the regions with the worst levels of both female and male sustainable development are located in the Center-South of the country, confirming the strong territorial gap present within the Italian Peninsula. Keywords: Gender differences; sustainable development; index; stacking method; territorial gap; Italian regions

1. Introduction 1.1 At the Origin of the Concept of Sustainable Development Sustainable development is a type of economic and social development that can be reconciled with environmental protection, social equity, and the rights of Gender Inequality and its Implications on Education and Health, 247–258 Copyright © 2023 Marianna Bartiromo and Enrico Ivaldi Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231022

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future generations. Therefore, it can be defined as “that development which enables the present generation to meet its own needs without compromising the ability of future generations to meet theirs” (WCED, 1987). The concept of sustainable development has entered the public debate only since the second half of the last century. In fact, between 1962 and 1972 two books came out that changed forever the vision of scholars and public opinion about sustainable issues. These two books were the book “Silent Spring” (Carlson, 1962) and the book “The Limits to Growth” (Meadows, Meadows, Randers, & Behrens, 1972). Both warned the world about the consequences of the economic development model adopted up to that moment, which would lead to a point of no return for the environment and future generations. Moreover, in 1969 there was an event that shocked the United States and the whole world: the Santa Barbara accident. This was one of the most serious oil spills in the sea. About 100,000 barrels of crude oil were spilled into the sea, causing the death of about 3,600 animals including seabirds, dolphins, seals, and sea lions (Clarke & Hemphill, 2002). This accident and the subsequent media coverage led to the creation of numerous environmental protection regulations and the birth of environmental days. In particular, the famous World Earth Day was born, which was attended on April 22, 1970, by about 20 million people (Rome, 2010). The increased attention to sustainable issues, moreover, led to the organization of the Earth Summit held in Rio de Janeiro in 1992. The Earth Summit, famous in history for being the largest conference in terms of number of participants, led to the resolution of five documents: the United Nations Framework Convention on Climate Change (UNFCCC), the Convention on Biological Diversity, Agenda 21, the Rio Declaration on Environment and Development, and the Forest Principles (Tokuç, 2013). The most relevant of these documents is undoubtedly Agenda 21 (United Nations Sustainable Development, 1992), which will guide the policies of participating countries until the entry into force of the 2030 Agenda in 2015. Agenda 21, in fact, contained within it detailed action plans with the aim of combating poverty, changing the production model to make it sustainable, the preservation of terrestrial and marine ecosystems, the prevention of deforestation, and the promotion of an increasingly sustainable agriculture (Toprak, 2013). Therefore, thanks to the Earth Summit there is an awareness about the urgency of adopting sustainable policies as soon as possible to guarantee future generations the same opportunities as today as stressed by the previous Brundtland Report (WCED, 1987). However, 20 years later during the United Nations Conference on Sustainable Development (dubbed Rio 120) (United Nations, 2012) it was realized that over the years there had been no improvement in sustainable development (Haines, Alleyne, Kickbusch, & Dora, 2012). For this reason, the participating states decided to take early action by initiating the process of defining a set of sustainable development goals (SDGs). This process resulted in the creation of a network, the Sustainable Development Solutions Network (SDSN), made up of scholars with expertise in the field (Sachs, 2014). Thanks to the SDSN, the famous 17 SDGs were created: (1) Defeat poverty;

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(2) Defeat world hunger; (3) Good health; (4) Quality education; (5) Gender equality; (6) Clean water and sanitation; (7) Renewable energy; (8) Good jobs and economic growth; (9) Innovation and infrastructure; (10) Reduce inequality; (11) Sustainable cities and communities; (12) Responsible consumption; (13) Fighting climate change; (14) Aquatic flora and fauna; (15) Terrestrial flora and fauna; (16) Peace and justice; and (17) Partnership for the goals.

1.2 From the Birth of the Commission on the Status of Women to Goal 5 of the 2030 Agenda The long journey toward gender equality began in 1945 thanks to the Charter of the United Nations (United Nations, 1945). The equality of women’s rights is in fact a fundamental principle of this organization (UN Women, 2009) to the point that in the Preamble of the Charter of the United Nations there is the reaffirmation of the “faith in fundamental human rights, in the dignity and worth of the human person, in the equal rights of men and women” (United Nations, 1945). And it is on this occasion that the UN established the Sub-Commission on the Status of Women (CeSPI Osservatorio di politica internazionale, 2012) which became the following year, precisely on June 21, 1946, Commission on the Status of Women (CSW) (UN-ECOSOC, 1946) thanks to the intervention of its first President Bodil Begtrup. The CSW consisted of 45 government representatives serving four-year terms and meeting once a year for two weeks to work on reports, research, and recommendations related to issues of interest to women’s rights (CeSPI Osservatorio di politica internazionale, 2012). For the period 1946–1962, the CSW focused primarily on major issues that needed immediate action in the field of equal rights (UN Women, 2009). And so it was that during these years the Commission drafted several conventions: • Convention on the Political Rights of Women on December 20, 1952 (UN

General Assembly, 1952); • Convention on the Nationality of Married Women on January 29, 1957 (UN

General Assembly, 1957); • Convention on Consent to Marriage, Minimum Age for Marriage and

Registration of Marriages on November 7, 1962 (UN General Assembly, 1962). Through the new political vision that emerged from the emergence of youth and feminist movements born around the 1960s, CSW focused more on women’s economic participation and all those social and cultural factors that determined women’s participation in development processes (Boserup & Trucchi, 1982; Pomeranzi, 2009). And so it was that 1975 was designated as the International Year of Women in the calendar of the United Nations (UNESCO, 1975) and that three key themes

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were adopted for the promotion of equal opportunities and rights: (1) women and development; (2) the promotion of equality; and (3) the recognition of the growing contribution of women to the consolidation of peace in the world (CeSPI Osservatorio di politica internazionale, 2012). In conjunction with this event, the first women’s conference was held in Mexico City. This conference, the first of its kind, resulted in a plan of action in which guiding principles were established that would guide intervention strategies until 1985 (UN Women, 2009). In addition, this event gave the impetus for the adoption in 1979 of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) which became effective two years later (UN Women, 2009). However, 1995 was the real turning point. In this year, in fact, in the wake of a decade punctuated by a series of annual conferences including the Earth Summit, the Fourth World Conference on Women was held in Beijing (CeSPI Osservatorio di politica internazionale, 2012). Thanks to this conference, two fundamental key words entered the public debate: empowerment and mainstreaming. The concept of women’s empowerment indicates all those processes through which women manage to gain power and control over their lives and acquire all those tools useful to make effective strategic choices (UN Commission on the Status of Women, 2002). The second concept, however, is much more difficult to define. However, it can be defined as all those strategies through which it is possible to combat gender discrimination (Mazey, 2001; Rittenhofer & Gatrell, 2012). However, 20 years after the Beijing World Conference, it is realized that perfect equality is still far from being achieved. For this reason, CSW contributed to the delineation of Goal 5 (Gender Equality) of the 2030 Agenda. This goal is foundational to sustainable development as the other goals are highly dependent on its achievement (Ritchie, Roser, Mispy, & Ortiz-Ospina, 2018). The current pandemic, however, has posed new challenges for achieving gender equality: one of the groups most affected by the effects of COVID-19 has been women. In fact, from an employment perspective, the impact of the pandemic has been greater for women than men. They are more susceptible to job loss than their male counterparts (ILO, 2020; OECD, 2020; UN, 2020), and this has been a setback for the professional careers of many women (Carli, 2020). In addition, job positions held by women are unlikely to be easily converted to remote work unlike male job positions (Alon, Doepke, Olmstead-Rumsey, & Tertilt, 2020). Finally, domestic confinement imposed by government restrictions has increased the likelihood of violence against women. As many as 7 out of 10 women, in fact, said that both physical and verbal violence from their partners increased because of home confinement (Women & Count, 2021).

2. Material and Methods As we have seen, sustainable development is a multidimensional phenomenon with many different facets. For this reason, it is necessary to use indicators,

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i.e., tools capable of measuring this complexity (Brul´e & Maggino, 2017). In this study we will use 50 indicators, chosen on the basis of data completeness, from the Benessere Equo e Sostenibile (BES) survey conducted by ISTAT. The indicators chosen are as follows: (a) Life expectancy at birth; (b) Healthy life expectancy at birth; (c) Mental health index; (d) Life expectancy without activity limitations at age 65; (e) Adequate nutrition; (f) Persons with at least a high school diploma (25–64 years old); (g) Participation in continuing education; (h) Cultural participation outside the home; (i) Reading of books and newspapers; (j) Employment rate (20–64 years old); (k) Transitions from unstable to stable jobs; (l) Employed in temporary jobs for at least 5 years; (m) Overeducated employees; (n) Job satisfaction; (o) Satisfaction with family relationships; (p) Satisfaction with friendships; (q) People you can count on; (r) Social participation; (s) Civic and political participation; (t) Voluntary activities; (u) Funding of associations; (v) General trust; (w) Trust in the Italian Parliament; (x) Trust in the judicial system; (y) Trust in parties; (z) Trust in the police and fire brigade; (aa) Perception of safety when walking alone in the dark; (bb) Satisfaction with one’s life; (cc) Satisfaction with leisure time; (dd) Positive judgment on future prospects; (ee) Satisfaction with the environmental situation; (ff) Knowledge workers; (gg) Cultural and creative occupation; (hh) Regular internet users; (ii) Road accident mortality (15–34 years); (jj) Excess weight; (kk) Smoking; (ll) Alcohol; (mm) Early exit from education and training system; (nn) Young people not working and not studying (NEET); (oo) Rate of nonparticipation in employment; (pp) Employees with low pay; (qq) Involuntary part-time; (rr) Penal institution crowding; (ss) Dissatisfaction with the landscape of the place of living; (tt) Concern about deterioration of landscape; (uu) Concern about climate change; (vv) Concern about loss of biodiversity; and (ww) Waiver of health benefits. The high number of indicators chosen in this study underlines the complexity of the phenomenon of sustainable development, that cannot be analyzed with the simple use of limited conceptual schemes (Alaimo, Ciacci, & Ivaldi, 2021). The Italian case was chosen because it presents a historical territorial gap between the northern regions, among the most developed in Europe, and the central-southern regions, which still present high economic and social inequalities.

2.1 The Stacking Method After identifying the indicators, the step to follow in the construction of an index is to make sure that they all have the same polarity. It, in fact, is a sign of the relationship between the chosen indicator and the phenomenon to be studied (Mazziotta & Pareto, 2015). However, some indicators show a negative polarity. For this reason, to properly aggregate all indicators, a transformation was conducted through the following formula: xij ¼ 100 2 xij

where xij is the value assumed by indicator j in unit i.

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At this point, one can proceed to calculate the index. In this study, we chose to use the stacking method (Exeter, Boyle, & Norman, 2011; Landi, Ivaldi, & Testi, 2017; Norman, 2010). The original method involves stacking the chosen indicators by reference years: k

n

+t ¼ 1 +i ¼ 1 xi;j;t kn vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 u n u k t+t ¼ 1 +i ¼ 1 xi;j;t 2 mj

mj ¼

sj ¼

kn

where i 5 1,2,. . .,n are the number of spatial specificities chosen, j 5 1,. . .,m are the indicators used, and t 5 1,. . .,k are the number of years considered. In this study, however, we chose to replace the reference years with gender and therefore the formulas used in this study can be represented as follows: n

n

+i ¼ 1 xi;j;F 1 +i ¼ 1 xi;j;M 2n vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2  2 u n u n t+i ¼ 1 xi;j;F 2 mj 1 +i ¼ 1 xi;j;M 2 mj mj ¼

sj ¼

2n

where i 5 1,2,. . .,n are the 20 Italian regions, j 5 1,2,. . .,50 are the sustainable development indicators, and F and M are the genders analyzed.

3. Results This section presents the analysis of the results obtained through the construction of the Gender Sustainable Development Index (GSDI). First, the results obtained through the application of the stacking method are reported in Table 21.1. Overall, the Italian region with the highest level of female sustainable development is Aosta Valley (GSDI F 5 19,428), followed by Friuli-Venezia Giulia (GSDI F 5 18,741) and Emilia-Romagna (GSDI F 5 15,748). In the last positions, on the other hand, are three southern regions, i.e., Calabria (GSDI F 5 228,350), Sicily (GSDI F 5 228,360), and Campania (GSDI F 5 229,398). On the other hand, regarding the region which has achieved the highest level of male sustainable development, we find Friuli-Venezia Giulia (GSDI M 5 19,962) followed by Emilia-Romagna (GSDI M 5 16,135) and Veneto (GSDI M 5 14,284). Also, in this case the last three positions are covered by Calabria (GSDI M 5 225,875), Campania (GSDI M 5 228,348), and Sicily (GSDI M 5 228,706). Only 8 out of 20 regions show higher levels of female sustainable development than male sustainable development. These regions are: Aosta Valley (GSDI F 5 19,428; GSDI M 5 13,883); Umbria (GSDI F 5 12,603; GSDI M 5 12,195); Marche (GSDI F 5 7,700; GSDI M 5 2,319); Lazio (GSDI F 5 6,475; GSDI M 5 3,213); Sardinia (GSDI F 5 1,170; GSDI M 5 27,196); Trentino-Alto Adige (GSDI F 5 0.651; GSDI M 5 21,699); Basilicata (GSDI F 5 25,584; GSDI M 5 29,921); and

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Table 21.1. GSDIs and Related Ranks.

Aosta Valley Friuli-Venezia Giulia Emilia-Romagna Veneto Umbria Tuscany Lombardy Marche Lazio Liguria Piedmont Sardinia Trentino-Alto Adige Abruzzo Basilicata Molise Apulia Calabria Sicily Campania

GSDI F

Rank F

GSDI M

Rank M

19.428 18.741 15.748 12.772 12.603 11.132 9.281 7.700 6.475 6.303 3.289 1.170 0.651 20.244 25.584 28.071 224.350 228.350 228.360 229.398

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

13.883 19.962 16.135 14.284 12.195 12.625 9.725 2.319 3.213 10.833 4.710 27.196 21.699 2.205 29.921 22.755 218.526 225.875 228.706 228.348

4 1 2 3 6 5 8 11 10 7 9 15 13 12 16 14 17 18 20 19

Source: Author’s own calculations from Italian National Institute of Statistics (ISTAT) data.

Sicily (GSDI F 5 228,360; GSDI M 5 228,706). From Table 21.1 it can also be seen that the 6 regions that show negative values for both GSDI F and GSDI M are all situated in the center-south of the Italian peninsula, that is, Basilicata, Molise, Apulia, Calabria, Sicily, and Campania. From Fig. 21.1 the Italian Regions that show a greater gap between the levels of GSDI F and GSDI M are Aosta Valley, Lazio, Sardinia, and Basilicata. While on the contrary, the regions that show a high gap between male and female levels are Liguria, Molise, and Abruzzo.

4. Discussion and Conclusion The road to achieving gender equality in Italy is still long and tortuous. However, in recent years the Italian peninsula has increased measures aimed at reducing inequalities on the ground.

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GSDI M

Aosta Valley Campania Sicily

20.000

Friuli-Venezia Giulia

15.000

Emilia-Romagna

10.000 5.000

Calabria

Veneto

0.000 -5.000 -10.000

Apulia

Umbria

-15.000 -20.000 -25.000

Molise

Tuscany

-30.000

Basilicata

Lombardy

Abruzzo

Marche

Trenno Alto Adige

Lazio Sardinia

Liguria Piedmont

Fig. 21.1. GSDIs Radar Chart. Source: Author’s own calculations from Italian National Institute of Statistics (ISTAT) data.

The first area on which it was decided to intervene is the strong territorial divide between the regions of the North and those of the South. In fact, as can be seen from Table 21.1, in both indices, the southern regions tend to be systematically grouped at the bottom of the ranking, unlike those in the North. For this reason, in early 2020, the Italian Ministry for the South and Territorial Cohesion adopted the so-called “Piano per il Sud 2030” (Ministro per il Sud e la Coesione territoriale, 2020). This measure aims to reduce the gaps between citizens and territories and creates new and good job opportunities for young people and women. Regarding the latter category, the objective of this plan is to guarantee equal access to both education and work. Particular attention has been paid to recognizing and valuing unpaid care work and domestic work through public services, infrastructure, and social protection policies (Ministro per il Sud e la Coesione territoriale, 2020). The following year of the entry into force of the Piano per il Sud 2030, the Italian government, to combat the effects caused by the COVID-19 pandemic, promulgated a new plan: the Piano Nazionale di Ripresa e Resilienza (PNRR) (Ministero dell’Economia e delle Finanze, 2021). This measure approaches gender

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inequality in a transversal manner. In fact, the three strategic axes (digitization and innovation, ecological transition, and social inclusion) are accompanied by the three horizontal priorities: promoting gender equality, reducing generational disparities, and favoring the rebalancing of territorial gaps (MEF, 2021). Interventions aimed at the first cross-cutting priority, that of promoting gender equality, account for more than 20% of the total or about 38.5 billion euros (MEF, 2021). One measure that is part of the PNRR is the so-called Family Act. This plan, which went into effect on April 27, 2022, through Law No. 32/2022, has two primary goals: parenting protection and work–life balance (di Rosa, 2022). To implement these objectives, the Family Act sets out a series of guidelines to be followed, including that of favoring female employment and the harmonization of family and work time through the equidistribution of care work. For this reason, among the measures forming part of this measure are: (1) increase in the length of the paternity leave period; (2) increase in the maternity leave allowance; (3) leave for pregnancy gynecological examinations also for caregivers; (4) the possibility of requesting work from home for both parents in order to devote themselves to their children; and (5) facilitations for ancillary domestic work (di Rosa, 2022). Another measure that is part of the PNRR is the Fondo per l’impresa femminile, which will start in May 2022. This fund is an incentive from the Italian Ministry of Economic Development that supports the birth, development, and consolidation of businesses led by women. This incentive is made up of nonrepayable grants and subsidized financing, of which 160 million euros of PNRR resources and 40 million euros are allocated by the 2021 Budget Law. This measure is aimed at four categories of women-owned businesses: (1) cooperatives or partnerships with at least 60% women members; (2) corporations with shares and members of the governing bodies formed by at least 2/3 women; (3) sole proprietorships with the owner being a woman; and (4) self-employed women with VAT numbers (MISE, 2022). Therefore, as can be seen, Italy, although it is a country in which strong inequalities persist, is making a considerable effort to achieve perfect gender equality both from an economic and social point of view. This work has demonstrated the gender differences and the strong territorial gap present in the Italian peninsula. The strength of this work lies in having created two different indices of sustainable development useful for measuring gender differences at a regional level. Another strength lies in the method chosen, the stacking method, which has a high replicability of results. Regarding the critical aspects of this work, a major limitation was the lack of complete data for all the indicators of the BES survey and therefore it was necessary to proceed with a selection of them based on completeness. The method chosen, finally, allows an absolute temporal comparison and for this reason it will be interesting to repeat this study once the abovementioned measures are put in place. The results have confirmed the historical territorial gap present among the Italian regions and the need for a targeted intervention aimed at reducing the gender gap present in the southern regions.

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Ministro per il Sud e la Coesione territoriale. (2020). Piano Sud 2030. Sviluppo e Coesione per l’Italia. Retrieved from http://www.ministroperilsud.gov.it/media/ 2003/pianosud2030_documento.pdf. Accessed on May 10, 2022. MISE. (2022). Fondo impresa femminile. Mise. Retrieved from https://www.mise.gov. it/index.php/it/incentivi/impresa/fondo-a-sostegno-impresa-femminile. Accessed on May 10, 2022. Norman, P. (2010). Identifying change over time in small area socio-economic deprivation. Applied Spatial Analysis and Policy, 3(2–3), 107–138. OECD. (2020). Women at the core of the fight against COVID-19 crisis. Paris: OECD Publishing. Retrieved from www.oecd.org/coronavirus/policy-responses/women-atthe-core-of-the-fight- againstcovidPomeranzi. (2009). A che punto siamo tra Nazioni unite, femminismo transnazionale e cooperazione: una lettura dell’agire delle donne nel mondo globalizzato. mimeo. Ritchie, R., & Mispy, O.-O. (2018). Measuring progress towards the sustainable development goals. Retrieved from SDG-Tracker.org Rittenhofer, I., & Gatrell, C. (2012). Gender mainstreaming and employment in the European union: A review and analysis of theoretical and policy literature. International Journal of Management Reviews, 14(2), 201–216. doi:10.1111/j.14682370.2012.00333.x Rome, A. (2010). The genius of earth day. Environmental History, 15(2), 194–205. doi: 10.1093/envhis/emq036 di Rosa, D. (2022, April 28). Family Act: le novit`a in arrivo per favorire la conciliazione tra vita e lavoro. Ipsoa. Retrieved from https://www.ipsoa.it/documents/lavoro-eprevidenza/amministrazione-del-personale/quotidiano/2022/04/28/family-actnovita-arrivo-favorire-conciliazione-vita-lavoro. Accessed on May 10, 2022. Sachs, J. D. (2014). The age of sustainable development. New York, NY: Columbia University Press. Tokuç, A. (2013). Earth Summit. In S. O. Idowu, N. Capaldi, L. Zu, & A. D. Gupta (Eds.), Encyclopedia of corporate social responsibility. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-28036-8_17 Toprak, Z. (2013). Agenda21 (UN). In S. O. Idowu, N. Capaldi, L. Zu, & A. D. Gupta (Eds.), Encyclopedia of corporate social responsibility. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-28036-8-246 UN. (2020). Policy brief: The impact of COVID-19 on women. New York, NY. Retrieved from www.un.org/sites/un2.un.org/files/policy_brief_on_covid_impact_ on_women_9_april_2020.pdf UNESCO. (1975). Report of the world conference of the international women’s year, Mexico City. 19 June–2 July 1975. Retrieved from https://documents-dds-ny.un. org/doc/UNDOC/GEN/N76/353/95/PDF/N7635395.pdf?OpenElement UN General Assembly. (1952, December 20). Convention on the political rights of women. A/RES/640. Retrieved from https://www.refworld.org/docid/3b00f08448. html. Accessed on May 9, 2022. UN General Assembly. (1957, January 29). Convention of the nationality of married women. A/RES/1040. Retrieved from https://www.refworld.org/docid/3b00f0674. html. Accessed on May 9, 2022. UN General Assembly. (1962). Convention on consent to marriage, minimum age for marriage and registration of marriages. Retrieved from https://www.refworld.org/ docid/456d89064.html. Accessed on May 9, 2022.

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UN Women. (2009). Short history of CEDAW convention. Retrieved from https:// www.un.org/womenwatch/daw/cedaw/history.htm. Accessed on May 9, 2022. UN-ECOSOC. (1946, June 21). ECOSOC resolution establishing the commission on the status of women. E/RES/2/11, New York, NY. United Nations. (1945, October 24). Charter of the United Nations. 1 UNTS XVI. Retrieved from https://www.refworld.org/docid/3ae6b3930.html. Accessed on May 9, 2022. United Nations. (2012, June 20–22). The future we want. Rio de Janeiro, Brazil: Outcome document on Sustainable Development. United Nations Sustainable Development. (1992). Agenda 21. In United Nations Conference on Environment & Development. Rio de Janerio, Brazil, 3 to 14 June 1992. WCED. (1987). Our common future. London: Oxford University Press. Women, U. N., & Count, W. (2021). Measuring the shadow pandemic: Violence against women during COVID-19. UN Women Data Hub.

Chapter 22

Association Between Crime Against Women and Income Inequality: A Study on Indian States Debarati Nandigrami and Ramesh Chandra Das

Abstract Crime against women is an entrenched issue in India in general and its states in particular. The existing literature unveils so many socioeconomic factors responsible behind such a social curse. Economic disparities in terms of income have also been identified as one of the crucial factors in determining the crime rates in some countries of the world. The present study seeks to examine whether income inequality has any sort of associations with the crime against women in the states of India. The study has observed rising trends of crime rates and per capita incomes across the states in India for the period 2000–2019 and crime rates in the states are positively and significantly correlated with rising inequality in income. There, thus, needs the policies related to reduction of crime against women and reduction of income inequality. The study thus suggests the interventions of the legislative system, governments’ tax policies toward the rich persons, public awareness programs, etc. to reduce violence against women. Keywords: Crime rate; PCNSDP; inequality; GINI; correlation; Indian states

1. Introduction Crime against women is a worldwide deep-rooted issue, in India, and is very evident. Cultural and social factors are connected with the development and dispersion of violent behavior in India. With different processes of socialization that men and women go through, men play the role of stereotyped gender wills to dominate and control, while women take up that of submission, dependence, and respect for authority. A female child grows up constantly called weak and in need of protection, whether physical, social, or economic. This misconstruction has led Gender Inequality and its Implications on Education and Health, 259–271 Copyright © 2023 Debarati Nandigrami and Ramesh Chandra Das Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231023

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to her exploitation at almost every stage of her life. According to the World Bank, 48% of the population in India belongs to women, but they are still repressed and distressed in various domains and are the victims of major violent crimes. Violence against women is acknowledged as a major health concern as it leads to mental and physical health problems for women. Violence against women incorporates sexual, physical, psychological, and economic abuse. India has a patriarchal society where culture has a credo that perpetuates violence against women. The question of the role of inequality in crime has been a matter of interest among many researchers and policy analysts. For population health, women’s empowerment, violent crime, risky behaviors, accidental death, and education. Income inequality plays an important role. Societies with smaller income differences between rich and poor tend to have better health and less violence. It has been found that homicides and assaults tend to be the most common where income inequality is highest (Das & Maity, 2020; Elgar & Aitken, 2010). On the other hand, it seems crime is also costly to the poor people in poor countries, particularly violent crimes can naturally lead to higher medical costs and loss of productivity that poor people in developing countries are not well equipped to bear (UN, 2005). From the work of Cui and Hazra (2017) it is seen that macroeconomic conditions significantly influence the level of crime. It is also seen that crime and macroeconomic factors like low GDP per capita, high inflation, and the unemployment rate are directly related. While most of the literature on this topic finds a positive effect of inequality on crime. The occurrence of violent crime against women has risen drastically in the past few years, reflecting their increasingly dejected status in society. This increasing trend can be imputed to many factors such as the stigmatization of the victims, lengthy judicial process, social taboos, increasing criminalization of politics and the continuous status of misery in society, political apathy to implement gender-sensitive laws and policies, and lack of will for strengthening and reforming police with more female personnel. The ramification of crime in India is wide. To control the same, the Government of India has taken several initiatives at different time points involving the states and the Union Territories. Recently, the Government of India has taken an initiative to end violence against women as a national priority, which resonates with the Sustainable Development targets of the United Nations on gender equality. The Prime Minister’s “Beti Bachao-Beti Padhao” initiative aims at equal opportunity and education for girls in India. The Ministry of Women and Child Development has taken several initiatives to ensure the safety and protection of women in India. Under the milieu, the present study focuses on the rationale behind the possible existence of a relationship between crime and income inequality in Indian states because there are no significant studies available in the existing literature to explain the relationship between crime rate and income inequality in depth in the country. That’s why the present study tries to fill the gap in the literature using the updated data.

2. Brief Literature Review Now we will check out some studies in the existing literature relevant to our work to justify the purpose of the present work. First, we focus on the studies related to the

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determinants of crime then we move on to the studies related to crime and income inequality. Watts and Zimmerman (2002) discussed the magnitude of some of the most severe forms of violence against women like intimate partner violence, sexual abuse by nonintimate partners, trafficking, and female infanticide, etc. It was also seen there are many potential perpetrators, including spouses and partners, parents, other family members, neighbors, and men in positions of power or influence. Demombynes and Ozler (2005) studied the effects of local inequality on property and violent crime in South Africa and found out that in general inequality leads to crime and also saw that ethical inequality and crime are correlative. Arora (2007) inspected that crime against women decreased when there was an increment in GDP, literacy rate, and sex ratio, although the study suggested a participatory approach to eliminating crimes against women. Mangoli and Ganapati (2009) have tried to present before the criminal justice system of India that Indian women are now not so safe and there is always a kind of feelings of threat generated among the women. Dutta and Husain (2009) showed that both inhibition and socioeconomic factors are important in explaining crime rates in Indian states. Durante (2012) finds population density has a significant positive association with property crime rates, while the GINI coefficient and the share of young people have a negative link with violent crime rates. Kumar, Nizamie, and Srivastava (2013) researched several facts about the repercussion of women’s mental health and violence against women and proposed mental health as a matter of question. Wolf, Gray, and Fazela (2014) observed that income inequality was associated with certain violent outcomes in high-, low-, and middle-income countries. Kahlon (2014) tried to recognize the area (Chandigarh) that are crime-prone and described that incident of crime is related to the social position of a group in the area and the availability of cop. Cui and Hazra (2017) observe a long-run relation between all the macroindicators and crime rate. Lower GDP per capita affects higher inflation rates, unemployment rates, and the total number of crimes in the country. Javier Cano-Urbina and Lance Lochner (2017) observe that average schooling has a positive impact on the depletion of the rate of arrests for violent and property crime. Banerjee (2018) investigated economic progression has a lead role to reduce crime against women and the fact that a woman chief minister doesn’t have any effect on actual and reported crime against women. Maity and Sinha (2018) deduced that poor women are more vulnerable to crime, whereas political women empowerment enables women to avoid crime. Zhang (2018) finds that high school dropouts are more likely to commit crimes since they can earn a lot of money from them. Das and Maity (2020) have revealed that there are long-run associations between crime rate and human development for four states, namely, Bihar, MP, Punjab, and Tamil Nadu. However, there is a causal influence from crime rates to human development in Andhra Pradesh, Madhya Pradesh, Uttar Pradesh, Maharashtra, and Rajasthan. Hazra (2020) observed that, among the macroeconomic factors, only Gross State Domestic Product per capita was found relevant in explaining total crime rates. However, the unemployment rate and price level, population density, socioeconomic factors like income inequality, and poverty rate, have a significant relationship with crime rates in India. Gupta, Sahoo, and Paltasingh (2022) examine the effect of HDI as positive on the encouragement of reporting of crimes and the

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negative on dowry death and cruelty by husband. Kelly (2000) studied that inequality had less effect on property crime but significant effect on violent crime. Elgar and Aitken (2010) inspected that income inequality correlated with trust and homicide and not with public expenditure and trust correlated with homicide from the study of the linkage between income inequality and rates of homicide in 33 countries. Aizer (2010) showed a direct relationship between the wage gap and domestic violence and crime against women in America. Narrowing this gap will improve the scenario. Rashada and Saraf (2016) show a direct union between income inequality and intimate partner violence in India. A one-unit increase in the GINI index increases sexual violence by 6.2% and a less severe form of violence by 2.1%. Nandan and Mallick (2020) aim to address two issues, one is whether gender equality promotes economic growth across Indian states, and the other, whether interstate differences in gender equality can explain per capita income disparities among the Indian states and confirm that gender equality leads to economic growth and that interstate differences in gender equality lead to inconsistency in per capita income among the Indian states.

3. Objectives and Hypotheses Objective of the study is to examine the trends of different types of crime against women and per capita net state domestic product (PCNSDP), their means and variances across the states. Then it goes for calculating inequality in crime and income, and correlating crime with inequality. The hypotheses of the study are: H0. Number of crime is constant over the years and across the states H1. Number of crime is increasing over the years and across the states H0. There is no correlation between crime and income inequality H1. There is positive correlation between crime and income inequality

4. Theoretical Underpinning The existing literature and the sociological aspects of crime against women provide us the frame for the theoretical connection between crime against women (CW) and the socioeconomic factors such as income level (Y), education (E), level of human development as measured by the HDI, youth unemployment (YU), income disparity or income inequality (IE), media (M), judicial structure (JS), good governance (GG), and police administration (PA), among others. Thus we can write the crime function as: CW ¼ f ðY ; E; HDI; YU; IE; M; JS; GG; PAÞ

Given fixed values of Y, E, HDI, YU, M, JS, GG, and PA, we can summarize the effect of income inequality upon the crime rate. Hence,

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CW ¼ f ðIEÞ

Where dCW/dIE # 0. It is our aim is to check whether the sign is positive or negative.

5. Data and Empirical Methodology This study is based on only secondary data on the number of incidences or crimes against women. The data for the crime among the states are obtained from the database of the National Crime Record Bureau (NCRB) and contain six major crimes such as Rape, Kidnapping, Dowry Deaths, Cruelty by Husband and Relatives, Molestation, and Sexual Harassment. The data for the net state domestic product (NSDP) were obtained from the database of the Reserve Bank of India (RBI) Handbook on the Indian Economy. In our analysis, we have taken two variables, which are the total no of crimes against women and the NSDP. We take into account the PCNSDP and incidence of crime in one lakh population. NSDP is measured in the 2004 base year price level. We have taken data for both the variables for the period 2000–2019. We have taken 29 states of India which are: Chhattisgarh, Goa, Gujarat Haryana Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya Mizoram, Nagaland, Odisha, Punjab, Rajasthan Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttarakhand, and West Bengal. For the analysis of trends of the variables, we fit line graphs and estimate the time coefficients by regression analysis for each of the states in India. In this analysis, we use descriptive statistics. Among the different descriptive statistics, we have mainly used arithmetic mean, standard deviation, and Pearson correlation coefficients. We got the average by calculating group of numbers and then dividing by the count of those numbers and the standard deviation (SD) by qffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ffi +ðx 2 xÞ using the following formula: n The formula for the correlation coefficient is:    + x2x y2y r ¼ correlðx; yÞ ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  2  + x2x + y2y

The statistical significance of the correlation coefficient is tested by the t test following the formula as given below: t ¼ r

 pffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðn–2Þ ð1 2 r2 Þ

with (n 2 2) degrees of freedom, n being the number of years. The GINI formula for computing inequality in crime rate as well as income is given below as:   ¼ G

 n n +i ¼ 1 +r ¼ 1 jyi 2 yrj 2nðn 2 1Þm

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There is an alternative representation of the GINI, a relatively better formula for computational purpose given by the Economic and Political Weekly Research Foundation, which is written as: GINI ¼ +

ð2i 2 n 2 1ÞXi n2 m

Where, i 5 rank of the states arranged in ascending order, n 5 number of observations, and Xi 5 value of crime rate or PCNSDP of that particular rank.

6. Results and Analysis In the objective section the study has mentioned whether the trends in the crime rates are increasing across the states during the period of study. The hypotheses are accordingly framed. The study has gone through first the presentation of the trends in crime rates and NSDP in Fig. 22.1 in four different panels. In the next step, the study goes for computing the correlation coefficient between the crime rate and NSDP for the states.

6.1 Objective 1: Trend Analysis Fig. 22.1 shows the total yearly rates of crime cases against women per lakh population in India in different years of the mentioned time period for the two sets of the states as given in Parts A and B. In panel A and panel B of Fig. 22.1, it is seen that the trends of the number of cases of the women-related crime are rising in most of the states except Tamil Nadu and Andhra Pradesh. Crime rate of the Indian states have risen over time showing a dreary scene of the states in terms of their women safety and development. Uttar Pradesh, Madhya Pradesh, Andhra Pradesh, and West Bengal belong to the top level states in most of the years, and Nagaland, Manipur, Tamil Nadu, and Sikkim are in the bottom position in most of the years. Tamil Nadu and Andhra Pradesh show a good sign since their crime rates are declining over time. Fig. 22.1, in its last two parts, Parts C and D, presents the trends of the NSDP in different states in our country during the time period 2000 to 2019. Showing the total yearly income in India in different years of mentioned time period yearly trend is as follows. The figure shows that the growth of PCNSDP in the 29 Indian states is rising during that specified time period. Goa leads the group of states followed by Sikkim and Bihar at the bottom level which is just preceded by Uttar Pradesh. The growth rate of PCNSDP is highest for Goa as well. It is also seen that the middle-income states are Karnataka, Himachal Pradesh, Punjab, Rajasthan, Meghalaya, etc. For better understanding of the gradients of the trends in the crime rates the study estimates the following regression equation: Crime rate ðCWÞ ¼ a 1 bt 1 u

Crime Against Women and Income Inequality Panel A: Trends of total number of crime (per lakh population) of the states

1

265

Mizoram Maharashtra

0.8

Arunachal Pradesh Chhattisgarh

0.6 0.4

Jammu & Kashmir West Bengal

0.2

Kerala Andhra Pradesh

0

Odisha

Panel B: Trends of total number of crime (per lakh population) of the states

0.3

Nagaland

0.25

Manipur Meghalaya

0.2

Bihar

0.15

Tamil Nadu

0.1

Sikkim Goa

0.05

Punjab

0

Uttarakhand

1400

Panel C: Trends of PCNSDP of the states

Bihar Uttar Pradesh Jharkhand Assam Andhra Pradesh Madhya Pradesh Manipur Odisha Chhattisgarh Jammu & Kashmir Rajasthan Meghalaya West Bengal Nagaland

1200 1000 800 600 400 200 0

4500

Panel D: Trends of PCNSDP of the states

4000

Tripura

3500

Karnataka

3000

Himachal Pradesh Arunachal Pradesh Uttarakhand

2500 2000 1500 1000

Punjab

500 2019

2018

2016

2017

2015

2014

2012

2013

2011

2010

2008

2009

2007

2005

2006

2004

2003

2002

2000

2001

0

Fig. 22.1. Yearly Trends of Crime Rate and Per Capita NSDP (Current Price) of the States. Source: Drawn by the Authors.

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Similarly, for the series for PCNSDP, the regression equation is: PCNSDP ¼ a 1 bt 1 V

It is now required to test whether estimated b (or b) is positive or negative and statistically significant. The study thus estimates both the equations and derived the estimated coefficients which indicate their gradients. The results are given in Table 22.1 (Column No. 6 and 7). It is observed from column 6 of the table that the estimated regression coefficients of crime rate on time (i.e., b) are positive and statistically significant except Andhra Pradesh and Tamil Nadu. The case of Andhra Pradesh is positive but insignificant in statistical terms. The computed t values are given in the parentheses of the column which are greater than that of the tabulated value of 2.09 at the degrees freedom 19 (5 20-1) at 5% level of significance. This suggests that the crime rates in the states of India except Andhra Pradesh and Tamil Nadu are rising over time statistically. The social scenarios of the states in India in overall sense are getting worse over the time. The results reject the null hypothesis in the first objective. On the other hand, there is only one state, Tamil Nadu, where the crime rate has been significantly decreasing in the study period as its value of b is (2) 300 with the absolute t value of 6.23. Hence, Tamil Nadu has progressed over time in maintaining a good social scenario as far as crime against women is concerned. The calculated regression coefficients of the per capita NSDP (PCNSDP) on time (i.e., b) are positive and statistically significant for all the states, as can be seen in column 7 of the table. The estimated t values are significantly greater than the tabulated value of 2.09 at the degrees of freedom 19 (5 20-1), at 5% level of significance are presented in the parentheses of the column. This suggests that the trends of the PCNSDP are rising over time, which is a good scenario as far as economic status is concerned. PCNSDP has increased at a high pace and statistically significant rate in major states including Arunachal Pradesh, Gujarat, Haryana, Kerala, Maharashtra, Sikkim, Tamil Nadu, and Goa. With the exception of Goa, Arunachal Pradesh, and Sikkim, the crime rate is high in these states as a result. The state scenarios show that in some states, a higher PCNSDP is linked to a higher propensity to commit crime. Also according to the first hypothesis of this study, the number of crimes is rising over time and across states, rejecting the null hypothesis.

6.2 Objective 2: Correlation Between PCNSDP and Crime Rate This section analyzes how PCNSDP is correlated with crime rate in different states. The correlation coefficient between the two is computed using the formula mentioned in the methodology section and the results are shown in Table 22.1 (Column 4). The coefficients are positive and statistically significant in all the states except Andhra Pradesh and Tamil Nadu. The results go with the regression results for the trend as given in Column 6. Therefore, crime rates and PCNSDP are highly correlated, and the states with high PCNSDP are associated with high crime rates in general perspective. But we do not establish any causal relationship

Table 22.1. Correlation and Regression Coefficients and GINI Values in Crime Rates and PCNSDP. PCNSDP GINI

Crime GINI

Correlation Coefficient(t)

2000 2001

0.073 0.206

0.489 0.260

2002 2003 2004 2005 2006 2007 2008

0.214 0.212 0.220 0.230 0.233 0.236 0.243

0.303 0.257 0.249 0.259 0.263 0.259 0.267

0.990 0.974 0.709 0.836 0.547 0.975 0.884

2009

0.260

0.290

0.808 (5.83)

2010 2011 2012 2013 2014 2015

0.259 0.268 0.255 0.255 0.273 0.282

0.312 0.301 0.290 0.259 0.263 0.302

0.945 0.946 0.842 0.916 0.965 0.739

20.17 (20.73) 0.902 (8.90) (30.85) (18.27) (4.27) (6.48) (2.77) (18.61) (8.06)

(12.29) (12.47) (6.63) (9.70) (15.78) (4.65)

States

Regression (t) for Crime Rate

Andhra Pradesh 47.27(0.22) Arunachal 13.13(7.23) Pradesh Assam 1354.41(13.9) Bihar 662.42(12.69) Chhattisgarh 192.08(3.46) Goa 19.23(7.2) Gujarat 203.73(4.05) Haryana 541.33(9.49) Himachal 42.32(6.99) Pradesh Jammu & 90.65(8.72) Kashmir Jharkhand 304.35(9.29) Karnataka 546.01(12.24) Kerala 355.44(10.30) Madhya Pradesh 823.79(6.58) Maharashtra 1373.10(9.44) Manipur 10.19(7.23)

Regression (t) for PCNSDP

43.25(14.29) 84.64(16.28) 34.31(16.13) 25.44(14.80) 44.81(24.70) 217.82(15.92) 94.76(16.11) 112.70(17.57) 70.78(18.79) 44.48(15.32) 32.62(18.81) 78.33(15.44) 82.36(16.87) 45.84(12.30) 88.31(22.74) 39.15(13.46)

Crime Against Women and Income Inequality

Year

267

268

Table 22.1. (Continued) PCNSDP GINI

Crime GINI

2016 2017 2018 2019

0.283 0.286 0.278 0.274

0.281 0.273 0.278 0.308

Source: Authors’ calculations.

Correlation Coefficient(t)

0.971 0.621 0.753 0.985 0.926 0.939 0.886 20.723 0.294 0.956 0.896 0.838

(17.25) (3.36) (4.85) (24.37) (10.42) (11.69) (8.12) (24.4) (1.30) (12.92) (8.56) (6.53)

States

Meghalaya Mizoram Nagaland Odisha Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal

Regression (t) for Crime Rate

26.90(14.33) 5.6(3.44) 3.12(5.55) 967.37(13.53) 211.48(8.38) 1292.84(9.08) 6.6(6.58) 2300.92(26.62) 41.65(3.01) 2313.06(7.84) 82.63(6.88) 1664.04(10.6)

Regression (t) for PCNSDP

46.99(24.74) 97.55(10.87) 51.99(18.57) 43.64(18.57) 69.33(22.22) 53.45(17.66) 188.08(12.57) 97.37(16.70) 62.67(12.31) 26.76(18.88) 84.49(22.97) 53.58(16.92)

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between the two. The two states Andhra Pradesh and Tamil Nadu produce negative correlation between the two which indicates the inverse association between income and rate of crime, but the t value is very small for Andhra Pradesh which means the association is insignificant. But for Tamil Nadu, the absolute t value is significantly high to reject the null hypothesis of no correlation between the two. So Tamil Nadu is a state where income growth is not associated with the rising crime rate. In other words, for Tamil Nadu the high income growth probably might be helping the crime rate to go down. The t values are very high for Assam and Odisha, which indicate very high magnitude of criminal activities in the two states. This study identified 13 states with high positive correlation, categorized as a correlation coefficient of over 0.9, including Arunachal Pradesh, Assam, Bihar, Haryana, Jharkhand, Karnataka, Madhya Pradesh, Maharashtra, etc. and 13 states with low positive correlation, having values less than 0.9, including Chhattisgarh, Goa, Gujarat, etc. After calculating the average PCNSDP of all the states, which is 13,276, the states are separated into below-average and above-average PCNSDP holding states. Those with above-average PCNSDP include Arunachal Pradesh, Goa, Haryana, Himachal Pradesh, Karnataka, Kerala etc., whereas states with below-average PCNSDP include Andhra Pradesh, Assam, Chhattisgarh, Gujarat, etc. Matching the states, it is found that the high average PCNSDP states holds the position of high average crime rates states and vice versa. It indicates that high-income earners are involved in high crime rates against women. Let us calculate inequality in income and crime rates using GINI index to make a comparison on the relationships between income inequality and crime rates (refer to Columns 2 & 3 in Table 22.1). A notably constant, gradual growth in PCNSDP inequality has been observed. In 2007, the GINI for PCNSDP reached its highest position, while in 2001, it reached its lowest point in an upward trend. The increase in PCNSDP occurred gradually between 2001 and 2008. The PCNSDP GINI index was largely stable from 2009 to 2010, before seeing an abrupt increase in 2011 and a sharp decline in 2012. Then, from 2013 to 2017, there was a continuous rise. Following that, it fell off over the following 2 years. Throughout the entire time frame, crime inequality fluctuated. From 2000 to 2003, it fluctuated before there was a little drop in crime, which was followed by a slow rise to the year 2006, a slight drop in crime, an increase in crime unexpectedly in 2008–2010, a decline in crime up until the year 2013, and then another fluctuation in crime noted in 2014, which continued until 2019. The lowest crime-related inequality was recorded in 2014, and the highest inequality was recorded in 2000. Computing correlation coefficient between the income inequality and crime inequality as measured by GINI index, a very high correlation coefficient of 0.896024878 is obtained. High correlation denotes a strong degree of association between the variables. Since there is a strong correlation between rising income inequality and rising crime rates, which is statistically supported by the t value of 10.29010976 and the probability value’s significance level of less than 1%, it is likely that rising income disparity in Indian states has likewise results in rising

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crime rates. With the exception of Tamil Nadu and Andhra Pradesh, the results demonstrate a positive link between income per capita and crime against women across all states. From this, it may be concluded that income disparities between the states may be blamed for the high rate of crime against women there. States with relatively high PCNSDP also tend to have higher rates of crime against women. In order to cure the social curse, it is thus recommended that the judicial system should intervene strongly, initiating public campaign and also including a redistribution of tax policies to lessen India’s PCNSDP disparity.

7. Conclusion The study has observed rising trends of crime rates and per capita incomes across the states in India for the period 2000–2019, and crime rates in the states are positively and significantly correlated with rising inequality in income. There, thus, is a need for policies related to reduction of crime against women and reduction of income inequality. The study thus suggests the interventions of the legislative system, government tax policies toward the rich persons, public awareness programs, etc. to reduce violence against women. Using technology to artificially intelligent policies and safety management and most importantly each soul needs a conscious mentality to stop any kind of crime. The present study does not explain the whole scenario of the crimes against women in Indian states. There are so many other factors affecting the crime rate. This field still needs more study and research.

References Aizer, A. (2010). The gender wage gap and domestic violence. The American Economic Review, 100(4), 1847–1859. Arora, J. (2007, March). An empirical investigation of the crimes against women in India. The Rights, 3(1). Banerjee, S. (2018, February 18). ‘Women on top’ and/or ‘economic progress’: What affects actual and reported crime against women? An analysis of socio-economic factors in India. MPRA Paper No. 84428, Retrieved from https://mpra.ub.unimuenchen.de/84428/ Cano-Urbina, J., & Lochner, L. (2017, October 22). The effect of education and school quality on female crime. Working Paper, NBER. Retrieved from https://www.nber. org/system/files/working_papers/w24061/w24061.pdf Cui, Z., & Hazra, D. (2017, July). Macroeconomic determinants of crime: Evidence from India. SSRN Electronic Journal. doi:10.2139/ssrn.3005019 Das, R. C., & Maity, N. (2020). Crime against women and human development: Do they have co-movements in Indian states? Vidyasagar University Journal of Economic, 25. Demombynes, G., & Ozler, B. (2005). Crime and local inequality in South Africa. Journal of Development Economics, 76, 265–292.

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Durante, A. (2012). Examining the relationship between income inequality and varieties of crime in the United States. A Senior Thesis in Economics, Spring. Retrieved from https://business.tcnj.edu/wp-content/uploads/sites/219/2013/07/Durante-thesis2012.pdf Dutta, M., & Husain, Z. (2009). Determinants of crime rates: Crime deterrence and growth in post-liberalized India. MPRA Paper 14478, University Library of Munich, Germany. Elgar, F. J., & Aitken, N. (2010). Income inequality, trust and homicide in 33 countries. European Journal of Public Health, 21(2), 241–246. Gupta, S., Sahoo, P. K., & Paltasingh, K. R. (2022). Does development deter crime against women? Panel evidence from India. Journal of Business and Socio-Economic Development, 2(1), 19–33. Hazra, D. (2020). What does (and does not) affect crime in India? International Journal of Social Economics, 47(4), 503–521. Kahlon, S. (2014). Crime against women in Chandigarh: A GIS analysis. International Journal of Management and Social Sciences Research (IJMSSR), 3(9), 82–87. Kelly, M. (2000, November). Inequality and crime. Review of Economics and Statistics, 82(4), 540–554. Kumar, A., Nizamie, S. H., & Srivastava, N. K. (2013). Violence against women and mental health. Mental Health & Prevention, 1(1), 4–10. Maity, S., & Sinha, A. (2018). Interstate disparity in the performance of controlling crime against women in India: Efficiency estimate across states. International Journal of Education Economics and Development, 9(1), 57–79. Mangoli, R. N., & Ganapati, M. T. (2009, December). Crime against women in India: A statistical review. International Journal of Criminology and Sociological Theory, 2(2), 292–302. Nandan, A., & Mallick, H. (2020, January 29). Does gender equality matter for regional growth and income inequality? An empirical analysis for the Indian states. Journal of International Development. Rashada, A. S., & Saraf, M. (2016). Income inequality and intimate partner violence against women: Evidence from India. No 222, Frankfurt School - Working Paper Series from Frankfurt School of Finance and Management, Frankfurt, Germany. United Nations. (2005). Crime and development in Africa. Mimeo. United Nations, New York, NY. Watts, C., & Zimmerman, C. (2002, April 6). Violence against women: Global scope and magnitude. The Lancet, 359(9313), 1232–1237. Wolf, A., Gray, R., Fazela, S., & March. (2014). Violence as a public health problem: An ecological study of 169 countries. Social Science & Medicine, 104, 220–227. Zhang, L. (2018). Violent crime and income inequality. Pomona, CA: California State Polytechnic University. Retrieved from https://scholarworks.calstate.edu/ downloads/zk51vk13p

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Chapter 23

Gender Responsive Budgeting Approach to Combating Climate Change Gamze Yildiz S¸ eren

Abstract Gender inequalities and climate change are global problems that concern the whole world. These two basic questions also have intersections with each other. Disruptions in natural life, usually due to human activities, lead to climate change over time. Climate change, on the other hand, deepens the already existing gender inequalities. Problems such as water scarcity, natural disasters, lack of access to clean water, and energy shortages are gender-responsive issues that affect women and men in different ways. All these factors, as supported in the literature, cause women to be in an even more disadvantageous position against climate change. One of the policy tools of states in the face of this problem is fiscal solutions. As a fiscal policy tool, government budgets can be used to eliminate the negative effects of climate change on women. This is called gender responsive climate budgeting (GRCB) in the literature. In order to apply GRCB, firstly sex-disaggregated data are required. In addition, institutional structures should be strengthened and strategic plans should be designed in a way that establishes the link between gender and climate change. This process should be carried out in a multistakeholder manner and the resources allocated for the financing of the problems should gain a gender-responsive structure. Keywords: Gender inequalities; climate change; global problems; government budgets; multistakeholder; gender responsıve budgeting

1. Introduction Climate change is one of the most important problems threatening the world today. Climate change threatens both food security and rural livelihoods. This situation may result in an increase in the difference between rich and poor. Therefore, climate change may cause women to become more impoverished. In Gender Inequality and its Implications on Education and Health, 273–284 Copyright © 2023 Gamze Yildiz S¸eren Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-83753-180-620231024

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the face of this problem, tackling public policies can be an effective solution. The gender dimension of climate change is a point that should not be overlooked. Gender-sensitive public policies can reveal a different dimension in combating climate change. Suggestions should be developed with a multidimensional approach. Gender responsive budgeting (GRB) is just that the distribution of resources gains a structure that takes into account disadvantaged segments. Integration of gender into budgets, development plans, and implementation areas through budgets can constitute an important dimension of combating climate change. The shape of climate finance can be a determinant of gender inequality as well as a determinant of sustainable development. Although there are positive steps taken, it is possible to state that current climate finance approaches are gender-blind. In this study, based on the effects of climate change on women, it is discussed to implement a gender responsive climate budgeting (GRCB) practice with the partnership of all stakeholders. At this point, some policy suggestions and evaluations are included.

2. The Link to Gender Inequality and Climate Change Although the fight against gender inequality has been fought by both governmental and nongovernmental organizations (NGOs) for 30 years, its connection with climate change is a relatively new but urgent problem. Problems in the climate affect gender inequality along with other inequalities. Women and girls in particular are disproportionately affected by climate change. In this context, the gender link of climate change is an issue that should be emphasized (Patel et al., 2021, p. 3). Women participate less in integration activities due to the inability of women to benefit from their right to education, their limited access to financial resources, and social and cultural norms. However, they have less institutional support (Islamic Relief, 2022, p. 7). Climate change means the deterioration caused by human activities beyond natural variables. The main reason for this is greenhouse gases. Behind the emissions are waste treatment, industrial processes, and burning of fossil fuels. As a result of these, global temperature increases and ecosystems are negatively affected (Eurostat, 2022). The climate change crisis, which poses a risk to national economies and the security of societies, affects marginalized groups/women and men in different ways. Those most affected by climate change, which deepens and exacerbates existing gender inequalities, are also disadvantaged people. Global shocks, COVID-19, global inequalities, and climate change are the risk and shock elements that summarize the year 2020. The progress of climate change negatively affects the economic opportunities of women who depend on climate-sensitive livelihoods. In this context, building policies that are gender sensitive, resistant to climate change, egalitarian, and fair can be an important step toward improving the situation of disadvantaged people (CABRI, 2022, p. 1; Hardiana et al., 2021, p. 9). In this context, although climate change is generally seen as a technical problem, it also has political and social aspects. For example, climate change is not gender neutral and can affect men and women differently (Dankelman, 2002,

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p. 24). Harvest problems, energy shortages, access to clean water, food shortages, natural disasters, diseases, migration, and conflicts may occur with the occurrence of climate change. Poor people are more affected as climate change affects their livelihoods. In this case, in terms of welfare, men and women are affected differently by climate change. Climate change affect breeds in contexts such as food security, health, water and energy resources, agricultural production, climate-related migration/conflicts, or natural disasters (Goh, 2012, p. 4). The effects of climate change on women can be classified as follows (Hardiana et al., 2021, p. 9): • Harvest Problems: Employment loss of women in the agricultural sector.

• • • • • •

Traditionally, women’s being a nutrient provider aggravates their domestic role. Energy Scarcity: The traditional requirement for women to gather energy for cooking and housework increases the workload. Clean Water: Lack of access to safe drinking water and adequate sanitation and hygiene facilities has a negative impact on pregnant women and children. Famine: Poverty as a result of famine poses the risk of discrimination in women’s education and early marriage. Natural Disasters: Women’s limited access to economic resources imposes greater burdens and safety risks on women during and after natural disasters. Disease: Increased burden for women who are the main caregivers for children, the elderly, and the sick. Migration: Security risks such as sexual violence and femicides

Studies that establish the gender/women and climate link are available in the literature. Studies in this area reveal that women are more negatively affected by climate change (Dankelman, 2010; Dankelman & Jansen, 2010; Denton, 2002; Escalante & Maisonnave, 2020; Khosla & Masaud, 2010; Masika, 2002; Negi, Sogani, & Pandey, 2010). In this context, the common point of research in the context of gender and climate change is that disadvantaged groups are not equally affected by climate change. It is concluded that discriminations such as gender, race, and class deepen inequality (Moosa & Tuana, 2014, pp. 682–683). It is noteworthy that the studies carried out were carried out especially in underdeveloped or developing countries. Here, the main argument behind the deepening of gender inequality by climate change is the phenomenon of women’s poverty. The fact that women are poorer than men, the agricultural structure of countries, and the social/cultural roles and duties imposed on women play a role in deepening these inequalities. Climate change is a global problem that has profound effects on the world ecosystem. Crop shortages, natural disasters, access to clean water, scarcity of natural resources, climate change, and increasing diseases can cause inequalities in society to deepen. Women’s daily needs are predominantly undertaken by women, and the phenomenon of women’s poverty leaves women vulnerable to climate changes (Ministry of Finance of the Republic of Indonesia & UNDP,

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2020, p. 1). The contexts in which gender equality and climate change should be examined in order for the relevant literature to develop are as follows (Pearse, 2017, p. 1): • Gender inequality dimension of the effects of climate change on women and

men • Climate management being sensitive to gender • The role of women in policies to be implemented against climate change • Steps to be taken to minimize the effects of climate change on gender

A country’s position against climate change largely determines its adaptation to changes. For example, a country that can quickly adapt to sea level rise, and a country that cannot do this, is more vulnerable to climate changes. In order to realize this defense, it is necessary to invest in measures sensitive to climate change. This situation also explains why the adaptation capacity of developed countries is high. In terms of women, factors such as occupation, education, and income can also determine the degree of vulnerability. Established socioeconomic structures and social norms disproportionately disadvantage women against climate change. This situation is not due to the vulnerability of women in society, but to their inadequate representation in decision-making processes and their limited access to information, representation, and resources. Therefore, countries where gender equality is not achieved or less achieved are more vulnerable to climate change (Tandon, 2020). Both developing and emerging market economies account for two-thirds of global greenhouse gas emissions. This situation leaves these economies more vulnerable to the dangers of climate change. Adaptation policies to be implemented create a significant financing need. However, high debts, limited budgets, and the increase in interest rates during the pandemic period also play an increasing role in government borrowing costs. This situation is one of the most important obstacles to meeting climate finance. At this point, public–private sector solidarity will be preferred, as well as a demand for funds from international institutions (Li, Natalucci, & Ananthakrishnan, 2022).

3. Gender Responsive Climate Policies: Assessment on the Basis of Fiscal Instruments In climate change debates, women are considered a marginalized group. (Rao, Lawson, Raditloaneng, Solomon, & Angula, 2019, p. 15). Putting the goal of ensuring gender equality at the center of climate change solution policies means integrating a gender perspective into policies that will reduce environmental and disaster risk. Equal participation of women/girls in decision-making processes is one of the most important channels in the fight against climate change. Developing women-oriented sustainable policies and ensuring resource management in this direction requires investing in gender-sensitive statistics and data. In this context, clean and renewable energy sources should be developed for a sustainable future, and technologies that women can use should be encouraged and

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investments should be made in these technologies (UNWomen, 2022a). Public spending is a central issue in climate management. It should improve service delivery capacity in the short term, increase infrastructure resilience in the medium term, and be consistent with decarbonization and environmental sustainability goals in the long term (Delgado, Eguino, & Lopes, 2021, p. 55). Strong fiscal measures need to be put in place for an economy that is resistant to climate change. There are various instruments such as public investment, carbon pricing, and the promotion of renewable energy and regulation. In order to manage the financial risks caused by climate change, the link between public policies and climate change must be established correctly. In addition, national/ sectoral development strategies should be established in line with the government’s commitments to reduce the effects of climate change. The financial instruments to be implemented to mitigate the effects of climate change should first be by preparing the financial framework that takes into account the income/ expenditure effects of climate policies. At this point, financial measures should include the requirements in the systematic analysis of climate impact, and expenditure items for climate change should be determined in the budgets and monitored in the next process. Although there was an interest in green budgeting in the late 2000s with the support of the United Nations in the world, it is not enough. Low-income South Asian countries such as Bangladesh and Nepal have adopted green budgeting. Accordingly, the environmental impacts of the expenditures are evaluated in advance. However, only 19 countries in the world apply climate budgeting (IMF, 2022, p. 21). There are several stages for gender equality and climate finance in a country. Following and implementing these stages will help to make more efficient and effective decisions at the point of financing. Effective steps for gender equality and climate finance are as follows (Unfcc, 2022, p. 4): • Gender Analysis: The relevant policy is carried out before it is implemented.

Gender disaggregated data are integrated into the process. • Inclusive Management Structures: Increasing coordination and participation

by strengthening institutional structures focusing on gender and climate. In addition, preparation of strategic plans in this direction. • Capacity Building: Supporting mainstreaming by developing the capacities of ministries and NGOs in order to provide climate finance and improve gender equality. • Resource Planning: Gaining a gender perspective of resources allocated for climate finance. While providing low-carbon economies in the face of climate change, gender equality should be ensured and women should be more involved in decision-making processes. Of course, there is a need for gender responsive climate finance instruments and resource allocations in order to implement these policies. At this point, the scarce public finance needs to be used effectively and efficiently. In addition, climate finance decisions should be taken at the point of

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accepting women’s rights as a human right. In this context, climate funds should not have a gender-blind structure (Schalatek, 2022, p. 1).

3.1 Gender Responsive Climate Budgeting (GRCB) Although climate change and gender have intersections, there is no priority area in policies and no clear budget is allocated. In this context, gender mainstreaming of the implemented policies is seen as an annex rather than a part of the process. Gender should be mainstreamed in all policy areas on climate change and comprehensive implementation plans, and a clear budget allocation should be put forward (Acosta et al., 2015, p. 1). The complex nature of the intersection of gender equality and climate change necessitates government intervention. Therefore, a holistic government response through fiscal policy tools is required. For climate change policies that do not deepen gender inequality, incentives for private investments and regulatory frameworks should be provided, and all these should be implemented with public budgets (Patel et al., 2021, p. 3). In this context, GRCB covers the reforms to be made on the basis of method implementation in all budget cycles in order to ensure that climate change and gender are taken into account. GRCB is also called double mainstreaming. It includes the coordinated and joint integration of both gender and climate changes into the budget (CABRI, 2022, p. 3).

3.2 GRCB Country Examples Country studies in the literature mainly focus on underdeveloped or developing countries. The starting point in country selection is gender inequalities, women’s participation in areas sensitive to climate change such as the agricultural sector, the existence of women’s poverty, and cultural/social norms deepening inequalities. For example, Indonesia is a country that is dependent on agriculture and where many women work in the informal sector. The fact that their livelihoods will be lost as a result of the change in climatic conditions poses a high risk for women. Climate changes increase the risks of natural disasters such as drought and flood. In addition, the traditional roles and responsibilities of women in meeting domestic needs cause the burden to be multiplied (Ministry of Finance of the Republic of Indonesia & UNDP, 2020, p. 1). In order to develop a performance-based budgeting system, the Indonesian government has established some mechanisms to improve the coherence of spending programs. One of them is Thematic Budget Tagging System (Gender Responsive Climate Budget Tagging), which is added to the budget document in order to monitor the output of the activity. System refers to the process of labeling in detail the outputs related to mitigating climate change and reducing its gender impacts (Unfcc, 2022, p. 17). Accordingly, various budget labeling systems have been established in order to emphasize the flow of public resources in thematic areas. The aim is to determine the groups and sectors with the greatest need and to ensure efficiency. Education, health, infrastructure, climate change mitigation and adaptation, and gender are

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some of the thematic areas in question. The one made on the climate theme of this labeling is called Climate Change Budget Tagging (CBT). Here, the government’s spending on climate change is tracked and was developed in 2014 (Hardiana et al., 2021, pp. 15–16). GRB studies in Indonesia are older than climate budgeting and started in 2010. Afterward, a common labeling was carried out by creating GRCB by combining climate change and gender. In 2021, with the support of the UNDP, Ministry of Finance, and Ministry of Women Empowerment and Child Protection, GRCB started labeling studies (Hardiana et al., 2021, pp. 17–19). Another GRCB application example is Bangladesh and Mexico. GRB has a long history in both countries and is stronger than climate budgeting. However, although there is progress toward the two, full integration has not yet been fully achieved in their overlap. Both GRB and climate change budgets are published in Bangladesh. The budget is based on a set of plans and policy frameworks. However, the climate budget does not include gender-sensitive indicators. There are still many steps to be taken before the plans turn into actions. Mexico has also integrated the perspective of climate change and gender into its budget processes. Progress, however, occurs at varying rates. Thematic annexes, in which public investments in gender equality and climate change can be monitored, are part of the budgets. However, in the case of this country, there are deficiencies in the integration of the intersection of gender and climate themes into the budget (Patel et al., 2021, p. 4). It is possible to come across both GRB and CRB applications in many African countries. The GRCB, on the other hand, offers countries an opportunity to build on their existing experience to develop techniques and approaches that cover both (CABRI, 2022, pp. 6–8). However, the effects of climate change with the COVID-19 pandemic will cause more women and girls to live in poverty in sub-Saharan Africa by 2030 (UNWomen, 2022b, p. 3). However, accountability of climate change and gender financing is still under development in most African countries. Lack of information and underdeveloped capacity are obstacles to accountability. For example, assessments on climate accountability in Ghana and Uganda indicate that efforts to integrate vulnerable groups into the budget process are insufficient. Rwanda, on the other hand, included dual mainstreaming in its national gender policy, gender, and climate change policies in 2021 (CABRI, 2022, pp. 6–8). Tanzania, Kenya, and Ethiopia include gender terms in their programs and plans and define gender as the most vulnerable group to climate change. But there is no mention of budget allocation for actions against women. In these countries, the capacity of institutions needs to be developed in order to include gender in climate change action plans (Aura, Nyasimi, Cramer, & Thornton, 2017, pp. 3–4). Making the country’s budget sensitive to climate and gender allows for more efficient, fair, and effective use of climate finance. Although gender and climate change reforms are implemented separately in countries, a more holistic approach has been developed recently. Double mainstreaming, supported by funding and international frameworks, has gained momentum. Using the CRB and GRB

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experiences, governments are trying to incorporate climate change gender into their budgets and national plans (CABRI, 2022, p. 3).

3.3 GRCB: Challenges and Solutions Although GRCB is a convenient tool for the effective and efficient use of public resources, it also brings some difficulties in the implementation stages. These difficulties can be listed as follows (Acosta et al., 2015, p. 4; Budlender, 2017, p. 39; CDKN, 2014; Hardiana et al., 2021, p. 22; Patel et al., 2021, p. 5–6; Li et al., 2022): • Ministries or related institutions having difficulties in GRB implementation

compliance • Inadequate implementation of regulations/mechanisms • Inadequate budget planning mechanisms • Lack of motivating systems in budgeting for climate change and gender

equality • Deficiencies in interministerial coordination • Structural constraints that make it difficult for women to access resources are

not adequately addressed. • Insufficient understanding of local ties/social-cultural relations in designing

environmental/agricultural policies • Requires a long-term effort • Policymakers have limited understanding of the concept of gender and limited

knowledge/capacity to turn knowledge into action. • Insufficient resources – financial, personnel, technical, research – allocated for

gender mainstreaming in climate change adaptation programs • COVID-19’s increasing borrowing costs by creating a significant constraint on

government budgets Although obstacles remain for the GRCB, it can be overcome with the joint efforts of the relevant ministries (Ministry of Finance, Ministry of Children, Ministry of Family, Ministry of Women) with the support of international organizations and relevant stakeholders. In addition, checklists can be created in order to monitor the results of climate change sensitive to gender. At this point, data separated by thematic areas are also important. Building the necessary capacity and monitoring policy outcomes are further steps to be taken (Hardiana et al., 2021, p. 23). In order to create this capacity, resources must be allocated. The GRCB covers the analysis and reporting of expenditures as well as allocations. Looking only at allowances creates an obstacle to seeing the difference between planned and actual expenditure (Budlender, 2017, pp. 39–40). As a result, the policies and plans to be created in order to implement an effective GRCB should provide a strong basis in the context of climate and gender. In order for the budget to be applicable, concrete policies/actions should be defined, the responsibilities of the actors involved in the process should be clearly defined,

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and budget costs should be calculated. Drawing the legal framework is the main starting point for policies to be included in the budget processes. Actions that will be made national priority by being guaranteed by law are very important at this point. The laws should also ensure the accountability of the budget, allowing the country’s actions to be traceable to climate and gender equality. At this point, sustainability is another situation that should be given importance. The next government needs to continue the steps taken toward climate change and gender equality. Otherwise, there is a risk that the policies will be periodic. Policies determined at the national level must be integrated into the international agenda. Knowing where the expenditures allocated to climate and gender are included in national budgets, and under which ministry and program they are carried out helps to clearly reveal the processes. In addition, all expenditures for both climate and gender should be included in the budget. Otherwise, an incomplete representation of the expenditures made in these two thematic areas will be presented. Generally, the GRCB reporting is undertaken by the finance ministries. Considering the fact that the personnel working here may not be familiar with the subject, necessary information and training activities should be implemented (Patel et al., 2021, pp. 5–6). Developing a gender-sensitive approach to climate change in the long term at the national and local level is possible with a climate policy framework that supports this. Development programs and plans should be prepared by adopting a gendered understanding in agriculture/natural resource management (Acosta et al., 2015, p. 1). In this process, efforts should be made to follow a multistakeholder path. Participation of parties such as NGOs, media, academia, and policymakers is necessary for accountability and capacity building. With the involvement of relevant actors, the demand for climate budgeting can be revived (CABRI, 2022, p. 8). In this context, budgets should aim to make climate policies and actions sensitive to gender, as well as attach importance to the establishment of monitoring and control mechanisms. Not only with an economic evaluation but also by examining social norms and values regarding why women are in a disadvantageous position in society, it should also be considered how stereotypes can be prevented in the long run. Thus, policy plans for increasing women’s representation, increasing their access to resources, and strengthening them can be made healthier (Atmadja, Liswanti, Tamara, Lestari, & Djoudi, 2020, p. 14).

4. Conclusion It is a phenomenon supported by the literature that gender inequality has deepened to the detriment of women in the face of climate change. Climate change is a global issue that needs to be addressed in the context of gender, and it is very important to adopt the GRB approach when considered on the basis of a solution. Using the budget tool in the fight against gender inequality and climate change can be considered as a solution. Although there are some difficulties, as examples in the world, there are application examples. For the CRB, some advice and precautions can be drawn in the light of the GRB. This study has tried to

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establish a link between climate change, gender, and public policies. In this direction, public policies should be implemented to not deepen gender inequality after climate change (especially in underdeveloped/developing countries). The defense mechanism to be established by countries against climate change has a financial dimension. In the case of public policies, the solution to this financial dimension can be provided by budgets. States can respond to the common intersection set of problems caused by gender and climate change with the GRCB. Here, policies need to acquire a comprehensive, accountable, and gender-sensitive structure. It is possible to summarize the steps to be taken as a priority for the implementation of the GCRB as follows: • • • • • • • •

Gender-disaggregated data Government will for policy action Sustainable and consistent policies Identifying the underlying cause of gender inequality at the country/region/ local level and getting to the root of the problem Transformation of social norms Awareness and training activities Building a multistakeholder process Ensuring women’s political representation and participation in decision-making processes

Studies in this field should increase by gaining a multidisciplinary structure. Because in order to understand which mechanisms work in the connection between climate change and gender, which threatens poor societies more, it will be beneficial for academic studies to intensify in this area.

References Acosta, M., Ampaire, E. L., Okolo, W., & Twyman, J. (2015). Gender and climate change in Uganda: Effects of policy and institutional frameworks. CCAFS Info Note. Atmadja, S. S., Liswanti, N., Tamara, A., Lestari, H., & Djoudi, H. (2020). Leveraging climate finance for gender equality and poverty reduction a comparative study. Center for International Forestry Research (CIFOR). Retrieved from https://www.cifor.org/publications/pdf_files/Reports/Climate-UNDP-Report.pdf Aura, R., Nyasimi, M., Cramer, L., & Thornton, P. (2017). Gender review of climate change legislative and policy frameworks and strategies in East Africa. CCAFS Working Paper no. 209. Budlender, D. (2017). Tracking climate change funding: Learning from gender-responsive budgeting. International Budget Partnership. Retrieved from https://internationalbudget.org/wp-content/uploads/gender-climate-budgetingsynthesis-april-2021.pdf CABRI. (2022). Gender and climate-change budgeting and finance: Lessons from the IBFCCA Programme. Policy Brief. Retrieved from https://www.cabri-sbo.org/ uploads/files/Documents/IBFCCA-Gender-and-CC-briefing-note_ENG_Final.pdf

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CDKN. (2014). How Kenya can turn its gender and climate change commitments into reality. Retrieved from https://cdkn.org/story/opinion-how-gender-issues-affectclimate-action-in-kenya?loclang5en_gb Dankelman, I. (2010). Gender and climate change: An introduction. London: Earthscan. Dankelman, I. (2002). Climate change: Learning from gender analysis and women’s experiences of organising for sustainable development. In R. Masika (Ed.), Gender, development, and climate change. Oxford: Oxfam. Dankelman, I., & Jansen, W. (2010). Gender, environment and climate change: Understanding the linkages. In I. Dankelman (Ed.), Gender and climate change: An introduction. London: Earthscan. Delgado, R., Eguino, H., & Lopes, A. (2021). Fiscal policy and climate change: Recent experiences of finance ministries in Latin America and the Caribbean. IDB. Retrieved from https://shs.hal.science/halshs-03371797/document Denton, F. (2002). Climate change vulnerability, impacts, and adaptation: Why does gender matter? In R. Masika (Ed.), Gender, development, and climate change. Oxford: Oxfam. Escalante, L., & Maisonnave, H. (2020). Assessing the impacts of climate change on women’s poverty and domestic burdens: A Bolivian case study. No. Hal-02970249, 20. Eurostat. (2022). Climate change – Overview. Retrieved from https://ec.europa.eu/ eurostat/web/climate-change Goh, A. H. (2012). A literature review of the gender-differentiated impacts of climate change on women’s and men’s assets and well-being in developing countries (pp. 1–44). CAPRi Work, International Food Policy Research Institute. Retrieved from https://www.worldagroforestry.org/sites/default/files/4.pdf Hardiana, M. D., Nathalia, D., Rochmawati, A. D., Charlotte, G., Kezia, U., Kurniawan, D. I., & Miftadi, L. Z. (2021). Gender responsive climate budgeting in Indonesia. UNDP Indonesia. Retrieved from https://www.undp.org/indonesia/ publications/gender-responsive-climate-budgeting-handbook IMF. (2022). Fiscal monitor: Fıscal polıcy from pandemıc to war. Retrieved from https://www.imf.org/en/Publications/FM Islamic Relief. (2022). Gender-based approach to climate change adaptation, a research paper from Lombok, West Nusa Tenggara, Indonesia. Birmingham: Islamic Relief Worldwide. Khosla, P., & Masaud, A. (2010). Cities climate change and gender: A brief overview. In I. Dankelman (Ed.), Gender and climate change: An introduction. London: Earthscan. Li, B., Natalucci, F., & Ananthakrishnan, P. (2022). How blended finance can support climate transition in emerging and developing economies. IMFBlog. Retrieved from https://www.imf.org/en/Blogs/Articles/2022/11/15/how-blended-finance-cansupport-climate-transition-in-emerging-and-developing-economies?utm_medium5 email&utm_source5govdelivery Masika, R. (Ed.). (2002). Gender, development, and climate change. Oxford: Oxfam. Ministry of Finance of the Republic of Indonesia & UNDP. (2020). Gender responsive climate change budgeting. Policy Brief. Retrieved from https://www.undp.org/ indonesia/publications/study-gender-responsive-climate-budgeting

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Index A. K. Sen’s capability approach, 214 Aboriginal people, 224–225 Aboriginal women, 224–226 media’s agenda setting theory, 226–227 method and results, 228–229 Age dependency ratio, 184 Agenda 21, 248 Agenda setting, 226–227 Air pollution, 126 Air pollution, level of, 128 Alkire–Foster methodology, 102–103 Allocative efficiency (AE), 58 Analysis of Variance (ANOVA), 95, 97 Angry fighters, 225 Anti-dowry Act, 219 Australian history, 225 Australian women, 224 Autoregressive distributed lag model (ARDL model), 184 Awareness and willingness among women, lack of, 84–85 Balika Samriddhi Yojana, 20–21, 44 Bangaru Thali, 44 Basic facility, 93 Beijing Declaration, 192, 194 Beijing Declaration and Platform for Action (BPA), 215–216 Beijing Platform for Action (BPfA), 194 Beijing Women’s Conference, 216 Benessere Equo e Sostenibile (BES), 250–251 Beti Bachao Beti Padhao Yojana (BBBP Yojana), 88, 114–115, 220, 259–260 Bhagyalaksmi Scheme, 44

Bill and Melinda Gates Foundation (BMGF), 194–195 Binary logit model, 104 Birth order, 115, 117 Body Mass Index (BMI), 95, 97 Capability, 214 Capability measure (CPM), 214 Charnes, Cooper, and Rhodes model (CCR model), 57–58 Child deprivation across indicators and level of deprivation between male child and female child, 105 Child deprivation ratio (CDR), 103–104 among male child and female child in West Bengal, 108 Child health, 102 Child Marriage Restraint Act, 219 Child sex ratio (CSR), 2, 12 data, data source, and research methods, 15–16 econometric estimation, 16–20 literature review, 13–14 policy implication, 20–21 research gap and scope of present study, 14–15 trend of OSR and CSR in India, 13 variable definitions, sample means, and range, 17 Childhood marriage, 155 Climate change, 2, 7, 273–274 gender responsive climate policies, 276–281 link to gender inequality and, 274–276 Climate Change Budget Tagging (CBT), 278–279

286

Index

Coefficient Effect (C), 24–25, 47 Cointegration analysis, 35 Cointegration test, 33, 35–36 and estimates of parameters, 185–188 Commission on the Status of Women (CSW), 249 Communication networks, 224 Condensed Course of Education for Adult Women, 93–94 Constant returns to scale (CRS), 57–58 Convenience sampling technique, 239 Convention of the Elimination of All Forms of Discrimination Against Women (CEDAW), 219, 249–250 Convention on Biological Diversity, 248 Correlation coefficient, 263 between PCNSDP and crime rate, 266–270 Coverage Gap Index (CGI), 102–103 COVID-19, 235–236 impact of COVID-19 on need of enhancing savings behavior level, 242 Crime against women (CW), 259–260, 262 Crime rate, 259–260 Cross-sectional dependence test (CD test), 63 Cross-sectional study, 238 Curriculum practices, 74–76 Data Envelopment Analysis (DEA), 57–58 Data2x platform, 194–195 Decision-making units (DMUs), 57–58 Decomposition, 52 Deen Dayal Upadhyay Grameen Kaushal Yojana (DDUGKY), 220 Degrees of freedom (d. f), 95 Department of Economic and Social Affairs (DESA), 154

Descriptive statistics, 184–185, 239–240 Developing countries, 24 Development, 91, 93, 142 Development of Women and Children in Rural Areas (DWCRA), 93–94 Diagnostics tests, 240–242 Dickey–Fuller Min-t test, 35 Digital India, 220 Digital media, 225–226 Digital participation, 229 Discrimination, 13 Disparities, 93 District Information System for Education (DISE), 61 Domestic science, 75 Domestic violence, 167 Double burden of malnutrition (DBM), 32 Dowry, 143–144 Dropout, 114–115, 117 birth order, 117 findings from NSSO data, 119–121 Dual-systems theory, 73 Earth Summit, 248 Econometric analysis of multidimensional child deprivation in West Bengal, 108–110 Econometric estimation, 16–20 Economic empowerment, 167 Economic growth, 127, 129 Education, 1–2, 56–57, 69–70, 79–80, 91, 93, 113–114, 127–128, 178 Education expenditures, 23–24, 44–45 Educational attainment, 179 Educational level of male-female, 96 Emerging market economies (EEs), 141–142 cultural factors, 143–144 data and methodology, 144–146 economic factors, 142–143 literature review, 142–144

Index research gap and objective of study, 144 results and findings, 146–148 Employment inequality, 80 Empowerment (see also Women empowerment (WE)), 1 Endogenous growth models, 56–57 Endowment Effect (E), 47 Engle–Granger Cointegration test, 35–36 Environment, 6 Environmentalists, 225 Equal Remuneration Act, 219 Error correction models, 35 European Commission, 214 Evidence and Data for Gender Equality Project (EDGE), 194–195 F-statistic, 185 F-test, 239, 242 Family Act, 255 Female as head of the household (FHH), 128–129 Female Labor Force Participation (FLFP), 5, 142 Female labor force participation rate (FLFPR), 4–5, 126, 217 factors affecting, 128–129 result of estimation of FLFPR equation, 133 Female life expectancy (FLE), 4–5, 127 factors affecting, 127–128 results of FLE equation, 130–133 Female literacy rate (FLR), 2, 12–13 effect of female literacy rate on variation of CSR, 14 Female work force participation rate (FWFPR), 2, 15 effect of female work force participation rate on variation of CSR, 14 Feminist theories, 70–74 liberal feminists, 70–71 Marxist feminists, 71–72 psychoanalytic feminists, 72–73

287

radical feminists, 72 socialist feminists, 73–74 Feminization of poverty, 204 Feminization-U hypothesis, 142–143 First-differentiated GMM estimator, 145–146 Fiscal instruments, assessment on basis of, 276–281 Fondo per l’impresa femminile, 255 Forest Principles, 248 Gender, 12, 69–70, 102, 177–178, 192, 214, 224 conferences for women’s emancipation and outcome, 215–217 feminist theories, 70–74 India story of position and status of women, 217–218 integration of gender in global goals, 195–199 objective of study, 215 school practices and representation in textbooks, 74–77 Gender analysis, 214 Gender bias, 14, 44–45, 102 data base, 103 literature review, 102–103 methodology, 103–104 results, 105–110 Gender budgeting, 188 Gender data, 6, 193–194 Gender Development Index (GDI), 12, 91, 93, 194 Gender differences, 255 Gender disaggregated data for policy action, 193–195 Gender disparity in education, 80 Gender Empowerment Measure (GEM), 12, 214–215 Gender equality, 1, 177–178, 192, 215 analysis, 2 examples, 1–2 implications, 2–5 and implications to other SDGs, 5–8

288

Index

recommendations for enhancing environment and urbanization policies to support, 206–209 Gender Equality and Women’s empowerment (GEWE), 215–216 Gender Equality Index (2010), 194 Gender gap in employment, 85–87 Gender inequality, 24, 31–32, 56–57, 79–80, 93, 102, 142, 154, 178, 215, 223 analysis of policy measures taken by government to reduce, 87–88 causes of gender inequality inhibiting global SD and achievement of SDGs, 154–155 data analysis and interpretation, 82–88 data and methodology, 25–26 data source and methodology, 82 descriptive statistics, 26 in India, 218–220 link to climate change and, 274–276 literature review, 24–25, 80–81 principle, 159 research gap and objective, 81–82 results, 26–28 Gender Inequality Index (2010), 194 Gender inequality index (GII), 25–26, 32, 130 analysis of GII and OW using time series modeling, 36 trend in GII and population of overweight women, 34 Gender norm, 142 Gender parity, 178–179 Gender practices, 69–70 Gender responsive budgeting (GRB), 273–274 Gender responsive climate budgeting (GRCB), 7, 273–274, 278 challenges and solutions, 280–281 country examples, 278–279

Gender responsive climate policies, 276–281 Gender statistics, 194 Gender studies, 69–70 Gender Sub Plan, 93–94 Gender Sustainable Development Index (GSDI), 7, 252 from birth of commission on status of women to goal 5 of 2030 agenda, 249–250 material and methods, 250–252 results, 252–253 sustainable development, 247–249 Gender violence (GV), 165–166 Gender-based violence literature review, 165–167 methodology and data source, 168–169 research gap and objectives, 167–168 result analysis, 169–172 Gender-related Development Index (GDI), 214–215 Gender-sensitive public policies, 273–274 Gendered discourses, 224 General Category States (GCS), 56–57 Generalized method of moments (GMM), 145–146 GINI coefficient, 260–262 Global Gender Gap Index (2006), 194 Globalization, 80 Good governance (GG), 262 Governance, 2 Government budgets, 280 Government expenditures, 26 Government of India, 13, 259–260 Gross fixed-capital formation (GFCF), 129 Hashtag, 226 Head count ratio (HCR), 15 Health, 2, 91, 93, 97–98, 178 Health expenditures, 23–24 Helpless victims, 225 Higher secondary (H.S.), 56–57

Index Home science, 75 Household expenditure during lockdown, 238 influence, 240–242 Household size, 129 Human capital development, 185 Human development, 91–93 Human Development Index (HDI), 91, 93, 214 Human development report (HDR), 12 Human flourishing, 214 Human poverty index (HPI), 214 Hunar Se Rozgar Tak, 220 Income disparity, 262 Income inequality (IE), 7, 259–260, 262 Index, 251 India, 33, 44–45, 56–57, 79–80, 102, 126, 166–167, 195, 236 employment trend, 217–218 gender inequality, social justice, and public action, 218–220 need for universal social protection, 218 story of position and status of women, 217–218 Indian states, 259–260 brief literature review, 260–262 data and empirical methodology, 263–264 objectives and hypotheses, 262 results and analysis, 264–270 theoretical underpinning, 262–263 Indian Succession Act, 219 Industries and politics, 156–157 Industry, 2 Integrated Child Development Services program (ICDS program), 88 Intensity of child deprivation (ICD), 103–104 among male child and female child in West Bengal, 108 Interaction effect (I), 47

289

International Union for Conservation of Nature (IUCN), 155 Intersectional approach, 193–194 Intersectionality, 193–194 Italian Regions, 253 Johansen Cointegration test, 35–36 Judicial structure (JS), 262 Kanya Jagriti Jyoti Scheme, 44 Kanyashree Prakalpa, 44 Kasturba Gandhi Balika Vidyalaya (KGBV), 87–88 Kishori Shakti Yojana (KSY), 2, 13, 17 descriptive statistics, 16 effect, 16–20 Kitakyushu City Basic Plan for Gender Equality, 209 Labor market, 217–218 Labor participation rate (LFR), 85 Ladli Scheme, 44 Least developed countries, 24 Liberal feminists, 70–71 Life expectancy, 127 Literacy rate, 95–96 Little Data Book on Gender, 194–195 Long-run relationship, 35–37 Low-and middle-income countries (LMICs), 32 Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), 220 Mahatma Gandhi National Rural Guarantee Act (MNREGA), 88 Mahila E-Haat, 220 Mahila Shakti Kendra, 220 Male literacy rate (MLR), 15 Marginal effect (ME), 130–133 Marx and Engels’ theory, 71 Marxist feminists, 71–72 Maternal education, 14

290

Index

Mean square (MS), 95 Media (M), 224, 262 agenda setting theory, 226–227 Middle East and North Africa region (MENA region), 156, 179, 181 Millennium Development Goals (MDGs), 166–167, 195–196 Ministry of Health and Family Welfare (MOHFW), 168 Ministry of Human Resource Development, 166–167 Ministry of Women and Child Development, 259–260 Mixed-method approach, 228 Mortality inequality, 93 MUDRA, 220 Multicollinearity, 240 test, 48 Multidimensional Child Deprivation Index (MCDI), 103–104 among male child and female child in West Bengal, 108 Multidimensional deprivation, 102 Multiple regression analysis, 241 Multistakeholder, 281 Natality inequality, 80, 93 National Apprenticeship Promotion, 220 National Commission for Women, 166–167 National Crime Record Bureau (NCRB), 263 National Family Health Survey (NFHS), 94–95, 116 unit level data, 102–103 National Family Health Survey 2019–20, fifth (NFHS-5), 167–168 National Mission for Empowerment of Women (NMEW), 93–94 National Perspective Plan (NTP), 219

National Programme for Education of Girls at Elementary Level, 93–94 National Programme of Nutritional Support to Primary Education, 93–94 National Rural Livelihood Mission, 220 National Sample Survey Office (NSSO), 3, 45, 236 data, 113–115 dataset, 44–45 findings from, 119–121 National Skills Mission, 220 National Urban Livelihood Mission, 220 Native women, 224 Neoliberal globalization, 214 Neoliberalism, 227 Net state domestic product (NSDP), 263 Nigeria, 178 empirical literature, 183 literature review, 179–183 method of study, 184 policy recommendations, 188 results, 184–188 stylized facts, 179–182 theoretical framework, 182–183 Nongovernmental organizations (NGOs), 274 North Bengal Region area of study, 94 data source and methodology, 94–95 education and health in, 94 health and nutrition, 97–98 objectives of study, 94 results, 95–97 Nutrition, 32, 97–98 database and methodology, 33 policy recommendation, 38 results, 33–37 Nutrition Landscape Information Systems (NLiS), 33

Index Oaxaca–Blinder decomposition analysis, 3, 44–45, 47 data, 45 factors influencing expenditure decisions in primary schools in rural India, 47–48 literature review and research hypothesis, 44–45 results, 48–52 Oedipus complex, 72–73 One-digit level occupations, 82 Output-oriented technical efficiency (OUTTE), 58, 62 determinants, 60–61 factors determining OUTTE, 63 methodology for finding outputoriented TE score, 59–60 result of OUTTE estimation, 61–63 variables, 64 Overall sex ratio (OSR), 12 Overweight (OW), 32 analysis of GII and OW using time series modeling, 36 trend in GII and population of overweight women, 34 Ownership inequality, 80 Pairwise correlation matrix, 240 Parental education, 48 Patriarchal society, 84 Patriarchy, 179–181 Patrilineality, 143–144 Patrilocality, 143–144 Per capita net state domestic product (PCNSDP), 2, 15, 262 Periodic Labor Force Surveys (PLFS), 217–218 Piano Nazionale di Ripresa e Resilienza (PNRR), 254–255 Piano per il Sud 2030, 254 Platform for Action (PfA), 192 Police administration (PA), 262 Pooling regression model, 16 Poverty (POV), 83–84, 126–127, 129, 143–144, 214

291

conferences for women’s emancipation and outcome, 215–217 India story of position and status of women, 217–218 objective of study, 215 trap, 215 Pradhan Mantri Kaushal Vikas Yojana (PMKVY), 220 Pre-Natal Diagnostic Test Act (PNDT Act), 20–21 Prime Minister Employment Generation Program (PMEGP), 220 Psychoanalytic feminists, 72–73 Public action in India, 218–220 Public expenditures, 5–6 Radical feminists, 72 Rashtriya Mahila Kosh (RMK), 2, 15, 17 descriptive statistics, 16 effect, 16–20 Ratio of female to male labor force participation rate (RLPR), 144–146, 148 Regression equation, 25–26, 46, 241–242 Regression model, 26–27 Religious groups, 44–45 Representation, 74–77 Reproduction Theory, 73–74 Reserve Bank of India (RBI), 15, 263 Right to Education Act (RTE), 114–116 Rio Declaration on Environment and Development, 248 Royal Commission of Aboriginal people, 225 Rural Development and Selfemployment Training Institute (RUDSETI), 220 Rural India, 126 data sources, 129–130 estimation methodology, 130

292

Index

factors influencing expenditure decisions in primary schools in, 47–48 literature review, 127–129 results, 130–133 Sarva Shiksha Abhiyan, 87, 114–115 Save the Girl Child, 20–21 Savings, 236–237 Scheduled Caste (SC), 15, 60–61 Scheduled Tribe (ST), 15, 60–61 School enrollment, 56–57 data set and sources, 61 determinants of technical efficiency, 60–61 empirical findings, 61–63 methodology of TE estimation, 58–60 School practices, 74–77 Schools, 113–114 Science, technology, engineering, and mathematics (STEM), 83 Self Help Group (SHG), 2, 13, 17, 88 descriptive statistics, 16 effect, 16–20 Sex disaggregated data, 193 Sex ratio (SR) (see also Child sex ratio (CSR)), 96–97, 128–129, 142 Simultaneous dependence, 126 Simultaneous panel model, 126–127 Skill India, 220 Social customs, beliefs, and practices, 84 Social empowerment, 167 Social expenditures, 179 Social Institutions and Gender Index (2010), 194 Social justice, 2, 214 conferences for women’s emancipation and outcome, 215–217 in India, 218–220 India story of position and status of women, 217–218 objective of study, 215

Social media, 224 Socialist feminists, 73–74 Solow model, 56–57 Special Category States, 56–57 Special opportunity inequality, 93 Spousal violence (SV), 168 Stacking method, 251–252 Standard deviation (SD), 263 Startup India, 220 Stationarity test, 33 STEP, 220 Structural break test, 33 Sukanya Samriddhi Yojana, 88, 114–115 Sum of squares (SS), 95 Sustainability, 154 Sustainable development (SD), 154, 247, 249 causes of gender inequality inhibiting global SD and achievement of SDGs, 154–155 Sustainable Development Goals (SDGs), 1, 154, 193, 204, 248–249 causes of gender inequality inhibiting global SD and achievement of SDGs, 154–155 gender disaggregated data for policy action, 193–195 gender equality and implications to other, 5–8 integration of gender in global goals, 195–199 methodology and data, 193 recommendations for enhancing WE to support achievement of SDGs, 155–159 Sustainable Development Solutions Network (SDSN), 248–249 Sustainable environment, 204–206 SWADHAR Greh, 220 System GMM technique, 145–146 t-test, 239

Index Technical Efficiency (TE), 3, 56–57 determinants of technical efficiency, 60–61 methodology of TE estimation, 58–60 Territorial gap, 251 Textbooks, 74–77 Thematic Budget Tagging System, 278–279 Tolerance, 48 Trend analysis, 264–266 Twitter, 229 Unified-systems theory, 73 Union territory (UT), 167 Unit root test, 184–185 United Nations (UN), 154, 204 United Nations Development Programme (UNDP), 12, 32, 93–94, 214 United Nations Framework Convention on Climate Change (UNFCCC), 248 United Nations Statistics Division (UNSD), 194 Universal social protection, India’s need for, 218 Unorganized sector, 235–236 data, 239 literature review and hypotheses development, 237–238 methodology, 238–239 policy recommendations, 244 questionnaire design, 238 results, 239–242 sampling design, 239 statistical tool, 239 study design, 238 Urbanisation of poverty, 204 Urbanization (URB), 2, 15, 129, 204 importance of and need for sustainable environment, 204–206 policies, 6, 207–208

293

Variable returns to scale (VRS), 57–58 Variance inflationary factor (VIF), 240 Vector error correction model (VECM), 33, 36–37 Violence against women, 259–260 Wald test, 169 Well-being, 214 West Bengal distribution of male child and female child by level of deprivation in, 105–108 econometric analysis of multidimensional child deprivation in, 108–110 MCDI, CDR, and ICD among male child and female child in, 108 Within-household gender bias, 44 Women agency, 16 Women Component Plan, 93–94 Women empowerment (WE), 5, 154, 166–167, 192 causes of gender inequality inhibiting global SD and achievement of SDGs, 154–155 importance of and need for sustainable environment, 204–206 literature review, 165–167 methodology and data source, 168–169 recommendations for enhancing environment and urbanization policies to support, 206–209 recommendations for enhancing WE to support achievement of SDGs, 155–159 research gap and objectives, 167–168 result analysis, 169–172 Women Helpline, 220

294

Index

Women’s Economic Opportunities Index (2010), 194 Women’s health, 31–32 Women’s political power (WP), 127–129 Work participation rates (WPRs), 85

World Bank, 194–195 World Earth Day, 248 World Economic Forum (WEF), 217 Youth unemployment (YU), 262