Higher Education Employment and Economic Development in India: Problems, Prospects, and Policies 9781032103044, 9781032360157, 9781003329862

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‘This book examines important issues and policies on higher education and employment which are essential for economic growth, human development, and well-being of the population. It is a rich collection of chapters by well-known scholars in this field. The editors made sincere efforts to put together this important volume which is a must read for all the stakeholders.’ S. Mahendra Dev, Director and Vice Chancellor, Indira Gandhi Institute of Development Research, Mumbai ‘This book covers the trisection of the three most significant challenges facing the Indian Economy – first, the transformation of higher education by giving it a broad and inclusive base which can also power India into the knowledge economy and give an overall impetus to development; second, that of employment where recent changes in terms of employment growth and unemployment have been extremely disturbing, especially among the educated youth, and finally the challenge of labour markets, where the precariousness of employment denoted by the recent migration crisis has highlighted the need for appropriate labour policy changes and universal social protection to promote decent work for all. The contributions in this book are a significant addition to the knowledge and policy base which seeks to address these three major challenges.’ Ravi S. Srivastava, Former Chairperson, Institute of Development, Studies, Jaipur; Director, Centre for Employment Studies, Institute for Human Development; President, Uttarakhand & Uttar Pradesh Economics Association; Former Professor of Economics and Chairperson, Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi; and Member, NCEUS, GoI ‘Put together in this volume is a collection of 17 insightful research chapters on higher education, employment, and development by about 25 scholars drawn from India and abroad. Recognising the powerful role of education as an income equalizer and as a significant contributor to growth and development, the authors unravel the intricate relationships between higher education and employment/unemployment informal and informal sectors, and development, besides probing quite a few emerging developmental issues. It is a very useful contribution to the analytical knowledge base on the subject. Students, researchers, and policy makers interested in development issues will find this volume edited by Prof R.K. Mishra and his colleagues to be a significant addition on to their resource material.’ Jandhyala B.G. Tilak, ICSSR National Fellow and Distinguished Professor, Council for Social Development, New Delhi; and Former Vice Chancellor, National University of Educational Planning & Administration, New Delhi

‘The book titled Higher Education, Employment, and Economic Development: Problems, Prospectus, and Policies deals with important issues of current concern relating to higher educational inequality, income inequality, jobless growth, and impact of global value chains and foreign direct investment on manufacturing employment. It shows that the relationship between educational inequality, and income inequality is mutually reinforcing and educational institutions in India are perpetuating inequality. It suggests policies to address current concerns particularly unemployment. The book provides a clear, rigorous, and through exposition of its themes. The book is valuable to policy analysts and students of economics and management.’ Radhakrishna Rokkam, Chairman, Centre for Economic and Social Studies, Hyderabad; Former Chairperson, Madras Institute of Development Studies, Chennai; Former Chairman, National Statistical Commission, Government of India; Former Director, Indira Gandhi Institute of Development Research, Mumbai; and Former Vice Chancellor, Andhra University, Visakhapatnam

Higher Education, Employment, and Economic Development in India

This volume examines the role of higher education and employment in economic development in emerging economies like India. It looks at the contours of higher education policies and the labour market dynamics to explore ways to address joblessness and income disparity. The book discusses themes such as quality and access to higher education, the shift towards private investment in higher education, demographic dividend and joblessness among youth, social and income inequalities, labour migration and employment, and the participation of women in the workforce, among others. It provides insights into the challenges relating to employment generation in the industrial sector. It also offers solutions and policy measures to move towards sustainable growth, better employment opportunities in various sectors of industries, and human development. Rich in empirical data, this volume will be of interest to students and researchers of education, economics, development studies, sociology, gender studies, and social and economic policy. Ram Kumar Mishra is a Senior Professor, ONGC Subir Raha Chair, and NLC Chair at the Institute of Public Enterprise, Hyderabad, India, and is a graduate of the International Management Program, SDA Bocconi, Milan, Italy. He has been a Fellow of the British Council and Commonwealth Secretariat. He has taught at the University of Bradford, United Kingdom, and was a Visiting Professor at Maison Des Sciences De L’ Hommes, Paris; University of Technology Mara, Malaysia; and Faculty of Economics, University of Ljubljana, Slovenia. He is a member of the UN Task Force on Standards of Excellence in Public Administration and Education. He has handled assignments for the Ministry of Finance, Ministry of Power, Ministry of Trade and Commerce, and Ministry of Heavy Industry and Public Enterprises, Government of India. He has been a management consultant to several organisations, including DFID, Deloitte, Adam Smith Institute, ADB, and Centre for Good Governance, Hyderabad, India. He is a member of the editorial boards of many international and national journals.

Sandeep Kumar Kujur is an Assistant Professor of Economics at the Department of Humanities and Social Sciences, Indian Institute of Technology Madras (IITM), Chennai, Tamil Nadu, India. Earlier, he was with the Institute of Public Enterprise, Hyderabad, India. He has been a Visiting Fellow at the Department of Economics, Jadavpur University, Kolkata, and a Visiting Faculty to the Department of Business Management, Central University of Odisha, Koraput. He holds a PhD in Economics from Jawaharlal Nehru University, New Delhi. His research interests include industrial economics, economics of technological change, and labour and development economics. K. Trivikram holds an MA and a PhD in Economics and is a Professor at the Institute of Public Enterprise, Hyderabad, India, with close to four decades of experience in teaching, training, and research. He has designed and conducted a good deal of management development programmes for central and state-level public enterprises for senior and middle-level executives, civil servants, and corporate executives. His areas of interest include business environment, public policy, corporate governance, and CSR.

Higher Education, Employment, and Economic Development in India Problems, Prospects, and Policies

Edited by Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

First published 2023 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 selection and editorial matter, Ram Kumar Mishra, Sandeep Kumar Kujur and K. Trivikram; individual chapters, the contributors The right of Ram Kumar Mishra, Sandeep Kumar Kujur and K. Trivikram to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record has been requested for this book ISBN: 978-1-032-10304-4 (hbk) ISBN: 978-1-032-36015-7 (pbk) ISBN: 978-1-003-32986-2 (ebk) DOI: 10.4324/9781003329862 Typeset in Sabon by Deanta Global Publishing Services, Chennai, India

Contents

List of figures List of tables List of contributors Foreword Prelude Acknowledgements List of abbreviations   1 Higher education and employment in India: An introduction

x xiii xvii xx xxiii xxvi xxviii 1

RAM KUMAR MISHRA, SANDEEP KUMAR KUJUR, AND K. TRIVIKRAM

PART 1

Higher education participation, employment, and income inequality

17

  2 Participation in higher education: The role of institutions

19

JANNET FARIDA JACOB

  3 Gender gaps in employment preferences among university graduates in India

37

PRADEEP KUMAR CHOUDHURY AND AMIT KUMAR

  4 Educational expansion and income inequality in India VACHASPATI SHUKLA

58

viii Contents PART 2

Quality and the role of higher education institutions in economic development

75

  5 Indian higher education: Introspecting the state of quality

77

K.M. JOSHI AND KINJAL AHIR

  6 The role and impact of academics’ societal engagement

95

HAIYANG CHEN AND SAM N. BASU

  7 The roles of agricultural universities in serving regional economic development

111

MIN ZHAO, SHUXIA REN, AND JUN DU

PART 3

Demographic dividend, joblessness, and informality

129

  8 Harnessing India’s demographic dividend: The way forward

131

JAJATI KESHARI PARIDA

  9 Jobless growth in India: Employment–unemployment of educated youth

151

MONA KHARE AND SONAM ARORA

10 Agricultural exports and informal sector in India: A macroeconomic perspective

166

ANIRBAN KUNDU

PART 4

Labour migration and female employment

197

11 Industrial and occupational distribution of migrant workers in India

199

MOHD. IMRAN KHAN

12 Understanding the decline in women’s employment in rural India RAJENDRA P. MAMGAIN AND KHALID KHAN

221

Contents  ix

13 Low female labour force participation: Evidence from urban Kerala 238 RENUKA S. AND ANU ABRAHAM

PART 5

Employment generation in the manufacturing sector

257

14 BRICS–EU global value chains trade and manufacturing employment 259 USHA NORI AND RAM KUMAR MISHRA

15 Employability of FDI in India’s manufacturing firms

280

SANJAYA KUMAR MALIK

16 Technology and labour in India’s manufacturing

298

RAM KUMAR MISHRA AND SANDEEP KUMAR KUJUR

17 Imported inputs and labour in India’s manufacturing

318

SANDEEP KUMAR KUJUR AND DITI GOSWAMI

Appendix Index

331 341

Figures

3.1 Share of Graduates Involved in Self-Employment and Salaried Jobs by Major States in India (21–65 years) 3.2 Distribution of Self-Employed Graduates by Socioeconomic and Educational Indicators 3.3 Distribution of Salaried Graduates by Socio-economic and Educational Indicators 3.4 Probability of Being into Salaried Job by Household Income Quintile and Gender 3.5 Probability of Being into Salaried Job by Household Income Quintile, Gender, and Location 3.6 Probability of Being into Salaried Job by Household Income Quintile, Gender, and Status of Technical Education 3.7 Probability of Being into Salaried Job by Household Income Quintile, Gender, and Education Level 3.8 Probability of Being into Salaried Job by Individual’s Age and Gender 3.9 Probability of Being into Salaried Job by Individual’s Age, Gender, and Location 3.10 Probability of Being into Salaried Job by Individual’s Age, Gender, and Status of Technical Education 3.11 Probability of Being into Salaried Job by Individual’s Age, Gender, and Education Level 4.1 Distribution of Workers according to Usual Status (Ps + ss) Approach by Employment Status for Rural and Urban Sector 6.1 The Three Components of Talent, Innovation and Place within the ASE Environment 7.1 Conceptual Framework 7.2 Distribution of 2018 Employed Graduates among Industries 8.1 Share of youth and elderly in total population and workforce in India, 1994–2019

44 44 45 49 49 50 51 52 52 53 54 70 106 117 124 134

Figures  xi

8.2 Share of informal employment (%) in the non-farm sectors in India 8.3 Daily earnings/wage (Rs.) of workers by their types of employment, 2017–2018 8.4 Youth unemployment and Not in Labour Force Education and Training (NLET) situation in India 8.5 Youth employment trends by their types of employment in India 9.1 Framework for a virtuous circle of economic growth (EG)/ human development (HD) growth 9.2 Long-term growth rates (LTGRs) for net state domestic product (NSDP) at factor cost (1993–94 to 2017–2018) 9.3 Unemployment trends by levels of education 14.1 Domestic employment in foreign final demand – Agriculture 14.2 Domestic employment in foreign final demand – Mining 14.3 Domestic employment foreign final demand – Textiles 14.4 Domestic employment foreign final demand – Manufacturing 14.5 Domestic employment foreign final demand – Chemicals 14.6 Domestic employment foreign final demand – Basic metals 15.1 Mean employment in domestic and foreign direct investment (FDI)-firms 15.2 Employment elasticity in foreign direct investment (FDI)-firms and domestic firms 15.3 Mean employment of high-tech and low-tech foreign direct investment (FDI)-firms 15.4 Mean employment in low-tech domestic and foreign direct investment (FDI)-firms 15.5 Mean employment in high-tech domestic and foreign direct investment (FDI)-firms 15.6 Mean employment in capital-intensive and non-capitalintensive foreign direct investment (FDI)-firms 15.7 Mean employment in capital-intensive foreign direct investment (FDI)-firms and domestic firms 15.8 Mean employment in non-capital-intensive domestic and foreign direct investment (FDI)-firms 15.9 Mean employment in exporting and non-exporting foreign direct investment (FDI)-firms 15.10 Mean employment in non-exporting domestic and foreign direct investment (FDI)-firms 15.11 Mean employment in exporting domestic and foreign direct investment (FDI)-firms 16.1 Accumulated labour employment change in the food and beverages industry

138 140 142 144 154 157 160 271 272 272 273 273 274 285 286 288 289 289 291 292 292 294 294 295 305

xii Figures

16.2 Accumulated labour employment change in wood and wood products industry 16.3 Accumulated labour employment change in publishing and printing industry 16.4 Accumulated labour employment change in the petroleum products industry 16.5 Accumulated labour employment change in the chemical and chemical products industry 16.6 Accumulated labour employment change in rubber and plastic products industry 16.7 Accumulated labour employment change in the nonmetallic mineral products industry 16.8 Accumulated labour employment change in the fabricated metal products industry 16.9 Accumulated labour employment change in the nonelectrical machinery and equipment industry 16.10 Accumulated labour employment change in the communications equipment industry 16.11 Accumulated labour employment change in the motor vehicles industry 16.12 Accumulated labour employment change in the furniture industry

305 306 307 307 308 309 310 310 311 312 313

Tables

  2.1 Descriptive statistics of variables 26   2.2 Determinants of attending/graduating by types of institutions 29   3.1 Summary Statistics of the Variables Used in the Logit Models 43   3.2 Determinants of Employment Preferences among Higher Education Graduates: Logit Estimates 46   4.1 Summary Statistics 66   4.2 Regression Results of Educational Inequality on Mean Years of Schooling 67   4.3 Regression Results of Income Inequality on Income 67   4.4 The Regression Results of Income Inequality on Education 69   4.5 Usual Status (PS + SS) Unemployment Rate for Person Age 15–29 Years, India 70   5.1 Institutional growth in higher education in India between 2010–11 and 2018–19 82   5.2 Performance of India in selected indicators of research output 85   5.3 Rate of unemployment at various levels of education (percentage)86   5.4 Students enrolled in various disciplines as a percentage of total enrolments in respective programs 87   6.1 Impact of North Carolina SBDTC 100   6.2 SBDTC in College of Business at Western Carolina University Goals in 2019 101   6.3 Regional Economic Impact Report of Western Carolina University (2012–13) 103   8.1 Demographic and employment profile of India, 1994–2019 133   8.2 Sectoral youth employment trends in India, 2005–2019 137   8.3 People living below the poverty line (BPL) by their employment status 141   9.1 Net state domestic product growth (base year: 2004–2005) 156   9.2 Employment status of higher education graduates (HEGs) 161

xiv Tables

  9.3 Education status of labour force 161 10.1 (Scenario A-1) Change (%) in quantity and price (case of rising world price of exportable food crops) 181 10.2 (Scenario A-2) Change (%) in quantity and price (case of rising world price of exportable cash crops) 182 10.3 (Scenario A-3) Change (%) in quantity and price (case of rising world price of exportable horticulture crops) 183 10.4 (Scenario A-1 to A-3) Change (%) in sectoral labour demand (case of rising world price) 184 10.5 (Scenarios A-1 to A-3) Changes (%) in wage rates and involuntary unemployment rate (%) (case of rising world price)185 10.6 (Scenario A-1 to A-3) Changes (%) in functional income (and savings) distribution of the households (case of rising world price) 186 10.7 Change (%) in macroeconomic indicators (case of rising world price) 186 10.8 (Scenario B-1) Change (%) in quantity and price (case of rising world price of exportable food crops and rising food crops productivity) 187 10.9 (Scenario B-2) Change (%) in quantity and price (case of rising world price of exportable cash crops and rising cash crops productivity) 188 10.10 (Scenario B-3) Change (%) in quantity and price (case of rising world price of exportable horticulture crops and rising horticulture crops productivity) 189 10.11 (Scenario B-1 to B-3) Change (%) in sectoral labour demand (case of rising world price of exportable crops and rising crop productivity) 191 10.12 (Scenario B-1 to B-3) Change (%) in wage rates and involuntary unemployment rate (%) (case of rising world price of exportable crops and rising crop productivity) 192 10.13 (Scenario B-1 to B-3) Change (%) in functional income (and savings) distribution of the households (case of rising world price of exportable crops and rising crop productivity)192 10.14 Change in macroeconomic indicators (case of rising world price of exportable crops and rising crop productivity) 193 11.1 Industrial distribution of male workers by migration status, 1987–88 to 2007–2008 205 11.2 Industrial distributions of female workers by migration status, 1987–88 to 2007–2008 206 11.3 Occupational distributions of male workers by migration status, 1987–88 to 2007–2008 208

Tables  xv

11.4 Occupational distributions of female workers by migration status, 1987–88 to 2007–2008 11.5 Dissimilarity index (Duncan index) 11.6 Industry and intra-industry occupation mix effects on male workers by migration status 11.7 Industry and intra-industry occupation mix effects on female workers by migration status 11.8 Compound annual growth rate of workers in different occupations from 1987–88 to 2007–2008 12.1 Gender-wise trends in labour force participation rates (LFPRs) and workforce participation rates (WPRs) in rural India (15+ years) 12.2 Age-group-wise female workforce participation rates (WPRs) in rural India 12.3 Usual principal activity status, rural female population of 15 years and above (%) 12.4 Sectoral changes in women’s employment (in lakhs) 12.5 Women workers withdrawing from workforce by their socio-religious group 12.6 Rural women workers by their educational level, 2004–5 and 2017–18 12.7 Results of logistic regression of work participation of rural women, 15 years and above 13.1 Adult female literacy and female labour force participation in South Asia 13.2 Labour force participation rate (%) in Kerala according to usual status (PS + SS)* 13.3 District-wise labour force participation rate in Kerala/1000 13.4 Percentage distribution of main workers, marginal workers, and non-workers by gender 13.5 Marriage and entry into the workforce 13.6 Marriage, childbirth, and withdrawal from workforce 14.1 Job creation through external trade in the EU and BRICS countries (persons in thousands – 2015) 14.2 Jobs generated by final goods trade and global value chains (GVCs) 2015 (persons, thousands) 14.3 Share of domestic employment in foreign final demand 14.4 Overall net gain from global value chains (GVC) participation during 2010–15 14.5 EU–BRICS trade through global value chains (2015) 14.6 Decomposition of the EU’s labour (2015) in its total trade with BRICS 14.7 Decomposition of the EU’s labour (2015) in its manufacturing trade with BRICS

209 211 212 214 217 222 223 225 226 227 228 230 239 242 243 244 248 250 265 266 267 267 269 270 270

xvi Tables

14.8 Decomposition of BRICS labour (2015) in its total trade with the EU 270 14.9 Decomposition of BRICS labour (2015) in its manufacturing trade with the EU 270 14.10 Job creation through forward and backward linkages in the EU 274 14.11 Job creation through forward and backward linkages in BRICS275 14.12 Sector-wise type of labour value added in exports (backward Linkage): BRICS 276 15.1 Distribution of cumulative foreign direct investment (FDI) inflows across different sectors of the economy, in Rs Crore 281 15.2 Average sales and average employment in manufacturing firms284 15.3 Output elasticity of employment in foreign direct investment (FDI)-firms and domestic firms 286 15.4 Characteristics of firms and employment elasticity of firms 288 15.5 Average employment in capital and non-capital-intensive foreign direct investment (FDI)-firms 290 15.6 Mean employment in exporting and non-exporting foreign direct investment (FDI)-firms 293 16.1 Elasticity of substation between capital–labour (K–L) in the manufacturing industry in India 303 17.1 Summary statistics, 2003–04 to 2014–15 321 17.2 Impact of imported inputs and labour use on GVA 322 17.3 Impact of imported inputs and labour use on TFP 324 17.4 Impact of imported inputs and labour use on wages 325 10.A1 Constant elasticity of substitution (CES) elasticity of transformation parameter (sVA 331 j ) of value added 10.A2 Values of relevant parameters 332 10.A3 (Scenarios A1–A3) Change (%) capital income and labour income of households (case of rising world price) 332 10.A4 (Scenarios B1–B3) Change (%) in factor income of households (case of rising world price of exportable crops and rising crop productivity) 333 11.A1 Components of occupational net shift (1988 to 2007– 2008) for total employment 334 11.A2 Components of occupational net shift (1988 to 2007– 2008) for total employment 336 15.A1 Foreign Direct Investment (FDI) inflows in India 338 15.A2 Description of variables 339 15.A3 Classification of manufacturing sector by technology 340

Contributors

Anu Abraham is a Senior Researcher at the Peace Research Institute Oslo (PRIO), Norway. She is a development economist whose research interests include social mobility, migration, labour, gender, and human capital development. Email: anu​.a​.abraham​@gmail​.​com. Kinjal Ahir is an Associate Professor & Coordinator, UGC Centre of Advanced Studies (CAS II), Post Graduate Department of Economics, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India. Her research interest lies in the economics of higher education, growth, and development. Email: kinjalahir​@gmail​.co​m. Sonam Arora is a Ph. D. Scholar at the National Institute of Educational Planning and Administration, New Delhi, India. Her primary areas of research interest are higher education, employment, economic growth and social sector development. Email: [email protected]. Sambhu N. (Sam) Basu is a Professor Emeritus of Finance and the former Dean of the Cotsakos College of Business at William Paterson University, USA. His main areas of research are banking and financial management, economic deregulation, and development. Email: basus1​@wpunj​.ed​u. Haiyang Chen is a Professor of Finance at College of Business, Western Carolina University Cullowhee, USA. His areas of interest are international financial management and economic development. Email: hchen​@email​ .wcu​.​edu. Pradeep Kumar Choudhury is an Assistant Professor of Economics, Zakir Husain Centre for Educational Studies, School of Social Sciences, Jawaharlal Nehru University, New Delhi. His research interests span a wide range of issues in education and development economics, and his recent works focus on privatisation of education and market policies, inequality in learning outcomes, skills and skill development, and family investment in education. Email: pradeepchoudhury​@jnu​.ac​​.in.

xviii Contributors

Jun Du is an Associate Dean and Professor, Department of Basic Disciplines, Shanxi Agricultural University, Jinzhong City, Shanxi Province, China. His major area of research is agricultural economics and regional development. Email: cxtdj​@163​.c​om. Diti Goswami is an Assistant Professor at the Indian Institute of Management Rohtak, India. Her research focuses on the issues related to development, primarily jobs, productivity, labour reforms, and industrialisation in the Indian economy. Email: dtgoswami93​@gmail​.c​om. Jannet Farida Jacob works with Ernst and Young LLP, Thiruvananthapuram, India. Formerly, she was a Research Fellow with National Institute of Public Finance and Policy, New Delhi. Her areas of research interests include social sector spending, particularly education and health. Email: jannetfarida​@gmail​.c​om. K.M. Joshi is a Professor of Economics of Higher Education, Department of Economics, Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India. He specialises in higher education policy, comparative higher education, and international education. Email: kmjoshi1972​@gmail​.co​m. Khalid Khan is an Assistant Professor at the Indian Institute of Dalit Studies, New Delhi, India. His main areas of research interest are the economics of education, inequality, and exclusion. Email: khan​.khalid7​@gmail​ .​com. Mohd. Imran Khan is an Assistant Professor of Economics at Sarla Anil Modi School of Economics, Narsee Monjee Institute of Management Studies, Mumbai, India. His primary research interests are in the field of labour and development economics. Email: mohdimran​.khan​@nmims​.e​ du. Mona Khare is Professor and Head of the Department of Educational Finance, CPRHE, National Institute of Educational Planning and Administration, New Delhi, India. Her main areas of interest are employability of educated youth, financing of education, educational internationalisation, and regional and spatial disparities in educational development. Email: mona​.khare14​@gmail​.​com. Amit Kumar is working as a Quantitative Research Associate at Young Lives India. He has done PhD in Economics from Zakir Husain Centre for Educational Studies, School of Social Sciences, Jawaharlal Nehru University (JNU), India. His research interests include financing of higher education, student loan market, and inequalities in education. Email: amit​.jnu2017​@gmail​.c​om.

Contributors  xix

Anirban Kundu is an Assistant Professor of Economics in the Department of Economics at CHRIST (Deemed to be University), Bangalore, India. His research interest lies in the informal sector and macroeconomics, and policy analysis in relation to the informal sector in India, with a focus on industry–agriculture interlinkages. Email: anirbankundu2006​ @gmail​.co​m. Sanjaya Kumar Malik is an Assistant Professor Institute for Studies in Industrial Development, New Delhi, India. His main areas of research interest are innovation and technological change, foreign direct investment, and labour economics. Email: mksanjaya​@gmail​.co​m. Rajendra P. Mamgain is a Professor of Economics at School of Social Sciences, Doon University, Dehradun. His major areas of research interest are employment, migration, livelihood, poverty, and social inclusion. Email: mamgain​.rp​@gmail​.​com. Usha Nori is an Associate Professor of Economics at the Institute of Public Enterprise, Hyderabad, Telengana, India. Her research interests include international trade and finance, agriculture, industry, and social sector development. Email: ushanori​@ipeindia​.or​g. Jajati Keshari Parida is an Associate Professor in the School of Economics, University of Hyderabad, Gachibowli, Hyderabad, Telangana, India. His main research areas are employment, migration and remittances, human development, poverty, and inequality. Email: jkparida​@uohyd​.ac​​.in. Shuxia Ren is a Teaching Assistant, College of Agricultural Economics and Management, Shanxi Agricultural University, Jinzhong City, Shanxi Province, China. Her research interest lies in agricultural economics and rural tourism destination marketing. Email: rsx​_stella​@sxau​.edu​​.cn. Renuka S. is a PhD scholar at the Department of Economics, Sacred Heart College (Autonomous), Thevara, Kerala, India. Her main areas of interest are female employment, education, household finance, and behavioural economics. Email: sreerenuka94​@gmail​.co​m. Vachaspati Shukla is Assistant Professor at the Sardar Patel Institute of Economic and Social Research, Ahmedabad, Gujarat, India. His research interest includes the economics of education, poverty, and inequality. Email: vachaspatishukla​@gmail​.c​om. Min Zhao is an Associate Dean and Professor, College of Agricultural Economics and Management, Shanxi Agricultural University, Jinzhong City, Shanxi Province, China. Her research interest lies in the areas of agricultural economics, development planning, and poverty alleviation. Email: sxaujmxyzm​@163​.c​om.

Foreword

The higher education sector in India is up for a significant overhaul. When fully implemented, the New Education Policy of 2020 is to change the face of the higher education sector in the most revolutionary way. Multidisciplinarity and flexibility in the curriculum are the two major reforms that have been introduced. Further, the sector has been made very competitive with the licensing of many private educational institutions. Additionally, the entry of foreign universities into the higher education landscape that has been in the offing for quite some time will be a reality shortly. India is also increasingly open to foreign scholars for both teaching and research. It has always been available to international students, although the number of international students admitted to Indian higher educational institutions is also likely to increase in the future. Against this state of flux in the higher education sector, 17 chapters analysing different aspects of India’s higher education sector are being brought out. In this context, the book is highly relevant and timely. Successive commentators have pointed to the quality of higher education in the country. One manifestation of this quality is the employability of Indian graduates, which varies considerably across the country. While the problem is common to science, engineering, humanities, and social sciences, it is much more severe in science, technology, engineering, and mathematics (STEM) subjects. The outturn of graduates in STEM subjects continues to be only a little over a quarter of the total number of graduates in the country. Within STEM, the ratio of science graduates continues to be more than Engineering and Technology graduates. The newly announced National Education Policy1 envisages the establishment of a new National Research Foundation (NRF) that will focus on funding research within the education system, primarily at colleges and universities. However, there is very little quantitative evidence to show that the demand for STEM graduates, especially for research, has increased as the investments in R&D have not kept pace with the increases in GDP. Another repeated concern about India’s human resources, especially in STEM subjects, is the widely varying quality and employability. At one end

Foreword  xxi

of the spectrum, there are prestigious higher education institutions such as the Indian Institutes of Technology, the Indian Institute of Science, and the Indian Institutes of Science Education and Research, the alumni of which have a very high reputation. The CEOs of some of the world’s leading technology companies, such as Microsoft and Google, are Indians who have had their initial education at these premier institutes. On the contrary, there are a large number of provincial universities which are not that well organised. The Wheelbox Employability Skill Test (WEST) has been conducted to measure employability over the last six years. Although it has increased from 34% in 2014 to almost 47% in 2019, it still means that one out of two graduates is not employable. Among the technology domains, Electronics and Communication Engineering (ECE) and Information Technology courses have the highest employability rate with 60.33% and 60.18%, respectively, and civil engineering is the lowest among all courses. According to this study, employability among ITI and polytechnic students is a big challenge. Apparently, despite the focus on improving the quality of such tertiary education, the employability of ITI and polytechnic graduates has been falling primarily due to a lesser focus on industry alliances and core employable skills. The prime minister officially launched the National Skill Development Mission on July 7, 2015, on World Youth Skills Day. The Mission has been developed to create convergence across sectors and states regarding skill training activities. Further, to achieve the vision of ‘Skilled India’, the National Skill Development Mission would consolidate and coordinate skilling efforts and expedite decision-making across sectors to achieve skilling at scale with speed and standards. It will be implemented through a streamlined institutional mechanism driven by the Ministry of Skill Development and Entrepreneurship (MSDE). Under the Mission, about 400 million people from across the country are expected to be trained by 2022. India has been experiencing cross border movement of highly skilled personnel abroad and primarily to the US for quite some time. Data compiled by the US National Science Board shows that in 2017, half of the foreignborn individuals in the US with an S&E highest degree were from Asia, with India (23%) and China (10%) as the leading countries of origin. This constant brain drain has to be seen as a knowledge asset, and efforts are made to take advantage of these highly qualified Indian diasporas in their host locations abroad for ideas and projects back home in the country. Recently the government has put in place schemes which deal with brain drain. The first one is 2017 introduced Visiting Advanced Joint Research (VAJRA) faculty scheme by the DST, enabling Non-Resident Indians (NRIs) and the overseas scientific community to participate and contribute to R&D in India. The Science and Engineering Research Board (SERB), a Statutory body of the DST, is implementing the scheme. VAJRA faculty will undertake research in S&T priority areas of the country wherein the capability and capacity

xxii Foreword

are needed to be developed. The VAJRA faculty will engage in collaborative research in public-funded institutions. The second is the National PostDoctoral Fellowship Programme (N-PDF) which provides PDF fellowships to Indian research scholars with doctoral degrees for two years. This, too, is administered by the SERB and has awarded 2,500 fellowships over the last two years. The scheme is essential to encourage Indians with PhDs. in STEM subjects to remain in the country, or those who have gone abroad for doctoral degrees to return home as the main route for brain drain is the education route whereby students go abroad for higher studies and then decide to stay back in those host locations by taking up employment. Sabharwal (2018) has shown, based on a field study, that some reverse brain drain from the US to India is occurring. According to this study, better career prospects in India, namely ample funding available for research, less competition for grants, ability to work on theoretical topics, and freedom in research objectives, emerged as the key factors that prompted return. Government programmes and increased research funding in India have improved job opportunities and security for scientists and engineers, who are deciding more frequently to leave faculty positions in the US to return home. But given the small sample size of just 83 returnees, it is doubtful that the study’s findings can be generalised. Higher Education, Employment, and Economic Development in India: Problems, Prospects, and Policies deals with these pertinent issues confronting our higher education sector in 17 different chapters. It is hoped that the book will precipitate a lively debate on all these issues facing the nation’s higher education system, which have become more critical now than ever before. If it has achieved this, it will serve as an essential contribution to our understanding of the country’s higher education sector. Sunil Mani Centre for Development Studies Trivandrum, Kerala

Note 1 https://mhrd​.gov​.in​/sites​/upload​_files​/mhrd​/files​/Draft​_NEP​_2019​_EN​_Revised​ .pdf (accessed on April 25, 2022)

Reference

1. Sabharwal, Meghna (2018). ‘Reverse Brain Drain: A Study of Indian Scientists and Engineers in Academia,’ Panel paper presented at Addressing the Problem of Reverse Brain Drain in U.S. Science, Technology, Engineering and Math (STEM) Fields, Evidence for Action: Encouraging Innovation and Improvement Conference, Association for Public Policy Analysis and Management, Washington, DC.

Prelude

I am delighted to write a prelude for this thought-provoking research work, entitled Higher Education, Employment, and Economic Development in India: Problems, Prospects, and Policies. The book starts with the introductory chapter, ‘Higher education and employment in India,’ and is then divided into 5 parts spread over 16 chapters. Each part contains three to four chapters under a sub-theme very well connected with the title of the book. Part 1 of the book focuses on ‘Higher education participation, employment, and income inequality.’ In this part, the initial chapter deals with the functioning of the system and public–private service providers. The chapter uses a multinomial logit model to examine the demographic, familial, regional, and gender characteristics that determine the probability of one’s attending public or private higher educational institution. The results show that lower-income households, women, and socially backward communities have a lower probability of attending a private institute, which suggests improving the quality of public institutions. Another chapter in this part analyses the gender differences in employment preferences among university graduates in India. It finds that young graduates are more likely to go for self-employment as compared to their older counterparts. And it also notices that females with postgraduate degrees and technical qualifications are more likely to be employed in salaried occupations than males. Education is said to be one of the key equalisers or differentiators of income distribution, and this aspect has been examined in another chapter in this part, where no significant relationship has been observed between levels of schooling and income inequality in rural areas, which is in contrast with the relationship observed for urban areas. In Part 2, the first chapter looks at how quality education leads to a higher degree of economic development as improved quality finally influences the performance of the higher education institutions and also that of students in terms of rate of return.

xxiv Prelude

In the chapter ‘The role and impact of academics’ societal engagement,’ the authors have studied the impact of universities on economic development in western countries as well as in emerging economies. The next chapter in this part presents a case study of Shanxi Agricultural University and its role in serving regional or local economic development in alignment with the Chinese government’s objective of achieving modernisation of agriculture and rural areas by 2035. It is suggestive of how higher educational institutions can be involved in pursuing development and welfare policies. The chapter ‘Harnessing India’s demographic dividend’ in Part 3 discusses the concerns arising out of 4.5 million people leaving agriculture and the inadequacy of job growth in non-farm sectors resulting in youth unemployment. It also underlines the concerns related to the rising share of the elderly population and suggests a number of welfare measures. Another chapter in this part highlights how we need to move to a virtuous and sustainable circle of higher gains through improved links among high growth, employment, and human development. The third chapter in this part is largely about scenario analysis considering the impact of higher prices in the global market on India’s agricultural exportables and how that impacts the income level of informal non-agricultural households. The suggestion of targeted government intervention is well argued with a view to striking a balance between growth and distribution. Part 4 of the book concentrates on labour migration and female employment. The first chapter in this part studies the industrial and occupational distribution of migrant workers in India. The chapter interestingly examines the relationship of complementarity or substitution between migrant and non-migrant workers in the labour market. The study finds that the occupations requiring higher skills have witnessed substantial growth, and non-migrants benefit from the jobs abandoned by migrants as it causes reorganisation of occupations within industries. The chapter ‘Understanding the decline in women’s employment in rural India’ shows that the decline has been widespread across different social groups, income strata, and states, though at varying rates. The withdrawal seems to be of a much greater extent among economically better-off groups due to higher affordability in waiting for a better job. The last chapter in this part presents a case study of Cochin Municipal Corporation in studying the determinants of female labour force participation in Kerala. Kerala, despite having the highest female literacy rate in India, has one of the lowest female labour force participation rates. The chapter also questions the notion of ‘family support’ as well as the ‘voluntary’ nature of the decision to remain outside the labour force and to exit the labour market after marriage and motherhood. Part 5 of the book relates to ‘Employment generation in the manufacturing sector’. The first paper discusses the interconnections between BRICS

Prelude  xxv

and the EU and majorly analyses the employment effects in these countries in respect of labour contents in exports and imports. The paper also finds that the Global Value Chain between BRICS and the EU expands employment in BRICS countries due to rising demand for foreign labour. The second chapter in this part examines the presumption of foreign direct investment to have played an important role in industrial change and employment in developing countries and finds that FDI firms offer lesser employment opportunities as compared to domestic firms, in case of both high technology and capital-intensive firms and low technology and low capital-intensive firms. The third chapter in this section finds that manufacturing industries with high capital-labour substitution are most likely to see a decline in labour employment. It happens despite the fact that production activity creates new employment opportunities, but structural change and technological effect work against the opportunities created by production activity. The last chapter in this part looks into the issue of effect of imported inputs and labour on the performance of a plant. It finds that imported inputs are not labour augmenting and thus may not improve employment in the manufacturing sector. It may, on the other hand, reduce wages. Thus, the plants should focus on reskilling the workers to match the needs of highquality imported inputs, which, in turn, increases the productivity of labour and thus the wages. We can appreciate that higher education in India has not yielded the desired results so far, both in terms of employment and economic development in the past. That’s why the New Education Policy – 2020 lays emphasis on imparting knowledge and its applicability and also discusses aligning education with well-defined skill sets at various levels of education. Undoubtedly, education and employment are universally acknowledged as two very critical aspects of economic development and its sustainability. In my opinion, this book attempts to enrich our understanding of the dynamic and transforming nature of the relationship between the participation of various institutions and income groups in higher education and their effect on employment and economic development. The book deserves to be read by academics, policymakers, and scholars working in these expanses of knowledge. V.K. Malhotra Member Secretary, Indian Council of Social Science Research, New Delhi

Acknowledgements

Higher education and employment are recognised as crucial factors for improving the economic well-being of the people. With the increasing unemployment rate, especially among the educated youth in a developing country such as India in recent years, there has been a renewed interest among academics and policymakers in unravelling and understanding the different dimensions of higher education and employment in economic development. In light of this background, the Institute of Public Enterprise (IPE), Hyderabad, India, organised a two-day international conference on higher education, employment, and economic development in December 2019. This edited book is a culmination of the presented papers at the conference. The volume presents 17 chapters under five major themes clustered around higher education, employment, and economic development. This collection of theoretically informed but empirically grounded chapters diagnoses the problems, discusses the prospects, and prescribes the policy for the possible solutions. Preparing this book was a year-long process, and its completion owes a great deal to many people. First, we express our deepest gratitude to all the valuable contributors to this book. Our sincere thanks to Prof. Sunil Mani, Director, Centre for Development Studies, Thiruvananthapuram, a scholar of international repute, who encouraged us for the conference and wrote the foreword for the book. We express our gratitude to Dr. V.K. Malhotra, Member Secretary, Indian Council of Social Science Research (ICSSR), Ministry of Education, Government of India, for his motivation and for presenting the prelude of the book. We are indebted to Dr. K.N. Jehangir, former Director, International Collaborations, ICSSR, for his support and invaluable inputs for the publication of this volume. We gratefully acknowledge the generous financial support provided by IPE, without which we could not have organised the conference. We would like to thank the faculty colleagues of IPE, particularly those who extended their support in rapporteuring the paper presentation and organising the conference. We

Acknowledgements  xxvii

owe a debt of gratitude to the two external reviewers who reviewed the manuscript. Finally, we are thankful to the editorial team of Routledge, without whose continuous support the book would not have seen the light of day. Ram Kumar Mishra Sandeep Kumar Kujur K. Trivikram

Abbreviations

ASE: Academics’ Social Engagement ASI: Annual Survey of Industries BRICS: Brazil, Russia, India, China, and South Africa CAGR: Compound Annual Growth Rate CET: Constant Elasticity of Transformation CGE: Computable General Equilibrium CPI: Consumer Price Index CSO: Central Statistical Office DIPP: Department of Industrial Policy and Promotion EG-HD: Economic Growth-Human Development EGINI: Education GINI EOS: Elasticity of Substitutions EPWRF: Economic and Political Weekly Research Foundation EU: European Union EUS: Employment-Unemployment Survey FDI: Foreign Direct Investment FLPR: Female Labour Force Participation Rate FWPR: Female Worker Population Ratio GDP: Gross Domestic Product GI: Government Institutions GoI: Government of India GST: Goods and Services Tax GVA: Gross Value Added GVCs: Global Value Chains HDI: Human Development Index HH: Households ICT: Information and Communication Technology IDA: Index Decomposition Analysis IIM: Indian Institute of Management IIT: Indian Institute of Technology

Abbreviations  xxix

IMI: LMDI: M&As: MLFPR: MNEs: MoSPI: MPCE: MWPR: NAS: NC: NCEUS: NCO: NIC: NMP: NSDP: NSSO: OBCs: OECD: PLFS: PS: RBI: Rs: SAM: SBDC: SBTDC: SC: SD: SRC: SS: ST: TFP: TM: UC Davis: U-I: U-I-G: UNCTAD: UNDP: UNESCO: UPSS: US: USDA:

Imported Intermediate Inputs Log Mean Divisia Index Merger & Acquisitions Male Labour Force Participation Rate Multinational Enterprises Ministry of Statistics and Programme Implementation Monthly Per Capita Expenditure Male Worker Population Ratio National Accounts Statistics North Carolina National Commission for Enterprises in the Unorganised Sector National Classification of Occupation – 1968 National Industrial Classification National Manufacturing Policy Net State Domestic Product National Sample Survey Office Other Backward Classes Organisation for Economic Co-operation and Development Periodic Labour Force Survey Principle Status Reserve Bank of India Indian Rupees Social Accounting Matrix Small Business Development Centre Small Business and Technology Development Center Scheduled Caste Standard Deviation Socio-religious categories Subsidiary Status Scheduled Tribe Total Factor Productivity Trade Matrix The University of California at Davis University and Industry University, Industry, and Government United Nations Conference on Trade and Development United Nations Development Programme United Nations Educational, Scientific and Cultural Organization Usual Principal and Subsidiary Status United States United States department of Agriculture

xxx Abbreviations

WCU: WIOD: WPI: WPR:

Western Carolina University World Input-Output Database Wholesale Price Index Workforce Participation Rate

Chapter 1

Higher education and employment in India An introduction Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

1.1 Background Education and employment are the two important indicators of economic development. From a national perspective, education enriches the stock of human capital that serves as a critical factor of production (Dutta, 2006). From an individual perspective, acquiring education contributes to people’s well-being intrinsically by enhancing human capability and individual freedom.1 Education raises well-being instrumentally by improving their skills and productivity, yielding employment and income (Dreze & Sen, 2002). Different levels of educational attainment are associated with varying levels of employment and wage. Particularly, acquiring a higher level of education improves the individual’s decision-making power and raises better employment opportunities and income (Azam, 2012; Khanna et al., 2016; Dhanaraj & Mahambare, 2019). This gain in individual employment and subsequent rise in income through the positive externalities of education (Tilak, 2008) creates new assets and raises living standards without making anyone worse (Thomas et al., 2001). Education is also contemplated to develop greater resilience to exogenous economic shocks (Bandyopadhyay, 2020). Therefore, education is considered one of the three primary parameters of the United Nations’ Human Development Index (United Nations Development Program [UNDP], 1990) and the Multidimensional Poverty Index (UNDP, 2010; Alkire & Santos, 2014; Alkire & Seth, 2015) to analyze the human progress. Human progress fostered by education is also manifested in better employment. The employment gain at the individual level directly increases the wage income and improves the standard of living. However, the decline in employment (or increase in unemployment) directly reduces the wage income (Arulampalam, 2001), which substantially affects food consumption expenditure (Campos & Reggio, 2015). The increase in unemployment creates permanent scars on an individual’s second employment spell (Arulampalam, 2001; Birkelund et al., 2017). The loss of employment negatively affects the physical and mental health condition of the affected individuals (Farre et al., 2018). The unemployed individual DOI: 10.4324/9781003329862-1

2  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

experiences a sub-optimal sense of activity (Golden & Perreira, 2015) and reduced social participation (Kunze & Suppa, 2017). This also adversely impacts the subjective well-being of the person (Crost, 2016) and triggers the risk of suicides and mortality from other diseases (Tapia Granados et al., 2014). Acquiring a higher level of education increases employment opportunities and is associated with higher returns (Dutta, 2006; Azam, 2012; Khanna et al., 2016; Dhanaraj & Mahambare, 2019). The educational expansion accounts for a major part of consumption convergence (Hnatkovska et al., 2012; Gupta et al., 2018) and healthcare expenditure (Mondal & Dubey, 2020) across different social groups in India. Education is also considered the most crucial factor in avoiding falling into the poverty trap (Thorat et al., 2017). Although educational expansion improves economic wellbeing, it has increased income inequality in India (Sehrawat & Singh, 2019), especially in urban areas (Kimija, 2006). Most of the existing studies, thus far, have examined primary and secondary educational expansion and its relationships with various development indicators in India. Few existing studies on higher education (Agarwal, 2007; Varghese & Malik eds., 2015; Tilak, 2018; Mittal et al., 2020) discussed the structure, growth, financing, and equity in higher education in India. No extensive study has been conducted yet on the relationship between higher education, employment, and economic development. This book fills this gap and examines the expansion of higher education and its relationship with employment and economic development. The gain in employment raises income (Merfeld, 2020) and expenditure on food and non-food consumables (Ravi & Engler, 2015; Maity, 2020), thereby improving overall economic well-being (Belot et al., 2007). Nonetheless, labour employment in India has been declining despite substantial economic growth (Tejani, 2016; Abraham, 2019; Sarkar et al., 2019). This persistent decline in economy-wide employment is attributed to reduced employment in the agriculture and manufacturing sectors (Roy Choudhury & Chatterjee, 2015; Mitra & Singh, 2019). The increase in education participation, the decline in child labour, the mechanization of agriculture, and the increasing standard of living in rural areas are the other reasons for the decline in the employment rate in India (Mehrotra et al., 2014). The slow growth of construction sector jobs is another important factor for a drastic decline in the employment rate in India in recent years (Mitra & Singh, 2019). The continuous decline in the employment rate in the Indian economy was to be reversed by the expansion in the manufacturing sector. However, like the aggregate Indian economy, the manufacturing industry itself has experienced jobless growth (Kannan & Ravindran, 2009; Thomas, 2013; Sen & Das, 2015). This phenomenon of jobless growth in the manufacturing industry is attributed to rigidity in the labour market (Kapoor, 2015), trade liberalization (Mehrotra et al., 2014; Sen & Das, 2015), changing nature of demand

Higher education and employment in India  3

for manufactured goods (Kannan & Raveendran, 2009), and technological advancement (Kujur, 2018). Advanced manufacturing technologies, such as robotics and artificial intelligence, have also negatively affected manufacturing employment, especially of intermediary skills in India (Sharma, 2016; Vashisht, 2018). Although these studies have dealt with employment fall at the aggregate economy and the sectoral level, detailed treatment of joblessness, demographic dividend, youth unemployment, informality, labour migration, and female labour force participation in varying economic sectors in urban and rural areas in recent years has been missing. This book provides an updated analysis of economy-wide employment change in India. The book also exclusively focuses on manufacturing employment in India and examines the impact of the Global Value Chains (GVC), Foreign Direct Investment (FDI), technological change, and imported inputs on employment generation and labour wages. Against this backdrop, the volume analyzes issues on higher education and labour employment in economic development in India. The book will be a novel contribution to the existing literature on five grounds. First, it examines the factors influencing participation in higher education. The linkages between higher education, employment, and income equality are examined. Second, it discusses the quality and efficiency of higher education institutions in India. The policy lessons drawn from the case study approach on the role of universities in employment generation and local area development are another noteworthy highlight. Third, the volume provides an up-to-date macroeconomic analysis of the demographic dividend in India. In addition, the part discusses the joblessness of educated youth, informal employment, and the informal economy in India. Fourth, the volume extends the existing literature on occupational change and migration across different economywide sectors in varying Indian states. It contributes toward understanding the macroeconomic perspective on declining women labour force participation in rural India, highlighting the gender and caste intersectionality of work. The use of field survey-based microeconomic analysis to explain the declining female employment adds a new dimension to female labour employment. Fifth, as manufacturing is considered a potential sustainable employment creator, the book focuses explicitly on employment generation in the manufacturing sector. The detailed analysis of the impact of emerging factors such as participation in GVCs, FDI firms, new technology, and imported inputs on employment change in the Indian industry underscores the employment dynamism in the manufacturing industry. Overall, besides contributing to the existing literature in five distinct ways, the issues analyzed in the book would help policymakers identify the emerging issues in higher education and employment in the economy. Considering the cross-cutting significance of higher education, employment, and economic development in an emerging economy, this book presents a collection of theoretically informed empirical and policy-oriented

4  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

papers that examines the role of higher education and employment in economic development. The up-to-date macro and microeconomic analysis of employment change is another important highlight of the book. The book also discusses the various critical aspects of manufacturing employment in detail. In the next section, Section 1.2, we rationalize the chapters covered in five different themes. Section 1.3 provides an overview of the chapters presented in all major parts of the book. The chapter concludes with key findings and identifies the scope for further research in Section 1.4.

1.2 Motivation There has been a policy shift toward increased private investment in higher education to meet the excess demand for high-skilled labourers. However, the higher education provided by the private sector comes with a higher cost of services than the public sector (Tilak, 2018). This inflated cost of higher education services, demographic, familial, regional, and gender characteristics may deter higher education participation of socially and economically disadvantaged groups. After participating in and completing higher education, graduates enter the labour market and engage in various jobs. With the job constraints in the labour market, educated youth in India may look for self-employment opportunities, which will reduce the overdependence on wage employment. This choice of employment (wage employment and selfemployment) may vary depending on the diverse social identity (specifically the gender) of the youth in India. A higher level of educational attainment ensures better earning opportunities in India. However, the higher returns to tertiary education may result in increasing wage inequality in urban India. Against this backdrop, Part 1 of the volume examines the participation in higher education, gender gaps in employment preferences among university graduates, and the impact of educational expansion on income inequality in India. The role of higher education institutions is considered crucial for the regional transition toward inclusiveness and sustainability. In pursuing these objectives, higher education institutions follow a three-step progression. In the first stage, higher education institutions focus on creating capacity by establishing physical infrastructure for teaching and learning. In the second stage of progression, higher education institutions emphasize improving the quality through their engagement in research and development for innovation. In the third stage, higher education institutions implement such indigenously developed technology for local development. As a developing country aiming to be a knowledge economy, Indian higher education institutions have been focusing on the first and the second stage of progression by creating and expanding the capacity and improving quality through their engagement in research and development. However, higher education

Higher education and employment in India  5

institutions in countries like the United States (US) and China have already achieved the first and second stages of progression. They are now focusing on the third stage- the local area development to enhance inclusivity and sustainability. As Indian higher education institutions are projected to be research-driven and self-sustainable in the future, which will play a critical role in local area development, understanding the existing quality of higher education institutions is a necessary prerequisite. It is also vital to draw policy lessons from countries already in the third stage of the progression of higher education institutions. Therefore, Part 2 of the book examines the quality and efficiency of higher education institutions in India. It also draws the policy lessons from case studies from the US and China on the role of universities in regional economic development. Like higher education, employment is another important indicator of economic development. In India, youth unemployment and the discouraged labour force are increasing. In addition, there has been an increase in the share and growth of the elderly population in India. This combination of increasing youth unemployment and the size of the elderly population underscores the need to harness the benefit of the demographic dividend before it is lost forever. The problem of youth unemployment during high economic growth rates creates widespread joblessness in the economy. Owing to widespread joblessness, the educated youth would fall back upon the agriculture and informal sector for employment and sustenance. This will affect the functional income distribution across formal-informal households in India. Therefore, the volume discusses the demographic dividend, joblessness, and informality in the economy in Part 3. Labour migration and female labour force participation are two crucial dimensions of the labour market in India. The theory of competitive labour market suggests that internal migration leading to competition between migrants and the locals lowers the wages in the host economy. The alternative explanation is that the migrants do not take away jobs but fill the labour needs of certain industries and occupations where the local labour supply is scarce. The aggregate labour supply may have increased because of various factors, including migration but the female labour force participation in the labour market has declined in India. Part 4 explores the occupational and industrial structure of migrants and non-migrants in this context. It also examines macro and microeconomic factors affecting the participation of women in the labour market in India. India has been trying to reverse the trend of declining employment by creating employment opportunities in the manufacturing sector. The GVCs, FDI, technological change, and imported inputs could affect employment and labour wages in manufacturing. Therefore, the book focuses exclusively on manufacturing employment in India. Trade integration through GVCs is considered a possible route to raise the shrinking manufacturing employment globally. Weighing on the outward-oriented economic

6  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

strategy, Brazil, Russia, India, China, and South Africa (BRICS) have intensified the GVC trade, particularly with European Union (EU), to generate new employment in the manufacturing sector. In addition to participating in GVCs, developing countries such as India have also been focusing on FDI to improve manufacturing employment. However, employment in India’s manufacturing varies across the sub-sectors, depending on the structure of capital–labour (K-L) use. Employment in industries with high K-L substitution might be adversely impacted by structural change or the application of advanced technology. This change in employment would affect the plant performances (total factor productivity and gross value added) and labour wages. However, plant performances and labour wage could also get affected by the simultaneous use of labour and imported inputs. Therefore, Part 5 of the volume analyses the impact of BRICS-EU GVCs trade on manufacturing employment. The book examines the impact of structural effect or technological change on employment. It also assesses the collective impact of imported inputs and labour on plant performances and labour wages in Indian manufacturing.

1.3 Overview of the chapters This book is divided into five major themes. The first theme of higher education participation, employment, and income inequality is discussed in three chapters (Chapters 2–4). The second theme of quality and the role of higher education institutions in economic development are covered in three chapters (Chapters 5–7). The third theme of demographic dividend, joblessness, and informality in India are discussed in Chapters 8–10, while the fourth theme of labour migration and female employment are examined in Chapters 11–13. The final theme of the book – employment generation in the manufacturing sector – is discussed in Chapters 14–17. Part 1 Higher education participation, employment, and income inequality The background of the theme is set up by Jacob (Chapter 2) by her exploration of the role of institutions in higher education participation in India. The author considers higher education policy as an institution that governs the functioning of the system and its public and private service providers. Using National Sample Survey (NSS) data, the chapter builds a multinomial logit model to ascertain the demographic, familial, regional, and gender characteristics that determine the probability of attending public or private higher educational institutions. The chapter shows that lower-income households, scheduled castes, tribes, Muslims, and women have a lower probability of attending private-unaided institutions than public institutions. As a policy implication, the chapter emphasizes the importance of increasing the

Higher education and employment in India  7

number of public institutions and enhancing the quality of the existing ones to ensure equity in access and efficiency. As participation in higher education influences the employment preferences of university graduates, Choudhury and Kumar (Chapter 3) analyze the employment preferences among university graduates in India through the lens of gender. Using the Periodic Labour Force Survey (PLFS) 2018–19 data, the authors analyze how the socioeconomic and demographic characteristics of the students matter in explaining employment preferences among university graduates. They find that female graduates have significantly higher chances of getting employment in salaried jobs (over self-employment) than males – with stark differences in the household’s economic status and region. Also, females with postgraduate degrees and technical qualifications are more likely to be employed in salaried occupations. They provide evidence that young graduates (particularly male youth) are more likely to go for self-employment than their older counterparts. The findings are instrumental in understanding the changing contours of the labour market for higher education graduates in India through a gender lens. Higher education participation and increasing returns to tertiary education may result in increasing income inequality in India. In its next chapter, by Shukla (Chapter 4), the book examines the association between educational expansion and income distribution using cross-section data from India. The chapter explores this independently for the rural and urban sectors due to the differential nature of the labour market. The chapter did not find any significant association between the levels of schooling and income inequality in the rural sector. However, in the urban sector, income inequality is positively related to the share of graduates in the workforce. Based on the findings, the author draws the economic implications for the labour market in India. Part 2 Quality and the role of higher education institutions in economic development Part 2 begins with a study by Joshi and Ahir (Chapter 5). The chapter assesses the various quality and efficiency parameters of higher education in India that contribute to economic development. The chapter employs three world university rankings that comprehensively analyze the quality of higher education institutes of the world. From these rankings, the authors identify the quality-specific parameters and provide an in-depth analysis of quality and efficiency parameters in higher education institutions in India. The exploratory analysis of the chapter demonstrates that the low performance in quality and efficiency parameters of higher education affects the performance of India’s economic development indicators. Therefore, the chapter suggests improvement in quality parameters like improving teaching and academic research output, and efficiency parameters like increasing

8  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

private rates of returns to higher education and match between demand and supply in job market to achieve greater economic development. Once the country has created the capacity and engaged in high-quality research, the higher education institutions of those counties are likely to focus on the application of indigenously developed technology in local area development. Accordingly, Chen and Basu, in Chapter 6, examine the role and impact of academics in local area economic development using a case study of Western Carolina University, US. The authors survey the role of universities and their impact on economic development in their communities in the local area. They show that, in addition to its contribution toward teaching, training, and counseling, the university has contributed toward the regional income and employment generation. Based on the analysis, the chapter provides some policy thoughts on the applicability of these issues in emerging countries such as India. Another study on the role of the university in serving regional economic development is discussed by Zhao, Ren, and Du in Chapter 7. In their study, the authors use Shanxi Agricultural University, China, as a case to demonstrate the roles of higher education institutions in spurring local economic development in China. Such roles mainly include providing talent support, establishing scientific and technological innovation service platforms, transforming scientific and technological achievements into practical productivity, and promoting interactions among stakeholders. Since there are some similarities between Chinese and Indian higher educational systems, the authors use this case to assess the strengths and weaknesses of the latter in adopting the mechanisms practiced in the former. Finally, the chapter draws on some policy implications for India, China, and other developing countries in Asia. Part 3 Demographic dividend, joblessness, and informality The stalemate in structural transformation and increasing enrollments in higher educational institutions in recent years is likely to exacerbate the problem of educated youth unemployment and employability issues. Hence, Parida (Chapter 8) analyzes the demographic dividend and identifies the sectors in which educated youth could be accommodated. Based on the secondary data taken from the population census and the employment–unemployment surveys (EUSs) and PLFS of the NSS, the chapter finds that the Indian economy is passing through a critical phase of demographic dividend and economic development. While about 4.5 million people are leaving agriculture every year, the non-farm sector job is not growing adequately to accommodate them. As a result, there is an upsurge in educated youth unemployment (18% and about 24 million) and the discouraged labour force. Moreover, the increasing share (from 8% to 10.2%) and growth (3% to 5.1%) of the elderly population put a question mark on the process of

Higher education and employment in India  9

harnessing demographic dividends in India. Based on these findings, the author argues that unless and otherwise, an integrated approach of development through a structured industrial policy for the promotion of micro and small enterprises along with infrastructure development and emigration and remittances policies is adopted, at the earliest, Indian economy is going lose it demographic dividend forever. Khare and Arora, in Chapter 9, explore the relationship between economic growth and human development via higher education graduate employment–unemployment trends in the major Indian states using historical data. The authors find that most states elude the virtuous circle of sustainable growth and are trapped in the vicious circle of unsustainable growth. The findings of the chapter highlight the need for strategic policy measures that strengthen the positive connection between high growth, better employment, and better human development, that is, the virtuous cycle of high gains. The widespread joblessness of the educated youth increases the dependence on the agriculture sector for employment and livelihood. As agriculture in India forms a major part of the informal sector, change in the world price of exportable agricultural affect the informal labour and informal economy. Kundu (Chapter 10) inquire about the macroeconomic implications of the rising world price of export on the informal economy. The author performs the scenario analysis using the Computable General Equilibrium Technique using the base-level Social Accounting Matrix of India for the year 2003–04. The chapter finds that despite the improvement in the functional income distribution of agricultural households due to the rising price of agricultural exportables in the world market in the short run, informal non-agricultural households experience deteriorating income distribution, which in turn reduces the real GDP growth of the economy. The chapter further analyzes whether the technical change in agriculture could play a significant role in arresting the rising domestic agricultural price. The author finds that in such a scenario, the real income of a certain section of the nonagricultural households improves at the cost of deteriorating the real income of the agricultural households. Hence, the chapter argues for targeted government intervention in specific households through complementary social welfare schemes to balance growth and distribution. Part 4 Labour migration and female employment This part focuses on labour migration and female employment in India. On labour migration, Khan (Chapter 11) explains the changes in the occupations between migrants and non-migrants during the last two decades from 1987– 88 to 2007–08. The author first presents the detailed descriptive statistics of industrial and occupational changes during the study period and further breaks down the change in occupations into primary components, namely

10  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

industry shift effect and occupational mix effects. It also helps to assess whether migrants are complementary or substitutes to non-migrant workers in the labour markets. The chapter reveals that occupations that require higher skills and education have reported a substantial growth rate after the economic reforms. Both migrants and non-migrant workers have benefited from occupational improvements. The segregation between migrants and non-migrants in occupations increases over time, from 1987–88 to 2007–08, and is higher among male workers than females. The choice of occupations over the period has changed among migrants and non-migrant workers, whereby non-migrants took up those jobs abandoned by migrants. Migrant workers are accommodated in the labour market not by creating more occupations but by reorganizing occupations within industries. On the macroeconomic trends in female employment in India, Mamgain and Khan, in Chapter 12, analyze the declining female labour force participation rate (FLFPR) in the labour market across different employment statuses, industry groups, socio-religious categories, and regions during 2004–05 and 2017–18. Simultaneously, they examine the education and income substitution hypothesis in the context of women’s decision to participate in work with the help of the econometric model. The chapter demonstrates that the decline in female employment has been widespread across different social groups, income strata, and states, albeit at a significantly varying rate. The major withdrawals from the workforce are observed in the case of those women working as ‘unpaid family labour’ in agriculture and ‘casual wage labour’ both in farm and non-farm sectors in rural India. The withdrawal seems prominent among relatively economically better-off groups, possibly due to their high affordability to wait for a better job. In the end, the chapter offers inputs for policy measures to provide decent livelihoods in rural areas on a big scale through demand-side interventions in the labour market. Renuka and Abraham, in Chapter 13, explore the micro-dynamics of societal norms and kinship ties in the decision of women to work in India. The authors go beyond macro statistics and use a case study approach from Kerala, a state characterized by the highest female literacy rate and one of the lowest female labour force participation in India. The authors delve deeper into this paradox and understand women’s decision to work or remain outside the labour force through a primary survey of 100 women in the working-age group. The chapter questions the notion of ‘family support’ to work as well as the ‘voluntary’ nature of the decisions to remain outside the labour force and to exit the labour market after marriage and motherhood, and rather urges to view these as consequences of social expectation borne by women in general. Part 5 Employment generation in the manufacturing sector The last section of the book discusses the impact of GVCs, FDI, technology, and imported inputs on employment and labour wage in Indian

Higher education and employment in India  11

manufacturing. Nori and Mishra, in Chapter 14, discuss the interconnections between BRICS and EU and analyses the employment effects in these countries in terms of labour content in exports, in imports, in the import content of exports, in the export content of imports, and in intermediates contained in imports that directly relate to GVC participation. The chapter uses various secondary sources of data and constructs the labour coefficients and finds that GVC trade between EU and BRICS expands employment in the latter due to rising demand for foreign labour. Based on the analysis, the chapter concludes that BRICS policies should be directed toward GVC intensification and industrial upgrading for a sizable increase in wage and employment gains. In developing countries like India, FDI is presumed to play an important role in generating new employment. Malik, in Chapter 15, analyzes the employability of FDI in India. Employing long-time-series data on India’s manufacturing firms from 2000–01 through 2017–18, the chapter underscores that FDI firms have lesser employability than domestic firms. Further controlling for different firms’ characteristics, the study finds that the low-tech, low-capital-intensive, and non-export-oriented FDI firms do not have better employability skills than the low-tech, low-capital-intensive, and non-export-oriented domestic firms, respectively. Moreover, the study unravels that FDI in high-tech, capital-intensive, and export-oriented firms has brought about a reduction in the employability of these firms compared to the high-tech, capital-intensive, and export-oriented domestic firms, respectively. It thus concludes that FDI should not be relied upon to boost employment in host developing countries. The employment creation varies across the manufacturing sub-sectors, depending on the structure of K-L use. The manufacturing industries with low K-L substitution depend majorly on labour. In contrast, industries with high K-L substitution rely more on fixed capital and advanced industrial technology to expand their production. However, these capital-intensive or labour-substituting industries are likely to create employment opportunities for high-skilled labourers. Therefore, Mishra and Kujur, in Chapter 16, examine whether these industries’ employment change is driven by activity effect, structural effect, or technological change. The chapter employs Annual Survey of Industries (ASI) data from 1998–99 to 2016–17 and a complete decomposition model. The chapter reveals that the industries with high K-L substitutions witness a decline in employment during this period. Although the expansion in the production activity created new employment opportunities, the structural change and technological effect have substantially reduced the overall labour employment in these industries. Based on the analysis, the study suggests developing a sector-specific employment policy to create employment opportunities for high-skilled labourers in the capital-intensive sectors. Changing employment patterns in manufacturing will affect plant performances (total factor productivity and gross value added) and labour wages.

12  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

The plant performances and labour wage will further change through the simultaneous use of labour and imported inputs. Therefore, Kujur and Goswami, in Chapter 17, examine the collective impact of imported inputs and labour on plant performances and labour wages. The authors use plantlevel ASI panel data from 2003–04 to 2014–15 and employ pooled OLS, fixed effects, and quantile regression models. They show that the simultaneous use of imported inputs and labour adversely impacts productivity and value added, especially among high performers. This means that imported inputs are not augmenting labour and may not boost employment in the manufacturing sector. On the other hand, the simultaneous use of imported inputs and labour reduces wages; however, this relationship is positive for the high wage quantiles. Therefore, the plants should focus on the reskilling of the workers to match the high-quality imported inputs, which, in turn, increases marginal productivity and wages. Their findings contribute to the debate on imported inputs in Indian manufacturing dominated by smalland medium-sized plants.

1.4 Concluding remarks Education and employment are the two critical aspects of economic development. Education improves the economic well-being of the people intrinsically as well as instrumentally. These positive externalities generated by education (particularly higher education) improve human capability, raising labour productivity and providing employment. The gain in employment generates income and helps create assets, and improves the standard of living. This is crucial for service-sector-dominated developing economies such as India, which is trying to become a manufacturing hub to absorb surplus labour in the economy. Through its various policies, the government has been trying to create more demand for labour to match the massive supply of labourers. In this context, an exclusive study on higher education and labour employment assumes great policy significance. The findings of the book add to the literature on higher education participation and the role of higher education institutions in regional economic development. The book also contributes toward the discussion on the demographic dividend, labour migration, female employment, and employment in manufacturing in India. From the perspective of policy, the findings of the book offer multiple new insights into the role of higher education and employment in economic development in a large developing economy. The specific findings on manufacturing employment are another vital contribution toward India’s policy discussion on creating new employment opportunities. Although the book has discussed the major issues of higher education and employment in India, it does not deal with some of the other emerging issues in the domain. The volume does not discuss vocational training and skill

Higher education and employment in India  13

development which has been considered a vital policy channel to improve the employability of educated youth. Although the book has discussed the role of the university in regional economic development using the case of the US and China, it does not supply an exclusive discussion on the role of higher education institutions in local area development in India. The volume lacks an exhaustive analysis of gender, caste, and their intersectionality in the informal labour market in India. Although the volume has covered some of the emerging issues and their relations to employment in the manufacturing sector, it has not considered the structural and technological dynamics and their corresponding impact on the employment of highly skilled labourers in the services sector. These issues are important, especially in a large developing economy like India, where higher education and employment policies have witnessed a massive transformation. Given the limitations of the book, four major issues emerge. (1) Can higher education play a comprehensive role in employment and regional economic development? Although the link between higher education participation, employment, and inequality has been examined in Part 1 and the role of higher education institutions in regional economic development in Part 2 of the book, a more comprehensive analysis of higher education in skill development, employment, and the local area development in India is needed. (2) Can an increase in formal sector employment across different social groups reduce socioeconomic disparity? Although some of the chapters in Parts 3 and 4 have analyzed the employment across different groups in the formal and informal sectors of the aggregate economy, a more detailed analysis of gender, caste, and their intersectionality in the informal labour market would add to our understanding of the dynamics of formal and informal employment in India. (3) Is manufacturing sector expansion a way forward to sustainable employment in India? Considering the sectoral space for manufacturing in the national economy, the objectives of the National Manufacturing Policy, 2011, and other recent initiatives such as ‘Make in India’ and ‘Aatmanirbhar Bharat’ or ‘self-reliant India’, the analysis of the fifth part unravels the impact of emerging factors on employment change in the manufacturing industry. However, generating sufficient employment opportunities for highly skilled labour in manufacturing, especially in the age of automation, may be unrealistic. The ‘one-size-fits-all’ approach for the manufacturing expansion may also not yield the desired results for employment creation for highly skilled labourers. So we ask (4) Can the evolving areas of the service sector generate new employment opportunities for highly skilled labourers in India? These emerging issues on higher education in regional development, including the interface between gender and caste in informal sector employment, and high-skilled labour employment in the tertiary sector could form the basis for future research.

14  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram

Note 1 In addition to the attainment (average of years of schooling), the value of education is important to freedom of a person in five different ways: intrinsic importance, instrumental personal roles, instrumental social roles, instrumental process roles, and empowerment and distributive roles (Dreze & Sen, 2002).

References Abraham, V. (2019). Jobless growth through the lens of structural transformation. Indian Growth and Development Review, 12(2), 182–201. Agarwal, P. (2007). Higher education in India: Growth, concerns and change agenda. Higher Education Quarterly, 61(2), 197–207. Alkire, S., & Santos, M. E. (2014). Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Development, 59, 251–274. Alkire, S., & Seth, S. (2015). Multidimensional poverty reduction in India between 1999 and 2006: Where and how?. World Development, 72, 93–108. Arulampalam, W. (2001). Is unemployment really scarring? Effects of unemployment experiences on wages. The Economic Journal, 111(475), F585–F606. Azam, M. (2012). Changes in wage structure in urban India, 1983–2004: A quantile regression decomposition. World Development, 40(6), 1135–1150. Bandyopadhyay, S. (2020). Gendered well-being: Cross-sectional evidence from poor urban households in India. Social Indicators Research, 151, 281–308. Belot, M., Boone, J., & Van Ours, J. (2007). Welfare‐improving employment protection. Economica, 74(295), 381–396. Birkelund, G. E., Heggebø, K., & Rogstad, J. (2017). Additive or multiplicative disadvantage? The scarring effects of unemployment for ethnic minorities. European Sociological Review, 33(1), 17–29. Campos, R. G., & Reggio, I. (2015). Consumption in the shadow of unemployment. European Economic Review, 78, 39–54. Crost, B. (2016). Can workfare programs offset the negative effect of unemployment on subjective well-being?. Economics Letters, 140, 42–47. Dhanaraj, S., & Mahambare, V. (2019). Family structure, education, and women’s employment in rural India. World Development, 115, 17–29. Dreze, J., & Sen, A. (2002). India: Development and Participation. New Delhi: Oxford University Press. Dutta, P. V. (2006). Returns to education: New evidence for India, 1983–1999. Education Economics, 14(4), 431–451. Farré, L., Fasani, F., & Mueller, H. (2018). Feeling useless: The effect of unemployment on mental health in the Great Recession. IZA Journal of Labour Economics, 7(1), 1–34. Golden, S. D., & Perreira, K. M. (2015). Losing jobs and lighting up: Employment experiences and smoking in the Great Recession. Social Science & Medicine, 138, 110–118. Gupta, P., Mallick, S., & Mishra, T. (2018). Does social identity matter in individual alienation? Household-level evidence in post-reform India. World Development, 104, 154–172.

Higher education and employment in India  15 Hnatkovska, V., Lahiri, A., & Paul, S. (2012). Castes and labour mobility. American Economic Journal: Applied Economics, 4(2), 274–307. Kannan, K. P., & Raveendran, G. (2009). Growth sans employment: A quarter century of jobless growth in India’s organized manufacturing. Economic and Political Weekly, 44(10), 80–91. Kapoor, R. (2015). Creating jobs in India’s organized manufacturing sector. The Indian Journal of Labour Economics, 58(3), 349–375. Khanna, S., Goel, D., & Morissette, R. (2016). Decomposition analysis of earnings inequality in rural India: 2004–2012. IZA Journal of Labour & Development, 5(1), 18. Kijima, Y. (2006). Why did wage inequality increase? Evidence from urban India 1983–99. Journal of Development Economics, 81(1), 97–117. Kujur, S. K. (2018). Impact of technological change on employment: Evidence from the organized manufacturing industry in India. The Indian Journal of Labour Economics, 61(2), 339–376. Kunze, L., & Suppa, N. (2017). Bowling alone or bowling at all? The effect of unemployment on social participation. Journal of Economic Behavior & Organization, 133, 213–235. Maity, B. (2020). Consumption and time-use effects of India’s employment guarantee and women’s participation. Economic Development and Cultural Change, 68(4), 1185–1231. Mehrotra, S., Parida, J., Sinha, S., & Gandhi, A. (2014). Explaining employment trends in the Indian economy: 1993–94 to 2011–12. Economic & Political Weekly, 49(32), 49–57. Merfeld, J. D. (2020). Moving up or just surviving? Non-farm self‐employment in India. American Journal of Agricultural Economics, 102(1), 32–53. Mitra, A., & Singh, J. (2019). Rising unemployment in India: A state-wise analysis from 1993–94 to 2017–18. Economic and Political Weekly, 54(50), 12–16. Mittal, P., Radkar, A., Kurup, A., Kharola, A., & Patwardhan, B. (2020). Measuring access, quality and relevance in higher education. Economic and Political Weekly, 55(24), 34–38. Mondal, B., & Dubey, J. D. (2020). Gender discrimination in healthcare expenditure: An analysis across the age-groups with special focus on the elderly. Social Science & Medicine. https://doi​.org​/10​.1016​/j​.socscimed​.2020​.113089 Ravi, S., & Engler, M. (2015). Workfare as an effective way to fight poverty: The case of India’s NREGS. World Development, 67, 57–71. Roy Choudhury, P., & Chatterjee, B. (2015). Analyzing “jobless growth” in postliberalisation India: a decomposition approach. The Indian Journal of Labour Economics, 58(4), 577–608. Sarkar, S., Sahoo, S., & Klasen, S. (2019). Employment transitions of women in India: A panel analysis. World Development, 115, 291–309. Sehrawat, M., & Singh, S. K. (2019). Human capital and income inequality in India: Is there a non-linear and asymmetric relationship?. Applied Economics, 51(39), 4325–4336. Sen, K., & Das, D. K. (2015). Where have all the workers gone? Puzzle of declining labour intensity in organized Indian manufacturing. Economic and Political Weekly, 50(23), 108–115.

16  Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram Sharma, S. (2016). Employment, wages and inequality in India: An occupations and tasks based approach. The Indian Journal of Labour Economics, 59(4), 471–487. Tapia Granados, J. A., House, J. S., Ionides, E. L., Burgard, S., & Schoeni, R. S. (2014). Individual joblessness, contextual unemployment, and mortality risk. American Journal of Epidemiology, 180(3), 280–287. Tejani, S. (2016). Jobless growth in India: An investigation. Cambridge Journal of Economics, 40(3), 843–870. Thomas, J. J. (2013). Explaining the ‘jobless’ growth in Indian manufacturing. Journal of the Asia Pacific Economy, 18(4), 673–692. Thomas, V., Wang, Y., & Fan, X. (2001). Measuring Education Inequality: Gini Coefficients of Education. The World Bank Policy Research Working Paper, 2525. Thorat, A., Vanneman, R., Desai, S., & Dubey, A. (2017). Escaping and falling into poverty in India today. World Development, 93, 413–426. Tilak, J. B. (2008). Higher education: A public good or a commodity for trade?. Prospects, 38(4), 449–466. Tilak, J. B. (2018). Private higher education in India. In Education and Development in India (pp. 535–551). Singapore: Palgrave Macmillan. UNDP (1990). Human Development Report 1990. United Nations Development Program. New Delhi: Oxford University Press. UNDP (2010). Human Development Report 2010: The Real Wealth of Nations: Pathways to Human Development. United Nations Development Program. New York: Palgrave Macmillan. Varghese, N. V., & Malik, G. (Eds.). (2015). India Higher Education Report 2015. London: Routledge. Vashisht, P. (2018). Destruction or polarization: Estimating the impact of technology on jobs in Indian manufacturing. The Indian Journal of Labour Economics, 61(2), 227–250.

Part 1

Higher education participation, employment, and income inequality



Chapter 2

Participation in higher education The role of institutions Jannet Farida Jacob

2.1 Introduction The demand for higher education rests mainly on two factors – the direct cost of education and the opportunity cost of education, which is the earnings forgone for acquiring higher education (Mincer, 1958; Mincer, 1974; Becker, 1975; Schultz, 1961). The high direct cost of education and opportunity costs, on the one hand, have a negative effect on the higher education participation decisions of individuals (Flannery & O’donoghue, 2009), and on the other hand, socio-economic background of individuals has a decisive role in the decision-making process (Basant & Sen, 2010). This raises the question of inclusion in the case of the Indian Higher Education system, where an institutional change is brought about through a policy shift towards greater private investment to meet the excess demand for high-skilled labour. The fact that higher education comes with a cost and private investment exacerbating it may have a deterring impact on higher education participation of socially and economically disadvantaged groups. Eventually, they may have to settle for lower levels of education eligible for low-paid low-/semi-skilled professions, adding to the existing wage inequality. Therefore, it is important to understand the role of institutions in higher education participation decisions of individuals, which may also explain, to some extent, the wage inequality resulting from unequal access to higher educational institutions. It is hypothesised here that the type of higher educational institution, in terms of ownership and management, has a decisive influence on the participation decisions of higher education aspirants. In other words, the study addresses the research question of the likelihood of individuals from different socio-economic backgrounds to attend public/private institutions for pursuing higher education. The study, thereby, contributes to the existing literature with an institutional analysis of the determinants of participation in higher education. Institutions of higher education in India may be public, private-aided and private-unaided, differing in their ownership, management and funding DOI: 10.4324/9781003329862-3

20  Jannet Farida Jacob

source. The public/government institutions are owned, managed and aided by the government; private-aided institutions (henceforth private-aided) are owned and managed by private agencies/organisations/trusts but funded by the government; and private-unaided institutions (henceforth privateunaided) are owned, managed and financed by private agencies. These three types of institutions have been functioning parallelly since many years, and private investment in higher education, focusing on specific professions, took shape even before economic reforms. The dwindling allocation of resources for higher education in the Five-Year Plans (Tilak, 1993), the decreasing higher education expenditure as a share of gross national product (GDP) and the resultant deteriorating quality of education in public higher educational institutions (Raman & George, 2015) set the advent of private investors in higher education with a profit motive (Kothari, 1986). Private sector participation in higher education in India gained momentum since the 1991 economic liberalisation (Tilak, 2012). The liberalisation of the economy has led to foreign investment, especially in the services sector, and has generated demand for skilled professionals (Raman and George, 2015). The oft-repeated rationale for privatising higher education is resource constraints in meeting the excess demand (Kaul, 2006; Rena, 2010). The process of privatisation of higher education was further strengthened by the World Bank statement that social returns to higher education are less than those of elementary and secondary education (World Bank, 1994), following which the government of India recommended the phasing out of subsidies in higher education. It must be mentioned here that increased participation of girl students and students of socially disadvantaged communities, especially scheduled castes (SC) and scheduled tribes (ST), would not have been possible in the absence of subsidies (Kapur & Mehta, 2007) and reservation policies (Weisskopf, 2004). The private higher educational institutions have expanded both in terms of their number and enrolment. The share of private-unaided higher educational institutions increased from 42.6% in 2001 to 63.21% in 2006 and their share of enrolments also increased from 32.89% to 51.53% in the same period (GOI, 2008). Currently, 78.6% of colleges are privately managed catering to 66.3% of the total enrolment (GOI, 2020). Given the increased participation in private higher educational institutions, an enquiry into the determinants of an individual’s decision to attend public or private institutions is vital. The study uses the National Sample Survey (NSS) data on Social Consumption: Education, 2014, to construct a multinomial logit model to ascertain the factors that determine the choice of institutions for higher education. In the rest of the chapter, Section 2.2 gives an account of the existing related literature, Section 2.3 elaborates the empirical specification, Section 2.4 explains the data along with descriptive statistics, Section 2.5 provides

Participation in higher education  21

the interpretation of the results and Section 2.6 discusses and concludes the chapter.

2.2 Related literature International studies on higher education provide a plethora of literature on choice of institution. Studies in Poland have found that professional advancement and university reputation determined participation in nonpublic universities, while marketing efforts, cost of studies and accessibility of financial aid determined participation in public university (Sojkin & Bartkowiak Pawełand Skuza, 2012). In the Netherlands, distance from home/residence has a negative effect, and regional/urban amenities have a positive effect on students’ choice of university (Sá, & Florax, 2004); whereas in Australia, teaching and learning, logistic factors, and transparency in information, determine students’ decision on universities (Bailey, Ifenthaler, Gosper & Kretzschmar, 2014); and in Scotland, England and Wales socio-economic class and fees are important factors while selecting a university (Heblich, Odendal-Wozniak & Timmins, 2012). Campus characteristics, academic quality, financial consideration and external factors are influential in Malaysia (Ariffin, Islam & Zaidi, 2014); physical and non-physical support systems including scholarship, learning environment and job prospects, are decisive in Thailand (Agrey & Lampadan, 2014); and quality of teaching are important determinants for South African students (Wiese, Heerden & Jordaan, 2010). Financial assistance, cost of tuition and availability of required programmes (Yusof, Ahmad, Tajudin & Ravindran, 2008; Wagner & Fard, 2009) as well as quality of education, administration standards, faculty qualification, and convenient and accessible location (Baharun, 2002) are also found to be influential in the choice of higher educational institutions. Studies found that a ban on affirmative action would lead to a substantial decline of minority students in the top tier colleges (Epple, Romano & Sieg, 2008) and result in a large migration of minority students out of the best schools into the lowest quality schools, and minority enrolment in the top quarter of American universities would decrease by a third (Hickman, 2013). Similarly, in the Indian context affirmative action (reservation policy) has enabled socially and economically backward students to access elite universities and have upward mobility in the ‘university quality hierarchy’ (Weisskopf, 2004). At this juncture, it must be mentioned that in India, university educational institutions in the government sector, like the Indian Institute of Technology (IIT), Indian Institute of Management (IIM) and colleges under central universities, excel in quality and efficiency, whereas the quality of education in affiliating colleges under state universities is below par, primarily, due to resource crunch (Kapur & Mehta, 2004). Contrarily,

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at the school level it is the resource-rich private sector that provides quality education (French & Kingdon, 2010). Therefore, the logical conclusion is to garner more private investment in higher education from national and international investors to achieve the goal of access, equity and excellence (Kaul, 2006).

2.3 Empirical specification As there are three main types of institutions in the Indian higher education system – public, private-aided and private-unaided institutions, a multinomial logit model is used to access an individual’s probability of attending these institutions. Higher education incurs additional investment and, hence, the household/individual decision to attend public or private higher educational institutions involves cost-benefit analysis (French & Kingdon, 2010). With the demand for higher education increasing, especially for technical education, private sector investment has increased considerably, accounting for 66.3% of the total enrolment in higher education (GOI, 2020). Despite this increasing participation in private higher educational institutions, much less has been researched into the determinants of attending public/private higher educational institutions in India. Among the factors that contribute to the decisions regarding the type of institutions, financial assistance, cost of education, quality of education, socio-economic class characteristics and college proximity are predominant in the international sphere. While, in India, studies focusing on ST, SC and Other Backward Class (OBC) students in elite institutions of higher education like IITs and medical colleges have found that affirmative action (AA) has undoubtedly contributed to the increased representation of ST/SC students in higher educational institutions (Mendelsohn & Vicziany, 1998). The AA is found to have a redistributive effect on the composition of socio-religious categories in elite higher educational institutions (Weisskopf, 2004). But AA is implemented only in government institutions. Studies report that ST and SC students are unevenly distributed across various courses in colleges and universities in India – roughly 75% of the STs and 60% of SCs opting for the relatively less-prestigious arts programmes (Chanana, 1993). These arts programmes are predominantly provided by government institutions and, therefore, the ST and SC students can be expected in more numbers in government colleges. Therefore, it can be hypothesised that socio-religious characteristics may have a decisive role in determining the choice of a higher education institution. Furthermore, it is found that boys outnumber girls among those ST and SC students who complete secondary education, a pre-requisite to pursue higher education, and are more likely to come from the uppermost strata of their population (Henriques & Wankhede, 1985). In yet another study, it is found that only 5% of the SC students in the sample had at least one

Participation in higher education  23

college-educated parent, while it was 32% for non-SC students, and 34% of SC students had illiterate parents, whereas only 11% of the non-SC students had illiterate parents (Aikara, 1980). However, it is also evidenced that there is a small segment of Dalit students who come from a relatively privileged socio-economic and family background and from English-medium schools (Kirpal & Gupta, 1999; Velaskar, 1986, p. 604). This also points to the more urban background of these families. Therefore, it is also hypothesised that gender, family education and socio-economic and geographic background are determining factors in deciding on the type of institutions to pursue higher education. Therefore, the equation for the multinomial logit model to ascertain the determinants of the choice of institutions at the higher education level – whether public, private-aided or private-unaided, is specified as follows:

Insti = a i + b1 Demi + b2Fami + b3Geoi + ei (2.1)

where Insti is a multinomial variable taking the value one if the type of institutions is private-aided, two if private-unaided and zero if public; Demi includes demographic characteristics as gender and socio-religious identities; Fami is a set of family characteristics as household size, parental education, number of dependents below 12 and above 65 years of age and family income; Geoi includes dummy variables for urban residence and state fixed effects, and ei is the error term.

2.4 Data and descriptive statistics The present study uses the nationally representative National Sample Survey data on Social Consumption: Education – 2014. The survey covers 65,926 households, spread over 36 states/union territories, comprising 36,479 rural households and 29,447 urban households with a total of 310,827 individuals. The data includes a comprehensive detail of household characteristics, demographic particulars of household members, education particulars of the currently attending persons, including expenditure and type of institutions, and current enrolment status and institution last attended of those not currently attending any educational institution. 2.4.1 Variables The multinomial outcome variable is the institutional characteristics of higher educational institutions denoted by type of institutions – public/ government, private-aided and private-unaided institutions. Government institution is the base category. The outcome variable is assessed from the perspective of those who have completed higher education and those who

24  Jannet Farida Jacob

are currently attending. Hence, the model is run separately for a completed sample and a currently attending sample. The explanatory variable covers three broad characteristics, namely, demographic, familial and geographic characteristics. The variable representing demographic characteristics is socio-religious categories including scheduled tribes, scheduled castes, other backward classes, Muslims and others, which include all other forward castes and religious categories. (‘Other’ may also include some backward communities too, which do not come under any of the above classifications.) The main variables representing familial characteristics are the education level of the head of household and family income denoted by monthly per-capita consumption expenditure (MPCE). The education of the head of household is a categorical variable with five possible levels of education, namely, illiterate, primary, middle, higher secondary and higher education. The reference category here is ‘illiterate’. Likewise, family income is also a categorical variable denoted by five MPCE quintiles,1 Q1–Q5 in ascending order from poorest to richest. The reference group here is Q5, the richest quintile. Geographical characteristics are captured by regional and state dummies. Since geographical differences have a varied effect on male and female choice decisions, gender variable is combined with regional dummies. Hence, the categorical variable capturing regional and gender differences takes the value one if rural male, two if rural female, three if urban male and four if urban female. The reference category here is urban male. The number of dependents – children below 12 years and elders above 65 years, and female-headed households are taken as additional control variables. The family being a crucial investor in human capital (Becker, 1994), the demographic and familial characteristics and geographic location influence the household’s decision on higher educational institutions. The studies on determinants of participation in higher education in the Indian context have found that a household’s decision on higher education participation is governed by its social composition, gender-related aspects, economic background and household education profile (Sundaram, 2006; Chakrabarti, 2009; Basant and Sen, 2010, 2014a, 2014b; Pramanik, 2015). Therefore, these characteristics are expected to have a significant influence on individuals’ decision on attending public/private-aided/private-unaided higher educational institutions. 2.4.2 Descriptive statistics The descriptive statistics of the multinomial logit model shows that STs, SCs and Muslims have higher mean attendance in public institutions than private-aided and private-unaided institutions in both completed and currently attending sample, more so for Muslims in the currently attending

Participation in higher education  25

sample (Table 2.1). At the same time, OBCs and others have higher mean in private-unaided institutions than private-aided and public institutions, in both samples, indicating their higher completion and participation in private-unaided institutions. It is also noted that where the household head is illiterate, private-aided institutions have higher mean completion, whereas public institutions have higher mean attendance. Where the household head has primary and below level of education, the mean attendance and completion are highest in private-aided institutions. Students whose household heads are higher secondary educated have higher mean completion in public institutions, and higher mean attendance in private-aided institutions with 33% participation. However, the mean participation and completion are the highest in unaided institutions where the household head is higher educated. Rural adult males and females have higher mean participation and completion in public institutions, whereas urban males and females have a higher completion rate in private-aided institutions. On the other hand, urban males have higher mean participation in private-unaided institutions, while mean participation is the highest in private-aided institutions for urban female students. Among the income quintiles, the lower quintiles have higher mean participation and completion in public institutions. The poorest quintile (Q1) has the highest mean participation and completion in public institutions with 8%, while their mean completion and participation rate is the lowest in private-unaided institutions with 2% and 3%, respectively. This is the same with Q2 with 15% mean completion and 18% mean participation in public institutions. Q3 and Q4 also have higher mean completion and participation in public institutions, but their mean completion and participation in private-aided and unaided institutions are less only by a few percentage points, respectively. Households with a greater number of dependent children of less than 12 years of age have the highest mean completion (69%) and participation (47%) in public institutions. On the other hand, in households with dependent elders above 65 years of age, the share of higher education completed is the highest in private-aided institutions (26%) and current participation is the highest in private-unaided institutions (27%). However, their mean current participation in higher education is the same in private-aided and public institutions (25%).

2.5 Empirical results The results of the multinomial logit model, run separately for currently attending and completed samples to estimate Equation (2.1), are presented in this section. Taking public institutions as the base category, the analysis investigates demographic, familial, regional and gender characteristics that

Mean

Public Std. dev. 0.175 0.310 0.486 0.268 0.490 0.332 0.340 0.308 0.440 0.484 0.394 0.391 0.461 0.464 0.253 0.314 0.375 0.387 0.499 0.958 0.554

0.126 0.133 0.106 0.263 0.372 0.193 0.189 0.305 0.313 0.069 0.111 0.169 0.183 0.469 0.600 0.265

Std. dev.

0.031 0.108 0.384 0.078 0.399

Mean

Private-aided

Outcome variable: completed higher education (HE)

Socio-religious category Scheduled tribe (ST) 0.060 0.237 Scheduled caste (SC) 0.142 0.350 Other backward class (OBC) 0.333 0.471 Muslim 0.088 0.284 Others 0.377 0.485 Education of head of household Illiterate 0.122 0.327 Primary and below 0.103 0.304 Upper primary/middle 0.139 0.346 Secondary/higher secondary/ 0.317 0.465 diploma Higher education 0.320 0.466 Sector and gender Rural male 0.268 0.443 Rural female 0.197 0.398 Urban male 0.264 0.441 Urban female 0.271 0.444 Monthly per-capita consumption expenditure (MPCE) quintiles Quintile 1 0.089 0.284 Quintile 2 0.159 0.366 Quintile 3 0.171 0.377 Quintile 4 0.192 0.394 Quintile 5 0.389 0.488 Children < 12 years 0.697 1.055 Elders > 65 years 0.198 0.477

Variable

Explanatory variables

Table 2.1 Descriptive statistics of variables

0.022 0.116 0.158 0.179 0.524 0.519 0.220

0.226 0.144 0.331 0.298

0.381

0.113 0.106 0.102 0.298

0.049 0.064 0.416 0.077 0.394

Mean

Private-unaided

0.147 0.321 0.365 0.384 0.500 0.883 0.497

0.419 0.351 0.471 0.458

0.486

0.317 0.307 0.302 0.458

0.216 0.245 0.493 0.267 0.489

Std. dev.

26  Jannet Farida Jacob

Source: author’s computation.

Socio-religious category ST SC OBC Muslim Others Education of head of household Illiterate Primary and below Upper primary/Middle Secondary/Higher secondary/ diploma Higher education Sector and gender Rural adult males Rural adult females Urban adult males Urban adult females MPCE quintiles Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Children< 12 years Elders > 65 years Female headed

Female headed 0.263 0.379 0.470 0.303 0.467 0.399 0.363 0.367 0.465 0.374 0.479 0.430 0.404 0.393 0.284 0.391 0.395 0.386 0.477 0.894 0.538 0.296

0.075 0.174 0.328 0.103 0.320

0.199 0.156 0.161 0.315

0.168

0.358 0.246 0.205 0.191

0.089 0.188 0.193 0.182 0.349 0.475 0.251 0.097

0.055 0.132 0.180 0.179 0.453 0.392 0.258 0.084

0.266 0.229 0.258 0.247

0.170

0.136 0.183 0.172 0.339

0.046 0.132 0.359 0.082 0.381

0.109 0.311 0.136 Outcome variable: currently attending HE

0.227 0.339 0.385 0.383 0.498 0.828 0.555 0.278

0.442 0.420 0.438 0.431

0.376

0.343 0.386 0.378 0.473

0.209 0.338 0.480 0.275 0.486

0.343

0.037 0.120 0.160 0.172 0.511 0.373 0.273 0.106

0.306 0.210 0.264 0.220

0.211

0.166 0.151 0.144 0.328

0.032 0.124 0.434 0.065 0.345

0.089

0.188 0.325 0.366 0.378 0.500 0.851 0.570 0.308

0.461 0.407 0.441 0.414

0.408

0.372 0.358 0.352 0.469

0.175 0.329 0.496 0.247 0.475

0.285

Participation in higher education  27

28  Jannet Farida Jacob

determine the probability of attending different types of higher educational institutions. 2.5.1 Demographic characteristics The variable capturing demographic characteristics is socio-religious categories (SRC), which is a categorical variable with others (forward groups) as the reference category, as mentioned in the previous section. The results show that moving from forward groups to ST, SC and Muslims each decrease the relative log odds of attending aided and unaided institutions each vs. public institutions, in both samples (Table 2.2). Moving from forward groups to ST decreases the relative log odds of attending aided vs. public by 0.56 for the currently attending sample, and by 0.39 for the completed sample, and of unaided vs. public, it decreases by 0.83 for the currently attending sample and by 0.54 for the completed sample. For the currently attending sample, moving from forward group to SC decreases the relative log odds of attending aided vs. public by 0.23 and of unaided by 0.12; and of aided vs. public decreases by 0.20 and unaided vs. public by 0.38 in the completed sample. The log odds of attending aided and unaided each vs. public decrease by 0.43 and 0.33, respectively, moving from forward groups to Muslims, in the currently attending sample, whereas in the completed sample the relative log odds of attending aided and unaided each vs. public decrease by 0.24 and 0.26, respectively. However, the log odds of attending unaided vs. public significantly increase by 0.28, moving from forward group to OBC and of aided vs. public decrease but are not significant. The coefficient for OBCs in the sample of those who have completed higher education is not significant. 2.5.2 Familial characteristics The variables gauging the influence of familial characteristics on the probability of attending and graduating from different types of institutions are the education level of the household head and family income. The number of dependent members in a household and female headedness of household are taken as additional control variables. 2.5.2.1 Education level of household head Taking illiterate household head as the base category, it is observed that in the currently attending sample, moving from illiterate to primary educated household head increases the relative log odds of attending aided by 0.24 and 0.34 in the completed sample, while it is insignificant in the case of unaided. When the education level of the household head moves from illiterate to middle level of education, the log odds of attending aided vs. public institutions increase but are insignificant in both samples; the log odds of

MPCE quintile 4

MPCE quintile 3

MPCE quintile 2

Monthly per-capita consumption expenditure (MPCE) quintile 1

Edu. of household head – HE

Edu. of household head – Sec./H. Sec.

Edu. of household head – Middle

Familial characteristics Edu. of household head – Primary

Muslims

Other backward class

Scheduled caste

Demographic characteristics Scheduled tribe

Variables

−0.834*** (0.071) −0.127** (0.061) 0.283*** (0.045) −0.327*** (0.064) −0.019 (0.069) −0.208*** (0.071) −0.017 (0.063) −0.093 (0.068) −1.070*** (0.097) −0.767*** (0.063) −0.517*** (0.054) −0.392*** (0.051)

−0.556*** (0.073) −0.239*** (0.069) −0.033 (0.050) −0.434*** (0.073) 0.240*** (0.079) 0.126 (0.080) 0.150** (0.073) −0.100 (0.080) −0.588*** (0.102) −0.461*** (0.069) −0.233*** (0.060) −0.175*** (0.057)

(0.145) −0.381*** (0.106) −0.281*** (0.094) −0.099 (0.085)

0.341*** (0.132) 0.024 (0.134) 0.004 (0.119) 0.035 (0.122) −0.389***

−0.387*** (0.114) −0.199* (0.105) −0.087 (0.075) −0.240** (0.111)

(0.194) −0.645*** (0.110) −0.527*** (0.095) −0.334*** (0.086)

0.163 (0.136) −0.373*** (0.143) −0.073 (0.121) −0.098 (0.124) −1.335***

−0.542*** (0.122) −0.381*** (0.113) 0.108 (0.073) −0.256** (0.114)

Private-unaided

Private-aided

Private-aided

Private-unaided

Higher education completed

Higher education attending

Table 2.2 Determinants of attending/graduating by types of institutions

(Continued )

Participation in higher education  29

−0.092*** (0.022) 0.026 (0.035) 0.128** (0.063) 0.053 (0.049) −0.178*** (0.056) −0.289*** (0.048) 0.412*** (0.075) 17,112 743.1

−0.106*** (0.025) 0.028 (0.039) −0.110 (0.074) −0.091 (0.056) −0.076 (0.062) −0.137** (0.054) −0.177** (0.087) 17,112 743.1

−0.089 (0.091) 0.115 (0.093) −0.031 (0.076) −0.691*** (0.135) 6,895 207.7

−0.080** (0.031) 0.103* (0.059) 0.015 (0.103) −0.104 (0.090) −0.155 (0.098) −0.170** (0.076) −0.306** (0.137) 6,895 207.7

−0.174*** (0.033) 0.006 (0.062) 0.071 (0.105)

Private-unaided

Private-aided

Private-aided

Private-unaided

Higher education completed

Higher education attending

Note: robust standard errors in parentheses; *** p < .01, ** p < .05, * p < .1. Source: author’s computation.

Observations Likelihood ratio (LR) chi-square test

Constant

Urban female

Rural female

Region and gender Rural male

Female headed

No. of elderly

No. of children

Variables

Table 2.2 Continued

30  Jannet Farida Jacob

Participation in higher education  31

attending unaided vs. public decrease significantly by 0.21 in the currently attending sample and by 0.37 in the completed sample. On the other hand, if the education level of the household head moves from illiterate to higher secondary, the log odds of attending aided vs. public increase by 0.15 and of unaided vs. public institutions decrease but are insignificant. The results are insignificant in both samples when the education level of the head of household moves from illiterate to higher educated. 2.5.2.2 Family income More interestingly, taking the highest income quintile (Q5) as the base, the results show significantly lower log odds of attending aided and unaided each vs. public institutions if the income quintile moves from Q5 to each lower quintiles Q4, Q3, Q2 and Q1, in both samples. The decrease is the highest when income quintiles move from Q5 to Q2 and Q5 to Q1, showing that they have lower log odds of attending aided and unaided each vs. public institutions in both samples, owing to their disadvantaged low-income status. Moving from Q5 to Q1 reduces the relative log odds of attending aided vs. public institutions by 0.58 in the currently attending sample and by 0.38 in the completed sample, and of unaided vs. public institutions by 1.07 in the currently attending sample and 1.33 in the completed sample. The relative log odds of attending aided vs. public institutions reduce by 0.46 in the currently attending sample and by 0.38 in the completed sample, and of attending unaided vs. public institutions reduce by 0.77 in the currently attending sample and by 0.65 in the completed sample, if income quintiles move from Q5 to Q2. Likewise, the relative log odds of attending aided and unaided each vs. public institutions reduce moving from Q5 to each Q3 and Q4, in both samples. 2.5.3 Geographic and gender characteristics As far as geographic and gender characteristics are concerned, the results reveal that in the currently attending sample, moving from urban male to rural female significantly reduces the relative log odds of attending unaided vs. public institutions by 0.17, and moving from urban male to urban female reduces the relative log odds of attending unaided vs. public institutions by 0.28. In the currently attending sample, moving from urban male to urban female decreases the relative log odds of attending in aided vs. public institutions by 0.14. In the rest of the cases the results are insignificant. In general, government higher educational institutions are increasingly being attended by women, irrespective of rural and urban differences. On the other hand, the results are insignificant for rural adult males in both samples.

32  Jannet Farida Jacob

2.6 Discussion and conclusion The major objective of this analysis was to ascertain the determinants of participation in different types of institutions of higher education in the context of increasing private investment in higher education. The analysis brings to light the profound role of the government in ensuring equitable participation in higher education. Even when the participation trends in public higher education institutions are found to be declining since 2004–5 (Jacob, 2018b), the lower income quintiles, SC/ST, Muslims and females have a higher probability of participating in government institutions of higher education, probably for its subsidised fee structure and affirmative action. While the need to augment investment in higher education to meet the excess demand for high-skilled labour is widely echoed, the cash-strapped government has sought refuge in private investments. These private investments are mostly directed towards professional courses, especially, medical and engineering courses, which have higher labour market returns (Jacob, 2018a). The wage premium associated with skill premium is already contributing to inequality in wage distribution and, hence, income distribution (Kijima, 2006; Lemieux, 2006). When the government goes for an institutional change through a policy shift towards more private investment, the expansion in higher education becomes skewed towards professional courses with a wage premium. As these private institutions have lower odds of attendance for underprivileged students, it paves the way for inequality in the distribution of skill premium. This would further exacerbate wage inequality and income inequality. Moreover, given the high dropout rate – 39.2% in rural and 55.4% in urban population in the age group between 16 and 24 due to financial constraints (GOI, 2015), government institutions, with the subsidised cost of education, could be a viable alternative to these dropouts. Therefore, government institutions have a greater stake in reducing the imbalances in participation among these communities by enhancing their reach through equity, expansion and efficiency. The study has significant policy implications in the context of the government’s policy towards an institutional change. The study delineates the factors responsible for hampering the higher education prospects of individuals, whereby the government may devise measures to overcome these obstacles. To make the higher education sector more inclusive, in terms of equity in access and participation, the government must develop inclusive institutions and effect institutional changes that are inclusive. For an inclusive higher education system with inclusive participation from all sections of society, the government, while increasing private investment in higher education, needs to enforce regulatory measures and ensure affirmative action to promote access to underprivileged groups in both public and private higher educational institutions.

Participation in higher education  33

The biggest challenge here is the poor quality and inefficiency of government institutions which has led to voting with the feet in preference for private institutions. The government may have to devote resources towards enhancing the quality and efficiency of existing government higher educational institutions and ensure equity and access through expansion in its numbers, alongside private investments. Private investment in higher education alone may not increase equitable access and participation; public investment is equally important. In fact, a complete withdrawal of public investment in higher education would prove detrimental to the weaker sections of the society including women.2

Notes 1 Monthly per-capita consumption expenditure is a common proxy for family income, as National Sample Survey Organisation (NSSO) data does not provide data on family income per se. Basant and Sen (2012) also uses MPCE in lieu of family income. 2 I extend my gratitude to Dr M. Imran Khan, Assistant Professor, Narsee Monjee Institute of Management Studies (NMIMS), Mumbai, for his comments and suggestions to improvise this analysis. I thank the Institute of Public Enterprise (IPE) for inviting this chapter to the International Conference on ‘Economic Development: Role of Higher Education Institutions in Employment’, where I garnered useful comments.

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Participation in higher education  35 Kapur, D., & Mehta, P. B. (2004). Indian Higher Education Reform: From HalfBaked Socialism to Half-Baked Capitalism. Center for International Development Working Paper No. 108. Harvard University. Kapur, D., & Mehta, P. B. (2007). Public Institutions in India: Performance and Design. Oxford University Press. Kaul, S. (2006). Higher education in India; seizing the opportunity. Indian Council for Research on International Economic Relations Working Paper, No. 179. Retrieved from http://www​.icrier​.org​/pdf​/WP​_179​.pdf. Kijima, Y. (2006). Why did wage inequality increase? Evidence from urban India 1983–99. Journal of Development Economics, 81(1), 97–117. https://doi​.org​/10​ .1016​/j​.jdeveco​.2005​.04​.008. Kirpal, V., & Gupta, M. (1999). Equality Through Reservations. Rawat Publications. Kothari, V. N. (1986). Private unaided engineering and medical colleges: Consequences of misguided policy. Economic and Political Weekly, 21(14), 593–596. Lemieux, T. (2006). Postsecondary education and increasing wage inequality. American Economic Review, 96(2), 195–199. https://doi​.org​/10​.1257​ /000282806777211667. Mendelsohn, O., & Vicziany, M. (1998). The Untouchables: Subordination, Poverty and the State in Modern India (Vol. 4). Cambridge University Press. Mincer, J. (1958). Investment in human capital and personal income distribution. Journal of Political Economy, 66(4), 281–302. https://doi​.org​/10​.1086​ /258055. Mincer, J. (1974). Schooling, Earnings and Experience. Columbia University Press. Pramanik, S. (2015). The effect of family characteristics on higher education attendance in india a multivariate logit approach. Higher Education for the Future, 2(1), 49–70. Raman, R., & George, K. (2015). Developments in higher education in India: a critique. eSocialSciences Working paper No. 7738. Retrieved from http:// csesindia​.org​/admin​/modules​/cms​/docs​/publication​/7​.pdf. Ravinder, R. (2010). Emerging trends of higher education in developing Countries. Scientific Annals of the ‘Alexandru Ioan Cuza’ University of Iasi: Economic Sciences Series, 301–316. Sá, C., Florax, R. J. G. M., & C, P. (2004). Determinants of the regional demand for higher education in the Netherlands: A gravity model approach. Regional Studies, 38(4), 375–392. https://doi​.org​/10​.1080​/03434002000213905. Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17. Sojkin, B., & Bartkowiak Pawełand Skuza, A. (2012). Determinants of higher education choices and student satisfaction: The case of Poland. Higher Education, 63(5), 565–581. Sundaram, K. (2006). On backwardness and fair access to higher education: results from NSS 55th round surveys, 1999-2000. Economic and Political Weekly, 41(50), 5173–5182. Tilak, J. B. G. (1993). Financing higher education in India: Principles, practice, and policy issues. Higher Education, 26(1), 43–67. Tilak, J. B. G. (2012). Policy crisis in higher education: Reform or deform? In K. N. Panikar & M. B. Nair (Eds.) Globalisation and Higher Education in India (pp. 37–61). Pearson.

36  Jannet Farida Jacob Velaskar, P. R. (1986). Inequality in higher education: A study of Scheduled Caste students in medical colleges of Bombay. Unpublished PhD Dissertation, Tata Institute of Social Sciences, Mumbai. Wagner, K., & Fard, P.-Y. (2009). Factors influencing Malaysian students’ intention to study at a higher educational institution. Chinese American Scholars Association, New York. Refereed Program of the E-Leader Conference at Kuala Lumpur, Malaysia. Retrieved from http://www.g- casa​.com​/P​DF/ malaysia/ Wagner​-Fard​.pd​f. Weisskopf, T. E. (2004). Impact of reservation on admissions to higher education in India. Economic and Political Weekly, 39(39), 4339–4349. Wiese, M., Heerden, C. H. Van, & Jordaan, Y. (2010). The role of demographics in students’ selection of higher educational institutions. Acta Commercii, 10(1). https://doi​.org​/10​.4102​/ac​.v10i1​.124. Yusof, M. B., Ahmad, S. N., Tajudin, M. B., & Ravindran, R. (2008). A study of factors influencing the selection of a higher education institution. UNITAR e-Journal, 4(2), 27–40.

Chapter 3

Gender gaps in employment preferences among university graduates in India Pradeep Kumar Choudhury and Amit Kumar

3.1 Introduction The gender differences in employment and earnings continue to be the focus of an extensive and growing literature in the domain of economics of gender, and several empirical works have offered a variety of new explanations for the variations (Choudhury, 2015; Meherotra & Parida, 2017). Studies evident that women’s access to the labour market is associated with socioeconomic development of a country (Chaudhary & Verick, 2014; Verick, 2018; ILO, 2022). For instance, participation of women in the job market boosts economic development, reduces poverty and inequality, improves welfare, works as a key factor in improving the bargaining power within households, and works as a coping mechanism to economic shocks that hit the household (Rhodes et al., 2017). Even if literature suggests several socio-economic benefits of women’s participation in the labour market, a declining trend in female labour force participation is observed worldwide, and more so in developing countries. For instance, the World Bank data shows a female labour force participation (FLFP) rate of 19 in India, as compared to the world average of 46 in 2021 (World Bank, 2022). Further, the ILO report on ‘Global Employment Trends for Youth 2022’ specifies the very low youth female labour market participation in India. In 2021, young Indian men account for 16 per cent of young men in the global labour market, while the corresponding share for young Indian women is just five per cent (ILO, 2022: 35). Literature finds that education is one of the important factors influencing FLFP, both in India and in several other countries’ contexts (Fatima and Sultana, 2009; Tam, 2011; Gaddis and Klasen, 2014; Meherotra & Parida, 2017; Chicoine, 2021; Lopez-Acevedo et al., 2021). The human capital theory predicts that with more education and skills, women acquire greater productivity; this, in turn, would increase women’s labour force participation (Goldin, 1990; England, Garcia-Beaulieu & Ross, 2004; Chatterjee, Desai & Vanneman, 2018). Furthermore, studies have discussed several other channels through which education improves women’s participation in DOI: 10.4324/9781003329862-4

38  Pradeep Kumar Choudhury and Amit Kumar

the labour market. For instance, Chicoine (2021) finds that each additional year of schooling led to a reduction in fertility which in turn is associated with an increase in labour market opportunity. Perhaps, the strong linkage between women’s education and their participation in the labour market (both directly, i.e., enhancing their skills and productivity, and/or indirectly by putting women in better socio-economic set-ups) has resulted in a significant increase in women enrolment in secondary and higher levels of education in several countries of the world. In 2019–20, females constituted 49% of the total enrolment in higher education in India, and this share was only 14.3% in 1970–71 (UGC Annual Report 1970–71, 1971; Ministry of Education, 2020). Thus, India has made substantial progress in increasing access to secondary and higher education for girls in the working-age group. One of the most predominant contemporary public policy debates in India is to boost self-employment opportunities among the educated youth. India is currently experiencing a demographic dividend where we find a substantial rise in the working-age population, and the recent policy initiatives aim to provide meaningful engagement in the labour market. An important policy goal of providing access to higher education and vocational training among youth is to enhance self-employment opportunities for the graduates. The National Education Policy (NEP) 2020 underlines the urgency of the need to hasten the spread of vocational education in India and aims to impart vocational skills and training to at least 50% of learners by 2025. Also, in the last couple of years, several policy initiatives such as skilling India, self-reliant India, and make in India have come up to transform the youth of the country from job seekers to job producers. It is argued that in the context of changing nature of work (World Development Report, 2019), providing foundational skills to the workforce will help in engaging themselves in self-employment activities, which will reduce the overdependence on wage employment. It is particularly true in developing economies such as India, which is experiencing prolonged jobless growth, tied with a huge presence of the informal sector. Studies in developing countries’ contexts in general also argue that the returns to education from self-employment are lower than for wage employment, which might be one of the principal causes of not motivating labourers to preferentially choose self-employment occupations. Although education expands an individual’s knowledge base and increases exposure to new opportunities, it increases the opportunity cost of being self-employed. In a study on India, Tamvad (2010) has also demonstrated that people with higher education do not prefer self-employment in the non-farm sector. However, one of the recent studies, while exploring the factors leading to unemployment among the educated versus uneducated in India, observed that the unemployment rate among the educated is not only higher compared to the uneducated, it is also increasing with higher levels of education (Bairagya, 2018). With the job constraints in the labour market, educated

Gender gaps in employment preferences among university graduates in India  39

youth in India may look for self-employment opportunities, but to claim this firmly, we need empirical/causal evidence. The higher education sector in India has undergone a massive expansion in terms of institutions and student enrolments in the past three decades. India has become one of the largest higher education systems in the world after China, with around 38.5 million students enrolled in 1,043 universities, 42,343 colleges, and 11,779 stand-alone institutions (MHRD, 2020). The recently adopted National Education Policy 2020 recommends increasing the gross enrolment ratio (GER) in higher education to 50% by 2035, which is currently at 27.1%. With the expansion of the higher education sector in India, youth from the middle and lower-middle classes are accessing higher education which was not the case before. Also, in the changing labour market conditions, the transition from university to work for the graduates may differ based on their social identity, household factors, and other socio-economic and cultural settings; gender inequality in labour market outcomes for graduates is a critical area for further enquiry. In this context, the two key questions addressed in this chapter is: How do graduates’ social identity, socio-economic settings, and demographic factors matter in their choice between salaried jobs and self-employment? How does the gender of the graduates interact differently with the occupational preferences of technical and non-technical graduates? Though the linkage between higher education and the labour market is a serious policy debate in India, firm academic engagement on labour market issues among university graduates in India is sparse. Further, we have extremely limited knowledge about gender inequality in the employment preferences of higher education graduates. We find hardly any study that explains the gender differences in employment preferences among university graduates in India. However, we find some studies examining the gender inequality in employment preferences and the wage gap in developed economies context. For instance, Morgan (1998) argues that jobs in fields of science and engineering have been traditionally male-dominated, and women find themselves at a disadvantage in terms of entry, pay, and promotions. Similarly, Graham and Smith (2005) find that female college graduates in the USA are less than half as likely as men to be employed in science and engineering and, if they are, earn about 20% less. Toumanoff (2005) found significant gender-related differences in salary offers at the time of initial appointment in the academic labour market in the USA, and interestingly, it has increased since 1990. Using the Periodic Labour using the Periodic Labour Force Survey 2018–19 data (National Statistical Office, Ministry of Statistics & Programme Implementation, 2020), this study aims to understand the gender differences in the engagement of graduates in self-employment activities vis-à-vis wage employment in India. Specifically, we analyse how the socioeconomic and demographic characteristics of the students matter in explaining the preferences between self-employment over wage employment.

40  Pradeep Kumar Choudhury and Amit Kumar

This study contributes to the literature in at least three ways. First, it explores the gender variations in the employment preferences of graduates in the context of the recent policy initiatives to make Indian youth self-reliant. Second, we explain the socio-economic and demographic contours of employment preferences of the graduates. Third, we relate the discussion on gender differences in graduates’ employment with the ongoing discourse on changing nature of work as discussed in the World Development Report 2019 (World Bank, 2019), though we have not examined this issue empirically in the chapter directly. We relate the gender variations in employment preferences of graduates in different age groups with the contemporary labour market challenges and policy initiatives. Possibly, this is the first study in India that explains the interaction between gender and employment preferences for university graduates using nationally representative household survey data. The rest of the chapter is structured as follows. Section 3.2 discusses the data, empirical design, and descriptive figures. The empirical results on determinants of employment preferences of graduates in India are discussed in Section 3.3. Section 3.4 concludes by outlining a summary of the findings, limitations of the study, areas for future research, and a few policy implications.

3.2 Data, empirical design, and descriptive accounts 3.2.1 Data We use  unit-level  data from the Periodic Labour Force Survey (PLFS) conducted from July 2018 to June 2019. It is a nationally representative household-level sample survey collected by the National Statistical Office (NSO) of the Government of India. It includes a sample of over 1,01,579 households (55,812 in rural areas and 45,767 in urban areas) and enumerates more than 420 thousand individuals. Besides individuals’ social and demographic characteristics, PLFS survey gathers details regarding the individual’s job contract, occupation, educational achievement, and household characteristics. The five major job categories covered in the survey include (i) Regular government jobs – the most prestigious jobs in India due to their security and stability quotient; (ii) Private regular jobs – second best in the overall job hierarchy; (iii) Contractual jobs – which are temporary jobs with a term of up to three years; (iv) Casual employment – which are not daily workers but vary from contracted jobs in terms of the form of work involved; and (v) Self-employment – workers in household enterprises which includes own-account workers, employer, and individuals who worked as helpers in household enterprises (unpaid family worker). PLFS also provides information on the completed level of education of individuals in various dimensions. For instance, it covers information on whether an individual has taken any technical education. This allows us to explore the linkage

Gender gaps in employment preferences among university graduates in India  41

between higher education (both general and technical) with the graduates’ employment choices. We have restricted the sample for our analysis to individuals in the age group 21–65 years who have completed graduation or above level of education. Further, the present analysis excludes those currently attending any level of education. The sample size after the restrictions is coming out to be 33,773, of which 26.7% are self-employed, 54.6% are salaried, 2.1% are casual labourers, and 16.6% are unemployed. Among the self-employed graduates, 67.8% are own-account workers, 11.8% are employers, and 20.4% are individuals who worked as helpers in household enterprises (unpaid family workers). As the majority of the graduates are salaried workers, the study examines the likelihood of being a regular salaried worker as compared to being into self-employment and how this varies with gender after controlling for other covariates. Thus, our final sample for the analysis includes the individuals who are either self-employed or engaged in salaried jobs. Our analysis does not include the graduates engaged in casual labour and are unemployed, whose share constitutes 18.7% of the total graduate sample. We have analysed the gender inequality in the probability of being into salaried jobs (over self-employment) by socio-economic, regional, and demographic characteristics. Of the total sample, the share of females is 42.6%. Close to three-fourths (72.2%) of the individuals are from urban areas. The caste-wise distribution indicates that the majority of individuals (45.7%) are from Upper Castes (UCs), followed by 34.8% from Other Backward Classes (OBCs) and 19.5% from Scheduled Castes (SCs) and Scheduled Tribes (STs). Further, more than three-fourths of the respondents (78%) were Hindu, 10.1% were Muslims, and 11.9% were from other religions. 3.2.2 Empirical design We examine the factors determining graduates’ choice for salaried jobs over self-employment in India. For this, we have used a logit regression model where the dependent variable is the probability of being in salaried jobs among graduates in the age group of 21-65 years. It is a dummy variable that takes value 1 for the individuals currently engaged in salaried jobs and 0 if they are engaged in self-employment. The econometric specification that we used in our study is as follows:

Y = a + b (Gender ) + g ( eco _ status ) + d ( age ) + qX + e

where a is the intercept, while b, g, and d are the coefficients of the main explanatory variables, θ is the coefficient vector of the other control variables, and e is the error term. X is the vector of the explanatory variables. We use the Usual Employment Status (considering both principal and subsidiary employment status) to estimate employment and unemployment.

42  Pradeep Kumar Choudhury and Amit Kumar

This specifically helps capture people who were working for long periods over the reference period of 365 days while bearing in mind their principal and subsidiary employment statuses. The explanatory variables used in the logit model are gender, caste, location, household consumption expenditure – a proxy to annual family income,1 family size, age, households’ head education, education level, and status of technical education. The summary statistics of the variables used in logit models are given in Table 3.1. The main variables of interest along which we examine the heterogeneity in the predicted probabilities of being into a salaried job are ‘household’s economic status’ and ‘age of the individual’. We hypothesise that there exists a stark inequality in the probability of being in salaried employment among males and females, and this gap varies significantly with socio-economic setup and demographic factors. We have estimated three separate logit regression models. Model 1 considers the analysis of the probability of being in salaried jobs by incorporating all the explanatory variables in the model, and the corresponding results are shown in Table 3.2. Similarly, to capture the gender disparity in the probability of being into salaried jobs, models 2 and 3 incorporate an additional interaction term, i.e., interaction effects of individual’s gender with household income (model 2) and individual’s age (model 3), and the regression estimations are given in Table 3.2. 3.2.3 Descriptive accounts We find that more than half of the graduates in India (53.8%) in the age group of 21–65 years were engaged in salaried jobs, while 30.6% were self-employed in 2018–19, and the rest are either casual labourers or unemployed. We notice significant regional inequality in the engagement of graduates in salaried jobs. For instance, three-fourths of the graduates from Puducherry are engaged in salaried occupations, while it is 38.5% in Bihar (see Figure 3.1). Overall, we find that more graduates from the states and UTs with better access to higher education (Puducherry, Chandigarh, Delhi, Tamil Nadu, Karnataka) are opting for salaried jobs compared to the states where the access to higher education is limited, such as Bihar, UP, Rajasthan, Madhya Pradesh, and Odisha. Given the regional inequality in access to higher education and employment opportunity among graduates, understanding the contours in the employment pattern among graduates at the sub-national level would reveal some interesting insights, which is beyond the scope of this chapter. Data shows significant gender and socio-economic variations in the employment pattern of higher education graduates. We note that 88.8% of self-employed individuals are males and their proportion is more as compared to salaried jobs, i.e., 74.6% (see Figures 3.2 and 3.3). This indicates that female graduates are more likely to be employed in salaried jobs than in self-employment. Social inequalities (by caste categories) in employment status reveal some interesting points. A clear hierarchy is observed wherein

– 6,639 6,640 6,640 6,640 6,640 6,640 6,603 6,605 6,604

employ_status gender caste location hh_cons_quintile family_size age head_edu edu_level tech_edu

Source: PLFS 2018–19, NSSO.

NOB

Variable

0.888 2.347 0.490 3.805 4.973 2.211 11.843 0.202 0.132

–

Mean

Self-Employed

– 0.315 0.713 0.500 1.319 2.208 1.054 5.255 0.401 0.338

SD – 0 1 0 1 1 1 0 0 0

Min 1 3 1 5 20 4 17 1 1

–

Max – 13,554 13,555 13,555 13,555 13,555 13,555 13,547 13,547 13,545

NOB

Salaried

Table 3.1 Summary Statistics of the Variables Used in the Logit Models

0.746 2.324 0.695 4.435 4.363 2.022 12.396 0.318 0.245

–

Mean – 0.435 0.724 0.460 0.965 1.885 0.989 4.921 0.466 0.430

SD – 0 1 0 1 1 1 0 0 0 1 3 1 5 17 4 17 1 1

–

Mean

20,195 0.637 20,193 0.798 20,195 2.332 20,195 0.621 20,195 4.206 20,195 4.584 20,195 2.090 20,150 12.196 20,152 0.276 20,149 0.204

Min Max NOB

Overall

0.481 0.402 0.720 0.485 1.147 2.030 1.017 5.051 0.447 0.403

SD

0 0 1 0 1 1 1 0 0 0

Min

1 1 3 1 5 20 4 17 1 1

Max

Gender gaps in employment preferences among university graduates in India  43

44  Pradeep Kumar Choudhury and Amit Kumar Self-Employed Salaried

53.8

Puducherry

Karnataka

Chandigarh

Goa

Tamil Nadu

Chhattisgarh

Delhi

Gujarat

Kerala

Maharashtra

Jharkhand

Jammu & Kashmir

West Bengal

Haryana

All India

Punjab

Andhra Pradesh

Uttarakhand

Odisha

Telangana

Madhya Pradesh

Himachal Pradesh

Rajasthan

Bihar

Uttar Pradesh

30.6

Figure 3.1 Share of Graduates Involved in Self-Employment and Salaried Jobs by Major States in India (21–65 years). Source: PLFS 2018–19, NSO.

General (48.8%)

Q5 (43.1%)

Engineering (33.0%)

Male (88.8%) Q4 (22.5%) OBC (37.1%)

Female (11.2%) Gender

SC (10.1%) ST (4.0%) Caste

Q3 (14.2%) Q2 (12.3%) Q1 (7.9%) Consumption Quintile

Others (67.0%)

Discipline

Figure 3.2 Distribution of Self-Employed Graduates by Socio-economic and Educational Indicators. Source: PLFS 2018–19, NSO.

STs and SCs are least represented in both salaried jobs and self-employment. The inter-caste distribution among both the self-employed and salaried workforce shows stark inequalities. That is, among the self-employed workforce, the highest share accounted for graduates belonging to UCs (48.8%), followed by OBCs (37.1%), and, as expected, SCs and STs were the least represented with 10.1% and 4% shares, respectively. A similar pattern is observed among the salaried workforce as well. Thus, being from a lower caste proves to be unfavourable compared to a UC individual being in any kind of employment, i.e., salaried or self-employed. Further enquiry would be interesting to examine the caste inequalities in salaried jobs by the government and private sector as the graduates from SCs, STs, and OBCs get reservations in government jobs. Gender and socio-economic variations in

Gender gaps in employment preferences among university graduates in India  45

Engineering (46.6%)

General (47.6%) Q5 (66.6%)

Male (74.6%)

OBC (37.2%) Q4 (19.3%) Female (25.4%) Gender

SC (11.3%) ST (3.9%) Caste

Q3 (7.5%) Q2 (4.3%) Q1 (2.3%) Consumption Quintile

Others (53.4%)

Discipline

Figure 3.3 Distribution of Salaried Graduates by Socio-economic and Educational Indicators. Source: PLFS 2018–19, NSO.

self-employed and salaried graduates provide us a rationale to find out the determinants of their employment choices. The employment status of an individual varies widely with households’ economic status, which is measured in terms of consumption expenditure in this study. Estimates reveal a huge difference between rich and poor graduates in terms of their participation in different types of employment – revealing a strong positive relationship between household income and being in salaried jobs or self-employment. The share of being self-employed increases with each successive income level and ranges from 7.9% for the poorest quintile to 43.1% for the richest quintile (see Figure 3.2). The corresponding share ranges from 2.3% for the poorest quintile to 66.6% for the richest quintile (see Figure 3.3). Thus, the economic status of households plays a more critical role in opting for self-employment over any other type of employment. This might be because graduates from rich family backgrounds might end up doing a business (which requires a huge amount of initial investment not only on building up human capital but also funding for infrastructure) over choosing salaried jobs. For higher education graduates, the discipline of study matters in their occupational choices. The graduates are grouped into engineering and others in our study. We find that a higher proportion of engineering graduates are involved in salaried jobs as compared to self-employment. Specifically, around one-third of the self-employed workforce holds a technical degree, and the corresponding share is close to half in the salaried workforce (see Figures 3.2 and 3.3). As the focus of this chapter is to examine the gender variations in employment preferences of university graduates, the following section looks at this issue in an empirical set-up.

46  Pradeep Kumar Choudhury and Amit Kumar

3.3 Gender differences in employment preferences of graduates in India: logit estimates The empirical results are discussed in three different parts in this section: (a) socio-economic determinants of employment preferences of higher education graduates, (b) the role of household’s economic status in explaining the gender variations in graduates’ employment preferences, and (c) how age (taken as a proxy for work experience) of the graduates' matters in minimising the gender differences in the probability of being in salaried jobs vis-à-vis self-employment. Logit estimates reveal a significant gender gap in employment preferences among higher education graduates in the workingage group, i.e., 21–65 years in India. Being female increases the chances of being in salaried jobs by 15.1% compared to their male counterparts (see

Table 3.2 Determinants of Employment Preferences among Higher Education Graduates: Logit Estimates

gender (ref. – female) caste_OBC (ref. – SC/STs) General location (ref. – rural) hh_cons_quintile (ref. – Q1) Q2 Q3 Q4 Q5 family_size age (ref. – 21–30 years) 31−40 years 41−50 years 51−65 years head_edu PG (ref. – UG) tech_edu (ref. – No) Interaction effect gender#hh_cons_quintile gender#age Constant Prob. > Chi2 Pseudo-R2 Observations

Simple model

Models with interaction terms

Eqn. 1

Eqn. 2

Eqn. 3

(Average marginal effects)

(Coefficients)

(Coefficients)

−.151*** (.008) −.113*** (.008) −.155*** (.008) .070*** (.007)

−1.006*** (.178) −.632*** (.047) −.835*** (.047) .358*** (.038)

−.366** (.164) −.636*** (.047) −.836*** (.046) .352*** (.038)

.031 (.022) .092*** (.021) .163*** (.020) .232*** (.020) −.015*** (.002)

– – – – −.075*** (.009)

.137 (.098) .409*** (.092) .737*** (.087) 1.082*** (.086) −.070*** (.009)

−.009 (.008) −.065*** (.009) −.101*** (.011) .000 (.001) .088*** (.008) .089*** (.009)

−.046 (.041) −.326*** (.047) −.491*** (.055) .000 (.004) .446*** (.039) .452*** (.044)

– – – .003 (.004) .448*** (.039) .443*** (.044)

– – – 0.000 .084 20,141

.056* (.040) – .945*** (.180) 0.000 .084 20,141

– −.011*** (.004) 1.350*** (.181) 0.000 .085 20,141

Source: Estimated from PLFS 2018–19, NSO. Notes: standard errors in parentheses;*** p < 0.01, ** p < 0.05, * p < 0.1

Gender gaps in employment preferences among university graduates in India  47

Table 3.2). Similar to our findings, Chatterjee et al. (2018) found that lesseducated women more often work on the family farm or as wage labourers, while college graduates are more likely to be found in the more secure (and prestigious) salaried positions (p. 870). It may be argued that the patriarchal set-up and the cultural constraints may be hindering women in opting for self-employment, even if they are educated. Further, the likeliness of having a salaried job varies widely across castes. A clear caste hierarchy is observed in the probability of being in salaried jobs, irrespective of other socio-economic factors. Compared to SCs/STs, the individuals belonging to OBCs and UCs are 11.3% and 15.5% less likely to opt for salaried jobs, respectively. This indicates that SC/ST graduates tend to opt for a salaried job over self-employment, whereas graduates belonging to OBC and UC have more chances of ending up in self-employment. Perhaps most SC/ST graduates come from lower or middle-class families who could not afford to start their own business, which might require a substantial amount of initial investment. Households’ location is statistically significant in determining the probability of being in salaried jobs in India. Graduates from urban areas have 7% more chances of doing a salaried job when contrasted with their urban counterparts. This might be due to the better availability of salaried job opportunities in urban areas and the existence of intense competition in business. The economic status of the household has a significant impact on the decision to opt for a salaried job. Estimates reveal that the probability of being in salaried job increases for each successive consumption quintile. For instance, graduates from the richest consumption quintile are 23.2 percentage points more likely to go for salaried jobs than their poorest counterparts. Do households with more members tend to choose salaried jobs over self-employment? Our estimates find a negative relationship between household size and the likeliness of being in salaried jobs. More clearly, having an additional family member decreases the chance of being salaried jobs by 1.5 percentage points. One possible explanation for this may be that households with more members would leave fewer resources for education which could corner the lower-order members to engage in self-employment rather than pursuing education and securing a salaried job in the future. However, we find that the educational attainment of the household head does not matter in a graduate’s decision to choose between a salaried job or self-employment. The age of the higher education graduates (taken as a proxy of experience) is negatively related to the probability of being in salaried jobs vis-àvis elf-employment. In comparison to the recent graduates, i.e., individuals in the age group of 21–30 years, graduates in every other age group are less

48  Pradeep Kumar Choudhury and Amit Kumar

likely to be in salaried jobs and this goes on decreasing with an increase in age. For instance, compared to recent graduates (21–30 years), graduates in the age group of 41–50 years and 51–65 years are 6.5% and 10.1% less likely to opt for salaried jobs. Also, we find that postgraduate students have an 8.8 percentage point higher chance of getting employed in salaried jobs than their undergraduate counterparts. Similarly, graduates with technical backgrounds are 8.9 percentage points more likely to opt for salaried jobs than graduates with no technical qualifications. 3.3.1 How does households’ economic status matter in explaining gender variation in graduates’ employment preferences? We interacted gender and household’s economic status (Equation 2) to examine the gender variations in employment preferences of higher education graduates between poor and rich households. We find that the predicted probability of choosing salaried jobs over self-employment is higher among females than their male counterparts across consumption quintiles. But this gap is the highest for the bottom consumption quintile (Q1) and the lowest for the top consumption quintile (Q5) (see Figure 3.4). The analysis clearly shows that female graduates from rich households are less likely to go for salaried jobs, which results in a declining gender gap. However, further disaggregation of gender across rural and urban areas suggests that female graduates in urban areas have the highest probability of being in salaried jobs, followed by female graduates from rural areas, urban males, and lastly, rural males (see Figure 3.5). These gaps vary significantly between poor and rich households. For instance, a male graduate from a rural area and belonging to a poor household has the least chance to opt for salaried jobs vis-à-vis self-employment, while a female graduate belonging to an urban area and a rich household has the highest probability of getting a salaried job. The PLFS data also provides information on whether the graduates have any technical qualifications during their entire study period – a certificate course or diploma or a degree. In the present analysis, we examine the impact of the type of technical background of the graduates on the probability of being in salaried jobs. Logit estimates reveal that graduates with a technical qualification are 8.9% more likely to be in salaried jobs. Such impact of technical qualifications on the choice of salaried job is noted higher among male graduates (9.8 percentage points) than females (5.6% percentage points). We also include an interaction effect of the technical

Gender gaps in employment preferences among university graduates in India  49

Pr(Salaried Job)

0.8

0.7

0.6

0.5

0.4 Q1 (Richest)

Q2

Q3

Q4

Household Income Quintile Female

Q5 (Richest)

Male

Figure 3.4 Probability of Being into Salaried Job by Household Income Quintile and Gender. Source: Estimated from PLFS 2018–19, NSO.

Pr(Salaried Job)

0.8

0.6

0.4

0.2 Q1 (Richest)

Q2

Q3

Q4

Household Income Quintile Female, Rural

Female, Urban

Male, Rural

Male, Urban

Q5 (Richest)

Figure 3.5 Probability of Being into Salaried Job by Household Income Quintile, Gender, and Location. Source: Estimated from PLFS 2018–19, NSO.

50  Pradeep Kumar Choudhury and Amit Kumar

background of the graduates with their household economic status and gender (Equation 2), and the results are interesting. The predicted probability of being into a salaried job is highest among female graduates with technical qualifications, followed by female graduates with no technical qualifications, male graduates with technical qualifications and male graduates with no technical qualifications, and again the gaps are minimum among rich households (see Figure 3.6). Besides, the level of higher education, i.e., undergraduate or postgraduate, also plays a significant role in graduates’ choice of employment. Results indicate that postgraduate individuals are 8.8% more likely to opt for salaried jobs over self-employment, and this chance is higher in the case of females (9.7 percentage points) than males (5.6 percentage points) (see Table 3.2). To measure the impact of education level in detail, we interacted the level of education with gender and household income quintile. We find significant and interesting differences in the probabilities of opting for a salaried job across these three factors. The predicted probability of being in salaried jobs is highest among females with postgraduate qualification, followed by females with undergraduate qualification, males with postgraduate qualification, and males with undergraduate qualification, with significant differences between poor and rich (see Figure 3.7). 0.9

Pr(Salaried Job)

0.8 0.7 0.6 0.5 0.4 Q1 (Richest)

Q2

Q3

Q4

Household Income Quintile

Q5 (Richest)

Female (No Technical Education)

Female (Technical Education)

Male (No Technical Education)

Male (Technical Education)

Figure 3.6  Probability of Being into Salaried Job by Household Income Quintile, Gender, and Status of Technical Education. Source: Estimated from PLFS 2018–19, NSO.

Gender gaps in employment preferences among university graduates in India  51

0.9

Pr(Salaried Job)

0.8 0.7 0.6 0.5 0.4 Q1 (Richest)

Q2

Q3

Q4

Household Income Quintile Female, UG

Female, PG & above

Male, UG

Male, PG & above

Q5 (Richest)

Figure 3.7 Probability of Being into Salaried Job by Household Income Quintile, Gender, and Education Level. Source: Estimated from PLFS 2018–19, NSO.

3.3.2 Age and gender gaps in employment preferences of graduates We also interacted gender and age of the individual (Equation 3) to examine the gender variations in employment preferences of higher education graduates at different levels of career, i.e., between the entry-level graduates and experienced ones. For both male and female graduates, we find a strong and negative relationship between age and their choice for salaried jobs over self-employment (see Figure 3.8). Though the chances of being in salaried jobs decrease for both male and female graduates, the degree of decrease in such probability is higher among male graduates. In other words, the gender gap in choice for salaried jobs increases with an increase in age and is highest when the graduates reach the age of retirement. Perhaps, male graduates tend to opt for doing business in the latter part of their career after gaining some experience from salaried jobs in the initial career phase. Looking at gender across sectors, estimates suggest that the impact of age on employment choice is greater among male graduates in rural areas, followed by male graduates in urban areas, rural females and lastly, urban females (see Figure 3.9). However, these gaps vary significantly at different levels of age. For instance, male graduates from the rural area and in the initial stage of their career have the least chance to opt for salaried jobs visà-vis self-employment, while female graduates belonging to the urban area and in the initial stage of their career have the highest probability of getting

52  Pradeep Kumar Choudhury and Amit Kumar

0.9

Pr(Salaried Job)

0.8

0.7

0.6

0.5 21

24

27

30

33

36

39

42

45

48

51

54

57

60

63

Age Female

Male

Figure 3.8 Probability of Being into Salaried Job by Individual’s Age and Gender. Source: Estimated from PLFS 2018–19, NSO. 0.9

Pr(Salaried Job)

0.8

0.7

0.6

0.5

0.4 21

24

27

30

33

36

39

42 Age

45

48

51

54

Female, Rural

Female, Urban

Male, Rural

Male, Urban

57

60

63

Figure 3.9 Probability of Being into Salaried Job by Individual’s Age, Gender, and Location. Source: Estimated from PLFS 2018–19, NSO.

Gender gaps in employment preferences among university graduates in India  53

a salaried job. This indicates that, with the increase in age, male graduates have a greater tendency to opt for self-employment over salaried jobs, especially in rural areas. The interaction of age with education type (technical or non-technical) and education level (undergraduate and postgraduate) enables us to estimate the impact of education on graduates' employment choices at disaggregated levels. As discussed above, there is a negative association between age and the decision to be in salaried jobs. However, this negative impact is noted the highest among the male graduates with no technical qualifications, followed by male graduates with technical education, female graduates with no technical education and female graduates with technical education (see Figure 3.10). This indicates that male graduates, especially those with technical qualifications, have higher chances of getting into self-employment in the later stages of their careers than their female counterparts. Besides, the level of higher education (undergraduate and postgraduate) also plays a significant role in graduates’ choice of employment. As discussed earlier, undergraduate individuals are more likely to opt for salaried jobs over self-employment, and this chance is higher in the case

0.9

Pr(Salaried Job)

0.8

0.7

0.6

0.5 21

24

27

30

33

36

39

42

45

48

51

54

57

60

63

Age Female (No Technical Education)

Female (Technical Education)

Male (No Technical Education)

Male (Technical Education)

Figure 3.10 Probability of Being into Salaried Job by Individual’s Age, Gender, and Status of Technical Education. Source: Estimated from PLFS 2018–19, NSO.

54  Pradeep Kumar Choudhury and Amit Kumar

0.9

Pr(Salaried Job)

0.8

0.7

0.6

0.5 21

24

27

30

33

36

39

42 Age

45

48

51

54

57

Female, UG

Female, PG & above

Male, UG

Male, PG & above

60

63

Figure 3.11 Probability of Being into Salaried Job by Individual’s Age, Gender, and Education Level. Source: Estimated from PLFS 2018–19, NSO.

of females than males. We also interacted the level of education with the gender and age of the individual. We find significant and interesting differences in the probabilities of opting for a salaried job across these three factors. Specifically, the interaction effect reveals that male undergraduates and postgraduate have higher tendencies to shift to self-employment from salaried jobs in their later career stages compared to their female counterparts (see Figure 3.11).

3.4 Conclusion While several empirical works have offered a variety of explanations for the declining female labour force participation in India, studies in understanding the contours of the labour market for higher education graduates through a gender lens are sparse. Using the Periodic Labour Force Survey 2018–19 data, this chapter analysed the gender differences in the employment preferences among university graduates in India. Specifically, we analyse how the socio-economic and demographic characteristics of the students matter in explaining such variations. Of the total graduates in the age group of 21–65 years, more than half (53.8%) are engaged in salaried jobs, while 30.8%

Gender gaps in employment preferences among university graduates in India  55

are self-employed, and the rest are either causal labourers or unemployed. The study reports some interesting insights upon examining the interaction of gender with variables such as economic status of the family, level of education, age, and location. We find that female graduates have significantly higher chances of getting employment in salaried jobs (over selfemployment) as compared to males – with stark differences by household’s economic status and region. Our analysis suggests that female graduates in urban areas have the highest probability of being in salaried jobs while male graduates from rural India have the least chance. Also, females with postgraduate degrees and technical qualifications are more likely to be employed in salaried occupations. We provide evidence that graduates in their early career (particularly male youth) are less likely to go for self-employment than those who are in later stages of their career. The findings are instrumental to understanding the changing contours of the labour market for higher education graduates in India through a gender lens. On the policy front, it is important to interrogate where we are lacking in attracting graduates to join self-employment platforms. It is felt that the policy should address the structural issues and challenges faced by higher education graduates in India to engage themselves in self-employment ventures. This is the case even when a clear policy focus is to make India selfreliant through policy interventions such as skilling India, make in India, vocal for local, etc. In fact, the National Education Policy 2020 aims in preparing professionals in cutting-edge areas that are fast gaining prominences, such as Artificial Intelligence (AI), 3D machining, big data analysis, and machine learning, in addition to genomic studies, biotechnology, nanotechnology, neuroscience, with important applications to health, environment, and sustainable living that will be woven into undergraduate education for enhancing the employability of the youth (p. 51). As our findings suggest that female graduates are less likely to opt for self-employment vis-à-vis salaried jobs, the policy should focus on generating business opportunities that attract more females. For example, giving institutional credit to females for start-ups may motivate them to engage in self-employment activities. Similarly, policy should also focus on alluring recent graduates towards self-employment as the study shows they prefer salaried jobs. This policy suggestion is critical in the context of ‘demographic dividend’ – a lifetime opportunity for the country to get educated youth to contribute to the country’s economic growth. This study explains the gender gap in employment preferences of higher education graduates in India. Our work is an initial foray to unfurl the labour market complexities of higher education graduates, and we urge future research on the issue. The ongoing COVID-19 pandemic has significantly impacted the labour market expectations of higher education graduates, and more so in developing economies such as India. Social scientists know little about the impact of the crisis on the employment choices of graduates and,

56  Pradeep Kumar Choudhury and Amit Kumar

more importantly, how gender plays a critical role in the entire discourse. This is an important area for future research. As this study focuses on the all-India level, future research is needed to uncover the regional dimensions of gender inequality in employment preferences, as we witness significant differences in the quality of higher education graduates at the state and regional levels. While we have examined the gender variations in employment preferences of technical and non-technical graduates in India, we need further research to find the differences among the graduates of different disciplines. For example, it is important to find out the gender inequality in employment patterns between engineering and management graduates.

Note 1 In this study, annual consumption expenditure of the household is used as proxy for its annual family income (economic status) as the NSO surveys do not collect data on family income.

References Bairagya, I. (2018). Why is unemployment higher among the educated? Economic and Political Weekly, 53(7): 43–51. Chatterjee, E., Desai, S., & Vanneman, R. (n.d.). Indian paradox: Rising education, declining women’s employment. Demographic Research, 38: 855–878. Chaudhary, R., & Verick, Sh. (2014). Female Labour Force Participation in India and Beyond. ILO Asia- Pacific Working Paper Series. ILO DWT for South Asia and Country Office for India, New Delhi. Chicoine, L. (2021). Free primary education, fertility, and women’s access to the labour market: Evidence from Ethiopia. The World Bank Economic Review, 35(2): 480–498. Choudhury, P.K. (2015). Explaining gender discrimination in employment and earnings of engineering graduates in India. Journal of Educational Planning and Administration, 29(3): 225–246. England, P., Garcia-Beaulieu, C., & Ross, M. (2004). Women’s employment among blacks, whites, and three groups of Latinas: Do more privileged women have higher employment? Gender and Society, 18(4): 494–509. Fatima, A., & Sultana, H. (2009). Tracing out the U-shape relationship between female labour force participation rate and economic development for Pakistan. International Journal of Social Economics, 36(1/2): 182–198. Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labour force participation: A Re-examination of the feminisation U hypothesis. Journal of Population Economics, 27(3): 639–681. Goldin, C.J. (1990). Understanding the Gender Gap: An Economic History of American Women. Oxford University Press, New York. Graham, J.W., & Smith, S. (2005). Gender difference in employment and earnings in science and engineering in the US. Economics of Education Review, 24(3): 341–354. https://data​.worldbank​.org​/indicator (accessed 5 September 2020).

Gender gaps in employment preferences among university graduates in India  57 ILO (2022). Global Employment Trends for Youth 2022: Investing in Transforming Futures for Young People. International Labour Organization, Geneva, Switzerland. DOI: https://doi​.org​/10​.54394​/QSMU1809 (accessed 12 December 2022). Lopez-Acevedo, G., Devoto, F., Morales, M., and Rodriguez J.R. (2021). Trends and Determinants of Female Labour Force Participation in Morocco: An Initial Exploratory Analysis IZA DP No. 14218. Institute of Labour Economics, Bonn, Germany. Mehrotra, S. & Parida, J.K. (2017). Why is the Labour Force Participation of Women Declining in India? World Development, 98(C): 360–380. MHRD (2020). National Education Policy 2020. Ministry of Human Resource Development, Government of India, New Delhi. Ministry of Education (2020). All India Survey on Higher Education 2019–20. Department of Higher Education, Ministry of Education, New Delhi. Morgan, L.A. (1998). Glass-ceiling effect or cohort effect? A longitudinal study of the gender earnings gap for engineers, 1982–89. American Sociological Review, 63(4): 479–483. National Statistical Office (2020). Periodic Labour Force Survey 2018–19. Ministry of Statistics & Programme Implementation, New Delhi. Rhodes, Francesca, Parvez, Anam and Harvey, Rowan (2017). An Economy that Works for Women: Achieving women's economic empowerment in an increasingly unequal world. Oxfam Briefing Paper, Oxfam GB, Oxford, UK. https://policy​-practice​.oxfam​.org​/resources​/an​-economy​-that​-works​-for​-women​ -achieving​-womens​-economic​-empowerment​-in​-an​-inc​-620195/ (accessed 20 Nov 2022). Tam, H. (2011). U-shaped female labour participation with economic development: Some panel data evidence. Economic Letters, 110(2): 140–142. Tamvada, J.P. (2010). The dynamics of self-employment in a developing country: Evidence from India, MPRA Paper No. 20042, Max Planck Institute of Economics, Germany, Available at: https://mpra​.ub​.uni​-muenchen​.de​/20042/ (accessed on 20 July 2021). Toumanoff, P. (2005). The effects of gender on salary-at-hire in the academic labour market. Economics of Education Review, 24(2): 179–188. UGC: University Grants Commission (1971). UGC Annual Report 1970–71. University Grants Commission, New Delhi. Verick, S. (2018). Female labour force participation and development. IZA World of Labour, 87(2): 1–11. World Bank (2019). World Development Report 2019: The Changing Nature of Work. World Bank, Washington, DC. World Bank (2022). The World Development Indicators. World Bank, Washington, DC.

Chapter 4

Educational expansion and income inequality in India Vachaspati Shukla

4.1 Introduction A common view is that education can play an important role in reducing income inequality. If the pool of workers with better educational achievement increases, it helps in reducing the inequality in the distribution of income in an economy. This proposition needs verification in the Indian context given the substantial progress in the educational expansion in India over the years.1 The said educational expansion has undoubtedly helped in bridging the inequality in educational opportunities (Desai and Kulkarni, 2008). However, it is not yet clear as to how expansion of educational opportunities in India has influenced the inequality in income distribution Higher level of educational attainment ensures better earning opportunities in India (Duraisamy, 2002; Dutta, 2006). However, its impact on the overall distribution of income remains unknown. The studies on wage inequality suggest that increasing returns to tertiary education result in increasing wage inequality in urban India (Kijima, 2006; Azam, 2012). The increase in urban wage inequality is attributable to the increase in the skill premium that is largely linked with the increase in demand for tertiary educated workers. Since the wageworkers form a very small fraction of the entire workforce of the country, this observation may not be adequate to explain observed income inequalities. In this background, the chapter is an attempt to analyse the impact of educational expansion on income inequality in India based on information obtained in the National Sample Survey Organisation’s 66th round employment-unemployment survey. The study is cross-sectional in nature where 20 major states, which further comprise 63 economic regions, are considered for the analysis. It is one of the first studies of its kind where impact of educational expansion on income inequality is examined in an intra-country perspective. In a situation of wide rural–urban differences, the entire analysis has been carried out independently for rural and urban sectors. As a prelude to the empirical exercise, the chapter begins with a comprehensive review of existing literature on the issue (Section 4.2). Section 4.3 discusses DOI: 10.4324/9781003329862-5

Educational expansion and income inequality in India  59

the proposed framework for analysis. Section 4.4 informs the data source and variables used. The empirical investigation is presented in Section 4.5. Finally, Section 4.6 concludes the findings.

4.2 A review of available evidence Existing literature does emphasise the role of education in shaping inequality in income distribution. Further, policymakers often justify higher educational spending as a highly effective tool for reducing income inequality. Schultz (1963) argued that increasing human capital ‘measured in terms of schooling’ as one way to lower income inequality and increased public spending on education might be one way to accomplish this. The human capital theory of earnings, stemming from the work of Becker (1962), Becker and Chiswick (1966), Mincer (1970; 1974), and Chiswick (1974), considers education in the form of human capital as a crucial determinant of differences in income level. The human capital model of earnings generally relates the dispersion of earnings with the level and dispersion of schooling and rates of return to schooling (Chiswick, 1974). The model provides a partial positive relation between schooling level and earnings inequality and also a positive relation between schooling inequality and earnings inequality. Therefore, while the model predicts a reduction in earnings inequality as a result of reduced schooling inequality, it predicts an increase in earnings inequality with increase in mean years of schoolings when other variables are held constant. Since, in reality, there is a simultaneous change in the level of schooling and schooling inequality, it is rather difficult to obtain a clear prediction regarding the effect of educational expansion on earnings inequality. It is even more difficult to obtain a prediction about the effect of educational expansion on income inequality. Knight and Sabot (1983) explain the impact of educational expansion on income inequality in terms of twin effects: ‘composition’ and ‘wage compression’ effect. The composition effect increases the relative size of the group with more education and tends initially to raise income inequality but eventually to lower it. On the other hand, the wage compression effect decreases the premium on education as the relative supply of educated workers increases, thereby lowering income inequality. For the wage compression, effect to work, the relation between the level of schooling and the rate of returns to schooling has to be negative. The development of human capital theory engenders considerable optimism about the effect of educational investment on income inequality. Since education is seen as a powerful, long-term instrument for equalising earnings and income distribution in the future, empirical verification of the role of education in reducing inequalities of income distribution has received considerable attention among researchers. These studies could be classified into two groups: (a) those adopting the educational variables as a flow

60  Vachaspati Shukla

measure like enrolment ratio and (b) those considering stock measure of schooling, like the educational attainment of the labour force. In the first group of studies, Chiswick (1971), based on the cross-section data from nine countries, observed direct and positive relation between earning inequality and educational inequality; therefore, lower educational inequality is expected to be an income equaliser. In another early work, Chiswick and Mincer (1972) studied changes in income inequality in the US on the time series data in relation to several variables, including the level of education and schooling inequality. The main observation made in the study is that although level and dispersion in schooling do affect income inequality, these effects were small over the period of studies. Psacharopoulos (1977) argues that a policy of more equal access to education might have the desired impact of making income distribution more equal. Winegarden (1979) concludes that higher average levels of schooling serve as an equaliser on income distribution, while educational inequality tends to generate disparities to a considerable degree. Ram (1981) has extreme reservations against these studies for using enrolment ratios as educational variables for explaining income inequality. He pointed out that it seems more plausible to view enrolment pattern as being the outcome of the present income distribution and suggested that such specifications are theoretically hazardous, especially in relation to the developing world where the schooling distributions assume rapid changes. On this count, it is rather difficult to interpret the regression results reported in the study that use current enrolment rates as regressors. The policy suggestion made in some studies about more equal access to schooling being an income equaliser could indeed be right; but such an income equalising effect could be expected to occur only in the future, and it seems unlikely that the suggested policy measure can be justified on the basis of the empirical evidence provided in these studies. The second group of studies include Ram (1984), Ram (1989), Park (1996), Checchi (2000), Gregorio and Lee (2002), and Mughal and Diwara (2011). Ram (1984) observed that a higher mean schooling level might be a mild equaliser; but the impact of educational inequality on income inequality seems to be very different from that suggested by earlier studies. He argued that there is nothing in the estimates to infer that a larger educational variance increases income inequality. Apart from reservation on the findings, Ram’s (1984) work cautions against deriving strong inferences based on such cross-section studies. In another work, Ram (1989) conducted a detailed review of several theoretical frameworks and empirical studies that link level and dispersion of schooling with income inequality. He pointed out that none of the theoretical constructs seems to generate a clear prediction about the effect of education on income inequality. In his empirical analysis, only a small impact of educational level on income inequality is observed.

Educational expansion and income inequality in India  61

Park (1996) offers a more comprehensive analysis of the subject than any previous studies. He observed that higher level of educational attainment of the labour force has an equalising effect on income distribution. The estimates also suggest that the larger the dispersion of schooling among the labour force, the greater the income inequality. A similar observation is made by Gregorio and Lee (2002) based on the analysis of a range of countries over the period 1960–1990. However, Földvári and Leeuwen (2011) in their empirical analysis find insignificant relationship between the inequality in mean years of schooling and income inequality. However, after accounting for the possible simultaneity using a two-stage least square analysis, authors find no relation for the non-OECD countries but a positive relationship is found in the case of OECD countries. Mughal and Diwara (2011) find a negative relationship between the Gini coefficient of consumption and mean years of schooling. In his intra-country analysis of Taiwan, Lin (2007) observed decline in income inequality in response to increase in the average level of schooling inequality. Checchi (2000) observed a U-shaped relationship between average years of schooling and income inequality with a lower turning point at 6.5 years in a panel data study covering 94 countries during 1965–1990. The author argued that initial expansion in education and income inequality seems to be negatively related; in more recent years further expansion in schooling in the world population has been accompanied by a widening in the dispersion of income distribution. Many other scholars tried to analyse the impact of education on inequalities but not in the spirit of human capital theory while in more general analysis of income inequalities. In one of the widely cited works, Ahluwalia (1976) concluded that there is clear evidence of a positive relationship between education and equality. Some scholars observed a negative relationship between the enrolments at the secondary level and inequality in income distribution (Papanek and Kyn, 1986; Bourguignon and Morrisson, 1990; Nielsen and Alderson, 1995; Alderson and Nielsen, 1999; Alderson and Nielsen, 2002; Wells, 2006). Simpson (1990) in his attempt to examine the determinants of income inequality in a panel of 58 countries for the period 1965–1975, using the combined primary and secondary enrolment rate as the educational variables, ended with an inverted U-shaped relationship between the income inequality and level of education. Crenshaw and Ameen (1994) find U-shaped relationship between the secondary school enrolment rate and income inequality measured in the Gin coefficient. Barro’s (2000) analysis suggests a negative association between primary enrolment and income inequality but positive relation between higher education and inequality. The fact that primary enrolment reduces the inequality in income is supported by Psacharopoulos et al. (1995) and Mughal and Diwara (2011) as well. Gruber and Kosack (2014) argued that higher primary enrolment

62  Vachaspati Shukla

is not always associated with lower income inequality. This relationship depends on the educational spending patterns prevalent in developing countries. In developing countries that focus their educational resources on primary students rather than students in their secondary schools and universities, increases in primary enrolment are associated with significantly lower inequality a decade later. In countries where governments concentrate their education spending on students in the upper levels, higher primary enrolment today is strongly associated with much higher future inequality. It is clear from the review of above studies that the expansion of education has a role in influencing the distribution of income. However, its direction is uncertain. It is largely determined by the evolution of the rate of return to education. The evidence suggests that level of economic development also influences the direction of the association between educational expansion and income inequality. Since the relation between the educational expansion and income inequality is dynamic and changes according to the evolution of returns that further depend on the level of economic development of the study area, results obtained from the analysis of one region may not hold true for another region. Therefore, prior to suggesting educational expansion as a tool to reduce income inequality, there is a need to study the direction of association between the educational expansion and income inequality.

4.3 A framework for analysis The impact of educational expansion on income inequality can be examined with the help of earning function. The human capital theory of earnings, initially proposed by Becker (1962) and subsequently taken up by Becker and Chiswick (1966), Mincer (1970; 1974), and Chiswick (1974), provides a fairly well-organised, although simplified, framework for studying the linkage between education and distribution of earnings. The model is formulated in terms of training periods which are completed before earnings begin. It, therefore, applies strictly to schooling rather than to all occupational training. Assume that no further investments in human capital are undertaken by individuals after completion of their schooling and that the flow of their earnings is constant throughout their working lives. Drawing the idea from Mincer (1974), the earnings ratio of income differing by S years of schooling is given by Equations (4.1) and (4.2):

YS = e rS (4.1) Y0



ln Ys = ln Y0 + rS (4.2)

Educational expansion and income inequality in India  63

Y0 is the level of earning without any schooling; YS is the level of earning with ‘S’ level of schooling; ‘r’ is the rate of returns for S level of schooling. The equation shows the logarithm of earnings to be a strict linear function of time spent at school. Taking variances of both sides, explained income inequality associated with education can be expressed as a function of the level of schooling and the rate of return to it, as presented in Equation (4.3):

s2 (ln Ys ) = s2 (r.S) (4.3)

The expansion of the right-hand side of the equation, however, depends on whether the two variables (r, S) are independent are not. If r and S are independent random variables, then

2

2

s2 (ln Ys ) = r s2 (S) + S s2 (r ) + s2 (S).s2 (r ) (4.4)

The equation reveals the fact that inequality in earning is large not only when ‘r’ ands2 (S) but also whens2 (r) and ‘S’ are large. With other variables held constant, increase in educational inequality [s2 (S)] leads to greater inequality in (lnY) earnings. If incomes are proportional to earnings, this also applies to inequality in logarithms of income. However, since the inequality in schooling not necessarily linearly related to the average level of schooling, the actual relation is non-linear. For a fixed value of S:

2

s2 (ln Ys ) = S s2 (r ) (4.5)

For a fixed value of r:

2

s2 (ln Ys ) = r s2 (S) (4.6)

Following Mincer (1970), if r can be taken as an index of individual ability, the interpretation is (a) individual differences in ability create greater relative differences in earnings at higher levels of schooling and (b) differences in schooling create greater differences in earning at higher levels of ability. If, however, r and S are dependent, then it takes the form of Equation (4.7);

2

2

s2 (ln Ys ) = r s2 (S) + S s2 (r ) + s2 (S).s2 (r ) + 2 r .S cov (r, S) (4.7)

If the covariance between the return to education and the level of education is negative, an increase in schooling can reduce the inequality in income. However, there is possibility of increase in inequality in income in the initial stage of educational development. For example, it might be

64  Vachaspati Shukla

that initially people with more ability earn more due to improved access to education which might increase inequality. This might be possible for the economy with very low level of education. However, if more people will receive education, the return on education will decline, reducing income inequality.

4.4 Data source and variables estimation Studies of this kind are confronted with two problems as regards the input data. First is the non-availability of information on household income distribution in India. In order to overcome this problem, monthly per capita household expenditure (MPCE)2 has been considered for the analysis. Second is the lack of comparable longitudinal data on income distribution. This problem cannot be resolved with the MPCE data as it is not available annually and also not strictly comparable over the years. Because of this reason, the present study is based on a cross-section analysis. It is carried out for 20 major states of the country, which is further divided into 63 regions in the light of wide intra-state variation following the NSSO’s classification of economic regions. All these 20 states together accommodate 97% of India’s population. One of the problems pointed out in the cross-country analysis is the comparability of data on educational attainment. That problem automatically gets resolved in the case of intra-country analysis. However, differences in the quality of education still remain contentious even in intra-country analysis, but it is expected to be much less in comparison with the cross-country analysis. Since there is no way to accommodate differences in the quality of education even in the intra-country situation, it remains one of the limitations of the present study. Another aspect of refinement adopted here lies in considering educational attainment in place of educational enrolment. Further, to account for greater sensitivity to income, this study entertains the educational attainment of workers rather than the general population. In the present analysis, data are obtained from NSSO’s 66th round employment-unemployment survey conducted during July 2009–June 2010. The educational level of workers in India is divided into eight categories: Illiterate, Below Primary, Primary, Middle, Secondary, Higher Secondary, Graduate, and Postgraduate and above. According to the education system in India, the years of schooling for each of the above education levels are 0, 2.5, 5, 8, 10, 12, 15, and 17, respectively. The Indian population, on the basis of sector of origin, is divided into two groups: rural and urban. The MPCE of rural and urban sectors cannot be comparable because of differences in price level and living standards. Therefore, every variable in the present case is estimated separately for both sectors. In addition, the rural and urban sectors widely differ in terms of

Educational expansion and income inequality in India  65

economics development, labour market conditions, etc.; a classification of this kind becomes necessary for a meaningful analysis. Prior to initiating the empirical analysis, there is a need to estimate three variables: level and dispersion of education, dispersion in MPCE (income). The level of education is measured by the mean years of schooling, which is estimated by Equation (4.8): n



m=

å x S (4.8) i i

i =1

where xi stands for the proportions of population, while educational levels i and Si stand for the years of schooling for educational level i. Since it is clearly visible from the discussion in Section 4.2, association between the education and income inequality also depends on the level of educational development. In the initial stage, primary education might influence the income distribution, while at the advance level of development graduation completed might influence the distribution of income. Therefore, three other educational variables were also considered for the analysis: primary completed, secondary completed, and graduation completed. The dispersion in education is measured in terms of education Gini (EGINI). The EGINI is measured by Equation (4.9): 1é ê m ê i =2 ë n



EGINI =

i -1

åå j =1

ù xi .xj Si - Sj ú (4.9) úû

where EGINI is the education Gini based on the schooling attainment distribution; µ is the average year of schooling for the concerned population; xi and xj stand for the proportions of the population with certain levels of schooling; si and sj are the years of schooling at different educational attainment levels; n is the number of categories in the schooling attainment data. The dispersion in education is also measured in terms of standard deviation (SD): n



SD =

å x (y - m) (4.10) i

i

2

i =1

The dispersion in income is measured in terms of the Gini coefficient, which is exactly one half of the relative mean difference, defined as the arithmetic average of the absolute values of differences between all pairs of income, computed as follows:

66  Vachaspati Shukla



GINIi =

n 1 é ê 2n 2m ê i =1 ë

ù

n

å å y - y úúû (4.11) i

j

j =1

where yi is the income of an individual and y1 ³ y2 ³ y3 ³ ....... ³ yn ; µ is the mean income for the concerned population; n is the size of the population (number of individuals).​

4.5 Empirical analysis Previous studies (Park, 1996) have attempted to analyse the relationship between income inequality and educational expansion by estimating a regression equation of the form given below (Equation (4.12)):

YINEQi = a0 + a1.Ei + a2 .EINEQi + a3. ln Yi + a4 .(ln Yi )2 + ei (4.12)

where YINEQ is measure of income inequality, E is measure of educational attainment, EINEQ is measure of inequality in educational attainment, and Y is measure of income level. In recent studies, the Gini coefficient of years of schooling (EGINI) is the most popular measure of educational inequality and mean years of schooling (µ) for the level of educational attainment. In the case of the present data set, empirical evidence suggests a negative and significant relationship between the mean years of schooling and EGINI (Table 4.2). Therefore, it is not wise to incorporate both the variables in the process of estimation. Equation (4.13) to be estimated now takes the forms suggested by Ram (1989).

YINEQi = a0 + a1.Ei + a3. ln Yi + a4 .(ln Yi )2 + ei (4.13)

Table 4.1  Summary Statistics Variables

Mean Years of Schooling Primary Completed Secondary Completed Graduation Completed Standard Deviation EGINI MPCE Gini Number of Observations

Rural

Urban

Max

Min

Max

Min

8.374 0.739 0.213 0.094 4.982 0.763 1751.3 0.450 63

2.188 0.153 0.028 0.011 3.250 0.243 560.3 0.133

10.416 0.827 0.510 0.318 6.272 0.586 2871.3 0.479 63

5.166 0.372 0.186 0.092 3.878 0.231 864.5 0.213

Educational expansion and income inequality in India  67 Table 4.2 Regression Results of Educational Inequality on Mean Years of Schooling

Intercept µ R2 N

Rural

Urban

.9419*** (53.64) −.0877 *** (−22.35) 0.8911 63

.9164*** (27.08) −.0665*** (−15.79) 0.8035 63

Source: Estimated from NSS 66th round employment-unemployment survey. Notes: The corresponding t-statistics are in parentheses with the estimated coefficients. The coefficient estimate is statistically significant at 1% (***).

Table 4.3 Regression Results of Income Inequality on Income  

Rural

Urban(1)

Urban(2)

Intercept ln Y ln Y2 R2 N

10.1110*** (3.85) −2.990*** (−3.91) .2260*** (4.06) 0.5133 63

−.3409 (−0.08) .0894 (0.08) .0003 (0.00) 0.1975 63

−.3565*** (−2.02) .0937** (3.87) – 0.1975 63

Source: Estimated from NSS 66th round employment-unemployment survey. Notes: The corresponding t-statistics are in parentheses with the estimated coefficients. The coefficient estimate is statistically significant at 1% (***) and 5% (**).

In a prelude to estimate the above equation, a non-linear quadratic relation between income inequality and income level is estimated. This is done in order to test the existence of the income Kuznets’s curve. The equation to be estimated can be represented by Equation (4.14):

GINIi = a0 + a1. ln Yi + a2 . ln Yi 2 + ei (4.14)

The equation is regressed taking the Gini coefficient of MPCE as a dependent variable on log of MPCE and its square in order to test the existence of the inverted U curve proposed by (Kuznets, 1955) for the relationship between the income distribution and level of income. The results of the estimated equations are presented in Table 4.3. It is estimated separately for the rural and urban sectors. In the rural sector, a U-shaped relationship between inequality and level of income is observed. The coefficients are significant at the 1% level with quite high value of R2. This result is similar to the findings of Park (1996), where it is estimated for the poorest 40 countries of the world. The U-shaped relationship shows that inequality in income automatically increases when the mean reaches at a certain level of income, as a kind of necessary statistical condition. However, the same is not observed in the case of the urban sector. None of the coefficients is found to be significant in the case of the urban sector. However, in the estimation of the linear

68  Vachaspati Shukla

equation for the urban sector, the coefficients are statistically significant at the 1% and 5% level respectively, with a low value of R2 in comparison to the earlier case. The above evidence suggests that different equations need to be estimated for the rural and urban sectors in order to analyse the impact of education on income inequality. The equation to be estimated for the rural and urban sector can be presented in Equations (4.15) and (4.16), respectively:

GINIi = a0 + a1Ei + a3. ln Yi + a4 .(ln Y )i 2 + ei (4.15)



GINIi = a0 + a1Ei + a3. ln Yi + ei (4.16)

These two equations are estimated for four different educational variables: mean years of schooling, primary completed, secondary completed, and graduation completed. The results of the estimated regression equation do not indicate any significant role of education in explaining income inequality (Table 4.4). The coefficient of mean years of schooling is found to be insignificant in both the rural and urban sectors. In both sectors, income inequality is associated with the level of income only. Estimates reveal a U-shaped relationship between inequality and the level of income in the rural sector but a positive relationship between these variables in the urban sector. The equation is estimated for the other three educational variables: primary completed, secondary completed, and graduation completed. In all these three cases, the level of education remains insignificant in the rural sector, while it is found to be positive and significant in the urban sector when graduation completed is considered for the analysis. This implies that the regions with a higher level of graduate population experience higher levels of income inequality. There might be many possible reasons behind the insignificant relationship observed between the level of educational attainment and income inequality. First, the predominance of self-employed workers might be one reason for the insignificant relation between education and income distribution. Selfemployed accounting for more than half of the total workers in the country (Figure 4.1) might constrain making any association between education and employment and thereby earnings, owing to two reasons: (a) earnings of the self-employed are least influenced by their educational level rather than other attributes like the activities in which they are self-employed and (b) the earnings of the self-employed are somewhat difficult to comprehend given the irregularity/uncertainty in earnings and are therefore not obtainable in surveys. Literature on returns to education in India suggests that returns to education for casual workers remain flat (Dutta, 2006). This implies that formal schooling does not have any bearing on the wage rate of casual workers.

Educational expansion and income inequality in India  69 Table 4.4 The Regression Results of Income Inequality on Education Rural Variables

(1)

(2)

(3)

(4)

Intercept

9.9381*** (3.67) .0013 (0.31)

9.9148*** (3.67) –

9.3861***(3.36)

9.2342*** (3.38)

–

–

–

.0165(0.37)

–

–

–

–

.1120 (0.77)

–

–

–

–

.3563 (1.14)

−2.9375*** −2.9300*** (−3.72) (−3.72) .2220*** (3.86) .2213*** (3.85) 0.5141 0.5144 63 63

−2.7725*** (−3.39) .2096*** (3.51)

-2.7261*** (−3.42) .2061*** (3.55)

0.5144 63

0.5144 63

−.3979 −.4642*** ***(−2.08) (−2.44) −.0040 (−0.59) –

−.2843 ***(−1.57) −.1927 (−1.10)

Mean Years of Schooling Primary Completed Secondary Completed Graduation Completed ln Y ln Y2 R2 N Urban Intercept Mean Years of Schooling Primary Completed Secondary Completed Graduation Completed ln Y R2 N

–

–

–

−.1137(-1.46) –

–

–

–

.1361(1.53)

–

–

–

–

.3388***(2.99)

.07786** (2.99)

0622** (2.48)

0.2275 63

0.2275 63

.1037 ** (3.50) .1181** (4.05) 0.2022 0.2251 63 63

Source: Estimated from NSS 66th round employment-unemployment survey. Notes: The corresponding t-statistics are in parentheses with the estimated coefficients. The coefficient estimate is statistically significant at 1% (***) and 5% (**).

Therefore, the impact of educational expansion on income inequality could at best remain limited to the segment of regular wage employment in the total workforce. Thus, in the given employment structure, one can expect an insignificant relationship between educational and income distribution in the rural sector, as observed in the analysis carried out in Section 4.5. This is not surprising given the very small proportion of regular workers (7.3%). However, regular workers have a greater share in the workforce in the urban sector (41.4%) in contrast with the rural sector (Figure 4.1). Second, the higher level of education not only increases the likelihood of an individual being employed in high-paid economic activities but also

70  Vachaspati Shukla

60 50 40

54.2

Rural 41.4

41.1

Urban

38.6

30 17.5

20 7.3

10 0

Self-Employed

Regular

Casual

Figure 4.1 Distribution of Workers according to Usual Status (Ps + ss) Approach by Employment Status for Rural and Urban Sector. Source: NSS 66th round employment-unemployment survey. Table 4.5 Usual Status (PS + SS) Unemployment Rate for Person Age 15–29 Years, India Education Not Literate Literate and up to Primary Middle School Secondary Higher Secondary Graduate and Above

Rural Male

Rural Female

Urban Male

Urban Female

2.2 2.9

0.0 1.4

3.8 4.1

2.6 2.0

4.0 5.0 7.8 16.6

3.9 6.8 22.2 30.4

5.4 5.9 10.9 13.8

8.1 20.5 19.1 24.7

Source: Estimated from NSS 66th round employment-unemployment survey.

induces greater unemployment among them. The NSS 66th round estimate reveals a positive relationship between the level of education and unemployment rate for the youth (15–29) (Table 4.5). Unemployment rate is the highest among the highly educated. High unemployment rate is the result of mismatch between demand and supply in the labour market. There might be two possible reasons for this. First is the lack of high-skilled job opportunity in the labour market. Second, higher education does not necessarily ensure those skills for which there is demand in the labour market. Third, theoretical roots of literature discussing the role of education in inducing economic growth and shaping income distribution originated from the human capital theory, where schooling is considered a proxy for human capital. However, schooling is not a perfect proxy for human capital as it represents the skills gained only through the formal educational system. No doubt, there are large sets of skills required for getting better-paid employment, and these do not necessarily come from the formal schooling system. Moreover, underestimation of human capital is intensified by considering

Educational expansion and income inequality in India  71

an illiterate person with zero human capital. Another problem in estimation of human capital through formal schooling is uniformity. This method of measurement does not differentiate among the various streams of educationsocial sciences, engineering, medical science, etc. which are expected to generate differential returns. Differences in learning achievement are another problem while considering schooling as the proxy for human capital.

4.6 Conclusion and policy implication The chapter attempted to analyse the relationship between education and income inequality in India. The analysis represents 20 major states of the country, further disaggregated into 63 economic regions. Given the evidence of wide rural–urban differences, the analysis was carried out independently for the rural and urban sectors of the country. The aim was to understand whether the mean and dispersion of education determine the distribution of income in the economy. It would have a significant bearing on policymaking towards fostering educational attainment to realise an equitable distribution of income. The onset of this exploration begins by analysing the relationship between the mean years of schooling and its dispersion. The analysis observes a negative relationship between the mean years of schooling and dispersion measured by the Gini coefficient of years of schooling. A similar attempt was made to test the relationship between the level and dispersion in income. In this case, results differ between the rural and urban sectors, with a U-shaped pattern in the rural sector as against a linear one in the urban sector. The analysis did not find any significant association between the levels of schooling and income inequality in both sectors. In the rural sector, the inequality in the distribution of income is only associated with the level of income. The insignificant relationship between the levels of education and income inequality in the rural sector is not unexpected. It happens because of the large share of self-employed workers in the rural sector where education may not serve any systematic role in determining their earnings. However, in the urban sector, income inequality is found to be positively related to the level of educational attainment, particularly when it is represented by the share of graduates. This implies that regions with a higher proportion of the workforce with graduation-level education and above experience a relatively higher level of income inequality. This too is not an unexpected result given the labour market conditions in urban India. Since the proportion of graduate workers is not substantial as yet, its adverse effect on income distribution through the composition and compression effect does not appear, as discussed by Knight and Sabot (1983). Therefore, it can be hypothesised that a continuous increase in the share of graduate workers may reduce the inequality in income distribution if conditions in the labour market remain unchanged. As more and more

72  Vachaspati Shukla

people move into the category of graduate workers motivated by the higher wage rates, it will ultimately reduce the inequality in income. An increase in the supply of graduate workers also bridges the wage premium between graduate and non-graduate workers.

Notes 1 The literacy rate in India has shown a spectacular increase in the recent decades. According to the data provided by population census, it increased from 52.21% in 1991 to 74.04% in 2011. The total number of universities and universitylevel institutions increased from 265 in 2002 to 533 in 2010. The number of colleges increased from 7,346 in 1991 to 25,951 in 2009, and the number of students enrolled increased from 4.9 million in 1991 to 13.6 million in 2009 (UGC 2011). 2 In some places in the chapter, monthly per capita household expenditure has been referred as per capita income.

References Ahluwalia, M. S. (1976). Inequality, poverty and development. Journal of Development Economics, 3(4), 307–342. Alderson, A. S., & Nielsen, F. (1999). Income inequality, development, and dependence: Reconsideration. American Sociological Review, 64(4), 606–631. Alderson, A. S., & Nielsen, F. (2002). Globalization and the great U-turn: Income inequality trends in 16 OECD countries. American Journal of Sociology, 107(5), 1244–1299. Azam, M. (2012). Changes in wage structure in urban India, 1983–2004: A quintile regression decomposition. World Development, 40(6), 1135–1150. Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5(1), 5–32. Becker, G. S. (1962). Investment in human capital: A theoretical analysis. The Journal of Political Economy, 75(5), 9–49. Becker, G. S., & Chiswick, B. R. (1966). Education and the distribution of earnings. The American Economic Review, 56(1/2), 358–369. Bourguignon, F., & Morrisson, C. (1990). Income distribution, development and foreign trade: A cross-sectional analysis. European Economic Review, 34(6), 1113–1132. Checchi, D. (2000). Does Educational Achievement Help to Explain Income Inequality? WIDER Working Paper No., 2000-11. Chiswick, B. R. (1971). Earnings inequality and economic development. The Quarterly Journal of Economics, 85(1), 21–39. Chiswick, B. R. (1974). Income Inequality: Regional Analyses Within a Human Capital Framework. National Bureau of Economic Research, Books. Chiswick, B. R., & Mincer, J. (1972). Time-series changes in personal income inequality in the United States from 1939, with projections to 1985. Journal of Political Economy, 80(3), S34–S66. Crenshaw, E., & Ameen, A. (1994). The distribution of income across nationalpopulations: Testing multiple paradigms. Social Science Research, 23(1), 1–22.

Educational expansion and income inequality in India  73 Desai, S., & Kulkarni, V. (2008). Changing educational inequalities in India in the context of affirmative action. Demography, 45(2), 245–270. Duraisamy, P. (2002). Changes in returns to education in India, 1983–94: By gender, age-cohort and location. Economics of Education Review, 21(6), 609–622. Dutta, P. V. (2006). Returns to education: New evidence for India, 1983–1999. Education Economics, 14 (4), 431–451. Földvári, P., & Leeuwen, van, B. (2011). Should less inequality in education lead to a more equal income distribution?. Education Economics, 19(5), 537–554. Gregorio, J. D., & Lee, J. W. (2002). Education and income inequality: New evidence from cross‐country data. Review of Income and Wealth, 48(3), 395–416. Gruber, L., & Kosack, S. (2014). The tertiary tilt: Education and inequality in the developing world. World Development, 54, 253–272. Kijima, Y. (2006). Why did wage inequality increase? Evidence from urban India 1983–99. Journal of Development Economics, 81(1), 97–117. Knight, J. B., & Sabot, R. H. (1983). Educational expansion and the Kuznets effect. The American Economic Review, 73(5), 1132–1136. Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45(1), 1–28. Lin, C. H. A. (2007). Education expansion, educational inequality, and income inequality: Evidence from Taiwan, 1976–2003. Social Indicators Research, 80(3), 601–615. Mincer, J. (1970). The distribution of labour incomes: A survey with special reference to the human capital approach. Journal of Economic Literature, 8(1), 1–26. Mincer, J. (1974). Schooling, Experience and Earnings. NBER Books. Mughal, M., & Diawara, B. (2011). Explaining Income Inequalities in Developing Countries: The Role of Human Capital. CATT Working Paper No. 2. Nielsen, F., & Alderson, A. S. (1995). Income inequality, development, and dualism: Results from an unbalanced cross-national panel. American Sociological Review, 65(5), 674–701. Papanek, G. F., & Kyn, O. (1986). The effect on income distribution of development, the growth rate and economic strategy. Journal of Development Economics, 23(1), 55–65. Park, K. H. (1996). Educational expansion and educational inequality on income distribution. Economics of Education Review, 15(1), 51–58. Psacharopoulos, G. (1977). Unequal access to education and income distribution. De Economist, 125(3), 383–392. Psacharopoulos, G., Morley, S., Fiszbein, A., Lee, H., & Wood, W. C. (1995). Poverty and income inequality in Latin America during the 1980s. Review of Income and Wealth, 41(3), 245–264. Ram, R. (1981). Inequalities in income and schooling: A different point of view. De Economist, 129(2), 253–261. Ram, R. (1984). Population increase, economic growth, educational inequality, and income distribution: Some recent evidence. Journal of Development Economics, 14(3), 419–428. Ram, R. (1989). Can educational expansion reduce income inequality in lessdeveloped countries? Economics of Education Review, 8(2), 185–195.

74  Vachaspati Shukla Schultz, T. W. (1963). The Economic Value of Education. Columbia University Press. Simpson, M. (1990). Political rights and income inequality: A cross-national test. American Sociological Review, 55(5), 682–693. U. G. C. (2011). Higher Education in India: Strategies and Schemes during Eleventh Plan Period (2007–2012) for Universities and Colleges. University Grant Commission. Wells, R. (2006). Education’s effect on income inequality: An economic globalisation perspective. Globalisation, Societies and Education, 4(3), 371–391. Winegarden, C. R (1979). Schooling and income distribution: Evidence from international data. Economica New Series, 46(181), 83–88.

Part 2

Quality and the role of higher education institutions in economic development



Chapter 5

Indian higher education Introspecting the state of quality K.M. Joshi and Kinjal Ahir

5.1 Introduction There is ample empirical evidence in the existing literature to suggest the role of higher education in economic development. Solow (1956) in the initial years identified technology as the ‘residual’ determinant explaining economic growth. Schultz (1961) later established the significance of ‘human capital’ and the benefits that accrue due to investment in human capital. Later Denison (1962), Becker (1975), Romer (1990), and Lucas (1988) contributed in explicitly endorsing the importance of education in building human capital through knowledge generation. The definition of economic development and the variables that conceptualize economic development have been refined and made more erudite with data availability over the period of time (Tilak, 2003). Traditionally, a challenge to Gross National Product as a measure of economic development got manifested in the form of Physical Quality of Life Index, as propounded by Morris D. Morris (1978), and later through various indices developed by United Nations Development Programme (UNDP) like Human Development Index, Inequality Adjusted Human Development Index, Gender Development Index, Gender Inequality Index, and Multi-Dimensional Poverty Index (UNDP, 2021a) followed by Millennium Development Goals [MDGs] (UNDP, 2021b) and Sustainable Development Goals [SDGs] (UNDP, 2021c). SDGs for the first time acknowledged explicitly the contribution of higher education in economic development by including goal 4 related to quality education. In particular, targets 4.3, 4.4, 4.5, 4.7 and respective indicators emphasize and articulate the contribution of higher education in sustainable development (UNDP, 2021d).1 In addition, the World Bank recommends investing in tertiary/higher education for various reasons like tertiary education facilitates long-term growth; increases workers’ employability, adaptability toward economic shocks; tertiary education has the highest returns on investment compared to other levels of education; and for wider individual and social benefits derived from tertiary education (World Bank, 2021). The World Bank also emphasizes the pivotal role played by digitalization in assuring economic development. DOI: 10.4324/9781003329862-7

78  K.M. Joshi and Kinjal Ahir

High quality of higher education is imperative for upskilling citizens. Losses of not investing in higher education include brain drain, lack of human and other resources to solve local problems using research capabilities, lack of teachers at all levels of education including tertiary, vocational, and professional education, and the resulting economic inequality among nations (World Bank, 2021). Despite this evidence of contributions made by higher education in economic development, challenges in developing countries compel prioritizing demands over available resources. Education competes with other prioritized needs like defense, agriculture, rural development, debt financing, subsidies, and transportation in India (Ministry of Finance [MoF], 2021). Within education, higher education competes with primary and secondary education, traditional education competes with technical education for the wants of limited resources (Ministry of Human Resource Development [MHRD], 2019a). Although, in the last two decades a clear upsurge is visible in access to higher education, both in terms of institutions and enrolment (MHRD, 2013, 2019b) while challenges associated with quality and efficiency in higher education in India still persist. The research questions of this study are: (1) Does higher education growth have an impact on selected global indexes that reflect the measure of economic development? (2) How does the quality of higher education in India contribute to employment and research opportunities? The chapter discusses the performance of higher education in India in view of the parameters used in the three global indexes, namely Global Competitiveness Report, World Digital Competitiveness Ranking, and Global Innovation Index, besides university rankings. It also presents the financing and access scenario of Indian higher education. An in-depth analysis of the impact of higher education quality on research output (in the form of innovation, patents, scientists) and employment is discussed in the chapter.

5.2 Methodology The analysis is thematic and exploratory in nature. The research is focused on exploring the contribution of the quality of the Indian higher education system to the economic development of a nation. The research builds upon largely three sequential themes, analysis of three global indexes assessing the specific aspects of economic development for various countries of the world, analysis of the performance of Indian higher education institutes in world university rankings, and in-depth analysis of the impact of quality of higher education in India on research output and employment. The first theme identifies three established indexes that are used as contemporary measures of specific aspects of development, and in doing so, these indexes assign an important role to higher education in a country’s development.

Indian higher education  79

Accordingly, the performance of higher-education-specific parameters in three global indexes, namely Global Competitiveness Report, World Digital Competitiveness Ranking, and Global Innovation Index, has been analyzed for India. It was observed that the parameters associated with higher education used in these indexes were specifically associated with the quality of higher education in a country. Such parameters include research and development outcomes in the form of research publications, patents, employability and skills of the graduates, and pupil–teacher ratio which is often used as a proxy for quality assessment. The performance of Indian higher education institutes in three world university rankings, i.e., Times Higher Education World University Rankings (THE), Quacquarelli Symonds (QS) World University Rankings and Academic Ranking of World Universities (ARWU), was also assessed to compare the quality globally.

5.3 India’s performance in selected global indexes Contemporary measurements of economic development focus on specific dimensions like short-term and long-term competitiveness of economies, digital competitiveness, and innovation performance of economies. Global Competitiveness Report aims at providing policy suggestions to enhance the short-term and long-term economic development of various economies (World Economic Forum [WEF], 2020a). The adoption and exploration of digital technologies that result in the economic development of various stakeholders in the economy, such as government, businesses, and society, are assessed in World Digital Competitiveness Ranking (Institute for Management Development [IMD], 2020). Since innovation drives economic growth and development, Global Innovation Index estimates the innovation performance of the world economies (Cornell University, INSEAD, World Intellectual Property Organization [WIPO], 2020). Notably, in each of these contemporary measures of economic development, the contribution of higher education is acknowledged. Higher education plays an essential role in knowledge production and dissemination. In the present day, where knowledge is the driving force for economic growth in knowledge economies, the performance of India in the following global indexes indicates the influence of Indian higher education in the development process. 5.3.1 Global Innovation Index (GII) 2020 Innovation is crucial for economic development. GII 2020 uses 80 indicators of innovation inputs and outputs to rank the innovation capabilities of various economies. For the first time, India entered the top 50 ranks in the world in GII with 48th rank. Consistent improvement in ranking over the last three years is greatly influenced by indicators related to higher education in India. Higher education indicators that have been identified as India’s

80  K.M. Joshi and Kinjal Ahir

strengths include the percentage of graduates in science and engineering, average score of the top three ranked Indian higher education institutes in QS ranking, citable documents, h-index, top three global R&D companies’ average expenditure, and ICT services exports as a percentage of global trade. At the same time, higher education indicators that have been identified as India’s weakness include tertiary inbound mobility, and expenditure on education (which includes expenditure on higher education) as a percentage of GDP, Government funding per pupil in secondary education as a percentage of GDP per capita, school life expectancy, and pupil–teacher ratio (in secondary education). Ranks for indicators like knowledge creation, knowledge impact, and knowledge diffusion are progressive. These indicators and India’s performance in the same explicitly highlight the importance of higher education in the development of an economy (Cornell University, INSEAD, WIPO, 2020). 5.3.2 Global Competitiveness Report 2020 The report assesses the competitiveness of an economy as compared to other economies using six indicators. The performance of India on one of the indicators relevant to higher education, namely ‘human capital’, has been poor. India was one of the worst-performing economies among G20 countries in the context of the adequacy of skill sets as measured by the percentage change in skill sets of all graduates, university graduates, and secondary school graduates. This result thus indicates the difficulty among the employers in finding appropriately skilled labour. Reskilling and upskilling the current workforce in skills emerging from newer challenges due to the COVID situation was the recommended short-term solution for the revival of economies. In the long run, the up-gradation of curricula for technical, vocational, and university education in tandem with the skills needed in the future has been suggested (WEF, 2020a). 5.3.3 IMD World Digital Competitiveness Ranking 2020 The ranking is an assessment of capacity and readiness to adopt and explore digital technologies for economic and social transformation. Rankings are provided for 63 countries, based on three indicators – ‘knowledge, technology, and future-readiness’ – and nine sub-indicators. India’s rank slipped by four positions to 48th rank, while last year it had climbed the same four positions to reach 44th rank. Many sub-factors in the ‘knowledge’ factor were closely linked with higher education. Again, the performance of Indian higher education was good for sub-factors, including R&D productivity by publications (rank 2) and graduates in sciences (rank 6). However, the subfactors where there is further scope for improvement include total R&D personnel per capita, pupil–teacher ratio in tertiary education, robots in

Indian higher education  81

education and R&D, higher education achievement, and high-tech patent grants. Sadly, data for women with degrees and female researchers was not presented. Knowledge, for India, was the best-performing indicator compared to technology and future readiness, being the other two indicators (IMD, 2020). The above global indexes that reflect the measure of the development of an economy have a specific focus on quality-related aspects of higher education. Hanushek and WoBmann (2007) explicitly emphasized the contribution of the quality of education over merely increasing access to education. While the efforts to increase financing and access to higher education in India are noteworthy, it proves to be insufficient with regard to the consistently growing aspirations to pursue higher education. Consequently, massive yet insufficient funding and access to higher education pose challenges to the quality of higher education in India, as manifested in poor performance in world university rankings, poor research output, and graduate unemployment.

5.4 Higher education in India: financing and access Reduced financing of higher education is detrimental to growth and its quality. For more than a decade, the public expenditure on higher education as a percentage of GDP in India has been less than one (Joshi and Ahir, 2016). The share of public expenditure on primary and secondary education as a percentage of GDP has been about three times and two times that of tertiary education. For a developing country like India, greater investments at initial levels of education are inevitable. However, as compared to the United States of America (USA) (0.9 percent) and Organisation for Economic Co-operation and Development (OECD) average (1 percent) (OECDiLibrary, 2021), India’s share of public expenditure on higher education is low especially considering that India’s higher education system is larger than that of the USA. In the Indian context, private institutional growth has been noteworthy. Along with this, the number of students pursuing higher education in private colleges (private aided and unaided) is about 47 percent of all the students enrolled in higher education. It displays that the private sector has played a vital role in increasing access to higher education in India. The number of higher education institutes in India has increased manifold making Indian higher education the largest in the world in terms of the number of institutes. During 2010–11 to 2018–19, a period of nine years for which comparable data is available, the number of universities increased by about 60 percent, and the number of colleges increased by about 21 percent as shown in Table 5.1 (MHRD, 2013, 2019a). To comprehend the number of institutions from a comparative perspective, the USA had 3,982 degree-granting post-secondary institutions (National Center for Education

82  K.M. Joshi and Kinjal Ahir Table 5.1 Institutional growth in higher education in India between 2010–11 and 2018–19 Institutional growth in higher education in India

2010–11

2018–19

Number of universities Number of colleges Privately managed universities as a percentage of total universities Privately managed colleges as a percentage of total colleges

621 32,974 32

993 39,931 39

73

77.8

Source: MHRD (2013, 2019b).

Statistics [NCES], 2021a) while China had a total of 2,956 higher education institutes by 2019 (World Education Services [WES], 2021). In India, about 39 percent of the universities in 2018–19 were privately managed as compared to 32 percent in 2010–11. In 2010–11, 73 percent of colleges were privately managed with 59 percent being private unaided colleges, 14 percent colleges were private aided, and 27 percent were government colleges. In 2018–19, 77.8 percent of colleges were privately managed with 64.3 percent being private unaided, 13.5 percent private aided, and 22.2 percent government colleges. Almost 9 percent growth in private unaided colleges is noticeable over a period of nine years, especially considering that colleges constitute a major part of the institutions of higher education in India (MHRD, 2013, 2019b). Private higher education is largely funded by the tuition fees of the students, thereby increasing the private expenditure on higher education. The household expenditure on higher education has remained high for urban areas as compared to rural areas, for private unaided institutes as compared to private aided institutes or government institutes, and for technical education as compared to general education or vocational education (Chandrasekhar, Geetha Rani, and Sahoo, 2016). Higher household private expenditure negatively affects the private rates of returns on investments in higher education by increasing costs for an individual. Combined with lesser public expenditure for higher education, if the rates of returns on investments in higher education are lesser, students would lack the motivation to pursue higher education. Lack of a skilled labour force trained in higher education negatively affects economic development (World Bank, 2021). Teachers are assessed for teaching and research quality (UGC, 2018). Teaching quality is extremely subjective and is thus largely assessed using a proxy, namely the pupil–teacher ratio. The pupil–teacher ratio in universities and its constituent units was 18, whereas the same, when measured for universities and colleges together, was 29 in 2018–19, up from 26.4 in 2010–11. About 70 percent of students study in colleges in India. Compared with two comparable higher education systems of China and the USA, according to the World Bank (2020), India’s pupil–teacher ratio was

Indian higher education  83

estimated at 25, whereas the pupil–teacher ratio for China was estimated at 19 and that for the USA at 12. In India, the high pupil–teacher ratio is a result of high enrolments in higher education and a relatively lesser number of faculties available. In terms of enrolment, India’s higher education is the second largest after China. About 37.4 million students were enrolled in higher education in India in 2018–19 (MHRD, 2013, 2019b). Students’ enrolment in higher education in China in 2018 was estimated at 45 million (WES, 2021) and that for the USA in 2018 at 19.6 million (NCES, 2021b). From 2010–11 to 2018–19, the absolute number of students enrolled in higher education in India increased by about 36 percent (from 27.5 million to 37.4 million). However, the Gross Enrolment Ratio (GER)2 for the USA has been above 85 for the last decade (Knoema, 2021) and that for China, about 50 in 2018 (WES, 2021). GER for India is dismally low at 26.3 percent (MHRD, 2019b). Although a rise in GER from 19.4 in 2010-11 to 26.3 in 2018–19 is appreciable since the absolute numbers comprised millions of students. The policy efforts are also focused on increasing the GER, and the target set in the National Education Policy 2020 is to achieve a GER of 50 by 2030 (MHRD, 2013, 2019b).

5.5 Quality of Indian higher education: world university rankings and the impact of quality on research output and employment In the global context, the quality of higher education is seen through the world university rankings. Besides this, the performance of higher education institutes in terms of knowledge production and dissemination is assessed through various criteria that reflect higher education quality. Teaching and research are assessed through established quality assessment parameters like pupil–teacher ratio, and research output and citations, respectively. The efficiency of higher education in India is assessed using established parameters in the literature (Ahir, 2007) that include the rate of unemployment at the level of higher education and the demand–supply mismatch in the job market for higher education pass-outs. The following four sections discuss the issues related to the quality of higher education in India. 5.5.1 Performance of India in world university rankings World university rankings have become very influential and vital for a comparative performance assessment of the quality of higher education institutes of the world, particularly since the 2000s. It assists various stakeholders in making informed decisions (Joshi, Desai, and Ahir, 2016). Performance-based decisions benefit each of the stakeholders and result in

84  K.M. Joshi and Kinjal Ahir

higher efficiency for higher education through lower drop-out rates, lower attrition rates, and efficient financial allocation. Most of the literature cites three world university rankings, namely Times Higher Education World University Rankings (THE), Quacquarelli Symonds (QS) World University Rankings, and Academic Ranking of World Universities (ARWU). In spite of several criticisms of world university rankings, it is difficult to ignore the dismal performance of Indian higher education institutes in these rankings (Joshi, Desai, and Ahir, 2016; Joshi and Ahir, 2017; Joshi, Ahir, and Desai, 2018). In THE 2021 rankings, none of the Indian higher education institutes ranked in the top 300 (THE, 2021). In QS ranking since 2018, the top-ranking institute’s rank has been continuously falling, with the highest rank being 172 of IITB in 2021 rankings (QS, 2021). In ARWU, the only Indian institute to mark its presence was IISc, but with a rank in the range of 501–600 in 2020 rankings (ARWU, 2021). Therefore, it’s a matter of concern that none of the Indian higher education institutes have been able to endorse its presence in the world university rankings. Policymakers have started focusing on improving performance through policy initiatives like the creation of ‘Institutes of Eminence’ (Joshi and Ahir, 2017). India has also created national rankings like the National Institutional Ranking Framework (NIRF) to identify better-performing higher education institutes. Further analysis of the performance of Indian higher education institutes in these rankings indicates that while access to higher education has increased in India, it’s the poor performance in terms of quality parameters that need to be improved. These world university rankings assess quality through parameters related to teaching, research, and internationalization of higher education institutes like a teacher-pupil ratio, ratios of research staff, and the ratio of international teachers and students as a percentage of the total, research income, medals, and awards won by staff and alumni, research output in reputed publications, citations of published research, and h-index of teaching and research staff. Accordingly, an attempt is further made to undertake an in-depth analysis of various parameters of quality and efficiency of higher education on the basis of the selected parameters identified above. 5.5.2 Assessment of quality of research in higher education in India Knowledge production in higher education is assessed through research output and its endorsement by world researchers from the same discipline. A consistent rise has been observed in the number of researchers per million of the population in India. It was 110 in 2000, 218 in 2015, and increased to 255 in 2017. India ranked 10th in the world in 2017 in terms of researchers per million of the population (DST, 2020a, 2020b). In terms of total documents and citable documents, India ranks in the top 10 countries in

Indian higher education  85 Table 5.2 Performance of India in selected indicators of research output Increase in research publications for India (2011–16) SCOPUS Web of Science SCI database National Science Foundation

Growth rate of scientific publications (2011–16) India

World

India’s share in global research output in 2016

India’s rank in the world in scientific publication output in 2018

50 36.5

8.4 6.4

1.9 3.7

5.4 4.1

5th 9th

83.1

10.7*

3.8*

5.3**

3rd

Source: DST (2020a). Notes: * During 2008–18, ** During 2018.

the world for the period 1996–2020. A few other performances of India in selected research output can be traced in Table 5.2. While research output is notable for India, the performance of India is low on the criteria - citations per document (India’s rank 199) and h-index (India’s rank 21) for the same period, especially considering comparable higher education of the USA and China (Scimago Journal Ranking [SJR], 2021). Most of the research publications in India are in the fields of chemistry, pharmacology and toxicology, and agricultural sciences (DST, 2020a). An appropriate amount of funding is inevitable to undertake research and development in higher education institutes, although the conditions in India in this regard need to be improved. The higher education sector accounted for 6.8 percent of the national research and development (R&D) expenditure from among various sectors in India in 2017–18 (Department of Science and Technology [DST], 2020a, p3). It was one of the lowest as compared to the USA (13 percent), Canada (41 percent), Australia (31 percent), and the UK (24 percent) (DST, 2020a, p7). The R&D expenditure as a percentage of GDP is only 0.7 percent (DST, 2020a). In terms of patent filing, India’s rank is seventh in the world. Although 33 percent of all the patents filed in India were by residents, 67 percent were by non-residents, primarily from the USA, Japan, Germany, and China (DST, 2020a). This is the case despite the fact that India ranked third in the world in terms of PhDs awarded in science and engineering disciplines. Among higher education institutes, the maximum number of patents were filed by all IITs together and Amity University (DST, 2020b) in 2017–18. 5.5.3 Employment scenario of graduates from Indian higher education Many benefits that both an individual and a nation derive from education are intangible (Viswanath, Reddy, and Pandit, 2009). Tangible benefits of

86  K.M. Joshi and Kinjal Ahir Table 5.3 Rate of unemployment at various levels of education (percentage) Level of education

Rate of unemployment

Level of education

Rate of unemployment

Illiterate Literate up to primary education Middle level of education Secondary level of education

1.1 2.4

Higher secondary Diploma/certificate courses Graduates Postgraduates

9.2 17.2

4.8 5.5

16.9 14.4

Source: PLFS (2019).

education are estimated using information related to the employment of those who have graduated in higher education vis-à-vis those who have not and analysis of the demand and supply gap in the job market. The unemployment rate is defined as the number of unemployed persons as a percentage of the total employed and unemployed persons in a country at a given point in time. Counter-intuitive to general perception, the unemployment rate for the period July 2018–June 2019 was the highest for higher education level as compared to other levels of education, as can be seen in Table 5.3 (Periodic Labour Force Survey [PLFS], 2019). Thus, unemployment rates were highest for post-higher secondary levels of education, i.e., higher education. Most researchers have estimated rates of returns on higher education to be greater than 10. An additional degree, at a higher education level, increases his/her earnings by more than 10 percent. Findings of initial studies of rates of returns on education in India suggested greater returns for primary and secondary levels of education. However, a rise in the private rates of returns to higher education in later studies justifies the investments in higher education for an individual, who may then expect to earn greater earnings (Agrawal, 2011; Duraisamy, 2002; Joshi and Ahir, 2019; Psacharopolous, 1973; Rani, 2014; Sahoo and Khan, 2020; Singhari and Madheswaran, 2016; Tilak, 1987). 5.5.4 ‘Demand–supply mismatch’ in the job market Several surveys have examined the lack of employability among graduates of higher education in India, particularly for engineering and management. The latest India Skills Report 2021 attempts to identify the skills demand and supply gap in India, particularly considering the COVID pandemic scenario. It surveyed 65,000 students to know the supply side employability and 50 employers from 15 industries to identify the demand variables to hire talent. Students were assessed for business communication, critical thinking, and numerical reasoning through a test and a psychometric assessment (Wheebox, 2021).

Indian higher education  87

On the demand side for jobs in the job market, it was observed that over the last eight years, overall employability has shown an increasing trend. By 2021, the employability of the students was 45.9 percent. It means that more than half of the students surveyed are non-employable. The most employable students were from the degrees and disciplines such as Bachelor of Engineering/Bachelor of Technology, Master of Business Administration, Bachelor of Arts, Bachelor of Commerce, Bachelor of Science, Master of Computer Applications, Polytechnic, and Bachelor of Pharmacy (Wheebox, 2021). Supply-side aspects for job opportunities in the job market showed that 47 percent of employers had a positive hiring intent in 2021 despite the pandemic. Sectors that were expected to hire the most in 2021 included pharma and health, engineering and manufacturing, energy, logistics, internet, software and hardware, and BFSI – banking, financial services, and insurance. The top five technical skills in demand included python programming, neural networks, cloud computing, supply chain, and general statistics, whereas the top five soft skills in demand included problem-solving, communication, active learning/resilience/flexibility, digital dexterity, and analytical and critical thinking (Wheebox, 2021). It can be further noted that out of the total 37.4 million students pursuing higher education in India majority of the students in higher education in India pursue undergraduate courses (79.76 percent share in 2018–19). Although very few of them pursue further postgraduation or research degrees like MPhil or PhD Only 10.81 percent are in postgraduation programs and merely 0.08 percent and 0.45 percent of total students in MPhil and PhD, respectively (MHRD, 2019b). Lower enrolments in research programs have implications related to research output associated with innovations and patents. As can be seen in Table 5.4, at the undergraduate level, a significant number of students pursue arts disciplines. In 2018–19, arts (32.69 percent), Table 5.4 Students enrolled in various disciplines as a percentage of total enrolments in respective programs Disciplines

Undergraduation

Postgraduation

PhD

Arts Science Commerce Engineering and technology Social sciences Management Indian languages Foreign languages Total enrolment

32.69 16.48 14.1 13.47 3.17 2.27 1.1 0.5 2,85,96,751

NA 14.78 11.33 4.58 18 15.98 7.5 4.89 39,75,286

NA 26.4 3.16 24.74 9.87 5.89 4.74 2.18 1,69,170

Source: MHRD (2019b). Note: NA: Not Available.

88  K.M. Joshi and Kinjal Ahir

science (16.48 percent), commerce (14.1 percent), and engineering and technology (13.47 percent) comprised 76.74 percent of students in undergraduate courses. In social sciences (3.17 percent), management (2.27 percent), Indian languages (1.1 percent), and foreign languages (0.5 percent), relatively lesser percentage of students enrolled at the undergraduate level. Although at the post-graduate level, the maximum number of students belong to the disciplines of social sciences (18 percent), management (15.98 percent), sciences (14.78 percent), commerce (11.33 percent), languages (Indian – 7.5 percent and foreign 4.89 percent) and engineering and technology (4.58 percent). As for the PhD program, maximum students belong to science discipline (26.4 percent), engineering and technology (24.74 percent), social sciences (9.87 percent), languages (Indian – 4.74, foreign – 2.18), and management (5.89 percent). Accordingly, across various levels of higher education, students belonging to disciplines like sciences, social sciences, engineering and technology, management, and languages, although small in terms of percentages ascend to PhD levels. (MHRD, 2019b). On the other hand, students belonging to arts and commerce were less likely to do postgraduation or PhD subsequently. Discipline choices at the level of PhD can impact the research output in terms of the number of publications and their citations. In terms of disciplines, the India Skills Report 2021 highlights that the skill gap was maximum in data science, artificial intelligence, and natural language processing. The most employable age group identified was 18–21, which coincides with the higher education eligible age cohort of 18–24 in India. While the employability of females has been largely higher than that of males, participation in the job market for 2021 was estimated at 36 percent and that for males at 64 percent. It is a clear indication of wastage of existing talent of females, which is not only a concern based on equity but also a major economic loss for the country’s GDP. Gender parity in higher education institutes should be sufficiently supported with appropriate laws in the job market to facilitate and accommodate eligible females (Wheebox, 2021). With regard to equity, gender parity in GER has been achieved, suggesting equal opportunities for access to females in higher education in India. However, gender bias can be witnessed in terms of the discipline choices between male and female students, with bias in favor of males for disciplines like ‘engineering’ and that in favor of females for disciplines like ‘education’. Certain socioeconomic factors influence the choices of pursuing higher education and the disciplines, as highlighted by Joshi and Ahir (2021). Another study (WEF, 2020b) identifies similar observations based on an analysis of the world. The importance of investing in human capital and social capital and reskilling and upskilling of employees has been recognized by employers. Higher education can play a key role in achieving this. The healthcare sector had the highest hiring trend in the world. Most of the jobs are in demand for data analytics, AI, and big data (WEF, 2020b).

Indian higher education  89

5.6 Conclusion and policy implications The demography of India offers an opportunity of unprecedented scale ever in history since independence. The median age of less than 30 in India is enviable for many countries. This stratum of the population is likely to be in the workforce for about three decades. Thus, if the youth of the country is trained for contributing as a skilled workforce, India can excel in the race whereby knowledge production and dissemination are detrimental to the development. In this context, higher education plays an important role in juxtaposing high-productivity labour in the job market. Despite growth in terms of the number of institutes and enrolments, Indian higher education faces many challenges related to quality, as exhibited in World University Rankings and India Skills Report 2021. Certain policy stimuli in the right direction can assure significant contribution of higher education to greater economic development. Maintaining a low pupil–teacher ratio can enhance the quality of knowledge dissemination and evaluation. With an ever-growing number of enrolments and even higher targets sets for enrolment, the only way to improve the pupil–teacher ratio is to increase the number of faculties. Since most of the institutional and enrolment growth is in private institutions, regulations regarding appropriate pupil–teacher ratio should be enacted in the private sector also. Researchers need grooming and training to enhance the qualitative publications and thereby improve citations and h-index. India has been home to several indigenous innovations, and citizens keep improvising various products and processes based on their needs. Higher education institutes can be instrumental in channelizing innovations and securing patents on such products by partnering with citizens or other academicians through knowledge sharing. Patent filing facilitation can enhance the number of patents filed in India. Research can be an expensive endeavor with uncertain outcomes. Government funding or government-channelized funding for research, innovation, and publication of research output can incentivize researchers to undertake good-quality research. It is imperative to establish an appropriate match between the demand for and supply of job opportunities. It requires an estimation of existing requirements for a skilland knowledge-equipped workforce and an estimation of required future manpower skill sets. The government should routinely undertake massive surveys to estimate the demand. The creation of expected enrolment opportunities on the basis of the required skill set would improve the prospects of seeking admissions to promising careers. Students should be groomed for crucial employability skills besides academics, like critical thinking, problem-solving, active learning, resilience, flexibility, and continuous technology adoption. Entrepreneurship should be majorly incentivized through easy access to necessary resources and guidance. When higher education pass-outs become job providers instead

90  K.M. Joshi and Kinjal Ahir

of job seekers, issues related to unemployment or lower private rates of returns would reduce substantially. Such entrepreneurial enthusiasts can not only provide employment but also contribute substantially to the GDP. Improvement in quality and efficiency would enhance the performance of Indian higher education in world university rankings. The enhancement in higher education quality will directly impact India’s position in global indexes that reflect the state of economic and human development.

Notes 1 Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational, and tertiary education, including university. Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs, and entrepreneurship. Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples, and children in vulnerable situations. Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable development (UNDP, 2021d). 2 Gross Enrolment Ratio is the proportion of the number of students enrolled in a particular level of education (irrespective of their age) to the number of citizens belonging to the relevant age cohort for that particular level of education.

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Indian higher education  93 Quacquarelli Symonds (2021). QS World University Rankings 2021. Quacquarelli Symonds Top Universities. Author. Retrieved as on 30 April 2021 from https:// www​.topuniversities​.com​/university​-rankings​/world​-university​-rankings​/2019 Rani, G. (2014). Disparities in earnings and education in India. Cogent Economics & Finance, 2(1), 941510. DOI: 10.1080/23322039.2014.941510 Romer, P. M. (1990). Endogenous technical change. Journal of Political Economy, 98, S71–102. Sahoo, N. and Khan, J. (2020). Why Internationalization of higher education can be a game-changer for India. Observer Research Foundation, Expert speak, India Matters. Retrieved as on 28 December, 2020 from https://www​.orfonline​ .org​/expert​-speak​/why​-int​erna​tion​alisation​-higher​-education​-can​-game​-changer​ -india/ Schultz (1961). Investment in Human Capital. American Economic Review, 51(1), 1–17. Scimago Journal Ranking (2021). Scimago Country Rank. Retrieved as on 18 May 2021 from https://www​.scimagojr​.com​/countryrank​.php​?order​=h​&ord​=desc Singhari, S. and Madheswaran, S. (2016). The Changing Rates of Returns to Education in India: Evidence from NSS Data. Working paper 358. ISEC, Bangalore. Retrieved as on 20 March, 2021 from http://www​.isec​.ac​.in​/WP​ %20358​%20-​%20Smritirekha​%20Singhari​%20and​%20S​%20Madheswaran​ %20-​%20Final​.pdf Solow (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70, 65–94. The World Bank (2020). Pupil Teacher Ratio, Tertiary. UNESCO Institute for Statistics. Retrieved as on 21 May 2021 from https://data​.worldbank​.org​/ indicator​/SE​.TER​.ENRL​.TC​.ZS Times Higher Education (2021). World University Rankings 2021. Times Higher Education World University Rankings. Author. Retrieved as on 30 April, 2021 from https://www​.tim​eshi​gher​education​.com​/world​-university​-rankings​/2021​ /world​-ranking#!​/pag​​e​/0​/l​​ength​​/25​/l​​ocati​​ons​/I​​N​/sor​​t​_by/​​rank/​s ort_order/asc/ cols/stats Tilak, J. B. G. (1987). The Economics of Inequality in Education. Sage Publications, New Delhi. Tilak, J. B. G. (2003). Higher Education and Development, in the Handbook on Educational Research in the Asia Pacific Region (Eds. J.P. Kleeves and R. Watanabe). Kluwer Academic Publishers, Dordrecht, pp. 809–26. United Nations Development Program (2021a). Global Human Development Indicators. Retrieved as on 22.7.21 from http://hdr​.undp​.org​/en​/countries United Nations Development Program (2021b). Millennium Development Goals and beyond 2015. Retrieved as on 22.7.21 from https://www​.un​.org​/millenniumgoals/ United Nations Development Program (2021c). Sustainable Development Goals. Retrieved as on 22.7.21 from https://sdgs​.un​.org​/goals United Nations Development Program (2021d). Sustainable Development Goal 4: Targets and Indicators. Retrieved as on 22.7.21 from https://sdgs​.un​.org​/goals​/ goal4 University Grants Commission (2018). UGC Regulations on Minimum Qualifications for Appointment of Teachers and other Academic Staff in Universities and Colleges and Measures for the Maintenance of Standards in Higher Education,

94  K.M. Joshi and Kinjal Ahir 2018. No. 271. University Grants Commission, New Delhi. Retrieved as on 21 May, 2021 from https://www​.ugc​.ac​.in​/pdfnews​/4033931​_UGC​-Regulation​ _min​_Qualification​_Jul201 8.pdf Viswanath, J., Reddy, K.L.N. and Pandit, V. (2009). Human Capital contributions to economic growth in India: An aggregate production function analysis. The Indian Journal of Industrial Relations, 44(3) January, 473–486. World Bank (2021). Understanding Poverty: Higher Education. Retrieved as on 20.7.21 from https://www​.worldbank​.org​/en​/topic​/tertiaryeducation#1 World Economic Forum (2020a). The Global Competitiveness Report, Special Edition 2020: How Countries are Performing on the Road to Recovery. World Economic Forum, Switzerland. ISBN 978-2-940631-17-9. Retrieved as on 11 May 2021 from http://www3​.weforum​.org​/docs​/WEF​_The​Glob​alCo​mpet​itiv​ enes​sRep​ort2020​.pdf World Economic Forum (2020b). The Future of Jobs Report 2020. World Economic Forum, Switzerland. Retrieved as on 11 May 2021 from http://www3​.weforum​ .org​/docs​/WEF​_Future​_of​_Jobs​_2020​.pdf World Education Services (2021). Education system profiles: Education in China. World Education News + Reviews. World Education Services. Retrieved as on 5 May, 2021 from Education in China (wes​.o​rg) Wheebox. (2021). India Skills Report 2021. Taggd, CII, AICTE, AIU, UNDP, Wheebox. Retrieved as on 5 May, 2021 from https://wheebox​.com​/assets​/pdf​/ ISR​_Report​_2021​.pdf

Chapter 6

The role and impact of academics’ societal engagement Haiyang Chen and Sam N. Basu

6.1 Introduction Higher education is an essential foundation for economic development (Porter, 2011). Academics’ societal engagement (ASE) with partners beyond campus has an increasing impact on economic development. This chapter aims to provide a comprehensive survey of ASE activities by reviewing published research on ASE. We define ASE broadly as any role that universities play in collaboration with outside partners. To our best knowledge, this is the first review that includes university research, teaching, and outreach activities. Based on the review, we develop a set of propositions of how ASE impacts economic development. Most of the research reviewed has been conducted in the West. We draw implications to help developing counties such as India formulate their ASE strategies. Our literature search has found hundreds of articles on ASE. Most of them have focused on activities related to the research mission of universities. Another strand of research has focused on the educational mission of universities, meaning teaching to prepare human capital for society. There is scant literature examining activities related to the university outreach mission. This chapter aims to fill the gap left in the literature. The chapter is organized as follows. Section 6.2 provides a survey of ASE related to university research, teaching, and outreach. This section also provides a case study of Western Carolina University. Based on the review, we develop a set of propositions about ASE on economic development in Section 6.3. Section 6.4 provides lessons that can be learned by India. Concluding remarks will be presented in the final section.

6.2 Academics’ societal engagement and economic development 6.2.1 Academics’ societal engagement through university research There is a voluminous body of literature focusing on university research cooperation with a third party beyond campus. Instead of reinventing the DOI: 10.4324/9781003329862-8

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wheel, we identify and survey eight recent studies reviewing papers published between 1997 and 2020. Examining emerging research patterns in university, industry, and government (U-I-G) interaction, Skute, Zalewska-Kurek, Hatak, and de Weerd-Nederhof (2019) identify research on perspectives of distance and partner complementarity, academic entrepreneurship, ecosystem, interaction channels, social relations, and policy implications. At the individual level, researchers ask questions about motives, incentives, etc. on university and industry (U-I) collaboration. At the organizational level, researchers ask what organizational characteristics explain success, factors influencing partner selection, etc. At the institutional level, researchers ask if U-I collaboration stimulates economic development, how U-I collaboration affects policy development, etc. Figueiredo and Ferreira (2021) provide a summary of four research clusters. The first one is motivations and barriers to U-I cooperation. Motivations include research development, financial resources, partner organization type, research of opportunities, human resources, and access to high technologies. Barriers include socio-economic conditions, lack of tradition, cultural differences, the ideology of individuals, bureaucracy, lack of organizational support, legal framework, etc. The second cluster is determinant factors including company size, activity sector, absence of risks, previous experiences, investment in R&D, knowledge research, and new products and processes. The third is government measures including political measures, programs/scholarships, legislation, and financing. The fourth is intersectoral technological cooperation including business competitiveness, marketing products, knowledge transfer, technology transfer, innovation in R&D, spin-offs, and patent development. Miranda and Pertuz (2021) identify five areas of research. They include individual and organizational determinants of U-I cooperation, impacts of U-I cooperation on industrial activity, the relation between scientific productivity and collaboration with the industry, the entrepreneurial university, and motivations of U-I cooperation. Rybnicek and Königsgruber (2019) review 103 U-I collaboration papers and distill success factors into three themes: flexibility, honesty, and clarity. At the institutional level, a partner must be flexible regarding one’s priorities as the other partner may have other priorities. Regarding the relationship between both partners, they advise that partners must be treated fairly, communicating openly to foster trust. Regarding the output factors, their summary suggests that partners must have clear purposes, plan realistically, agree on responsibilities, and define roles right at the beginning. Pertuz, Miranda, Charris-Fontanilla, and Pertuz-Peralta (2021) identify 17 success factors in four internal facilitators: structure, strategy, knowledge, and relationships. Regarding the structure, they identify the size of the company, the internal structural characteristics, and institutional support to collaborative processes, the existence of intellectual property policies, the

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capacity and technological intensity of the company, geographic proximity with university partners, and location of the firm in areas of high business density, the existence of governance mechanisms for collaboration processes and the management and motivation of human talent and collaboration teams. Regarding organizational relationships, they find previous experience in collaborative processes and R&D, trust among the members of the collaboration, effective communication between the members of the collaboration, shared objectives, and mutual understanding of the needs and relevant aspects of the collaboration process, the ability to share resources and costs in the cooperation process, and the encouragement of joint scientific production with universities. Perkmann, Salandra, Tartari, McKelvey, and Hughes (2021) provide determinants of academic engagement. Individual characteristics such as male, prior experience, and research productivity have a positive impact on academic engagement. Organizational and relational context such as peer effects has a positive impact. The authors suggest that the impact of other individual characteristics, organizational and relational context, and institutional context need to be further investigated. De las Heras-Rosas and Herrera (2021) review 349 U-I-related open innovation articles and find the focus of the papers has been on SMEs, Helix models, entrepreneurship, or commercialization. In recent years, there has been a trend toward research into entrepreneurship, key aspects of R&D such as strategy and cooperation, or education management. Da Silva, Kovaleski, and Pagani (2021) review 112 technology transfer papers published before December 2019. They identify key approaches and elements of technology transfer (agents, technology, mechanisms, policies, barriers, supporters, models, and effects and impacts). 6.2.1.1 University research engagement and economic development in action Porter (1990, 2004, 2011) and Porter and Council of Competitiveness, Monitor Group, and on the FRONTIER (2002) explore the specific issues of ASE. Porter (2011) states that the economic development strategy is to enhance productivity and competitiveness, which is the only way to create jobs in the long run. Productivity is driven by the business environment, cluster development, and policy coordination. To offer the most productive environment for business, the states must protect and enhance higher education and research institutions and relentlessly improve the public education system. Higher education is an essential foundation for economic development. Clusters are interconnected companies and institutions in a field located in a specific geographic location (Porter, 1998). Clusters influence competitiveness and universities play a significant role in developing the clusters. Harvard and MIT play an instrumental role in developing the Massachusetts life science cluster (Porter, 2011). Similarly, academic institutions help develop the

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oil and gas cluster in the Huston area. Piiparinen, Russell, and Post (2015) demonstrate how Pittsburgh has revitalized its economy through the contributions of Carnegie Mellon and the University of Pittsburgh. Youtie and Shapira (2008) show how Georgia Tech helps transform the region from an agricultural to an innovation-driven economy. Shaffer (2015) shows North Carolina’s Research Triangle Park founded by NC State, Duke, and the University of North Carolina at Chapel Hill helps the state economic boom. Total employment in Raleigh-Durham-Cary, NC, was more than tripled from 286,000 to 1.03 million during 1970 and 2007. The total employment number in 2019 stood at 1.38 million (data from https://apps​.bea​ .gov​/iTable​/iTable​.cfm​?reqid​=70​&step​=1​&acrdn=6). Other examples can also be found in Silicon Valley in California (Reis, 2019), Route 128 in Massachusetts (Earls, 2002), and the wineries in New York’s Finger Lakes (Shaffer, 2015). The wineries in Napa and Sonoma Valley in California are another great example (Porter, 2011). The University of California at Davis (UC Davis) has been intimately involved with the growth and development of the now-famous wine industry in California (Ryan, 2014; UC Davis Department of Viticulture and Enology, 2020). 6.2.2 Academics’ societal engagement through university teaching Since the world’s first universities were established around the 1200s, their main purpose has remained the same: to serve their students. Celebrating Columbia University’s 250th anniversary, President Bollinger states, “Universities remain meaningful because they respond to the deepest of human needs, to the desire to understand and to explain that understanding to others” (Bollinger, 2003). The needs are fulfilled by universities that not only improve students’ qualify for life but also prepare them to join the workforce to promote economic development. Lehmann, Christensen, Thrane, and Jørgensen (2009) examine university engagement and regional sustainability initiatives in Denmark. They adopt a view that universities impact sustainable development by providing human and intellectual capital (graduates and knowledge) as well as natural capital, production capital, and social capital (institutions). They analyze two successful cases of engagement of Aalborg University and conclude that universities play critical roles at the national as well as the regional level and contribute to economic development. Wang, Yu, Chen, Zhang, Wiedmann, and Feng (2015) state that engineering students cannot integrate what they learn in the classroom into real-world situations. The authors propose a simulating industry model where teaching is closely related to real-world situations. In the model, U-I cooperation must be strong and sustainable before, during, and after students’ academic studies. The approach works well. Bethel (2017) also build a U-I partnership to provide students with real-world experience using project-based learning. The

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outcome and feedback are very positive and U-I relationships also improve economic development in the region. Yoshino, Pinto, Pontes, Treinta, Justo, and Santos (2020) create a new multicriteria framework based on skills and competencies needed. The model assumes a strong U-I partnership, where the university provides labs, instructors, and students, and the industry partners provide real-world problems and solutions. The outcome of this framework is positive. It generates innovative projects, new products, startups, and patents. Students, companies, and society are receiving value-added benefits. Persadie, Sangster, Ameerali, Soodeen, Maharajh, and Ramkhalawan (2020) focus on graduate students and develop programs to foster their entrepreneurial spirit and managerial capabilities. Through U-I partnership, their program uses real-world industry projects, exposes students to industry operational challenges, and hones their soft skills of leadership, teamwork, and conflict management. Industry experts are invited to teach how to translate theory into practice through industry immersion projects. The program has yielded positive results for the graduates, the university, and the industry. 6.2.3 Academics’ societal engagement through university outreach In the US, large land-grant universities were set up in the 1860s to support the local economy. Other state universities have specific community service missions. This is also true in other western countries (Sánchez-Barrioluengo, 2014). While outreach is stated in universities’ mission statements, we find very little research on this topic. Combining more than 70 years of experience as administrators and professors, we see that our employers have exerted significant emphasis on outreach. We use outreach activities at Western Carolina University as an example. Western Carolina University (WCU) is a part of 16 public universities in the University of North Carolina System. As of fall 2020, WCU had 12,243 students. Since its founding in 1889, WCU has been committed to educating students from rural Western Carolina. Rural American communities face special economic and societal challenges. The rural population in the US declined from 54.4% of the total population in 1910 to 19.3% in 2010 (Ratcliffe, Burd, Holder, & Fields, 2016). The proportion of rural adults 25 and older with a bachelor’s degree or higher increased from 15% to 20% vs. 26% to 34% in urban areas. The estimated rural employment in the second quarter of 2018 was 1.8% below its pre-recession level in 2008 vs. 8.2% above that level in urban areas. Rural America included 14% of the nation’s population but accounted for only 4% of employment growth from 2013 to 2018 (US Department of Agriculture, 2018). Rural Americans are more likely to die from heart disease, cancer, unintentional injury, chronic lower respiratory disease, and stroke than their

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urban counterparts. Children in rural areas with mental, behavioral, and developmental disorders face more challenges than children in urban areas (Centers for Disease Control and Prevention, 2017). Other challenges include a lack of policy to assist rural areas, the growing opioid crisis, lack of broadband (American Library Association, 2019), limited housing choices, transportation options, etc. According to the North Carolina Institute of Medicine (2014) and discussion of the Leadership Cashiers, a community leadership program, Western North Carolina (NC) faces similar challenges. WCU and Its College of Business have worked hard to help the community overcome these challenges. For over 130 years, WCU has provided affordable, comprehensive education to residents in NC. Students at WCU enjoy low tuition of $500 per semester through the NC Promise Tuition Plan and a low-cost book rental program where students pay $150 for all of the textbooks needed in a semester. WCU plays a significant role in providing quality affordable education opportunities. The Small Business and Technology Development Center (SBTDC) in the College of Business at WCU helps small businesses and entrepreneurs in Western NC. It is a part of the larger NC SBTDC, an organization of the University of North Carolina System. Its mission is to provide education and resources for small and mid-sized businesses, entrepreneurs, and community leaders to innovate and succeed. Table 6.1 shows NC SBTDC’s role and impact on the state economy. NC SBTDC has 11 regional centers throughout NC. Administered by the College of Business at WCU, the Western Center covers 14 rural counties. Table 6.2 presents the goals of SBDTC in the College of Business at WCU. These two tables show that SBTDC has actively promoted economic development in the state and regions by helping business people solve a wide range of issues and challenges. Table 6.1 Impact of North Carolina SBDTC 2016 Accomplishments

Impact

Contracts from federal, state, and local agencies and prime contractors Capital obtained Hour of counseling Clients who worked with the SBTDC Job created New business started Internship for graduate and undergraduate Hours contributed by students for SBTDC clients Increased revenue by companies helped by SBTDC The new tax generated by $1 invested

$228,595,421 $123,861,431 54,484 4,524 2,431 99 630 25,333 12.9% $2.51

Source: North Carolina Small Business and Technology Development Center.

The role and impact of academics’ societal engagement  101 Table 6.2 SBDTC in College of Business at Western Carolina University Goals in 2019 Category

Metric

Annual goal

Economic impact

Total capital Business starts Job created SBA cases SBA counseling hours Single-year long-term clients Pre-venture Small business Mid-size business Others No of events No of attendees Program income

$10,000,000 50 300 335 6,130 136 12% 45% 41% 2% 10 350 $17,500

Counseling Segmentation

Training

Source: WCU SBTDC director report.

The College’s other outreach programs include: •

•

•

•

Rural school students lack career opportunities (Ohlson, Shope, & Johnson, 2020). The College has worked with the rural school district and county government to offer a Career Day and Jobs Fair where high school students did a career self-assessment and then interacted with local businesses and faculty and staff from the College to discuss careers. The College manages an Agricultural Mediation Program for the State of North Carolina and the State of Virginia. It is a collaborative program among WCU’s College of Business, the US Department of Agriculture, and the rural community. Faculty facilitate farmers with delinquent and distressed loans and help them, and creditors resolve their disputes in a non-adversarial setting outside the traditional legal processes. The College participates in a Farm-to-Pantry gleaning program that facilitates people to pick produce left in the field after harvest and deliver thousands of pounds of produce from local farms and gardens to local pantries every year. The College’s Corporation for Entrepreneurship & Innovation provides business services to local companies and entrepreneurs. The Center for the Study of Free Enterprise provides economics research and thought leadership in NC and beyond. North Carolina Data Dashboard provides a one-stop, multi-purpose information source of NC with a wide variety of county-level and industry-by-county-level data for the region.

Other units within WCU have outreach initiatives. •

WCU helps Dillsboro, a small tourist town 9 miles away from campus, to revitalize its economy after the 2008 economic downturn.

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•

•

•

WCU helped develop questionnaires to improve business operations (Grunwell & Ha, 2014). Faculty and students developed maps and brochures, assisted in planning and promoting events, launched an app to connect customers with the merchants, and partnered with the city to publish a book, A Guide to Historic Dillsboro. The sentiment in the community is that WCU brings hope to Dillsboro and helps the town to get back on its feet. Community and Economic Engagement and Innovation Office worked with outdoor community partners in the region to organize the inaugural 2018 Outdoor Economy Conference (Western Carolina University, 2018). Western NC is the biggest outdoor industry hub east of the Rockies. In 2016, it supported more than 260,000 jobs, $8.3 billion in salaries, generated $28 billion in consumer spending, and $1.3 billion in state and local taxes. After the success of the inaugural conference, the Office participated in the 2019 and 2020 Conferences (Western Carolina University, 2019; 2020) and will sponsor the 2021 Conference. Rapid Prototype Center creates a dynamic environment for hands-on prototyping and experimentation. Faculty and staff work with business partners to address their specific needs of product commercialization and process improvement. Department of Political Science and Public Affairs runs local government training programs for public officials and personnel in the 26 westernmost counties of NC (Boylan, n.d.).

WCU’s outreach impact on economic development has been recognized. WCU is a Carnegie Community Engaged University. Jackson County, where WCU is located, received the second place in the nation as the most economically dynamic micropolitan area. 6.2.4 Quantitative analysis of impact of academics’ societal engagement Academics’ societal engagement has a direct economic impact on the community. While there are some concerns about the methodologies of economic impact studies (Siegfried, Sanderson, & McHenry, 2007; Bowen & Qian, 2017), the studies are a standard activity for many universities in the West. Guidelines have been published by the Association of Public and Land-grant Universities and the Association of American Universities (2014). In the 2012–13 year, NC universities added $63.5 billion to state income and 14.6% of the total gross state product. They hired 186,076 employees with payroll and benefits of $10.7 billion and spent another $11.3 billion on its operations (Economic Modeling Specialists International, 2015). The California State University’s impact was $27 billion in 2019; the system supported 209,400 California jobs and generated $1.6 billion in state

The role and impact of academics’ societal engagement  103 Table 6.3 Regional Economic Impact Report of Western Carolina University (2012–13) Types of economic impact

Added regional income

Equivalent to # new jobs

WCU operations spending WCU construction spending WCU alumni spending WCU research spending WCU visitor spending WCU student spending Total impact

$166.7 million $2.3 million $266.7 million $849.7 thousand $34.8 million $39.9 million $511.3 million

2,945 79 5,643 15 897 895 10,474

Source: Western Carolina University.

and local taxes (Office of the Chancellor, the California State University, 2021). In 2011–12, universities in the UK generated £73 billion of output and employed 378,250 people and additional 373,794 full-time equivalent employees, 2.7% of all UK employment. The universities contributed £39.9 billion to the UK’s GDP, 2.8% of the country (Universities UK, 2014). WCU was responsible for injecting $901.8 million in revenue statewide and $511.3 million into Western NC during 2012–13 (Western Carolina University, 2013). Table 6.3 summarizes the findings. Lin (2004) investigates the impact of higher education on the economy in Taiwan from 1965 to 2000. He shows that one additional percent of people who completed higher education is estimated to increase GDP by 0.19%. Engineering and natural sciences graduates have a more prominent impact, while humanities majors do not. Amendola, Barra, and Zotti (2020) use a sample of about 50,000 Italian graduates in 2011 and find a positive association between the number of graduates and gross domestic product per capita at the local level. Analyzing nearly 15,000 universities in 78 countries, Valero and Van Reenen (2019) find a 10% growth in the number of universities is positively associated with a 0.4% rise in GDP per capita in the region. Bonaccorsi, Biancardi, Sánchez-Barrioluengo, and Biagi (2019) conduct an exploratory study and find in most cases university outputs are positively related to the performance of firms located in the neighborhood. The impact is largest for the engineering or technology field followed by the business, while basic sciences are inconclusive and humanities insignificant. Orazbayeva, Plewa, Davey, and Galán-Muros (2019) conduct a survey of experts and develop a comprehensive future research agenda in the area of U-I cooperation.

6.3 Propositions of academics’ societal engagement impact Our extensive review of ASE literature shows that universities influence economic development positively. Based on our analysis, we develop the following propositions. First, universities, large or small, public or private, research or teaching-intensive, contribute positively to economic

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development. Second, university research contributes to knowledge creation and knowledge transfer. University research builds innovation ecosystems where people share ideas and collaborate openly and efficiently. Third, universities develop future workforce well as they improve students’ employability and earning power. Forth, through outreach activities, universities play a critical role in regional economic as well as social, cultural, and community development, such as improving healthcare, enhancing cultural lives, and building coalitions to address community issues. These propositions will help researchers to further their investigation of the role and impact of ASE. New theories can be developed, and existing theories can be applied to enhance our understanding of ASE. Specific testable hypotheses can be developed and tested to shed new light on critical issues in this area. Some phenomena and impacts are difficult to quantify at the present time. We encourage researchers to develop new methods and improve the existing methods to deepen our understanding of the role and impact of ASE.

6.4 Academics’ societal engagement applications to India The Ministry of Human Resource Development (India) noted that while the number of educational institutions saw more than fiftyfold growth in the last six and a half decades, the level of available manpower remained inadequate (Digital Management and Leadership, 2018). The National Knowledge Commission noted that while very large in absolute numbers, the percentage of the Indian population enrolled in higher education has been at around 7%, roughly one-half of the average for Asia (Gujar, n.d.). Without addressing the needs of all students, the higher education scheme will wind up with a few pockets of high global excellence, such as the campuses of the Indian Institute of Technology (IIT) and the Indian Institute of Management (IIM). The quantity expansion in this area has been “unbelievably inadequate, making the challenges threatening on dual fronts of quantity and quality” (Kurian, 2016). The Confederation of Indian Industries has an objective of having a very large base of the appropriately skilled labour pool and promoting entrepreneurship and growth of the enterprise. Higher education in India is multi-modal, where the IITs and IIMs are globally competitive, while most of the rest of the institutions might face varying degrees of quality control issues. This issue of improving quality across the board faces a significant trade-off function against the need to train large volumes of students. Additionally, the vast majority of undergraduate students attend various colleges under a university, which is responsible for providing the degree. These affiliated colleges might vary significantly in terms of their academic standards, faculty quality, and available financial resources. There is a collective appreciation by the Indian government, regulatory agencies, and NGOs as well as the private sector about higher education’s

The role and impact of academics’ societal engagement  105

significance for economic development and job creation. For curricular updates, it is important to create a consistent process that considers the interests of all stakeholders. Regular and structured feedback from business and industry is particularly relevant. More than individual courses, successful degree programs are responsible for creating/enhancing the quality and reputations of universities. Increasing employability also needs close and consistent U-I collaboration. Organizing job fairs, where prospective employers can visit and talk/interview with students, is critical for a sustained employability drive. To better educate students, employers must be motivated to come to the university to provide advice to faculty for curriculum changes and seek out its students in the forms of internships, practicum, and ultimately employment. Another important point is to get the alumni involved in the process. Mid-size institutions, particularly at the level of affiliated colleges in India, should further develop these activities. The research and innovation area has been the traditional domain of high research universities in India. It will imply heavy lifting on mid-size players. We suggest defining innovation more broadly and aim to develop a mindset of innovation. The famous Dabbawallas in Mumbai who have the legendary reputation of zero mistakes in delivering thousands of home-cooked lunches are a good example (Thombe & Sinha, 2010). The future growth of the country and equitable distribution of such growth rely on mid-level institutions. It is crucial to pay attention to the role and impact made by this relatively under-noticed sector.

6.5 Concluding remarks This chapter shows the positive role and impact of universities (Jyotishi & Gavazzi, 2021). They drive knowledge creation, innovation, and entrepreneurship in the 21st-century economy. Equally important, universities educate the future workforce. Last but not least, universities play a critical role in regional economic development through community outreach. The role and impact of ASE are well presented in a diagram by the Association of Public and Land-grant Universities (2015). Universities engage the community through teaching (talent development), research (research and innovation), and service (stewardship of place). When a university has effective programs in all three realms and their intersections, the university is achieving high-impact economic engagement (see Figure 6.1). Universities are engaging with the community to perform a much broader range of activities beyond campus. ASE is a trend in the West and throughout the world. Expecting this trend to continue, or even accelerate, we encourage researchers to further investigate relevant issues of ASE and help us better understand universities’ contributions. There is room for improvement in universities (Feldman & Desrochers, 2003). The faculty’s main responsibility remains teaching and research. At

106  Haiyang Chen and Sam N. Basu

Human Capital and Talent Development

• Research, Creative Works, ProblemSolving, and Entrepreneurship

Research and Innovation

• Cradle-to-grave Human Capital and Talent development

Stewardship of Place

• Community-connected Institutions and Stewardship for Vibrant Communities

Figure 6.1 The Three Components of Talent, Innovation and Place within the ASE Environment. Source: Association of Public and Lang-grant Universities, 2015.

the same time, they need to use their expertise to reach out to help the community. Governments must protect and enhance higher education institutions (Porter, 2011). Unfortunately, we have seen a decade of declining support for public universities from state governments in the US. Public universities lost 15% of state subsidies in 2018 compared to the recession year of 2008. The public and the state legislature must act to keep the public universities public so that they can expand their role in economic development and employment growth.1

Note 1 The authors would like to thank the two anonymous reviewers for their comments and suggestions and the editors, Professors Ram Kumar Mishra, Sandeep Kumar Kujur, and K. Trivikram, for their assistance and support.

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110  Haiyang Chen and Sam N. Basu Valero, A. & Reenen, J. V. (2019). The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68, 53–67. doi: 10.1016/j.econedurev.2018.09.001 Wang, Y., Yu, Y., Chen, M., Zhang, X. Y., Wiedmann, H., & Feng, X. (2015). Simulating industry: A holistic approach for bridging the gap between engineering education and industry. Part I: A Conceptual Framework and Methodology. The International Journal of Engineering Education, 31(1a), 165–173. Western Caroline University. (2013). Regional economic impact report. Retrieved April 19, 2021 from https://www​.wcu​.edu​/WebFiles​/PDFs​/15​-123​-Economic​ -Impact​-Report​-Western​-Version​.pdf Western Carolina University. (2018). 2018 Outdoor Economy Conference at Western Carolina University. Retrieved April 19, 2021 from https://www​.wcu​ .edu​/WebFiles​/OEC​-Program​.pdf Western Carolina University. (2019). WCU, Regional partners planning expanded Outdoor Economy Conference in Asheville. Retrieved April 19, 2021 from https:// www ​. wcu​. edu​ /stories​ /posts​ /News​ /2019​ /09​ / wcu​-regional​-partners ​-planning ​ -expanded​-outdoor​-economy​-conference​-in​-asheville​/index​.aspx Western Carolina University. (2020). Virtual outdoor economy conference to tackle ‘the future of the outdoors’. Retrieved April 19, 2021 from https:// outdoorindustry​.org​/press​-release​/virtual​-outdoor​-economy​-conference​-to​-tackle​ -the​-future​-of​-the​-outdoors/ Yoshino, R. T., Pinto, M. M. A., Pontes, J., Treinta, F. T., Justo, J. F. & Santos, M. M. D. (2020). Educational test bed 4.0: A teaching tool for industry 4.0, European Journal of Engineering Education, 45(6), 1002–1023. https://doi​.org​ /10​.1080​/03043797​.2020​.1832966. Youtie, J., & Shapira, P. (2008). Building an innovation hub: A case study of the transformation of university roles in regional technological and economic development. Research Policy, 37(8), 1188–1204.

Chapter 7

The roles of agricultural universities in serving regional economic development Min Zhao, Shuxia Ren, and Jun Du

7.1 Introduction It is widely acknowledged that regional economic development requires human capital (Baldwin and McCracken, 2013), scientific and technological innovation (Huggins and Izushi, 2007), and collaborations among stakeholders (Schalin, 2011). Higher education institutions (HEIs) have become increasingly treated as the engine of economic development (Schalin, 2011) on account of their roles in the aforementioned aspects. Issues concerning “agriculture, rural areas, farmers” are always the top priority of the Chinese government. Under the guidance of the “two-stage development strategic plan”, proposed on the 19th National Congress of the Communist Party of China, the goals and tasks of the Rural Revitalization Strategy and Agriculture Modernization by 2035 are stated as: decisive progress will be made in rural revitalization, and the modernization of agriculture and rural areas will be basically accomplished (The 19th National Congress of the Communist Party of China, 2018). Therefore, in the new era of rural revitalization, agricultural universities in China, as a representative advanced force in academic and scientific research on agriculture-related problems, have the responsibility and ability to serve local economic development (Li, 2020). However, most existing studies on the roles of higher educational institutions in stimulating national or regional economic development focus on developed countries (e.g., Baldwin and McCracken, 2013; Gitterman and Coclanis, 2011; Huggins and Johnston, 2009; Shin, 2012) and certain developing countries in Africa (Ogunnubi and Shawa, 2017). And studies on the mechanisms of agricultural universities in serving regional agricultural economy are limited, except in the Chinese-language literature (e.g., Li, 2020). Moreover, current studies prefer to investigate the relationships between higher education and regional economic growth by adopting quantitative research methods. However, due to methodological limitations, the findings of such studies may be misleading (Baldwin and McCracken, 2013). Hence, this research on the roles of agricultural universities in serving local DOI: 10.4324/9781003329862-9

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agricultural economic development in China, adopting case study methodology, is meaningful. Gaps in broad themes including higher education, regional economy, and employment can be filled, which is the first contribution of this chapter. Furthermore, the case study in this research will be used to assess the strengths and weaknesses of Indian higher educational system, which is, to the best of the authors’ knowledge, the first to compare Chinese and Indian higher educational systems and provide policy implications for India and other developing countries in Asia of utilizing HEIs to stimulate regional economic development. This can be regarded as the second contribution of this research. This chapter chooses Shanxi Agricultural University (SXAU), which is situated in Taigu District, Shanxi Province, to provide some empirical evidence. Agricultural economy in Taigu District is always ranked at the top in China and contributes greatly to the regional economy (Zhang, Liang, & Chen, 2020). As the only agricultural university in Shanxi Province, Shanxi Agricultural University has played a huge role in leading the development of local agricultural science and technology, training agricultural professionals, and promoting the development of the local rural economy.

7.2 Literature survey This section reviews the most relevant literature on the roles of higher educational institutions in stimulating regional economic development and forms a conceptual framework. 7.2.1 Talent support Human capital theory (Becker, 1964) provides the main theoretical foundation for the assertion that education can play an important role in stimulating regional economic growth (Baldwin and McCracken, 2013). This theory advocates that skills, knowledge, and abilities can be obtained via education which is an investment in human capital, activating a series of future benefits (Mincer, 1994). Specifically, it has been suggested that human capital is closely related to productivity (Holland, Liadze, Rienzo, & Wilkinson, 2013). Thus, education can stimulate economic growth by enhancing human capital and increasing labour productivity. Becker and Lewis (1993) further state that higher education can reinforce human capital and improve labour efficiency via producing, diffusing, and transmitting academic knowledge. It is supplemented by Owusu-Agyeman and Fourie-Malherbe (2019) that higher education may also need to consider training skills that are required by employers. Nevertheless, Tight (2004) claims that education is supposed to be a process by which learners develop skills of critically analyzing issues, instead of a means of training practical skills for the job setting. But given the fact that most of the

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university graduates apply for jobs in private industry upon graduation, HEIs are still supposed to purposely develop students’ competencies that are needed by employers (Owusu-Agyeman, 2016). In a word, HEIs have the responsibility and ability to enhance human capital by imparting academic knowledge and training students on the skills assessed by employers. The reinforced human capital can further improve labour efficiency and productivity, contributing to economic growth. Nonetheless, from the regional perspective, due to the disparity in local economy, talented graduates prefer to migrate to economically advanced regions (Venhorst, 2013). Specifically, the migration of graduates hinders the stimulating effects of higher education on economic growth in emigration areas but benefits the immigration areas. The brain drain among regions needs to be seriously considered, especially in China, where the inequality of the regional economy is enormous (Wang and Vallance, 2015). Apart from enhancing human capital and improving labour productivity, HEIs are also deemed to play roles in lowering the unemployment rate. However, this is only if the economy can effectively utilize the mounting supply of university graduates (Wang and Liu, 2011). The oversupply of highly educated graduates also causes unemployment and jeopardizes economic development (Wang and Liu, 2011). Hence, higher education can also spur regional economic development by lowering unemployment rate but only when higher education development and economic growth are mutually reinforcing one another. 7.2.2 Scientific and technological support Another fundamental theory that underpins the statement, that education can be treated as the engine of economic growth, is the endogenous growth theory. This theory implies that knowledge plays a key role in fuelling economic development (Psacharopoulos, 2006), especially in the era of knowledge economy. On account of the continuous research carried out and new knowledge frequently generated (Wang and Liu, 2011), HEIs have always been regarded as pivotal sources of knowledge that can be utilized to achieve economic development (Huggins and Johnston, 2009). Since knowledge undoubtedly plays important role in innovation, HEIs have been valued most by the government when formulating local innovation and economic growth policies (Cooke, 2004). Governments are attaching further importance on transferring and commercializing university-generated knowledge (Kitson, Howells, Braham, & Westlake, 2009; Sainsbury, 2007) to encourage innovation and sustain regional economies (Huggins and Johnston, 2009). More specifically, universities are actively engaged in regional innovation systems and serve as “knowledge transceivers” (Huggins and Johnston, 2009, p.1090) to obtain knowledge globally and transfer locally (Cooke, 2005). Consequently, many universities have

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incorporated and emphasized regional commitment and innovation capacity in their mission statements (Lawton Smith, 2007). And it is found that students graduated from doctoral and postdoctoral programmes in HEIs are particularly willing to bring the most advanced technologies from HEIs into the industry and help to develop new products (Bradshaw, Kennedy, & Davis, 2003). Therefore, Wolf (2002) contends that higher education is essentially not an engine of economic development but still a motivator to economic growth through scientific and technological innovation. However, it is discussed that the research role of HEIs is structured to purely pursue academics, which renders HEIs face the dilemma between satisfying the needs of industry and conducting basic research (Etzkowitz and Kemelgor, 1998). Moreover, personnel in HEIs prefer to improve academic rankings instead of serving the public which can stimulate economic growth (Mattoon, 2006). This requires a trade-off between basic research and applied research in HEIs (Schalin, 2011). Experience from leading regions is that a separate publicly funded research institution needs to be established to dedicate to applied research (Huggins and Johnston, 2009). Apart from conducting applied research and serving the public, HEIs may also contribute to regional economic growth by encouraging an environment for entrepreneurship (Jones and Vedlitz, 1993) due to spatial proximity and knowledge spillover (Lawton Smith, 2007).

7.2.3 Intellectual support The term “triple helix” is often used by scholars to describe the collaborations among the government, enterprises, and HEIs (Schalin, 2011). The triple helix model regards HEIs as academic entrepreneurs (Huggins and Johnston, 2009) that are greatly engaged in activities such as founding spin-off companies (Huggins, 2008) to help the government inspect policy implementation and to provide consulting services for private firms. This relationship effectively creates a win-win-win situation for the three parties involved (Schalin, 2011). Specifically, HEIs can receive funding from the other two parties for research, while the companies can both utilize the research achievements and employ trained graduates from universities (Schalin, 2011). Most importantly, from the perspective of the government, regional economy can be sufficiently activated by the triple helix (Schalin, 2011). Theoretically, the relationship between HEIs and corresponding firms can be justified by the social capital theory (Owusu-Agyeman and Fourie-Malherbe, 2019). Sustainable development of the HEI–industry cooperation depends on the mission statement of the HEI (Fai, De Beer, & Schutte, 2018). Overall, HEIs can facilitate regional economic growth through reaching collaborations with the government and private firms, namely triple helix.

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7.3 Methodology and data used During the process of literature searching and reading, the authors found that current studies prefer to investigate the relationships between higher education and regional economic growth by adopting quantitative research methods. However, the findings of certain such empirical studies can be misleading, possibly due to methodological limitations (Baldwin and McCracken, 2013). The reasons for revealing limitations can be as follows. Firstly, it takes time for innovated workable products to be highly saleable; that is to say, it would be a long time for transferred innovations to exert economic impacts, “some of which may not occur for generations” (McMahon, 1993, p.107). Thus, ascertaining a proper lag time for research on the effect of innovations on economic growth is difficult but important. Secondly, especially when studying the impacts of higher education on economic revitalization, it is biased to evaluate such impacts from a short-term perspective (Wang and Liu, 2011). Therefore, this chapter adopts a qualitative research method, namely case study methodology, and the case study will be used to assess the strengths and weaknesses of the Indian higher educational system. All the data analyzed in this chapter is collected from official statistics in SXAU.

7.4 Results This section presents the results of the case study about the roles of SXAU in serving regional economic development in Taigu District, Shanxi Province. 7.4.1 Demand-oriented professional talent support for local agriculture and rural areas development 7.4.1.1 Recruitment of well-educated talents for “Shanxi Agricultural Valley” The construction of “Shanxi Agricultural Valley” is a strategic plan implemented by the Shanxi Provincial CPC Committee and the provincial government, aimed at promoting the long-term development of “agriculture, rural areas, farmers” and a prosperous society in a comprehensive way. As the core of this strategic plan, SXAU plays an important role in recruiting welleducated agricultural talents to meet the need of constructing the Valley. The following measures are specifically taken in this recruitment: setting up special talent funds, launching a forum for young doctors, adopting a quick recruitment mode of “one person, one policy” and a flexible employment mechanism, setting up seven academic workstations, and recruiting leading talents in the field of functional agriculture and functional food, for example, the academic Qiguo Zhao, who is the founder of functional agriculture. Besides, the recruitment of 132 PhD graduates from high-ranking

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HEIs, such as China Agricultural University and Northwest Agricultural and Forestry University, since 2016 has been accomplished. 7.4.1.2 Training of local agricultural talents Since the implementation of constructing “Shanxi Agricultural Valley”, the disciplines of “Functional Agriculture” and “Functional Food” have been set up in SXAU, which leads the cultivation of professional talents in these two subjects within China. Moreover, relevant colleges within SXAU have conducted broad research projects on functional agriculture and functional food, with the former research field being selected for the construction plan of the Shanxi Service Industry Innovation Discipline Group. The development strategies of organic dry-land agriculture, functional agriculture, and horticultural industry have been approved and optimized, promoting interdisciplinary integration and strengthening talent cultivation. With more than 110 years of development, SXAU has formed a professional discipline system with multidisciplines developing coordinatively. There are 16 teaching institutes, such as the College of Agriculture, College of Animal Science, College of Grassland Science, College of Food Science and Engineering, College of Agricultural Economics and Management (CAEM), etc. The majors cover eight categories, including agriculture, science, engineering, economics, management, literature, law, and art. There are 63 undergraduate majors, 67 master’s degree programmes, 44 doctoral degree programmes, and 8 postdoctoral research mobile stations. SXAU has trained more than 130,000 intellectuals of all kinds within the agriculture discipline. The employment rate of 2018 undergraduates is 92.29%. From the perspective of the nature of employer, “Private enterprises and Individual enterprises” are still the type of employer that absorbs the most graduates, accounting for 49.84%. From the aspect of the industry, the proportion of graduates working in “agriculture, forestry, animal husbandry and fishing industry” is the highest (13.46%; see Figure 7.2). From the standpoint of regions, due to the implementation of Rural Revitalization Strategy and the introduction of entrepreneurship policy, the regions where graduates are employed gradually show polarization. Employment in provincial capital cities has increased by 2.95% compared with the previous year (2017), while employment in rural areas also witnesses a rise of 3.68% over 2017. 7.4.1.3 Innovating the model of practical teaching and training of students’ practical ability Taking the reform of practical teaching in College of Agricultural Economics and Management of SXAU as an example, it regards the cultivation of students’ innovative and practical ability as the core, optimizes

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Higher Education Inistitutions (HEIs) Human Capital Theory

Endogenous Growth Theory

Education

Knowledge

Practical Skills

Acadamic Knowledge

Enhancing Human Capital Improving Labour Productivity

Lowering Unemployment Rate Talent Support

Innovation

Regional Innovation Systems

“Triple Helix” “Academic Entrepreneurs”

Knowledge Spillover

Technology Transformation

The Government

Private Firms

Entrepreneurship Scientific & Technological Support

Intellectual Support

Regional Economic Development

Figure 7.1 Conceptual Framework. Source: Summarized by the authors (2021).

the practical teaching links, and constructs a multi-level, progressive practical teaching system, including professional cognition practice, curriculum learning practice, social practice, economic issues investigation and training, graduation internship, graduation thesis guidance, extracurricular science and technology competition, etc. Especially from 2015 to 2017, to serve the implementation of the Poverty Alleviation Strategy and the Rural Revitalization Strategy, a “2311” Social Practice Education Model has been formed. The number “2” stands for serving two major national strategies— “resolutely win the battle against poverty” and “implementing rural revitalization”. Practical education should keep pace with the times. CAEM closely follows the two national strategies formulated by the central government and designs and organizes practical education according to the needs of economic development and specifically agricultural economic development of Shanxi Province. The number “3” stands for three kinds of practical training modes— teaching practice mode, cooperative education and training mode, and integrated practical education mode. Firstly, the teaching practice bases, from the on-campus simulation laboratory to the off-campus Shanxi Fenjiu Group, Shandong Shouguang Vegetable Expo Garden, etc., provide broad platforms for students’ professional practice. Multi-level and multi-type practice-based tours stimulate students’ enthusiasm and interests in agriculture and develop their self-learning and self-thinking ability. Secondly, formulation of the cooperative education mode takes poverty alleviation as

118  Min Zhao, Shuxia Ren, and Jun Du

the core and forms a mode of “government + base + practice” with local governments. Students are recruited as Third-Party Evaluation Groups to evaluate poverty alleviation implementation in the poor counties of Shanxi Province. This practice helps local governments promote poverty alleviation project, innovates the practical education mode towards collaboration between HEIs and government, and advances students’ ability of knowing, learning, loving, and serving agricultural industry. Thirdly, the integrated practical education mode includes projects such as local industrial plan project, rural revitalization planning project, etc. Students are required to participate in project planning, field investigation, farmer training, policy advocacy, report writing, etc. Activities within the “project-practice-person educating model” enhance students’ ability of practical innovation and awareness of serving rural areas. The number “1” stands for an innovation-deepening education and teaching system. The practical education and training modes in CAEM highlight the position of students, train students’ cognitive ability, stimulate students’ dedication, construct an open learning environment for them, create ways to obtain knowledge through various channels, provide opportunities to apply what they have learned to social practice, and promote their selfeducation and self-development capabilities. The number “1” means to achieve a goal. Students’ participation in social practice activities, such as poverty alleviation, and rural revitalization planning is both practical education and professional education of national conditions and agricultural situations, helping to realize the goal of cultivating a team of trained agricultural and rural management talents, who know agriculture and love rural areas and farmers. Given the students’ professional knowledge and familiarity with national situations, some cities and counties began to recruit our graduates with a separate channel. 7.4.2 Establishment of agricultural science and technology innovation service platforms by SXAU for local agriculture development Shanxi Province is located in the Loess Plateau, where mountainous areas account for more than 80% of the total territory of the province. Frequent drought, frost, and other natural disasters make it difficult for Shanxi to become the main production base of crops and agricultural products. However, it is a well-deserved “coarse cereal kingdom” and a high-quality dry and fresh fruit belt on the Loess Plateau in the world. Fruit, livestock, vegetables, traditional Chinese herbs, edible fungi, and other specific agricultural products are the leading industries of Shanxi. To cope with the needs of regional economic and industrial development of Shanxi, SXAU has established scientific research and innovation service platforms to supply better scientific and technological support for local economy.

The roles of agricultural universities  119

7.4.2.1 The resources of SXAU SXAU is a well-known century-old agricultural university in China. It is a co-funded university by Shanxi Provincial Government and the Ministry of Agriculture of the PRC as a national agricultural and rural informatization demonstration base. It has a national technical innovation centre for coarse cereals, a scientific observation and experimental station for crop cultivation, and cultivated land conservation in the Loess Plateau region of north China, supervised by the Ministry of Agriculture. A comprehensive experimental station for the long-term treatment of the Chinese herbs industry technology system and the Ministry of Agriculture Modern Seed Industry Enhancement Project—“pear’s original seed preservation and cultivation base”—are all launched in SXAU. There are one province-level collaborative centre, four collaborative innovation centres, three key laboratories, nine engineering (technology) research centres, four industrial technological innovation research institutes (strategic alliance), one key innovation team, and one key research base of humanities and social sciences in Shanxi Province. In addition, there are Genetically Modified Biological Product Composition Supervision and Testing Center, Pesticide Registration Test Qualification Unit, the national Computer Quality Supervision and Inspection Center (Shanxi branch), and six other qualification centres of the Ministry of Agriculture. SXAU has advantages in more than ten research fields, such as wheat, soybean, grain, sheep, alpaca, grassland ecology, veterinary medicine, edible fungi, etc. There are 4 scientists in charge of the national modern agricultural industry technology system, 5 chief experts, and 22 position experts in the provincial modern agricultural industrial system. There are 2 top young scientists of Shanxi Province, 4 leaders in disciplines and technology, 3 leading talents in emerging industries, 18 outstanding young academic leaders, and 8 scientific and technological innovation teams. From 2013 to 2017, more than 900 scientific and technological achievements have been achieved.

7.4.2.2 Establishment of agricultural science and technology innovation service platforms Focusing on the development strategy of organic dry-land agriculture, functional agriculture (food), and specific agriculture in Shanxi Province, SXAU promotes scientific and technological innovation. The university also focuses on the research of industrial technology of main functional crops and processing technology of functional foods. The integrated technology system of wheat, grain, water, and fertilizer with high efficiency, machinery and skill matching, and simplified cultivation process was established. The firstclass international millet genome database was established; a new variety

120  Min Zhao, Shuxia Ren, and Jun Du

of high-quality millet strain enriched with stomach-nourishing components and a high-quality and high-yield millet strain suitable for mechanized cultivation were cultivated. Functional foods such as edible fungi, dairy products, coarse crops, brewed products, and others were studied. More than ten products, such as selenium-rich millet, selenium-rich noodle, miscellaneous grain Taigu cake, quinoa rice wine, instant food, millet porridge etc., have been developed. Fifteen national invention patents, 46 utility model patents, and 8 national standards have been approved. According to the requirements of “International Vision and National Standard”, SXAU has launched R&D platforms such as Shanxi Functional Agriculture Research Institute, Shanxi Functional Food Research Institute, Shanxi Functional Food Quality Inspection and Testing Center, National Functional Grain Technology Innovation Center, Grain Functional Genome and Big Data Center, etc. Besides, SXAU has organized multidisciplinary scientific research teams and integrated many disciplines with superior scientific research strengths, conducting scientific and technological research, and undertaking a number of national and provincial key scientific and technological innovation projects, such as Shanxi Functional Agriculture Key Technology Research and Demonstration Project. Shanxi Industrial Technology Research and Demonstration Area of Main Functional Crops Project focuses on key technology research and functional food processing, setting up a high-tech innovation service platform for the development of functional agriculture and modern agriculture in Shanxi in the fields of grain, animal husbandry, intensive processing of agricultural products (food), and other resources. 7.4.3 Continuous scientific and technological achievements transformation and promotion services 7.4.3.1 Setting up specialized technical service and promotion institutions to enhance the overall planning of social services The “Agriculture, Countryside and Farmers” Service Center is the management institute of agricultural scientific and technological achievements promotion and social services in SXAU. It is responsible for the overall coordination of scientific and technological services and promotion. In 2013, the centre merged with the Office of the New Rural Development Research Institute of SXAU. As an independent department, it is equipped with full-time staff, strengthening the design, organization, assessment, promotion, and outreach of social service works in SXAU. There are hundreds of technology promotion teams in 38 fields, including more than 600 academics and more than 1,000 doctoral students, postgraduates, and undergraduates. From 2015 to 2017, more than 40 new technologies have been popular in cooperation with more than 80 agricultural enterprises and more

The roles of agricultural universities  121

than 1,000 agricultural management subjects in 52 counties and districts, benefiting more than 1 million farmers. In 2018, 161 advanced applicable technologies and 80 new varieties were popularized, supporting nearly 100 characteristic products such as Taihang millet, jujube Fengbao, Chinese dwarf cherries series, spore powder, beehive honey, etc. 7.4.3.2 Shaping outstanding social services teams to solve agricultural economy issues During the process of technology promotion for many years, 15 outstanding social service teams have been shaped, including Planning and Evaluation Team from CAEM, Traditional Chinese Herb Team from College of Life Sciences, Edible Fungus Team of College of Food Science and Engineering, etc. These teams have participated in many activities, such as “One Village, One Product, One County and One Industry”, “Special Actions on Science and Technology Poverty Alleviation”, “Poverty Alleviation Implementation Third-Party Evaluation”, etc., contributing greatly to rural revitalization of Shanxi. They have not only been favourably received by the industry and the provincial government but also won a good reputation for SXAU within the region. 7.4.3.3 Closely following the Poverty Alleviation Strategy and Science and Technology Poverty Alleviation There are 119 counties, with 58 of them in Shanxi, which constitute the main battlefield for China to extricate itself from poverty. SXAU has set up a leading group on accurate poverty alleviation, headed by the secretaries and deans of colleges, with science and technology, education and industry as the main focuses, and talents as the main support to help farmers solve technical problems in production and life. In recent years, SXAU has selected and sent 31 poverty alleviation teams to carry out long-term designated support to poor areas. More than 200 cadres and teachers carry out poverty alleviation work in villages. In 2019, SXAU selected 5 people to work as deputy county governors, 5 people as first secretaries in rural areas, and sent 200 scientific and technological personnel. More than 5,000 graduate students and undergraduates went to poor counties to carry out technical guidance and cumulative promotion of various new varieties and new technologies. Moreover, SXAU has closely followed the Poverty Alleviation Strategy by conducting six specific forms of poverty alleviation, namely Special Technology Poverty Alleviation, Targeted Poverty Alleviation by Special Funds, Education Poverty Alleviation, Intelligence Poverty Alleviation, sending special science and technology commissioners, and Consumption Poverty Alleviation, providing necessary driving force for regional economic growth.

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7.4.4 Effective interactions between SXAU and local economy under the “Five-in-One” Model 7.4.4.1 “Five-in-One” Service Model The “Five-in-One” Model contains five entities including SXAU, the government, agricultural technology promotion departments, agricultural science and technology enterprises, and agriculture operation subjects. Social service of HEIs is a new force, with its advantages lying in motivating effective collaborations with the government, agricultural technology promotion departments, agricultural science and technology enterprises, and agriculture operation subjects. Agricultural universities provide experts and technical support, while governmental departments provide organizational and financial support, and experts from agricultural technology promotion departments cooperate organically. Besides, agricultural science and technology enterprises provide new products and new varieties, and agriculture management subjects implement technical demonstration. SXAU has broken the campus “wall” and promoted deep integrations of “political, industrial, academic and research”. Specifically, SXAU has established cooperative relationships with relevant governmental departments in 52 counties and districts in 11 prefectures and cities of the province, more than 80 agricultural enterprises, more than 1,000 agriculture management subjects, and jointly participated in social services and agricultural technology promotion works, directly or indirectly benefiting more than a million farmers. 7.4.4.2 “Three Service Carriers” The “Three Service Carriers” refer to scientific and technological service and technical training carrier with the experts involved as the main mode; the technical demonstration and popularization application carrier with the demonstration base as the main mode; and the market docking and talent training carrier with the mass entrepreneurship and innovation base of university students as the main mode. In recent years, five expert academies and two bases have been established in Zezhou County of Jincheng City, and an expert compound has been established in Wenxi, which trains more than 2,000 local agriculture practitioners every year. Five agricultural comprehensive demonstration bases, 25 industrial demonstration boutique bases, and 40 industrial demonstration bases have been built in Shanxi, and more than 40 new varieties have been popularized. In cooperation with Shanxi Juxin Agricultural Science and Technology Co., Ltd. and Shanxi Xuefeng Xing Agricultural Science and Technology Co., Ltd., SXAU has built a University Student Entrepreneurship Park and an “Internet” Agricultural Entrepreneurship Park, which have made obvious achievements in mass entrepreneurship and practical innovation education for students in SXAU.

The roles of agricultural universities  123

7.4.4.3 “Two Achievements” The “Two Achievements” are as follows. First, SXAU has achieved remarkable results in serving the transformation and upgrading of local industries and improving the level of science and technology. The main results are that SXAU has effectively promoted the proposal and construction of “Shanxi Agricultural Valley”, and at the same time it supported Taigu District to obtain the first batch of state-level agricultural high technology zone. Second, SXAU has achieved remarkable results in promoting scientific research and practical education, so that mass entrepreneurship and practical innovation education in SXAU have led to its winning the honorary titles, such as the First Batch of Mass Entrepreneurship and Innovation Demonstration Colleges and Universities in China and the Second Batch of Mass Entrepreneurship and Innovation Demonstration Bases in Shanxi Province. SXAU adheres to the service mission of “writing papers on the land and popularizing science and technology to the land of Shanxi”. SXAU has been serving “agriculture, rural areas and farmers”, actively promoting the “Five-in-One” social service model, building the “Three Service Carriers”, highlighting the “Two Major Achievements”, effectively giving full play to the roles of agricultural social services, and making positive contributions to the agricultural efficiency and farmers’ income in Shanxi Province. And it will continuously promote the sustainable development of itself and local economy and maintain the win-win situation between universities and local agricultural economy.

7.5 Discussion As is illustrated in Figure 7.1, the common roles of HEIs in stimulating regional economic development can be categorized into three dimensions, namely talent support, scientific and technological support, and intellectual support. Nevertheless, it needs to be reminded that the migration of graduates causes the brain drain which may hinder the stimulating effects of HEIs on local economy, and the oversupply of tertiary educated individuals also jeopardize economic growth. Moreover, research funding may vary among HEIs, rendering the disparity of research and innovation capabilities within HEIs and the exaggeration of regional inequality. By implicitly adopting the conceptual framework to analyze the case of SXAU, it can be stated that SXAU, as a publicly funded agricultural university in China, is a strong performer in serving local economic development from all three aspects. The university is not only highly committed to its mission statement of becoming both a research-oriented and an application-oriented university and proactively engaged in social services and regional innovation system but also developing its own characteristics of serving regional economy.

124  Min Zhao, Shuxia Ren, and Jun Du

Farming, Forestry, Animal Husbandary and Fishery Information transmission, Computer Services and Software Industries Education Other Construction Public Administration and Social Organization Scientific Research, Technical Services and Geological Exploration Banking Manufacturing Industry Wholesale and Retail Trade Real Estate Culture, Sports and Entertainment Production and Supply of Electricity, Gas and Water Water, Environmental and Public Facilities Management Transport, Warehousing and Postal Services Hotels and Catering Servies Leasing and Business Servies Mining Health, Social Security and Welfare Neighborhood Services 0.00%

5.00%

10.00%

Figure 7.2 Distribution of 2018 Employed Graduates among Industries. Source: Official statistics of Shanxi Agricultural University for internal reference only (2018).

Given that both India and China are developing countries in Asia and have both encountered the problem of regional economic disparity (Jha and Kumar, 2017), the roles of HEIs in spurring local economic growth can be attached further importance and utilized to mitigate regional inequality. Moreover, the practices of SXAU can be foremost used to assess the strengths and weaknesses of the Indian higher educational system so that certain policy implications could be laid out in the next section to employ the roles of HEIs in fostering regional economic growth. From the perspective of talent support, although certain Indian HEIs, for instance Indian Institutes of Technology, provide vocational and technical education, seeming to enhance human capital, the textbooks used in HEIs are highly focused on theoretical knowledge while ignoring cultivating the practical ability of students. Besides, India, as one of the largest food producers, has 10% of the global arable land, the number of students enrolled in agriculture-related disciplines only takes 1%. Such unbalanced distribution of students among disciplines probably limits the sustainable development of the national economy. From the view of scientific and technological support, Indian HEIs meet the same problem as Chinese HEIs that research funding varies among universities, leading to the shortage of funding, high-quality academics, and

The roles of agricultural universities  125

research activities in public HEIs. Nevertheless, the top-class universities, notably the Indian Institutes of Technology, can not only receive funding from the government but also have a well-developed alumni network which allows expanding channels of obtaining funding. From the point of collaboration with stakeholders, the Indian government encourages HEI–industry relationships but the governmental investment in higher education is insufficient, with the higher education budget only taking 1.25% of Indian GDP in 2017. To summarize, compared with SXAU, which can be a representative of Chinese HEIs, the Indian HEIs have both advantages and disadvantages in exerting stimulating effects on regional economic development.

7.6 Conclusion and policy implications Based on the discussion above, four policy implications could be provided for India and possibly other developing countries in Asia. Firstly, the cultivation and training of talents in HEIs is supposed to take the needs of local economic development into account and serve national economic growth policies. Secondly, the development of colleges and universities students’ practical ability requires to be attached further importance. Thirdly, it could be a meaningful attempt to train students collaboratively by the government, private firms, and the HEIs. Fourthly, governmental spending on higher educational systems is expected to increase so as to encourage research activities and innovation and to attract high-quality academics. An additional policy implication that could be given for SXAU is that an alumni network is a worthy endeavour. To conclude, this research investigates the roles of HEIs, especially agricultural universities, in serving regional economic development by applying case study methodology. The case of SXAU is analyzed by applying the conceptual framework derived from the literature review and is used to assess the strengths and weaknesses of Indian higher educational system. Based on the discussion, four policy implications are provided for India and other developing countries in Asia, and one additional policy implication is given for SXAU, hoping it sustainably serve the local economy.1

Note 1 We would like to express our deep and sincere gratitude to Shanxi Agricultural University, our families, and our friends for all the support received during the writing of this chapter. This work is supported by The Annual Shanxi Province Philosophy and Social Science Planning Project in 2020 – Thoroughly Implementing the Instructions of General Secretary Xi Jinping on a Keynote Speech during the Inspection of Shanxi Province: Studies on Improving Agriculture Comprehensive Effectiveness and Competitiveness in Shanxi Province based on the “Specialty” and “Excellence” (Project Number: 2020ZD016); The Scientific Research Funding Program for the

126  Min Zhao, Shuxia Ren, and Jun Du Returned Overseas Chinese Scholars by Department of Education of Shanxi Province – Studies on the Establishment of Platforms for the Exportation of Coarse Cereals in Xinzhou Shanxi (Project Number: 2020-071); and The Shanxi Federation of Humanities and Social Sciences Annual Key Project Research Program 2020-2021 – Studies on the Development of Agricultural Insurance within Small Farmers in Shanxi Province (Project Number: SSKLZDKT2020058).

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The roles of agricultural universities  127 Jones, B., & Vedlitz, A. (1993). Higher education, business creation, and economic growth in the American States. In Higher Education and Economic Growth. Kluwer Academic Publishers. Kitson, M., Howells, J., Braham, R., & Westlake, S. (2009). The Connected University: Driving Recovery and Growth in the UK Economy. National Endowment for Science, Technology and the Arts. Lawton Smith, H. (2007). Universities, innovation, and territorial development: A review of the evidence. Environment and Planning C: Government and Policy, 25, 98–114. Li, W. K. (2020). Exploration and practice of rural revitalization in higher agricultural colleges: Take the Northeast Agricultural University as an example. China Agricultural Education, 21(6), 10–16. Mattoon, R. (2006). Higher education and economic growth. Chicago Fed Letter, 222b, 1–7. McMahon, W. (1993). The contribution of higher education to R&D and productivity growth. In Higher Education and Economic Growth. Kluwer Academic Publishers. Mincer, J. (1994). The production of human capital and the lifecycle of earnings: Variations on a theme. National Bureau of Economic Research, NBER Working Paper #4838. Ogunnubi, O., & Shawa, L. B. (2017). Analyzing South Africa’s soft power in Africa through the knowledge diplomacy of higher education. Journal of Higher Education in Africa, 15(2), 81–108. Owusu-Agyeman, Y. (2016). Investigating the determinants of adults’ participation in higher education. Cogent Education, 3(1), 1–20. Owusu-Agyeman, Y., & Fourie-Malherbe, M. (2019). Workforce development and higher education in Ghana: A symmetrical relationship between industry and higher education institutions. Industry and Higher Education, 33(6), 425–438. Psacharopoulos, G. (2006). The value of investment in education: Theory, evidence, and policy. Journal of Education Finance, 32(2), 113–136. Sainsbury, D. (2007). The Race to the Top: A Review of Government's Science and Innovation Policies. The Stationery Office. Schalin, J. (2011). State investment in higher education: Rethinking the impact on economic growth. In A Way Forward: Building a Globally Competitive South. University of North Carolina Press. Shin, J. C. (2012). Higher education development in Korea: Western university ideas, Confucian tradition, and economic development. Higher Education, 64(1), 59–72. The 19th National Congress of the Communist Party of China. (2018). Available at: http://www​.gov​.cn​/zhengce​/2018​-02​/04​/content​_5263807​.htm. Tight, M. (2004). Key Concepts in Adult Education and Training. Routledge. Venhorst, V. A. (2013). Graduate migration and regional familiarity. Tijdschriftvoor Economische en Sociale Geografic, 104, 109–119. Wang, X., & Liu, J. (2011). China’s higher education expansion and the task of economic revitalization. Higher Education, 62(2), 213–229. Wang, X., & Vallance, P. (2015). The engagement of higher education in regional development in China. Environment and Planning C: Government and Policy, 33, 1657–1678.

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Part 3

Demographic dividend, joblessness, and informality



Chapter 8

Harnessing India’s demographic dividend The way forward Jajati Keshari Parida

8.1 Introduction Indian economy had been passing through a phase demographic dividend, in which the share of working population was the highest (Aiyar and Mody, 2011; Joe et al., 2018; Mehrotra, 2015). But with the falling total fertility1 rate, and the rising share of the elderly population (it increased from 8.2 percent to about 10.5 percent during 2011–2012 and 2019–2020), it is expected that this demographic advantage is going to be over soon.2 In this context, the decline of overall labour force participation rates (Kannan and Raveendran, 2019; Parida, 2015; Mehrotra and Parida, 2021) and an upsurge in youth unemployment (Mehrotra and Parida, 2019, 2021) are certainly put as questions on the process of harnessing the demographic dividend in India. Hence, the main question of this study is what could be the possible ways to harness the demographic dividend in India? The main objectives of this chapter are to examine the pattern of youth unemployment in India and to identify the sectors in which educated youth could be accommodated. Moreover, this chapter provides a discussion on the effectiveness of the previous policy measures and suggests measures to reduce the extent of youth unemployment in order to sustain the process of economic growth to harness the demographic dividend in India. This chapter is organized into six sections. Section 8.2 provides the sources of data. Section 8.3 provides the context of the demographic dividend in India, while Section 8.4 explains the broad sectoral youth employment trends, quality of jobs and the situation of rising unemployment and its implications on poverty in India. Section 8.5 provides a discussion on the effectiveness of past and present initiatives of the government. It also outlines a road map that could help tackle the problem of rising youth unemployment so as to sustain economic growth in India. Finally, Section 8.6 concludes the chapter.

DOI: 10.4324/9781003329862-11

132  J. K. Parida

8.2 Sources of data This chapter is based on secondary data. The Employment and Unemployment Survey (EUS) and Periodic Labour Force Survey (PLFS) of the National Sample Survey (NSS) and Census population data are used. Furthermore, the enterprise surveys conducted during 2010–2011 (67th round) and 2011–2012 (73rd round) are also used. The indicators like workforce, labour force, unemployed, Not in Labour Force Education and Training (NLET), labour force participation rate and unemployment rates are calculated using both EUS and PLFS data. These estimates are adjusted further to the Census population3 to obtain absolute numbers. Employment and unemployment figures are computed using the Usual Principal and Subsidiary Status (UPSS) of persons. The sectoral employment figures are calculated using the National Industrial Classification (NIC) codes. Formal– informal employments are computed using the information on enterprise type, number of workers in the enterprise, types of job contracts, availability of social security benefits etc. The enterprise survey is used to provide a broad picture of informality by estimating the number and share of unregistered and informal enterprises.

8.3 The context of demographic dividend in India The total population in India has continued to increase from about 0.89 billion to about 1.33 billion during 1993–94 and 2018–2019 (Table 8.1). During this period, the number of youth (age group 15–29 years) has increased from about 240 million to 362 million, while the number of elderly population (aged 60 years and above) has also increased from 59 million to about 135 million (a massive rise during the post-2004–2005 period). It is noted that while the growth of the total and youth population has declined marginally (between 1993–94 and 2004–2005 and post-2005 periods), the growth rate of elderly population is actually increased. The growth rate of the total population has declined from 2.1 percent per annum during 1993–94 and 2004–2005 to 1.5 percent per annum during 2004–2005 and 2018–2019. Similarly, the growth rate of youth population has declined from 1.9 percent per annum during 1993–94 and 2004–2005 to 1.8 percent per annum during 2004–2005 and 2018–2019. In contrast, the growth rate of elderly population has increased from 3 percent per annum during 1993–94 and 2004–2005 to 5.1 percent per annum during 2004–2005 and 2018–2019. While the falling growth rate total fertility rate during the post-2001 Census period could be the reason for the falling growth rate of youth population (and overall population), an improved life expectancy and the falling death rate due to an improved healthcare system could be the reason for the high growth rate of elderly population in India (Aiyar and Mody, 2011; Joe et al., 2018).

1999–2000

1007.0 399.5 9.0 264.8 137.4 7.8 70.9 27.0 0.03

891.6 374.0 7.2 239.8 133.7 6.4 59.1 25.3 0.01

Total population (all ages)

1993–94

Absolute numbers (million)

78.6 30.9 0.05

289.2 154.1 8.9

1092.8 459.1 10.8

2004–2005

100.3 37.1 0.03

329.6 138.0 9.0

1227.6 474.2 10.6

2011–2012

134.8 38.7 0.12

361.8 113.8 23.9

1328.1 468.8 29.0

2018–2019

Note: UPSS, Usual Principal and Subsidiary Status. Source: author’s estimation based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data, and Census projected population for respective survey years.

Population Workforce (UPSS) Unemployed (open) Youth (aged 15–29 years) Population Workforce (UPSS) Unemployed (open) Elderly (aged 60 years and above) Population Workforce (UPSS) Unemployed (open)

Indicators by age groups

Table 8.1 Demographic and employment profile of India, 1994–2019

Harnessing India’s demographic dividend  133

134  J. K. Parida

Moreover, it is noted that the share of elderly population has consistently been rising in India. That means the Indian economy is now approaching quickly towards an ageing society. In India, the share of elderly population was 6.6 percent in 1993–94, which had increased to 7.2 percent during 2004–2005. It further increased to 8.2 percent during 2011–2012 and to 10.2 percent during 2018–2019, respectively (Figure 8.1: Panel B). On the other hand, the share of elderly population in the total workforce has also increased from about 7 to 8.2 percent during 1993–94 and 2018–2019 (Figure 8.1: Panel B). The share of the elderly in the total workforce is likely to increase further as the Indian economy gradually advances towards an ageing society (Mehrotra, 2015), because a large proportion of the total workforce in India is either engaged in agriculture and allied activities or in the low-paid informal service sectors. In contrast, the share of youth in the total workforce has constantly been declining despite the fact that their share in the total population still

Panel A: Share of youth (age 15 to 29 years) in total population and workforce 40

35.8

% of Youth

35 30

26.9

Population

34.4

33.6

26.3

26.5

Workforce

29.1

25

27.2

26.9

24.3

20 15

1993-94

1999-00

2004-05 Year

2011-12

2018-19

Panel B: Share of elderly (age 60 years & above) in total population and workforce

12 % of Elderly

10 8

Population 6.6 6.8

10.2

Work force 7.0 6.8

7.2 6.7

8.2 7.8

8.2

6 4 2 0 1993-94

1999-00

2004-05

2011-12

2018-19

Year

Figure 8.1 Share of youth and elderly in total population and workforce in India, 1994– 2019. Source: author’s estimation and plot based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data, and Census projected population for respective survey years

Harnessing India’s demographic dividend  135

continues to rise. The share of youth in the total population increased from 26.5 percent to about 27.2 percent during 2004–2005 and 2018–2019. But their share in the total workforce has declined from about 36 to 33.6 percent during 1993–94 and 2004–2005, and further to 29 percent during 2011–2012 and to 24.3 percent during 2018–2019, respectively (Figure 8.1: Panel A). The falling share of the youth population in the total workforce could be due to both supply and demand side factors. On the supply side, a large number of youth (both boys and girls) were attending higher education (Mehrotra and Parida, 2019). Hence, they could be remained out of the labour force for a considerable period of time (but a portion of them had already started joining the labour force, which had been reflected by a massive increase in educated unemployment). The negative income effect due to the rising standard of living, falling incidence of income poverty (Chauhan et al., 2016) and stagnant real wages (Mehrotra and Parida, 2021) might have discouraged many youths from joining the labour force. On the other hand, demand-side factors like lack of industrialisation, slow growth of the manufacturing sector, falling investment and output growth in the infrastructure sector, skill mismatch issues (NSDC, 2013; Mitra, 2013; Mehrotra, 2014; Singh & Parida, 2020) and growing mechanisation/ automation in agriculture (Kannan & Raveendran, 2012; Mehrotra et al., 2014) might have negatively affected the growth of youth employment in both rural and urban India. As a consequence, the number of open4 unemployed youth increased massively to an unprecedented high level in recent years. While total unemployment (based on UPSS) increased from about 7 million to about 11 million during 1993–94 and 2011–2012, it increased even more rapidly during post-2012 periods to reach 29 million. Among the total unemployed, youth alone constitutes about 24 million (about 82.5 percent). This is an indication of the labour market crisis that the Indian economy is currently passing through. Unless this problem is addressed, at the earliest, it is likely to affect economic growth. This will have negative implications on the process of harnessing the demographic dividend in India. The sectoral youth employment patterns and their unemployment scenario are explored in the next section in detail.

8.4 Rising youth employment challenges in India 8.4.1 Sectoral employment trends The demographic advantage that the Indian economy is currently having is characterized by a rise in the share of the working-age population (and youth) in the total population (Mehrotra and Parida, 2019). But the falling total and youth employment is the indication of the crisis, which the

136  J. K. Parida

Indian economy is currently passing through. While youth unemployment increased by about 2.1 million per annum) during 2011–2012 and 2018– 2019 (Table 8.1), the number of youth in the workforce actually declined by 3.5 million per annum during the same period (Table 8.2). This implies the fact that those who are completing their education and training are not joining the workforce. The depth of the labour market crisis could also be explained by the rate of decline in youth employment. The youth workforce had declined by 3.2 million per annum during 2004–2005 and 2011–2012, but during the last decade (between 2011–2012 and 2018–2019) it declined by 3.5 million per annum (Table 8.2). As a result, youth employment had declined from about 154 to 114 million during 2004–2005 and 2017–2018. The sectors that contributed to this decline are explained below. Agriculture and allied sector alone has contributed the maximum to the decline of youth workforce. This is as expected, and it is good news. With an improved level of education, youth are expected to go for non-farm sector jobs. The rising agricultural mechanization and growing enrolments in higher education and training are among the main reasons behind this trend. Youth employment in agriculture declined at the rate of 3.1 million per annum (about 22 million decline in total) during 2011–2012 and 2017– 2018, in addition to a 3.6 million per annum decline (about 25 million decline in total) during 2004–2005 and 2011–2012. During this period manufacturing sector also recorded a 4.7 million decline in jobs for youth (in addition to 0.3 million job losses during 2004– 2005 and 2011–2012). As a result, the share of employment of youth in the manufacturing sector has declined from 16 to 15 percent. According to Mehrotra and Parida (2019) “falling manufacturing jobs is the opposite of the goal of ‘Make in India’, and the opposite of what is desirable if the process of structural transformation is to be sustained to harness the demographic dividend in India”. Though labour-intensive sub-sectors of the manufacturing sector, viz., wearing apparel, textiles, furniture, non-metallic mineral products and wood products (Parida, 2015) together have contributed to the growth of employment opportunities for the overall population, these sub-sectors have failed to attract the educated youth. Because these labour-intensive sub-sectors mainly accommodate low-skilled job seekers. Moreover, the quality of jobs in these sectors is poor (without written job contracts and provision of social security). Hence, it is questionable whether the registered segment of these sub-sectors will help generate enough jobs to accommodate youth job seekers. The non-manufacturing sector (mostly construction and utilities), which has been creating about 1.1 million per annum jobs for the youth populace during 2004–2005 and 2011–2012, has started registering a decline of (0.2 million per annum) youth workforce during 2011–2012 and 2017–2018 (Table 8.2). This declining youth employment in the construction and utility

85.7 22.4 11.6 34.5 154.2 163.1 56.8 69.5

60.7 22.1 19.4 35.7 138.0 147.0 99 83.7

39.0 17.4 18.0 39.4 113.8 137.7 126.6 97.5

55.6 14.5 7.5 22.4 100 – – –

44.0 16.0 14.1 25.9 100 – – –

2011–2012

2004–2005

2018–2019

2004–2005

2011–2012

Share of employment (%)

Absolute number of youth (million)

34.3 15.3 15.8 34.6 100 – – –

2018–2019

Note: NLET, Not in Labour Force Education and Training. The bold values provide the total/aggregate values. Source: author’s estimation based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data, and adjusted to Census population.

Agriculture Manufacturing Non-manufacturing Service Work Force (WF) Labour force (LF) Participating in education Youth NLET

Broad sectors

Table 8.2 Sectoral youth employment trends in India, 2005–2019

Harnessing India’s demographic dividend  137

138  J. K. Parida

sectors is an outcome of the slowdown of the infrastructure sector’s growth rate. This implies the fact that the low-skilled youth job seekers (illiterates or having up to the primary and middle level of education) are also not finding alternatives, particularly those who tend to lose their livelihoods (in rural and suburb areas) due to growing mechanisation in agriculture. Hence, those who are leaving agriculture will also likely join the group of unemployed youth to form a “reserve army”. The only sector which registered a positive growth of jobs for youth is the services. The service sector alone has registered about 0.2 million per annum growth of jobs for youth during 2005–2012, and about 0.5 million per annum during 2011–2012 and 2018–2019. Hence, this sector is likely to play a crucial role in creating employment opportunities for educated job seekers, in the years to come. But the quality of jobs in this sector is mostly poor (Mehrotra and Parida, 2019), gig jobs and jobs without a written contract or social insurance. To attract educated youth towards this sector, the formalization of jobs is important. 8.4.2 On the quality of jobs, earnings and poverty

Share of Informal Employment (%)

The poor quality of jobs in both industry (manufacturing and non-manufacturing together constitute industry) and service sectors may discourage educated youth from entering the job market, particularly, those youth who still play the role of added family labour, instead of the breadwinner. A positive development towards formalizing the Indian economy has been noticed. The share of informal5 employment in both industry and service sectors has declined marginally during 2004–2005 and 2017–2018 (Figure 8.2). This reduction needs to be promoted further.

100

90.9

97.8

90.0

85.7

80

97.6 79.8

97.5

94.7

92.6

89.7

71.4

60 33.3

40 20 0

11.1

40.0 16.8

14.3

2004-05 2011-12 2017-18 MANUFACTURING

2004-05 2011-12 2017-18 NON-MANUFACTURING Govt./public sectors

20.4

2004-05

28.3

2011-12 2017-18 SERVICES

Private sectors

Figure 8.2 Share of informal employment (%) in the non-farm sectors in India. Source: author’s estimation and plot based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data

Harnessing India’s demographic dividend  139

In this context, the rising share of informal employment in the government or public sector enterprises across the sectors (Figure 8.2) needs to be given special attention. Because this has happened during the last one and half decades, and it contrasts with the objective of achieving decent jobs in India. In India, the government or public sector enterprises contribute negligibly to the overall growth of non-farm sector jobs in India. Only about 13 percent of total non-farm workers are employed in government or public sector enterprises. Since a substantial proportion of jobs is created by private sectors, measures to formalize private sector jobs will help achieve the objective. In the private sector, among the regular salaried workers, still about 90 percent of them are hired without any written job contracts or with a written contract for less than a year (Mehrotra and Parida, 2019). This implies that a substantial change on the ground is yet to happen to reduce the volume of informality, which seems to be a challenging job in the context of changing labour market conditions and employment relations due to globalisation. However, the Chinese have succeeded in reducing their informality through an improvement in the labour force participation rate of both men and women. They have achieved it through a structured industrial and skilling policy along with a few reform measures. India could also have achieved this by now, had a structured and strategic industrial policy been adopted in the past (during the early 1980s or 1990s). But unfortunately, we are still far away from these measures until today. This poor quality of jobs is further reflected by the very low average daily earning/wage level of the workers (Figure 8.3: Panels A–D). On average, casual workers and self-employed workers earn less than Rs. 200 per day (nominal wage rate during 2018–2019). While self-employed employers tend to earn about Rs. 500 per day on average, regular salaried workers earn even less than Rs. 500 per day (as in 2017–2018). This should be worrying as workers in the unorganized sector and informal workers in the low-skilled service sectors and industry are more vulnerable to the risk of retrenchment in case of the slightest economic shock. At the same time, labour laws will need to be re-examined by state governments if this trend towards informal employment is to be stemmed. Because of the poor quality of jobs and low level of earnings, a higher share of the Indian labour force is living below the poverty line. Though development programmes like MGNREGA helped improve the wage rates (Shah et al., 2015) and had a knock-on effect on the standard of living of the rural populace (Mehrotra et al., 2014), it had never sustained the poverty reduction because of informality. As the employment contract and earning level of the workers in the informal sector are uncertain, in the changing labour market conditions they are more vulnerable to income poverty. This is clearly evident in the case of the persons, who are engaged as casual

.001

.0015

0

0

3000

Daily Earning (Rs)

2000

4000

1000

Daily Earning (Rs)

2000

3000

Panel B: Self-employed (Employers)

1000

4000

5000

Freq. Density

Freq. Density

0

.002

.004

.006

.008

0

5.0e-04

.001

.0015

.002

0

0

2000 Daily Wages (Rs.)

3000

500

Daily Wage (Rs.)

1000

1500

Panel D: Casual Employees

1000

Panel C: Regular salaried Workers

2000

4000

Figure 8.3 Daily earnings/wage (Rs.) of workers by their types of employment, 2017–2018. Source: Author’s estimation and plot based on Labour Force Survey (LFS) unit-level data, 2017–2018

0

.001

.002

.003

0

5.0e-04

Freq. Density

Panel A: Self-employed (Own A/C Workers)

Freq. Density

.002

140  J. K. Parida

Harnessing India’s demographic dividend  141 Table 8.3 People living below the poverty line (BPL) by their employment status Employment status

Percentage of people living below the poverty line (BPL) 2004–2005

2011–2012 2017–2018

Types of employment Self-employed (Own Account 35.6 Workers plus unpaid family helpers) 6.5 Self-employed – employers Regular salaried workers 18.8 Casual workers 56.7 Unemployed 29.4 43.1 Not in labour force Employment status (formal vs informal) Formal jobs 7.9 Informal jobs 42.1 Total 41.2

24.8

35.9

4.8 10.2 38.4 20.8 29.6

9.4 16.7 44.2 31.1 38.2

4.7 28.8 28.1

7.1 37.2 36.3

Source: author’s estimation on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data.

labour and as self-employed (Table 8.3). The incidence of poverty among these informal workers declined during 2004–2005 and 2011–2012, but it increased again during the post-2011–2012 periods. It is expected that informal sector workers would not be able to spend a higher share of their income (which is transitory in nature) on the improvement of the level of human capital (on quality education and training) of their family members. Hence, their family members are less likely to access quality (formal sector) jobs that could reduce their household-level poverty on a sustainable basis. Though the quality of jobs matters for harnessing the demographic dividend, it is still better to get an informal job instead of being unemployed. If a large chunk of the working-age population remains unemployed, it will be the worst scenario for the demographic dividend.

8.4.3 Educated youth unemployment is at the rise In India, the youth unemployment rate increases with rising levels of education and training. This contrasts with the belief that rising years of schooling will help improve youth employability. The overall youth unemployment rate (based on UPSS) in India increased from 6.1 to 17.4 percent during 2011–2012 and 2018–2019. The number of open unemployed youth (Table 8.2) also jumped from 9 to 24 million (difference between youth LF and WF [Work Force]). For each level of education, the unemployment rate increased during 2011–2012 and 2017–2018 (Figure

142  J. K. Parida

Panel A: Youth unemployment rates by level of education (in %) 40 30

23.8

21.3

19.2

20

14.4

13.7 8.3

7.1

10

3.0

1.7 0

36.2

35.8

Illiterate

Upto Primary

37.3 33.0

18.8

18.5

10.8 6.1

5.9

4.5

17.8

Middle Secondary Higher Graduate Secondary 2011-12

PG and Above

With Vocational Technical Education degree

Total

2017-18

Absolute number (million)

Panel B: Youth unemployed and NLET in India (million) 150 100 50 0

100.2

83.7

69.5

8.9

9

2004-05

2011-12 Unemployed

25.1

2017-18 NLET

Figure 8.4 Youth unemployment and Not in Labour Force Education and Training (NLET) situation in India. Source: author’s calculation and plot based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data

8.4: Panel A). Among illiterates, it increased from about 2 to 7 percent. For youth having up to the primary level of education, it increased from 3 to 8 percent. For middle school education, it increased from 4.5 percent to about 14 percent. For secondary education, it increased from 6 percent to about 14.5 percent. Among youth with higher secondary education, it increased from 11 to 24 percent. Among graduates, it increased from 19 to 36 percent. Postgraduate youth unemployment increased from 21 to 36 percent. This is really massive at the higher education level. These statistics clearly show how the general education system has completely failed in improving youth employability in India. Furthermore, it is noted that even technical and vocational education and training are also not helpful. The unemployment rate among youth having technical education and training was the highest (37.3 percent). In the case of formally vocationally trained youth, this rate was 33 percent. The incidence of unemployment almost doubled between 2011–2012 and

Harnessing India’s demographic dividend  143

2017–2018 across the education categories (in general education and technical and vocational training). This could be one of the reasons behind the rising NLET (Not in Labour Force, Education and Training) population in India. 8.4.4 Growth of disheartened labour force The slow growth (or scarcity) of non-farm jobs and the rising open unemployment together might have resulted in a massive increase in the size of disheartened youth. Youth “Not in Labour Force, Education and Training (NLET)” increased in India by about 2 million per annum during 2004– 2005 and 2011–2012. This has further increased by about 3 million per annum during 2011–2012 and 2017–2018. About 100.2 million youth are found as NLET during 2017–2018 (Figure 8.4: Panel B). The situation is alarming because an additional 127 million youth (Table 8.2) are currently attending education and training (in addition to those currently unemployed or currently NLET). After completing education/training they would either search for jobs or would remain NLET. If they join the labour market, the unemployment rate would increase further. But if they prefer to remain NLET, it would increase the volume of the disheartened labour force or the so-called “potential reserve army”. These increased NLET youth along with the elderly population (which started growing6) would constitute the total demographic liability of the economy as a whole. However, if measures are taken to create non-farm jobs in both rural and urban areas, India could still harness its demographic dividend. In the next section, the possible ways to harness demographic dividend are discussed.

8.5 On harnessing the demographic dividend 8.5.1 Role of self-employment programmes It is clear that despite the government’s measures7 to promote the growth of self-employment activities, the number of youth engaged as self-employed (in both farm and non-farm sectors) has declined in India. It declined from 81 million to 63 million during 2005–2012 and further to 49 million during 2017–2018. This might have happened due to several reasons. First, educated youth might not have acquired the required level of skill that could help them to set up their own business. Second, the inclination of youth for self-employed activities is shattered, perhaps due to either a low level of earnings or the existing constraints (mostly financial and other kinds of legislations etc.) in setting up new businesses. Hence, youth instead prefer to look for a regular salaried job. This is pretty clear from the number of youth engaged as regular salaried workers. The number of regular salaried employment among youth has also consistently been rising in India.

144  J. K. Parida

Number of workers (Million)

1993-94 100 80

71 69

2004-05

2011-12

2018-19

81 63 49

60 40

15 17

20 0

1999-00

SE

22

29

35

RE

48 51 51 46 30

CL

Type of Employment

Figure 8.5 Youth employment trends by their types of employment in India. Source: author’s calculation and plot based on National Sample Survey (NSS) and Periodic Labour Force Survey (PLFS) unit-level data

Youth engaged as regular salaried (with and without written job contracts or social security measures) increased from 22 million to 35 million during 2005–2018 (Figure 8.5). 8.5.2 Does vocational education and training help? Though the number of vocationally and technically trained youth has increased during 2004–2005 and 2018–2019, their workforce participation has declined substantially. It is already noted from the previous section that open unemployment rates among vocationally (formally trained) and technically trained youth have increased. In this context, although the “new education policy – 2020” is likely to increase the supply of vocationally trained youth further, due to the introduction of vocational training curricula at the school level, it will have very little impact on the labour demand conditions of the industries. Hence, supplementary measures including the development of infrastructure (roads, canals, electricity and warehouses etc., for promoting agricultural growth), and local industrialisation (maybe either through public-private partnerships (PPP) or private model) are necessary. The development of agriculture and agro-based industries will have both forward and backward linkages on the growth of large-scale industries and on the expansion of service sectors. These measures will definitely help improve the quality jobs through the reduction of informality over the long run in India. However, in the short and medium run, promoting emigration could be a better strategy to reduce the extent of rising open, educated youth unemployment in India.

Harnessing India’s demographic dividend  145

8.5.3 Role of emigration and remittances policies India consistently ranked first among the top remittance-receiving countries of the world with a receipt of about US$ 69 billion (about 2.6 percent of India’s GDP) during 2017, keeping its competitors like China, Philippines, Mexico, France, Nigeria and Pakistan much behind (Rath, 2003; Parida and Raman, 2018; Parida, 2019). Various research studies conducted on India and other developing countries have also found that the inflow of remittances has far-reaching positive and developmental consequences. These consequences include poverty and inequality reduction, raising domestic savings and investment in human capital, boosting economic growth through increased domestic demand for goods and services etc. (Parida et al., 2015). The best example are the states of Kerala, Punjab, Tamil Nadu and Gujarat. During the last three decades, these states have developed substantially relative to other states of India, due to the huge inflow of remittances (Noushad et al., 2022). The inter-state (rural–urban) migration is also crucial for sustaining the structural transformation process and boosting economic development in India. Because people from relatively backward states like Bihar, Jharkhand, Odisha, UP and North-East India migrate to other states like Kerala, Punjab, Haryana, Gujarat, Maharashtra, Tamil Nadu, and Karnataka either in search of employment opportunities or for better sources of earning (Parida and Raman, 2020; Parida, 2019). This internal labour flow not only brings equilibrium in the labour market (to fill the labour demand–supply gaps) by increasing labour productivity (through the reduction in under-employment and unemployment), but it also contributes to poverty reduction, increased domestic savings and improved demand for goods and services. Hence, internal labour flow helps speed up the process of urbanization. Therefore, it is argued that a suitable emigration and remittance policy will not only help ease the labour market problems (by reducing the extent of youth unemployment) in India, but it will also cause large-scale inflows of foreign exchange reserves. Inflows of foreign exchange reserves would help improve the balance of payment position of India.

8.5.4 Role of unregistered, micro- and small enterprises The overall growth of non-farm sector jobs is driven by the enterprises which hire less than ten total workers (Mehrotra and Parida, 2019). These enterprises contributed about 68 percent of the total non-farm employment during 2017–2018. In manufacturing their share is 61 percent, while in nonmanufacturing and services their share is about 66 and 71 percent, respectively, during 2017–2018 (Mehrotra and Parida, 2019). This result shows why the share of informal and unorganized sector employment is still so high in India, despite a rise in the number of registered enterprises. Because

146  J. K. Parida

even though these enterprises might have registered themselves under GST to continue their business (and pay sale tax), they could not provide employment with social security benefits to their employees because of the size of their business. As per the Economic Survey (2019), the share of dwarf firms (which remained micro or small firms) is still very high within the registered enterprises, while their contribution to registered employment generation is quite negligible. Due to the predominance of tiny enterprises and informality in the industrial sector, it is very difficult to harness economies of scale. Furthermore, the adoption of new technologies and regular upgradation is difficult. Hence, it can be stated that the growth of the size of enterprises (in both manufacturing and service sectors) is necessary to boost the growth of quality jobs as well as to ensure the global competitiveness of Indian enterprises. Suitable measures (pro-growth measures for the enterprise) should be taken that would help micro- and small firms to grow into medium- and then large-scale enterprises in the long run. 8.5.5 Prospects of infrastructure development One of the overarching constraints in India’s development policy is the failure to develop power and transport infrastructure in line with the needs of industry (Maira, 2014). Good physical infrastructure in terms of improved transportation, uninterrupted power supply and adequate land along with flexible regulations (with respect to bureaucratic controls) regarding safety, pollution, inspections, licencing and labour conditions is needed to improve the competitiveness of our manufacturers globally. Given the fact that about 99 percent of unregistered enterprises are microenterprises, these enterprises are likely to be concentrated in small towns (10 million population) and large cities, it is the middletier cities that must now receive the greater attention of the policymakers. 8.5.6 Necessity of industrial and employment policies The most important root cause of the problem on the jobs front in India is that we have not had a manufacturing policy ever since economic reforms

Harnessing India’s demographic dividend  147

began. Almost all countries in East and South East Asia had an industrial policy. The most well-known are Japan (where the industrial policy was led by the Ministry of Trade and Industry, MITI), China (where the process was led by the State Planning Commission and its successor, the National Development and Reforms Commission) and so on. The problem in India has been that since economic reforms began, the deregulation (or the end of industrial licencing), domestic liberalisation and the opening up of the economy are considered (ironically) to be proxies for manufacturing policy. But necessary as these were, these merely provided the context in which industrial policy should have been pursued with the objective of enhancing manufacturing growth. The absence of policy meant that the global trends favouring de-industrialisation (Rodrik, 2012) were internalized by the Indian economy very quickly. The absence of industrial policy since 1991 is admitted in the 12th Five-Year Plan as well. There were many elements of industrial policy that allowed China to become the global manufacturing hub, or the “factory of the world”. These reform measures include strategy for reforms in agriculture, development of small and marginal enterprises (SMEs), foreign direct investment, vocational education ecosystem etc. First, China started its reform process in 1979 with agriculture; industry-related reforms came almost a decade later. This allowed food prices to remain low and demand for industrial goods to rise and facilitated the shift of labour from agriculture to industry and services. This process has been much weaker in India on all these counts, because of slow agricultural growth. Second, the Chinese government followed “yizhenyi pin” policy with one town or area specialising in one product. Clustering based on the promotion of Township and Village Enterprises (TVEs) was occurring based on both an industrial and territorial specialisation. TVEs’ development was facilitated indirectly by the government policy of ‘leaving the land but not the village’ – which favoured investment in social infrastructure in small-sized towns, reducing internal migration to big cities. Decentralisation encouraged local governments to support TVEs. The expansion of China’s agricultural bank and rural credit cooperatives relieved their credit constraints, which are one of the main problems facing SMEs in all developing countries. The efforts led to the low labour costs of rural enterprises and their market orientation made TVEs internationally competitive exporters of labour-intensive products. The clustering of enterprises in a sector was an essential ingredient of success. Third, there was a clear strategy underlying the gradual opening up to the Foreign Direct Investments (FDI). FDI into China was small till the end of the 1990s, especially via Hong Kong, Singapore and Taiwan, with their large Chinese populations. In the early reform periods, China allowed FDI only in the form of joint ventures (except in the Special Economic Zones), because the authorities felt this form was better suited to tapping advanced

148  J. K. Parida

foreign technology. It was not until 1986 that wholly foreign-owned enterprises were permitted outside the SEZs. FDI was encouraged to focus on export-oriented manufacturing. The initial focus was on labour-intensive exports of manufactures, and later they focused on technology-intensive exports. Lastly, it can be stated that China is a manufacturing giant in the world, because of its ability to: (a) build a foundation of technical and vocational education and training (TVET) over many years and (b) continuously upgrade the TVET system in response to China’s growing share in world manufacturing output. Reforming our skill development system can move India to a higher growth trajectory, especially its manufacturing sector, whose share in GDP and employment is stagnant for over two decades. Moreover, in India, we need an additional National Employment Policy that would be designed: (i) to ensure an effective Labour Market Information System to establish an effective national coordinating and monitoring mechanism involving both state and civil society organizations to enhance employment promotion and creation; (ii) to improve the skill and human capability of women, and their ability to adapt to changing labour markets conditions by providing them a supportive ecosystem; (iii) to ensure equal access to employment opportunities for marginalized sections and other backward classes (OBCs); (iv) to promote emigrations and encourage remittance inflows; and (v) to lay down strategies to incorporate the informal sector under the fold of labour legislation, social security and decent earnings as per the international standards.

8.6 Conclusion and policy suggestions Indian economy is passing through a critical phase of economic development. While about 4.5 million people are leaving agriculture every year, the non-farm sector’s job is not growing adequately (grown only about 2 million per annum during post-2012 period) to accommodate them. Moreover, the number of youth joining the labour force after completion of their education and training is on the rise. This results in an upsurge in educated youth unemployment (18 percent and about 24 million) as well as the growth of disheartened labour force (youth completed education and training but still neither in jobs nor searching for jobs actively). Since the growth of the youth population (aged 15–29 years) already started declining with a corresponding rise in the share (from 8 to 10.2 percent) and growth (from 3 to 5.1 percent) of elderly population, necessary measures to create jobs should be taken at the earliest. Otherwise, the Indian economy is going to lose its demographic dividend forever and it will become an ageing society without sufficient economic prosperity like that of the developed countries of the Global North. In this context, it is argued that an integrated approach to development through a structured industrial policy for the promotion of micro- and

Harnessing India’s demographic dividend  149

small enterprises along with infrastructure development and emigration and remittances policies is necessary.

Notes 1 As per the Census population projection (by Govt. of India), Total Fertility Rate (TFR) is expected to decline from 2.34 during 2011–2015 to 1.72 during 2031– 2035 and it will continue to decline at this pace. 2 This demographic advantage is going to disappear during post 2040, and India will become an ageing society forever. 3 The projected population data from the report of Ministry of Health and Family Welfare, Government of India (2019–2020) is used. 4 It includes those persons who continued to seek jobs or available for jobs during the entire year or major part of the year preceding the date of survey 5 As per the definition of National Commission for Enterprises in the Unorganised Sector (NCEUS) report published in 2007. 6 Share of elderly population in India increased from 8.6 (Census, 2011) to 9.8 percent (PLFS, 2017–2018). 7 The self-employment schemes/programmes of the Government of India include: IRDP, JRY, SGSY, NFWP, NRLM, SGRY, PMKVY, HRIDAY, PMEGP etc.

References Aiyar, S. S., & Mody, A. (2011). The Demographic Dividend: Evidence from the Indian States. IMF Working Papers, 1–31. Chauhan, R. K., Mohanty, S. K., Subramanian, S. V., Parida, J. K., & Padhi, B. (2016). Regional estimates of poverty and inequality in India, 1993–2012. Social Indicators Research, 127(3), 1249–1296. Economic Survey. (2019). Nourishing Dwarfs to Become Giants: Reorienting Policies for MSME Growth. Economic Survey 2018-19, Ministry of Finance, Government of India, Volume I, Chapter 3, pp.57–77. Joe, W., Kumar, A., & Rajpal, S. (2018). Swimming against the tide: Economic growth and demographic dividend in India. Asian Population Studies, 14(2), 211–227. Kannan, K. P., & Raveendran, G. (2019). From jobless to job-loss growth. Economic and Political Weekly, 54(44), 38–44. Kannan, K.P. & Raveendran, G. (2012). Counting and profiling the missing labour force. Economic and Political Weekly, 47(6), 77–80. Maira, A. (2014). Jobs, growth, and industrial policy. Economic and Political Weekly, 49(34), 35–39. Mehrotra, S. (2014). India's Skills Challenge: Reforming Vocational Education and Training to Harness the Demographic Dividend. Oxford University Press. Mehrotra, S. (2015). Realising the Demographic Dividend: Policies to Achieve Inclusive Growth in India. Cambridge University Press. Mehrotra, S., & Parida, J. K. (2019). India’s Employment Crisis: Rising Education Levels and Falling Non-agricultural Job Growth (CES, Working Paper, 2019– 04). Centre for Sustainable Development. Azim Premji University.

150  J. K. Parida Mehrotra, S., & Parida, J. K. (2021). Stalled structural change brings an employment crisis in India. The Indian Journal of Labour Economics, 64 (2), 281–308. Mitra, A. (2013). Can Industry be the Key to Pro-Poor Growth? An Exploratory Analysis for India (Asia-Pacific Working Paper Series). New Delhi: ILO. Noushad, A. P., Parida, J. K., & Raman, R. K. (2022). Low-skilled emigration, remittances and economic development in India. Migration and Development, 11 (3), 389–419. NSDC (2013). District and Industries Wise Skill Gap Studies, the Report of the National Skill Development Corporation (NSDC), The Ministry of Skill Development And Entrepreneurship, Government of India. Parida, J. K. (2015). Growth and prospects of Non-farm employment in India: Reflections from NSS data. The Journal of Industrial Statistics, 4(2), 154–168. Parida, J. K. (2019). Rural-urban migration, urbanization, and wage differentials in urban India. In Internal Migration, Urbanization and Poverty in Asia: Dynamics and Interrelationships (pp. 189–218). Singapore: Springer. Parida, J. K., & Raman, K. R. (2018). India: Rising trends of international and internal migration. In Triandafyllidou, A. (Ed.) Handbook of Migration and Globalisation (pp. 226–248). Northampton: Edward Elgar Publishing. Parida, J. K., & Raman, R. K. (2020). Migration and urbanization. In Rajan, S.I. and Sumeetha, M. (Ed.) Handbook of Internal Migration in India (pp. 449–461). New Delhi: Sage Publication. Parida, J. K., Mohanty, S. K., & Raman, K. R. (2015). Remittances, household expenditure and investment in rural India: Evidence from NSS data. Indian Economic Review, 50 (1), 79–104. Rath, D. (2003). Workers Remittances: An important and stable source of external development finance. In Maimbo, S. M. and Ratha, D. (Ed.), Remittance; Development Impact and Future Prospects, World Bank. 19–51. Rodrik, D. (2012). No more growth miracles, Project Syndicate, Cambridge, available at http://www​.project​-syndicate​.org​/commentary​/no​-more​-growth​ -miracles​-by​-dani​-rodrik, accessed on 20/06/2013. Shah, M., Mann, N., & Pande, V. (2015). MGNREGA Sameeksha: An Anthology of Research Studies on the Mahatma Gandhi National Rural Employment Guarantee (2005) Act 2006–2012 (No. id: 6749). Singh, S., & Parida, J. K. (2020). Employment and earning differentials among vocationally trained youth: Evidence from field studies in Punjab and Haryana in India. Millennial Asia, 0976399620964308.

Chapter 9

Jobless growth in India Employment–unemployment of educated youth Mona Khare and Sonam Arora

9.1 Introduction Today, as India aspires to become a knowledge economy, it is not just targeting a high rate of growth but decent employment. Three major developments over the past few years that have been instrumental in propagating/ hindering these aspirations are globalization, technological advancement and competition. The first, which started with economic liberalization in the early 1990s, is often quoted to be a major trend changer for India’s low rate of growth (Hindu rate of growth) to export-led high growth. However, the same could not be said about its impact on employment as India’s postliberalization high rate of growth is often quoted as jobless growth. To add to the stigma of jobless growth, recent years’ technological advancement and rising competition challenge the country’s demographic dividend. With around 62 percent of its population in the working age group (15–59 years) and more than 54 percent of the total population below 25 years of age, India has the potential to become one of the world’s fastest-growing economies if it can harness the virtuous circle of sustained high growth – high human development (HD) leading to higher employment/income to make use of technological advancement and competition in its favour. The virtuous circle of growth and vicious circle of poverty are based on the interplay of indicators, and in order to maximize the potential, it becomes critical to examine India’s performance. These indicators viz. economic growth (EG) and HD (via education and employment) are inter-dependent and mutually reinforcing. They shape the overall development of the economy. This chapter explores the relationship between growth and human development via higher education graduate employment–unemployment trends in major Indian states.

9.2 EG–HD and employment Education becomes an important instrument of growth in both the chains (a) from EG to HD and (b) from HD to EG in many ways. First, an DOI: 10.4324/9781003329862-12

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individual’s human capital means his general skill level which varies with levels of education. The theory of human capital focuses on the fact that the way an individual allocates his time over various activities in the current period affects his productivity in future periods and, hence, the accumulation of human capital (Lucas, On the Mechanics of Economic Development, 1988). Education by way of enhancing labour productivity has a potential threat of “endangering transitional growth” towards a higher equilibrium level of output (N.G., Romer, & Weil, 1992). Second, education enhances people’s capabilities, creativity and productivity, and consequently, the human capital market remains differentiated due to a higher demand for more educated and skilled workers in the labour market. Such imperfections of the human capital and labour market perpetuate inequalities of income (T.W.Schultz, 1993). Third, education brings a change in the nature of job skills demanded that determines the labour market growth trajectory (Brynjolffson & McAfee, 2014). Fourth, education contributes to technological capability and technical change in the industry, growth in exports and investment decisions of policymakers and managers who further influence the volume of domestic and foreign investment. Further, it has been argued that even “unskilled workers” in a modern factory need a minimum level of literacy which is acquired in primary and lower secondary school (Woods, 1994). Also, secondary and tertiary education represent critical elements in the development of key institutions, of government, the law and the financial system, among others, all essential for EG (Ranis, Stewart, & Ramirez, Economic Growth and Human Development, 2000). Hence, it is clearly evident that education impacts aggregate growth rate (GR) through diverse channels, consequently making EG and HD mutually important with the educational status of the masses being a prominent factor. HD, the best indicator of welfare, finds its theoretical underpinnings in the capability approach which holds “a person’s capability to have various functioning vectors and to enjoy the corresponding well-being achievements” (Sen, 1985). Achievements in HD make critical contributions to EG. It enters the HD function through reduced poverty, more equally distributed income, higher household spending on food, health and education, higher government spending on social expenditures and a more efficient HD improvement function (a production function that relates the inputs into HD). And HD enters the EG through a higher investment rate, more equitable distribution of income and more appropriate economic policy (Ranis, Stewart, & Ramirez, Economic Growth and Human Development, 2000). Thus, one can see a mutually reinforcing role between EG and HD. It may be said that EG is dependent on productivity (employment) and productivity is affected by education (skills and levels). The EG/HD chain triggering employment (productivity) becomes even more important in countries like India, with the demographic bulge at the centre that is continuously swelling the pool of job-seekers. Studies show that among educated

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job-seekers, it is the percentage of graduates who have witnessed the greatest increase as the share of job-seekers with graduate and above degrees is increasing fastest (Khare, 2014). Studies across the globe have also proven that the income elasticity of higher education (HE) is much higher than that of all other levels of education (Bank T. W., 2002). Studies in several developed countries reveal that the rate of unemployment declines steadily as the level of education of labour market participants rises (Ghose, Majid, & Ernst, 2008). The market age of today is beset with imperfections of all kinds, and the newly emerging human capital market and the labour market are no exceptions. The imperfections in the human capital market are twofold – supplyside educational inequalities and demand-side employment inequalities. The interplay of these two inequities, in turn, propagates economic inequality, which may become a vicious circle of unequal growth. The two-way relationship between EG and HD can also become reinforcing as evidenced by literature such that nations may enter into an upward spiral of development, i.e. the virtuous cycle of high growth and large gains in HD, or get trapped into a poverty trap, i.e. the vicious cycle of low growth and low rates of HD (Ranis, Stewart, & Ramirez, 2000; Ranis, 2004). The third case may be that of lopsided EG, i.e. relatively good growth and relatively poor HD or vice versa. Studies have shown that most of the EG-lopsided nations are not able to sustain their growth momentum and later move to the vicious cycle of low EG/low HD (Ranis, Stewart, & Ramirez, 2000). Such experiences prove that HD is a necessary pre-requisite for long-term sustainable growth. However, equally important is the distributive angle of growth, increased investment, higher productivity and improved employment so as to leverage successes in HD into sustainable EG – the sustained upward spiral of virtuous EG. Khare, (2019) uses the following framework (Figure 9.1) to explain the virtuous cycle of EG/HD growth. She finds that growth in Indian states has had a very feeble but positive relationship with unemployment which proves that high-growth states do not necessarily have low unemployment (Khare, 2019) and, hence, better HD. With the above background, in this chapter, we use employment as a proxy of HD (of which education is a key component) and explore employment–unemployment trends among the educated youth in India.

9.3 Empirical approach What is the relative status of Indian states in experiencing this EG/HD relationship in terms of improved employment so as to leverage successes in HD into sustainable EG is the major concern of this chapter. The main question that this chapter attempts to explore is whether the states that experienced a high rate of growth over a long period of time can be deemed to be better off in terms of better employment scenarios and better status of

154  Mona Khare and Sonam Arora

FRAMEWORK FOR VIRTUOUS CYCLE OF EG/HD GROWTH HIGH GROWTH

HIGHER PCNSDP

BETTER EMPLOYMENT QUALITY

LOW POVERTY & UNEMPLOYMENT

HIGHER EDUCATION STATUS

LESSER INEQUALITIES

SUSTAINED HIGH & EQUITABLE GROWTH

Figure 9.1 Framework for a virtuous circle of economic growth (EG)/human development (HD) growth

youth education. The chapter thus traces the experience of Indian states by analyzing the relationship between growth, HE and employment to see if targeting a higher rate of growth in itself is a sufficient condition to achieve sustained HD. The chapter is divided into three sections. Section 9.4.1 analyzes the growth profile and income inequality of major Indian states post-liberalization. Section 9.4.2 highlights the issue of jobless growth in India by reflecting upon the employment/unemployment status of the educated. Section 9.4.3 explores the relationship between EG, HE (HD) and employment and attempts to identify the states trapped in the vicious circle of unsustainable growth. Unsustainable growth, here, may be defined as lopsided EG leading to a downward spiral of the vicious circle of low educational development and poor employment status (HD). Employment status (ES) is measured by graduate unemployment rates (URs) and the proportion of regular salaried in the graduate plus population as a proxy to employment quality. EG is measured as the long-term logistic growth of net state domestic product (NSDP) at constant prices (2004–2005 prices) over a period of time, and the proportion of graduate plus population in the labour force is taken as a proxy to HD (ES for HD). The chapter uses expansive quantitative and qualitative data drawn from a wide range of secondary sources including national and international reports, web sources, National Sample Survey Organization reports, National Accounts Systems estimates and other researches. The employment

Jobless growth in India  155

indicators are computed using unit-level data from the employment and unemployment survey of the National Sample Survey Office (NSSO) for 2004–2005 and 2011–2012 and periodic labour force survey (PLFS) for 2017–2018, while growth indicators are computed using statistics on Indian economy of the Reserve Bank of India (RBI).

9.4 Analysis 9.4.1 Growth trends in major Indian states India has been the dominant economic power globally for more than threefourths of known economic history (Maddison, 2007). The GR of gross domestic product (GDP) has accelerated since the 1980s and more so postliberalization after the 1990s. The convergence theorem (Barro, 1991) postulates that when the GR of an economy accelerates, initially some regions with better resources would grow faster than others. But after some time, when the law of diminishing marginal returns set in, the GRs would converge. Literature also suggests that this growth convergence may not necessarily lead to income equality. Due to differential marginal productivity of capital, this may lead to income inequality across regions (Bhattacharya & Sakthivel, 2005). With India having become the fifth largest economy in the world in 2019 and aspiring to be the third largest by 2025, in this section we analyze India’s situation in terms of growth across states and income inequality. Studies reveal that India’s objective of “growth with equity” continues to elude it and remains a matter of grave concern for policymakers as regional disparities in growth remain high despite good macro-economic trends (Datt & Ravallion, 2002; Khare, 2019; Kundu & Varghese, 2010). In order to examine this situation across the major states of the country in the post-reform period, long-term growth rates (LTGRs) of net state domestic product (NSDP) were calculated (base year 2004–20051) for the period starting from 1993–94 to 2017–2018 (Table 9.1). For mapping the sustainability of growth, i.e. whether growth could be sustained in the long term, we divide the entire (1993–94 to 2017–2018) period into three sub-periods: period I (1993–94 to 2000–2001), period II (2000–2001 to 2011–2012) and period III (2011–2012 to 2017–2018). Based on the LTGRs (1993–94 to 2017–2018), states can be ranked as shown in Figure 9.2. The top three states of Gujarat, Maharashtra and Haryana are also the ones that recorded relatively higher GRs in earlier periods (Table 9.1). On the other hand the bottom most states of Assam, Punjab, Uttar Pradesh and Odisha continued to remain at the lower rungs of the ladder in most of the time periods. Out of these states at the bottom, only Assam and Madhya Pradesh have shown some improvement in recent years. In the sub-period 2006–2007 to 2011–2012, the states of Bihar, Madhya Pradesh and Rajasthan performed meticulously with a rise in the

6.48 4.47 6.49 7.97 7.42 6.83 6.75 6.30 5.97 6.84 5.27 5.00 6.69 6.82 5.21 6.07 5.65 6.29 0.92 14.67

Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Odisha Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal All India Mean SD CV

7.20 6.33 6.84 7.64 6.83 6.22 7.49 5.71 7.34 6.62 4.87 4.64 5.25 5.56 5.71 5.50 6.61 6.23 0.95 15.22

2011–2012 to 2017–2018 5.87 3.56 6.00 7.66 7.24 6.69 6.12 6.18 5.15 6.55 5.14 4.86 6.86 6.93 4.74 5.95 6.60 5.97 1.06 17.84

1993–94 to 2011–2012 5.08 1.73 4.89 4.57 5.24 6.52 5.54 4.38 3.44 4.02 2.79 3.81 5.67 5.43 3.45 5.81 5.40 4.52 1.26 27.79

1993–94 to 2000–2001 6.01 4.67 7.39 9.18 7.98 6.32 6.37 7.03 5.74 8.10 6.31 5.51 6.68 8.22 5.51 5.38 7.00 6.65 1.24 18.65

2001–2002 to 2011–2012 4.93 3.71 2.97 9.01 6.66 5.88 5.47 6.63 2.72 7.46 6.08 3.54 3.84 6.45 3.85 4.43 5.70 5.23 1.77 33.91

2001–2002 to 2005–2006

5.16 4.71 8.30 7.86 7.16 5.36 5.38 6.01 6.76 6.31 4.44 5.37 7.10 7.09 5.57 4.85 6.42 6.09 1.17 19.21

2006–2007 to 2011–2012

Source: computed compounded average growth rates (CAGRs) for net state domestic product (NSDP) at factor cost, Reserve Bank of India (RBI) Database from National Accounts Statistics (NAS), Central Statistics Office (CSO), Ministry of Statistics and Programme Implementation, Government of India.

1993–94 to 2017–2018

State

Table 9.1 Net state domestic product growth (base year: 2004–2005)

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Jobless growth in India  157

9 8 7

7.97

7.42

6

6.84 6.83 6.82 6.75 6.69 6.49 6.48 6.30 6.07 5.97

5

5.27 5.21 5.00

4.47

4 3 2 1 Assam

Punjab

Uttar Pradesh

Odisha

Madhya Pradesh

West Bengal

Kerala

Andhra Pradesh

Bihar

Rajasthan

Karnataka

Tamil Nadu

Himachal Pradesh

Maharashtra

Haryana

Gujarat

0

Figure 9.2 Long-term growth rates (LTGRs) for net state domestic product (NSDP) at factor cost (1993–94 to 2017–2018). Source: computed GRs for NSDP at factor cost, Reserve Bank of India (RBI) database from National Accounts Statistics (NAS), Central Statistics Office (CSO), Ministry of Statistics and Programme Implementation, Government of India

GR as compared to the precedent sub-period till 2005–2006. It can clearly be noticed that Gujarat (one of India’s most industrialized states), along with Maharashtra and Odisha, slid down as compared to the precedent sub-periods. In the subsequent sub-period 2011–2012 to 2017–2018, the states of Tamil Nadu and Rajasthan also lost their earlier period higher ranks. It can also be seen that the GRs of Andhra Pradesh, Assam and Karnataka improved significantly which led the states to realize sustained GRs throughout the period till 2017–2018. Hence, it can be understood that the relative rankings of the states in terms of GRs have not undergone any major changes over the long period, with only a few stray cases here and there during sub-periods, which too could not be sustained for long. Assessing the growth performance of the states on the yardstick of sustainable growth as reflected in maintaining consistently higher GR (Khare, 2019), they can be categorized into the following categories: Category I: Sustained High GRs – the states that seem to have been maintaining high GRs during all the periods and sub-periods are Gujarat, Haryana and Maharashtra. Category II: Sustained Moderate GRs – this includes the states of Andhra Pradesh, Karnataka and Kerala that have maintained medium GRs.

158  Mona Khare and Sonam Arora

Category III: Sustained Low GRs – the states that have been consistent at lower GRs are West Bengal, Punjab, Uttar Pradesh and Odisha. Category IV: Unsustained High/Low GRs – this category includes states that realized a combination of high and low GRs in the periods/sub-periods. Such states are Rajasthan, Himachal Pradesh and Tamil Nadu that slid from high to low ranks, as well as Bihar, Assam and Madhya Pradesh that moved from low to high ranks. It can clearly be seen that the states that could sustain a high or moderate rate of growth are the developed ones and the ones that could not include largely the group of underdeveloped states. One can, however, note with some satisfaction that few from the latter category could break through the low growth trap in recent years. But there is every possibility that these may observe a similar fate as that of other low-growing states that rose to higher levels of growth in certain sub-periods but could not keep up their relative ranks in later periods by sustaining a high rate of growth (Khare, 2019). Although there has been one positive feature that the inter-state disparity in growth has reduced in subsequent sub-periods consistently as revealed by coefficients of variation sliding down from 28 to 19 percent and 15 percent, respectively, it gives little hope for all states to be able to sustain high growth due to following reasons. First, the erstwhile Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh (BIMARU) states that either fall into category III or category IV continue to be under-performers on various other counts. The 2018 Multidimensional Poverty Index shows that multidimensional poverty is highly acute and significant in three states of Bihar, Uttar Pradesh and Madhya Pradesh (Initiative, 2018). Similarly, as per United Nations Development Programme’s (UNDP) Human Development Index (HDI), 2017, the ranks of Haryana, Himachal Pradesh, Tamil Nadu and Karnataka have seen a significant jump and Rajasthan with Uttar Pradesh continue to remain at the bottom of the ranks in the last 27 years. Moreover, Rajasthan and the other states of Uttar Pradesh, Madhya Pradesh and West Bengal are the worst performing in terms of HDI (Ghosh, 2019). Second, Observations on state-wise per capita income (PCI or PCNSDP) give an alarming sign. Despite registering higher GRs in the recent decade, the less developed states (LDS) have not been able to touch the per capita income mark of the developed states (DS). With the sustainability of high GR by category III and category IV states seen as a challenge, the concerns about their catching up with DS continue to remain a difficult proposition. Third, although the coefficient of variation (CV) in GRs has gone down in period II and period III, the inter-state disparities in PCNSDP have grown from a high of 35 percent in 2000–2001 to 42 percent in 2011– 2012 and 44 percent in 2017–2018, as measured by the CV in per capita NSDP.

Jobless growth in India  159

The analysis, thus, indicates that states falling in categories III and IV are likely to remain trapped in the vicious circle of low growth or lopsided growth and may not be able to enjoy the virtuous circle of EG/HD despite realizing high growths for short intermittent durations. Once they fall into the vicious circle, they will not be able to draw the benefits of HD (education and better employment), thus hampering their sustained growth. The GRs of Indian states have had a very feeble but positive relationship with unemployment which proves that high-growth states do not necessarily have low unemployment (Khare, 2019) and, hence, better HD. In light of this analysis, it would be interesting to explore the relationship between EG growth and educated unemployment (HD) in the next section. 9.4.2 Graduate employment–unemployment in Indian states In the event of EG, employment gains importance and agriculture as a job provider loses its importance calling for job creation in non-agricultural activities. This is how labour market inequality is linked with economic inequality because the hierarchical structure of the labour market is based upon the skills and authority of the employers to choose from the pool of job-seekers. Individuals raise their productivity by spending time and money on education and training (Becker, 1962), but productivity can also be tied to positions, that is, jobs (Thurow, 1975). Different jobs have different skill requirements, which may not only mean that individuals with varying amounts and types of abilities and qualifications get selected for them but also that individual skills develop more in some jobs than in others (Faraks, 2003; Kohn, Schooler, & Miller, 1983). Thus with economic and population growth, employment growth should rise and unemployment should decline. The productive sectors of the economy should generate enough employment to absorb job-seekers, especially the educated (more so those with HE degrees), due to increasing demand for new high-order skills arising out of technological advancements in the changing job requirements. A closer look at the unemployment trends in India from 1993–94 to 2017–2018 across education levels will give a clearer picture (Figure 9.3). Not only is India facing jobless growth, but it is the unemployment of the educated that is on the rise. The open unemployment rate (UR) in India has risen sharply between 2011–2012 and 2017–2018. Although the UR has risen across all education categories, the increase is the highest for those with graduate plus degrees, the technical education and vocational education categories. The URs of diploma/degree holders and post-graduates increased from 11.40 percentage (pc) to 20.63 pc and 9.06 pc to 16.70 pc, respectively, from 2004–2005 to 2017–2018. A state-wise analysis of unemployment trends of higher education graduates (HEGs) provides a very challenging scenario. Though the URs declined in the initial period, there was a sharp rise from 2011–2012 to 2017–2018

160  Mona Khare and Sonam Arora

40

37.3

35.9

Unemployment Rate (Open) %

35

33.0

30 23.8

25

16.1

20 15.1

15

0

19.8 17.3

19.0

17.8

14.4

14.4

11.6

10 5

21.5

7.1 0.7

9.1 5.0

4.3

3.6

5.4

1.0

Illiterate

1983

Primary

Secondary

1987-88

Higher Graduate and With Secondary Above Techincal education

1993-94

1999-00

2004-05

Vocationally Trained 2011-12

Total

2017-18

Figure 9.3 Unemployment trends by levels of education. Source: based on National Sample Survey (NSS) various rounds reports, periodic labour force survey (PLFS), 2017–2018

for all the states. The rise in URs has been the highest among Andhra Pradesh, Bihar, Tamil Nadu, Madhya Pradesh and Himachal Pradesh between 2004– 2005 and 2017–2018. Among all the states, Gujarat is the only state where the lowest UR in all three periods and Kerala stood first consistently with the highest UR of HE graduates (URoHEG) (Table 9.2). The above analysis confirms that growth does not necessarily have some positive impact in keeping URoHEG low and cannot be considered a sufficient condition for triggering the virtuous circle of EG–HD as the decline in URs could not be sustained. Also, many category I and category II states of the previous section do not fall into the category of states with a high decline in URoHEG. In order to have a look at the quality aspect of the employment of HE graduates vis-à-vis GRs, an analysis was undertaken for the proportion of HEGs in regular/salaried workers for the selected states (Table 9.3). It can be seen that states like Gujarat, Haryana, Karnataka, Maharashtra and Tamil Nadu are consistently better states, while states like Assam, Bihar, West Bengal and Uttar Pradesh consistently remain lowly ranked even on this count. But, surprisingly Bihar, Gujarat, Haryana and Odisha along with Assam are also the states that witnessed the highest increase in regular/salaried HEG workers. Once again it can be seen that despite registering a positive increase these BIMARU states could not match the developed states on the employment quality count.

Jobless growth in India  161 Table 9.2 Employment status of higher education graduates (HEGs) State

Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Odisha Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal All India

Unemployment rate of HEGs

Proportion of HEGs in regular/ salaried workers

2004

2011

2017

2004

2011

2017

26.56 46.56 24.30 13.30 27.30 35.37 20.83 51.94 12.73 15.81 47.40 27.76 21.60 25.00 20.78 26.56 27.74

30.47 41.61 22.22 6.47 18.30 20.14 18.94 43.33 12.67 10.81 30.41 14.36 22.63 22.86 17.88 33.41 22.91

51.61 44.00 43.36 19.28 31.05 48.18 29.88 55.56 29.13 24.73 51.34 34.07 33.03 42.18 30.74 35.59 37.73

6.39 5.76 8.36 6.22 9.25 8.40 5.78 12.15 6.26 9.11 9.00 9.27 5.77 10.25 9.96 6.39 8.02

14.64 10.34 11.52 9.00 20.36 13.53 12.51 20.38 9.79 15.32 11.74 11.58 12.48 18.51 13.23 15.32 13.77

27.04 15.10 15.66 17.30 16.68 14.08 21.38 31.95 16.39 21.37 14.19 17.49 20.09 37.38 18.43 18.02 20.16

Source: computed using National Sample Survey (NSS) various rounds and periodic labour force survey (PLFS) unit-level data.

Table 9.3 Education status of labour force State

Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Odisha Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal All India

Proportion of graduate + labour force 2004

2011

2017

47.70 25.19 12.62 43.09 36.83 27.21 50.46 34.17 33.71 45.76 21.75 40.41 34.74 52.63 23.72 47.70 36.10

48.07 26.09 23.81 55.29 44.68 40.28 54.55 43.67 47.06 57.87 35.02 59.57 37.23 58.97 32.62 38.98 43.98

30.85 40.44 23.45 53.01 47.49 25.45 53.83 37.12 40.83 54.57 33.48 48.72 39.26 47.80 29.26 38.77 40.27

Source: computed using NSS various rounds and periodic labour force survey (PLFS) unit-level data.

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In order to assess the HD angle through ES, the shifts in the proportion of HEGs in the labour force for the select states were analyzed (Table 9.3). A parallel rise in regular/salaried employment and in the labour force of HEGs is a significant indicator as it reflects an improvement in the quality of employment as well as ES of the workforce conditions. The states that witnessed such a trend between 2004–2005 and 2017–2018 are Bihar, Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Odisha and West Bengal. While the states where regular/salaried employment declined sharply, despite a rise in the labour force of HEGs, are Andhra Pradesh, Himachal Pradesh, Tamil Nadu and West Bengal. Once again a mixed bag of states in the desirous and un-desirous changes the category. 9.4.3 EG–HD–ES: The virtuous circle of growth Trying to establish if the states with sustainable high growth are also able to sustain virtuous HD, a cross-mapping of the indicators analyzed in the above section with LTGRs was undertaken. The states could then be categorized as follows: Category I: a virtuous circle of high growth and large gains in HD – Gujarat, Haryana, Maharashtra and Karnataka. Category II: a vicious circle of low growth and low rates of HD – Punjab, Odisha, Uttar Pradesh and Assam. Category III: lopsided circle growth, i.e. relatively good growth and relatively poor HD or vice versa – Himachal Pradesh, Tamil Nadu, Rajasthan, Bihar, Andhra Pradesh, Kerala, West Bengal and Madhya Pradesh. It can clearly be seen that of the 16 major states analyzed only 4 deem to be experiencing a virtuous circle of high growth and large gains in HD which are also the developed states of the country on various other parameters. The majority of those that fall into the category of vicious circle carry a legacy of being lowly developed. Most of the states fall into category III experiencing lopsided EG–HD circle and face the risk of falling into category II, if not taken care of. Both category II and category III states can thus be quoted as being vulnerable.

9.5 Conclusion and policy implications It is evident from the above analysis that most of the states in the country elude the virtuous circle of high EG–HD–ES and are trapped in the vicious circle of unsustainable growth. Targeting a high EG is desirable but not sufficient enough to trigger the virtuous circle of high EG–HD unless it is also sustained for a long period by exploiting the region-specific comparative advantages to the best. Several states, despite experiencing higher growth

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and positive increase in employment scenarios in certain sub-periods, could not match the per capita income or employment aggregates of the consistent good performers as there is hardly any change in the relative position of the states in the long run on almost all counts. The need, therefore, is to not just target high growth but sustained long-term EG, lesser ups and downs that call for “stabilisation policies”. Inequality seems to be a greater menace than low EG in itself to achieve the virtuous growth circle (Khare, 2019). The problem of low growth/lopsided growth in the states is topped with problems of the vicious circle of employment and education. With growth having little dent on unemployment (jobless growth, growth in less productive jobs, casualization etc.), the tendency of a vicious circle of poor employment leading to poor education, i.e. the low ES – low HD trap, is likely to continue in future. The policy implication of the above analysis is that the country needs to enhance and strengthen the links between EG–HD–ES. We need comprehensive state-specific policies that bridge the gap between education and decent employment and target overall growth and development. As a policy measure, the country should aim at a minimum threshold level of compulsory education (secondary level) to develop skilled and quality human resources capable of ensuring a regular inflow of income (Khare, 2019) that has the capacity to push the states to a higher trajectory of skilled workers (with HE qualifications). Creating more regular and stable jobs by improving productivity through restructuring the state economies towards high-growth sectors with higher employment elasticities needs to be part of a comprehensive policy for quality employment. If the first condition for creating a roadmap to better employment and education is a sustained high EG, the second is simultaneous interventions to improve HD and ES. It is important to keep regional dimensions in focus so as to identify the relative advantages of individual states in sectoral/human/ environmental aspects in order to aid the state economy’s transition from low productivity to high productivity trajectory of respective dominant sectors. Only then can there be hope to kick start a virtuous circle of high EG–HD.

Note 1 The revised base year 2011–2012 is widely contested by policymakers and academicians as the inflated growth estimates are hard to reconcile with other economic correlates (Nagraj, 2018).

References Bank, T. W. (2002). Constructing Knowledge Societies: New Challenges for Tertiary Education. Washington, DC: The World Bank.

164  Mona Khare and Sonam Arora Barro, R. J. (1991). Economic Growth in a Cross Section of Countries. Quarterly Journal of Economics, 106 (2), 407–443. Becker, G. S. (1962). Investment in Human Capital: A Theoretical Analysis. Journal of Political Economy, 70 (5), 9–49. Bhattacharya, B. B., & Sakthivel, S. (2005). Regional Growth and Disparity in India: Comparison of Pre- and Post-Reform Decades. Economic and Political Weekly, 39 (10), 1071–1077. Brynjolffson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company Inc. Datt, G., & Ravallion, M. (2002). Is India's Economic Growth Leaving the Poor Behind? Journal of Economic Perspectives, 16 (3), 89–108. Faraks, G. (2003). Cognitive Skills and Non-Cognitive Traits and Behaviors in Stratification Process. Annual Review of Sociology, 29, 541–562. Ghose, A. K., Majid, N., & Ernst, C. (2008). The Global Employment Challenge. Geneva, Switzerland: International Labour Organization. Ghosh, S. K. (2019, March). Human Development Index Across Indian States: Is the Glass Still Half Empty? Retrieved 05 13, 2021, from SBI ECOWRAP: https:// www​.sbi​.co​.in​/documents​/13958​/14472​/Ecowrap​_20190308​.pdf Initiative, O. P. (2018). Global Multidimensional Poverty Index. Oxford: University of Oxford. Khare, M. (2014). Employment, Employability and Higher Education in India: The Missing Links. Higher Education for the Future, 1 (1), 39–62. Khare, M. (2019). Exploring the Relationship Between Economic Growth, Employment and Education in Indian States. In R. Govinda, & M. Poornima (Eds.), India’s Social Sector and SDGs: Problems and Prospects (pp. 264–282). London: Routledge. Kohn, M. L., Schooler, C., & Miller, J. (1983). Work and Personality: An Inquiry into the Impact of Social Stratification. Norwood NJ: Ablex. Kundu, A., & Varghese, K. (2010). Regional Inequality and Inclusive Growth in India: Identification of lagging states for strategic intervention. Oxfam India Working Paper Series in Collaboration with Institute for Human Development. Delhi: Institute for Human Development. Lucas, R. E. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22 (1), 3–42. Maddison, A. (2007). Contours of the World Economy 1-2030: Essays in Macro Economic History. Oxford: Oxford University Press. N.G., M., Romer, D., & Weil, D. (1992). Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), 407–437. Nagraj, R. (2018, December 02). India's GDP Debate: What Explains Reduced Growth Rates Under the UPA? Retrieved May 12, 2021, from The Wire: https:// thewire​.in​/macro​/indias​-gdp​-debate​-what​-explains​-reduced​-growth​-rates​-under ​ -the​-upa Ranis, G. (2004). Human Development and Economic Growth. Center Discussion Paper No. 887. New Haven, CT: Economic Growth Centre, Yale University. Ranis, G., Stewart, F., & Ramirez, A. (2000). Economic Growth and Human Development. World Development, 28 (2), 197–219.

Jobless growth in India  165 Schultz, T. W. (1993). Origins of Increasing Returns. Oxford: Basic Blackwell Scientific Publications. Sen, A. (1985). Well-Being, Agency and Freedom: The Dewey Lectures 1984. The Journal of Philosophy, 82 (4), 169–221. Thurow, L. (1975). Generating Inequality. New York: Basic Books. Woods, A. (1994). North-South Trade, Employment and Inequality: Changing Fortunes in a Skill-driven World. Oxford: Oxford University Press.

Chapter 10

Agricultural exports and informal sector in India A macroeconomic perspective Anirban Kundu

10.1 Introduction In India, agriculture and allied sector are considered pivotal to the inclusive and sustainable growth of the economy due to their wide-ranging forward and backward linkages with the rest of the economy. It is envisaged that the 1991 New Economic Policy regime that brought about changes in industrial and trade policies in India would eventually bring forth a favourable condition for agriculture, improving its terms of trade that ultimately promotes export and growth of the tradable agricultural sector (Bhalla and Singh, 2009). However, it is observed that such policies failed to bring forth the desired outcome (Bhalla and Singh, 2009), and Indian agriculture experienced a growth deceleration during 1990–93 to 2003–2006. Despite the slowdown in agricultural growth, India has emerged among the 15 major exporters of agricultural products, such as cotton, rice (basmati), pepper, sugar, oil meals and meat in the world market (Government of India, 2016). Over a period of about 25 years, India’s share of agricultural export to the world export has also increased from a negligible share of 1 percent in 1980 to 2.50 percent in 2014 (Government of India, 2016). Despite a significant increase in area under cultivation since 2003–2004 accompanied by the growth in major exportable high-value agricultural commodities like cotton, soybean and maize, an export ban on low-value agricultural commodities, especially rice, wheat and edible oils, has also been imposed from time to time in order to stabilise the domestic price of the commodities (Government of India, 2016). On the one hand, the rising domestic price of agricultural commodities often leads to an export ban; on the other hand, new opportunities to expand the Indian agricultural market outside the domestic periphery provide incentives to domestic producers to expand their market that could contribute to the overall growth of the economy. However, export growth alone cannot ensure overall economic growth since the macro-linkages between the agriculture and the non-agricultural sectors of the economy also play a crucial role in determining the overall growth of the economy. Our focus is on the informal DOI: 10.4324/9781003329862-13

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non-agricultural sector in India since a large segment of the informal nonagricultural sector depends on agriculture through forward and backward production linkages as well as labour market linkages. Besides, a large segment of the Indian workforce (almost 80 percent) is employed in the informal sector as well. Hence, any policy or external shock that induces changes in agriculture would definitely have a deeper impact on the informal sector’s livelihood and income, which has further broader implications in terms of the economic development in India. In this study, we enquire about the macroeconomic implications of the surge in the world price of agricultural exportables on the income distribution across informal nonagricultural households in India and the resultant impact on the overall growth of the economy. Price signalling to the export market gives incentives to the agricultural producers to reap the benefits from exporting to the world market by diverting the supply from the domestic market towards the international market. Given the agricultural supply rigidity in the short run, diversion of supply from the domestic market leads to the spiralling of food inflation due to demand–supply mismatch in the domestic economy. Spiralling food inflation could affect the income distribution across informal non-agricultural households of the economy. One way of stalling the demand–supply mismatch in the domestic market is to remove the supply-side rigidities in agriculture by introducing technological change within this sector. Hence, we further examine through the counterfactual scenarios whether enhancing agricultural productivity could dampen price inflation and help in improving the income distribution across households. In this regard, we further examine, through a series of counterfactual scenarios, the impact of increasing farm productivity alongside the rising export price on income distribution and the overall growth of the economy. In order to perform the multisectoral analysis we use a static short-run (one period) computable general equilibrium (CGE) model based on the social accounting matrix (SAM) of India (India-SAM) for the year 2003– 2004. CGE analysis is considered as a semi-empirical analysis. Hence, CGE-based study is extensively used for policy analysis irrespective of the time aspect. SAM captures the inherent structural specification of the Indian economy, considering this feature, the study used 2003–2004 SAM constructed by the author (Kundu, 2019). The use of CGE model helps us to understand the microeconomic reaction of an individual entity, such as agricultural and non-agricultural producers and households to various exogenous shocks like rising world price of agricultural commodities and increasing farm productivity, as well as its macroeconomic impact in terms of overall economic growth of the country. The rest of the study is organised as follows. First, in Section 10.2 we discuss the literature review followed by the empirical approach of the study in Section 10.3, which is further divided into three sub-categories. Simulation results and their discussion are

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depicted in Section 10.4, and finally, the conclusion and policy implication of the study are highlighted in Section 10.5.

10.2 Literature survey Economic growth leads to structural transformation and the resultant movement of labour from the agricultural sector to the other growing sectors of the economy. Gollin (2010) shows that around 30 developing countries experience productivity growth out of the reallocation of labour from agriculture to other sectors from 1960 to 2000. Strikingly, the average labour productivity growth (not total factor productivity growth) in agriculture in these countries is faster than their non-agricultural counterpart. Specific to the case of Pakistan, India and Indonesia, Golin (2010) further highlights that owing to the lower marginal productivity of labour in agriculture, labour moves out and consequently drives up the total factor productivity growth in agriculture from 1960 to 2000. Hence, sectoral labour mobility has a wider implication in terms of the overall growth of the economy. Agricultural development and its implication on a country’s overall growth and development process is a well-known theoretical conjecture and already outlined in the so-called Mellor hypothesis in a general equilibrium setting. The crux of the Mellor Hypothesis is central to the farm productivity. Improving farm productivity and income level of the farmers induces industrial growth and finally export competitiveness in both agriculture and non-agricultural sectors. Expanding industrial sector, in turn, absorbs the labour and resources from the agricultural sector. Hence, within the dualistic development model, subsistence sector, majorly agriculture, is considered as the pool of reserve labour and the challenge is to transfer this labour to the modern industrial sector. The well-known works of Lewis (1955) and Fei and Ranis (1964) revolve around this dualistic structure. This is the standard way of looking into agricultural productivity and the overall growth of the economy in a general equilibrium framework. Until the mid-1980s, the focus was on agricultural policies focusing on pricing policy or trade policy and their impact on the overall economy in the form of output, resource utilisation and income distribution; however, in the latter period, the focus shifted towards the indirect impact of economy-wide policy on agricultural incentives (Schiff and Valdés, 2002). In a general equilibrium framework, agricultural incentives could be captured by observing the favourable terms of trade or relative price of agricultural to non-agricultural products (Schiff and Valdés, 2002). A few studies are worth mentioning here. However, most studies within partial equilibrium settings do not take into consideration the relative price changes and their impact on intersectoral resource flow in the long run (Schiff and Valdés, 2002). Nonetheless, this aspect is captured through the general equilibrium approach. For instance, based on simulation exercise, the study of

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Mundlak, Cavallo and Domenech (1989) shows that from 1913 to 1984, Argentina heavily taxed its agricultural export and therefore distorted the terms of trade; otherwise, Argentina could have experienced a higher growth rate if the appropriate policy could be undertaken to reap the benefit of comparative advantage from agricultural export. An econometric study by Coeymans and Mundlak (1993) on Chile’s mining and agricultural sectors during 1962–82, within a five-sector general equilibrium framework, highlights that economy-wide policy would have benefited the economy in a better way in terms of sectoral allocation of labour, capital and the overall economic growth of the country, rather than sector-specific (mining and agriculture) policy intervention. This is so since a large share of the mining and agricultural sectors in the Chilean economy is tradable. Hence, one can argue that any policy intervention or exogenous shock has wider implications in terms of the movement of labour and capital across sectors of an economy. A cross-country study of Schiff and Valdés (1992) shows that direct domestic agricultural price stabilisation policy reduced price volatility and insulated agriculture from the adverse shock of world price volatility. This study is based on 18 developing countries from the 1960s till the mid1980s. However, the removal of direct agricultural price intervention had a mixed outcome on the income level of rural and urban poor households, although the rural poor gained. A simulation study by Dorosh and Valdés (1990) on Pakistan’s protectionist measures between 1983 and 1987 shows that import tariff, import quota and anti-export biased policy severely restricted the farm income from five major crops. Real exchange rate appreciation due to the commodity boom (the socalled ‘Dutch Disease’), and the favourable terms of trade, leads to declining growth of other exportable sectors. Dutch disease and the agricultural sector are very much linked and it has crucial macroeconomic implications. For instance, the coffee sector’s boom in Columbia due to rising coffee prices created an accumulation of foreign assets. Edwards (1985) shows that such a phenomenon raised the money supply that resulted in an inflationary situation in the Colombian economy. Higher growth of inflation appreciated the real exchange rate, given the nominal exchange rate and that ultimately affected the export competitiveness of the non-coffee tradable sectors. Another classic example of Dutch Disease is Nigeria during the 1970s. Oyejide (1993) found that the Nigerian oil sector boom adversely affected the growth of the agriculture exportable which was the major source of export revenue prior to the oil boom. Although agriculture’s share of non-oil GDP declined by around 50 percent, however, agricultural employment fell by only 20 percent due to the low degree of labour mobility in agriculture. Dick et al. (1983) argue, citing the case of a boom in world coffee export prices and its impact on the Kenyan economy, fiscal policy and monetary policy need to be designed in such a manner that it would keep the domestic absorption constant. This is a plausible measure if the export-oriented

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agricultural sector has weak linkages with the domestic sectors. On the other extreme, the policy adjustment should be such that it allows the shift of resources from traded to domestic-oriented sectors by inducing the real exchange rate appreciation. Karingi and Siriwardana (2003), in a similar line of thought, show the impact of the dual terms of trade effect on Kenya during 1970. Kenyan economy experienced dual shocks – one due to the international coffee export price boom and the other due to the international crude oil price hike. While the coffee export price boom (positive terms of trade effect) brought windfall gain, on the contrary, increasing international crude oil price (negative terms of trade) put pressure on import bills. Finally, their simulation exercise shows that the boom in the agricultural sector outweighs the adverse impact of rising imported manufacturing input prices, and the economic growth was accompanied by labour demand across all three sectors of the economy. The above-mentioned brief literature survey highlights the impact of exogenous policy shocks on agriculture and its macroeconomic implications. India is no exception to such a scenario. However, India-centric studies are majorly focused on the effects of agricultural price policy, food subsidy and policy concerning the intersectoral allocation of investment on the income distribution across social groups (for detailed analysis, see de Janvry and Sadoulet, 1987). Nonetheless, linking agricultural price shock with the macroeconomy is not highlighted in Indian studies. This study is an attempt to this end. In this study the focus is on the informal sector in India, and it links the agricultural price shocks (through export prices) and the informal economy within a general equilibrium framework to understand its macroeconomic implications. In the following section, we discuss the methodology used in this study.

10.3 Empirical approach of the study The empirical approach of the study is sub-divided into three categories. First, we discuss the India-SAM used in our analysis followed by a description of scenarios that are employed to address the research questions. Second, we explain the CGE model structure, the PEP 1–1 model developed by Decaluwé et al. (2009), with our modification suitable to the current context, and finally, the model calibration and specification are presented. 10.3.1 Social accounting matrix for India and the description of the scenarios India-SAM is constructed based on the Input–Output Transaction Table for the year 2003–2004. However, the discussion on the methodology adopted to construct the SAM is outside the purview of the present context (for details, see Kundu, 2019); hence, we highlight a few important

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components of India-SAM, which are related to our analysis. Since agriculture is the focus of our study, the agricultural sector in India-SAM is divided into three broad sectors, namely, food crops, cash crops and horticulture crops. The agricultural allied sector is merged with fishery, logging and forestry and mining and quarrying activities to make a unified agro-allied and resource-based sector (AGALD). Apart from agriculture, the non-agricultural sector is further divided into various sub-sectors with their respective formal–informal counterparts. These are manufacturing of food and beverages in the formal sector (FodBvrF) and manufacturing of food and beverages in the informal segment (FodBvrI); the formal segment of manufacturing of textiles and wearing apparel (TextF) and the informal segment of textiles and wearing apparel (TextI); formal agro-based industry (AGROMF) and informal agro-based industry (AGROMI);1 nonagro-based industry in the formal sector (NAGMF) and non-agro-based industry in the informal sector (NAGMI);2 formal capital goods sector (CAPMF) and informal capital goods sector (CAPINF);3 among the services, formal infrastructure services (INFRF) and informal infrastructure services (INFRI) are considered;4 finally, other services in the formal segment (OTSRF) and other services in the informal segment (OTSRI) are considered for the analysis.5 Three types of labour are considered in the India-SAM, viz. formal regular labour (FRL), who are employed in the formal sector; informal regular labour (IRL) who are employed in both formal and informal sectors and casual labour (CL) who are informal in nature but employed in both formal and informal sectors. Apart from these three categories of labour, land and capital are considered as other two factors of production. Households, both rural and urban, in India-SAM are divided among 13 groups based on the household’s major source of income. These are as follows: marginal and small agricultural self-employed rural household (MSA-HH), medium and large agricultural self-employed rural household (MLA-HH), agricultural rural labour household (AL-HH), formal capitalist household (cap-HH), own account rural household (ROA-HH), rural establishment household (REstb-HH), own account urban household (UOA-HH), urban establishment household (UEstb-HH), rural formal wage labour household (RFWL-HH), rural informal wage labour household (RIWL-HH), urban formal regular wage labour households (UFrWL-HH), urban informal regular wage labour households (UIrWL-HH) and, finally, urban informal casual wage labour household (UIcWL-HH). In order to understand the exogenous price shocks and their macroeconomic implication within the economy, we perform the following simulations at the sectoral level: Scenario A-1: a 20 percent increase in the world price of export of food crops

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Scenario A-2: a 20 percent increase in the world price of export of cash crops Scenario A-3: a 20 percent increase in the world price of export of horticulture crops Scenario B-1: a 20 percent increase in the world price of export of food crops along with a 20 percent increase in the technical change in the food crops sector Scenario B-2: a 20 percent increase in the world price of export of cash crops along with a 20 percent increase in the technical change in the cash crops sector Scenario B-3: a 20 percent increase in the world price of export of horticulture crops along with a 20 percent increase in the technical change in the horticulture crops sector 10.3.2 Computable general equilibrium model In this section, we describe the CGE model developed by Decaluwé et al. (2009), which is applied in our context. We highlight only the relevant equations. We begin with the sectoral production function. It is assumed that the production function of an output is a nested function: at the top-level sectoral output j (XSTj ) is a fixed-coefficients Leontief production function of value added (VAj ) and aggregate intermediate consumption (CI j )

VAj = v j XSTj (10.1)



CI j = ioj XSTj (10.2)

where ioj = Leontief coefficient of intermediate consumption and v j = Leontief value added coefficient. At the bottom level VAj is a constant elasticity of substitution (CES) production function of composite labour (LDC j ) and composite capital (KDC j ):

VA j

VAj = B

(

)

-1

- rVA rVA ébVALDC -rVA j + 1 - bVA KDC j j ùú j (10.3) j j êë j û

where BVA = CES scale parameter of VA, j bVA j = CES share parameter of VA and rVA = CES elasticity parameter of VA; -1 < rVA < ¥. j j By composite labour (or capital), we mean the aggregate of various categories of labour (or capital). A firm’s derived demand for composite labour and capital emerges from profit maximisation or cost minimisation. From

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CES production function, we obtain the following relative demand for composite labour to capital as:

é bVA RC j ù LDC j = ê j VA ú ë 1 - b j WC j û

sVA j

KDC j (10.4)

where RC j = Rental rate of composite capital, WC j = Wage rate of composite labour and sVA = CES elasticity of transformation parameter of VA; 0 < sVA j j   chi2 Pseudo R2

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separated. The schemes of widow pension definitely help women to reduce their economic vulnerability, as can be seen in the relative differences in the odds ratio of joining work between divorced and widow women. A rise in the values of the odds ratio in the case of divorced women indicates their rising compulsions to go to work over the years in the absence of any social protection schemes such as that for widow women. The likelihood of widows joining the workforce tended to increase since 2004–5 despite the window pensions being in place, possibly due to a lack of information about pension programmes, inadequate coverage and amount (Gupta, 2018). Education is an important endowment factor that helps women enter the labour market and determines their earnings (Shultz, 2002; Desai and Joshi, 2019). Here we have attempted to understand the educational attainment along with the effect of caste on the probability of rural women joining the workforce. ST woman with below primary education is considered as a reference category. The probability of women joining the workforce takes vaguely a ‘U’ shape curve—being higher for those with below primary and higher education as compared to those with school education. This broad pattern is true across different social groups. However, caste emerges as a significant factor in determining the likelihood of participation of women in work. The odds of joining the workforce among those with below primary level education and belonging to SC, OBC and Other social groups is lower by 34, 40 and 44 per cent, respectively, as compared to their ST counterparts. Among those with school-level education, the odds of women joining the workforce are the lowest among OBC than illiterate ST, followed by SC and Others. Similarly, the likelihood of rural women with higher education joining the workforce is significantly lower among all social groups except STs than the STs with below primary level of education. However, significant inter-caste differences are seen in the odds ratios of joining the workforce for those with higher education—probability being the highest among STs, followed by Others, OBCs and least among SCs. The probability of joining the workforce has certainly improved over the years for those with higher education, though at a varying rate among different social groups. This suggests that chances of getting jobs, particularly regular salaried jobs in transport and other services, have become much brighter for those with higher education despite a large withdrawal of rural women from the workforce in recent periods. At the same time, the rate of unemployment among highly educated women is the highest as compared to others in rural areas. This also means that along with measures to improve the educational levels of women, efforts need to be made to expand employment opportunities outside the farm sector in a big way. So far religious background is concerned, the odds of joining the work force is 36 per cent higher among Hindus and ORMs than Muslims. The odds ratio has gradually reduced over time which implies reducing interreligious group disparity in WPR among rural women.

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12.4.2 Impact of household characteristics Whether occupational income-related typology of rural households based on their major source of income and income cohort (quintiles) has any effect on the likelihood of women joining the workforce can be deduced from Table 12.7. NSSO/PLFS categorises rural households into six categories based on a major source of income—self-employed in agriculture (SEA), self-employed in non-agriculture (SENA), regular salaried (RS), casual labour-agriculture (CLA), casual labour-non-agriculture (CLNA) and others (OS). In terms of mean per household expenditure, at the top are RS households, followed by OS, SENA, SEA, CLNA and CLA at the bottom. The reference category used here is CLNA households. Due to the change in the categorisation of households in 2011–12, we will only compare logit model results for the latest two rounds of data, i.e. 2011–12 and 2017–18. Some interesting features can be deduced. First, the probability of women joining the workforce is lower in SENA and OS but is higher in the case of SEA and CLA households. It is obvious due to the labour-intensive nature of agriculture and the dependence of agricultural wage labour households mainly on their wage labour. Agriculture still employs 73 per cent of the total rural female workforce. Second, the odds ratio tended to increase for each category of households with respect to the reference group CLNA. This deviation of workers from CLNA to agricultural activities, particularly among land-owning households, could be due to the contraction of jobs in the construction and manufacturing sectors. We have tried the model with and without monthly per capita expenditure (MPCE). The result shows the possibility of endogeneity between the MPCE and MPCE quintile since the standard error reduces notably when MPCE is dropped. So, only the income quintile is considered in the analysis. Further, the results are not affected due to the endogeneity between the MPCE quintile and occupational background. So, both covariates are included in the model. The reference category is the lowest income quintile (0–20 per cent). We find that the probability of women joining the workforce tends to improve with the increase in their household income group, which peaks at the middle income (40–60 per cent) quintile and thereafter reduces for the top 40 per cent. This also broadly shows an inverted ‘U’ shaped relation between income and work participation, particularly in 2011–12/2017–18. The odds ratio increased during 2004–5 and 2011–12 and reduced in 2017–18. This means that likelihood of women joining the workforce from relatively better-off households improved in 2011–12 but deteriorated in 2017–18 compared to the bottom 20 per cent of household. A significant positive ‘income effect’ on female WPRs is seen across different income quintiles except at the top income quintile. This might be due to the fact that household reliance on women’s incomes may fall at the top

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income quintile and more of their time is allocated to domestic activities and childcare (Neff et al., 2012). However, given the reduction of workers in the non-farm sector, this is possibly an employment crisis rather than an income effect.

12.5 Conclusions and policy implications This chapter analysed the pattern of decline in women’s participation in work in rural India since 2004–5. The decline has been widespread across different economic sectors, employment statuses, income groups and social categories, albeit at a varying rate. While the major decline in women WPRs in the age group 15–24 years has been in favour of education, it has been largely in favour of ‘domestic works’ in the other age groups. The major withdrawals from the workforce are observed in the case of those women working as ‘unpaid family labour’ in agriculture and ‘casual wage labour’ both in farm and non-farm sectors. A large proportion of women who were engaged in ‘domestic duties but also engaged in free collection of goods for their household use’ also shifted in favour of ‘domestic work’ only. This could have been partly due to measurement issues of women’s work and partly due to the growing use of cooking gas facilitated by Ujjawala Yojana, purchased vegetables and growing use of readymade garments among rural households. Across income quintiles, the shape of women’s WPRs changed from a traditional inverted ‘U’ shaped in 2004–5 to a falling straight line, depicting a positive impact on women’s work participation with rising household income. It is observed that despite a large decline in the number of rural women workers in agriculture, it still employs about three-fourths of them. While part of such a decline was offset by nearly 3 per cent annual growth in employment opportunities in the rural non-farm sector, mainly due to a surge in the construction sector, during 2004–5/2011–12, the same could not sustain due to a sharp contraction in construction, manufacturing and trade sub-sectors during 2011–12/2017–18, in which women from ST, SC and OBC communities mainly worked as casual labour. Amidst such a widespread decline in women’s employment, there has been an increase of about 3.9 million regular salaried employment for them, benefitting mostly those belonging to ORM and upper-caste Hindu. The logistic regression results bring several interesting aspects of the probability of rural women joining the workforce. These variables, however, do not capture the demand-side aspects of their participation in the labour market such as investment due to limitations of available data at the household level. While education emerges as a significant predictor of joining the workforce, its iteration with social groups of women shows the differing impacts of a similar level of education on different caste groups. The higher likelihood of divorced/separated and widowed women joining

Decline in women’s employment in rural India  235

work underscores the limited coverage of social protection for such women. Household characteristics of rural women, such as a major source of income, income class and the number of children in the household, have a significant impact on determining their likelihood of participating in the labour market. Improvement in women’s education improves participation in work, whereas they tend to withdraw from participating in the labour market when their income reaches a certain threshold level, i.e. towards the top two income quintiles. In brief, a larger part of the decline in the number of women in the workforce in recent periods is attributed to the significant shrinkages in employment opportunities in rural India. 12.5.1 Policy implications The declining employment opportunities in India, particularly for rural women, deserve immediate attention. The situation would have been worsened due to economic disruptions and related reverse migration during the COVID-19 pandemic, and the recovery may take some time (Mamgain, 2021). Since most of the withdrawal from the workforce happened among rural women working in the agriculture sector and that too in the case of those with the lowest educational attainments including a large number of illiterates, the policymakers should keep in mind these facts while designing policies and programmes for promoting employment opportunities for women as well as men both in farm and non-farm sectors on a large scale, particularly for the post-COVID-19 period for faster economic recovery. The policy focus should not be merely on enhancing the participation of women in the labour market but also on creating opportunities for decent work that will, in turn, contribute to the economic empowerment of women. Recently, Reddy and Mamgain, (2020) provide comprehensive policy suggestions for faster recovery from COVID-19-related economic disruptions leading to the transformation of the rural economy in a shorter span of time. For this transformation to happen in rural India, it is essential to provide (i) a range of quality infrastructure including education and health, (ii) good foundational education, (iii) skill training in the context of fast-changing skill demand landscapes, (iv) cluster-based approach of agriculture and micro, small and medium enterprises (MSME) development, (v) increased financial support, (vi) branding of local products, (vii) adding these products and services to value chains with ensured remunerative incomes, and (viii) institutional reforms including promoting equal rights. This transformative journey would be much easier, faster yet inclusive by augmenting the capacities and capabilities of local-level institutions such as Panchayati Raj Institutions on a sustained basis. This requires massive public investment through enhanced budgetary allocations at least over the next five years along with incentivising private investment in rural areas in a big way. While doing so, every care should be taken to create an enabling

236  Rajendra P. Mamgain and Khalid Khan

environment by improving access to and relevance of education and training programmes, skills development and social protection programmes such as access to child care, maternity protection and provision of safe and accessible transport, along with the promotion of a pattern of growth that creates decent job opportunities. The existing policies and programmes aimed at promoting enterprise development, particularly among women and SCs/STs need to be geared up in a mission mode. Every care should be taken that well-intentioned policy initiatives such as MUDRA (Micro Units Development & Refinance Agency) loan scheme do not fall prey to weak design, implementation and monitoring. These measures will help in improving India’s progress towards fulfilment of SDG-8 on decent employment, SDG-1 on the end of poverty and SDG-5 on gender parity and SDG10 on reduced inequalities by 2030.1

Note 1 The paper substantively draws on the research work by authors, entitled “Withdrawal of women from work in rural India: Trends, causes and policy implications”, SRSC Working Paper- 1/2021, S.R Sankaran Chair, National Institute of Rural Development and Panchayati Raj, Hyderabad. The authors are grateful to Professors D.N. Reddy, Indira Hirway and K.P. Kannan for their valuable suggestions. The chapter also benefitted from the valuable comments from the participants of the International Seminar on ‘Economic Development: Role of Higher Education Institutions in Employment’, organised by the Institute of Public Enterprises, Hyderabad, 10–11 December 2019. Views expressed are of authors own. Other usual disclaimers also apply.

References Chand, R., Srivastava, S. K. & Singh, J. (2017). Changing structure of rural economy of India implications for employment and growth, Discussion Paper, National Institution for Transforming India- NITI Aayog, November. Desai, S. & Joshi, O. (2019). The paradox of declining female work participation in an era of economic growth, The Indian Journal of Labour Economics, 62(1), 55–71. Esteve-Volart, B. (2004). Gender Discrimination and Growth: Theory and Evidence from India, DEDPS 42, London School of Economics and Political Science, London, January. Ghose, J. (2017). Who works in India: The implications of defining work in the Indian statistical system, in Kannan. K.P., Mamgain, Rajendra P and Rustagi, P. (eds.), Labour and Development. Essays in honour of Prof. T.S. Papola. Academic Foundation, New Delhi. Gupta, S. (2018). Who does not get caught by social safety nets? IDEAS for India, May. Hirway, I. (2012). Missing labour force: An explanation. Economic and Political Weekly, 57(37), 67–72.

Decline in women’s employment in rural India  237 IMF (2018). Pursuing Women's Economic Empowerment, International Monetary Fund, Policy Paper, May, Washington DC. Kannan, K.P. & Ravindran, G. (2019). From jobless to job-loss growth gainers and losers during 2012–18, Economic and Political Weekly, 45(44), 38–44. Kapsos, S., Bourmpoula, E, & Silberman, A. (2014). Why is female labour force participation declining so sharply in India?, ILO Working Papers 994949190702676, International Labour Organization, Geneva. Klasen, S. & Pieters, J. (2012). Push or pull? Drivers of female labour force participation during India’s economic boom, Working Paper No. 6395, IZA Discussion Paper Series, Institute for the Study of Labour, Bonn. Mckinsey Global Institute (2018). The Power of Parity: Advancing Women’s Equality in Asia Pacific, April. Neff, D., Sen, K. & Kling, V. (2012). The puzzling decline in rural women’s labour force participation in India: A re-examination, Indian Journal of Labour Economics, 55(3), 408–429. Oxfam India (2016). An Economy That Works for Women, Oxfam Annual Report 2016–17, New Delhi. Reddy, W.R. & Mamgain, Rajendra P. (2020). Revival and reconstruction of rural livelihoods amidst Covid-19: Policy responses, opportunities and way ahead, SRSC Policy Paper Series-1/2020, S.R. Sankaran Chair (Rural Labour), National Institute of Rural Development and Panchayati Raj, Hyderabad. Schultz, P.T. (2002). Why governments should invest more to educate girls, World Development, Elsevier, 30(2), 207–225. Singh, Av. & Ozanne, A.G. (2017). Revisiting the Decline in India’s Female Labour Force Participation: The Rise of Machines and Security Risks, Discussion Paper No. 1712, University of Otago, Dunedin.

Chapter 13

Low female labour force participation Evidence from urban Kerala Renuka S. and Anu Abraham

13.1 Introduction There has been an increasing presence of women in the labour market in the twentieth century, but the female labour force participation rate (FLFPR) varies across regions and countries (Devi, 2002). Middle East, North Africa, South Asia, Latin America, and the Caribbean lie at the lower end of the spectrum, while South-East Asia and Pacific, Sub-Saharan Africa, and East Asia lie at the higher end; i.e. these rates are higher than the global average levels of female labour force participation (Chaudhary & Verick, 2014). Unlike western industrialised countries, the South Asian region presents an interesting contradiction between growth and employment because in spite of its impressive growth rate, the female labour force participation rate continues to be dismal.1 In the context of India, FLFPR is much below the global average and lower than its South Asian counterparts like Maldives, Sri Lanka, and Bangladesh (Table 13.1). India also records the globally evident gender gap in labour force participation rate (LFPRs). For instance, according to the National Sample Survey Organisation’s (NSSO) employment–unemployment surveys (EUS) and Annual Reports of Periodic Labour Force Survey (PLFS), the FLFPR is less than half of the male labour force participation rate (MLFPR). Furthermore, FLFPRs indicate a rural–urban gap with urban areas registering less than twice the FLPR in rural areas and urban LFPR consistently falling below 20 per cent. Considering the nature and quality of paid work, women are mostly engaged in low-productivity and low-paying jobs. Studies also document the segmentation of the Indian labour market based on gender, type of employment, wages, sector, and residential location (Duraisamy M and P Duraisamy, 2016). Why is this low female labour force participation rate of concern? Apart from contributing to a country’s growth, labour market participation also affects the economic and social well-being of women. Keeping aside the status symbol, work enhances financial autonomy, standard of living, self-esteem, and greater participation of women in familial decision-making, as seen in the DOI: 10.4324/9781003329862-17

Low female labour force participation  239 Table 13.1 Adult female literacy and female labour force participation in South Asia Country

Adult female literacy (%)

Female labour force participation (%)

Afghanistan Bangladesh Bhutan India Nepal Maldives Pakistan Sri Lanka

30 71 57 66 60 98 46 91

22 36 59 21 83 42 22 35

Source: The World Bank, 2018.

literature (Devi, 2002; Jose, 2012). Besides, women’s work participation not only ensures their personal well-being but has an inter-generational effect in improving the educational attainment of children (Afridi, Mukhopadhyaya & Sahoo, 2016) and can lead to better occupational outcomes, especially among daughters (Miller, 2015). Hence, women’s employment is said to ‘yield a growth premium in the GDP trends’, by 27 per cent (International Monetary Fund [IMF] as seen in Chapman & Mishra, 2019) which India has so far failed to absorb due to the widespread presence of women outside the labour force. Given the context, we use an in-depth micro-level survey to examine the reasons for a low level of female labour force participation, continued absence from the labour force after the first withdrawal, and possible interventions to improve women’s participation in the workforce. Studying the determinants of female labour force participation at the local level will help us to obtain a nuanced understanding of the factors influencing women’s entry and exit from the labour market, which often goes unnoticed in a macro-level study. Further, as a micro-level study placed in an urban setting, we hope to answer why urban FLFPR is persistently lower than rural FLFPR seen at the national and sub-national levels. Further, this study adds to empirical works exploring societal norms, kinship, and family structure in women’s decision to work and the broader theme of voluntary unemployment among women. In this context, the specific research questions addressed by this chapter are (i) how important are societal norms, family, and kinship ties in explaining FLFPR in urban areas? (ii) does it influence women’s entry and exit decisions from the labour market and re-entry after their first withdrawal? and (iii) what possible interventions could improve women’s economic role in the household? We attempt to answer the above questions using qualitative information from the field using primary data collected from Ernakulam district – the most urbanised district in Kerala, India. Kerala assumes relevance on several grounds: Kerala is the state in India that has high human capital development (as per HDI [Human Development Index] statistics), comparable to developed countries and scores high on social sector indicators such as

240  Renuka S. and Anu Abraham

health and education (Pillai, 2008). According to the 2011 Census, Kerala has the highest overall literacy rate (94 per cent), with the highest female literacy rate in India. If education is hypothesised to be a significant determinant of labour market participation, Kerala presents a paradoxical situation of high female literacy and low female labour force participation – a trend also seen in many of the South Asian countries except Nepal and Bhutan (Table 13.1).

13.2 Literature survey Literature on the labour market offers numerous empirical works on female labour force participation. Historically, women’s participation in the labour market has been lower than that of men and many reasons have been attributed to the same. Some of the reasons discussed are higher educational attainment or enrolment rate leading to a late joining of the labour market (Eapen, 2004; Lahoti & Swaminathan, 2013; ILO, 2013; Chaudhari & Verick, 2014), a view that female incomes are secondary sources of income and are not required in high-income households (ILO, 2013; ; Abraham, 2013; Chaudhari & Verick, 2014), poor working condition, wage discrimination (Mehra & Gummage, 1999; ILO, 2013), increase in male education (Bhalla & Kaur, 2011), and increased competition with men for job opportunities (ILO, 2013). The phenomenon of ‘voluntary’ withdrawal of women from the labour market has also been a focus of many studies (Eapen, 2004; Sudarshan & Bhattacharya, 2009; Abraham, 2013; Chatterjee, Rama & Murugai, 2017). While some studies give marriage as an explanation for the declining share of women in the labour force (Das, Chandra, Kochhar, & Kumar, 2015), others suggest that there are no signs of such withdrawal (Unni, 1996). Chapman and Mishra (2019) identified patriarchal social norms and the disproportionate burden of care work and domestic obligation as two of the major factors responsible for low FLFPRs in India. Economic growth and female labour force participation are believed to show a U-shaped relationship, known as the U-shaped feminization hypothesis. The hypothesis states that the relation between economic development and women’s labour force participation rate follows a U shape. According to Goldin, (1995) women’s presence is marked in the labour force at extremely low levels of income but falls with the increase in income when they recede to unpaid household chores, indicated by the falling portion of the U-shaped curve. Again, with the increase in education and the ‘value of women’s time in the market’ women start taking up paid jobs, indicated by the rising portion of the U-shaped curve. However, India holds very little evidence or violates the feminization U hypothesis (Bhalla & Kaur, 2011). In fact, the relationship between economic growth and female labour force participation shows an inverted U shape in the Indian context (Lahoti & Swaminathan, 2013).

Low female labour force participation  241

Women’s employment shows few atypical characteristics. In India, the female workforce participation rate (FWPR) in the rural area is more than that of the urban area. Ahmed, (1979) reveals the clustering of women in casual jobs, with wages lower than men. Abraham, (2013) lays down the problem of missing labour force or de-feminization of the labour force in India. The author finds two main reasons, inter alia, for the phenomenon of de-feminization of labour: withdrawal from the labour force and women being competed out by men in the labour market. The most literate state in India, Kerala, suffers from educated unemployment among women (Sebastian & Navaneetham, 2012). Among those employed, a vast majority is clustered in the lowest rungs of the professional hierarchy – nursery teachers, nurses/heath technicians, steno/typists, and other clerical jobs (Eapen, 2004). Women are mostly engaged in jobs with long working hours and low wages, which do not require high levels of education or technical skills. Most of the regularly employed females in Cochin are engaged in clerical and related jobs (Prakash, 2002). Literature shows the widespread presence of educated unemployment in Kerala (Sebastian & Navaneetham, 2012). Panickar, (2016) finds a perfect positive correlation between urbanisation and female work participation in Kerala. It can be inferred from the literature that female labour force participation is low in India both in absolute and relative terms, in spite of the rising trends in different parts of the world. Women bear the double burden of work and family responsibilities, which causes them to exit the labour market at an early stage. Most of the studies on female labour force participation in India are based on macro-statistics except for a few studies that used primary surveys in Thrissur district (Devi, 2002), parts of urban Delhi (Sudarshan & Bhattacharya, 2009), and outskirts of Jaipur (Jose, 2012) among others. In this context, this study attempts to find more evidence for the causes of low labour force participation and ‘voluntary’ unemployment among women in Kerala despite high levels of literacy. The chapter first uses secondary data from various sources to understand the macro-economic trends and patterns of FLFPR of the study and compares it to the trends of the district and the state. The secondary data sources include the 2011 Census, Annual Report of Periodic Labour Force Survey (PLFS, 2017–18) of the Government of India, Reports of the NSSO, Gender Statistics 2014–15 of the Government of Kerala, and The World Bank database. Further the study delves into the primary data using descriptive statistics to understand the motivations to work or stay outside the labour force.

13.3 Background of the study Analysing the PLFS Annual Report (2017–18), we find that the labour force participation rate differs across gender, education, and region. The female

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labour force participation rate (FLFPR) is lower than male labour force participation rate (MLFPR) across all educational categories in India and Kerala. FLFPR is the highest among graduates followed by higher secondary level of education both at the national and state level. Besides, there is a rural–urban divide at the national and state level, with LFPR across educational categories being comparatively more in the rural areas. 13.3.1 Trends and patterns of female labour force participation in Kerala It is known that Kerala holds an exceptional position in the Indian economy as it fares high in terms of the HDI and other social sector indicators compared to other Indian states, earning itself credit as the Kerala Model of Development. Even though Kerala is the most literate state in India, the labour force participation rate (LFPR) is lower than the national average and is the lowest among all South Indian states (Table 13.2). The fact that nearly 80 per cent of women in the state and its sublevels are not active participants in the labour market calls for a deeper analysis of the situation (Table 13.4). 13.3.2 District-wise labour force participation in Kerala Examining the LFPR in the state, we find that the FLFPR in Kerala is very low at 23.8 per cent compared to 54.7 per cent MLFPR in the rural area. Table 13.3 shows that even in districts like Ernakulam with a high overall FLFPR of 53.6 per cent, the region-wise distribution of FLFPR is still at a low 27 per cent with the urban FLFPR being lower than the rural FLFPR (which is also the case in most of the districts). Overall labour force participation in the rural areas of Ernakulam district is more than in urban areas, which is similar to the national and state level trends. The fact that Ernakulam district has a large share of women outside the labour force makes it suitable to undertake a micro-level study to examine the motivations to work as well as withdraw from the labour force among women in Kerala. The study placed in an urban setting helps to gauge the reasons for the predominance of low female labour force participation in the Table 13.2 Labour force participation rate (%) in Kerala according to usual status (PS + SS)* Region

Kerala

Tamil Nadu Karnataka Andhra Pradesh Telangana

India

Rural Urban Rural + Urban

36.4 36.8 36.3

46.1 41.3 43.9

37 36.8 36.9

41.4 37.9 40

50.2 41 47.1

44.9 38.9 42.5

Source: data compiled from Periodic Labour Force Survey (PLFS, 2017–18) Annual Report. *Note: PS+SS = Usual Principal Status and Subsidiary Status

Kasargod Kannur Wayand Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki Kottayam Alapuzha Pathanamthita Kollam Thiruvananthapuram Kerala

1 2 3 4 5 6 7 8 9 10 11 12 13 14

319 221 232 142 140 248 185 271 433 301 298 211 235 292 238

Rural female 223 175 199 107 106 178 194 265 343 219 383 228 152 328 225

Urban female

Source: Gender Statistics 2014–15, Government of Kerala.

District

Sl No 542 396 431 249 246 426 379 536 776 520 681 439 387 620 463

Total female 552 572 581 554 416 535 547 631 599 599 570 568 547 589 547

Rural male

Table 13.3 District-wise labour force participation rate in Kerala/1000

515 534 505 519 454 539 522 600 538 538 535 617 512 554 545

1067 1106 1086 1073 870 1074 1069 1231 1137 1137 1105 1185 1059 1143 1092

Urban male Total male 871 793 813 696 556 783 732 902 1032 900 868 779 782 881 785

Rural (M + F) 738 709 704 626 560 717 716 865 881 757 918 845 664 882 770

Urban (M + F)

1609 1502 1517 1322 1116 1500 1448 1767 1913 1657 1786 1624 1446 1763 1555

Total (M + F)

Low female labour force participation  243

244  Renuka S. and Anu Abraham

urban area in general. The widespread presence of nuclear family and high standard of living in the urban area presents a conflict of interest between the care work at home and paid work in the labour market, which together shape women’s decision to participate in the labour force. Besides, the study is relevant from a global perspective wherein female labour force participation shows a persistently declining trend. 13.3.3 Labour force participation in Kanayannur taluk Table 13.4 presents a gender-wise distribution of workers and non-workers in Kanayannur taluk along with the district level and the state level figures. Male main workers constitute 51 per cent of the total male population in Kanayannur taluk compared to 17 per cent of female main workers. Similarly, the share of male marginal workers is 4 per cent compared to 3 per cent of female marginal workers. Total male workers constitute 56 per cent and female workers constitute 21 per cent in the taluk. On the other hand non-workers comprise 44 per cent males and 79 per cent females. The non-participation of females in the labour market is evident from the data of non-workers in Kerala and its sublevels. We also understand that the percentage distribution of workers and non-workers in Kanayannur taluk is quite similar to Ernakulam district, substantiating the choice of the survey.

13.4 Data and methodology The chapter is mainly based on primary data consisting of a sample of 100 women in the working age group of 15–64 years that was collected using a purposive sampling method. Kochi Municipal Corporation of Kanayannur taluk (sub-district) in Ernakulam district in Kerala was chosen as the area for the survey. The wide gap between female literacy rates (87.76 per cent) and female working population (21 per cent) in Kanayannur sub-district serves as a receptacle of high female literacy and low female labour force Table 13.4 Percentage distribution of main workers, marginal workers, and non-workers by gender

Kanayannur taluk Ernakulam district Kerala

Gender

Main workers

Marginal workers

Total workers Non-workers (main + marginal workers)

Male Female Male Female Male Female

51.43 17.33 50.18 14.95 44.79 12.37

4.55 3.54 6.21 5.26 6.62 4.14

55.98 20.87 56.39 20.21 51.41 16.51

Source: District Census Handbook, Ernakulam and Census, 2011.

44.02 79.13 43.61 79.79 47.27 81.77

Low female labour force participation  245

participation. In the 2011 Census, among the taluks, the percentage of urban population is the highest in Kanayannur taluk 818,432 (96.13 per cent). The urban context of our study is evident from the fact that Ernakulam district is the most urbanised district in Kerala and the sub-district of Kanayannur has the highest percentage of the urban population. Cochin Municipal Corporation is the urban centre of Kanayannur taluk and also one of the most urbanised centres of Kerala. A scheduled questionnaire was administered to the respondents to obtain the required information. To improve accuracy and avoid nonresponse to items, the survey was conducted through face-to-face interviews with the respondents. Though the sample size is small, measures were taken to ensure the representativeness of heterogeneities such as age, education, marital status, household type, and socio-religious and economic backgrounds. A pilot survey was conducted in Thevara, in Cochin Corporation in January 2017. The questionnaire was modified after the pilot study and fieldwork was carried out in the urban metropolis of Kochi Municipal Corporation, during January–March 2017. The questionnaire had a general section capturing details regarding the socio-demographic and economic conditions of the respondents followed by two separate sections addressing questions to women who are (i) currently part of the labour force and (ii) outside the labour force to understand in depth the motivations for both joining employment as well as withdrawing from the same.

13.5 Descriptive statistics Most of the respondents in the survey were from the prime working age group (25–54) and 83 per cent of the respondents were married. Educational attainment of the respondents shows that three-quarters of the respondents were highly educated (42 per cent as graduates, 22 per cent as post-graduates, and 10 per cent professionally qualified women) and among the rest, 4 per cent had a diploma, 12 per cent had senior secondary education and 9 per cent secondary education. This indicates the sample of respondents in the study has a relatively high level of education. Educational specialization shows the following distribution: 26 per cent Commerce, 13 per cent Humanities and Arts, 11 per cent Science, 9 per cent Medicine and Health, 7 per cent Engineers, 12 per cent other specialization like business, law etc. (22 per cent women have not stated their educational specialization). This shows that women in STEM are only a small proportion and the highest specialization exists in Commerce, followed by Humanities and Arts, both traditionally considered as ‘suitable’ for women. Examining the household-level characteristics of the respondents, it was found that three-quarters of the respondents lived in nuclear families, and while 12 per cent survived on less than Rs. 10,000 per month, 35 per cent

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had monthly income between Rs. 20,000 and Rs. 30,000, and the same proportion earned above Rs. 30,000. In the survey responses, 55 per cent of respondents were Hindus, 38 per cent were Christians, and 7 per cent were Muslims. Half of the respondents surveyed belonged to forward groups (51 per cent), 32 per cent were Other Backward Communities (OBC), 14 per cent were Scheduled Caste (SC), and 3 per cent were Scheduled Tribes (ST). These distributions are in line with the general demographic distributions of urban households in Kerala and hence make the findings of this study generalizable.

13.6 Results and discussion of the primary survey This section presents the results of the field survey conducted to understand the role of societal norms, kinship, and family ties in women’s decision to enter and exit the labour market. 13.6.1 Women’s labour force participation Among the survey respondents, 59 per cent were employed and 4 per cent were job seekers, i.e. 63 per cent of women were in the labour force, while 37 per cent were outside the labour force. Among those employed, 50 per cent held upper white-collar jobs, 15 per cent held lower white-collar jobs, 10 per cent were self-employed, and the rest (15 per cent) held blue-collar jobs. This is consistent with the distribution of female workers among different occupational groups at the state and national levels, indicating the predominance of regular employment among urban women. 13.6.1.1 Labour force participation of respondents according to age Almost all of the women in the age group of 15–34 years (most of the respondents were above the age of 18) reported their employment status as ‘employed’, and those belonging to the 25–34 age group constituted the highest proportion of the employed women. The labour force participation decreased post-35 years of age, which could be attributed to a higher reproductive load or increased care work at home (caring for parents or child care). It is noteworthy that none of the respondents post-35 years of age were actively searching for a job during the survey period. 13.6.1.2 Labour force participation of respondents according to education Among different educational categories, the highest level of labour force participation is registered among graduates (28 per cent), which is followed by women with secondary education (13 per cent), higher secondary and diploma holders (11 per cent), and professionally qualified women (9 per cent). Women with primary education are almost absent in the labour force. But the relation between employment status and education is puzzling because, while the labour force participation is the highest among graduates

Low female labour force participation  247

(27 per cent), the percentage of women outside the labour force is the highest among the post-graduates (20 per cent). In spite of the hypothesised positive relation between education and female labour force participation, the levels remain low (as per the macrodata) and more women tend to exit the labour market post-35 years, as the study revealed. Verick, (2018) states that in countries like India, the relationship between FLFP and education is not linear, but U shaped, i.e. LFPR is high for the least educated women, low for those educated up to high school levels, and again high for those who have attained more than the secondary level of education. The relationship between education and FLFP depends on the relative strength between (i) the high opportunity cost of abstaining from work as educational attainment increases the prospects of entering the labour market (income effect) and (ii) the substitution of leisure for work as income from work increases (substitution effect) (Chaudhary & Verick, 2014). Moreover, as educational attainment increases, women tend to postpone their marriage and motherhood, which positively influences FLPR due to the absence of care duties (Chaudhary & Verick, 2014). Given this context, we presume that women’s non-participation or exit (if not entry) into the labour market in urban areas would have been influenced by non-economic or situational factors dictated by the conventional responsibilities associated with gender. 13.6.1.3 Labour force participation of respondents according to marital status Labour force participation by marital status shows that nearly half of the married women participate in the labour force and the number of married women who are employed (46 per cent) is more than the number of married women outside the labour force (36 per cent). Most of the respondents who are single, widow, and divorcee tend to remain in the labour force. This shows that unmarried women (including divorcee or widow) have a greater probability to participate in the labour market over an extended period of time, unlike married women who are known to bear the double burden of work–family balance on the one hand, and the financial security of their spouse on the other, both of which are absent in the case of an unmarried woman (mostly a single parent or widow). Among the women outside the labour force, married women constitute the highest share. Such a high percentage of working women and homemakers in the ‘Married’ category prompts us to conclude that subjective factors would play a crucial role in women’s decision to work.2 Literature shows an inverse relationship between the number of children and FLFP (Dasgupta & Golder, 2005; Masood & Ahmed, 2009 as seen in Chaudhary & Verick, 2014). Social norms and expectations about women’s work and a disproportionate burden of care duties on women especially in urban areas are cited as one of the many reasons for low FLFPR (Verick, 2017).

248  Renuka S. and Anu Abraham

13.6.2 Nature of employment among the working women Out of the 59 per cent of working women, 90 per cent of women are not the principal earning member of the family, and 46 per cent of the employed women are regular private sector employees compared to 15 per cent working in the government sector. Twenty per cent each are self-employed or work in the unorganized sector. This shows the concentration of women in private sector employment. Further, 91 per cent of women are engaged in regular salaried employment, 7 per cent work in shifts and a meagre 2 per cent does part-time job. The number of hours spent at work shows that 64 per cent of women work 5–8 hours a day. None of them work less than 2 hours per day or more than 11 hours per day. This shows the active participation of women in the workplace. Working hours are in compliance with the labour laws in India (i.e. working hours should not exceed 48 hours per week or 9 hours per day). Among the various reasons, the chief motivating factor for women to work is the financial security and self-reliance that comes with regular employment (55 per cent). This is substantiated by the fact that 48 per cent of working women use a major portion of their monthly income to meet personal requirements. Desire to contribute to household income and expenses was cited by 42 per cent, as the motive to work, and 3 per cent had no specific reason. This confirms the existing literature which signifies the role of paid work in making women’s economic contribution to the households more evident, increasing their financial independence from male members, and commanding greater respect within the family (Jose, 2012). An interesting relation between marital status and nature of work is that 47 per cent of married women who were employed entered the labour force prior to marriage, or 53 per cent of women entered the labour force after marriage. On further dissecting the post-marriage entry, we found that 31 per cent of married working women entered the labour market after marriage but before childbirth, and only 22 per cent entered the labour market after childbirth (Table 13.5). Contrary to the findings of Sudarshan and Bhattacharya, (2009) that there is a declining percentage of working women entering the labour market post-marriage and post-motherhood, we see that Table 13.5 Marriage and entry into the workforce Time of entry

Percentage

Pre-marriage Post-marriage before childbirth Post-marriage after childbirth Total

47 31 22 100

Source: primary data.

Low female labour force participation  249

marriage and motherhood might be acting as a push factor for women’s entry into the labour market. 13.6.3 Importance of kinship, family ties, and labour force participation This section examines the role of societal norms, kinship, and family ties on female labour force participation. We first try to understand the expectations of working women, their priorities, and confidence in work–family balance, which is crucial during the period between their entry and exit from the labour market. Kinship ties and familial expectations about women’s responsibilities influence women’s participation in the labour market – in our survey, 73 per cent of working women said families showed a positive attitude towards work. The remaining 27 per cent either discouraged (2 per cent) or seemed indifferent to women’s work (8 per cent) or expected them to undertake household responsibilities along with work (17 per cent). This often leaves them with the choice to prioritize work and/or family. In our study 74 per cent of working women gave equal importance to work and family. Nineteen per cent prioritized family over work, whereas 7 per cent prioritized work over family. This self-created pressure to balance work and family with an average level of confidence (49 per cent) to do the same makes them heavily dependent on a strong support system like maids (46 per cent), parents (37 per cent), and husband (17 per cent) to prolong their stay in the labour market. While societal norms, kinship, and family ties influence women’s participation in the labour market, does it deter their entry and haste their exit? In our sample, 37 per cent of women are outside the labour force. Among them, more than half (57 per cent) never ever joined the labour market and ‘preferred’ to ‘voluntarily’ stay outside the labour force. Their ability to participate in the labour market is limited by childcare and domestic obligations as suggested by 38 per cent and 16 per cent of the respondents, respectively. Other reasons such as limited career-oriented skills and understanding (11 per cent), lack of family support and unmatched job preference (8 per cent respectively), and health issues (5 per cent) were also suggested. Fourteen per cent of respondents ‘prefer to stay at home’. In other words, the ‘voluntary’ decision to remain outside the labour force can be ascribed to an internal conflict between economic role and traditional gender-specific role, with non-working women often settling for the latter to prescribe to the societal norms (Zhou, 2017). Now, considering withdrawal from the labour market, among those who withdrew from work (43 per cent), two-thirds of the women suggested domestic obligations such as childcare (69 per cent), followed by family responsibility (19 per cent), personal health issues and medical advice to stay away from work (12 per cent). The withdrawal from work is not a

250  Renuka S. and Anu Abraham Table 13.6 Marriage, childbirth, and withdrawal from workforce Time of withdrawal

Percentage

Pre-marriage Post-marriage before childbirth Post-marriage after childbirth Total

13 25 62 100

Source: primary data.

temporary, short-term phenomenon and often leads to the permanent exit of women from the labour market as about half of the respondents who withdrew from the labour market have already been outside the labour force for longer than 5 years, and about 80 per cent reported lack of interest in re-entering the labour force. On examining the time of withdrawal, we found that women’s withdrawal from employment was predominantly related to marriage and motherhood as 62 per cent withdrew from the labour force post-childbirth, while 25 per cent withdrew post-marriage but before childbirth (Table 13.6). Thus motherhood plays an important role in women’s withdrawal and exit from the labour force. The findings of Sudarshan and Bhattacharya, (2009) also indicate the general indifference among non-working women to re-enter work and the role of household and childcare responsibility in the withdrawal and continued absence of women in the labour market. However, our study found that, compared to the state of being married, the added responsibility from motherhood seems to exert a greater influence on women’s withdrawal from work. In fact, the role of marriage in women’s participation in the labour market is inconclusive as post-marriage entry and exit are high compared to the pre-marriage levels. Instead, motherhood is the major influence on women’s decision to enter, exist, and exit the labour market. We find that entry into the labour market post-motherhood is less than pre-motherhood entries, whereas exit from the labour market post-motherhood exceeds the pre-motherhood exits. This can be ascribed to the reinforcement of conventional societal expectations – of women as caregivers (Fletcher, Pande & Moore, 2017; Zhou, 2017), deterring their entry and hastening their exit, especially in the absence of a strong kinship and familial ties for the shared responsibility towards a work–family balance.

13.7 Findings and policy implication Low levels of female labour force participation have been a subject matter of discussion and debate in the literature. Micro-level studies play a vital role in capturing the perceptions and prejudices towards work, of not just women but also their family members who influence women’s decision to work. Existing literature indicates the mediating role of family (Eapen, 2002) and

Low female labour force participation  251

marriage and motherhood (Sudarshan & Bhattacharya, 2009; Devi, 2002) in women’s employment decision. To the best of our knowledge, at the time of our study, not many works have empirically examined the role of societal norms, family ties, and kinship in women’s labour market participation in Kerala. The results of our study confirm other micro-level studies (Devi, 2002; Sudarshan & Bhattacharya, 2009; Jose, 2012). Future research can explore a larger sample, including rural areas. The role of marital status in women’s decision to work is inconclusive. Examining the entry and exit with respect to marital status, we found that percentage of labour market entry post-marriage is more than pre-marriage entry. And the post-marriage exit of women from the labour market is higher than the pre-marriage exit. Thus, we may state that the influence of marital status on women’s decision to enter and exit the labour market is inconsistent. Rather motherhood proves to be an important determinant of women’s entry, withdrawal, and exit from the labour market given the findings that post-motherhood entry into the labour market is less than premotherhood entry, and post-motherhood exit is more than pre-motherhood exit from the labour market. We may deduce that withdrawal from work stems from the double burden of work–family balance, their average level of confidence to balance work and family, and the need for a support system to achieve the same. And most importantly, the behavioural expectations borne by women to be the primary caregiver. Respondents also strongly suggest the need for family’s approval and material support in order to be able to work outside of the home. Hence strong kinship and familial ties are crucial for women’s paid work. For instance, Gonzalez, (2006) documented the role of public and/or private support in externalising women’s domestic and care work and improving their labour market presence. Child-bearing and child-rearing responsibilities after motherhood are found to be the major reason for women to stay outside the labour force and also for working women to withdraw from the labour market. Very often withdrawal from work manifests into a permanent exit from the labour force because women generally do not plan a re-entry, also evident in the study by Sudarshan and Bhattacharya, (2009). Along with marital status, we find that education is not an important determinant of women’s decision to work as women with high educational levels have also withdrawn from the labour force, reinstating the paradox of highest female literacy and low female labour force participation in Kerala and furthering the cause of non-economic factors such as societal norms, kinship, and family ties in labour market decision-making. Thus, the findings of the study reinforce the role of behavioural factors and hence behavioural public policy in influencing women’s exit (if not entry) from the labour market. Although pro-women and work–family policies such as maternity leave, crèche facility etc. exist at the institutional level, what women need beyond such interventionist policies are

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small nudges at the household level to enter and exist in the labour market. The complexity of the issue is in its highly subjective nature – an anchoring bias in the gendered perception of household duties versus paid work outside home – which lead them towards the irrational decision of exiting the labour market in the name of child care and domestic duties. This calls for a need to de-bias the gendered expectations on labour market participation that is required not just to channelise women’s human capital into the growth process of the economy but also to realise the goals of gender equality.

13.8 Conclusion This study examined the determinants of female labour force participation by analysing women’s entry and exit choices. We first presented an overview of female labour force participation at the state and district level of the Kerala economy and then examined the situation at the local level through a field survey. FLFPR in Kerala is lower than the national average and the lowest among South Indian states. Educated unemployment and jobless growth, though an undesired feature of India’s growth process, has a disturbing gender dimension highly unfavourable for women. We may state that in an urban setting, subjective factors (manifested in personal decisions) and situational factors (such as the double burden of work and family responsibility, child care, aged parents, personal health issues etc.) influence women’s withdrawal from work in the first place, and continued absence after the first withdrawal resulting in a permanent exit from the labour market. In other words, responsibilities associated with motherhood act as a catalyst in women’s non-participation in the labour market.

Notes 1 Retrieved from https://www​.worldbank​.org​/en​/events​/2020​/02​/18​/south​-asia​ -women​-in​-the​-workforce week#​:~:te​xt=Wo​men%2​0in%2​0Sout​h%20A​sia%2​ 0cont​inue,​%25%2​0vers​us%20​80%25​%20fo​r%20m​en. 2 Widespread presence of both working and non-working women who are married is also found in the study of Sudarshan and Bhattacharya, (2009) in the context of Urban Delhi. Such a conflicting observation among married category is found in studies based on large samples such as Sudarshan and Bhattacharya, (2009) as well as the studies based on small samples like the present one.

References Abraham, V. (2013). Missing Labour or Consistent Defeminisation. Economic and Political Weekly, 48(31), 99–108. Afridi, F., Mukhopadhyay, A., & Sahoo, S. (2016). Female Labour Force Participation and Child Education in India: Evidence from the National Rural Employment Guarantee Scheme. IZA Journal of Labour & Development, 5(7).

Low female labour force participation  253 Afridi, F., Mukhopadhyay, A., & Sahoo, S. (2016). Female labour force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme. IZA Journal of Labour & Development, 5(1), 1–27. Ahmed, K. (1979). Studies of Educated Working Women in India Trends and Issues. Economic and Political Weekly, 14(33), 1435–1440. Bhalla, S. S., & Kaur, R. (2011). Labour Force Participation of Women in India: Some Facts, Some Queries. Asia Research Working Paper No. 40. London: London School of Economics and Political Science. Chapman, T., & Mishra, V. (2019). Rewriting the Rules: Women and Work in India. ORF Special Report No. 80. New Delhi: Observer Research Foundationhttps:// orfonline ​ . org ​ / wp ​ - content ​ / uploads ​ / 2019 ​ / 01 ​ / ORF ​ _ Special ​ _ Report ​ _ 80 ​ _ WomenWork​.pdf Chatterjee, U., Murgai, R., & Rama, M. (2017). What explains the decline in female labour force participation in India? Retrieved August 10, 2020, from http://www​ .ideasforindia​.in​/article​.aspx​?article​_id​=1566 Choudhary, R., & Verick, S. (2014). Female Labour Force Participation in India and Beyond. ILO Asia Pacific Working Paper Series. Geneva: ILO. Das, S., Chandra, S., Kochhar, K., & Kumar, N. (2015). Women Workers in India: Why So Few Among So Many. IMF Working Paper No. 15/55. Washington, DC: IMF. Dasgupta, P., & Goldar, B. (2005). Female Labour Supply in Rural India: An Econometric Analysis. New Delhi: Institute of Economic Growth. Department of Economics and Statistics. (2016). Gender Statistics 2014–15. Thiruvananthapuram: DES. Directorate of Census Operations Kerala (2011). District Census Handbook Ernakulam. Thiruvananthapuram: Directorate of Census Operations Kerala. Devi, L. (2002). Education, Employment, and Job Preference of Women in Kerala: A Micro-level Case Study. CDS Discussion Paper No. 42. Thiruvananthapuram: Centre for Development Studies. Duraisamy, M., & Duraisamy, P. (2016). Gender Wage Gap across the Wage Distribution in Different Segments of Labour Market, 1983–2012: Exploring the Glass Ceiling or Sticky Floor Phenomenon, Applied Economics, 48 (43), 4098–4111. Eapen, M. (2004). Women’s Work and Mobility: Some Disquietening Evidenced from Indian Data. CDS Working Paper No. 358. Thiruvananthapuram: Centre for Development Studies. Eapen, M. & Kodoth, P. (2002). Family Structure, Women’s Education and Work: Re-examining the High Status of Women in Kerala. CDS Working Paper No. 341. Thiruvananthapuram: Centre for Development Studies. Fletcher, E. K., Pande, R., & Moore, C. T. (2017). Women and Work in India: Descriptive Evidence and Review of Potential Policies. CID Faculty Working Paper No. 339. Cambridge: Centre for International Development, Harvard University. Goldin, C. (1995). The U-Shaped Female Labour Force Function in Economic Development and Economic History. In Investments in Women’s Human Capital. Schultz: University of Chicago Press. Gonzalez, M. J. (2006). Balancing Employment and Family Responsibilities in Southern Europe- Trends and Challenges for Social Policy Reform. Revue Francaise des Affaires Sociales, 1(5), 189–214.

254  Renuka S. and Anu Abraham International Labour Organisation. (2013). Global Employment Trends 2013 Recovering from a Seconds Jobs Dip. Geneva: ILO. Jose, S. (2012). Women's paid work and well-being in Rajasthan. Economic and Political Weekly, XLVII (45), 48–55. Kanayannur Taluk. (n.d.). Retrieved August 10, 2020, from https://indikosh​.com​/ subd​/674087​/kanayannur Kerala State Planning Board. (2015). Kerala Economic Review. Thiruvananthapuram: State Planning Board. Lahoti, R., & Swaminathan, H. (2013). Economic development and female labour force participation in India. IIM Bangalore Research Paper, (414). Masood, T., & Ahmad, I. (2009). An Econometric Analysis of Inter-state Variation in Women’s Labour Force Participation in India. MPRA Paper No. 1927. Munich Personal RePEc Archive (Aligarh Muslim University). http://mpra​.ub​ .unimuenchen​.de​/19376​/1​/MPRA​_paper​_19376​.pdf [15 Oct 2014]. Mehra, R., & Gammage, S. (1999). Trends, Countertrends, and Gaps in Women’s Employment. World Development, 27(3), 533–550. Miller, C. C. (2015, May 15). Mounting Evidence of Advantages for Children of Working Mothers. The New York Times. https://www​.nytimes​.com​/2015​/05​ /17​/upshot​/mounting​-evidence​-of​-some​-advantages​-for​-children​-of​-working​ -mothers ​ . html#:~ ​ : text ​ = Yet ​ % 20evidence ​ % 20is​ % 20mounting​ % 20that​ , time​ %20with​%20them​%20​%E2​%80​%94​%20they​%20do. Ministry of Statistics and Programme Implementation. (2019). Annual Report Periodic Labour Force Survey (July 2018–June 2019). New Delhi: National Statistical Office. Panickar, R. C. (2016). Urbanization and Female Workforce in Kerala. Southern Economist, 55(7), 13–18. Pillai N. V. (2008). Infrastructure, Growth and Human Development in Kerala. MPRA Paper No. 7017 Prakash, B. A. (2002). Urban Unemployment in Kerala: The Case of Kochi City. Economic and Political Weekly, 37(39), 4073–4078. Sebastian, A., & Navneetham, K. (2012). Gender, Education and Work: Determinants of Women’s Employment in Kerala. In Kerala’s Demographic Future: Issues and Policy Options. Academic Foundation. Sudarshan, R. M., & Bhattacharya, S. (2009). Through the Magnifying Glass: Women’s Work and Labour Force Participation in Urban Delhi. Economic and Political Weekly, 44(48), 59–66. The World Bank Data. (2018a). Literacy Rate, Adult Female (% of Females Ages 15 and above) [Data file]. Retrieved August 10, 2020, from https://data​.worldbank​ .org​/indicator​/SE​.ADT​.LITR​.FE​.ZS The World Bank Data. (2018b). Labour Force Participation Rate, Female (% of Female Population Ages 15+) (Modelled ILO Estimate). [Data file]. Retrieved August 10, 2020, from https://data​.worldbank​.org​/indicator​/SL​.TLF​.CACT​.FE​ .ZS Unni, J. (1996). Women's Employment in Newly Industrialising Countries [Review of the Book Women and Industrialisation in Asia]. Economic and Political Weekly, 31 (39), 2679–2680. Verick, S (2014). Female Labour Force Participation in Developing Countries. IZA World of Labour, 2014, 87.

Low female labour force participation  255 Verick, S. (2017). The paradox of Low Female Labour Force Participation. ILO Working Papers. Geneva: International Labour Organization. Verick, S. (2018). Female Labour Force Participation and Development. IZA World of Labour, 2018, 87v2. Zhou, M. (2017). Motherhood, Employment, and the Dynamics of Women’s Gender Attitudes. Gender and Society, 31(6), 751–776.

Part 5

Employment generation in the manufacturing sector



Chapter 14

BRICS–EU global value chains trade and manufacturing employment Usha Nori and Ram Kumar Mishra

14.1 Introduction Trade integration intensifies in recent years with the emergence of global value chains (GVCs). Higher participation of developing economies in GVCs resulted in increased trade at about 33 percent in 2011. Countries are now specializing in specific goods rather than developing whole industries for their exports. This fragmentation of production is expected to have greater implications on the labour market. In the past, while a greater amount of literature focused on the implications of trade and employment, the link between GVCs and employment is of recent origin. Many studies in the literature evidence a positive relationship between GVC participation and employment. GVC participation leads to productivity growth of the firms, thus creating job opportunities. This process of fragmentation in production is experimented with by industrialized countries especially Germany the first in the 1970s itself to reduce the costs of production by relocating their production base to developing countries due to lower wage levels. With the passage of time, globalization has intensified the fragmentation of production, thus resulting in the offshoring of jobs. There is a widespread belief that offshoring of jobs endangers domestic employment in the home country. This fear of losing jobs is however negated over a period. Thomas Farole (2016) in his study finds that GVCs created manufacturing jobs in some countries, while others have seen a shift in demand for labour from manufacturing to services and from lower to higher skills. Against this backdrop, the present chapter therefore examines whether GVCs have a deleterious effect or positive benefit on Indian manufacturing employment, particularly when India’s trade is consistently rising with the European Union (EU). Fragmentation of production is more pronounced in the manufacturing sector, triggering the advanced nations to enjoy a greater share in global production networks due to their strong manufacturing base. For instance, the EU’s (European Union-28) growing integration in GVCs is evident in its growing share of foreign value added in the manufacturing sector (Reinhilde Veugelers, 2013). On the other hand, emerging economies, in particular DOI: 10.4324/9781003329862-19

260  Usha Nori and Ram Kumar Mishra

BRICS (Brazil, Russia, India, China and South Africa), have become important partners to the EU due to lower labour costs. Products designed and manufactured in the EU would be assembled in countries like China and India. BRICS form a better supplier of products as they enjoy a higher comparative cost advantage in the industries, viz., electrical equipment, transport equipment, textiles, chemicals and basic metals, whereas the EU has high foreign value-added content. Viewing from the employment dimension, it is evident from the existing studies that integration within GVCs helps generate more employment through productivity and scale effects. ‘Labour-intensive manufacturing activities have been outsourced to developing countries with low-cost human resources, especially in East Asia (World Bank, 2017). Furthermore, significant changes have occurred in labour force composition since the spread of GVCs’ (OECD, 2013). Literature evidences the fact that relocation of labour-intensive, low value-added activities results in the expansion of manufacturing jobs in emerging economies. Divergent views emerge on GVCs manufacturing employment. According to Rodrik (2013), GVC participation may not boost employment but help in absorbing advanced technologies. He further argues that technologies associated with GVC participation provide diminishing possibilities of substitution of unskilled labour or other factors of production. Baldwin (2014) views that GVCs tend to boost productivity and employment through entry into global manufacturing goods markets but make industrialization less meaningful. Lopez-Gonzalez (2016) and Constantinescu et al. (2019) find positive effects from importing intermediates on a country’s value added as well as on employment, particularly in services. Mixed responses on the employment effects of GVC integration prompted us to study the job effects of GVCs with special reference to BRICS and EU countries due to their growing GVC manufacturing trade. Moreover, the rise of the BRICS as the most influential group in the international arena, due to its aggressive outward-oriented policies, has become a preferred destination to forge economic and trade relations. Shrinking global employment further forces every nation to adopt new policy agendas in their national policies and India is no exception. Since India’s share of the manufacturing sector in employment is virtually on a declining trend with intermittent spikes over the years, it is pertinent to explore ways to accomplish the employment goal. One of the possible routes identified is to participate in GVCs. India’s attempts in its Make in India programme perhaps enable the country to participate in GVCs and open doors for employment generation. Therefore, it is pertinent to understand the GVC trade and its employment effects between the two trade blocs. Accordingly, the chapter firstly discusses the employment dynamics of GVCs scanning the literature. Secondly, it attempts an empirical validation to see the effect of GVCs on Indian manufacturing employment by

Impact of BRICS–EU GVC trade on employment  261

adopting the Maren Lurweg et al. (2009) model. Finally, the chapter concludes with policy suggestions.

14.2 Job creation effects of GVC participation: literature review A substantial body of both theoretical and empirical research focuses on the relationship between GVC trade and employment. The traditional theory of trade postulates that trade creates jobs. According to the neo-classical models, workers find alternative jobs when they lose in one sector. In the long run, the labour market gets cleared when workers who were laid off find new jobs. Trade affects workers through a mere change in equilibrium wages. This assumption however is often contested due to the limitations of the theory. The traditional theory is drawn in isolation leaving out the effects of investment flows on trade and it talks about the trade of finished goods but not of intermediate goods which can have far-reaching implications on employment. This is because labour demand in an open economy is affected not only in import-competing industries but in all industries using foreign inputs to produce final goods. Employment effects of trade are further analyzed by the type of labour used in production. Some studies used labour as a homogeneous factor, while others allow for different skill levels among workers. Feensta and Hanson (1996) find that relocation of skilled labour from one nation to another results in higher relative wages of skilled workers in both countries when the shift in more skilled labour-intensive production activities takes place in the country that is engaged in production and export of intermediate goods. Grossman and Rossi-Hansberg (2006) opine that offshoring affects the wages of different types of labour. Offshoring of production activities does bring benefits to all if the impact on factor prices is not excessive. Falk and Wolfmayr (2005) find that imported materials from low-wage countries exert a significant negative impact on total manufacturing employment in the economies in question. Their findings show that an increase in imported materials from low-wage countries had decreased employment by at least 0.26 percentage points per year over the period 1995–2000. Conversely, the share of imported inputs from high-wage countries has a positive impact on aggregate employment. Therefore, imports from highwage countries and domestic employment seem to be complements rather than substitutes. Hijzen, Görg and Hine (2005) investigate the link between international sourcing and the skill structure of labour demand in the United Kingdom and find that narrow outsourcing has a negative effect on the demand for all types of labour. However, the lower the skills, the higher would be the impact of international sourcing on aggregate employment. Similarly, Geishecker (2004)

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observes that international outsourcing in German manufacturing industries is not significant in determining the relative demand for low-skilled workers. Marin (2004) finds that German multinationals tend to offshore skill and R&D-intensive activities to Eastern Europe. Heavy investments in manufacturing industries and offshoring activities resulted in the creation of jobs in Germany. Offshoring has enabled German firms to save 65–80 percent of their labour costs, helping them to remain competitive in a highly competitive environment. However, a few studies have shown a negative relation arguing that the size of an economy often matters in bringing growth in employment. Sen (2019) finds that trade integration has a positive impact on manufacturing employment via scale effects but a negative impact via the productivity effects. More qualitative studies on GVCs also favour labour upgradation through GVC participation (Gereffi, 1994; Kaplinsky, 2000; Barrientos et al., 2010). Baldwin (2014) views that GVCs might facilitate entry into global manufacturing goods markets but at the same time make industrialization less meaningful as capability building is not guaranteed and diminish productivity growth in the long run. Most recent studies have carried out individual country studies mainly selecting high-income countries (Meng, Xiao, and Ye, 2018). Shepherd and Stone’s (2012) empirical study on OECD (Organization for Economic Cooperation and Development) and emerging markets finds a positive and strong relationship between labour outcomes and GVC participation. Their study highlights that firms with the strongest international linkages (GVC participation) have high employment levels and are more conspicuous in emerging markets than in OECD. Higher linkages have also resulted in higher wages. GVCs cause a structural transformation through exports wherein people are pulled out of less productive activities and pushed to more productive manufacturing jobs. Lopez-Acevedo and Robertson (2016) find that with a 1 percent increase in apparel output, there is a 0.3-0.4 percent increase in employment in the apparel sector. Banga (2016) finds that greater backward linkages have a negative influence on employment growth in the Indian context, more so in the non-manufacturing industries. Further, forward linkages could not offset the loss from the former due to weaker GVC participation in labour-intensive sectors. GVC participation is expected to enhance skills among labour. Thomas, Claire and Deborah’s (2018) study focusing on both backward and forward GVC integration finds that GVC integration brings in relative changes in the demand for skilled labour. World Development Report (2020) demonstrates the experiences of developing economies (e.g. Vietnam, Mexico) and finds that GVC leads to skilled employment growth. Taking cognizance of the views emerging from various studies, the discussion in the following paragraphs centres around the empirical analysis and its outcomes.

Impact of BRICS–EU GVC trade on employment  263

14.3 Empirical approach 14.3.1 Data and methodology To examine the employment effects of GVC trade, the input–output approach is generally followed, using the World Input–Output Database (WIOD). The present chapter uses the TIVA (Trade in Value Added) data published by OECD on the following variables: total exports, domestic value added in exports of intermediate goods, foreign value added in exports of intermediate goods, domestic value added in exports of final goods, foreign value added in exports of final goods and so on. OECD data is used directly as it extracts data from World Input–Output Database (WIOD) and constructs the tables that offer insights into the globalization of value chains, by providing information on the domestic value content in exports and foreign value added in exports of final goods. WIOD provides input–output tables and bilateral trade data for 43 countries and 56 sectors, which comprise 85 percent of the world GDP. WIOD further contains employment data consistent with the input–output matrix. A separate account called the SEA (Social Economic Accounts – 2016) contains employment data in terms of the number of persons engaged, total hours worked and total hours worked by skill types for every country and each sector. With these accounts, we calculate labour coefficients, which allow us to extract labour content embodied in domestic value added in exports of final goods, and foreign value added in exports of final goods. Finally, time series data is extracted for the period 2000–15. 14.3.2 Empirical model Employing the methodology adopted by Maren Lurweg et al. (2009), the study uses the demand equation as

LC * ( I - A )

-1

´ TM

(I − A)−1 is the inverse input coefficient, showing how many units of intermediate production of sector i are needed to produce one unit of final demand for goods of sector j directly and indirectly. LC is a diagonal matrix having labour coefficients as diagonal entries. The labour coefficient for each sector illustrates how many jobs are needed to produce one unit of output. Therefore, the labour coefficient for a specific sector i is calculated as follows: employmenti/outputi. The input–output matrix provides data on employment (persons in employment) and output for each sector. By multiplying the diagonal matrix LC by (I − A)−1, the number of jobs which are directly and indirectly needed for the production of one unit of final demand is calculated. Finally, the

264  Usha Nori and Ram Kumar Mishra

matrix LC × (I − A)−1 is multiplied by TM (trade matrix). TM is a diagonal matrix having, as entries, for example, the net exports of each sector. By multiplying LC × (I − A)−1 by TM, we obtain a measure of the number of jobs needed to produce net exports. Capturing the dynamics of the contents provided by Maren Lurweg and others, the present study extracts the data on total employment and total output data for all EU-28 and BRICS countries and calculates the labour coefficients. Further, these coefficients are multiplied with the trade matrix having entries of backward and forward linkages using the TIVA Database.

14.4 Results and discussion The increase in vertical specialization or global value chains is measured in terms of (i) value-added exports and (ii) upstreamness of a country or industry from final demand. The first one measures domestic value-added embodied in exports. It is usually reported as a share of total exports and value-added export share. Value added is denoted as the extent of foreign value-added embodied in domestic goods that are exported (i.e., multiple national border crossings). Secondly, upstreamness measures the distance of production from final demand. Greater upstreamness implies greater fragmentation of production, which is consistent with increased global value chains. The empirical analysis of the employment effects of GVC trade is decomposed into four components: labour content (1) in exports, (2) in imports, (3) in the import content of exports and (4) in the export content of imports. The last two components relate strictly to a country’s participation in GVCs. Table 14.1 presents labour demand from final goods trade and labour demand from GVC trade. The total domestic labour demand for each country is the sum of labour demand by domestic exports and domestic content of imports. The sum of the rest is counted as the total foreign labour demand resulting from each country’s trade position in 2015. Looking at the values of total domestic and foreign labour demand, it is quite clear that, in 2015, most of the countries demanded more domestic labour than foreign labour through exports. Greater demand for domestic labour is seen in the highly developed nations of the EU, viz., France, Germany, the Netherlands, Spain and the UK. Among the BRICS, only China and Brazil depended on domestic labour in production. Split between final goods trade and intermediate goods (GVC trade) evidences that 142 million jobs were created through final trade and 47 million jobs through GVC trade from the selected countries (Table 14.2). Countries that demanded the highest amounts of labour from GVC trade include Belgium, France, Germany, Ireland, Italy, the Netherlands, Spain and the UK from the EU group and Brazil and China from BRICS group. Further, it is apparent that India’s presence in GVC trade is lower than that of many EU as

Impact of BRICS–EU GVC trade on employment  265 Table 14.1 Job creation through external trade in the EU and BRICS countries (persons in thousands – 2015) Domestic demand for labour Country/Region

Demand for labour in total exports

Austria 678.20 Belgium 1403.06 513.23 Czech Republic Denmark 108.21 Estonia 216.37 Finland 495.01 4710.67 France Germany 10181.81 Greece 729.82 Hungary 6.12 Ireland 1510.92 Italy 4091.62 Latvia 203.89 Lithuania 445.41 Luxembourg 293.93 The Netherlands 2439.81 Poland 907.23 Portugal 1059.83 Slovak Republic 871.29 Slovenia 335.14 Spain 3342.97 Sweden 124.20 United Kingdom 6548.51 Bulgaria 567.01 Croatia 49.30 Cyprus 117.59 Malta 117.06 Romania 402.49 India 1105.00 Russian Federation 217.10 Brazil 2448.01 China 9675.76 South Africa 974.43

Foreign demand for labour

Demand for Demand for labour in export labour in total content of imports imports

Demand for Labour in import content of exports

331.87 625.08 196.41 53.98 93.97 232.08 2311.98 5177.73 368.28 2.57 705.34 1790.10 100.22 225.53 76.01 1192.94 405.94 487.44 309.17 155.59 1548.33 64.38 3627.66 228.42 27.32 64.55 31.38 199.43 539.89 115.89 1164.13 3056.94 414.90

179.73 478.03 201.61 31.66 75.31 128.10 1006.21 2137.15 178.85 2.64 607.22 908.20 45.58 140.75 202.34 681.10 241.68 301.37 390.14 108.79 758.71 25.72 987.33 205.47 9.88 32.67 69.23 92.23 77.35 45.30 4101.63 176.70 220.11

622.07 1376.56 466.65 93.70 201.85 511.09 4916.48 8164.58 789.00 5.50 1098.46 3718.26 202.34 452.99 244.24 2138.53 853.46 1059.00 841.18 284.51 3147.05 109.00 7054.72 576.70 47.74 117.86 108.93 413.80 684.88 277.41 20386.56 1202.38 1042.38

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Domestic labour demand and foreign labour demand values are higher in these nations. Hence, these values are expressed in bold. Source: Authors’ own calculations based on Organization for Economic Cooperation and Development (OECD) – Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

266  Usha Nori and Ram Kumar Mishra Table 14.2 Jobs generated by final goods trade and global value chains (GVCs) 2015 (persons, thousands) Countries

Final goods

GVC

Austria Belgium Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg The Netherlands Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom Bulgaria Croatia Cyprus Malta Romania United States India Russian Federation Brazil China South Africa Total

1300.28 2779.62 979.88 201.92 418.23 1006.11 9627.16 18346.39 1518.82 11.63 2609.38 7809.89 406.23 898.40 538.18 4578.34 1760.70 2118.83 1712.48 619.66 6490.03 233.21 13603.24 1143.72 97.04 235.46 225.99 816.30 22908.60 1789.89 494.52 22834.57 10878.14 2016.81 143139.40

511.61 1103.12 398.03 85.64 169.28 360.18 3318.19 7314.88 547.13 5.21 1312.56 2698.30 145.81 366.28 278.36 1874.04 647.62 788.81 699.31 264.38 2307.05 90.10 4614.99 433.90 37.20 97.22 100.62 291.67 6786.91 617.25 161.20 5265.77 3233.64 635.01 47616.38

Note: Domestic Labour demand and foreign labour demand values are higher in these nations. Hence, these values are expressed in bold. Source: Authors’ own calculations based on Organization for Economic Cooperation and Development (OECD) – Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

well as BRICS nations. On the other hand, Brazil and China’s engagement in GVC has maintained the same level as that of industrialized nations of the EU. A higher share of jobs is sustained by foreign final demand. Table 14.3 clearly evidences that GVC participation did not increase much demand for labour in India during 2005–15. Except for South Africa, all other BRIC nations witnessed a slump in demand. The EU on the other hand had shown a marginal rise in labour demand due to a fall in output.

Impact of BRICS–EU GVC trade on employment  267 Table 14.3 Share of domestic employment in foreign final demand Country

2005

2010

2015

EU-28 Brazil China India Russian Federation South Africa

9.9 15.8 16.8 12.9 19.1 20.5

11.7 10.3 14 13.9 16 20.5

13.9 12.8 12.2 12.2 18 21.6

Note: EU, the European Union. Source: Trade in Value Added (TIVA) Database.

Table 14.4 Overall net gain from global value chains (GVC) participation during 2010–15 Country/Region

Austria Belgium Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg The Netherlands Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom Bulgaria Croatia Cyprus Malta Romania Brazil Russia India China South Africa

2010 

2015 

2010 

2015 

2010 

2015 

Forward linkage

Backward linkage

Ratio of forward to backward linkage

20.2 20.3 17.7 19.2 17.9 19.1 19.4 20.9 18.4 13.2 12.6 17.5 20.1 15.4 12.3 23.3 20.3 15.9 17 16.9 16.8 20.7 22.6 14.1 12.5 17.6 6.9 20 23.9 23.9 15.4 16.2 24.3

28 33.5 37.2 28 33.9 29.4 22.1 21.5 23 47.5 24.7 40.7 21.8 32.8 60.4 23.6 26.9 26.9 43.9 33.9 21.9 24.3 17.5 34.1 20.7 25.4 64.3 21.1 9.6 9.7 23.6 21.1 18.7

0.72 0.61 0.48 0.69 0.53 0.65 0.88 0.97 0.80 0.28 0.51 0.43 0.92 0.47 0.20 0.99 0.75 0.59 0.39 0.50 0.77 0.85 1.29 0.41 0.60 0.69 0.11 0.95 2.49 2.46 0.65 0.77 1.30

21.3 20.2 19.4 18.1 16.8 21.4 21.2 21.9 15.8 16.2 12.3 18.6 19.0 16.8 10.6 21.3 21.5 15.4 18.8 20 17.6 21.5 23.7 16 12.4 16.8 7.0 21.3 19.6 19.6 14.9 17.5 20.1

26.5 34.1 39.3 29.3 34.8 25.9 21.4 21 24.5 43.1 23.7 40.2 22.4 31.6 68.8 27.9 26.6 28.4 44.8 32.5 22.7 20.7 15.1 36.2 20 27.8 59.1 22.9 12.5 10.8 19.1 17.3 22.6

Source: Authors’ calculations using Trade in Value Added (TIVA) Database.

0.80 0.59 0.49 0.62 0.48 0.83 0.99 1.04 0.64 0.38 0.52 0.46 0.85 0.53 0.15 0.76 0.81 0.54 0.42 0.62 0.78 1.04 1.57 0.44 0.62 0.60 0.12 0.93 1.57 1.81 0.78 1.01 0.89

268  Usha Nori and Ram Kumar Mishra

India could not absorb more domestic labour due to lower backward participation. Overall net gains from GVC participation can be gauzed in terms of ‘net value-added’ by participation in GVCs. This is calculated using a ratio of forward-to-backward linkages to estimate the extent of net gains. A ratio greater than 1 implies that a country’s domestic value added in intermediate exports is higher than its foreign value-added content of gross exports. This ratio improved from 0.65 to 0.78 for India from 2010 to 2015. However, India’s net gains from international linkages have not been greater, thus clearly indicating the slow pace of GVC trade (Table 14.4). 14.4.1 Impact of EU–BRICS GVC trade on employment The rise of BRICS economies in global trade with a share of 25% of the world GDP and 20% of global trade (2019) exhorted the advanced nations to become major partners with the bloc. Major trade partner being EU-27 has a share of 18.3 percent and is followed by the USA (12.5%), Japan (6.3%), South Korea (5.8%) and Hong Kong (5.5%). The surge in trade and investment flows between the two blocs has become the main route to get well connected to GVCs. These nations penetrate deeply into each other’s markets by specializing in specific products within a value chain, rather than producing the final product. Moreover, two-way trade is mostly in industrial products (95.7%), leaving scope for offshoring in the assembly lines of manufacturing. While the EU’s import trade in manufactures stands at 70.3% of its total imports, BRICS constitute about 86.3% of their total imports. The primary products constitute about 26.5% of the EU and 12% of BRICS. The unbundling of production is expected to have implications for labour markets. Fragmentation in production is evident wherein the trade in intermediate goods is higher than in final goods (Table 14.5). The EU GVC trade is high with Russia among BRICS and is followed by Brazil and South Africa. Employment behaviour in terms of GVC trade between the EU and BRICS in terms of value added in final demand which constitutes one of the most important factors of employment behaviour shows that participation in GVC trade with BRICS resulted in the EU’s demand for foreign labour than domestic labour (Table 14.6). Particularly, the growing manufacturing trade between the two areas is resulting in demand for BRICS labour for the production of goods in the EU (Table 14.7). BRICS on the other hand witnessed an increase in domestic demand for labour domestically and overseas. The positive relation between GVC and employment growth supports the arguments of others. Employment creation is seen both directly within exporting firms and indirectly through the demand for goods within the domestic economy (Table 14.8). The extent to which GVCs interact with domestic labour depends on the linkages

25,484.9 (68.86) 176,158.2 (50.02) 38,577.2 (53.72) 131,114.7 (80.34) 11,058.8 (63.57)

11,526.40 (31.14) 37,011.3 176,026.4 (49.98) 352,184.6 33,233.8 (46.28) 71,811.1 32,088.0 (19.66) 163,202.8 6,338.3 (36.43) 17,397.1

44,422.7 (63.31) 174,551.7 (58.73) 41,494.4 (61.31) 60,139.1 (55.76) 15,163.8 (56.71)

25,745.9 (36.69) 122,637.5 (41.27) 26,186.5 (38.69) 47,705.8 (44.24) 11,577.6 (43.29)

Final goods

Intermediate goods

Total exports

Intermediate goods

Final goods

EU imports from BRICS (US$ mn)

EU exports to BRICS (US$ mn)

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Source: Trade in Value Added (TIVA) Database.

Brazil China India Russia South Africa

Country/Region

Table 14.5 EU–BRICS trade through global value chains (2015)

70,168.6 297,189.2 67,680.9 107,844.8 26,741.4

Total imports

Impact of BRICS–EU GVC trade on employment  269

270  Usha Nori and Ram Kumar Mishra Table 14.6 Decomposition of the EU’s labour (2015) in its total trade with BRICS EU domestic value added (DVA) in foreign (BRICS) final demand (US$ mn) Brazil China India Russia South Africa

31,562.2 291,682.5 58,437.9 128,084.5 12,684.0

EU demand for EU foreign value added domestic labour (FVA) in domestic final (persons in thousands) demand (US$ mn)

EU demand for foreign labour

62.2 575.5 115.3 252.7 25.0

118.3 515.9 122.4 170.2 41.5

59,989.9 261,479.0 62,059.7 86,274.7 21,063.8

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Source: Authors’ calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

Table 14.7 Decomposition of the EU’s labour (2015) in its manufacturing trade with BRICS EU/BRICS

Brazil China India Russia South Africa

EU DVA in foreign (BRICS) final demand (US$ mn) 6,971.2 153,897.0 17,659.2 25,011.4 2,927.6

EU demand for EU FVA in Demand for Foreign domestic labour domestic final labour (persons in (persons in thousands) demand (US$ mn) thousands) 13.7 303.6 34.8 49.3 5.7

18,216.4 115,780.7 20,918.7 29,328.4 8,267.2

35.9 228.4 41.2 57.8 16.3

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Source: Authors’ calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

Table 14.8 Decomposition of BRICS labour (2015) in its total trade with the EU BRICS/EU

Brazil China India Russia South Africa

BRICS DVA in BRICS demand for foreign (EU) final domestic labour demand (US$ mn) (persons in thousands) 59,989.9 261,479.0 62,059.7 86,274.7 21,063.8

646.2 1150.8 168.2 50.3 226.8

BRICS FVA in BRICS demand for domestic final foreign labour (EU) demand (US$ mn) (persons in thousands) 31,562.2 291,682.5 58,437.9 128,084.5 12,684.0

339.9 1283.8 158.3 74.8 136.6

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Source: Authors’ calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

Table 14.9 Decomposition of BRICS labour (2015) in its manufacturing trade with the EU BRICS DVA in foreign BRICS demand for BRICS FVA in BRICS demand (EU) final demand domestic labour domestic final for foreign labour (US$ mn) (persons in thousands) demand (US$ mn) (persons in thousands) Brazil China India Russia South Africa

18,216.4 115,780.7 20,918.7 29,328.4 8,267.2

196.2 509.5 56.7 17.1 89.0

6,971.2 153,897.0 17,659.2 25,011.4 2,927.6

75.0 677.3 47.8 14.6 31.5

Note: EU, the European Union; BRICS, Brazil, Russia, India, China and South Africa. Source: Authors’ calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

Impact of BRICS–EU GVC trade on employment  271

of exporting firms to domestic input-supplying firms. The findings confirm that greater access to the EU markets supports jobs in BRICS both directly and indirectly. In other words, forward linkages have a positive impact on employment and are realized in manufacturing employment (Table 14.9).

14.4.2 Sectoral employment composition Sector-wise, when looked at, employment levels increased in the EU across the board, due to foreign final demand push. The reversal trend is seen, however, among BRICS except for Brazil and South Africa. India witnessed a drop in demand for labour in labour-intensive sectors such as agriculture, mining, manufacturing and textiles. On the other hand, chemicals and non-metallic products showed an increase in labour (Figures 14.1–14.6). Dependency on foreign final demand is inevitable for nations that are highly integrated with production chains. Further down the line, GVC trade is happening because of greater value addition within the economies. Job gains are observed due to forward linkages in both EU and BRICS areas. There is a clear indication that the EU experiences job gains from GVC trade in the Chemicals and Pharmaceutical products and transport sectors. On the other hand, textiles, electronics and machinery in China, wood products, food products and textiles in Brazil, IT and other information services and textiles in India contributed to employment growth (Tables 14.10 and 14.11).

40

35.9

33.7

35

30.2

28.7

30

26.1

23.3

25 20

17.1

15

15.6

10

7.5

13.4

10.6

10.2

9.7

5 0

EU 28

Brazil

China 2005

16.6 12.8

8.6 9.5 7.9 India 2010

Russian Federation

South Africa

2015

Figure 14.1 Domestic employment in foreign final demand – Agriculture. Source: Trade in Value Added (TIVA) Database.

272  Usha Nori and Ram Kumar Mishra 80

74.9

70

60.9 54.9 62.2

60 50 40 30 20

73.8 73.3 74

47.2 44.7 30.9

19.1 21.8 23.3

69.6 70.8

26.2

29.3

23.4

10 0

EU 28

Brazil

China 2005

India 2010

Russian Federation

South Africa

2015

Figure 14.2 Domestic employment in foreign final demand – Mining. Source: Trade in Value Added (TIVA) Database. 70

65.6

60

58

50.5

50 40 30 20

23.7 17.8 20.4

10 0

EU 28

36.4

21.2 11.5

41.2 38.9 21.6

9.1

Brazil

18.5 China 2005

India 2010

15.8

Russian Federation

21.5

34.3 28.1

South Africa

2015

Figure 14.3 Domestic employment foreign final demand – Textiles. Source: Trade in Value Added (TIVA) Database.

The sustainability of GVC-induced employment growth thus achieved can be retained only when heavy investments are doled out on skill enhancement programmes. At present, every nation is spending huge amounts to augment the skills of labour. Skill upscaling if accompanied by GVC trade, nations attain higher productivity growth and become competitive in technologically advanced manufacturing industries which further leads to innovation, higher productivity and job creation. Further, cognitive skills and readiness to learn are crucial for performance in GVCs (OECD, 2017).

Impact of BRICS–EU GVC trade on employment  273 45

40.5

40 35 30 25 20

28.5 24.3 19.7

25

34.2

33.0

30.0

31.4

40.7 37.4

34.8

32.2

31.8 28.0

26.7

19.214.9

15 10 5 0

EU 28

Brazil

China 2005

India 2010

Russian Federation

South Africa

2015

Figure 14.4 Domestic employment foreign final demand – Manufacturing. Source: Trade in Value Added (TIVA) Database. 45

42.1

40 35 30 25 20

30.8 26.3 21.1

31.5 25

37.3 38.1 27.9 24.5 17.7 18.5 18.6

19.2 14.9

15

40.6 33.4 34.1

10 5 0

EU 28

Brazil

China 2005

India 2010

Russian Federation

South Africa

2015

Figure 14.5 Domestic employment foreign final demand – Chemicals. Source: Trade in Value Added (TIVA) Database.

To gauge the employment effect of each country’s participation in GVCs by skill level, we analyze the trends using the data on labour value added on exports of the selected nations. It is quite evident that all BRICS countries have low-skilled workers, thus limiting the participation in GVC trade with those countries where foreign demand for labour accommodates typically high-skilled workers (Table 14.12).

274  Usha Nori and Ram Kumar Mishra 70 49

50 40 30 20

58.8 53.2 58.5

58.3

60

32.2 27.7 22.5

37.9 31

46.5 31.8 29.1

26.7 18.7

27.4 28.8 28

10 0

EU 28

Brazil

China 2005

India 2010

Russian Federation

South Africa

2015

Figure 14.6 Domestic employment foreign final demand – Basic metals. Source: Trade in Value Added (TIVA) Database.

Table 14.10 Job creation through forward and backward linkages in the EU Sector

Forward linkage

Backward linkage

(DVA) demand for labour (FVA) demand for Labour in export content of in import content of imports exports EU Basic metals Chemicals and pharmaceutical products Construction Electronics IT and other information services Machinery Textiles Transport Wood products Food and tobacco products

94.8 641.2

4.6 21.3

8.7 48.3 121.4 182.3 133.5 381.8 85.8 101.8

0.9 9.0 3.2 8.7 11.6 54.1 8.6 5.8

Source: Authors’ calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

23.0 10.8 0.3 5.9 6.6 16.8 63.4 41.4 119.5 112.0

Basic metals Chemicals and Pharmaceutical products Construction Electronics IT and other information services Machinery Textiles Transport Wood products Food and tobacco products

89.7 90.2 0.0 675.5 4.3 323.2 1124.6 95.7 121.9 63.5

China 4.4 6.7 0.0 3.5 105.2 6.0 73.4 5.9 12.9 19.6

India 7.8 2.9 0.4 1.7 2.8 1.4 0.7 2.4 4.9 1.3

Russia 2.7 0.7 0.1 0.7 0.5 2.0 1.1 2.1 1.3 0.8

South Africa 4.6 2.4 0.0 1.6 0.4 3.7 8.7 11.6 17.2 14.0

Brazil 10.8 16.3 0.0 249.6 0.5 61.3 127.8 19.2 18.6 5.5

China 2.0 2.2 0.0 2.0 8.7 2.7 14.4 2.1 2.3 1.5

India

1.1 0.6 0.1 0.5 0.5 0.4 0.2 0.7 0.9 0.2

Russia

0.6 0.4 0.0 0.4 0.2 0.8 0.5 1.3 0.4 0.2

South Africa

Note: Domestic labour demand and foreign labour demand values are higher in these nations. Hence, these values are expressed in bold. Source: Author’s calculations using Trade in Value Added (TIVA) and World Input–Output Database (WIOD) – Socio-Economic Accounts Database.

Brazil

Sector

(DVA) demand for labour in export content of imports (FVA) demand for labour in import content of exports

Table 14.11 Job creation through forward and backward linkages in BRICS

Impact of BRICS–EU GVC trade on employment  275

1.93 6.02 8.65 7.28 7.31 6.72 5.89 7.62 6.63 7.51 5.03 6.45 37.32 4.44 7.42 6.93 7.25 7.75

28.87 39.29 43.07 33.40 36.54 50.46 43.36 38.27 26.11 40.05 41.50 26.92 38.84 37.85 38.28 36.80 35.55 53.44

Agr., forestry, fisheries 3.76 Beverages and tobacco products 8.57 Construction 9.97 Chemical, rubber, plastic products 8.34 Metal products 8.25 Leather products 10.33 Wood products 8.38 Machinery and equipment 9.61 Metals 7.22 Mineral products 9.28 Manufactures 8.47 Minerals 7.67 Public administration /Defence/ 37.79 Health/Education Processed foods 7.42 Paper products, publishing 9.08 Textiles 7.57 Transport equipment 8.86 Wearing apparel 10.32

Source: World Bank – WITS (World Integrated Trade Solutions) Database.

China

43.43 35.47 40.44 31.57 43.10

52.82 38.71 39.77 32.28 32.70 42.52 38.94 31.58 28.13 33.04 30.95 30.83 33.29 4.90 6.88 5.95 7.15 6.95

1.18 5.90 8.36 4.25 5.15 6.20 5.99 5.75 4.31 5.30 5.87 4.30 46.52

India

35.32 32.65 31.33 26.36 35.12

35.30 36.98 42.27 18.33 22.95 38.60 43.57 20.25 15.71 24.60 28.69 23.52 31.95 4.27 5.55 6.33 7.05 5.67

2.19 4.96 5.95 6.72 7.95 7.94 5.80 10.46 5.91 6.98 7.49 5.88 36.72

Russia

31.98 26.85 34.31 25.53 31.51

45.89 30.84 31.84 23.99 36.65 50.38 40.38 36.68 25.01 34.97 49.17 25.77 29.72

8.51 9.24 9.20 6.47 9.72

5.07 8.17 9.36 8.17 9.24 8.68 7.94 9.09 4.75 9.08 6.93 7.89 36.68

South Africa

29.12 32.21 32.49 22.86 37.38

26.62 28.30 33.51 24.24 35.26 32.04 36.19 29.04 15.92 32.94 24.54 34.61 29.74

Unskilled LVAX Skilled LVAX Unskilled LVAX Skilled LVAX Unskilled Skilled LVAX Unskilled LVAX Skilled LVAX Unskilled LVAX share bwd (%) share bwd share bwd (%) share bwd LVAX share bwd share bwd (%) share bwd share bwd (%) (%) (%) share bwd (%) (%) (%)

Brazil

Sector

Skilled labour value added in exports (LVAX) share bwd (%)

Table 14.12 Sector-wise type of labour value added in exports (backward Linkage): BRICS

276  Usha Nori and Ram Kumar Mishra

Impact of BRICS–EU GVC trade on employment  277

14.5 Conclusion and policy implications This study finds that GVC-related cross-border production-sharing activities were the most important force driving employment between BRICS and the EU. The rise of GVCs significantly contributed to domestic employment growth in BRICS than in the EU due to the export content of imports. This could be attributed to the labour-intensive assembling of final goods. Sector-wise details reveal that China stands on top among BRICS for the final assembly of numerous manufactured products that are labour intensive, especially textiles and electronics. Textiles though constitute an important sector in India and Brazil, employment expansion is stifled due to China’s dominance. Pharmaceuticals, where India has a strong base, again could not add to new jobs due to the EU’s supremacy achieved through technological innovation. Whatever the benefits so far derived is the result of the EU’s advancement in GVC trade with BRICS in a few sectors. Perhaps, a rise in the EU’s intra-trade caused the group to move away from outside countries, but this is not apparent with all products. The EU’s dependency on BRICS (Chinese and Indian markets) for textiles and IT, other primary products and mineral products from Russia, Brazil and South Africa products is paramount for its external trade. BRICS linkage in GVC trade may have implications for labour markets in terms of changes in the size and composition of the labour market, labour productivity, skilled and unskilled labour, wage differentials between skilled and unskilled labour and so on. GVC trade may induce wage and employment gains only if there is access to skilled labour. Most of the developing nations lag in the supply of skilled workforce and the bias towards skilled labour may bring in complexities. Flexible labour markets in BRICS may have the tendency to foster requisite skills among domestic labour and transfer knowledge from foreign firms (EU). Further, encouragement of labour-intensive industries results in a greater share of women labour force participation in total employment. BRICS countries would find a way to move towards the production of higher-end products through GVCs which in turn generate employment benefits in the host countries. Backward linkages in BRICS lead to more value addition to exports, thus rising employment growth, but these benefits may get eroded if imports of intermediate goods displace domestic production. Similarly, in countries like India, GVC trade necessitates domestic value addition (forward linkages) in exports of labour-intensive industries such as textiles and garments which led to greater employment generation. Therefore, first, BRICS policies should be directed towards GVC intensification and industrial upgrading. This can be realized much faster if BRICS as a group moves forward with a common objective in the production value chains. Second, the upgradation of skills is mostly needed for sectors like

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agriculture, construction, leather, textiles, manufacturing, minerals and apparel that are highly labour intensive in nature. Third, exploit technology spill overs, with a strong focus on skills development, which in turn supports an adaptable labour force positioned to maximize the dynamic potential of GVCs. Also enable BRICS to specialize in the upstream production of medium and high technology parts and help them move up the value chains and reap the benefits of GVCs. Fourth, increase the supply side capabilities of firms and individuals as well, to absorb new knowledge and adapt to new technologies and processes for productivity gains. Fifth, enhance participation of local firms in the production network, and finally, steps should be taken to enhance the capacity building of small firms to participate in GVCs.

References Baldwin, R. (2014). Trade and industrialization after globalization’s second unbundling: How building and joining a supply chain are different and why it matters. In R. C.Feenstra and A. M.Tayler (Eds.), Globalization in an Age of Crisis: Multilateral Economic Cooperation in the Twenty-first Century (pp. 165– 214). University of Chicago Press. Banga, K. (2016). Impact of global value chains on employment in India. Journal of Economic Integration 31(3), 631–73. Barrientos, S., G. Gereffi and A. Rossi (2010). Economic and Social Upgrading in Global value chains: Developing a Framework for Analysis. Working Paper, No. 2010/03, Capturing the Gains 2010. Ben, S. and Stone, S. (2012). Global Production Networks and Employment: A Developing Country Perspective. OECD Working Paper- TAD/TC/WP (2012)29. Constantinescu, C., Mattoo, A., and Ruta, M. (2019). Does vertical specialisation increase productivity? The World Economy, 42, 2385–2402. Falk, M. and Wolfmayr, Y. (2005). Employment Effects of Outsourcing to Low Wage Countries: Empirical Evidence for EU Countries. WIFO Working Papers No. 262. Farole, T. (2016). Do global value chains create jobs? IZA World of labour. https:// www​.researchgate​.net​/publication​/306920349​_Do​_global​_value​_chains​_create​ _jobs. Feenstra, R. C. and Hanson, G. H. (1996). Globalisation, outsourcing and wage inequality. American Economic Review, 86(2), 240–245. Geishecker, I. (2004). Outsourcing and the demand for low-skilled labour: Exemplary Evidence from German manufacturing industries. In Meulders, D., Plasman, R. and Rycx, F. (Eds.), Minimum Wages, Low Pay and Unemployment. Palgrave Macmillan. Gereffi, G. (1994). The organization of buyer-driven global commodity chains: How US retailers shape overseas production networks. In G. Gereffi and M. Korzeniewicz (Eds.), Commodity Chains and Global Capitalism (pp. 95–122). Greenwood Press. Grossman, G. M. and Rossi-Hansberg, E. (2006). Trading tasks: A simple theory of offshoring. NBER Working Paper Series No. 12721.

Impact of BRICS–EU GVC trade on employment  279 Hijzen, A., Görg, H. and Hine, R. C. (2005). International outsourcing and the skill structure of labour demand in the United Kingdom. Economic Journal, 115(506), 860–878. Kaplinsky, R. (2000). Globalisation and unequalisation: What can be learned from value chain analysis? Journal of Development Studies, 37, 117–146. Lopez-Acevedo, G and Robertson, R (2016). Stitches to Riches? Apparel Employment, Trade, and Economic Development in South Asia. World Bank. Lopez-Gonzalez, J. (2016). Using Foreign Factors to Enhance Domestic Export Performance: A Focus on Southeast Asia (OECD Trade Policy Papers, No. 191). OECD Publishing. Lurweg, M. and Westermeier, A. (2009). Jobs Gained and Lost through Trade: The Case of Germany. CAWM Discussion Paper No. 18. Lurweg, M., Oelgemöller, J. and Westermeier, A. (2010). Sectoral Job Effects of Trade – An Input-Output Analysis for Germany. CAWM Discussion Paper No. 19. Marin, D. (2004). A nation of poets and thinkers – Less so with eastern enlargement? Austria and Germany. CEPR Discussion Paper No.4358. Meng, B., Xiao, H., and Ye, J. (2018). A Global Value Chain Based Structure Decomposition Analysis on the Change of Employment. Mimeo. OECD Report (2013). Interconnected Economies: Benefitting from Global Value Chains. Synthesis Report. OECD Skills Outlook (2017). Skills and Global Value Chains. OECD Library. Reinhilde Veugelers (2013). Manufacturing Europe’s Future. Bruegel Blueprint 21. Rodrik, D. (2013). Unconditional convergence in manufacturing. The Quarterly Journal of Economics, 128, 165–204. Sen, K. (2019). What explains the job creating potential of industrialisation in the developing world? The Journal of Development Studies, 55, 1565–1583. Skills and Global Value Chains, OECD Skills Outlook Report 2017. OECD Publishing. https://doi​.org​/10​.1787​/9789264273351​-en World Bank. (2017). Global Value Chain Development Report 2017. Measuring and Analyzing the Impact of GVCs on Economic Development. World Development Report (2020). Trading for Development in the Age of Global Value Chains. World Bank Group.

Chapter 15

Employability of FDI in India’s manufacturing firms Sanjaya Kumar Malik

15.1 Introduction Employment creation is the key challenge that developing countries have been dealing with for time immemorial. This challenge can be met through modern sector employment, and the absence of such employment opportunity is compelling the millions to resort to agriculture and informal activities for their survival. For modern sector employment, there is an urgency for industrial policy to promote diversification of production activities, facilitate the restructuring of existing activities and foster coordination between public and private entities to make all this plausible (Felipe and Hasan, 2006, p. 7). Apparently, it is argued that foreign direct investment (FDI) by multinational enterprises (MNEs) could play an important role in such industrial change as they own knowledge of markets, technologies and distribution channels (UNCTAD, 2007). For instance, East Asian growth and industrial development are greatly attributed to FDI (Dobson and Chia, 1997). The policymakers from developing countries are therefore luring MNEs with several fiscal and monetary incentives and relaxation in trade regulations to attract FDI. Regardless of its high policy relevance, the labour market research on FDI has thus far been limited to labour productivity and wages, and marginal attention has been given to understand the employment effect of FDI in developing countries. A study of 19 sub-Saharan African countries by Coniglio, Prota and Seric (2015) finds that in comparison to domestic firms, foreign firms tend to generate more employment in these countries. Another study by Peluffo (2015) affirms the positive and significant effect of FDI on employment generation in Uruguayan manufacturing firms, and the author nevertheless underscores that FDI in Uruguay is akin to an increased demand for white-collar labour compared to blue-collar labour. Further, studies by Karlsson, Lundin, Sjoholm and He (2009) and Waldkirch, Nunnenkamp and Alatorre Bremont (2009) for China and Mexico, respectively, corroborated the presence of FDI is found to have positively influenced employment creation in manufacturing firms in these countries. The aforementioned DOI: 10.4324/9781003329862-20

FDI in India’s manufacturing firms  281 Table 15.1 Distribution of cumulative foreign direct investment (FDI) inflows across different sectors of the economy, in Rs Crore Sl. No. 1

Name of sector

Agriculture and allied sector 2 Industry sector 2.1 Industry-less manufacturing 2.2 Manufacturing 3 Services sector 1 + 2 + 3 Total FDI inflows

Aug. 1991 to Mar. 2000 April 2000 to Dec. 2019 638 (0.30)

7,858.85 (0.29)

1,53,581.79 (71.68) 64,122.39 (29.93) 89,459.40 (41.76) 60,025.77 (28.02) 2,14,245.6 (100)

14,82,373.94 (56.22) 4,12,339.65 (15.64) 10,70,034.29 (40.58) 11,46,129.18 (43.47) 26,36,895.05 (100)

Notes: industry-less manufacturing includes mining and quarrying, gas, electricity, construction and water supply. Values in parenthesis are in percentage. Source: foreign direct investment (FDI) Factsheet – April to December 2019, FDI Statistics, DIPP; and sector-wise break-up of FDI approved during August 1991–March 2000), FDI Newsletter, Department for Promotion of Industry and Internal Trade (DPIIT).

researches confirm the employment growth owing to the presence of foreign firms in host developing countries. In light of the above, the present study intends to analyse the employability of FDI (i.e., employability of FDI-firms) in India. India has continuously been undertaking numerous internal and external reforms to become an investor-friendly nation since 1991. These reforms have resulted in a substantial FDI inflow from US$ 97 million to US$ 39 billion between 1990–91 and 2017–2018 (Reserve Bank of India, 2018).1 The industry sector accounts for the lion’s share of total FDI inflows, as obvious from Table 15.1 which shows the distribution of accumulated FDI inflows among different sectors of the country. And around 41 percent of FDI inflows went to manufacturing from August 1991 through December 2019. The present study therefore focusses on analysing the employability of FDI in Indian manufacturing firms only. This study is an important contribution to labour economics research in developing countries. An analysis of the employability of FDI reveals that FDI-firms have lower employability as compared to domestic firms in India. Further controlling for different characteristics of firms, the FDI-firms are not seen to have better employability when they are compared to domestic firms in India. This chapter is organised as follows. The next section discusses the related literature on the contribution of FDI to employment generation in the host country. The required database and methodology of the study are presented in Section 15.3. Section 15.4 analyses the employability of FDI-firms in India’s manufacturing industries, and it also analyses how different characteristics of firms influence the employability of FDI-firms. The last section concludes and offers relevant policy suggestions.

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15.2 Related literature There are several channels that mediate the employment effect of FDI in the host country. FDI can generate employment directly when MNEs start new subsidiaries or new industries in the host country (Karlsson et al., 2009). Increased FDI inflows however bring about a reduction in demand for labour by foreign firms when the technologically superior FDI inflows make them more efficient in the usage of labour in the host country. Further, the higher transfers of superior technologies from the parent company to subsidiaries increase the productivity of the latter and thus helping them minimise the usage of labour in the host country (Benacek et al., 2000; Conyon et al., 2002; Girma et al., 2002). FDI inflows are heterogeneous in nature and have heterogeneous impact on employment in the host country. In what follows, we briefly discuss how the heterogeneous nature of FDI influences the employment generation in the host country. (i) When the MNEs enter into labour-intensive industries, they tend to generate higher employment than when they are in less labour-intensive industries in the host country (Jenkins, 2006). (ii) The mode of entry of FDI inflows affects the employment generation in the host country. For instance, a greenfield FDI has a higher potential for job creation in the host country as it is a new venture or new establishment by a foreign entity which would certainly generate jobs which were not in existence before; whereas brownfield FDI inflows (i.e., cross-border Mergers and Acquisitions [M&As]), which are the employment-acquiring investment, have the lower likelihood for employment generation in the host country (Dunning and Lundan, 2008). In the short run, the brownfield FDI would generate jobs if the merged or acquired domestic firm by a foreign entity is financially stable or efficient; otherwise, it would bring about retrenchment of jobs in the host country (Bagchi-Sen, 1991). (iii) Export-platform FDI is generally backed by the desire to discover an efficient location from where the output could be exported easily and profitably to other countries (Pradhan, 2006). The strong desire of export-platform FDI to utilise the locational advantages (e.g., cheap labour, raw materials, other intermediate inputs and so on) makes it one of the important channels for job creation in the host country. In addition to the abovementioned direct effects of FDI on employment generation, there are some indirect effects of FDI inflows on employment in the host country. Technology spillovers from FDI can influence employment generation in the host country. As already mentioned, MNEs own most of the technologies and are responsible for sizeable global research and development (R&D) expenses, and these technologies due to their non-rival nature are likely to be spilled over and influence the output and employment in the host country. Further, through the competition effect, FDI can influence the demand for labour in the host country. The entry of multinationals in the host country intensifies competition which leads

FDI in India’s manufacturing firms  283

to the restructuring of domestic firms and thereby affects employment in the host country. In this case, FDI brings about a reduction in employment generation in the host country as the technological superiority of foreign firms drives away the non-competitive or weak domestic firms (Coniglio et al., 2015). Furthermore, foreign subsidiaries establish backward linkages with local firms, and they can influence the demand for labour in the host country. When the foreign subsidiary purchases locally produced intermediate goods, the demand addressed to supplying industries could increase and thereby accelerating the demand for labour in the host country (Jude and Silaghi 2016). In the case of cross-border M&As, the foreign takeovers of domestic firms would destroy employment, when they abolish their linkages with domestic firms while restructuring the acquired firms (Geishecker and Hunya, 2005). If the merged or acquired firms replace the traditional domestic suppliers with imports, it would generate a negative effect of FDI on employment in the host country.

15.3 Data and methodology 15.3.1 Data The major database of the study is the ProwessIQ provided by the Centre for Monitoring Indian Economy (CMIE). The firm-level data on important variables, viz. sales, wages and salaries, equity holding information, gross fixed assets, export, imports and so on for a period of 18 years spanning from 2000–2001 to 2017–2018 are extracted from the ProwessIQ. The sample of the study consists of 1179 firms and of the total sample 945 are domestic and 234 are FDI-firms. The second important database is the Annual Survey of Industries (ASI) which is provided by the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. For calculating labour or employment of manufacturing firms or companies, the average wage at the three-digit level of the National Industrial Classification (NIC), 2008, is taken from the ASI database. Apart from these databases, in order to normalise the sales variable, wholesale price indices (WPI), taken from the Office of Economic Adviser, Ministry of Commerce and Industry, Government of India, is employed. For aggregative data on FDI inflows, the database on the Indian economy from the Reserve Bank of India and the FDI Newsletter from the Department for Promotion of Industry and Internal Trade (DPIIT), Ministry of Commerce and Industry, Government of India, are employed.2 15.3.2 Methodology The aim of this chapter is to analyse the employability of FDI in India’s manufacturing sector. We have estimated the output elasticity of employment of

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firms to see the employability (or labour absorption capacity) of FDI.3 The output elasticity of employment of a firm measures the responsiveness of employment to the output growth of the firm.4 In this chapter we basically compare the employability of FDI-firms with that of domestic firms to see whether FDI-firms have fared well or not in India.

15.4 Employability of FDI in manufacturing firms The aim of this study is to analyse the employability of FDI-firms in India’s manufacturing industries during 2000–2001 to 2017–2018. In the first step, we analyse the employment creation and employability of FDI-firms, and in the second step, how the different characteristics of firms affect the employability of FDI-firms are discussed. The following sub-sections discuss the detailed analysis of these steps, respectively, in Sections 15.4.1 and 15.4.2. 15.4.1 Employability of FDI-firms Table 15.2 presents the average sales and employment of FDI-firms and domestic firms in the manufacturing industries. The average sales of domestic firms increased around three-fold from 10.5 billion rupees to 29 billion rupees during 2000–2001 to 2017–2018, registering a growth rate of 6.7 percent, and corresponding to the sales’ growth, the employment of domestic firms Table 15.2 Average sales and average employment in manufacturing firms Year

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 2017–2018 Total

Domestic firms

Foreign direct investment (FDI)-firms

Sales (million Rs) Employment

Sales (million Rs) Employment

10516.11 10431.25 11292.05 12005.59 12966.91 14298.02 16671.23 18684.85 18975.37 19562.88 21761.21 23772.09 24258.49 24401.43 24628.76 26333.30 28524.29 28958.26 19335.56

6734.88 6801.23 7377.36 8075.03 9421.76 10990.20 12618.74 13897.33 14600.48 15469.79 17424.40 18378.48 19320.74 19471.80 20344.93 21651.44 23315.40 23746.93 14980.05

Source: author’s calculation using CMIE.

2059 2021 2028 2050 2236 2493 2578 2881 3022 3121 3094 3169 3093 3252 3168 3233 3297 3614 2800

2723 2717 2915 2828 3144 3432 3794 3811 3904 4067 4230 4546 4429 4675 4504 4613 4680 5335 3908

FDI in India’s manufacturing firms  285

grew at a meagre 3.5 percent from 2059 to 3614 during the study period. Similarly, the sales of the FDI-firms grew by around 8 percent from Rs. 6.7 billion to Rs. 23.7 billion and brought about the employment generation from 2723 to 5335, registering a growth of around 4 percent during the study period. These numbers indicate that the employment generation in FDI-firms is higher than that of domestic firms, and this is apparent from Figure 15.1. Although employment in FDI-firms is higher than that in domestic firms, the employability of the former is lower than that of the latter. During the study period, the employment elasticity of FDI-firms (0.615) is smaller than the employment elasticity of domestic firms (0.648). As shown in Figure 15.2, employment elasticities in both FDI-firms and domestic firms are more or less fluctuating in nature; nonetheless, the employment elasticities of domestic firms are higher than those of FDI-firms for most of the years during the study period. This implies that the employability of FDI-firms has been lower than that of domestic firms during the period under consideration. This indicates that regardless of the employment generation, the domestic firms have more employability as compared to the employability of FDI-firms during 2000–2001 through 2017–2018. Table 15.3 reports the industry-wise employment elasticity of FDI-firms and domestic firms. In most of the industries, the employment elasticities of domestic firms are higher than those of FDI-firms, only in four industries, viz. food, tobacco, wearing apparel and coke and refined petroleum, and FDI-firms showed a higher ability to generate employment compared to domestic firms. It thus implies that an increase in the output of FDI-firms leads to less employment generation as compared to the employment generation from Domestic Firms

FDI-firms

6000 5000 4000 3000 2000 1000

20 00 -0 20 1 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.1 Mean employment in domestic and foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

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FDI-Firms

Domestic Firms

0.83 0.82 0.81 0.8 0.79 0.78 0.77 0.76 0.75 0.74

8

7

-1

-1

20

17

6 20

16

5

-1

-1

20

15

4 20

14

3

-1

20

13

2

-1

20

12

1

-1

20

11

0

-1

20

10

9

-1

20

09

8

-0

20

08

7

-0

20

07

6

-0

20

06

5

-0

20

05

4

-0

-0

20

04

3 20

03

-0

02

20

20

01

-0

2

0.73

Figure 15.2 Employment elasticity in foreign direct investment (FDI)-firms and domestic firms. Source: author’s calculation using CMIE Table 15.3 Output elasticity of employment in foreign direct investment (FDI)-firms and domestic firms NIC code

Name of industry

FDI-firms

Domestic firms

10 11 12 13 14 15 16 17 19 22 23 24 25 20 21 26

Food products Beverages Tobacco products Textiles Wearing apparel Leather and related products Wood and wood products Paper and paper products Coke and refined petroleum products Rubber and plastics products Other non-metallic mineral products Basic metals Fabricated metal products Chemical and chemical products Pharmaceuticals Computer, electronic and optical products Electrical equipment Machinery and equipment Motor vehicles, trailers and semi-trailers Other transport equipment All manufacturing firms*

0.699 0.417 1.162 0.796 0.914 0.683 0.988 0.620 0.646 0.605 0.275 0.526 0.422 0.496

0.585 0.635 0.827 0.827 0.338 0.661 1.153 0.726 0.536 0.701 0.786 0.634 0.703 0.607 0.621 0.567

0.496 0.725 0.718

0.562 0.776 0.755

0.747 0.615

0.794 0.648

27 28 29 30 All

Note: * denotes fixed panel regression result. Source: author’s calculation using CMIE.

FDI in India’s manufacturing firms  287

the similar increase in output of domestic firms in India’s manufacturing industries. It is apparent from the above that the employment generation in FDIfirms is higher than that in domestic firms; however, as indicated by the employment elasticities of FDI-firms, the FDI-firms have lower employability as compared to the domestic firms in Indian manufacturing industries. There are the following possible explanations for the lower employability of FDI-firms in the manufacturing industries. The technological superiority of FDI-firms helps them economise the usage of labour in manufacturing and thereby lowering the demand for labour and employability of them in India. Further, the entry of MNEs leads to a restructuring of the existing (production) structure of the FDI-firms, and the structural change favours the usage of skilled labour, as the technologies employed in FDI-firms are skilled-biased in nature, which thus reduces the employability of FDI-firms in comparison to domestic firms. 15.4.2 Different characteristics of firms and employability of FDI-firms In order to see how the different characteristics of firms affect the employment and employability of FDI-firms, the employment and employability of FDI-firms in terms of their technology level, capital intensity and trade orientation are discussed in Section 15.4.2.1–15.4.2.3, respectively. 15.4.2.1 Technology differences and employment in FDI-firms Figure 15.3 portrays the mean employment in high-tech and low-tech FDIfirms. Here, the high-tech firms are firms belonging to high-technologyintensive industries (or high-tech industries) and the low-tech firms are from low-technology-intensive industries (or low-tech industries).5 It is shown in Figure 15.3 that compared to the high-tech FDI-firms, the low-tech FDIfirms are found to have generated more jobs throughout the study period. In addition, as indicated from the employment elasticities reported in Table 15.4, a one percent increase in output brings about a 0.68 percent increase in employment in the low-tech FDI-firms, whereas a similar increase in output leads to a 0.58 percent increase in employment in the high-tech FDI-firms in the manufacturing industries. It is clear that not only do the low-tech FDIfirms have higher employment generation compared to the high-tech FDIfirms, but they also have a higher labour absorption capacity as compared to the labour absorption capacity of high-tech FDI-firms. However, the employment in low-tech FDI-firms does not seem impressive when they are compared to the employment of low-tech domestic firms in the manufacturing industries. Figure 15.4 shows that average employment

288  Sanjaya Kumar Malik

High-tech FDI-firms

Low-tech FDI-firms

5000 4500 4000 3500 3000 2500 2000 1500 1000 500

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.3 Mean employment of high-tech and low-tech foreign direct investment (FDI)firms. Source: author’s calculation using CMIE Table 15.4 Characteristics of firms and employment elasticity of firms Characteristics of firms

Employment elasticity of foreign direct investment (FDI)-firms

Employment elasticity of domestic firms

High-tech Low-tech Capital intensive Non-capital intensive Exporter Non-exporter

0.58 0.68 0.52 0.71 0.59 0.60

0.64 0.66 0.57 0.72 0.69 0.60

Source: author’s calculation using CMIE.

in both low-tech FDI-firms and low-tech domestic firms is growing at a similar rate during the study period; nonetheless, the labour absorption capacity of low-tech FDI-firms is seen to have been slightly higher than that of lowtech domestic firms, as apparent from employment elasticities reported in Table 15.4. This implies that FDI in low-tech firms is likely to influence the employability of low-tech FDI-firms in India. In contrast, when the employment in high-tech FDI-firms is compared to the employment in high-tech domestic firms, FDI in high-tech industries is found to have improved the employment situations in high-tech industries in India (Figure 15.5). However, the employment elasticity of hightech FDI-firms is lower than that of high-tech domestic firms (Table 15.4). This implies that the output growth in high-tech FDI-firms brings about less employment growth as compared to the employment growth owing

FDI in India’s manufacturing firms  289

Low-tech domestic firms

Low-tech FDI-firms

5000 4500 4000 3500 3000 2500 2000 1500 1000 500

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.4 Mean employment in low-tech domestic and foreign direct investment (FDI)firms. Source: author’s calculation using CMIE High-tech domestic firms

High-tech FDI-firms

3500 3000 2500 2000 1500 1000 500

01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

20

20

00

-0

1

0

Figure 15.5 Mean employment in high-tech domestic and foreign direct investment (FDI)firms. Source: author’s calculation using CMIE

to the output growth in high-tech domestic firms. We have the following explanation for the lower employment absorption capacity of FDI in hightech manufacturing industries. The high-tech FDI are more likely to upgrade the production structure and production methods in the host country; for instance, the superior technology transfers from the parent company to the

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subsidiaries in the host country tend to improve the production and productivity of the high-tech FDI-firms and thus enable them to economise the usage of labour in the production. Therefore, the high-tech FDI has less labour absorption capacity in the manufacturing industries in India. 15.4.2.2 Differences in capital intensification and employability of FDI-firms Table 15.5 presents how the FDI-firms with different levels of capital intensity—capital-intensive FDI-firms and non-capital-intensive or low-capitalintensive FDI-firms—affect the employability of FDI-firms.6 Capital-intensive (high-capital-intensive) firms are defined as firms whose capital intensity is at least the third-quartile value of the sample in terms of capital intensity. The low-capital-intensive firms are on the other hand located below the thirdquartile value of the sample in terms of the capital intensity of the firms. As shown in Table 15.5, employment generation in capital-intensive FDI-firms as well as in non-capital-intensive FDI-firms does not show any apparent growth during the study period. Nonetheless, employment creation in non-capital-intensive FDI-firms is substantially higher as compared to capital-intensive FDI-firms throughout the period of study (Figure 15.6). Further, non-capital-intensive FDI-firms have a higher potential to generate employment compared to capital-intensive FDI-firms which is evident from their employment elasticities (Table 15.4). Table 15.5 Average employment in capital and non-capital-intensive foreign direct investment (FDI)-firms Year

Capital-intensive FDI-firms

Non-capital-intensive FDI-firms

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 2017–2018 All

1605 1536 1642 1492 1417 1682 1352 1538 1387 1137 993 817 1000 1178 751 608 840 796 1367

4419 4193 3986 3825 4024 4021 4424 4352 4589 4764 4818 5115 4934 5170 5035 5270 5140 5878 4743

Source: author’s calculation using CMIE.

FDI in India’s manufacturing firms  291

Capital-intensive FDI-firms

Non-capital intensive FDI-firms

7000 6000 5000 4000 3000 2000 1000

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.6 Mean employment in capital-intensive and non-capital-intensive foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

However, the comparison of FDI-firms with domestic firms in terms of their capital intensities shows a different finding with respect to employment generation in India. As Figure 15.7 indicates, FDI in capital-intensive firms does not show any increasing trend of employment; instead, it takes a decreasing trend of employment, registering a negative growth of 5 percent during the period of study. Additionally, as reported in Table 15.4, the employment elasticity of capital-intensive FDI-firms is significantly lower than that of capital-intensive domestic firms. The lower employment and lower employability of capital-intensive FDI-firms can be attributed to the following. FDI in capital-intensive firms induces capital intensification that minimises labour employment in production and thereby reducing the demand for labour by capital-intensive FDI-firms. Furthermore, the increase in FDI in non-capital-intensive firms shows a flat increase in employment (2 percent) during the period under consideration, and the employment generated in non-capital-intensive FDI-firms is seen to be higher than that generated in non-capital-intensive domestic firms (Figure 15.8). Though the employment generated by non-capital-intensive FDI-firms is higher compared to the employment generated by non-capital-intensive domestic firms, still the employability of non-capital-intensive domestic firms (i.e., 3.5 percent) is higher than that of non-capital-intensive FDI-firms during the study period. Notwithstanding the high employment compared to low-capital-intensive domestic firms, the employability of

292  Sanjaya Kumar Malik

Capital-intensive domestic firms

Capital-intensive FDI-firms

3500.00 3000.00 2500.00 2000.00 1500.00 1000.00 500.00

20 00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0.00

Figure 15.7 Mean employment in capital-intensive foreign direct investment (FDI)-firms and domestic firms. Source: author’s calculation using CMIE Non-capital intensive domestic firms

Non-capital intensive FDI-firms

7000 6000 5000 4000 3000 2000 1000

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.8 Mean employment in non-capital-intensive domestic and foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

non-capital-intensive FDI-firms is marginally lesser than that of non-capitalintensive domestic firms (Table 15.4). 15.4.2.3 Trade orientation and employability of FDI-firms Trade orientation of foreign firms is likely to affect the employability of FDI-firms in the host country. Export-oriented FDI, as argued in the

FDI in India’s manufacturing firms  293 Table 15.6 Mean employment in exporting and non-exporting foreign direct investment (FDI)-firms Year

Exporting FDI-firms

Non-exporting FDI-firms

2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 2017–2018 All

3186.39 3216.83 3259.57 3277.26 3705.59 4576.06 4684.37 4374.55 3747.08 4323.04 4220.91 4691.86 4043.87 4118.16 4074.62 3914.93 3903.43 4841.67 3995.96

2438.92 2386.65 2682.95 2552.40 2716.70 2606.89 3206.31 3425.42 4011.91 3909.74 4234.79 4438.22 4727.28 5105.62 4847.03 5142.59 5139.96 5486.86 3850.01

Source: author’s calculation using CMIE.

literature, is likely to generate more employment, as it is backed by the motive to exploit the location-specific advantages in the host country. The export-oriented firm or the exporting firm is defined as a firm whose netexports (i.e., exports-imports) are at least one percent of the total sales of the industry to which the firm belongs. However, the export-oriented FDI does not improve the employment generation in India, as shown in Table 15.6. Further, as apparent from Figure 15.9, the exporting FDIfirms have flat growth of employment (i.e., 1.55 percent), whereas the non-exporting FDI-firms are growing around 6 percent (5.61 percent) during the period under consideration. This implies that FDI in nonexporting firms leads to employment generation in the manufacturing industries which is again clear from the higher employment elasticity of non-exporting FDI-firms in comparison to exporting FDI-firms during the study period (Table 15.4). Further comparison of non-exporting FDI-firms with non-exporting domestic firms shows that employment of non-exporting foreign firms on average is higher than that of non-exporting domestic firms (Figure 15.10). This implies that FDI in non-exporting firms seems to have generated employment in the manufacturing industries. Nonetheless, the FDI in non-exporting firms is not found to have improved the employability of non-exporting firms as the employment elasticities of both non-exporting FDI-firms and domestic firms are the same (Table 15.4).

294  Sanjaya Kumar Malik

Exporting FDI-firms

Non-Exporting FDI-firms

6000 5000 4000 3000 2000 1000

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.9 Mean employment in exporting and non-exporting foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

Non-exporting domestic firms

Non-Exporting FDI-firms

6000 5000 4000 3000 2000 1000

20 00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.10 Mean employment in non-exporting domestic and foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

The comparison of exporting FDI-firms with exporting domestic firms is portrayed in Figure 15.11, which shows that employment of exporting FDI-firms is marginally higher than the employment of exporting domestic firms and both are growing at a meagre rate of around 1.5 percent during 2000–2001 to 2017–2018. However, the employment elasticity of exporting domestic firms is substantially higher than that of exporting FDI-firms during the period under consideration (Table 15.4). This implies that the

FDI in India’s manufacturing firms  295

Exporting domestic firms

Exporting FDI-firms

6000 5000 4000 3000 2000 1000

20

00 20 01 01 20 02 02 20 03 03 20 04 04 20 05 05 20 06 06 20 07 07 20 08 08 20 09 09 20 10 10 20 11 11 20 12 12 20 13 13 20 14 14 20 15 15 20 16 16 20 17 17 -1 8

0

Figure 15.11 Mean employment in exporting domestic and foreign direct investment (FDI)-firms. Source: author’s calculation using CMIE

growth of output in export-oriented foreign firms is translating into lower employment generation as compared to the employment generation, owing to the output growth of the export-oriented domestic firms.

15.5 Conclusion and policy suggestions The employment generation, which has been a monumental task of the developing world, can be accomplished through modern sector development and industrial change. FDI is presumed to have played a vital role in industrial and modern sector development. Nevertheless, labour market research has hitherto paid marginal attention to understanding the employability of FDI in host developing countries. The present study has therefore analysed the employability of FDI in India’s manufacturing firms. Our analysis has found that the employment of FDI-firms has outnumbered the employment of domestic firms during 2000–2001 through 2017–2018. Regardless of high employment generation, the employability of FDI-firms—which is measured by the employment elasticity of FDI-firms—is seen to have been lower than that of domestic firms throughout the study period. This finding implies that increased output growth in FDI-firms leads to lesser employment growth as compared to the employment growth of domestic firms due to their increased growth of output. Further controlling for different characteristics of firms (namely technology differences, capital intensiveness and trade orientation), the low-tech, low-capital-intensive and non-export-oriented FDIfirms are not seen to have better employability compared to the low-tech,

296  Sanjaya Kumar Malik

low-capital-intensive and non-export-oriented domestic firms, respectively. The application of advanced technologies in low-tech, low-capital-intensive and non-export-oriented FDI-firms brings about increased productivity and simultaneously economises the usage of labour in production. Therefore, these FDI-firms do not bring about better employability compared to their domestic counterparts in India’s manufacturing industries. Moreover, it is seen that FDI in high-tech, capital-intensive and export-oriented firms leads to a reduction in labour absorption capability compared to the hightech, capital-intensive and export-oriented domestic firms in manufacturing industries. The following can be put forth to explain the above findings. FDI-firms, which are technology-intensive, capital-intensive and export-oriented in nature, tend to employ more superior technologies in manufacturing that help them economise the usage of labour in production and thereby reducing the labour demand and employability of them. Needless to mention that MNEs own, control and produce most of the world’s technologies, and the advent of industry 4.0—which is characterised by advanced technologies, artificial intelligence, machine learning, data mining and mobile robotics—is expected to automate two-thirds of jobs in developing regions (World Bank, 2016). FDI by MNEs is likely to be embodied the feature of Industry 4.0. Hence, FDI is presumably to automate or to do away with the existing employment in lieu of creating new employment in host developing countries. In sum, it can be said that FDI is not likely to bring about new employment in the host country and it cannot thus be relied upon to boost employment in host developing countries, like India; particularly as observed in this study, FDI which is technology-, capital-, and export-intensive is not at all expected to bring about employment in host developing countries.

Acknowledgements The author is thankful to the anonymous referee for the useful comments on the chapter. Any errors that remain are author’s only.

Notes 1 FDI inflow is calculated as net of repatriation/disinvestment. 2 See Table 15.A2 in Appendix for details of the variables. 3 Employability and labour absorption capacity are used interchangeably throughout. 4 For the sake of shortness, hereafter ‘output elasticity of employment’ will be referred as ‘employment elasticity’. 5 See Table 15.A3 in Appendix for the detailed classification of industries according to technology-intensiveness. 6 Capital intensity is defined as the ratio of capital stock to sales of the firm, and here capital stock is estimated using perpetual inventory method (see Table 15.A2 in Appendix for detailed description of capital stock estimation).

FDI in India’s manufacturing firms  297

References Bagchi-Sen, S. (1991). Employment in foreign-owned manufacturing firms in the United States: The impact of modes of entry. Tijdschrift voor economische en sociale geografie, 82(4), 282–294. Benacek, V., Gronicki, M., Holland, D., & Sass, M. (2000). The determinants and impact of foreign direct investment in Central and Eastern Europe: A comparison of survey and econometric evidence. Transnational Corporations, 9(3), 163–212. Coniglio, N. D., Prota, F., & Seric, A. (2015). Foreign direct investment, employment and wages in Sub‐Saharan Africa. Journal of International Development, 27(7), 1243–1266. Conyon, M. J., Girma, S., Thompson, S., & Wright, P. W. (2002). The impact of mergers and acquisitions on company employment in the United Kingdom. European Economic Review, 46(1), 31–49. Dobson, W., & Chia, S. Y. (Eds.). (1997). Multinationals and East Asian Integration. Institute of Southeast Asian Studies. Dunning, J. H., & Lundan, S. M. (2008). Multinational Enterprises and the Global Economy. Edward Elgar Publishing. Felipe, J., & Hasan, R. (2006). The challenge of job creation in Asia. ERD Policy Brief, 44. Asian Development Bank. Geishecker, I., & Hunya, G. (2005). Employment Effects of Foreign Direct Investment in Central and Eastern Europe (No. 321). WIIW Research Report. Girma, S., Thompson, S., & Wright, P. W. (2002). Why are productivity and wages higher in foreign firms?. Economic and Social Review, 33(1), 93–100. Jenkins, R. (2006). Globalization, FDI and employment in Viet Nam. Transnational Corporations, 15(1), 115. Karlsson, S., Lundin, N., Sjöholm, F., & He, P. (2009). Foreign firms and Chinese employment. World Economy, 32(1), 178–201. Peluffo, A. (2015). Foreign direct investment, productivity, demand for skilled labour and wage inequality: An analysis of Uruguay. The World Economy, 38(6), 962–983. Pradhan, J. P. (2006). Quality of Foreign Direct Investment, Knowledge Spillovers and Host Country Productivity: A Framework of Analysis. ISID Working Paper No. 11, Institute for Studies in Industrial Development, New Delhi. Reserve Bank of India. (2018). Database on Indian Economy. Reserve Bank of India. UNCTAD (2007). The Least Developed Countries Report. UNCTAD. Waldkirch, A., Nunnenkamp, P., & Alatorre Bremont, J. E. (2009). Employment effects of FDI in Mexico's non-maquiladora manufacturing. The Journal of Development Studies, 45(7), 1165–1183. World Bank. (2016). World Development Report 2016: Digital Dividends. World Bank Publications.

Chapter 16

Technology and labour in India’s manufacturing Ram Kumar Mishra and Sandeep Kumar Kujur

16.1 Introduction The employment situation in India has worsened in recent times. The total number of people unemployed in India has increased from 10.8 million in 2011–2012 to 28.5 million in 2017–2018. This has shot up India’s unemployment rate to 6.1% in 2017–2018, the highest since 1977–78 (Mitra & Singh, 2019). To counter the problem of increasing unemployment rate, especially among the educated youth (Mehrotra & Parida, 2019), the Government of India initiated the National Manufacturing Policy (NMP), 2011 (Kujur & Goswami, 2021). This policy emphasized the role of the manufacturing industry in creating sustainable employment in the economy. However, the employment creation potential in the manufacturing sector varies across different sub-groups, depending on the structure of the sector. The manufacturing industries with low capital-labour (K–L) substitution rely primarily on using more labourers to manufacture their output. These industries offer greater scope to create employment opportunities through the expansion of production activity and structural change. Other sets of manufacturing industries with high K–L substitution utilize more advanced industrial technologies. This situation displaces labour and polarizes jobs. Nonetheless, it offers new employment opportunities for very high-skilled labourers. Existing studies on Indian manufacturing examined the factors affecting their employment. These studies have attributed the phenomenon of jobless growth in Indian manufacturing to various demand-side factors, including the rigidity in the labour market, trade liberalization, growth in real wages, and changing nature of demand for manufactured goods (Kannan & Raveendran, 2009; Sankaran et al., 2010; Sen & Das, 2015; Kapoor, 2015). In addition, employment in Indian manufacturing is negatively impacted by technological change (Kujur, 2018; Majumdar, 2018) and productivity (Goswami, 2022). The use of new industrial technologies such as robotics and artificial intelligence has also displaced labourers and polarized the job in some sectors of Indian manufacturing (Sharma, 2016; Vashisht, 2018; DOI: 10.4324/9781003329862-21

Technology and labour in India  299

Vashisht & Dubey, 2019). In such a case, understanding the employment change in aggregate manufacturing may not provide sufficient insight to formulate sector-specific manufacturing employment policies, especially during employment crises. Therefore, based on the estimate of K–L substitution of Indian manufacturing industries (Goldar et al., 2013; Chaurey & Soundararajan, 2019), we identify and classify the industries into: (i) industries with high K–L substitution and (ii) industries with low K–L substitution. Industries with low K–L substitutions may not experience a large decline in employment in a developing economy because of its expanding economic activities. This set of industries also may not affect the employment of highly skilled labourers. However, the industries’ high K–L substitutions may experience a decline in employment because of their inherent reliance on capital and the adoption of new technologies. The dependence on capital in these industries will mainly affect the employment of highly skilled labourers. Therefore, this study focuses explicitly on the employment change in industries with a high level of K–L substitution. Employment in industries with high K–L substitution might be negatively impacted by a structural shift or technological change. Therefore, the chapter examines whether employment in industries with high K–L substitution is affected by the scale effect, structural effect, or technological change. The analysis will provide an understanding of the employment change in the technology-driven manufacturing industries in India. The present study adds to the domain of manufacturing employment in two distinct ways. First, disaggregated three-digit-level industrial data from 1998–99 to 2016–2017 provides a detailed understanding of the factors driving employment change at the industrial sub-sectors. This will benefit the industry to suitably reallocate capital and labour, especially in the age of manufacturing automation. Second, this study will guide designing the employment policy of the technologically oriented manufacturing sectors, which will improve the employment of highly skilled labourers and reduce the educated youth unemployment in India. Following this, the rest of the chapter is organized as follows. Section 16.2 reviews the related literature in the domain. The empirical framework and data used are presented in Section 16.3, while Section 16.4 presents the results. Section 16.5 discusses the findings with policy implications.

16.2 Review of related literature The increased competition from foreign firms following the trade reforms forced the domestic firm to relax labour laws and provide the opportunity to move towards capital-intensive production. This results in the retrenchment of less productive labourers and the phenomenon of jobless growth (Mukherjee, 2012, 2014). The phenomenon of jobless growth in the Indian

300  Ram Kumar Mishra and Sandeep Kumar Kujur

economy is well established in the literature (Bhalhotra, 1998; Sharma, 2006; Thomas, 2013; Kannan, 2013; Pattanaik & Nayak, 2013; Tejani, 2016; Abubakar & Nurudeen, 2019; Abraham, 2019). The absolute decline in employment in India is attributed to the employment fall in the agriculture and manufacturing sector (Choudhury & Chatterjee, 2015; Mitra & Singh, 2019). The increase in participation in education, decline in child labour, mechanization of agriculture, and increasing standard of living in rural areas due to the rise in real wage are other factors that reduce the employment rate in India (Mehrotra et al., 2014). The slow growth of construction sector jobs is also another important determinant for a drastic decline in the employment rate in India in recent years (Mehrotra & Parida, 2019). Like the Indian economy, the manufacturing industry, too, has experienced a decline in employment (Nagraj, 2004, 2011). This decline in manufacturing employment is because of inflexible labour laws (Kapoor, 2015), increasing demand for capital-intensive goods (Kannan & Raveendran, 2009), and an unfavorable trend of the domestic real exchange rate (Mazumdar & Sarkar, 2004). In addition, the availability of cheap capital following the trade liberalization (Sankaran et al., 2010; Mehrotra et al., 2014; Sen & Das, 2015) increased the capital or technological base (Kujur, 2018; Majumdar, 2018) reduced the employment in the industry. The application of advanced industrial technology such as robotics and artificial intelligence has also reduced the required labour per unit of output (Vashisht, 2018) and polarized the jobs in Indian manufacturing (Sharma, 2016; Vashisht & Dubey, 2019).1 All these studies have been conducted at the aggregate level, which seems to recommend the one-size-fits-all policy for employment creation. However, the factors affecting employment change vary within the manufacturing sub-sectors. The existing literature in the domain has paid marginal attention to this topic. The present study bridges this gap and adds to the domain of literature.

16.3 Empirical framework and data 16.3.1 Methods The decomposition method can be applied to analyze the contribution of pre-determined factors on the input use change (Ang & Zhang, 2000). We apply the perfect decomposition method to examine the impact of the scale effect, structural effect, and intensity effect (technology effect) on the total employment change in the identified manufacturing industries in India. The input–output and index decomposition analysis (IDA) is applied in the literature to examine factor input change. The decomposition methods are based on the Laspeyres index and the Divisia index, which have three major variants: input use approach, input intensity approach, and input coefficient approach. All these versions of the Laspeyres index are limited by large

Technology and labour in India  301

residuals and failed in satisfying other statistical tests. These, however, have been handled by the Log Mean Divisia Index (LMDI). The additive and multiplicative versions of LMDI Method-I and LMDI Method-II have been developed in the literature (Ang et al., 2003; Ang, 2015). Based on Ang’s (2004, 2015) suggestions, we apply LMDI-I to examine the contribution of three major pre-identified factors on the total employment change in the specific manufacturing industries in India.2 We take IDA identity

V=

åV = åx

x ¼¼ xn,i (16.1)

1,i 2,i

i

i

i

where V is the aggregate of labour employment with n factors ( x1, x2 ,, xn ) contributing to changes in V over time, and i is the subcategory of the aggregate. Our goal is to calculate the contributions of the n factors to the change in the V between 0 and T. We obtain this by using the additive decomposition method

DVtot = V T - V 0 = DVx1 + DVx2 +¼¼+ DVxn . (16.2)

where tot is the total change in aggregate. The right-hand identity is associated effects given in Equation (16.1). Using the LMDI approach, we get the effect of the kth factor on the righthand side of Equation (16.2) is DVxk =

åL (V

T i

i

=

å i

æ xT ö ,Vi0 ln ç k0,i ÷ è xk,i ø

)

æ xkT,i ö ViT - Vi0 l n ç ÷ ln ViT - ln Vi0 è xk0,i ø



where L ( a, b ) = ( a - b ) / ( ln a - ln b ) is the logarithmic mean of two positive numbers a and b, and L ( a, a ) = a. We consider three main components to decompose the total employment change. These are (i) magnitude of total output considered as scale effect, (ii) sectoral output share in aggregate taken as structural change effect, and (iii) sectoral labour share in output referred to as intensity effect or technology effect (Liu et al., 1992; Sun, 1998; Kujur, 2018). Equation (16.1) is performed for the employment decomposition in industries with high K–L substitutions in India as

L=

åL = åQ Q Q = åQS I (16.4) Qi Li

i

i

i

i

i i

i

302  Ram Kumar Mishra and Sandeep Kumar Kujur

where

Q=

åQ : total output inallsectors i

i



L=

åL : total labour employment inallsectors i

i



æQ Si = ç i èQ

ö ÷ : activity share of sector i ø



æL Ii = ç i è Qi

ö ÷ : labour intensity of sector i ø

where DLtot is the change in the total labour demand from year 0 to T and is reflected by LT - L0 . Here, we decompose DLtot into—the change in production (activity effect, DLact ), the change in structure within the industry (structural effect, DLstr ), and the change in labour intensity (intensity effect, DLint ). Using Equation (16.2), we get the additive form of decomposition as given

DLtot = LT - L0 = DLact + DLstr + DLint (16.5)

where

DLact =

æ QT ö wi ln ç 0 ÷ èQ ø

å i



DLstr =

å i



DLint = wi =

æ IT wi ln ç i0 è Ii

å i



æ ST ö wi ln ç i0 ÷ è Si ø ö ÷ ø

LTi - L0i ln LTi - ln L0i

16.3.2 Data The elasticity of substitution ( E ) between capital–labour ( K - L ) input is an important indicator of labour employment in the industry. Employing constant elasticity of substitution production function in annual time series of Annual Survey of Industries (ASI) data, Goldar et al. (2013) computed the elasticity of substitution between K and L for 22 National Industrial

Technology and labour in India  303

Classification (NIC)-2004-based manufacturing industries at a two-digit level. We use the reported elasticity of substitution and compute its median value (0.80). We take this median value as the cut-off point and classify the industries with a value of E greater than the cut-off point as industries with high K - L substitution, while industries with value E lesser than the cut-off point are taken as industries with low K - L substitution (Table 16.1).

Table 16.1 Elasticity of substation between capital–labour (K–L) in the manufacturing industry in India NIC-2004/EPWRF Description of the industry concorded series

K–L substitution

15 16 17 18

0.94 0.64 0.64 0.66

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Manufacture of food products and beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel, dressing, and dyeing fur Tanning and dressing of leather, manufacture of luggage, handbags, saddlery, harnesses, and footwear Manufacture of wood and of products of wood and cork, except furniture, manufacture of articles of straw, and paint materials Manufacture of paper and paper products Publishing, printing, and reproduction of recorded media Manufacture of coke, refined petroleum products, and nuclear fuel Manufacture of chemical and chemical products Manufacture of rubber and plastic products Manufacture of other non-metal minerals Manufacture of basic metals Manufacture of fabricated metal products except machinery and equipment Manufacture of non-electrical machinery and equipment (Not Elsewhere Classified [NEC]) Manufacture of office accounting and computing machinery Manufacture of electrical machinery apparatus NEC Manufacture of radio, television and communication equipment and apparatus Manufacture of medical, precision, and optical instruments and clocks Manufacture of motor vehicles, trailers, and semi-trailers Manufacture of other transport equipment Mfg. of furniture, manufacture NEC

Source: Goldar et al. (2013).

0.56 0.97 0.73 0.93 0.84 0.88 0.80 0.81 0.54 0.81 0.87 0.73 0.73 0.82 0.74 0.86 0.71 0.87

304  Ram Kumar Mishra and Sandeep Kumar Kujur

We use NIC-2004-based ASI data at a three-digit level from 1998–99 to 2016–2017, provided by the Central Statistics Office, Ministry of Statistics and Program Implementation, Government of India. The data is compiled and electronically made available by India Time Series, Economic and Political Weekly Research Foundation (EPWRF, 2019). The database provided the harmonized series that concorded the data of NIC-1998, NIC2004, and NIC-2008. We use the total number of persons engaged as an indicator of labour input used in output production in the individual manufacturing industry. The nominal value of the gross value of output is deflated by the appropriate wholesale price indices (WPIs) to get its real values. The WPIs at 2011–2012 base price are retrieved from EPWRF (2019).

16.4 Results The decomposition results in industries with high K–L substitution show a diverse impact of activity, structural, and technological change. We discuss them below. 16.4.1 Food and beverages industry The accumulated total employment in the food and beverages industry has declined during 1998–99 to 2016–2017 (Figure 16.1). The total employment in the industry has been raised by the activity effect, while the structural effect and technological effect reduced it. The expansion in production increased the total demand for labour in the industry, accounting for 91% of the total employment change in absolute value during this period. However, the positive contribution of activity effect on employment in the industry has been reduced by the negative effect of structural and technological changes. The structural change effect has reduced employment generation in the industry, accounting for about −7% of the total employment change. The accumulated labour intensity effect (or technology effect) reduced employment by −183% of the total change in the absolute value of total employment.3 16.4.2 Wood and wood products industry In the wood and wood product industry, the absolute number of labour employment has declined between 1998–99 and 2016–2017 (Figure 16.2). The rise in the economic activity in the industry raised its total labour demand, accounting for about 522% of the total change in the labour employment change in the industry. The change in the structure of the industry has also increased the labour demand in the industry, and this accounts for around 25% of the total change in labour employment in the industry. However, the adoption of new technology has reduced employment in the industry significantly by −648%.

Technology and labour in India  305

1

Millions

0.5 0 ∆Ltot

∆Lact

∆Lstr

∆Lint

-0.5 -1 -1.5

Figure 16.1 Accumulated labour employment change in the food and beverages industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data 0.03 0.02

Millions

0.01 0 -0.01

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.02 -0.03 -0.04

Figure 16.2 Accumulated labour employment change in wood and wood products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

16.4.3 Publishing and printing industry The publishing and printing industry has witnessed a significant change in employment from 1998–99 to 2016–2017 (Figure 16.3). The accumulated increase in production activity improved the employment scenario, contributing about 15% of the total employment change in the industry during the

Millions

306  Ram Kumar Mishra and Sandeep Kumar Kujur

0.02 0.01 0 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09

∆Ltot

∆Lact

∆Lstr

∆Lint

Figure 16.3 Accumulated labour employment change in publishing and printing industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

period. However, the structural change reduced labour use by −9% in the overall change in labour use. Further, the negative labour intensity resulting from new technology has reduced the industry’s labour use by −105% of the total change in employment. 16.4.4 Petroleum products industry The absolute number of labour employment in the petroleum products industry has declined during the study period (Figure 16.4). The expansion in economic activity enhances employment in the industry by about 136% of the total employment change. The structural change in the industry too has favored labour use and increased the total labour demand by about 22%. However, the advancement in the technology has negatively impacted labour employment in the sector. This has reduced employment by about −258% of the total employment change. 16.4.5 Chemical and chemical products industry Labour employment in the chemical and chemical products industry has witnessed a significant decline between 1998–99 and 2016–2017. This is attributed to the activity effect, structural effect, and technological change (Figure 16.5). The rise in production activity has generated employment in the sector by about 8% of the total labour use change in the industry. However, the structural effect and technological change have reduced the

Technology and labour in India  307

0.06 0.04

Millions

0.02 0 ∆Ltot

∆Lact

∆Lstr

∆Lint

-0.02 -0.04 -0.06 -0.08

Figure 16.4 Accumulated labour employment change in the petroleum products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

0.1 0 ∆Ltot

∆Lact

∆Lstr

∆Lint

-0.1

Millions

-0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8

Figure 16.5 Accumulated labour employment change in the chemical and chemical products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

308  Ram Kumar Mishra and Sandeep Kumar Kujur

sector’s labour demand by about −10% and −97% of the total change in employment, respectively. 16.4.6 Rubber and plastic products industry The rubber and plastic products industry witnessed a fall in employment between 1998–99 and 2016–2017, contributed by the scale effect, structural effect, and intensity effect (Figure 16.6). The increase in production raised the labour used, accounting for about 99% of the total employment change in the sector. The structural change in the industry, however, has negatively impacted labour employment. The negative impact of structural change has limited employment by −4% of the overall employment change. Likewise, the application of better industrial technology has reduced employment opportunities in the sector. This accounts for about −195% of the total employment change during the period. 16.4.7 Non-metallic mineral products industry The overall employment in the non-metallic mineral products industry during 1998–99 to 2016–2017 has experienced a decline (Figure 16.7). The increase in output has generated labour employment opportunities in the industry, accounting for about 799% of the total employment change in the industry. The structural change has also created new employment in the industry. The creation of employment through structural change is around 0.2 0.15 0.1

Millions

0.05 0 -0.05

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.1 -0.15 -0.2 -0.25 -0.3

Figure 16.6 Accumulated labour employment change in rubber and plastic products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

Technology and labour in India  309

0.4 0.3 0.2 Millions

0.1 0 -0.1

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.2 -0.3 -0.4

Figure 16.7 Accumulated labour employment change in the non-metallic mineral products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

25% of the overall employment change in the industry. Contrary to the activity effect and structural effect, the improvement in technology has markedly reduced employment in the sector. It reduced employment by around −923% of the total employment change in the industry during this period. 16.4.8 Fabricated metal products industry Labour employment in the fabricated metal products industry has declined (Figure 16.8). The rise in the production scale increased labour employment by 78% of the total employment change in the sector. Although structural change reduced employment, its effect is significantly lesser. The estimated overall structural change effect contributed around −01% of the total employment change in the sector. The intensity effect has significantly reduced employment, accounting for about −177% of the total employment change in the industry. 16.4.9 Non-electrical machinery and equipment industry The non-electrical machinery and equipment industry’s labour employment during 1998–99 to 2016–2017 has experienced a decline (Figure 16.9). During this period, the accumulated increase in economic activity has raised employment in the industry by 58% of the total change in employment. The

310  Ram Kumar Mishra and Sandeep Kumar Kujur

0.15 0.1

Millions

0.05 0 ∆Ltot

∆Lact

∆Lstr

∆Lint

-0.05 -0.1 -0.15 -0.2 -0.25

Figure 16.8 Accumulated labour employment change in the fabricated metal products industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

0.4 0.2 0 Millions

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.2 -0.4 -0.6 -0.8 -1

Figure 16.9 Accumulated labour employment change in the non-electrical machinery and equipment industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data

employment gain emerging from the rising scale has been reduced because of the industry’s structural change and technological change. The estimated structural change reduced employment by −01%, while the technological effect contributed around −157% of the total employment change in the industry during this period.

Technology and labour in India  311

16.4.10 Communications equipment industry Unlike other manufacturing industries, employment in the communications equipment industry has experienced a positive trend (Figure 16.10). The increase in employment between 1998–99 and 2016–2017 has positively contributed to both scale and structural effects. The expansion in production increased the total employment in the industry by 87% of the total change in employment. Similarly, the structural change within the industry favored the use of more number of labourers, which contributed around 190%. However, the use of high-quality technology has reduced the total employment in the industry by −176% of the total employment change. 16.4.11 Motor vehicles industry The marginal increase in labour employment in the motor vehicles industry in India during 1998–99 to 2016–2017 has primarily contributed by the activity effect (Figure 16.11). The rise in economic activity significantly increased employment in the industry and contributed around 15905% of the total employment change in the industry. The other two pre-defined factors, such as structural effect and intensity effect, reduced employment opportunities in the sector. The change in the structure and technology accounts for around −96% and −15709% of the total employment change in the industry, respectively.

0.25 0.2 0.15

Millions

0.1 0.05 0 -0.05

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.1 -0.15 -0.2 -0.25

Figure 16.10 Accumulated labour employment change in the communications equipment industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data.

312  Ram Kumar Mishra and Sandeep Kumar Kujur

2.5 2 1.5

Millions

1 0.5 0 ∆Ltot

∆Lact

∆Lstr

∆Lint

-0.5 -1 -1.5 -2 -2.5

Figure 16.11 Accumulated labour employment change in the motor vehicles industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data.

16.4.12 Furniture industry In the furniture industry, total employment has declined during the period considered (Figure 16.12). The overall expansion of production activity increased employment, accounting for around 836% of the total employment change in the industry. The increasing employment emerging out of growing production is curtailed by the change in the structure and technology used in the industry. The structural change compressed employment by −05%, while technology use reduced employment by −931% of the total employment change in the industry.4

16.5 Discussion and policy implications The manufacturing industries with high K–L substitutions are most likely to witness a decline in total labour employment. This will affect the employment opportunities of high-skilled labourers. The present study employs NIC2004-based three-digit ASI data, and LMDI-I-based complete decomposition method to analyze whether the change in employment in the industries with high K–L substation is driven by activity effect, structural effect, or technological change during 1998–99 to 2016–2017. We find that accumulated labour employment in these sectors has declined during this period. The employment fall in these sectors has been impacted by the activity, structural, and technological change. The increasing production scale (activity effect) raised the

Technology and labour in India  313

0.80 0.60

Millions

0.40 0.20 0.00 -0.20

∆Ltot

∆Lact

∆Lstr

∆Lint

-0.40 -0.60 -0.80

Figure 16.12 Accumulated labour employment change in the furniture industry. Notes: (1) DLtot : change in labour demand from year 0 to year T . (2) DLact : activity effect, DLstr : structural effect, and DLint : intensity effect. (3) A positive number shows the effect that increases the total labour demand. (4) A negative number shows the effect that decreases the total labour demand. Source: authors’ calculation using Annual Survey of Industries (ASI) data.

employment opportunity and thus increased overall employment in these sectors. Although the structural change within the industry (structural effect) has unequal effects on the employment change, in most of the identified manufacturing sectors, the structural change has favored capital use, which reduced the demand for labourers. The increasing employment opportunities created by the activity effect and partly by the structural effect in some sectors have been compressed by the higher use of technology in different operations domains (intensity effect), thus reducing employment in these sectors. Considering the varying effects of all the pre-defined factors on employment change across the industries, the study suggests developing a sector-specific employment policy to improve high-skilled employment in these sectors. The policymakers should specifically emphasize production growth, which will create more high-skill employment opportunities in these otherwise high K–L substitution sectors. Attention should be paid to structural change within the industry to realize its positive impact on employment. More specifically, the government should exploit and take advantage of the positive impact of the structural change in the wood and wood products industry, petroleum products industry, nonmetallic mineral products industry, and communications equipment industry to generate more employment. The greater use of technology reduced employment in industries with high K–L substitutions. This suggests that the government should design a sector-specific employment policy to reduce job loss. Protecting the labourers and promoting high-skilled labour employment in these sectors would reduce the educated youth unemployment in the economy.

314  Ram Kumar Mishra and Sandeep Kumar Kujur

Acknowledgments The authors would like to thank the participants of the international conference held at Institute of Public Enterprise, Hyderabad, India (December 2019) for their helpful discussion. The authors would also like to thank the reviewers for their valuable suggestions on an earlier draft of this chapter. The standard disclaimer applies.

Funding This work is part of the major research project ‘Labour displacement potential of technology adoption: Firm-level evidence from Indian manufacturing industry’ (F. No. 02/29/ST/2017-18/RP/Major), supported by the Indian Council of Social Science Research (ICSSR), Ministry of Education, Government of India, New Delhi.

Notes 1 The impact of the use of advanced industrial technology on labour employment and job polarization in various countries context has been studied by several researcher using task-based approach (Bessen, 2012; Autor & Dorn, 2013; Autor, 2015; Frey & Osborne, 2017; Artnz et al., 2017; and Acemoglu & Restrepo, 2018). 2 Refer these studies to know more about the advantages of LMDI-I method. 3 The technological improvements measured in terms of total factor growth in India’s manufacturing are well documented in the literature (Topalova & Khandelwal, 2011; Maiti, 2013; Arnold et al., 2016). 4 The annual decomposition results and its corresponding percentage share in the total change in employment in each sector will be made available upon request.

References Abraham, V. (2019). Jobless growth through the lens of structural transformation. Indian Growth and Development Review, 12(2), 182–201. Abubakar, J., & Nurudeen, I. (2019). Economic Growth in India, Is It a Jobless Growth? An Empirical Examination Using Okun’s Law. The Indian Journal of Labour Economics, 62(2), 307–317. Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. Ang, B. W. (2004). Decomposition analysis for policymaking in energy: Which is the preferred method?. Energy Policy, 32(9), 1131–1139. Ang, B. W. (2015). LMDI decomposition approach: A guide for implementation. Energy Policy, 86, 233–238. Ang, B. W., & Zhang, F. Q. (2000). A survey of index decomposition analysis in energy and environmental studies. Energy, 25(12), 1149–1176. Ang, B. W., Liu, F. L., & Chew, E. P. (2003). Perfect decomposition techniques in energy and environmental analysis. Energy Policy, 31(14), 1561–1566.

Technology and labour in India  315 Arnold, J. M., Javorcik, B., Lipscomb, M., & Mattoo, A. (2016). Services reform and manufacturing performance: Evidence from India. The Economic Journal, 126(590), 1–39. Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157–160. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labour market. American Economic Review, 103(5), 1553–97. Bessen, J. (2012). More machines, better machines... or better workers? The Journal of Economic History, 71(1), 44–74. Bhalotra, S. R. (1998). The puzzle of jobless growth in Indian manufacturing. Oxford Bulletin of Economics and Statistics, 60(1), 5–32. Chaurey, R., & Soundararajan, V. (2019). Banning contract work: Implications for input choices and firm performance. Accessed from https://repository​.iimb​.ac​.in​/ handle​/2074​/13955 on December 08th, 2022. Choudhury, P. R., & Chatterjee, B. (2015). Analyzing "jobless growth" in postliberalisation India: A decomposition approach. The Indian Journal of Labour Economics, 58(4), 577–608. EPWRF (2019). India Time Series. Economic and Political Weekly Research Foundation, Sameeksha Trust, Mumbai, India, http://epwrfits​.in​/ TypesOfASIStateWise​.aspx. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254–280. Goldar, B., Pradhan, B. K., & Sharma, A. K. (2013). Elasticity of substitution between capital and labour inputs in manufacturing industries of the Indian economy. The Journal of Industrial Statistics, 2(2), 169–194. Goswami, D. (2022). Productivity and job reallocation: Evidence from the Indian manufacturing. International Journal of Manpower. https://doi​.org​/10​.1108​/IJM​ -07​-2020​-0345. Kannan, K. P. (2013). The growth-employment in India since the advent of economic reforms: Is there a ‘virtuous circle? The Indian Journal of Labour Economics, 56(2), 175–190. Kannan, K. P., & Raveendran, G. (2009). Growth sans employment: A quarter century of jobless growth in India’s organised manufacturing. Economic and Political Weekly, 44(10), 80–91. Kapoor, R. (2015). Creating jobs in India’s organised manufacturing sector. The Indian Journal of Labour Economics, 58(3), 349–375. Kujur, S. K. (2018). Impact of technological change on employment: Evidence from the organised manufacturing industry in India. The Indian Journal of Labour Economics, 61(2), 339–376. Kujur, S. K., & Goswami, D. (2021). National manufacturing policy: A reality check. Economic and Political Weekly, 56(45–46), 20–24. Liu, X. Q., Ang, B. W., & Ong, H. L. (1992). Interfuel substitution and decomposition of changes in industrial energy consumption. Energy, 17(7), 689–696.

316  Ram Kumar Mishra and Sandeep Kumar Kujur Maiti, D. (2013). Market imperfections, trade reform and total factor productivity growth: Theory and practices from India. Journal of Productivity Analysis, 40(2), 207–218. Mazumdar, D., & Sarkar, S. (2004). Reforms and employment elasticity in organised manufacturing. Economic and Political Weekly, 39(27), 3017–3029. Mehrotra, S., & Parida, J. K. (2019). India’s employment crisis: Rising education levels and falling non-agricultural job growth. Ajim Premji University, CSE WP-2019-04. Mehrotra, S., Parida, J., Sinha, S., & Gandhi, A. (2014). Explaining employment trends in the Indian economy: 1993–94 to 2011–12. Economic and Political Weekly, 49(32), 49–57. Majumdar, R. (2018). Technology and labour market: Insights from Indian manufacturing sector. The Indian Journal of Labour Economics, 61(2), 321–338. Mitra, A., & Singh, J. (2019). Rising Unemployment in India: A Statewise Analysis from 1993–94 to 2017–18. Economic and Political Weekly, 54(50), 12–16. Mukherjee, S. (2012). Revisiting the Apparent Paradox: Foreign Capital Inflow, Welfare Amelioration and Jobless Growth with Agricultural Dualism and Nontraded Intermediate Input. Journal of Economic Integration, 27(1), 123–133. Mukherjee, S. (2014). Liberalisation and jobless growth in developing economy. Journal of Economic Integration, 29(3), 450–469. Nagaraj, R. (2004). Fall in organised manufacturing employment: A brief note. Economic and Political Weekly, 39(30), 3387–3390. Nagaraj, R. (2011). Growth in organised manufacturing employment: A comment. Economic and Political Weekly, 46(12), 83–84. Pattanaik, F., & Nayak, N. C. (2013). Trends and forecasting of employment intensity of growth in India. Journal of the Asia Pacific Economy, 18(3), 438–459. Sankaran, U., Abraham, V., & Joseph, K. J. (2010). Impact of trade liberalization on employment: The experience of India’s manufacturing industries. The Indian Journal of Labour Economics, 53(4), 587–405. Sen, K., & Das, D. K. (2015). Where have all the workers gone? Puzzle of declining labour intensity in organised Indian manufacturing. Economic and Political Weekly, 50(23), 108–115. Sharma, A. N. (2006). Flexibility, employment and labour market reforms in India. Economic and Political Weekly, 41(21), 2078–2085. Sharma, S. (2016). Employment, Wages and Inequality in India: An Occupations and Tasks Based Approach. The Indian Journal of Labour Economics, 59(4), 471–487. Sun, J. (1998). Changes in energy consumption and energy intensity: A complete decomposition model. Energy Economics, 20(1), 85–100. Tejani, S. (2016). Jobless growth in India: An investigation. Cambridge Journal of Economics, 40(3), 843–870. Thomas, J. J. (2013). Explaining the ‘jobless’ growth in Indian manufacturing. Journal of the Asia Pacific Economy, 18(4), 673–692. Topalova, P., & Khandelwal, A. (2011). Trade liberalization and firm productivity: The case of India. Review of Economics and Statistics, 93(3), 995–1009.

Technology and labour in India  317 Vashisht, P. (2018). Destruction or polarization: Estimating the impact of technology on jobs in Indian manufacturing. The Indian Journal of Labour Economics, 61(2), 227–250. Vashisht, P., & Dubey, J. (2019). Changing task content of jobs in India: Implications and the way forward. Economic and Political Weekly, 54(3), 44–52.

Chapter 17

Imported inputs and labour in India’s manufacturing Sandeep Kumar Kujur and Diti Goswami

17.1 Introduction National Manufacturing Policy (NMP), 2011, aims to expand the manufacturing activity in India and raise its contribution in Gross Domestic Product (GDP) from 15% to 25% in 2022. The policy also emphasizes sustainable performance and employment creation (Kujur & Goswami, 2021). The performance of the industrial sector, measured in terms of gross value added (GVA) and total factor productivity (TFP), has been steadily increasing over the last decade. The growth rate of manufacturing GVA in India has increased from 6.4% in 2003–04 to 7.8% in 2014–15. Likewise, the TFP growth rate1 has also increased from 1.6% in 2003–04 to 2.5% in 2014–15 (Das et al., 2018). One of the crucial factors of this steady increase in the performance of the industry is the use of a wide variety of high-quality imported inputs (Goldberg et al., 2009; 2010; Topalova & Khandelwal, 2011). The share of imported inputs (IMI) in total input used in the organized manufacturing industry in India has gradually increased from 20.91% in 2003–04 to 22.77% in 2014–15. The usage of imported inputs has a significant positive impact on the performance of Indian manufacturing (see Section 17.2). Considering the dominance of the small- and medium-size labour-intensive factories, which constitute about 85% of India’s manufacturing (Kapoor, 2018), it would be interesting to analyze the impact of simultaneous use of imported inputs and labour on productivity performance and wage structure (McMillan & Rodrik, 2011, Bollard et al., 2013; Goswami, 2022). In particular, in this chapter, we examine the above relationship across the productivity and wage quantiles. Therefore, this study analyzes the collective impact of the imported inputs and employment on productivity performances and wages. The collective impact of imported inputs and employment in productivity and wages will help understand whether these imported inputs augment labour or capital. Moreover, the study will help identify the group of factories that has the potential to substitute imported inputs with domestic supply and create more employment. This will, in turn, help India’s manufacturing become ‘self-reliant’ or ‘Aatmanirbhar’ (GoI, 2020).2 DOI: 10.4324/9781003329862-22

Imported inputs and labour in India’s manufacturing  319

After the introduction (Section 17.2), the remainder of the chapter is structured in the following way. Section 17.2 presents the methodology adopted in the study, while the data and summary statistics are presented in Section 17.3. The impact of the imported inputs on GVA, TFP, and wages is discussed in Section 17.4. The economic implications of the study are highlighted in Section 17.5, while the final section, Section 17.6, draws the major conclusions and provides the policy implications.

17.2 Review of existing studies This section reviews the related literature that analyzes the static and dynamic gains of imported inputs (Romer, 1990; Grossman & Helpman, 1991; Kasahara & Rodrigue, 2008). At the international and national level, one set of literature analyzes the impact of import liberalization of final output on the performance. In contrast, the other set examines the impact of input liberalization on the TFP of the manufacturing sector in different countries. The import liberalization policy reduced the level of tariffs levied on imported inputs from abroad, which allowed manufacturing firms to access new and high-quality inputs. Due to access to new and high-quality inputs, domestic firms produce various high-quality products (Amiti & Khandelwal, 2013; Colantone & Crino, 2014). An increase in the use of imported inputs also significantly increases the TFP (Amiti & Konings, 2007; Kasahara & Rodrigue, 2008; Kasahara & Lapham, 2013; Yu, 2015; Halpern et al., 2015; Elliott et al., 2016; Olper et al., 2017; Forlani, 2017; Imbruno, 2019). Further, the use of imported inputs has increased the volume and variety of high-quality exports of the firms (Turco & Maggioni, 2013; Bas & Strauss-Kahn, 2014, 2015; Fan et al., 2015; Feng et al., 2016; Xu & Mao, 2018; Bbaale et al., 2019; Hornok & Murakozty, 2019). The usage of inputs sourced from abroad has raised the export competitiveness of manufacturing firms (Mazzi & Foster-McGregor, 2019). In India, trade liberalization provided access to a previously unavailable wide range of new and high-quality inputs from foreign countries. This access to new inputs increased the ability of Indian firms to manufacture new products (Goldberg et al., 2009). The reduction in tariff on imported inputs accounts for an average of 31% of new products introduced by India’s domestic firms (Goldberg et al., 2010). Moreover, the reduction in input tariff helped access better inputs that increased firm productivity (Topalova & Khandelwal, 2011). This increase in productivity is specifically observed among the large-sized formal sector firms in India (Nataraj, 2011). Further, with a 10-percentage point decline in input tariffs, firms in the state with high judicial efficiency gain an additional 3.6 percentage points in TFP compared to a firm in the state with a median level of judicial efficiency (Ahsan, 2013). In addition to this, Sharma (2014, 2016), Goldar (2015), Rijesh (2019), and Kujur (2019) find that the use of imported input

320  Sandeep Kumar Kujur and Diti Goswami

is positively linked to the productivity growth in the Indian manufacturing. Moreover, the use of imports also substantially increased the Indian manufacturing exports (Sharma, 2016). However, the focus thus far has been on imported inputs and their impact on growth and export performance. Little attention has been paid to understanding the impact of the simultaneous use of imported inputs and labour on the plant performance and labour welfare in the manufacturing industry in India. This chapter addresses this gap in the literature and provides empirical evidence on the link between the simultaneous use of imported inputs and labour on plant performances and wages. The study contributes toward understanding the static gains from the use of imported inputs, and trade-off between the use of imported inputs and employment in relation to plant performance and labour welfare. This will help us have a policy relevance for the Indian manufacturing to be selfreliant. It will also provide deeper understanding on the ways to create more employment opportunities in the sector.

17.3 Empirical design 17.3.1 Model We use panel data econometric model to investigate the impact of imported inputs and employment on performance and wage in India’s manufacturing plants. We employ a fixed effect (FE) model in the natural logarithm, as shown in Equation 17.1.

Yijt = a i + b1IMI1ijt + b2L2 ijt + b3IMI*L3ijt + dX ijt + g t + Îijt (17.1)

where i, j, t denotes plants, industry (two-digit), and year, respectively. Yijt represents plant-level variables: GVA, TFP, and wages. IMIijt is the imported inputs for plant i of industry j at time t, and b1 is the coefficient for IMI. Lijt is the labour input, and IMI * Lijt is the interaction of the simultaneous use of imported inputs and labour. b3 is the coefficient of interest in our study. Xijt is the vector of other plant characteristics like capital and age of the plant. g t is the year fixed effect, and Îijt is the error term in the model.3 The ordinary least square (OLS) models the statistical relationship between the independent variables and the conditional mean of the dependent variable. However, considering the heterogeneous composition of TFP and wages in the industry, we use quantile regression models for panel data in addition to the OLS and FE model. The quantile regression models the relationship between the independent variables and conditional quantiles of the dependent variable, rather than the conditional mean of the dependent variable. Thus, it provides a more comprehensive picture of the effect of simultaneous use of imported inputs and labour on the quantiles of GVA, TFP, and wages in manufacturing plants.

Imported inputs and labour in India’s manufacturing  321 Table 17.1 Summary statistics, 2003–04 to 2014–15 Variable

Obs.

Mean

Std. Dev.

Min

Max

Log TFP Log GVA Log EM Log IMI Log C Log L Log Age Log IMI*L

281,358 282,584 374,920 71,129 378,296 375,809 397,623 71,047

6.562 10.929 9.476 12.029 10.412 3.432 50.178 60.747

1.094 1.984 1.846 2.426 2.685 1.425 253.732 25.586

−4.928 −1.139 −5.465 −5.153 −5.596 0.000 0.000 −34.118

15.358 21.769 18.953 23.400 22.624 11.704 2014 231.188

Source: Authors’ calculation based on ASI data.

17.3.2 Construction of variables and data We estimate the TFP of the manufacturing plants in India using a semiparametric approach developed by Levinsohn & Petrin (2003) under the framework of the Cobb-Douglas production function.4 GVA is calculated as output minus intermediate inputs. The nominal values of total output, GVA, and IMI are deflated by the suitable wholesale price indices (WPIs) of respective industrial output at 2004–05 prices, while the value of fixed capital (C) is deflated by the WPI of machinery and machine tools. The total number of persons engaged (L) is the labour size. The consumer price index (CPI) of rural labourers and industrial workers at 2004–05 prices is used as a deflator to arrive at the actual value of wages and salaries, including the bonus of the employees (EM). We subtract the year of initial production, as reported in the database, from the current year (2020) to get the age of the plant. We consider only the positive values for independent variables. The plant-level data used in this study has been obtained from the Annual Survey of Industries (ASI) from 2003–04 through 2014–15, provided by the Central Statistical Office (CSO), Ministry of Statistics and Program Implementation, Government of India. We use the plants that have a minimum of three observations over all the years studied. We include both the census and the sample sector of the ASI data after applying appropriate weights provided by ASI. The summary statistics of the data used are presented in Table 17.1. The panel data used in this study is not balanced due to the missing observations.

17.4 Results 17.4.1 Imported inputs, labour, and GVA Table 17.2 presents the results of the pooled OLS (POLS), FE, and quantile regression for panel data. We find that the use of imported inputs

(Coefficient: t-value)

(Coefficient: t-value) 0.144*** (0.020) 0.155*** (0.010) 0.268*** (0.040) 0.505*** (0.030) 0.021*** (0.000) –0.012*** (0.000) Yes 66324 431.23 0.000 0.254 p: 0.000 p: 0.000

FE (robust)

POLS (robust)

0.109*** (0.010) 0.261*** (0.000) 0.016 (0.010) 0.663*** (0.010) −0.0001*** (0.000) −0.004*** (0.000) Yes 66324 13180.91 0.000 0.792 – –

2

1

0.089*** (0.020) 0.192*** (0.020) 0.064 (0.080) 0.732*** (0.000) −0.0002*** (0.000) −0.004 (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.25)

3

0.073*** (0.000) 0.270*** (0.000) −0.048*** (0.000) 0.704*** (0.000) −0.0001*** (0.000) −0.001 (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.50)

4

0.096*** (0.010) 0.318*** (0.000) −0.057*** (0.010) 0.683*** (0.000) −0.0001*** (0.000) −0.004*** (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.75)

5

Source: Authors’ estimate based on ASI data. Notes: POLS: Pooled OLS, FE: Fixed effect, Q: Quantile, IMI: Imported inputs, EM: Emoluments, L: Labour, C: Capital, IM*L: Interaction of imported inputs and labour. *p 0.10, **p 0.05, ***p 0.01. Constant not reported. Robust standard errors in parentheses.

Log IMI Log C Log L Log EM Log Age Log IMI*L Year dummies N F-statistics p-value R2 Hausman test Modified Wald test

Independent variables

Dependent variable: Log GVA

Table 17.2 Impact of imported inputs and labour use on GVA

322  Sandeep Kumar Kujur and Diti Goswami

Imported inputs and labour in India’s manufacturing  323

significantly increases GVA in the manufacturing plants in India. The use of imported inputs raises the GVA of the plants across the quantiles. However, the usage of more labour negatively affects GVA, especially for those in the upper quantiles. The plant that increasingly uses imported inputs and employs more units of labour in the production experiences a significant reduction in GVA. The reduction in GVA is significant for the plants in higher quantiles. This indicates that the imported inputs are not labour augmenting, especially among the plants within higher GVA quantiles. 17.4.2 Imported inputs, labour, and TFP The estimates of the regression equation 1 suggest that the use of imported inputs in the production significantly raises the TFP of manufacturing plants in India (Table 17.3). This study supports the findings of the existing literature on the positive impact of the use of imported inputs on TFP (Amiti & Konings, 2007; Kasahara & Rodrigue, 2008; Topalova & Khandelwal, 2011). The positive impact stimulated by imported inputs is observed to be stronger in plants within the higher quantiles. This means that imported inputs improve TFP mainly for the plants that already have higher productivity. On the other hand, an increase in labour use has a negative impact on TFP. The results further reveal that the plant that accelerates the use of imported inputs and employs more units of labour in the production experiences a significant reduction in TFP. This significant decline in TFP is observed among the plants within higher quantile. 17.4.3 Imported inputs, labour, and wages We also notice that the increased use of imported inputs has a significant negative impact on the wages in manufacturing plants in India (Table 17.4). This implies that the use of imported inputs reduces the marginal product of labour. Further, higher use of labour significantly raises their emoluments. Plants that increase the use of imported inputs and employ more labour units experience lower wages. However, at the higher quantile of the wages, the simultaneous use of imported inputs and labour has a significant positive effect on wages. This suggest that these labourers are highly skilled workers that complement the technologically advanced imported inputs.

17.5 Discussions This section presents the economic interpretation of the results. The use of high-quality imported inputs equipped with better technology from abroad

(Coefficient: t-value)

(Coefficient: t-value) 0.144*** (0.020) −0.098*** (0.010) −0.175*** (0.040) 0.505*** (0.030) 0.021*** (0.000) −0.012*** (0.000) Yes 66324 101.78 0.000 0.091 p: 0.0000 p: 0.0000

FE (robust)

POLS (robust)

0.109*** (0.010) 0.008** (0.000) −0.427*** (0.010) 0.663*** (0.010) −0.0001*** (0.000) −0.004*** (0.000) Yes 66324 2296.23 0.000 0.425 – –

2

1

0.062*** (0.000) -0.056*** (0.010) −0.435*** (0.020) 0.704*** (0.020) −0.0001 (0.000) 0.002 (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.25)

3

0.058* (0.030) 0.002 (0.030) −0.521*** (0.060) 0.704*** (0.000) −0.00003 (0.000) 0.002 (0.010) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.50)

4

0.103*** (0.000) 0.064*** (0.000) −0.507*** (0.000) 0.687*** (0.000) −0.0001*** (0.000) −0.004*** (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.75)

5

Source: Authors’ estimate based on ASI data. Notes: POLS: Pooled OLS, FE: Fixed effect, Q: Quantile, IMI: Imported inputs, EM: Emoluments, L: Labour, C: Capital, IM*L: Interaction of imported inputs and labour. *p 0.10, **p 0.05, ***p 0.01. Constant not reported. Robust standard errors in parentheses.

Log IMI Log C Log L Log EM Age of the plant IMI*L Year dummies N F-statistics p-value R2 Hausman test Modified Wald test

Independent variables

Dependent variable:TFP

Table 17.3 Impact of imported inputs and labour use on TFP

324  Sandeep Kumar Kujur and Diti Goswami

(Coefficient: t-value)

(Coefficient: t-value) 0.098*** (0.010) 0.107*** (0.010) 0.789*** (0.020) 0.100*** (0.000) −0.0005 (0.000) −0.012*** (0.000) Yes 66324 1187.68 0.0000 0.5836 p: 0.0000 p: 0.0000

FE (robust)

POLS (robust)

−0.025*** (0.000) 0.098*** (0.000) 0.590*** (0.010) 0.281*** (0.000) 0.0002*** (0.000) 0.006*** (0.000) Yes 66324 25794.31 0.0000 0.8833 – –

2

1

0.117 (0.080) 0.167*** (0.030) 0.868*** (0.130) 0.167*** (0.060) 0.0004*** (0.000) −0.017 (0.010) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.25)

3

−0.045*** (0.000) 0.093*** (0.000) 0.518*** (0.000) 0.325*** (0.000) 0.0002*** (0.000) 0.008*** (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.50)

4

−0.122*** (0.020) 0.186*** (0.020) 0.359*** (0.030) 0.205*** (0.030) 0.001*** (0.000) 0.021*** (0.000) Yes 66324 – – – – –

(Coefficient: z-value)

Q (0.75)

5

Source: Authors’ estimate based on ASI data. Notes: POLS: Pooled OLS, FE: Fixed effect, Q: Quantile, IMI: Imported inputs, EM: Emoluments, L: Labour, C: Capital, IM*L: Interaction of imported inputs and labour. *p 0.10, **p 0.05, ***p 0.01. Constant not reported. Robust standard errors in parentheses.

Log IMI Log C Log L Log GVA Age of the plant IMI*L Year dummies N F-statistics p-value r2 Hausman test Modified Wald test

Independent variables

Dependent variable: Log emoluments

Table 17.4 Impact of imported inputs and labour use on wages

Imported inputs and labour in India’s manufacturing  325

326  Sandeep Kumar Kujur and Diti Goswami

raises the performance of the plants and thereby generates static gains in manufacturing (Kasahara & Rodrigue, 2008; Grossman & Helpman, 1991). One-unit use of imported inputs significantly increases the GVA and TFP across the quantiles. Nonetheless, the significant positive impact induced by the use of imported inputs has been observed to be crucial in the plants with higher quantiles of GVA and TFP. The high-quality inputs imported from abroad are processed by highly skilled labourers who manufacture better output varieties and thus command higher wages. Therefore, the study found that a 1% increase in imported inputs raises wages by 9.8%. However, this positive impact of imported inputs on wages is not the same across the wage quantiles of Indian manufacturing. Plants already in the higher wage quantiles reduce the real wage of the labour with an increase in imported inputs. This might be because of a diminishing marginal product of the labour at the higher quantile of the wages. Labour can complement the increase in imported inputs until it becomes redundant, where its marginal product does not register an increase. The increase in labour use significantly reduces the GVA and TFP of Indian manufacturing. The strong negative impact of labour on the GVA and TFP in higher quantile plants indicates that the plants with higher GVA and TFP rely less on labour. Therefore, adding extra units of labour increases the overall cost of production. This, in turn, impacts the GVA and TFP of the plants negatively. The study further reveals that increased employment in the plants raises the labour wages, with a strong positive impact observed in the factories with lower quantiles of wages. This implies that factories that pay low wages to their workers and add more labour to the production will increase the average wage. This might be because of an increase in the marginal product of the workers brought by labour. The combined use of imported inputs and labour has a significant negative impact on the GVA and TFP of the plants and wages of the labourers. If the manufacturing plant expands its operations by employing more labourers and simultaneously increases its use of imported inputs, it will experience a decline in the GVA and TFP. This decline is explained by the increase in the number of skilled labourers to manage the high-quality imported inputs, which increases the cost of production and negatively impacts the GVA and TFP. Therefore, the imported inputs are capital-intensive and technologically advanced and are not labour augmenting. The increase in the number of skilled labourers reduces the marginal product of labour and consequently reduces the average wages of the labourers in the plant. Therefore, we notice that the simultaneous increase in imported inputs and labour has a negative impact on wages. Nonetheless, the plants in higher quantiles exhibit a significant increase in wages because of their higher skill and better bargaining power.

Imported inputs and labour in India’s manufacturing  327

17.6 Conclusions and policy implications Does the use of imported inputs improve the performance of the manufacturing plants? How does the use of labour affect plant performances? What trends does plant performance exhibit due to the simultaneous movement of the imported inputs and labour? How do all these relationships manifest themselves at every quantile of the heterogeneous performances of the plants? As observed in many existing studies, we find that imported inputs generate static gains and increase the performance of the manufacturing plants in India, measured in terms of GVA and TFP. This study further concludes that the more productive plants experience a higher increase in the GVA and TFP for a corresponding one-unit increase in the use of imported inputs. Thus, the study suggests that the higher use of imported inputs in the manufacturing sector in India should be encouraged. On the other hand, the greater use of labourers reduces the performance of the plant significantly. In this scenario, the study suggests the plants should initiate the process of reskilling of the labourers to improve their marginal product. The increase in the marginal productivity of the labourers will contribute directly to raising the overall productivity of the plant. The simultaneous use of imported inputs and labourers reduces the performance of the manufacturing plants, especially among the upper quantiles. This means that imported inputs are not labour augmenting and may not improve the employment scenario in the manufacturing sector. Overall, while imported inputs raise productivity, the simultaneous use of imported inputs and labour negatively impacts employment in manufacturing. Therefore, the one-size-fits-all policy such as ‘Aatmanirbhar Bharat’ needs industry-specific introspection. Further, we ask: How does the use of imported inputs determine the welfare of the workers? Does the use of more units of labour improve the wage rate? How does the simultaneous movement of the imported inputs and labourers affect the worker wages? How do all these relationships vary at the quantile? The use of imported inputs increases wages in the manufacturing plants in India. However, it negatively impacts the wages of the plant in upper quantiles. As the increasing use of imported inputs in production makes labour redundant, the price of the labour falls. Therefore, the plants should focus on the reskilling of the labourers to match the skill requirement of the imported inputs equipped with advanced technology. As a result, the greater use of workers increases the wages in manufacturing. The positive impact is noticed in the lower quantile of wages. This indicates that the factories that provide low remuneration and add more units of labour witness a rise in the average wage. On the other hand, as the imported inputs are technologically advanced, the simultaneous use of imported inputs and labour reduces the

328  Sandeep Kumar Kujur and Diti Goswami

marginal productivity of the labour, which leads to a reduction in wages. Nonetheless, the use of imported inputs and highly skilled labour in the plants in higher wage quantiles increases the remuneration of the workers further. Therefore, the plants should focus on the reskilling of the workers to match the high-quality inputs imported from abroad, which will increase marginal productivity and wages. These analyses will help us deeply understand the relationship between external dependence, employment, performance, and labour welfare in Indian manufacturing. This is crucial at this juncture, where the emphasis is on the evolution of a self-sufficient ‘Atmanirbhar Bharat’ proposed by the Indian government. Although the present study contributes to developing a detailed understanding of the static gains from the use of imported inputs, future research should focus on evaluating the collective impact of imported inputs and the age of the plant, which influences the absorptive capacity and productivity. Such research would greatly enrich the literature on gains accrued by an emerging country from imported inputs.

Acknowledgments The authors would like to thank the participants in the international conference held at the Institute of Public Enterprise, Hyderabad, India (December 2019) for their helpful discussion. The authors would also like to thank the reviewers for their valuable suggestions on an earlier draft of this chapter. We are responsible for any remaining errors.

Notes 1 TFP is measured using Tornqvist aggregation. 2 The Government of India announced a special economic package on May 12, 2020, with an aim of making India self-reliant. 3 We applied the Hausman (1978) test to check whether there is a significant difference between fixed and random effect estimators. We also apply the modified Wald test to examine the group-wise heteroscedasticity in the fixed effect regression model. 4 This methodology solves the endogeneity problem in the production function.

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Appendix

Appendix 10A Table 10.A1 Constant elasticity of substitution (CES) elasticity of transformation parameter (sVA j ) of value added Activity

sVA j

Food crops Cash crops Horticulture crops Agro-allied activities Food and beverages (F) Food and beverages (I) Textile and wearing apparel (F) Textile and wearing apparel (I) Other agro-based industry (F) Other agro-based industry (I)

0.78 0.78 0.78 0.78 1.09 1.09 0.89 0.89 0.86 0.86

sVA j Non-agro based industry (F) Non-agro based industry (I) Capital goods industry (F) Capital goods industry (I) Infrastructure services (F) Infrastructure services (I) Other services (F) Other services (I)

1.06 1.06 0.9 0.9 1.6 1.6 1.22 1.22

Source: calculation is based on the estimates provided by Pradhan and Sahu (2008) and Goldar, Pradhan and Sahu (2013).

332 Appendix Table 10.A2 Values of relevant parameters Description of elasticity parameters

Symbol

Constant elasticity of substitution (CES) elasticity of substitution of composite labour Constant elasticity of transformation (CET) elasticity of transformation between local sales and exports CES Elasticity of substitution of composite commodity

s LD j

0.2

s Xjx

2

s mM

2

Income elasticity of Consumption across commodities Frisch parameter Wage rate of formal regular labour (at the base level) Wage rate of informal regular labour (at the base level) Wage rate of casual labour (at the base level) Rental rate across sectors (at the base level) Price of local product (at the base level) Price elasticity of indexed transfers and parameters Exchange rate (at the base level) Export price (at the base level) World price of import (at the base level)

Value

0.7 −1.5 1.2 1.1 1 1 1 1 1 1 1

Note: parametric values are exogenously determined. Source: author’s own compilation.

Table 10.A3 (Scenarios A1–A3) Change (%) capital income and labour income of households (case of rising world price) Food crops

Cash crops

Hort. crops

Household type

Capital income

Labour income

Capital income

Labour income

Capital income

Labour income

MSA-HH MLA-HH Cap-HH ROA-HH REstb-HH UOA-HH UEstb-HH

2.86 2.86 −1.05 −1.05 −1.05 −1.05 −1.05

−0.72 −0.72 −1.82 −0.72 −0.72 −0.72 −0.72

1.64 1.64 −0.74 −0.74 −0.74 −0.74 −0.74

−0.54 −0.54 −1.45 −0.54 −0.54 −0.54 −0.54

2.17 2.17 −0.88 −0.88 −0.88 −0.88 −0.88

−0.62 −0.62 −1.62 −0.62 −0.62 −0.62 −0.62

Source: author’s compilation based on India-social accounting matrix (SAM) 2003–2004.

Appendix  333 Table 10.A4 (Scenarios B1–B3) Change (%) in factor income of households (case of rising world price of exportable crops and rising crop productivity) Food crops Household type

Capital income

MSA-HH −0.25 MLA-HH −0.25 1.76 Cap-HH ROA-HH 1.76 REstb-HH 1.76 UOA-HH 1.76 1.76 UEstb-HH

Cash crops

Hort. crops

Labour income

Capital income

Labour income

Capital income

Labour income

1.60 1.60 0.59 1.60 1.60 1.60 1.60

−0.59 −0.59 0.25 0.25 0.25 0.25 0.25

0.08 0.08 −0.73 0.08 0.08 0.08 0.08

0.35 0.35 1.72 1.72 1.72 1.72 1.72

1.45 1.45 0.63 1.45 1.45 1.45 1.45

Source: author’s compilation based on India-SAM 2003–2004.

Net shift

Col. 7 (5 − 6)

Col. 8 (4 − 3)

Col. 9 (2 − 4)

Col. 10 ([8 − 7]/7)

53 50 −69

77 81 −224 −93 1050 4 3221 49 167

1988

Expected change

Col. 6 (3 − 1)

Div. 1 2691879 12023876 4234941 6020842 9331997 1543062 7788935 1785901 6003034 23 Div. 2 2351512 10749799 3699466 5018467 8398287 1347954 7050333 1319001 5731332 19 Div. 3 3916411 6722019 6161409 7975536 2805608 2244998 560610 1814126 −1253517 324 Div. 4 2905179 5168737 4570510 5722651 2263558 1665331 598227 1152140 −553914 193 Div. 5 15066219 22884644 23702605 31475871 7818425 8636386 −817961 7773266 −8591227 −950 Div. 6 73439186 94512918 115536618 95417771 21073732 42097432 −21023700 −20118847 −904853 96 Div. 7 18501225 28934286 29106654 34485510 10433061 10605429 −172368 5378856 −5551224 −3121 Div. 8 4858083 11940506 7642874 9837216 7082423 2784791 4297632 2194342 2103290 51 Div. 9 52322292 84400020 82314919 80912206 32077728 29992627 2085101 −1402713 3487814 −67 Total 176285143 277336805 277336805 277336805 101051662 101051662 0 0 0 B. Female non-migrant Div. 1 320346 820193 315679 550358 499847 −4667 504514 234679 269835 47 Div. 2 452408 1504941 445817 976443 1052533 −6591 1059124 530626 528498 50 Div. 3 854902 1426784 842448 1832473 571882 −12454 584336 990026 −405689 169

Col. 5 (2 − 1)

2007–2008 Actual total change weighted by 1988 occp distr. within 40 indust.

Col. 4

Percentage net shift due to

Occupation shift and interaction effect

Col. 3

2007–2008 total 2007–2008 weighted by 1988 occp distr.

Col. 2

Number of workers

Col. 1

Components of net shift

Industry shift Occupation Induseffect shift and try shift interaction effect effect

Occupational category

A. Male non-migrant workers

Table 11.A1 Components of occupational net shift (1988 to 2007–2008) for total employment

Appendix 11A

334 Appendix

335875 1380177 16404886 4110549 535236 14213543 38625058

2757199 3136046 4291482 3449447 8296535 2557810 10079211 3772329 7425746 45926164

881381 1835901 2222272 661581 3748899 48593841 7566504 1162301 28552422 95366983

722875 2052166 12950565 4222938 433346 14491251 38625058

3601239 4829771 2708420 2613127 6618459 3971026 7266447 4823875 9493799 45926164

1541052 2365402 1997475 506512 3105722 45752069 5452948 554220 34091585 95366983

1210276 3174246 2198347 721819 4494601 46746539 10384638 978781 25287719 95341767

2745301 3245619 3553419 2934066 8166038 3767809 9917428 3297270 8163936 45926164

754943 2227859 13074524 5609198 809061 12733554 38617979

1297009 1857065 1382157 323329 2067701 32297070 3357882 232394 26185793 68961113

2417864 3483797 866539 1132643 3057631 2873228 2940504 3204812 6306709 26214903

382035 651585 −3696841 51621 −109803 67583 −571010

637338 1327564 1606954 478398 2710878 35138842 5471438 840475 20646630 68961113

1573824 1790072 2449601 1968963 4735707 1460012 5753268 2153266 4238656 26214903

−4965 −20404 −242520 −60768 −7913 −210125 −571010

659671 529501 −224797 −155069 −643177 −2841772 −2113556 −608081 5539163 0

844040 1693725 −1583062 −836320 −1678076 1413216 −2812764 1051546 2068053 0

387000 671989 −3454321 112389 −101890 277708 0

328895 330776 1338345 −808844 −23925 −200872 60239 −215307 745702 −1388879 −1847303 −994470 2818134 −4931690 −183520 −424561 −3264703 8803866 −25216 25216

−11898 855938 109573 1584152 −738064 −844999 −515381 −320939 −130498 −1547579 1210000 203217 −161784 −2650981 −475059 1526605 738190 1329863 0 0

419069 −32068 847681 −175693 −3330362 −123959 1498649 −1386260 273825 −375715 −1479990 1757697 −7079 7079

50 253 11 −39 −116 65 −133 30 −59

−1 6 47 62 8 86 6 −45 36

108 126 96 1333 −269 −533

50 −153 89 139 216 35 233 70 159

101 94 53 38 92 14 94 145 64

−8 −26 4 −1233 369 633

Note: Div. 1 (Legislators, Senior Officials and Managers), Div. 2 (Professionals), Div. 3 (Technicians and Associate Professionals), Div. 4 (Clerks), Div.5 (Service Workers and Shop and Market Sales Workers), Div. 6 (Skilled Agricultural and Fishery Workers), Div. 7 (Craft and Related Trades Workers), Div. 8 (Plant and Machine Operators and Assemblers) and Div. 9 (Elementary Occupations).

Div. 4 340840 Div. 5 1400581 Div. 6 16647406 Div. 7 4171317 Div. 8 543149 Div. 9 14423668 Total 39196068 C. Migrant male Div. 1 1183375 Div. 2 1345974 Div. 3 1841881 Div. 4 1480484 Div. 5 3560828 Div. 6 1097798 Div. 7 4325943 Div. 8 1619063 Div. 9 3187090 Total 19711261 D. Migrant female Div. 1 244043 Div. 2 508337 Div. 3 615318 Div. 4 183183 Div. 5 1038021 Div. 6 13454999 Div. 7 2095066 Div. 8 321826 Div. 9 7905792 Total 26405870

Appendix  335

1988

Col.3

Col.5 (2 − 1)

8691078 7711427 4653238 4630142 18052212 114622417 23231081 9013627 86646325 277251531 474820 602269 555027 366324 1176446 16257785 2719138 276754 16185288 38613853

315679 445817 842448 335875 1380177 16404886 4110549 535236 14213543 38625058

499847 1052533 571882 382035 651585 −3696841 51621 −109803 67583 −571010

9331997 8398287 2805608 2263558 7818425 21073732 10433061 7082423 32077728 101051662 −4667 −6591 −12454 −4965 −20404 −242520 −60768 −7913 −210125 −571010

1543062 1347954 2244998 1665331 8636386 42097432 10605429 2784791 29992627 101051662

504514 159141 1059124 156451 584336 −287421 387000 30449 671989 −203731 −3454321 −147101 112389 −1391411 −101890 −258482 277708 1971745 0 −11205

57 57 −269 10 691 4 3409 32 208 345373 32 902672 15 871757 −49 356551 8 875720 −30 −3307220 4 1503800 −1238 156592 254 −1694037 710 11205

3332798 3038372 2068781 538595 4832432 −20109499 5703205 2926879 −2246305 85274

68 85 149 92 130 96 1338 −154 −610

43 43 369 90 −591 96 −3309 68 −108

Occupation Industry shift Occupa- Industry shift effect and interac- tion shift shift and tion effect effect interaction effect

7788935 4456137 7050333 4011961 560610 −1508171 598227 59632 −817961 −5650392 −21023700 −914201 −172368 −5875573 4297632 1370753 2085101 4331405 0 −85274

Net shift

Percentage net shift due to

Col.9 (2 − 4) Col. 10 ((8 − 7)/7)

Components of net shift Col.6 (3 − 1) Col.7 (5 − 6) Col.8 (4 − 3)

2007–2008 to- Actual change Expected tal weighted by change 1988 industry distr.

Col.4

4234941 3699466 6161409 4570510 23702605 115536618 29106654 7642874 82314919 277336805

2007–2008 total weighted 2007–2008 by 1988 occp distr.

Col.2

Number of workers

Col.1

Div. 1 2691879 12023876 Div. 2 2351512 10749799 Div. 3 3916411 6722019 Div. 4 2905179 5168737 Div. 5 15066219 22884644 Div. 6 73439186 94512918 Div. 7 18501225 28934286 Div. 8 4858083 11940506 Div. 9 52322292 84400020 Total 176285143 277336805 B. Female non-migrant Div. 1 320346 820193 Div. 2 452408 1504941 Div. 3 854902 1426784 Div. 4 340840 722875 Div. 5 1400581 2052166 Div. 6 16647406 12950565 Div. 7 4171317 4222938 Div. 8 543149 433346 Div. 9 14423668 14491251 Total 39196068 38625058

Occupational category

A. Male non-migrant workers

Table 11.A2 Components of occupational net shift (1988 to 2007–2008) for total employment

336 Appendix

2757199 3136046 4291482 3449447 8296535 2557810 10079211 3772329 7425746 45926164

881381 1835901 2222272 661581 3748899 48593841 7566504 1162301 28552422 95366983

3601239 4829771 2708420 2613127 6618459 3971026 7266447 4823875 9493799 45926164

1541052 2365402 1997475 506512 3105722 45752069 5452948 554220 34091585 95366983

1603967 2378364 1589660 1815711 1536835 48385923 3427907 418312 34210469 95366979

3673804 4457943 3077130 3377857 7368193 2627592 7175981 5615795 8522588 45896884 1297009 1857065 1382157 323329 2067701 32297070 3357882 232394 26185793 68961113

2417864 3483797 866539 1132643 3057631 2873228 2940504 3204812 6306709 26214903 637338 1327564 1606954 478398 2710878 35138842 5471438 840475 20646630 68961113

1573824 1790072 2449601 1968963 4735707 1460012 5753268 2153266 4238656 26214903 659671 722585 529501 542463 −224797 −632611 −155069 1154130 −643177 −2212064 −2841772 −207918 −2113556 −4138598 −608081 −743990 5539163 5658048 0 −4

844040 916605 1693725 1321897 −1583062 −1214352 −836320 −71590 −1678076 −928342 1413216 69783 −2812764 −2903231 1051546 1843466 2068053 1096842 0 −29280 −62915 −12962 407815 −1309199 1568887 −2633854 2025041 135908 −118884 4

−72565 371828 −368710 −764730 −749734 1343434 90466 −791920 971211 29280 110 102 281 −744 344 7 196 122 102

109 78 77 9 55 5 103 175 53 −10 −2 −181 844 −244 93 −96 −22 −2

−9 22 23 91 45 95 −3 −75 47

Note: Div. 1 (Legislators, Senior Officials and Managers), Div. 2 (Professionals), Div. 3 (Technicians and Associate Professionals), Div. 4 (Clerks), Div.5 (Service Workers and Shop and Market Sales Workers), Div. 6 (Skilled Agricultural and Fishery Workers), Div. 7 (Craft and Related Trades Workers), Div. 8 (Plant and Machine Operators and Assemblers) and Div. 9 (Elementary Occupations).

C. Migrant male Div. 1 1183375 Div. 2 1345974 Div. 3 1841881 Div. 4 1480484 Div. 5 3560828 Div. 6 1097798 Div. 7 4325943 Div. 8 1619063 Div. 9 3187090 Total 19711261 D. Migrant female Div. 1 244043 Div. 2 508337 Div. 3 615318 Div. 4 183183 Div. 5 1038021 Div. 6 13454999 Div. 7 2095066 Div. 8 321826 Div. 9 7905792 Total 26405870

Appendix  337

338 Appendix

Appendix 15A Table 15.A1 Foreign Direct Investment (FDI) inflows in India Year

FDI inflows (US$ million)

1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 2017–2018

97 129 315 586 1343 2144 2842 3562 2480 2167 4031 6125 5036 4322 5987 8901 22739 34728 41738 33109 29029 32952 26953 30763 35283 44907 42215 39431

Source: Reserve Bank of India.

Appendix  339 Table 15.A2 Description of variables Variable

Description

Foreign Direct Investment (FDI)-firm Output

Firms with at least 10 percent foreign equity holdings ProwessIQ are defined as FDI-firms or foreign firms.

Employment

Capital stock

Data sources

This variable is the sales of the firms deflated ProwessIQ by output price indices at two-digit level of NIC-2008. This variable (i.e., number of employees) is ProwessIQ computed by dividing the reported salaries and and Annual wages at the firm level by the average wage rate Survey of of the industry (three-digit industry) to which Industries firm belongs. We have taken use of the ASI data (ASI) for the calculation of wage rate of industry. At the time of this study, the ASI data was available only up to 2015–2016. We have extrapolated the values for the remaining years of the study. This variable is constructed using the perpetual ProwessIQ inventory method by taking 2000–2001 as base year. First, the base year gross fixed assets (GVA), which is in historical cost, is converted into GVA at replacement cost based on revaluation factor computed following Srivastava (1996). Second, using the base year GVA at replacement cost, investment series and annual rate of depreciation (i.e. 5 percent), the perpetual inventory method is applied to construct the capital stock. Finally, the capital stock at replacement cost is deflated by the wholesale price index for machinery and machine tools (base: 2011 – 2012 = 100) to arrive at real capital stock.

Source: authors’ compilation.

340 Appendix Table 15.A3 Classification of manufacturing sector by technology NIC-code (2008)

Description of industries Low-technology industries

10 11 12 13 14 15 16 17 19 22 23 24 25 20 21 26 27 28 29 30

Food products Beverages Tobacco products Textiles Wearing apparel Leather and related products Wood and wood products Paper and paper products Coke and refined petroleum products Rubber and plastics products Other non-metallic mineral products Basic metals Fabricated metal products High-technology industries Chemical and chemical products Pharmaceuticals Computer, electronic and optical products Electrical equipment Machinery and equipment (Not Elsewhere Classified [NEC]) Motor vehicles, trailers and semi-trailers Other transport equipment

Source: Organisation for Economic Co-operation Development, 2011.

Index

Page numbers in italics indicate figures, bold indicate tables in the text, and references following “n” refer endnotes. 1991 New Economic Policy 166 2018 Multidimensional Poverty Index 158 Abraham, Anu 10 Abraham, V. 241 Academic Ranking of World Universities (ARWU) 84 Academics’ societal engagement (ASE): Confederation of Indian Industries 104; employability 105; propositions 103–104; quantitative analysis 102–103, 103; talent, innovation and place 105, 106; university outreach 99–102, 100, 101; university research 95–98; university teaching 98–99 affirmative action (AA) 20–22 agricultural exports: agricultural employment 169; agricultural incentives 168; computable general equilibrium (CGE) model 167, 172–178; constant elasticity of substitution (CES) elasticity of transformation parameter (σVAj) 331; counterfactual scenarios 167; domestic price of 166, 169; Dutch Disease 169; economic development 167; elasticity parameters 332; exogenous policy shocks 170; forward and backward production linkages 167; general equilibrium framework 169; growth deceleration during 1990–93 to 2003–2006

166; labour productivity growth 168; macroeconomic implications 167; Mellor hypothesis 168; model calibration 178–180; PEP 1–1 model 170; price signalling 167; pricing policy/trade policy 168; simulation exercise 170; social accounting matrix (SAM) of India (India-SAM) 167; spiralling of food inflation 167 Agriculture Modernization 111 agro-allied and resource-based sector (AGALD) 171 agro-based sector 194n1 Ahir, Kinjal 8 Ahluwalia, M.S. 61 Ahmed, K. 241 Alatorre Bremont, J.E. 280 Ameen, A. 61 Ameerali, A. 99 Amendola, A. 103 Ang, B.W. 301 Annual Reports of Periodic Labour Force Survey (PLFS) 238 Annual Survey of Industries (ASI) data 302, 305 Armington function 176 Arora, Sonam 9 Baldwin, R. 260, 262, 278 Banga, K. 262 Barra, C. 103 Barro, R.J. 61 Basu, Sam N. 8

342 Index Becker, G.S. 59, 62, 77 Becker, W. 112 Bethel, C.L. 98 Bhattacharya, S. 250, 251, 252n2 Biagi, F. 103 Biancardi, D. 103 Bonaccorsi, A. 103 Brazil, Russia, India, China, and South Africa (BRICS) 5–6 BRICS–EU global value chains (GVCs): decomposition of BRICS labour 270; decomposition of EU’s labour 270; demand equation 263; employment behaviour 268; employment expansion 277; flexible labour markets 277; forward and backward linkages 274, 277; fragmentation of production 259, 264; job creation effects 261; labour-intensive manufacturing activities 260; labour coefficient 263; labour demand 264, 265; OECD data 263; offshoring of jobs 259; overall net gain, 2010–15 267, 268; participation and employment 259; qualitative studies 262; social economic accounts (SEA) 263; two-way trade 268; value-added exports 264; World Input–Output Database (WIOD) 263, 266, 270, 274, 275 Browning, H.L. 202 capital intensification 290, 291; capitalintensive FDI-firms 290, 290–292, 291, 292; non-capital-intensive FDIfirms 290, 290–292, 291, 292 caste inequalities 44 Cavallo, D. 169 Chapman, T. 240 Charris-Fontanilla, A. 96 Chatterjee, E. 47 Checchi, D. 60, 61 Chen, Haiyang 8 Chen, M. 98 Chicoine, L. 37 Chilean economy 169 China, higher education institutes 82, 124 Chiswick, B.R. 59, 60, 62 Choudhury, P.K. 7 Christensen, P. 98 Claire 262

clear caste hierarchy 47 Cobb-Douglas production function 321 Coeymans, J.E. 169 College of Agricultural Economics and Management (CAEM) 116 “composition shift effect” 203 computable general equilibrium (CGE) model 9; aggregate consumption expenditure (CTHh) 174; aggregate intermediate consumption (CIj) 174; Armington function 176; composite commodities (Qm) 176; constant elasticity of transformation (CET) aggregation function 176; cost due to aggregate intermediate consumption (PCIj) 177; cost due to value added (VAj) 177; government’s sources of revenue 174; households’ income (YHh) 173; investment demand 175; Leontief production function of value added (VAj) 172; linear expenditure system 175; for nonimport-competing informal goods and services 177; for non-imported informal goods and services 178; price of composite traded goods and services (PCm) 177; price paid for the imported product (PMm) 177; price paid on the local product (PDi) 177; production function of composite labour (LDCj) 172; semi-empirical analysis 167 Confederation of Indian Industries 104 Coniglio, N.D. 280 Constantinescu, C. 260 construction sector jobs 2 consumer price index (CPI) 174, 179, 321 COVID-19 pandemic 55 Crenshaw, E. 61 Davey, T. 103 Deborah 262 Decaluwé, B. 170, 172 De las Heras-Rosas, C. 97 ‘demand–supply mismatch’ 86 Demi 23 demographic dividends 8, 9, 12, 38, 55; demand-side factors 135; emigration and remittances policies 145; employment opportunities 136, 145, 148; and employment profile

Index  343 of India, 1994–2019 133; growth rate of elderly population 132; growth rate of youth population 132; industrial and employment policies 146; infrastructure development 146; inter-state (rural–urban) migration 145; labour-intensive sub-sectors 136; labour market crisis 135, 136; negative income effect 135; “new education policy – 2020” 144; selfemployment schemes/programmes 149n7; supplementary measures 144; unregistered, micro- and small enterprises 145–146; vocational training curricula 144; youth employment challenges 135 Denison, E.F. 77 Department of Political Science and Public Affairs 102 de Weerd-Nederhof, P.C. 96 Dick, H. 169 direct cost of education 19 distribution of workers according to usual status (Ps + ss) approach 70 Diwara, B. 60, 61 Domenech, R. 169 domestic employment in foreign final demand 267; agriculture 271, 271; basic metals 271, 274; chemicals 271, 273; forward and backward linkages 271, 274, 275; manufacturing 271, 272, 273; mining 271, 272; sectorwise type of labour value added in exports 273, 276; textiles 271, 272 Du, Jun 8 Duncan/dissimilarity index 202, 210–211, 211, 218 Dutch Disease 169 economic growth (EG)/human development 152; aggregate growth rate (GR) 152; chain triggering employment 152; income elasticity 153; unsustainable growth 154; virtuous circle of growth 151, 153, 154, 162 educational expansion on income inequality: ‘composition’ effect 59; dispersion in education 65; distribution of workers according to usual status (Ps + ss) approach 69, 70; earnings ratio of income 62–63;

educational inequality on mean years of schooling 66, 67; educational level of workers 64; educational spending patterns 62; educational variables 68; education Gini (EGINI) 65, 66; EINEQ 66; empirical verification 59, 61; formal schooling system 70, 71; Gini coefficient 61, 67, 71; higher educational spending 59; human capital theory 59, 61, 62; income inequality on education 68, 69; income inequality on income 67, 67; inequality in earning 63; intracountry analysis 64; Kuznets’s curve 67; level of education 65, 68; lower educational inequality 60; monthly per capita household expenditure (MPCE) 64, 67; National Sample Survey Organisation’s 66th round employment-unemployment survey 58, 64; policymakers 59; regression equation 66; schooling inequality 59; summary statistics 66; two-stage least square analysis 61; usual status (PS + SS) unemployment rate 70, 70; ‘wage compression’ effect 59; YINEQ 66 educational level of workers 64 education Gini (EGINI) 65, 66 education level of household head 28, 31 Employment and Unemployment Survey (EUS) 132, 201 employment change decomposition results 304, 306, 307, 308; in publishing and printing industry 305–306, 306; in rubber and plastic products industry 308, 308; in wood and wood products industry 304, 305 employment gain 1, 11, 159, 277, 310 employment patterns in manufacturing 11 employment preferences 7 employment scenario of graduates 85 employment–unemployment surveys (EUSs) 8, 201 “endangering transitional growth” 152 export-oriented firm 292–295, 293, 294, 295 Falk, M. 261 family income 24, 31

344 Index Farole, T. 259 Feenstra, R.C. 261 Fei, J.C.H. 168 female college graduates, USA 39 female labour force participation rate (FLFPR) 238; de-feminization of labour 241; in-depth micro-level survey 239; inter-generational effect 239; in Kerala see Kerala, female labour force participation rate (FLFPR); labour force participation rate (LFPRs) 238; in rural area 238, 241; in South Asia 238, 239; U-shaped feminization hypothesis 240; ‘voluntary’ withdrawal of women 240 female labour market participation: college graduates 47; female labour force participation (FLFP) rate 37; ‘Global Employment Trends for Youth 2022’ 37; gross enrolment ratio (GER) 39; human capital theory 37; less-educated women 47; National Education Policy (NEP) 2020 38, 39; policy initiatives 38; secondary and higher education 38; self-employment opportunities 38, 39; socio-economic benefits 37; unemployment rate 38; vocational education and training 38 Feng, X. 98 Ferreira, J.J. 96 Figueiredo, N. 96 “Five-in-One” service model 122 Five-Year Plans 20 fixed effect (FE) model 320 food and beverages in the formal sector (FodBvrF) 171 foreign direct investment (FDI) employability 11, 147, 148, 338; accumulated FDI inflows in different sectors of country 281, 281; Annual Survey of Industries (ASI) 283; backward linkages 283; brownfield FDI inflows 282; for China and Mexico 280; competition effect 282; direct effects of 282; exportplatform FDI 282; firm-level data 283; greenfield FDI 282; indirect effects of 282; internal and external reforms 281; in manufacturing firms see manufacturing firms,

FDI employability; modern sector employment 280; ProwessIQ 283; technology spillovers 282; in Uruguay 280; variables description 339 formal agro-based industry (AGROMF) 171 formal capital goods sector (CAPMF) 171 formal infrastructure services (INFRF) 171 formal regular labour (FRL) 171 formal segment of manufacturing of textiles and wearing apparel (TextF) 171 Fourie-Malherbe, M. 112 Galán-Muros, V. 103 Geishecker, I. 261 gender disparities 90n1 gender gaps in employment preferences 4; and age 51, 52–54 , 53, 54; caste-wise distribution 41; casual employment 40; clear caste hierarchy 47; contractual jobs 40; econometric specification 41; economic status of households 45; graduates involved in self-employment and salaried jobs 42–43, 44; households’ economic status 44, 48, 49–51 , 50; logit estimates 46; logit regression models 41, 42, 46; Periodic Labour Force Survey (PLFS) survey 40, 48; policy initiatives 40; private regular jobs 40; regular government jobs 40; salaried jobs 41, 42, 44, 46–48; self-employed graduates distribution 42, 44; in self-employment activities 39; socioeconomic and demographic contours 40; Usual Employment Status 41 gender inequality 39 gender ratio 229 Geoi 23 German manufacturing industries 262 Gini coefficient 61, 67, 71 Global Competitiveness Report 2020 80 ‘Global Employment Trends for Youth 2022’ 37 Global Innovation Index (GII) 2020 79–80 Global Value Chains (GVC) 3

Index  345 Gnanasekaran, K.S. 202 Goldar, B. 179, 302, 319 Goldin, C. 240 Gollin, D. 168 Gonzalez, M.J. 251 Görg, H. 261 Goswami, Diti 12 Graham, J.W. 39 Gregorio, J.D. 60, 61 Gross Domestic Product (GDP) 318 Gross Enrolment Ratio (GER) 83, 90n2 Grossman, G.M. 261 Gross National Product 77 gross value added (GVA) 318, 321, 322, 323, 326 growth trends in major Indian states 155; 2018 Multidimensional Poverty Index 158; Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh (BIMARU) states 158; coefficient of variation (CV) 158; convergence theorem 155; growth with equity 155; long-term growth rates (LTGRs) 155, 157; net state domestic product growth (NSDP) 155, 156; state-wise per capita income 158; sustained high GRs 157; sustained low GRs 158; sustained moderate GRs 157; unsustained high/low GRs 158 Gruber, L. 61 Hanson, G.H. 261 Hanushek, E. 81 Hatak, I. 96 Hausman, J.A. 328 He, P. 280 healthcare expenditure 2 Herrera, J. 97 higher education graduates (HEGs), employment status 160, 161 higher education institutions progression 4; in China 8 Hijzen, A. 261 h-index 85, 89 Hine, R.C. 261 Hughes, A. 97 human capital market, imperfections 153 human capital theory 37, 59, 61, 62, 70, 112 Human Development Index (HDI) 158

IMD World Digital Competitiveness Ranking 2020 80–81 imported inputs (IMI): Annual Survey of Industries (ASI) data 313; CobbDouglas production function 321; consumer price index (CPI) 321; fixed effect (FE) model 320; gross value added (GVA) 318; high-quality exports 319; import liberalization policy 319; one-size-fits-all policy 327; ordinary least square (OLS) models 320; plant-level variables 320; productivity growth 320; quantile regression models 320; reduction in tariff 319; total factor productivity (TFP) 318; wages 323, 325; wholesale price indices (WPIs) 321 import liberalization policy 319 index decomposition analysis (IDA) 300–301 Indian Higher Education: during 2010–11 to 2018–19 81, 82; assessment of quality of research 84; employment scenario of graduates 85–86, 86; financing 81; gender parity 88; Global Competitiveness Report 2020 80; global indexes 78, 79; Global Innovation Index (GII) 2020 79; h-index 85, 89; IMD World Digital Competitiveness Ranking 2020 80; knowledge production 84; pupil–teacher ratio 79, 80, 82–83, 89; quality and efficiency 90; R&D productivity 80; research and development outcomes 79; research publications 85; in World University Rankings 83–84 India Skills Report 2021 88 industrial shift effect 200 informal agro-based industry (AGROMI) 171 informal capital goods sector (CAPINF) 171 informal segment of textiles and wearing apparel (TextI) 171 Insti 23 institutions: affirmative action (AA) 20, 22; cost-benefit analysis 22; Demi 23; demographic characteristics 24, 28, 29–30; descriptive statistics 24–25, 26–27; dwindling allocation

346 Index of resources 20; elite institutions 22; familial characteristics 24, 28; FiveYear Plans 20; geographic and gender characteristics 24, 31; Geoi 23; Insti 23; multinomial logit model 20, 22–25; National Sample Survey data on Social Consumption: Education – 2014 23–25; for non-SC students 23; ownership and management 19; private-aided institutions 20; private sector participation 20, 22; private-unaided higher educational institutions 20; privatisation process 20; public/government institutions 20, 32, 33; reservation policy 21; of ST/SC students 22; subsidies 20 inter-caste distribution 44 international studies 21 “International Vision and National Standard” 120 Jacob, J.F. 6 jobless growth 300; economic liberalization 151; growth trends in major Indian states 155–159, 156, 157; labour productivity 152; statespecific policies 163 joblessness of educated youth 3, 9 Jørgensen, T.H. 98 Joshi, K.M. 7 Justo, J.F. 99 Kar, S. 194n6 Karingi, S.N. 170 Karlsson, S. 280 Kenyan economy 170 Kerala, female labour force participation rate (FLFPR): according to age 246; according to education 246–247; according to marital status 247; behavioural public policy 251; chief motivating factor 248; child-bearing 251; child-rearing responsibilities 251; district-wise labour force participation 242, 243, 244; educational specialization 245; family support 10; family ties 249; female labour force participation 10; female literacy rate 10; in Kanayannur taluk 244, 244; kinship 249; marriage and entry 248, 248, 250; micro-level studies 250, 251;

motherhood 250, 251; nature of employment 248, 248–249; professional hierarchy 241; purposive sampling method 244; role of marital status 251; scheduled questionnaire 245; social sector indicators 242; withdrawal from work 249–251; work–family balance 249, 251 Khan, Imran M. 33n2 Khan, Khalid 10 Khan, M.I. 201, 204 Khare, M. 9, 153 Kleemans, M. 200 Knight, J.B. 59, 71 Konigsgruber, R. 96 Kosack, S. 61 Kovaleski, J.L. 97 Kujur, S.K. 11, 12, 319 Kundu, Anirban 9 labour force, education status 160, 161 labour force participation rates (LFPRs) 222, 222 labour migration 3, 5, 8, 9, 24, 197 labour productivity 152 Laspeyres index 300–301 Lee, J.W. 60, 61 Leeuwen, van, B. 61 Lehmann, M. 98 Leontief production function 172 Levinsohn, J. 321 Lewis, D. 112 Lewis, W.A. 168 Lin, C.H.A. 61, 103 literacy rate in India 72n1 Log Mean Divisia Index (LMDI) 301 Lopez-Acevedo, G. 262 Lopez-Gonzalez, J. 260 Lucas, R.E. 77 Lundin, N. 280 Lurweg, M. 261, 263, 264 macroeconomic closures 180 Magruder, J. 200 Maharajh, A. 99 ‘Make in India’ 136 male labour force participation rate (MLFPR) 238, 242 Malik, Sanjaya Kumar 11 Mamgain, Rajendra P. 10, 235 manufacturing firms, FDI employability: average sales and

Index  347 average employment 284, 284; capital intensification 290, 290–292, 291, 292; classification of 340; employment generation 286, 287; industry-wise employment elasticity 285, 286, 286; mean employment 285, 285; technological superiority 287; trade orientation 292–295, 293, 294, 295 manufacturing industries: activity effect 312, 313; Annual Survey of Industries (ASI) data 302; capitalintensive goods 300; decomposition method 300, 301; demand-side factors 298; elasticity of substitution (E) 302; employment change decomposition results 304–312, 305–312; index decomposition analysis (IDA) 300, 301; Laspeyres index 300–301; Log Mean Divisia Index (LMDI) 301; National Industrial Classification (NIC)-2004based manufacturing industries 304; robotics and artificial intelligence 298, 300; sector-specific employment policy 313; structural effect 313; three-digit-level industrial data, 1998–99 to 2016–2017 299 manufacturing of food and beverages in the informal segment (FodBvrI) 171 Marin, D. 262 Marjit, S. 194n6 McKelvey, M. 97 Mean Divisia Index (LMDI) 301 Mehrotra, S. 136 Mellor hypothesis 168 MGNREGA development programmes 139 Micro Units Development & Refinance Agency (MUDRA) loan scheme 236 migrant workers: adverse employment effect 201; Assamese workers 201; in China 200; “composition shift effect” 203; compound annual growth rate of workers 217, 217; Duncan/ dissimilarity index 202, 210–211, 211, 218; endogeneity bias of 201; formal and informal segmentation 200; impact of 199; industrial distribution of female workers, 1987–88 to 2007–2008 204, 206, 207; industrial distribution of male

workers, 1987–88 to 2007–2008 204, 205; industrial shift effect 200, 203, 218; industry and intraindustry occupation mix effects on female 211, 214–215; industry and intra-industry occupation mix effects on male 211, 212–213; internal migration 199, 200; lower poverty rates 199; negative employment effect 200; negative impact of 200, 201; net shift 216; occupational and industrial structure 200, 203; occupational shift effect 200, 203, 218; primary adjustment mechanism 201; shiftshare analysis 202–204, 211, 218; in Turkey 200; unemployment rate 201 Mincer, J. 59, 60, 62, 63 Ministry of Agriculture Modern Seed Industry Enhancement Project 119 Miranda, L.F. 96 Mishra, Ram Kumar 11 Mishra, V. 240 monthly per-capita consumption expenditure/monthly per capita household expenditure (MPCE) 24, 33n1, 64, 67, 233 Morgan, L.A. 39 Morris, Morris D. 77 Mughal, M. 60, 61 Multidimensional Poverty Index 1 Mundlak, Y. 169 National Classification of Occupation-1968 (NCO-68) 201, 202 National Education Policy 2020 39, 55, 57 National Employment Policy 148 National Industrial Classification (NIC) codes 132 National Institutional Ranking Framework (NIRF) 84 National Knowledge Commission 104 National Manufacturing Policy (NMP) 13, 298, 318 National Sample Survey data on Social Consumption: Education – 2014 23–25 National Sample Survey Organisation (NSSO) 33, 40, 58, 64, 155, 219n2, 222, 238 negative employment effect 200

348 Index “new education policy – 2020” 144 non-agro-based industry in the formal sector (NAGMF) 171 non-agro-based industry in the informal sector (NAGMI) 171 non-agro-based sector 194n2 non-exporting FDI-firms 292–295, 293, 294, 295 non-migrant workers 10, 199–204, 207, 210, 211, 216–218; females, white-collar jobs 210; male, lower blue-collar occupations 210 Nori, Usha 11 North Carolina Institute of Medicine 100 North Carolina SBDTC 100, 100 Nunnenkamp, P. 280 occupational mix effects 10 occupational shift effect 200, 203 “one person, one policy” 115 opportunity cost of education 19 Orazbayeva, B. 103 ordinary least square (OLS) models 320 Owusu-Agyeman, Y. 112 Oyejide, T.A. 169 Pagani, R.N. 97 Panchayati Raj Institutions 235 Panickar, R.C. 241 Parida, J.K. 8, 136 Park, K.H. 60, 61, 66, 67 Peluffo, A. 280 Periodic Labour Force Survey (PLFS) survey 7, 39, 40, 48, 132 Perkmann, M. 97 Persadie, N. 99 Pertuz, V. 96 Pertuz-Peralta, L. 96 Petrin, A. 321 Physical Quality of Life Index 77 Pinto, M.M.A. 99 Plewa, C. 103 Pontes, J. 99 poverty alleviation strategy 117, 121 Pradhan, B.K. 179 Prota, F. 280 ProwessIQ 283 Psacharopoulos, G. 60, 61 Qiguo Zhao 115 Quacquarelli Symonds (QS) World University Rankings 84

Ram, R. 60, 66 Ramkhalawan, A. 99 Ranis, G. 168 Reddy, W.R. 235 Reenen, J.V. 103 regional inequality 42 Ren, Shuxia 8 Renuka, S. 10 reservation policy 21 Rijesh, R. 319 Robertson, R. 262 Robinson, S. 179 Rodrik, D. 260 Romer, P.M. 77 Rossi-Hansberg, E. 261 Rural Revitalization Strategy 111, 116, 117 Rybnicek, R. 96 Sabot, R.H. 59, 71 Sahoo, A. 179 Salandra, R. 97 salaried jobs 39, 41, 42, 44–47, 49–54, 51, 54, 55, 224, 232; graduates from urban areas 47; postgraduate students 48; SC/ST graduates 47; successive consumption quintile 47 Sánchez-Barrioluengo, M. 103 Sangster, N. 99 Santos, M.M.D. 99 Schiff, M. 169 Schultz, T.W. 59, 77 The Scientific Research Funding Program 125n1 self-employment opportunities 4, 38, 39 self-employment schemes/programmes 149n7 Sen, K. 262 Seric, A. 280 Shanxi Agricultural University (SXAU), China 8; advantages 119; “Agriculture, Countryside and Farmers” Service Center 120; conceptual framework 117; doctoral and postdoctoral programmes 114; “Five-in-One” service model 122; “government + base + practice” mode 118; integrated practical education mode 118; integrated technology system 119; intellectual support 114; international millet genome database 119; “International

Index  349 Vision and National Standard” 120; knowledge transceivers 113; national strategies 117; poverty alleviation strategy 117, 121; practical teaching 116; practical training modes 117; professional discipline system 116; projectpractice-person educating model 118; R&D platforms 120; regional innovation systems 113; resources of 119; Rural Revitalization Strategy 117; science and technology poverty alleviation 121; scientific and technological innovation 119; Social Practice Education Model 117; social services teams 121; teaching institutes 116; “Three Service Carriers” 122; triple helix model 114; “Two Achievements” 123 The Shanxi Federation of Humanities and Social Sciences Annual Key Project Research Program 2020-2021 126n1 Sharma, A.K. 179, 201 Sharma, C. 319 Shepherd 262 shift-share analysis 202–204 Shukla, Vachaspati 7 Simpson, M. 61 Singelmann, J. 202 Siriwardana, M. 170 Sjoholm, F. 280 Skute, I. 96 Small Business and Technology Development Center (SBTDC) 100, 100 Smith, S. 39 social accounting matrix (SAM) of India (India-SAM) 167 social economic accounts 263 social inequalities 42 Social Practice Education Model 117 social protection schemes 232 Solow 77 “Sons of the Soil” (Weiner) 199 Soodeen, D. 99 stabilisation policies 163 stark inequalities 44 Stone, S. 262 Sudarshan, R.M. 250, 251, 252n2 survey-based microeconomic analysis 3 sustainable development goals (SDGs) 77

Taiwo, O. 194n6 Tamvad, J.P. 38 Tartari, V. 97 teaching quality 82 technical and vocational education and training (TVET) 148 theory of competitive labour market 5 Thomas 262 Thrane, M. 98 Tight, M. 112 Times Higher Education World University Rankings (THE) 84 Tornqvist aggregation 328 total factor productivity (TFP) 318–320, 323, 324, 326 Total Fertility Rate (TFR) 149n1 Toumanoff, P. 39 tradable crops export, exogenous rise of world price effects: capital income and labour income of households 332; cash crops 180, 182; food crops 180, 181; functional income distribution 185; horticulture crops 180, 183; households distribution 186; luntary unemployment rate 185; macroeconomic indicators 186; sectoral labour demand 184; wage rates 180, 185 tradable crops export, simultaneous rise world of price effects: allocation of labour 190, 191; cash crops productivity 188; factor income of households 333; food crops productivity 187; functional income distribution 190, 193; Hicks neutral productivity 185–186; horticulture crops productivity 189; involuntary unemployment rate 190, 192; macroeconomic indicators 193; productivity shocks 186; wage rates 190, 192 trade integration 5 trade liberalization 319 Treinta, F.T. 99 triple helix model 114 “two-stage development strategic plan” 111 U-I cooperation 96, 98 U-I partnership 98, 99 Ujjawala Yojana 234 unemployment rate (UR) 86, 86, 159, 160, 160, 298

350 Index United Nations Development Programme (UNDP) 77 United Nations’ Human Development Index 1 university quality hierarchy 21 university research, Academics’ societal engagement (ASE): determinant factors 96; determinants of academic engagement 97; and economic development in action 97–98; flexibility, honesty, and clarity 96; governance mechanisms, collaboration processes 97; government measures 96; at individual level 96; at institutional level 96; internal facilitators 96–97; intersectoral technological cooperation 96; motivations and barriers 96; research patterns in university, industry, and government (U-I-G) interaction 96 UR see unemployment rate (UR) USA, higher education institutes 81 U-shaped feminization hypothesis 240 Usual Principal and Subsidiary Status (UPSS) 132 usual status (PS + SS) unemployment rate 70, 70 Valdés, A. 169 Valero, A. 103 Verick, S. 247 ‘wage compression’ effect 59 wage inequality 4, 19, 32, 58 Waldkirch, A. 280 Wang, Y. 98 Weiner, Myron 199 Western Carolina University (WCU) 99–103, 103 wholesale price indices (WPIs) 283, 304, 321 widow pension 232 Winegarden, C.R. 60 WoBmann, L. 81 Wolf, A. 114 Wolfmayr, Y. 261 women’s workforce decline: casual wage labour 223; during COVID-19 pandemic 235; disaggregation by age group 224; domestic duties 224, 234;

education level-wise withdrawals 228, 228; sectoral changes 226, 226; socio-religious pattern 227, 227; System of National Accounts (SNA)-extended activities 224; Time Use Survey 224; unpaid family labour 223, 224, 225, 234; see also women’s work participation rate (WPR) women’s work participation rate (WPR): age-group-wise 221, 223, 223; cluster-based employment opportunities 221; gender inequality 221; gender-wise trends 222, 222; gross domestic product (GDP) 221; household characteristics 229, 233–235; ‘income effect’ 233; logistic regression 229, 230, 234; personal characteristics 229 World Bank 77 World Development Report 40, 262 World Input–Output Database (WIOD) 263 world university rankings 7 WPR see women’s work participation rate (WPR) YINEQ 66 “yizhenyi pin” policy 147 Yoshino, R.T. 99 youth employment challenges: daily earnings/wage of workers 139, 140; educated youth unemployment 141, 142, 144; growth of disheartened labour force 143; MGNREGA development programmes 139; of people living below the poverty line (BPL) 141, 141; in private sector 139, 248; quality of jobs 138, 139; sectoral employment trends 135, 137, 138; share of informal employment 138, 138, 139; structured industrial and skilling policy 139 youth unemployment 5, 131 Yu, Y. 98 Zalewska-Kurek, K. 96 Zhang, X.Y. 98 Zhao, Min 8 Zotti, R. 103