Structural Transformation of Bangladesh Economy: A South Asian Perspective (South Asia Economic and Policy Studies) 981160763X, 9789811607639

Citation preview

South Asia Economic and Policy Studies

Mustafa K. Mujeri Neaz Mujeri

Structural Transformation of Bangladesh Economy A South Asian Perspective

South Asia Economic and Policy Studies Series Editors Sachin Chaturvedi, Director General, RIS for Developing Countries, New Delhi, India Mustafizur Rahman, Distinguished Fellow, Centre for Policy Dialogue (CPD), Dhaka, Bangladesh Abid Suleri, Executive Director, Sustainable Development Policy Institute, Islamabad, Pakistan Dushni Weerakoon, Executive Director, Institute of Policy Studies of Sri Lanka, Colombo, Sri Lanka

The Series aims to address evolving and new challenges and policy actions that may be needed in the South Asian Region in the 21st century. It ventures niche and makes critical assessment to evolve a coherent understanding of the nature of challenges and allow/facilitate dialogue among scholars and policymakers from the region working with the common purpose of exploring and strengthening new ways to implement regional cooperation. The series is multidisciplinary in its orientation and invites contributions from academicians, policy makers, practitioners, consultants working in the broad fields of regional cooperation; trade and investment; finance; economic growth and development; industry and technology; agriculture; services; environment, resources and climate change; demography and migration; disaster management, globalization and institutions among others.

More information about this series at http://www.springer.com/series/15400

Mustafa K. Mujeri · Neaz Mujeri

Structural Transformation of Bangladesh Economy A South Asian Perspective

Mustafa K. Mujeri PKSF Bhaban Institute for Inclusive Finance and Development (InM) Dhaka, Bangladesh

Neaz Mujeri Center for Research Initiatives Dhaka, Bangladesh

ISSN 2522-5502 ISSN 2522-5510 (electronic) South Asia Economic and Policy Studies ISBN 978-981-16-0763-9 ISBN 978-981-16-0764-6 (eBook) https://doi.org/10.1007/978-981-16-0764-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

For our parents/grandparents Muhammad Mujibur Rahman and Zubaida Rahman who always wanted us to become compassionate humans and all members of the ‘Sampreeti’ family who have supported and continue to support us at all times.

Preface

While there exists a large body of literature on structural transformation in the advanced countries, the analysis on structural transformation in today’s developing countries is limited, especially on whether these countries are following similar paths or, if the path is different, which factors explain these differences and what their implications for the future development of these countries are. The Bangladesh economy provides a useful case study to examine the above and related issues due to its spectacular transformation from a country with dire predictions and pessimistic prophecies during the 1970s to one of Asia’s most remarkable phoenixes with amasing and unexpected success stories in recent years. The developments achieved by Bangladesh are among the fastest transformations in modern history, and Bangladesh’s achievements do not fit the traditional pathways of human and social development. No doubt, differential sectoral productivity growth and economic liberalisation have caused many of these transformations, but Bangladesh’s unique neoliberal development model under which social progress has far outstripped economic growth has also played a major role. The role of the state has also been significant in pursuing sound macroeconomic policies, enhancing disaster management capacity, raising investments in public health and education, creating partnerships with NGOs and civil society, reducing population growth, and encouraging overseas labour migration. The present book argues that Bangladesh’s structural transformation over the last fifty years has largely been driven by social changes led by women empowerment. The relationship between the formal and informal labour markets and the role of the informal sector in stimulating structural transformation have also been significant. During most of the period of growth miracle since the 1990s, external trade (e.g. rapid growth in exports of readymade garments) further accelerated the transition out of agriculture into industry and services. The structural and policy reforms, both economy-wide and sector-specific, carried out since the mid-1980s, prepared the macroeconomy to effectively respond to the micro-signals for change and adopt appropriate transformations. These micro–macro transmissions and their role in overall transformation are seldom acknowledged in the traditional development literature.

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For the future, along with aiming to promote inclusive structural transformation, it would be important for Bangladesh to differentiate between different activities within services since the economy has become highly dominated by the services sector and various service activities tend to use very different skill intensities of labour. Human capital has played an important role in the process of structural transformation in the Bangladesh economy through transferring resources both from the goods-producing sector to the services sector and between tradable and nontradable services. For Bangladesh, the heterogeneous quality of labour across sectors is probably important to understand the low labour productivity in agriculture relative to non-agricultural activities. At its present level of development, structural transformation is important for Bangladesh both for sustaining the process of capital deepening and raising productivity. Capital deepening will add to rising total factor productivity and make labour productivity growth still higher. Further, Bangladesh can experience a ‘productivity growth bonus’ as it converges to an employment structure akin to higher middleincome countries and realise the potential of structural transformation as a source of productivity growth. We would like to express our deep gratitude to our teachers, friends, and relatives from whom we have benefited much through discussions and exchange of ideas over the years. We are grateful to Dr. Qazi Kholiquzzaman Ahmed, Chairman of the Institute for Inclusive Finance and Development (InM) and the Palli KarmaSahayak Foundation (PKSF), for his encouragement and support to complete the study. We are grateful to Professor Sanat Kumar Saha of Rajshahi University for his advice, constant support, and encouragement. We also express our gratitude to Farhana Nargis, Nahid Akhter, and J. Joha for providing valuable assistance with research and graphics. We also express our deep gratitude to Springer Nature for publishing the book and sincere thanks to Nupoor Singh, Daniel Joseph Glarance, Anil Chandy, and William Achauer for helpful editorial and other suggestions and encouragement throughout the process of finalising the book. Finally, the largest part of the credit for writing this book goes to Zinnatoon Nadira for her valuable encouragement and support, especially by shouldering all responsibilities of the family in the absence of which we could not have written the book. We also acknowledge the deep encouragement and support provided by Hasib Mamtaz and Moonzeba Mujeri as well as Aliyah Mamtaz and Rayhan Mamtaz who, despite their busy activities and different professions, helped us in numerous ways to nurture many of our thoughts and ideas. Dhaka, Bangladesh

Mustafa K. Mujeri Neaz Mujeri

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 A Multidimensional View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Bangladesh’s Structural Transformation . . . . . . . . . . . . . . . . . . . . . . 1.4 Primacy of Equality Horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Organisation of Chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 5 7 8 10 12

2 Structural Transformation: Theory and Global Evidence . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Measures of Structural Transformation . . . . . . . . . . . . . . . . . . . . . . . 2.3 Gains from Structural Transformation . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Structural Transformation: Global Trends . . . . . . . . . . . . . . . . . . . . . 2.4.1 Historical Trends in Developed Countries . . . . . . . . . . . . . . 2.4.2 Recent Trends in Developed and Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Structural Transformation and Premature Deindustrialisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Structural Transformation and Development Theory . . . . . . . . . . . . 2.5.1 The Neoclassical Growth Models . . . . . . . . . . . . . . . . . . . . . 2.5.2 The Structuralist Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 The New Structural Economics . . . . . . . . . . . . . . . . . . . . . . . 2.5.4 The New Latin American Structuralism . . . . . . . . . . . . . . . . 2.5.5 The Value Chain Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.6 Resource-Based Industrialisation . . . . . . . . . . . . . . . . . . . . . . 2.6 Empirical Evidence on Structural Transformation . . . . . . . . . . . . . . 2.6.1 Manufacturing as the Engine of Economic Growth . . . . . . . 2.6.2 Role of ST in Labour Productivity Growth . . . . . . . . . . . . . . 2.6.3 Structural Change Within Manufacturing . . . . . . . . . . . . . . . 2.6.4 Industrial Upgrading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Premature Deindustrialisation: Role of Services Sector . . . . . . . . . 2.8 Structural Transformation and Development . . . . . . . . . . . . . . . . . . .

13 13 14 15 17 17 18 19 19 21 22 24 25 25 26 27 28 28 29 30 32 34 ix

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2.8.1 Structural Transformation and Labour Market Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Structural Transformation, Employment, and Poverty . . . . . . . . . . . 2.9.1 Structural Transformation and Human Development . . . . . 2.10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34 35 36 37 38

3 Structural Transformation in South Asia: An Overview . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Structural Transformation in South Asia . . . . . . . . . . . . . . . . . . . . . . 3.2.1 South Asian Growth Surprises . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 GDP Growth and its Composition . . . . . . . . . . . . . . . . . . . . . 3.2.3 Changes in Macro Aggregates . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 A Dynamic Panel Model for South Asia . . . . . . . . . . . . . . . . 3.3.2 Methodology and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Empirical Results and Implications . . . . . . . . . . . . . . . . . . . . 3.4 Factors Influencing ST in South Asia . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Reforms for Transition and Growth . . . . . . . . . . . . . . . . . . . . 3.5 Poverty and Human Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Multidimensional Poverty in South Asia . . . . . . . . . . . . . . . . 3.6 South Asia: A Region of Growing Inequality . . . . . . . . . . . . . . . . . . 3.6.1 Income and Wealth Inequality . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Landlessness and Rising Inequality . . . . . . . . . . . . . . . . . . . . 3.6.3 Gender Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.4 Rising Informality in Employment . . . . . . . . . . . . . . . . . . . . 3.7 Inequality in Access to Basic Services . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Access to Water and Sanitation . . . . . . . . . . . . . . . . . . . . . . . 3.7.2 Access to Health Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.3 Access to Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.4 Inequality in Fiscal Regime . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Export Sophistication in South Asian Countries . . . . . . . . . . . . . . . . 3.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 46 48 51 51 54 55 56 60 61 63 64 65 66 67 68 70 73 73 74 79 80 82 85 86

4 Structural Transformation in Bangladesh: Trends and Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Economic Structure Before Independence . . . . . . . . . . . . . . . . . . . . . 4.3 Growth and Structural Transformation, 1971–2020 . . . . . . . . . . . . . 4.3.1 Growth of GDP and GDP Per Capita . . . . . . . . . . . . . . . . . . . 4.3.2 Changes in Uses of GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Changes of GDP by Industrial Origin . . . . . . . . . . . . . . . . . . 4.3.4 Savings and Capital Formation . . . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Sectoral Composition of GDP . . . . . . . . . . . . . . . . . . . . . . . .

89 89 90 91 92 93 95 95 98

Contents

4.4

Economic Growth and Structural Transformation . . . . . . . . . . . . . . 4.4.1 Structural Transformation in the Bangladesh Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Interdependence, Linkages, and Leading Sectors . . . . . . . . 4.5 Changes in Sectoral Share of Employment . . . . . . . . . . . . . . . . . . . . 4.6 Uniqueness of Bangladesh’s Structural Transformation . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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99 100 102 105 114 115

5 Bangladesh’s Rural Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Economic Structure and Agriculture in South Asia . . . . . . . . . . . . . 5.2.1 Nature of Rural Transformation in Bangladesh . . . . . . . . . . 5.2.2 Urge for Diversifying Rural Livelihoods . . . . . . . . . . . . . . . . 5.3 Changes in Agriculture in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Growth in Crop and Horticulture . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Drivers of Crop Sector Growth . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Changes in Resource Base of Agriculture . . . . . . . . . . . . . . . 5.3.4 Production Organisation: Land Reforms and Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Non-crop Sector in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Growth and Development of Fisheries . . . . . . . . . . . . . . . . . 5.4.2 Livestock and Poultry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Women in Non-crop Agriculture . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Forestry Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Agribusiness Development for Rural Transformation . . . . . . . . . . . 5.5.1 Marketing Linkages in Agriculture . . . . . . . . . . . . . . . . . . . . 5.5.2 Value Chain Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Commercialisation of Bangladesh Agriculture . . . . . . . . . . . . . . . . . 5.6.1 Agricultural Diversification . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Climate Change Mitigation and Adaptation . . . . . . . . . . . . . . . . . . . 5.7.1 Climate Change and Bangladesh Agriculture . . . . . . . . . . . . 5.7.2 Climate Change Adaptation: Modifying Agriculture . . . . . 5.7.3 Protecting Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Contract Farming and Small Farmer Implications . . . . . . . . . . . . . . 5.9 The Rural Non-farm Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Rural Transformation: Future Directions . . . . . . . . . . . . . . . . . . . . . . 5.11 Rural Transformation: Policy Priorities . . . . . . . . . . . . . . . . . . . . . . . 5.12 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 117 119 120 121 123 125 126 130 132 137 137 141 143 144 147 147 148 149 150 151 152 153 154 156 157 163 164 165 166

6 Industrial Transformation in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Industry Sector in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Employment Structure and Industry Sector Dynamics . . . . 6.2.2 Evolution of Industrial Policy Framework . . . . . . . . . . . . . . 6.2.3 Changes in Manufacturing Industry . . . . . . . . . . . . . . . . . . . .

171 171 172 173 174 176

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6.3 6.4

Trade Liberalisation and Manufacturing . . . . . . . . . . . . . . . . . . . . . . Sources of Manufacturing Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Structure of Manufacturing Industry . . . . . . . . . . . . . . . . . . . 6.4.2 SMEs and Cluster Development . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Cottage and Microenterprises . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 The RMGs Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Role of FDIs in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Challenges to Manufacturing Growth . . . . . . . . . . . . . . . . . . . . . . . . . 6.8 Policies for Stimulating Manufacturing Growth . . . . . . . . . . . . . . . . 6.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

179 181 183 183 188 191 196 199 200 202 203

7 Services Sector in Bangladesh: Changes and Prospects . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Modernising Services Sector in South Asia . . . . . . . . . . . . . 7.2 Bangladesh’s Services Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Employment Dynamics in Services Sector . . . . . . . . . . . . . . . . . . . . 7.4 Software and ITES in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 ICT Industry in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Logistics and Transportation: Megaprojects . . . . . . . . . . . . . 7.4.3 Challenges and Issues in High Services Growth . . . . . . . . . 7.5 Digital Financial Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Women’s Digital Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Digital Inclusion of the Poor . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Rising Remittances and Their Impact . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 Remittances, Household Income, and Asset Accumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

205 205 206 209 210 215 215 217 219 220 222 223 224

8 Inclusive Structural Transformation: Policy Agenda . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Bangladesh’s ST: Need for an Inclusive Agenda . . . . . . . . . . . . . . . 8.3 Developing Entrepreneurship and Enterprises . . . . . . . . . . . . . . . . . 8.4 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Creating Productive Employment . . . . . . . . . . . . . . . . . . . . . 8.4.2 Interaction Between Formal and Informal Economy . . . . . . 8.4.3 Countering Labour Market Imbalances . . . . . . . . . . . . . . . . . 8.4.4 4IR and Small Farmer Development . . . . . . . . . . . . . . . . . . . 8.5 ST and Shared Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

229 229 232 233 236 238 241 242 244 245 246

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9 Unexpected Challenges: Covid-19 and Cyclone Amphan . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Cyclone Amphan in May 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Global Covid-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Covid-19 and Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Socioeconomic Ramifications of Covid-19 . . . . . . . . . . . . . . . . . . . . 9.5.1 Impact on Employment and Labour Market . . . . . . . . . . . . . 9.5.2 Healthcare System and COVID-19 Response . . . . . . . . . . . 9.5.3 Gender Dimension of Response . . . . . . . . . . . . . . . . . . . . . . . 9.6 Dimensions of Human Vulnerability . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Need for Multi-sectoral Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.1 Rapid Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.2 Medium and Long-Term Response Framework . . . . . . . . . . 9.7.3 Covid-19 and the Informal Economy . . . . . . . . . . . . . . . . . . . 9.7.4 Covid-19 and the Microfinance Sector . . . . . . . . . . . . . . . . . 9.8 Fiscal and Monetary Policy Framework for Covid-19 . . . . . . . . . . . 9.9 Exploiting Potentials of the ‘Gig Economy’ . . . . . . . . . . . . . . . . . . . 9.10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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249 249 250 251 252 253 255 255 256 257 260 260 264 265 267 268 269 271 272

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

About the Authors

Mustafa K. Mujeri holds a Ph.D. in Economics from McMaster University in Canada and is currently the Executive Director of the Institute for Inclusive Finance and Development (InM) in Dhaka, Bangladesh. During his professional career, he has worked in different national and international organisations including teaching in Rajshahi University in Bangladesh and University of Queensland in Australia, National Expert in the Bangladesh Planning Commission, Research Director in the Centre on Integrated Rural Development in Asia and the Pacific (CIRDAP), UNDP Adviser in Cambodia, Chief Economist in the Bangladesh Bank (central bank) and Director General of the Bangladesh Institute of Development Studies (BIDS). He has written and edited several books and published widely in national and international journals. Neaz Mujeri holds an MBA in Finance from Independent University Bangladesh and B.Comm. in Business Management from Ryerson University in Canada. He is currently the Executive Director & CEO, of the Center for Research Initiatives in Dhaka, Bangladesh. In the past, he has worked in various positions in the Career Development and Employment Centre and the Students Union of the Ryerson University. He has also worked as Senior Economist in different research projects of the Research and Information System (RIS) in India and organisations in Dhaka.

xv

Abbreviations

ADB AFC AML/CFT BBS BDT BGMEA BMI BPO CBD CEDAW CMSEs CPI DFS DOC DTW EIA EOI EXPY FAO FDI FTZ GDP GDS GFCF GFP GHI GMP GNI GVC

Asian Development Bank Asian Financial Crisis Anti-Money Laundering /Countering Financing of Terrorism Bangladesh Bureau of Statistics Bangladesh Taka Bangladesh Garments Manufacturers and Exporters Association Body Mass Index Business Process Outsourcing Convention of Biological Diversity Convention on the Elimination of all Forms of Discrimination against Women Cottage, Micro, and Small Enterprises Consumer Price Index Digital Financial Services Day-Old Chick Deep Tube Well Environmental Impact Assessment Export-Oriented Industrialisation Export Sophistication Food and Agriculture Organisation (of the United Nations) Foreign Direct Investment Free Trade Zone Gross Domestic Product Gross Domestic Savings Gross Fixed Capital Formation Good Farming Practice Global Hunger Index Good Manufacturing Practice Gross National Income Global Value Chain

xvii

xviii

HDI HYV ICT ILO INR ISIC ITES LDC LLP MFA MFS MOP MSMEs NGO-MFI NIC NPL NRB ODA PPP R&D REER RMG RNF SAARC SAP SDGs SEZs SMEs SNA SPS ST STW TBT TFP THE TNC TSP UNCTAD UNDP UNESCAP UNICEF UNIDO

Abbreviations

Human Development Index High-Yielding Variety Information and Communication Technology International Labour Organisation Indian Rupee International Standard Industrial Classification Information Technology Enabled Service Least Developed Country Low Lift Pump Multi Fibre Arrangement Mobile Financial Services Murate of Potash Micro, Small, and Medium Enterprises Non-government Organisation and Microfinance Institution Newly Industrialised Country Nonperforming Loan Non-Resident Bangladeshi Official Development Assistance Purchasing Power Parity Research and Development Real Effective Exchange Rate Readymade Garment Rural Nonfarm South Asian Association for Regional Cooperation Structural Adjustment Programme Sustainable Development Goals Special Economic Zones Small and Medium Enterprises System of National Accounts Sanitary and Phyto-Sanitary Structural Transformation Shallow Tube Well Technical Barriers to Trade Total Factor Productivity Total Health Expenditure Trans National Corporation Triple Super Phosphate United Nations Conference on Trade and Development United Nations Development Programme United Nations Economic and Social Commission for Asia and the Pacific United Nations Children Fund United Nations Industrial Development Organisation

Abbreviations

UNRO USD VAT WHO WTO

xix

United Nations Relief Operation United States Dollar Value-Added Tax World Health Organisation World Trade Organisation

List of Figures

Fig. 2.1 Fig. 3.1

Fig. 3.2

Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 4.1 Fig. 4.2 Fig. 5.1 Fig. 5.2

Structural transformation and sectoral shares of GDP and employment. Source Authors’ illustration . . . . . . . . . . . . . . . Growth in GDP and GDP per capita in South Asian countries. Source World Bank, World Development Indicators (WDI) database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Labour productivity growth and initial level of labour productivity in South Asia. Source Authors’ construction based on ILO (2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Income poverty in South Asian countries, 2013. Source Based on World Bank, PavcalNet . . . . . . . . . . . . . . . . . . . . . . . . . Changes in poverty in South Asian countries. Source Based on World Bank, PavcalNet . . . . . . . . . . . . . . . . . . . . . . . . . Multidimensional poverty in South Asia. Source Based on data from OPHI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life expectancy at birth in different world regions, 2017. Source data.worldbank.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infant mortality rates in world regions, 2017. Source data.worldbank.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Density of health workers in South Asian countries. Source World Health Organisation, 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . Out-of-school rates by world regions, 2016. Source uis.unesco.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tax revenue in different geographic regions. Source Data.workbank.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth in GDP and GDP per capita in Bangladesh (at constant 2006 prices). Source World Bank, 2018 . . . . . . . . . . Labour force participation rate in Bangladesh. Source BBS, Labour Force Survey, various years . . . . . . . . . . . . . . . . . . . Irrigated area in Bangladesh, 1972–2019. Source BBS, Statistical Yearbook, different years . . . . . . . . . . . . . . . . . . . . . . . . Use of chemical fertilisers in Bangladesh, 1972–2019. Source BBS, Statistical Yearbook, different years . . . . . . . . . . . .

18

49

56 63 64 65 76 76 77 79 81 92 107 127 128 xxi

xxii

Fig. 7.1

Fig. 7.2

List of Figures

Employment in Services Sector of Bangladesh Source BBS, Labour Force Surveys; World Bank, World Development Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Export of Software and ITES from Bangladesh. Source Bangladesh Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

211 216

List of Tables

Table 2.1 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 3.15 Table 3.16 Table 3.17 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7

Value-added shares by sectors, 1950–2005 (per cent) . . . . . . . . Long-term development in south asian countries . . . . . . . . . . . . Sectoral contribution to GDP in South Asian countries . . . . . . . Changes in macro aggregates in South Asian countries . . . . . . . Summary statistics of key indicators . . . . . . . . . . . . . . . . . . . . . . Labour productivity growth, structural transformation, and openness in South Asia: LSDV estimation results . . . . . . . Gini coefficients of income in South Asian countries . . . . . . . . Palma ratio in South Asian countries . . . . . . . . . . . . . . . . . . . . . Quintile share ratio in South Asian countries . . . . . . . . . . . . . . . Gender gap index in South Asia . . . . . . . . . . . . . . . . . . . . . . . . . Vulnerable employment in South Asia . . . . . . . . . . . . . . . . . . . . Access to safe drinking water in South Asian countries . . . . . . Access to proper sanitation facilities in South Asia . . . . . . . . . . Total and private health expenses in South Asian countries . . . Child malnutrition in South Asia . . . . . . . . . . . . . . . . . . . . . . . . . Global hunger index of South Asian countries, 2019 . . . . . . . . . Inequality in education in South Asian countries . . . . . . . . . . . . Export shares by quintiles of productivity values in South Asian countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GDP and employment by sectors in Bangladesh (former East Pakistan), 1959–1960 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Labour in agriculture and non-agriculture in Bangladesh (former East Pakistan), 1951–1970 . . . . . . . . . . . . . . . . . . . . . . . Bangladesh’s GDP growth rate at constant prices . . . . . . . . . . . GDP and GDP per capita of Bangladesh . . . . . . . . . . . . . . . . . . Distribution by uses of GDP at current prices, percentages . . . . Distribution of GDP by industrial origin at constant 2006 prices, percentages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GNI per capita and gross domestic savings in selected countries (per cent of GDP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 47 50 52 57 59 66 66 67 69 71 74 75 77 78 78 80 84 90 91 92 93 94 96 97 xxiii

xxiv

Table 4.8 Table 4.9 Table 4.10 Table 4.11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16 Table 4.17 Table 4.18 Table 4.19 Table 4.20 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 5.13 Table 5.14 Table 5.15 Table 5.16 Table 5.17 Table 5.18 Table 5.19

List of Tables

Average and marginal savings in Bangladesh (at 2006 constant prices) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sectoral composition of GDP and growth rates (at 1995– 96 constant prices) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average share (per cent) of value added by broad sectors, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in value-added share in GDP of subsectoral activities, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . High and low backward and forward linkages of production sectors, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . Sectoral share in GDP and employment . . . . . . . . . . . . . . . . . . . Gender differences in sector composition of employment . . . . . Labour force by level of education . . . . . . . . . . . . . . . . . . . . . . . Changes in status in employment . . . . . . . . . . . . . . . . . . . . . . . . Indexes of real wages in major sectors . . . . . . . . . . . . . . . . . . . . Employment and labour productivity by broad sectors . . . . . . . Different categories of enterprises in Bangladesh . . . . . . . . . . . Number of enterprises and employed labour, 2013 . . . . . . . . . . Agriculture output and employment shares in South Asian countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Farming, landless and agricultural labour households in rural Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sub-sectoral shares of GDP and composition of agricultural value added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Annual growth of agriculture, 1990–2015 . . . . . . . . . . . . . . . . . Growth performance of major crops in Bangladesh . . . . . . . . . . Liberalisation of agricultural input markets in Bangladesh . . . . Changes in land use pattern in rural areas, 1984–2008 . . . . . . . Density of agricultural machinery in Bangladesh . . . . . . . . . . . Major provisions of land-related policies and acts in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land tenure forms in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . Changes in tenancy and related indicators in Bangladesh agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of non-crop agriculture in GDP and agricultural GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Annual fish production in Bangladesh . . . . . . . . . . . . . . . . . . . . Profitability of major aquaculture production systems in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Freshwater pond farming systems in Bangladesh . . . . . . . . . . . . Domestic production of livestock products . . . . . . . . . . . . . . . . . Employment and average time allocation in rural Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest area in South Asia, 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . Major types of forests in South Asia . . . . . . . . . . . . . . . . . . . . . .

98 99 100 101 103 105 106 108 109 109 110 111 112 120 122 124 124 125 129 130 132 134 135 136 137 138 139 139 142 143 144 145

List of Tables

Table 5.20 Table 5.21 Table 5.22 Table 5.23 Table 5.24 Table 5.25 Table 5.26 Table 5.27 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 6.11 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7 Table 7.8 Table 9.1

xxv

Climate change scenario, Bangladesh . . . . . . . . . . . . . . . . . . . . . Production and marketing of major crops . . . . . . . . . . . . . . . . . . Technology generation and Innovations in Bangladesh agriculture during 2004–14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in seasonal mean temperatures in Bangladesh, 1948–2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of rural household income in Bangladesh, 1988– 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of rural population by employment type . . . . . . . . Duration of employment and labour productivity in RNF occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rural household income from farm and non-farm sources . . . . Growth and sectoral share of industry subsectors in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sectoral composition of employment in Bangladesh . . . . . . . . . Employment category and value-added per labour in industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Capital-Labour Ratio and Labour Productivity by Size Structure of Manufacturing Enterprises . . . . . . . . . . . . . . . . . . . Wage and employment growth in manufacturing, 2013-2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Employment elasticity of manufacturing industries . . . . . . . . . . Weighted mean of tariff rate applied in Bangladesh . . . . . . . . . Sub-sectoral sources of manufacturing growth, 1988-2010 . . . . Structure of industrial enterprises and employed labour, 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exports of RMGs of Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . Bangladesh’s market share in global apparel market . . . . . . . . . Growth and Relative Changes of Services Sector in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth Performance of Services Subsectors in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Employment Category and Value Added per Labour in Services Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Type of Services Sector Establishments in Bangladesh . . . . . . . Ongoing Megaprojects in Bangladesh . . . . . . . . . . . . . . . . . . . . Three Major Components of DFS . . . . . . . . . . . . . . . . . . . . . . . . Roles of Different Participants in Mobile Banking . . . . . . . . . . Average Annual Income of Remittance-Receiving Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bangladesh’s monetary, financial, and fiscal responses for Covid-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

146 150 151 152 158 159 159 160 172 173 175 177 177 178 180 182 184 192 195 210 210 212 214 218 220 221 225 261

Chapter 1

Introduction

Abstract Bangladesh, like most developing countries, has been experiencing a moderate but steady structural transformation (ST) moving the economy away from traditional agricultural activities to relatively modern manufacturing and services sectors. The challenge for Bangladesh is to manage the ST process and economic institutions to increase and diversify incomes, reduce risks and uncertainties, lessen poverty and inequalities, and enhance social achievements. The key for Bangladesh is to integrate three development dimensions of desirable structural changes, growth to reduce income and productivity gaps (convergence), and enhanced equality. The interactions between ST and social development are critical for Bangladesh since technology has radical impacts on social interactions, leading to adaptation and regeneration of social relations. New ICTs are changing social relations, and rising overseas labour migration is transmitting social relations across geographies providing new ways of adaptation. These developments indicate the need for embracing a multi-sectoral and interdisciplinary view on ST in Bangladesh to include institutional changes that mediate social outcomes.

1.1 Introduction The shift of resources (such as labour) from the lower (agriculture) to the higher (non-agriculture) productivity activities is traditionally taken as an inevitable consequence of the development process. Practically no country has developed without industrialising first, except probably the few with an abundance of natural resources or land. In the rural areas, with the dominance of subsistence agriculture, the trend is for labour to move to manufacturing or services employment with higher wages. As labour moves out from the rural areas, real wages tend to rise creating incentives in agriculture to adopt new technologies and mechanisation which also increases productivity. In the urban areas, industries employ workers with stable jobs and higher incomes. Over time as capital and skill accumulation further deepens, shift into more advanced skill-intensive and higher value-added activities takes place and

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. K. Mujeri and N. Mujeri, Structural Transformation of Bangladesh Economy, South Asia Economic and Policy Studies, https://doi.org/10.1007/978-981-16-0764-6_1

1

2

1 Introduction

the process continues. This, in a nutshell, is the mainstream narrative of development across the world. The analysis of the links between economic growth and structural composition of output and employment has followed mainly two schools of thought. The neoclassical school—with its homogeneous aggregate output—argues that the output structure hardly matters for economic growth. On the other hand, economists in the structural tradition argue that economic growth changes the composition of both GDP and employment, and this change is the driver of sustained economic growth and rising incomes. The current consensus, however, is that these two schools are not mutually contradictory, and economic growth and composition of aggregate output involve a cause and effect relationship. In this context, the evidence on structural transformation (ST) in an economy is highly convincing. Historical data on most of the present-day developed countries show that these countries had gone through a common transformation—from agriculture to manufacturing and then to services. In the early stages, a country is mostly agriculture-based, labour productivity is low, and the economy changes rather slowly. With rising labour productivity, economic growth starts to pick up along with higher wages. As the prospect of rapid productivity growth in agriculture is limited, labour migrates to manufacturing having greater scope for productivity growth. As the economy matures, enhanced productivity and wages lead to a shift of labour to the services sector over time. Thus, rising GDP per capita is associated with a declining share of agriculture in GDP and a rising share of value added initially in manufacturing and then in services. A similar trend is also observed in sectoral shares of employment to total employment for these three sectors. Empirical evidence in support of such a transition is well documented (e.g. Kuznets 1973; Chenery 1960; Lewis 1954; Syrquin 1988; Baumol 1967). It is in this sense that ST—traditional paradigms of which offer alternative theories on the drivers of ST—is one of the essential ingredients of modern economic growth. As a stylised fact of the development process, ST is the reallocation of economic activities across the broad sectors of the economy (agriculture, industry, and services) that is associated with the process of modern growth (Kuznets 1966). With ST as one of the major stylised facts, the recent literature shows that extending the one-sector growth models to incorporate ST can explain a variety of the development dynamics (for a review, see Herrendorf et. al. 2013). The standard one-sector model does not take into account the process of intersectoral reallocation of economic activity or ST; as in the neoclassical model, growth is the outcome of saving, physical and human capital accumulation, and innovation (Solow 1956; Herrendorf et al. 2013). Still, ST has received a lot of attention in the policy debate of the developing countries to promote efficient sectoral reallocation of economic activities, primarily through government intervention. Globally, the empirical analysis shows that the transfer of labour and other inputs to more productive activities is a driver of growth as hypothesised by Lewis (1954). The process can, however, be both growth-enhancing and growth-reducing depending on the nature of reallocation of labour (McMillan and Rodrik 2011). Their analysis shows that ST in most of Asia has been growth-enhancing as labour moved from lower

1.1 Introduction

3

to higher productivity sectors. In contrast, the opposite happened in Sub-Saharan Africa and Latin America. One of the most optimistic models of economic development through ST is provided by Lewis (1954). The model focuses on a two-sector economy where the transfer is from the traditional to the modern sector. The driver of development is the movement of the abundant factor of production (labour) from the ‘traditional’ or ‘non-capitalist’ sector (having low productivity and wage) to the ‘modern’ or ‘capitalist’ sector (with higher productivity and wages).1 Some analysts consider the model as still relevant to the present-day developing countries (see, for example, Gollin 2014). Along similar lines, Seers (1963) identifies several structural characteristics to differentiate the developed from the developing countries: (i) factors of production: a literate and skilled labour force, high capital intensity in all sectors, and welldeveloped transport and power systems; (ii) sectors of the economy: commercial agriculture and manufacturing diversified with high share; (iii) public finance: reliance on direct taxes and adequate expenditure on social security; (iv) foreign trade: diversified exports with high price and income elasticities and primary product imports; (v) household consumption: moderately equal income distribution post-tax and low share of food expenses in household expenditure; (vi) savings and investment: welldeveloped financial intermediation and high investment; and (vii) dynamic influences: no chronic tendency to macroeconomic deficits, slow population growth, and high urbanisation. However, many of the above characteristics have changed in the globalised world, e.g. industrialisation is now more equated with the process of horizontal integration into global value chains (GVCs) and foreign direct investment (FDI) rather than a vertically integrated manufacturing structure. The ST process of the present-day developed countries mostly involved diversifying from agriculture and traditional manufactured goods (e.g. food and beverages, garments, and textiles) into more sophisticated manufacturing and services leading to rising productivity and income. Traditionally, such reallocations of labour and other resources across economic activities accompany ST and economic growth. However, the modern concept of ST considers three aspects involving sectoral, factoral, and integrative dimensions (Herrendorf et al. 2013; Sumner 2017). The sectoral dimension covers inter- and intra-reallocation of activities towards higher productivity and thus includes the traditional measures of ST. The factoral dimensions highlight the drivers in terms of factors of production and productivity while the integrative aspect covers global integration in terms of trade and investment patterns. Thus, the traditional and largely unidimensional view of ST highlights that sustained economic growth is driven by productivity growth (both within and across sectors) and ST, and these two transformations must work steadily to ensure sustainability. Kaldor (1966,1967, 1968) emphasises the interactions among economic

1 In

his model, Lewis does not equate traditional with agriculture and modern with manufacturing; although, for simplicity, this is how the model has been interpreted in the mainstream development literature.

4

1 Introduction

sectors in the growth process of modern economies, arguing in particular that manufacturing is the main engine of growth as highlighted by the export-for-growth models. Further, Kuznets (1979) identifies ST as one of the six characteristics of modern economic growth while Herrendorf et al. (2013) argue that explicit consideration of an economy’s sectoral composition is critical to understanding a number of economic and labour market outcomes, including income convergence, productivity, and wage inequality. Rapid globalisation and related developments have, however, led many analysts to rethink whether the same diagnosis as suggested by the traditional ST process is still relevant. At present, the structure of production is rapidly changing with automation and global production fragmentation indicating that low labour costs are no longer adequate for enjoying competitiveness. On the contrary, automation might lead to reverse reorganisation of labour-intensive manufacturing to the developed countries. This may also shift focus more towards raising complementarities between labour and capital requiring different sets of labour skills along with investments in the required human skills. This shows that, even if manufacturing plays the key role in creating better-paid jobs, significant adjustments are needed in the traditional ST agenda. The prospects for the transition of the developing countries into low-cost manufacturing are gloomier than before in a globalised world; hence, future growth of the developing countries would have to adopt a different blueprint in the digitalised and automated world. Further, a fresh look at improving agriculture and services is warranted for the present developing countries. Despite rapid technological advances, agricultural productivity remains low in most of these countries, indicating the need for increasing incomes in agriculture for rapid and inclusive growth. Further, with rapid urbanisation, agricultural productivity will have to rise rapidly to ensure food security of the rising share of the non-agricultural population. The dependence on food import may not be a sustainable solution particularly if more protectionist policies are followed and the global trade environment deteriorates which, along with the threat of climate change, strongly reinforce the need for raising the resilience and rapid growth of the agriculture sector. Similarly, the services sector needs creative approaches to facilitate required growth. Usually, the services activities get higher thrusts for growth in an urban and digitalised economy. The challenge, however, is to strengthen the rapid and productive job creation process boosted by the industry sector. The process requires skilled workers to sustain job creation especially in tradable and modern services. The focus on skilled services, on the other hand, brings out the challenge of absorbing the vast quantities of less-skilled labour who exist in the developing countries. While artificial intelligence (AI) creates risks for services as automation does for manufacturing, the benefits emerging from the digitalised economy could be unlimited. For example, almost at no marginal cost, businesses can enter into the new and vast markets almost instantaneously. Thus, possibilities do exist in the modern globalised world, but the key challenge for the developing countries relates to building up a successful services sector that requires heavy investments in human capital and technologies and many of which may not deliver short-term benefits. Nevertheless, the

1.1 Introduction

5

services sector has the possibility of emerging as another core component of growth for the developing countries in the long run.

1.2 A Multidimensional View The common feature of the traditional ST analysis is its exclusive focus on economic factors with little emphasis on other dimensions of development. As a consequence, not much theoretical or empirical analysis has been done on how the various dimensions (e.g. economic, social, and environmental) of development are interrelated and influence the ST process although it is widely agreed that these dimensions are not mutually exclusive. Moreover, the strategies that prioritise investments in noneconomic factors in development may have a significant catalytic effect in advancing ST in the developing countries. As factors of production move from the lower productivity to the higher productivity activities, ST occurs not only in economic dimensions, but also in social institutions and beliefs (Kuznets 1971). From a broader perspective, the concept of ST is thus consistent with the present conceptualisation of development in which the transformation allows most people to have greater assets and incomes along with wider choices over economic and noneconomic dimensions of human well-being. The multidimensionality of development also brings out different connotations relating to the social sphere into the forefront of the ST analysis. Social development essentially ensures that all people can lead an empowered life with better quality. Higher incomes, technology, and good public policies are needed for the people to live a better life, enjoy higher consumption, and access education and other social amenities. This aspect of social development is supported by economic dimensions in society. However, there are other social dimensions, such as the distribution of wealth and income, and these distributional issues have their own space in social development. In addition, ST issues are also linked with empowerment, social differentiation, gender and social inequality, power relations and social conflicts, social and economic exclusion, traditions and beliefs, and other sociocultural norms that violate the overriding concern that ‘no one is left behind’. In the low-income countries having acute imbalances in factor returns across sectors, factor reallocation favouring the higher productivity sectors makes ST and economic growth highly interlinked involving both economic and social transformations. However, despite recognising these linkages, the traditional interpretation of ST runs in terms of economic transformation alone. The underlying reason for the relegation of the social dimensions of transformation in ST may relate to a dearth of data and limitations of the analytical methodologies to integrate social transformations as components of the ST process. However, the modern concept of development is not merely economic transformation; it is essentially a socially disruptive process. With development, the sectors undergo differential rates of growth creating losers (in the slow-growing sectors) and gainers (in the faster growing sectors) in society (Syrquin 1988). These differential

6

1 Introduction

impacts require policy actions and institutional adjustments as components of ST to minimise the costs of and the resistance to ST. Thus, the role of the state in managing ST requires action to minimise social tensions and conflicts among different interest groups and support the weaker segments in society (Kuznets 1971).2 Thus, from a broader perspective, ST means putting qualitative changes in the production structure at the centre of the growth dynamics of a country. In the present globalised world, such changes are shaped by rapid global integration and growth in productivity and employment through the building of capacities, knowledge, and learning. Similarly, environmental sustainability can be achieved only if there is ST along with inclusive technological transformation with the right balance between the knowledge-intensive sectors and diversification towards sectors with rising domestic and external demands. In the real world, faster growth paths for productivity, output, and employment over time are required for ST that needs to integrate two types of efficiency. The first is the Schumpeterian efficiency, where sectors with high-productivity growth support knowledge and capacity expansion for innovation to drive further productivity gains. The second is the Keynesian efficiency which is specialisation in sectors having higher growth in both domestic and external demand that favours output and employment expansion. For ST, it is necessary to strengthen the dynamic sectors, both in terms of technological capacity and adequate demand expansion. Supportive industrial policy is also necessary in two complementary directions: (i) capacity building and increased competitiveness of the existing sectors; and (ii) diversifying towards new, high-productivity sectors that are sustainable and environmentally efficient. Also, policies to promote productivity among micro, small, and medium-sized enterprises (MSMEs) will provide large dividends in terms of creating jobs, serving as hubs for technology dissemination and adoption, and better income distribution. On the other hand, virtuous ST will not work if more high-tech enclaves are created or changes are concentrated within the most efficient edge of the production system alone. The need is to synergise ST across the entire economy creating backward and forward linkages and supporting intermediate productivity sectors. The approach should be a combination of both ‘pull-from-the-top’ and ‘push-from-the-bottom’ forces, such that the structure of employment changes towards a labour shift from low-productivity sectors to higher productivity ones. In the long run, the outcome is a more diversified distribution with less income inequality as more labour moves to medium- and high-productivity sectors.

2 Syrquin

(1988) calls such interventions as ‘minimal development state’, although the role may go well beyond the classical role of protecting the citizens against violence, theft, and fraud and enforcement of contracts and related issues.

1.3 Bangladesh’s Structural Transformation

7

1.3 Bangladesh’s Structural Transformation The quest for development and improving the people’s well-being and socioeconomic conditions are the crucial challenges facing Bangladesh. Since independence in 1971, as the country has developed, inter-sectoral movement of economic activity has taken place (see Mujeri and Mujeri 2020). It has also changed across subsectors within the same sector. In both cases, the driving forces of ST have resulted in reallocation of economic activity towards higher productivity activities, but these are not adequately recognised in Bangladesh’s development analysis. Over the years, Bangladesh has experienced a moderate but steady ST; moving the economy away from traditional agricultural activities to relatively modern manufacturing and services sectors. In 1991, agriculture’s share in total employment was about 70 per cent, which declined to 40 per cent in 2017. Correspondingly, the shares of industry and services rose to 21 per cent and 39 per cent, respectively. Agriculture’s contribution to GDP (with over 65 per cent share in the 1970s) fell to about 14 per cent in 2017, while the share of services rose to 52 per cent and that of industry to 34 per cent. The above shows that employment in agriculture has not fallen as rapidly as its share in output; while the reverse is true for employment in industry. As a result, the services sector is the growth-driver of the Bangladesh economy at present with two-thirds of the incremental growth emerging from the services sector. The pattern of ST in the Bangladesh economy deviates from the traditional pattern of most Western and South East Asian economies where the shift took place first from agriculture to industry followed by a transfer to services. The traditional pattern of ST enables to transfer the labour force from agriculture to industry at the initial stages which has not happened in Bangladesh to the required extent as industry (especially manufacturing) did not expand to the required extent. As a result, the unskilled rural labour continues to subsist in agriculture; while those who are forced or moved out from the rural areas have joined the urban informal and the slum sector. The growth pattern essentially highlights the link between poverty and low-productive employment due to inadequate manufacturing growth. Additionally, the pattern reflects the failure of the goods-producing sectors to grow at reasonable rates to ensure satisfactory shares in the country’s GDP. Over the coming decades, favourable conditions for higher services growth are likely to be created in Bangladesh. With rapidly rising real GDP per capita, services demand will further increase reinforcing the GDP growth. In the sector, the demands for services which are mostly intermediate consumption (such as producer and government services) are likely to grow faster and these have strong multipliers. The growth of these dynamic and intensive ICT user services will generate employment opportunities especially for the educated youth. For the government, the services sector is a vast but relatively unexploited potential tax-base, and its growth can therefore have important long-term implications for the fiscal policy.

8

1 Introduction

1.4 Primacy of Equality Horizon Bangladesh has become a lower middle-income country in 2015 and fulfilled all three criteria for graduation from the least developed country (LDC) status in 2018 and is likely to emerge as a developing country in 2024. In the post-LDC graduation period, productive ST will be crucial for Bangladesh since sectoral diversification, human capital, technological innovation, labour mobility, and productivity growth are closely linked with ST. The challenge for Bangladesh, therefore, is to manage its ST process and economic institutions in a manner that shapes incentives and triggers productive transformations which will increase and diversify incomes, reduce risks and uncertainties, lessen poverty and inequalities, and enhance social achievements. The ST path towards greater equality can be set through creating new highproductivity sectors and dissemination of technology throughout the production system. This will create additional job opportunities in higher productivity sectors, raise the labour force participation rates especially for women, and lower both unemployment and informality. All these factors will create positive impacts on poverty and inequality. The process also generates a sustainable path to equality, since the changes occur in the production structure which endogenously creates productive jobs, builds transformative capacities, and broadens the high-productivity segments in the economic structure. These transformations are important complementary routes to social policy and fiscal measures for assisting the most disadvantaged and poorest segments in Bangladesh society. If the production structure is polarised, redistributive fiscal and social mechanisms have limited space to resolve the problems of inequality and equitable growth in a sustainable manner. The key will be to target both jobs and human development opportunities within an integrated ST framework. Since production linkages are part of ST, well-targeted linkages exploitation through policies will ensure more equal sharing of gains in society. The integration of social and redistributive policies is necessary to improve income distribution and reduce vulnerability. These policies also provide the intertemporal linkages which enable structural change-oriented policy to achieve their longer term redistributive impacts. More importantly, well-designed social policy protects the most disadvantaged during the disruptive transition period of ST and from the social impacts of external shocks. When ST leads to narrower productivity gaps, production structure diversifies and aggregate productivity gains are achieved, all of which lead to equality and labour benefits. The key aspect of ST for Bangladesh is, therefore, to integrate three dimensions of development: (i) ST in the production structure; (ii) reduction of domestic and global income and productivity gaps (convergence); and (iii) promotion of equality. The three dimensions are integrated and jointly determine the country’s growth path. In view of the longer term goals of building a just and equitable society with shared prosperity, Bangladesh’s development has to ensure consistent progress in all three fronts covering structural change, convergence, and equality. Many developing countries (e.g. in Latin America and the Caribbean) have made progress on one or the

1.4 Primacy of Equality Horizon

9

other of these dimensions. Over the past decades, many countries have reduced the income gap but not the technology or productivity gaps. Although income distribution has somewhat improved in a few of these countries through revitalisation of the labour market and more vigorous social policies, this failed to sustainably generate an adequate quantity of quality jobs. Further, more than two-thirds of Bangladesh’s production system is characterised by informality. Within this overwhelming duality, ST towards closing the productivity gap and creating quality jobs is a big challenge that requires coordinated and complementary policy responses on all fronts. No doubt, the fast-expanding international technology frontier creates new challenges and new opportunities for Bangladesh, and the need is to move closer to the economies at the leading edge of innovation. For broad-based and sustainable services-led growth, Bangladesh needs to adopt a coherent and integrated services policy. Further, rather than implementing ad hoc reforms in services, Bangladesh needs services reform as part of an overall strategy to ensure consistency and uniformity in the depth and pace of reforms across the sectors. With strong inter-linkages between different services, reform in a particular service is unlikely to yield the desired outcome in the absence of corresponding reforms in other complementary services. Such an integrated services policy should also define the sequence as well as the pace of reforms. We must, however, recognise that interactions between ST and social development, in most cases, are not well understood, and hence the views widely differ globally, and this is more so in Bangladesh. Moreover, technology has radical impacts on forms of social interactions, leading to adaptation and regeneration of social relations. For example, new ICTs are changing social relations, and Facebook allows an individual to be connected to many more people than ever before. The rapidly increasing international migration from Bangladesh transfers social relations across geographies, providing new ways of adaptation while maintaining parts of the old identities. All these developments indicate the need for the adoption of a multisectoral and interdisciplinary view on ST and social development in particular local contexts covering the institutions that mediate social outcomes. No doubt, public policy has the potential to promote ST and social development simultaneously and institutions may be developed and evolved for promoting ST, including social policies for inclusive development in Bangladesh. The connotation of ST is therefore more comprehensive and inclusive in Bangladesh than the one outlined in the traditional view. In this book, this inclusive view has been termed as the South Asian perspective of ST, and the approach differentiates South Asian challenges from the traditional largely unidimensional ones. In essence, ST in the South Asian context carries a deeper and wider connotation in view of the challenges that South Asia face due to its large population size, high burden of poverty, rising inequalities, and, above all, its compulsion and deep commitment to achieve sustained and inclusive development within a short span of time which requires a South Asian ‘brand’ of ST.

10

1 Introduction

1.5 Organisation of Chapters The introductory remarks of this chapter are followed by a brief review of the relevant theories and global empirical evidence on the nature and dominant trends of ST across different regions of the world in Chap. 2. The chapter also stresses that the process of ST has several stylised facts. Productive ST involves both horizontal and vertical evolution along with diversification and technological upgrading. The theoretical and empirical analysis on ST suggests that the developing countries need to promote economic growth with inclusive and productive ST, with productivity enhancements and job creation at the core. Chapter 3 illustrates the nature and dynamics of ST in South Asia using several indicators. An important dimension of the South Asian ST process is acute inequalities and the trend towards increasing inequalities rather than bridging the gap. Overall, the countries in South Asia reveal their unethical and unfair biases in favour of the rich and the elite. As a result, the countries fail to collect adequate revenues for ensuring basic services to the population especially the poor, and allow the powerful to amass abundant wealth through every conceivable channel. Chapter 4 highlights Bangladesh’s ST process since independence which is distinct on several counts. In Bangladesh, the ‘deviant’ pattern of ST involves a high share of the services sector in total GDP at relatively low income per capita. Bangladesh’s agricultural modernisation model characterises the sequencing of chemicalisation and mechanisation. The pattern of industrialisation has been closely linked with urbanisation, and the developments are more akin to the ‘production cities’ paradigm. The services sector, on the other hand, is heterogeneous with a small share of highly productive tradable services and a disproportionately large segment of low-paid nontradable activities. Chapter 5 discusses rural and agricultural transformation in the Bangladesh economy and policies to boost ST and agricultural growth in the country; the route being technological change and crop diversification in favour of high-value crops. The analysis stresses the complexities of integrating the macro and longer term perspectives with the short-term political imperatives and highlights the need for combining reforms with the longer term economic impacts to draw policy guidelines for future development and ST of the Bangladesh economy. Chapter 6 provides an analysis of the nature of industrial (mostly manufacturing) transformation in the country. Over the last few decades, Bangladesh has embarked on liberalisation and labour-intensive industrialisation, which could potentially generate productive jobs and promote the required ST. This characterised rapid export and employment growth, especially in the highly export-oriented segment of the manufacturing industry, lead to striking growth in the RMGs sector. However, the process could not create a transformative export-oriented industrialisation strategy that would diversify exports and lead to a broad-based export structure. The chapter argues that, for Bangladesh, the policies for enhancing employment and inclusive ST should be well coordinated with policy priorities to increase manufacturing productivity and exports with emphasis on both domestic and export-oriented industries in the country.

1.5 Organisation of Chapters

11

Chapter 7 argues that developing the services sector in Bangladesh can generate far-reaching benefits, especially due to the sector’s labour-intensive nature, which can generate productive jobs for the country’s large labour force. Further, with extensive synergies of different services with other sectoral activities, the sector can lift productivity throughout the entire economy. These synergies are most evident in high-value service industries such as finance, ICT, and business services. Since highly productive services are education intensive, secondary, tertiary, and technical education and improvement in the quality at all levels are essential for the transition to modern services. Chapter 8 discusses the policy implications for accelerating inclusive ST in Bangladesh. Although the country’s deviation from the classical path is not due to ‘premature deindustrialisation’, concerns exist relating to the possibility of its inability to benefit fully from the manufacturing sector to raise overall productivity and employment through its widespread spillover effects. In Chap. 9, recent unexpected impediments that have affected the growth and ST momentum of the Bangladesh economy are discussed covering the cyclone Amphan and Covid-19 pandemic in 2020. Bangladesh needs to instal an adequate automatic stabiliser that can be expanded when needed. In the context of Covid-19, Bangladesh should design a comprehensive package of measures that could simultaneously provide stimulus to the economic activities and protect jobs especially in the informal and gig economy. The sectoral patterns of employment show a strong gender dimension indicating the need for a careful consideration of the gender implications of ST. The challenge for Bangladesh is to identify how to ensure a more inclusive ST within a relatively short period, as the country seeks economic and social transformation along with ‘leaving no one behind’. Three issues are important for Bangladesh to explore in this context: (i) nature and depth of ST and inclusive growth; (ii) how best to deal with the distributional tensions that rapid ST entails; and (iii) how to achieve rapid economic and social transformation in an inclusive manner. No doubt, the path towards achieving inclusive ST is complex in Bangladesh. The manufacturing subsector could not create the required number of jobs and generate rapid absolute and relative productivity gains in the economy. In the recent decades, the services sector has largely led to both output and employment growth. However, services subsectors with strong labour absorptive capacity have low average productivity. For the future, the aim for Bangladesh would be to skillfully manage the ‘developer’s dilemma’ of achieving both ST and inclusive growth for reducing poverty and arresting the rising trend of income inequality. A well-articulated domestic policy regime and pattern of resource accumulation along with supportive globalisation policies are needed for Bangladesh to reach the cherished high-income status by 2041.

12

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References Baumol, W.J. 1967. Macroeconomics of unbalanced growth: The anatomy of urban crisis. American Economic Review 57: 415–426. Gollin, D. 2014. The lewis model: a 60-year retrospective. Journal of Economic Perspectives 28 (3): 71–88. Herrendorf, B., R. Rogerson and A. Valentinyi (2013). Growth and Structural Transformation. NBER Working Paper No. 18996, Cambridge, MA: National Bureau of Economic Research. Kaldor, N. 1966. Causes of the Slow Rate of Economic Growth in the United Kingdom. Cambridge: Cambridge University Press. Kaldor, N. 1967. Strategic Factors in Economic Development. Ithaca, NY: New York State School of Industrial and Labor Relations. Kuznets, S. 1966. Modern Economic Growth: Rate, Structure and Spread. New Haven, CT: Yale University Press. Kuznets, S. 1971. Economic Growth of Nations. Cambridge, London: Harvard University Press, Mass and Oxford University Press. Kuznets, S. 1979. Growth and Structural Shifts. In Economic Growth and Structural Change in Taiwan, ed. W. Galenson. London: The Postwar Experience of the Republic of China, Cornell University Press. Lewis, W.A. 1954. Economic Development with Unlimited Supplies of Labour. The Manchester School 22 (2): 139–191. McMillan, M. and D. Rodrik. 2011. Globalization, Structural Change and Productivity Growth. In: Making Globalization Socially Sustainable, ed. M. Bacchetta, and M. Jansen. Geneva: International Labour Organization. Mujeri, M.K., and N. Mujeri. 2020. Bangladesh at Fifty: Moving beyond Development Traps. London: Palgrave Macmillan. Seers, D. 1963. The Limitations of the Special Case. Oxford Bulletin of Economics and Statistics 25 (2): 77–126. Solow, R. 1956. A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics 70 (1): 65–94. Sumner, A. 2017. The Developer’s Dilemma: The Inequality Dynamics of Structural Transformation and Inclusive Growth. In: ESRC GPID Research Network Working Paper 1, ESRC Global Poverty and Inequality Dynamics Research Network, Economic and Social Research Council, London. Syrquin, M. 1988. Patterns of structural change’, chapter 07. In Handbook of Development Economics, vol. 1, 203–273. Elsevier: Amsterdam.

Chapter 2

Structural Transformation: Theory and Global Evidence

Abstract The conceptual scrutiny and empirical evidence indicate that ST involves changes in structures of production and employment along with improvement of tangible and intangible infrastructure in the economy. The process refers to stylised facts that highlight its links with economic growth, horizontal and vertical evolution, and diversification and technological upgrading. For South Asia, the key is to promote economic growth with structural and productive transformation, implying that productivity enhancements within sectors should not come at the expense of job creation. A virtuous process of inclusive ST transforms a country beyond growth to reduce poverty, enables the poor to acquire higher human development, enhances the government’s fiscal capacity and strengthens institutions, widens social protection measures, and increases expenditure on essential public services.

2.1 Introduction Economic development and the concept of ST are strongly related in explaining economic progress across countries. In the development analysis—as one abandons the simplistic world of homothetic preferences, unbiased technological change, and perfect mobility and markets featuring instant adjustments and includes the real-world complexities—ST emerges as a central feature of development which determines the rate and pattern of economic growth. Development policies should, therefore, try to anticipate desirable ST and facilitate it by removing obstacles and correcting for market failures. Inefficient policies, on the other hand, can hamper growth by blocking the required changes in socioeconomic structure or by pushing them towards a growth-hampering path. For instance, ‘forced industrialisation’ may accelerate growth but only for a short time and at very high costs, and this is not likely to be sustained. In addition to a theoretical construct, ST is a conflictive process that requires both individual and societal adaptations. There may emerge, for example, the need for a large reallocation of populations from the rural to the urban areas, especially in the early stages of development. These changes require mechanisms for facilitating © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. K. Mujeri and N. Mujeri, Structural Transformation of Bangladesh Economy, South Asia Economic and Policy Studies, https://doi.org/10.1007/978-981-16-0764-6_2

13

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migration, in which the government may have to play the important role as the facilitator among different group interests and as the mitigator of the adverse effects of these changes. Traditionally, the concept of ST is the transfer of labour and other factors of production from the low-productivity sectors to the high-productivity ones or from the low-productivity to higher productivity activities within the same sector or both. It is regarded particularly beneficial for developing countries as these countries have significant structural heterogeneity—existence of inter-sectoral productivity gaps characterised by a limited pursuit of segmented high-productivity activities which results in low levels of growth and development. In low-income countries, structural heterogeneity widely persists at both sectoral and subsectoral levels. Relative labour productivity, measured in terms of output– labour ratio, in agriculture, industry (manufacturing and nonmanufacturing), and services, shows that the gaps in sectoral productivity are the most at low levels of income (UNIDO 2013).1 At low-income levels, nonmanufacturing industries (such as electricity, gas, water supply, other utilities, and construction and mining activities) usually reflect high productivity as these usually remain more capital-intensive, and hence, labour productivity also tends to be high. At higher levels of income, manufacturing emerges as more productive with greater capital intensity. Economic activities also differ in terms of the strength of their linkages with other sectors/activities in the economy. In the developing countries, high- and low-productivity activities (e.g. traditional and modern sectors) usually have relatively weak linkages which also reduce the prospects of both ST and technological change. Further, there is a negative relationship between differences in inter-sectoral productivity and average labour productivity (McMillan and Rodrik 2011).

2.2 Measures of Structural Transformation In the empirical literature, ST is typically measured by changes in sectoral shares in total economic activity. The common indicators of measuring ST are the shares of employment, value added, and consumption in the respective aggregates for the economy. The level of employment is measured either by the number of workers or the total number of working hours. Value added may also be expressed in nominal terms or in real terms. It becomes obvious that any difference between the employment and value-added shares across sectors reflects the variations in labour productivity. Sometimes, the sectoral export shares (as percentages of GDP) are also used to measure structural transformation. For measuring ST, the employment shares and value-added shares of sectors in total employment and total value added are computed. Assuming there are ‘n’ sectors in the economy, total employment is the total of the number of workers in each sector. 1 The

distinction between subsectors and activities is useful in heterogeneous sectors, e.g. industry and services, but not in agriculture which has more uniform productivity levels.

2.2 Measures of Structural Transformation

15

Similarly, the total value added is the total of the value added in each sector. Using ‘L’ for total employment and ‘Y’ for total value added, these can be expressed as follows: L =

n 

L i and Y =

I = 1

n 

Yi,

i = 1

where Li is the employment in sector i and Yi is the value added in sector i. Finally, the sectoral shares of employment and value added are obtained as follows: βi = L i /L and λi = Yi /Y, where βi and λi are the shares of sector i in total employment and value added, respectively. These shares, however, have limitations in measuring ST. The employment shares, for example, may not reflect real changes in labour input due to sectoral differences in hours worked or in labour quality. The value-added shares (at current prices) also do not distinguish between changes in quantities and prices across sectors. Further, alternative indicators may display different behavioural patterns leading to diverse conclusions. For example, the choice of the commodity space (e.g. final consumption or consumption value added) has differences in identifying the causal factors of ST (Herrendorf et al. 2013).

2.3 Gains from Structural Transformation Both static and dynamic gains can be derived from ST. The static gain is the economywide increase in labour productivity as more labour moves to the higher productivity sectors. Dynamic gains, on the other hand, are generated over time due to skill upgrading and positive externalities from better technologies and higher capabilities. Productive ST results when both static and dynamic gains are achieved. Several stylised facts are observed between sectoral employment shares and relative labour productivity in developing countries (UNIDO 2013). First, tradable services and nonmanufacturing industries have the highest labour productivity but have the lowest shares of the employed labour.2 As a country develops, tradable services gain importance due to their tradable element and their use of modern technologies such as ICT; however, these are highly skill-intensive. Expansion of these 2 As per the ISIC (Revision 3), tradable services are transport, storage, and communications; financial

intermediation; and real estate activities. Nontradable services cover wholesale and retail trade; hotels and restaurants; and other community, social, and personal services. Non-market services include public administration and defence; education; health; and social work.

16

2 Structural Transformation: Theory and Global Evidence

services generates quality jobs, but many developing countries lack the required skilled labour force. As a result, ST towards tradable services does not create good jobs for the majority of the labour force. This explains the inability of India’s ICT service industry to emerge as a major employment (and hence growth) driver for the majority in the country (Ray 2015). In a similar vein, nonmanufacturing industries can experience high-productivity growth, but these tend to have little linkages with the rest of the economy. As such, nontradable services and agriculture are the two main sources of employment in the developing countries. However, low labour productivity in these activities generates low wages and limited scope for skill accumulation indicating the need to transfer labour to high-productivity activities for productive ST. In addition, nontradable services are characterised by the dominance of informal and vulnerable employment. Hence, ST towards these services does not create quality employment (Szirmai et al. 2013). In terms of labour productivity and job-creating capacity, manufacturing ranks between tradable and nontradable services. Manufacturing usually has lower productivity but higher employment potential than tradable services, while it has higher productivity but lower employment potential than nontradable services. In the real world, ST towards manufacturing is usually taken as industrialisation. In an economy, ST is an ongoing process associated with development. At a low level of income, output and labour remain mostly concentrated in agriculture whereas, as income rises, the shares of total production and total employment start to concentrate on manufacturing and services. Similarly, the economic structure undergoes evolution as ST pushes upgradation towards the production of sophisticated goods with modern technologies. This leads to both production diversification and process upgradation in sectors. Successful transition of industrial structures in such directions, however, requires facilitating institutions and infrastructure, the evolution of which does not take place on its own. Institutional discordance often emerges as the greatest obstacle to ST, especially in the middle-income countries (Schneider 2015). For the developing countries, diversification is the key to development. While developed countries produce a wide diversity of goods and services, the developing countries typically produce a limited basket. The importance of diversification (or horizontal evolution) of production is evident from the empirical regularity in a large number of developing countries (Imbs and Wacziarg 2003). As poor countries develop, sectoral production and employment become more diversified. The diversification process continues and employment concentration declines as income per capita rises. Further, ST leads to the production of increasingly sophisticated goods. Industrial upgrading initiates a process of moving towards the production of goods with high-value addition and greater productivity. The determinants of the nature and direction of such transformation are, however, complex and country-specific. Among these determinants, factor endowments and public policies have received particular attention. Factor endowments influence the direction of ST through determining a country’s comparative advantages (for discussion on recent aspects of comparative advantages, see Lin and Chang 2009; Lin and Monga 2010; Lin 2011). The literature

2.3 Gains from Structural Transformation

17

also identifies the availability of natural resources as a factor behind slow industrialisation. Recent evidence, however, shows a weak empirical relationship between the degree of sophistication of exports and endowments of human capital or institutional quality (Rodrik 2006). Nevertheless, the changes in a country’s productive structure through ST depend on a large number of factors including endowments and policy decisions. As such, the process ST involves large-scale changes; with new sectors/industries emerging as drivers of job creation and technological upgrading, and improvement of infrastructure serving the needs of the emerging industries. Moreover, the need for coordination assumes a critical role as a significant determinant of transaction costs and return to investment of the enterprises. The weak market forces alone are unlikely to ensure efficient resource allocations during the transition phases, and the developing countries can effectively use well-articulated industrial policies to expand the horizon of their static comparative advantage and diversify into new and more sophisticated activities.

2.4 Structural Transformation: Global Trends Since the early years of modern development, global trends point out to several stylised facts on ST. Ideally, since ST is a continuous process, time series data over a relatively long period of time are needed to examine the trends. Moreover, this also raises the issue: how realistic is it for the currently developing countries to exhibit the same regularities that the present-day developed countries displayed during their transition phases? One should also note that long time series may also suffer from differences in quality since the past data typically are not likely to have the same quality as the recently collected data.

2.4.1 Historical Trends in Developed Countries The development pattern of the present-day developed countries exhibited a broad trend: a shift from agriculture towards industry (manufacturing) and services activities. Major inputs (e.g. labour and capital) progressively moved away from agriculture into non-agriculture activities. In the process, self-employment was replaced by wage employment. Over the last two centuries, economic growth in the presently developed countries resulted in declining shares of employment and value added of agriculture and rising shares of industry and services. In addition, employment and value-added shares of manufacturing showed a hump-shaped pattern—rising at lower levels of GDP per capita, reaching a peak at medium levels, and declining afterwards (Fig. 2.1). There were two prominent empirical regularities as well (Herrendorf et al. 2013). First, at low levels of income, agriculture’s share in employment remained considerably above

18

2 Structural Transformation: Theory and Global Evidence

Fig. 2.1 Structural transformation and sectoral shares of GDP and employment. Source Authors’ illustration

its value-added share showing an employment bias towards agriculture. Second, both employment and value-added shares in services remained significant throughout the development process. The rate of increase of value-added share of services sector, however, showed acceleration at a GDP per capita of around USD 8,100. Further, the value-added share of manufacturing reached its highest level at similar income levels, and services sector gradually emerged as the main engine of growth afterwards.

2.4.2 Recent Trends in Developed and Developing Countries The analysis of recent trends covering most of the present developed and developing countries also confirms the regularities discussed above (see, for example, Herrendorf et al. 2013). First, the share of employment in agriculture decreases with income, whereas the employment share in services rises, and second, employment share in manufacturing shows a pattern which is inverse U-shaped (also see Rodrik 2009). In particular, the declining share of employment in agriculture has many implications. With productivity differential favouring non-agricultural activities over agriculture, the movement of labour from agriculture to non-agriculture raises average labour productivity in the economy. Moreover, higher income resulting from the ST process creates additional demand for manufactured goods and services providing further impetus to expanding manufacturing and services activities. The evidence also shows that the employment share of manufacturing rises until reaching around 30 per cent, it flattens out afterwards, and then actually starts declining. This is roughly consistent with the pattern of the present-day developed

2.4 Structural Transformation: Global Trends

19

countries discussed above; the major difference, however, is that the peak occurs at a relatively lower level (of employment share) of 30 per cent compared with around 40 per cent for the currently developed countries. This probably reflects a movement towards a new pattern of industrialisation with lower peaks of employment in manufacturing and a bias towards services at earlier stages of transformation relative to earlier periods of ST. One can also observe a relatively strong positive association between the share of employment in services and per capita income in recent periods.

2.4.3 Structural Transformation and Premature Deindustrialisation The trends of ST, as discussed above, suggest that the countries tend to ‘deindustrialise’ (i.e. the shares of manufacturing in employment and value added in respective totals tend to decline) at certain levels of income per capita. Empirical evidence shows that the world has largely deindustrialised over the last six decades. This is true, especially since the 1990s, both for the ‘mature’ developed countries and for many developing countries. Table 2.1 shows the value-added shares of agriculture, industry (and separately for manufacturing), and services in GDP for selected countries. The table shows that China, Thailand, Malaysia, and India were the most industrialised countries (in terms of value-added share) in Asia in 1950, and by 1980, most of these countries had further industrialised including South Korea and the Philippines. However, the situation had changed significantly by 2005; many of the countries had further industrialised but others (e.g. China and Sri Lanka) exhibited lower industrialisation levels. In other words, these countries have ‘deindustrialised’. The services sector benefited from the process. The regional averages show that the shares of manufacturing in value added increased in Asian countries in recent decades, but a deindustrialisation process had set in Latin America and Africa. Historically, although deindustrialisation has taken place in countries that had fully developed, the paradox is that many countries now deindustrialise at lower levels of income. Several empirical studies show that the shares of manufacturing employment and value added in their respective totals reached their peak levels and started to decline at lower than earlier levels of GDP per capita (Felipe et al. 2014; Palma 2005; Rodrik 2016). This phenomenon is widely referred to as ‘premature deindustrialisation’ which is often interpreted as the rise of services sector as a new (and additional) engine of economic growth for the developing countries.

2.5 Structural Transformation and Development Theory Since the Industrial Revolution (now called the First Industrial Revolution)—adoption of new manufacturing processes in Europe and the US from about 1760 AD to

47

46

48

S. Korea

Sri Lanka

Thailand

15

Developed countries1

42

19

19

28

14

15

12

13

17

7

19

14

21

7

31

11

9

16

10

12

4

9

8

7

11

10

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7

43

40

36

50

36

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41

41

32

41

31

29

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33

37

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39

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57

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IND

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13

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10

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12

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14

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5

MAN

48

42

39

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41

45

48

48

47

38

46

38

29

36

SER

1980

4

21

25

10

25

23

28

16

25

30

23

36

30

32

AG

36

35

32

40

33

29

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37

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25

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25

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21

IND

24

20

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24

22

22

18

24

26

16

22

17

40

14

MAN

59

44

43

50

42

48

43

47

36

46

36

40

21

48

SER

2005

2

16

26

7

13

10

17

3

14

21

8

18

13

20

AG

28

34

30

37

35

44

27

40

32

27

50

28

48

27

IND

17

18

12

18

24

35

15

28

23

19

30

16

34

17

MAN

70

51

45

56

52

46

56

56

54

51

42

54

40

53

SER

Note are in current prices. Developed countries include Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Norway, Sweden, Switzerland, United Kingdom, and United States. AG stands for agriculture, IND for industry, MAN for manufacturing, and SER for services. Source Szirmai 2012:409

1 Figures

44

41

Developing countries

Latin America

Africa

49

22

Asia

Averages

61

40

Malaysia

42

55

India

Philippines

51

China

Pakistan

61

Bangladesh

1960 SER

AG

MAN

AG

IND

1950

Table 2.1 Value-added shares by sectors, 1950–2005 (per cent)

20 2 Structural Transformation: Theory and Global Evidence

2.5 Structural Transformation and Development Theory

21

around 1840 AD—the rapidity of economic growth has mostly been determined by manufacturing growth. Since World War II, several European countries (e.g. Austria, Finland, Greece, Ireland, Portugal, and Spain) catched up during the 1950 and 1960s using manufacturing growth; while East Asia replicated the same model in the 1970 and 1980s. At present, China, Malaysia, Thailand, and Vietnam are following a similar path (Studwell 2014). Over the years, the theoretical and empirical literature has provided multiple explanations of the ST process and development in general. The First Industrial Revolution was fuelled by progressive technological progress resulting in sustained economic growth. Most classical economists favoured laissezfaire (a French term meaning ‘let alone’) economics with the driving principle that the less the government was involved in the economy, the better off the business would be and the society. The government’s intervention should be limited to preserving property, life, and individual freedom, and the natural laws that govern market forces and the economy—which the British economist Adam Smith called the ‘invisible hand’—should be allowed to work freely.3 Under laissez-faire, the price system regulates what is to be produced and how, and hence ST takes its course as the market reallocates factors to more productive sectors that offer better returns. Although it was a dominant intellectual framework during much of the eighteenth and nineteenth centuries, its major shortcoming was the neglect of, among others, the key role of technological change and industrial upgrading in economic growth which separates modern economic growth from the classical approaches. In the present literature, three separate approaches to theoretical perspectives are available: first, growth theories in the neoclassical tradition; second, structuralist development theories; and third, new structuralist economics essentially aiming at reconciling the first two schools.

2.5.1 The Neoclassical Growth Models The one-sector Solow growth model (Solow 1956) is the pioneering neoclassical growth analysis which expands the Harrod–Domar model (Harrod 1939; Domar 1946). The traditional neoclassical growth models are built on several critical assumptions: (i) production functions are aggregates implying, among others, that all firms and industries use the same technology; (ii) constant returns to scale prevails in production such that there are no economies of scale; (iii) all markets are perfectly competitive; and (iv) technological change is neutral, meaning that technological change equally improves the productivity of both labour and capital. As such, these models do not capture several important features of economic growth including the ST process. Technological progress is also exogenous. Recent endogenous growth 3 The concept of laissez-faire (and hence capitalism) is widely criticised since it has moral conflicts

with no protection for the weakest. The proponents of laissez-faire argue that if all in society pursue their own interests, benefits of all will automatically be ensured. The opponents argue that laissez-faire leads to poverty and economic inequality. John Maynard Keynes was a major critic of laissez-faire economics and he argued for government intervention in specific cases.

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models have, however, extended the one-sector framework that is consistent with the stylised facts of ST especially how changes in the shares of output and employment are generated. Technological progress in these models results from innovation through investing in research and development (R&D). The opportunities for technological spillovers also expand leading to increasing returns to scale (Acemoglu et al. 2001; Aghion and Howitt 1992; Glaeser and Shleifer 2002; Jones 1998; Romer 1987, 1990). However, these models are still criticised for their inability to explain the complexity of the ST process within a realistic framework (Dosi 1982; Freeman and Louça 2001; Malerba et al. 1999; Nelson and Winter 1982; Silverberg 2001; Silverberg and Verspagen 1994).

2.5.2 The Structuralist Framework The structuralist economics examines the relationship between the structure of production and economic growth. The structural economics builds on the proposition that economic development depends on ST: ‘It is impossible to attain high rates of growth of per capita or per worker product without commensurate substantial shifts in the shares of various sectors’ (Kutznets 1979: 130). The structuralist approach (e.g. Rosenstein-Rodan 1943; Nurkse 1953; Lewis 1954; Myrdal 1957; Hirschman 1958) is based on several assumptions as follows: Path-dependency: The growth process creates dynamic economies of scale and externalities giving rise to cumulative effects, as firms develop the capacity to produce better quality goods at lower costs.4 Structural heterogeneity: In developing countries, modern economic activities usually persist with traditional informal activities having low productivity. The dual economy models (e.g. Lewis 1954; Ranis and Fei 1961; Temple 2005; Ranis 2012) highlight the reallocation of labour from traditional to modern activities to drive economic growth. Urban modern manufacturing: While manufacturing is the engine of growth, some empirical regularity known as Kaldor’s law (Kaldor 1957, 1966) suggests that higher manufacturing growth ignites faster GDP growth, manufacturing labour productivity growth, and aggregate labour productivity growth. Manufacturing is considered important as it creates increasing returns to scale in both static and dynamic terms. This reduces production costs for firms, facilitates division of labour, and results in more efficient production (Kaldor 1966). The Verdoorn law holds that output growth is associated with productivity growth (Verdoorn 1949). The interaction between scale economies and the market size is 4 For

instance, the emerging East Asian economies in East Asia benefited from economic policies that allowed the firms to engage in learning and acquiring competitiveness in manufacturing. However, reverse dynamics may also occur when adverse shocks that affect economic activity (such as financial and/or debt crises) can create long-term negative effects on growth. See Easterly 2001.

2.5 Structural Transformation and Development Theory

23

also emphasised as this facilitates the replacement of traditional methods of production by modern ones (Rosenstein-Rodan 1943). The modernisation process has to be large scale as the market is important for the structuralist school since sustaining production growth needs rising aggregate demand. Sufficient demand ensures fuller utilisation of resources that will facilitate the ST process. Strong effective demand is a necessary condition for growth (Kaldor 1957, 1966; Taylor 1991). Manufacturing also facilitates capital accumulation as manufacturing activities have higher capital intensity than agriculture and services activities (Chenery et al. 1986). In developing countries, a process of ST towards manufacturing has other benefits as manufacturing is the seedbed of technological progress (Szirmai 2012; Chenery et al. 1986). Manufacturing production requires modern technologies, and the accumulation of new capital goods embodies the latest state-of-the-art technologies facilitating embodied technological change.5 The dynamic returns to scale in manufacturing help the workers to assimilate new knowledge termed as the disembodied technological progress (Szirmai 2012).6 Further, manufactured products have strong inter-sectoral linkages creating complementarities across industries (Cornwall 1977; Hirschman 1958). The backward linkages occur when an industry needs inputs from others while forward linkages induce investments in downstream industries (e.g. production of steel may stimulate the emergence of machinery industry). These linkages also facilitate the spillover of knowledge and technology from manufacturing to other sectors. One can also identify the price and income elasticity of demand advantages for the manufacturing goods.7 Engel’s law suggests that lower per capita income results in a larger share of expenditure on agricultural goods (Engel 1857). As income rises, demand shifts from agricultural to manufactured goods. Further, the price and income elasticities of demand are high for manufacturing products giving an additional advantage for manufacturing which further stimulates the demand for intermediate inputs and capital goods. Without industrialisation, a country will have to import manufactured goods which, with high price and income elasticities, may lead to shortages of foreign exchange and worsened balance of payment problems (Chenery et al. 1986). Further, there are two types of the services output—traditional services and services needed by manufacturing activities (Kaldor 1968). The second one complements manufacturing activities and therefore grows as a result of the expansion of manufacturing activities.8 5 An

analysis on R&D expenditures of 36 developed countries in 2008 in major sectors shows that manufacturing is the most R&D-intensive industry. See, Lavopa and Szirmai (2012). 6 However, in modern times, rapid innovation also takes place in the services and agriculture sectors as many activities in these sectors have become capital-intensive and knowledge-based (e.g. biotechnology and bioengineering in agriculture and ICT in services). 7 Price and income elasticities of demand refer to the rate of change in quantity demanded of a good (or a service) as its price changes (price elasticity) or as income changes (income elasticity). 8 The ST process also involves a shift of labour towards services although productivity gains are lower which is called the ‘cost disease’ or the ‘structural burden hypothesis’. See, Baumol 1967; Baumol

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2 Structural Transformation: Theory and Global Evidence

Within the structuralist tradition, the Latin American structuralist school has significant specificities of its own (Prebisch 1950; Singer 1950). According to the Latin American school, the developing countries could lose their chances of industrialising by specialising in resource-intensive industries having a comparative advantage. The consequence follows from the direction of ST resulting in declining terms of trade creating balance-of-payments constraint. The exchange rate may also follow cyclical appreciation due to commodity price booms. The outcomes may be debt crisis and industrial competitiveness erosion, having serious consequences for the domestic manufacturing activities. In the context of developing countries with comparative advantage to specialise in resource-intensive industries, policy decisions and strategies are therefore important in diversifying away from natural resources for promoting productive ST. The current terms of trade controversy are more concerned with the manufacturing export prices from developing countries and those from developed countries. The focus is on the types of manufacturing goods produced by these countries. It is argued that the types of manufacturing goods exports by the developing countries have the disadvantages of the commodities highlighted under the Prebisch hypothesis (UNCTAD 2002, 2005). The terms of trade of the manufacturing exports by the developing countries show a downward trend since the mid-1970s relative to the developed countries (Maizels 2000; Minford et al. 1997; Rowthorn 1997; Sarkar and Singer 1991; Zheng and Zhao 2002). The specialisation of the developing countries is in low-tech and low-skill-intensive manufactures. The argument is that an export-oriented diversification strategy towards manufacturing in general does not necessarily resolve the terms-of-trade disadvantage of the developing countries in the absence of upgrading and technological change needed to acquire the capacity to export high-tech and high-skill-intensive manufacturing products.

2.5.3 The New Structural Economics The dominance of the Washington Consensus led to a progressive decline in the analysis of ST in the 1980 and 1990s. However, since the early 2000s, there has been a revival of interest in ST largely due to the mixed results of the policies advocated by the Washington Consensus (Priewe 2015). The new structural economics contains elements of both neoclassical and structuralist perspectives. While it recognises the importance of changes in the production structure for development, it also maintains that these structural changes should be consistent with comparative advantages (Lin 2011; Lin and Treichel 2014). Accordingly, firms need to move up the industrial ladder and attain competitiveness in producing capital- and skill-intensive goods. This helps to improve both factor endowments and production structures (Ju et al. 2009). The critics, however, point et al. 1985. The Cornwall model (engine of growth hypothesis model) assumes that manufacturing and overall economic growth are mutually reinforcing. See, Cornwall (1977).

2.5 Structural Transformation and Development Theory

25

out that excessive dependence on current factor endowments may not facilitate structural change and restructuring putting limits on a country’s development potential (Lin and Chang 2009). The pursuit of ST requires the acquisition of new capacities, such as production in strategic industries even if the right factor endowments are not as yet in place.

2.5.4 The New Latin American Structuralism The Latin American structuralism’s revival has two strands: one focuses on the exchange rate (Bresser-Pereira 2012; Ocampo 2014) and the second combines the structuralist and Schumpeterian arguments on ST and technological progress. It explains how productive heterogeneity and the direction of ongoing ST hamper technological change and development. The Latin American economies have wide-scale heterogeneity; where resource-based industries are more productive and technologically advanced in general, and manufacturing industries are less productive. In such situations, ST towards resource-based industries limits industrialisation and acquisition of new capabilities as embodied in the dynamic comparative advantage called ‘latent comparative advantage’ (Bielschowsky 2009). The Schumpeterian or evolutionary economics, on the other hand, focuses on the role of innovation and analyse how capabilities affect learning and development (Dosi et al. 2000; Lall 1992). The approach holds that the technological change possibilities vary across industries, and its speed depends on the dynamics of ST (Dosi et al. 1990). It maintains that comparative advantages are not endowed; these have to be created through learning and innovation. Successful economies are able to accommodate more dynamic activities, characterised by scale economies, sharp learning curves, technological progress, productivity growth, and descent wages (Salazar-Xirinachs et al. 2014).

2.5.5 The Value Chain Approach The present production structure is increasingly becoming more fragmented at the global level creating the global value chains (GVCs). Value chains refer to the activities needed to transform a product from its conception to final consumption (Gereffi and Fernandez-Stark 2011). The GVC of a final product is defined as ‘the value added of all activities that are directly and indirectly needed to produce it’ (Timmer et al. 2014: 100). The rapid rise of GVCs indicates that production activities are increasingly being undertaken under global production networks and are fragmented across countries (Gereffi 2015). At present, countries increasingly participate in global trade by specialising in one or several tasks of a value chain, rather than specialising in all tasks required

26

2 Structural Transformation: Theory and Global Evidence

for producing a product (Baldwin 2012).9 As prices of labour and capital needed to perform different tasks vary, the share of value that a country adds depends on the task in which it specialises, and consequently, its income and employment. Hence, it is important for ST of a country whether it supplies critical high-tech components or is responsible for simple assembly (Milberg et al. 2014). Given the pervasiveness of GVCs, the implications of production fragmentation for the ST process are important. The GVCs are typically led by the transnational corporations (TNCs). The concentration of GVCs with a few TNCs gives them the power to control the management of these networks and limits the technological opportunities available to the host (developing) countries. As a consequence, firms in developing countries remain constrained to producing low-value-added products and face pressures to keep labour costs unduly low. In the real-world situation, firms can use four major channels to improve their position in the GVCs (Humphrey 2004): • Upgrading products: Adopt more sophisticated product lines with higher value added. • Upgrading processes: Utilise new technologies and innovations to adopt more efficient production techniques. • Adopt functional upgrading: Participate in more sophisticated tasks in the GVCs (e.g. production of high-tech components and design). • Upgrading chain: Use acquired capabilities in a GVC to enter into another GVC. In most cases, low-income countries try to increase the domestic value addition of their exports by functional upgrading (Milberg et al. 2014). Middle-income countries, on the other hand, prefer product and process upgrading and aim to establish their own brands to avoid the middle-income trap. The GVCs may have their roots in regional value chains; while these regional value chains may provide useful grounds for the developing country firms to become competitive in the global market through accumulating capabilities and boosting competitiveness (Banga et al. 2015).

2.5.6 Resource-Based Industrialisation Traditionally, many resource-rich countries suffer from the Dutch disease in which the manufacturing industry suffers from adverse impact on long-run economic growth (Auty 1993; Collier 2007; Frankel 2012; Sachs and Werner 1995).10 Since many commodities may suffer from large price fluctuations and long-run deterioration in 9 For

example, for any single product, some countries may specialise in its design and prototype, others may produce inputs and components, and yet others may specialise in assembling the final product. 10 The term ‘Dutch disease’ has originated from the economic crisis in the Netherlands in the 1960s that followed its discovery of gas reserves in the North Sea. It is the negative consequence from a rise in the value of a nation’s currency from the exploitation of a natural resource and its impact on the overall economy. The Dutch disease decreases the export price competitiveness of the country’s

2.5 Structural Transformation and Development Theory

27

their terms of trade (Erten and Ocampo 2012; Ocampo and Parra 2003), resourcerich developing countries suffer the most from such commodity price fluctuations. Moreover, such production tends to remain as an enclave activity that reinforces structural heterogeneity. Since the early 2000s, the global economy has experienced frequent commodity price booms (Kaplinsky and Farooki 2011). Further, these have been fuelled by increased financial speculation (Tang and Zhu 2015; Zhang and Balding 2015). The proponents of natural resources-based industrialisation strategies maintain that natural resources can lead to industrialisation as these activities are becoming increasingly dynamic and R&D-intensive (Kaplan 2012; AfDB et al. 2013; Andersen et al. 2015). They also highlight fiscal linkages and consumption linkages. Fiscal linkages channel the revenues from natural resources into other industries and broader development programmes. The consumption linkages also promote industrialisation, as higher incomes raise demand in other sectors (Andersen et al. 2015). In reality, most developing countries benefitting from the commodity price booms became net capital exporters and experienced terms-of-trade shocks and exchange rate regime changes (UNCTAD 2008).

2.6 Empirical Evidence on Structural Transformation The empirical analysis on value added per capita growth and employment changes in agriculture, industry, and services across countries and regions brings out several interesting findings (UNCTAD 2005, 2008). First, higher reductions in agricultural employment (e.g. in East, South, and Southeast Asia) are associated with faster economic growth than in regions (e.g. Sub-Saharan and Northern Africa) with lower reductions in agricultural employment. Second, rising shares of industrial employment are associated with faster economic growth as seen in most Asian countries relative to countries in Latin America and Northern and Sub-Saharan Africa. Finally, changes in services employment and GDP growth do not reveal any strong relationship largely due to the highly heterogeneous nature of the service activities. The ST towards low-productivity services does not show a strong association with economic growth. The industry sector includes manufacturing, mining, utilities, and construction activities, and these subsectors are very different in terms of their labour productivity and capacity to absorb labour. The empirical relationship between growth in GDP per capita and the manufacturing share in GDP shows that its rising share is accompanied by faster growth in GDP per capita. However, the relationship between the manufacturing share in GDP and economic growth is weaker than the similar relationship between the industry’s employment share and economic growth (Felipe et al. 2014; Rodrik 2016). manufactured goods and increases imports through a higher value of the local currency. In the long run, unemployment may result as manufacturing jobs move to lower cost countries.

28

2 Structural Transformation: Theory and Global Evidence

However, the existence of a strong relationship, as discussed above, does not necessarily show that ST generates higher economic growth. Several studies examine the impact of ST on economic and productivity growth which may be discussed under four broad groups: (i) manufacturing as the engine of economic growth; (ii) role of ST in labour productivity growth; (iii) structural change within manufacturing; and (iv) industrial upgrading.

2.6.1 Manufacturing as the Engine of Economic Growth The structuralists argue that manufacturing has characteristics that can make the sector as the engine of economic growth. Aside from several early studies (Cornwall 1977; Cripps and Tarling 1973; Kaldor 1967), Rodrik (2009) shows that rising shares of value added and employment of industry in GDP and total employment, respectively, are associated with higher economic growth. Other studies also suggest that manufacturing is the engine of economic growth (e.g. for Southeast Asia, see Felipe 1998; for South Africa see Tregenna 2007; and Chandrasekhar 2007; Kathuria and Raj 2009; and Ray 2015 for India). A Schumpeterian view on the issue examines the role of technological change in manufacturing growth (Fagerberg and Verspagen 2002; Szirmai and Verspagen 2015) and shows that manufacturing has a more important role prior to 1973 while services are positively associated with GDP growth in all time periods due to growing importance of services activities. Szirmai and Verspagen (2015), while testing the engine of growth hypothesis, find that manufacturing is an engine of growth, while services do not always have similar impacts. They also report that economic growth has a positive association with manufacturing growth, especially in countries with a more educated workforce indicating the importance of investments in human capital. In addition, Rodrik (2013) shows that manufacturing productivity tends to converge to the technological frontier which is unconditional such that it does not depend on other variables (e.g. quality of policies/institutions, geography, and infrastructure). The analysis by Foster-McGregor et al. (2015) confirms that a bigger manufacturing sector is significantly related with longer periods of economic growth, thus contributing both to triggering and sustaining economic growth in a country.

2.6.2 Role of ST in Labour Productivity Growth Labour productivity growth can be achieved through several routes, such as through capital accumulation, technological change, economies of scale, or learning. During ST, labour shifts from low- to high-productivity sectors raising aggregate labour productivity (reallocation effect). Labour productivity growth may also take place through changes in relative output prices (inter-sectoral terms-of-trade effect).

2.6 Empirical Evidence on Structural Transformation

29

Different studies (e.g. de Vries et al. 2015; McMillan and Rodrik 2011; Timmer and de Vries 2009; Timmer et al. 2014) suggest that ST has the lowest contribution to overall labour productivity change in high-income countries; while it plays a key role in the developing countries through multiple ways. In some regions (Latin America and Africa), the routes have been through the movement of labour from higher to lower productivity activities while, in others (e.g. Asia), a reverse movement has taken place.

2.6.3 Structural Change Within Manufacturing As manufacturing activities differ considerably, ST is not a shift of labour only from agriculture to manufacturing; labour also moves from less productive to more productive manufacturing activities, e.g. from light (less capital-intensive) to heavy (more capital-intensive) manufacturing (Chenery et al. 1986; Hoffman 1958), which has been called by Timmer and Szirmai (2000) as the ‘structural bonus hypothesis’. Several authors have used the shift-share decomposition method to analyse manufacturing and identify the contribution of different activities within manufacturing (Timmer and Szirmai 2000; Fagerberg and Verspagen 1999; Fagerberg 2000; Peneder 2003).11 Timmer and Szirmai (2000) find that structural change has a positive impact when inputs are shifted to (i) activities with higher productivity; (ii) activities having more rapidly growing productivity; and (iii) activities having higher Verdoorn elasticity (taken as the elasticity of total factor productivity growth to output growth). Peneder (2003), while analysing the cases of services sector and technology-driven and human-capital-intensive manufacturing, shows that the rising employment share of services has a negative effect on GDP growth, which confirms Baumol’s structural bonus hypothesis. On the other hand, increases in the shares of technology-driven and human-capital-intensive manufacturing exports have a significant effect on GDP growth. However, the net effect of ST covering both services and manufacturing industries is weak because of the dominance of both positive and negative effects. Moreover, the level of employment in the electrical machinery industry can be a significant determinant of manufacturing productivity growth due to its high capacity to spur productivity growth in manufacturing activities (Fagerberg and Verspagen 1999). New technologies of the present era are also creating ST which is different from the earlier periods. As is observed, ‘New technology, in this case the electronics revolution, has expanded productivity at a very rapid rate, particularly in the electrical machinery industry, but without a similarly large increase in the share of that

11 The shift-share accounting-based decomposition analyses the productivity growth impact of structural change. The method decomposes total change into its structural components. See, Fagerberg (2000).

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2 Structural Transformation: Theory and Global Evidence

industry in total employment’ (Fagerberg 2000: 409). This weak effect on employment is a matter of concern since it reduces the poverty-reducing capacity of modern ST especially in countries with large populations.

2.6.4 Industrial Upgrading Essentially, modern ST involves industrial upgrading covering both diversification and sophistication processes which are analysed using two different approaches: the product space approach and the gross value chain (GVC) approach. The product space approach builds on the structuralist view which postulates that the products that a country produce and export affect its rate and pattern of economic growth (Hausmann and Kliger 2007; Hausmann et al. 2007, 2011; Hidalgo et al. 2007). It also holds that a country is unlikely to produce a good in which it lacks the required knowledge and expertise. Thus, issues like learning, capability, and technological change become the critical factors in ST. The production possibilities define the spaces in which the economy operates. For example, the product space contains all the exported commodities by the country, and the distance between two of these commodities determines the probability of producing one if the country produces the other. In the product space, ST is the country’s movement from one commodity that it produces to another which is close to it, and the ‘closeness’ is determined by the knowledge and capability to produce the commodities. In the space, two commodities are considered close if the knowledge/capability to produce them are similar, and these are far apart if a completely new set of skills are needed to produce the other. The product space thus provides a network of commodities for each country, in which the country can move from one commodity to another. The process brings in economic diversification and production of increasingly more sophisticated and higher value-added commodities. Hausmann et al. (2007) have developed a quantitative index of export sophistication (EXPY) which is highly linked with the per capita income of a country.12 The study also shows that the initial level of EXPY is positively related to the subsequent rate of economic growth indicating a positive correlation between the degree of sophistication of the export basket and higher growth (see Fortunato and Razo 2014). The methodology can be used to identify the productive diversification path for developing economies (see Hausmann and Klinger 2008, for Colombia; Felipe et al. 2013

12 The EXPY is calculated in two steps. First, using the six-digit HS coding system (with more than 5,000 commodities), weighted average of the incomes of the countries exporting each traded commodity is computed, where the weights are the revealed comparative advantage of each country in that commodity (normalised so that the weights sum up to one) which gives the income level of that commodity (referred to as PRODY). In the second step, EXPY is calculated as the weighted average of the PRODY for each country, where the weights are the shares of each commodity in the country’s total exports.

2.6 Empirical Evidence on Structural Transformation

31

for China; Jankowska et al. 2012 for Asia and Latin America; and Fortunato et al. 2015 for Ethiopia). On the other hand, the implications of GVCs for ST have been analysed by using input–output matrices. These studies suggest that global production is mostly concentrated in East Asia; while the lead enterprises belong to the developed countries and globalisation is highly visible in a few industries, such as clothing and textiles, electronics, and automotive industries (De Backer and Miroudot 2013; Timmer et al. 2014; UNCTAD 2014). Another common feature is that the developed countries benefit more from participation in GVCs than the developing countries as these are involved in low-value-added activities with little scope for upgrading (Milberg et al. 2014). Although the developing countries are increasingly participating in the GVCs, the developed countries capture the most in terms of value addition (Banga 2013). The participation in GVCs occurs both through forward and backward linkages. Usually, forward linkages provide higher domestic value creation than backward linkages. In the GVCs, the developed countries have the highest forward to backward linkage ratios indicating that these countries are the largest net gainers from participation in the GVCs. The analysis by Timmer et al. (2014) shows that the share of capital and highskilled labour in the gross value added in GVCs has increased over time while the share of low-skilled labour has declined. This indicates that countries that specialise in capital-intensive stages of production (e.g. developed countries) gain more from the GVCs than those that specialise in labour-intensive stages of production (UNCTAD 2012). While the developing countries should move up the value chain, lowervalue-added activities have some benefits for these countries in terms of employment generation and learning through production and interactions with other GVC participants. Industrial upgrading through export sophistication and value chain upgrading has also been related to the middle-income trap. Felipe et al. (2012), in their analysis, show that ST, export sophistication, and diversification help countries to avoid the middle-income trap.13 Lee (2013) suggests that the countries need to go for upgradation and diversification of their economies to avoid the middle-income trap. For successful development, the key is to pursue the process of structural and technological change simultaneously. The index of structural modernisation, developed by Lavopa and Szirmai (2014), combines both structural change and technological change components. The ST component is defined by the employment share in the modern sector (industry and tradable services). The technological change component is measured by the labour 13 This relates to a situation, in which middle-income countries fail to move forward due to constraints inhibiting greater specialisation in production and employment and reliance on innovation, and inability to shape new products and processes (Gill et al. 2007). The Asian Development Bank (ADB) uses the term to refer to ‘countries stagnating and not growing to advanced country levels’ and attributes the cause of the phenomenon to the failure to ‘make a timely transition from resource-driven growth, with low cost labour and capital, to productivity-driven growth’ (ADB 2011).

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2 Structural Transformation: Theory and Global Evidence

productivity of the modern sector, relative to that of the United States taken as the world technological frontier. The index shows that only countries that have skillfully managed both transformations simultaneously (e.g. South Korea, Singapore) have succeeded in catching up with the developed world. Others are caught in the lowand middle-income traps.

2.7 Premature Deindustrialisation: Role of Services Sector In the traditional pattern of ST, as discussed above, one of the empirical regularities is that as the countries develop, the process of deindustrialisation starts beyond a relatively high level of income. In other words, the relationship between manufacturing employment and income per capita shows a stable inverted-U relationship (Rowthorn 1994). In the ST literature, a distinction is often made between two types of deindustrialisation: (i) positive deindustrialisation (taken as a natural outcome of sustained economic growth in developed countries) and (ii) negative deindustrialisation (which may occur at any level of income) (Rowthorn and Wells 1987). In the case of positive deindustrialisation, the key driver is the rapid productivity growth in manufacturing which reduces employment while output continues to expand. The displaced workers are absorbed in the services sector since the demand patterns shift towards services as income rises. The share of employment in services sector rises while that of manufacturing declines (Baumol 1967; Baumol et al. 1985). As positive deindustrialisation is the outcome of economic dynamism, this is taken as a desirable outcome of development. Negative deindustrialisation occurs when falling manufacturing output or higher productivity in manufacturing creates low manufacturing employment and incomes (Rowthorn 1994; Rowthorn and Wells 1987). Palma (2005) indicates an unstable relation between manufacturing employment and income per capita along with the apprehension that many developing countries would deindustrialise before they reach high incomes. He further shows that the level of income per capita at which manufacturing employment started to decline in the past has dropped from USD 20,645 in 1980 to USD 9,805 in 1990 and further to USD 8,691 in 1998. Several factors may have worked in the process, such as labour-displacing technological progress and the rise of GVCs that favour the concentration of labour-intensive stages of production in the labour-abundant and low-wage developing countries. Further, the trend of manufacturing employment to reach peaks at increasingly lower levels of income per capita confirms the trends towards premature deindustrialisation. The relocation of labour-intensive production activities has benefited the Asian countries (e.g. Bangladesh, Cambodia, and Vietnam through readymade garments), leading to the expansion of manufacturing employment and output (i.e. industrialisation) in these countries. However, the countries in many other regions (e.g. Africa and Latin America) could not successfully integrate into the GVCs, which have contributed towards their premature deindustrialisation. Further, the Dutch disease

2.7 Premature Deindustrialisation: Role of Services Sector

33

is also considered as a determinant of premature deindustrialisation in many of these countries. In the process, manufacturing becomes less labour-intensive with a fall in the manufacturing share in GDP. This reduces the long-term growth prospects in the absence of ‘growth-pulling effects of manufacturing’ (Tregenna 2009: 459). Further, countries with high shares of manufacturing employment in the past can enjoy higher incomes at present (Felipe et al. 2014). In recent decades, Asia is the only developing region that has succeeded in maintaining a strong manufacturing sector while Latin America and Sub-Saharan Africa show intense deindustrialisation processes (Rodrik 2016; UNCTAD 2003). In recent times, an increasing trend in the incidence of informal jobs in manufacturing is observed although informality is typical of the services sector activities. The manufacturing firms are observed to increasingly outsource their service activities to services sector enterprises (Manyika et al. 2012).14 The rise in manufacturing-related services is taking place in both developed as well as in fast-growing developing countries and regions. Manufacturing firms are increasingly outsourcing activities such as business services and transportation to service sector enterprises. In practice, services enterprises face many challenges for establishing such linkages, such as their informal nature, inadequate human capital, and low usage of ICT and other modern technologies (Salazar-Xirinachs et al. 2014). The recent decline in the importance of manufacturing in economic growth has led some analysts to argue that the services sector or some of its components can replace manufacturing as the engine of economic growth or can become an additional engine (Ghani and O’Connell 2014; Acevedo et al. 2009; Felipe et al. 2009). The arguments are also drawn from the experience of India where ICT-enabled services are growing fast in recent decades (Chakravarty and Mitra 2009; Dasgupta and Singh 2006; Ghani and Kharas 2010; Joshi 2011). Rodrik (2014), on the other hand, argues that tradable services enjoy higher productivity levels mostly due to their usage of modern technologies like ICT. But tradable services require skilled labour, a scarce resource in most developing countries. Further, the workers who leave the agriculture sector are mostly unskilled with low education, and imparting training and modern skills to these labourers is difficult and requires time.15 In the developing countries, the dominant trend is to absorb the labour (including those thrown out of agriculture) in nontradable services, such as retail trade, informal transport, retail shops, restaurants, or hotels. These services are efficient in absorbing labour, but have little scope for enhancing productivity due to their limited scale and the small size of the local market. In manufacturing, on the other hand, even small developing countries can adopt export-led strategies to enhance growth. 14 Manufacturing-related services are defined as services required for producing and delivering manufacturing products. Business services have close links with manufacturing production, followed by trade, financial intermediation, and inland transportation services. See, UNIDO (2013). 15 For example, providing training to a farmer to work in a readymade garments factory is easier than training him/her to work in a bank or in ICT services; so that manufacturing activities can provide a more readily accessible employment for the displaced agricultural workers due to agricultural productivity increases.

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In the context of the developing countries, the paradox, therefore, is ‘Economic activities that are good at absorbing advanced technologies are not necessarily good at absorbing labor’ (Rodrik 2013: 171). A trade-off seems to work in the services sector as well: tradable services can absorb technologies but these are not good job creators; while nontradable services are good at absorbing labour, but these have low capacity to absorb technologies. This explains why many economists believe that a services-led model of development is unlikely to generate rapid employmentgenerating growth in the same way that manufacturing growth can deliver (e.g. Rodrik 2014).

2.8 Structural Transformation and Development The structure of production affects different aspects of social and human development through multiple pathways. It is well recognised that ST brings pervasive social transformations, such as higher urbanisation and secularisation, and demographic transitions to low fertility rates (Kuznets 1966). These reflect desirable social goals and ST creates important opportunities for the developing countries to attain these goals; but there also exist challenges, such as rapidly rising rural migration, urban planning, and social spending. Further, the developing countries must also explore the role of ST in employment generation and reduction of poverty and inequality, more specifically between ST and human development, in the context of the sustainable development goals (SDGs).

2.8.1 Structural Transformation and Labour Market Changes The process of ST brings about several important changes, such as higher labour force participation rate of women, increased rural–urban and international labour migration, and declining fertility. Each of these has impacts on household incomes and overall income distribution. The global labour force participation rate is declining over the last decades for both women and men. Moreover, women’s participation rate is lower than that of men, and women’s participation in paid work is lower than for men. Thus greater participation of women in the labour force can have important implications on the labour market and the wage levels. Rural–urban migration has both positive and negative effects. As a source of remittances, it contributes to development in the rural areas and raises household incomes. On the other hand, it increases urban challenges, especially if migration rates exceed the urban job creation rates (Lall et al. 2006; Todaro 1980). On the other hand, international migration may have the opposite effect. However, if international migration involves the scarce educated and skilled labour, this

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may adversely affect domestic development. Migration provides a relatively stable source of remittances especially to the middle-income countries. In Bangladesh, for example, remittances accounted for USD 18.32 billion in 2019, while net official development assistance (ODA) and foreign direct investment (FDI) accounted for about USD 3 billion and USD 3.6 billion, respectively. For these countries, remittances are a source of foreign exchange at the national level while these are important sources of household income. For the longer term, however, the impact will depend on whether and how effectively the remittance-receiving country manages to avoid the dependency on remittances.

2.9 Structural Transformation, Employment, and Poverty Structural transformation has clear implications for employment and poverty. Three mechanisms are distinguished through which economic growth affects employment and poverty: direct, indirect, and induced impacts (Lavopa and Szirmai 2012). The direct impact comes through new jobs and labour reallocations within and among sectors. For new jobs, additional employment is created with a positive impact on employment and income. In the case of reallocation of workers, Labour moves from lower to higher productivity activities and sectors through reallocations with higher productivity and wages. The indirect impact on employment and poverty depends on the nature and strength of the links between the expanding sectors/activities and the overall economy: the impact will be larger with stronger linkages. The induced impact will be generated through the growth of economic activities through multiplier effects creating more employment and generating higher productivity and incomes. Several empirical studies on the relationship between ST, employment, and poverty use the decomposition analysis to investigate the relationship between ST and employment generation. The main concern is the social dimension of growth: economic growth without employment is unlikely to deliver inclusive development. In this context, the ‘socially necessary rate of growth’ is defined as the growth that delivers both productivity and employment growth, and growth is taken as socially sustainable when the growth of labour productivity and employment exceeds 3 per cent (Pieper 2000). The above generalisations, however, do not consider the higher labour force participation rates in developing countries mainly due to higher participation of women in the labour force. In the case when labour force grows at faster rates, the above rates may not be adequate to guarantee social inclusion. Moreover, employment trends alone may not be adequate to capture the employment challenges since remaining unemployed is a ‘luxury’ for the poor. Most of the poor are either underemployed or involved in low-quality employment and, in such cases, although ‘underemployment growth’ contributes to employment growth, this does not generate adequate incomes with an impact on poverty. Further, greater participation in labour-intensive global manufacturing trade (e.g. RMGs for Bangladesh) has lowered the global market prices and consequently wages in many developing countries (UNCTAD 2010).

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Low wages also limit domestic aggregate demand to sustain growth, further limiting employment growth. Even several types of technological change may negatively impact employment growth due to high labour-displacing components weakening the link between GDP and employment growth (UNCTAD 2010). There also exists a trade-off between labour productivity growth and employment (Kucera and Roncolato 2012). Some developing countries in Asia are observed to have experienced ‘jobless growth’ in recent years. For example, although several service activities (e.g. trade, restaurants, and hotels) are significant employment generators in the developing countries, these are low contributors to labour productivity growth. These activities are good in absorbing labour but not necessarily good in absorbing technologies. The structural transformation pattern of the least developed countries (LDCs) shows that agriculture contributes significantly to aggregate productivity growth (UNCTAD 2014).16 Rapid agricultural productivity growth accelerates ST by enabling the surplus labour to move to higher productivity non-agricultural activities. Thus, productivity growth in agriculture is a prominent feature in the development policies of these countries (Szirmai et al. 2013). Several cross-country studies find that agricultural growth has the most sizable effect on poverty reduction in the poorer countries. As income rises, agriculture’s role in poverty reduction declines while secondary sectors gain more importance (see Christiansen and Demery 2007). Further, poverty reduction in both rural and urban areas in India is found to be associated more with growth in agriculture and services sectors than in the industry sector (Ravallion and Datt 1996); while in China, agriculture is seen to reduce poverty the most (Ravallion and Chen 2007). However, despite some empirical evidence contrary to the importance of the industry sector in poverty reduction, ST towards manufacturing is positively associated with a number of indicators of social inclusiveness (Lavopa 2015; UNIDO 2015). As the share of manufacturing employment in total employment increases, poverty is likely to decline. The key will be to promote ST that is productive and create employment in sectors having above-average labour productivities.

2.9.1 Structural Transformation and Human Development A virtuous process of ST can be a powerful driver of improvements in social and human development and enable a country to make steady progress in all aspects of development. A cross-country analysis of the links (using the ST component of the Divisia index) shows close relationships between ST and human development (UNCTAD 2014). It shows that the countries that enjoy a more rapid ST process can 16 The

study uses the Divisia index to decompose total labour productivity and employment–population ratio into sectoral contribution effects. The index is the components’ shares in total valueweighted sum of logarithmic growth rates. The first step is to define total labour productivity as the ratio of total real value added to total employment. See, Sato 1976.

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also create a stronger relationship between GDP growth and poverty reduction. The impact of economic growth on poverty reduction is observed almost negligible in countries where the productivity growth component of ST is low. This shows that if the countries grow but do not undergo desirable ST, then economic growth alone is unlikely to achieve any significant poverty reduction. The results further indicate that ST enables the developing countries to create enabling conditions for the people to access both education and educational benefits through creating productive jobs. This happens since production activities agglomerate through ST which makes it easier to provide education. In addition, ST provides incentives to both parents and children to acquire education since various skill demands and returns increase through ST. A fast and inclusive ST process can also improve the living conditions of the most vulnerable segment of society.

2.10 Concluding Remarks The analysis highlights that the process of ST that accompanies and fosters socioeconomic development has several stylised facts. In general, ST is associated with economic growth, especially when directed towards industry and manufacturing. In addition, productive ST needs evolution in both horizontal and vertical directions for which both diversification and technological upgrading are essential. The theoretical and empirical analysis on ST and decomposition of labour productivity growth in order to disentangle the effect of ST bring into the forefront some key aspects of the relationship between ST and development: • Sustained economic growth results in higher value-added and employment shares of industry and services sectors in GDP and total employment, respectively, and consequent declining shares of agriculture, and the process is led initially by expanding manufacturing activities. • Manufacturing is the classical engine of productivity growth and ST, while services are the main sources of labour absorption. • Productivity gains in agriculture are critical to sustaining inclusive ST and economic growth, poverty reduction, and human development in the developing countries. • Inclusive development in the low-income countries should explicitly accommodate the ST processes as these have a significant impact on both the quantity and quality of growth. The nature and speed of growth and ST largely shape the trajectory of poverty reduction and social and human development path of a country. • For the developing countries, rather than pursuing economic growth as a singular objective, it is worthwhile to promote economic growth with productive and inclusive ST to maximise the impact of ST and economic growth on social and human development.

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Rodrik, D. 2014. Are Services the New Manufactures? Project Syndicate. October 13. Available at https://www.project-syndicate.org/commentary/are-services-the-new-manufactures-bydani-rodrik-2014-10. Rodrik, D. 2016. Premature Deindustrialization. Journal of Economic Growth 21: 1–33. Romer, P.M. 1987. Growth based on Increasing Returns due to Specialization. American Economic Review 77 (2): 56–62. Romer, P.M. 1990. Endogenous Technological Change. Journal of Political Economy 98 (5): S71– 102. Rosenstein-Rodan, P. 1943. Problems of Industrialisation of Eastern and South-eastern Europe. Economic Journal 53: 202–211. Rowthorn, R. 1994. Korea at the Cross-Roads. Economic and Social Research Council Centre for Business Research Working Paper No. 11. Cambridge: University of Cambridge. Rowthorn, R. 1997. Replicating the Experience of the Newly Industrialising Economies. Economic and Social Research Council Centre for Business Studies Working Paper 57. Cambridge: University of Cambridge. Rowthorn, R., and J.R. Wells. 1987. De-industrialization and Foreign Trade. Cambridge: Cambridge University Press. Sachs, J.D., and A.M. Werner. 1995. Natural Resource Abundance and Economic Growth. NBER Working Paper No. 5398. Cambridge, MA: National Bureau of Economic Research. Salazar-Xirinachs, J.M., I. Nübler, and R. Kozul-Wright (eds.). 2014. Transforming Economies: Making Industrial Policy Work for Growth. International Labour Organization, Geneva: Jobs and Development. Sarkar, P., and H.W. Singer. 1991. Manufactured Exports of Developing Countries and Their Terms of Trade since 1965. World Development 19: 333–340. Sato, K. 1976. The Meaning and Measurement of the Real Value Added Index. Review of Economics and Statistics 58 (4): 434–442. Schneider, B.R. 2015. Designing Industrial Policy in Latin America: Business-State Relations and the New Developmentalism. New York: Palgrave Macmillan. Silverberg, G. 2001. The Discrete Charm of the Bourgeoisie: Quantum and Continuous Perspectives on Innovation and Growth. Research Policy 31: 1275–1289. Silverberg, G., and B. Verspagen. 1994. Learning, Innovation and Economic Growth: A Long-Run Model of Industrial Dynamics. Industrial and Corporate Change 3: 199–223. Singer, H.W. 1950. The Distribution of Gains between Investing and Borrowing Countries. American Economic Review 40 (2): 473–485. Solow, R. 1956. A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics 70 (1): 65–94. Studwell, J. 2014. How Asia Works: Success and Failure in the World’s Most Dynamic Region. New York: Grove Press. Szirmai, A. 2012. Industrialisation as an Engine of Growth in Developing Countries, 1950–2005. Structural Change and Economic Dynamics 23 (4): 406–420. Szirmai, A., and B. Verspagen. 2015. Manufacturing and Economic Growth in Developing Countries, 1950–2005. Structural Change and Economic Dynamics 34(C): 46–259. Szirmai, A., M. Gebreeyesus, F. Guadagno, and B. Verspagen. 2013. Promoting Productive Employment in Sub-Saharan Africa: A Review of the Literature. UNU-MERIT Working Paper 2013–062 prepared for the Knowledge Platform Development Policies of the Ministry of Foreign Affairs of the Netherlands, United Nations University—Maastricht Economic and Social Research Institute on Innovation and Technology. Tang, K., and H. Zhu. 2015. Commodities as Collateral. Available at http://papers.ssrn.com/sol3/ papers.cfm?abstract_id=2355674. Taylor, L. 1991. Income Distribution, Inflation, and Growth: Lectures on Structuralist Macroeconomic Theory. Cambridge, MA: MIT Press. Temple, J. 2005. Dual Economy Models: A Primer for Growth Economists. The Manchester School 73 (4): 435–479.

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Timmer, M.P., and G. de Vries. 2009. Structural Change and Growth Accelerations in Asia and Latin America: A New Sectoral Dataset. Cliometrica 3 (2): 165–190. Timmer, M.P., and A. Szirmai. 2000. Productivity Growth in Asian Manufacturing: The Structural Bonus Hypothesis Examined. Structural Change and Economic Dynamics 11 (4): 371–392. Timmer, M.P., G. de Vries, and K. de Vries. 2014. Patterns of Structural Change in Developing Countries. GGDC Research Memorandum 149, Groningen Growth and Development Centre. Netherlands: University of Groningen. Todaro, M. 1980. Internal Migration in Developing Countries: A Survey. In Population and Economic Change in Developing Countries, ed. R.A. Easterling. Chicago, IL: University of Chicago Press. Tregenna, F. 2007. Which Sectors can be Engines of Growth and Employment in South Africa? An Analysis of Manufacturing and Services. Paper presented at the HSRC EGDI Roundtable on The Changing Character of Industrial Development: What Implications for Growth, Employment and Income Distribution? Human Sciences Research Council Employment Growth and Development Initiative. Tregenna, F. 2009. Characterising Deindustrialisation: An Analysis of Changes in Manufacturing Employment and Output Internationally. Cambridge Journal of Economics 33: 433–466. UNCTAD. 2002. Trade and Development Report 2002: Global Trends and Prospects, Developing Countries in World Trade. Geneva and New York: United Nations. UNCTAD. 2003. Trade and Development Report 2003: Capital Accumulation, Growth and Structural Change. Geneva and New York: United Nations. UNCTAD. 2005. Trade and Development Report 2005: New Features of Global Interdependence. Geneva and New York: United Nations. UNCTAD. 2008. Trade and Development Report 2008: Commodity Prices, Capital Flows and the Financing of Investment. Geneva and New York: United Nations. UNCTAD. 2010. Trade and Development Report 2010: Employment, Globalization and Development. Geneva and New York: United Nations. UNCTAD. 2012. World Investment Report 2012: Policies for Inclusive and Balanced Growth. Geneva and New York: United Nations. UNCTAD. 2014. Trade and Development Report 2014: Global Governance and Policy Space for Development. Geneva and New York: United Nations. UNIDO. 2013. Industrial Development Report 2013: Sustaining Employment Growth: The Role of Manufacturing and Structural Change. Vienna: United Nations Industrial Development Organization. UNIDO. 2015. Industrial Development Report 2016: The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development. Vienna: United Nations Industrial Development Organization. Verdoorn, P.J. 1949. Fattori che regolano lo sviluppo della produttività del lavoro. L’Industria 1: 3–10. Zhang, M., and C. Balding. 2015. Carry Trade Dynamics under Capital Controls: The Case of China. Available at http://ssrn.com/abstract=2623794. Zheng, Z., and Y. Zhao. 2002. China’s Terms of Trade in Manufactures, 1993–2000. UNCTAD Discussion Paper No. 161. Geneva.

Chapter 3

Structural Transformation in South Asia: An Overview

Abstract The nature and dynamics of ST in South Asia are complex in which social, economic, and political processes are ingredients of both processes and outcomes. An important dimension is acute inequalities in all aspects of social and economic life. Universal access to basic services and equal opportunities for all are yet to become the cornerstones of the South Asia’s ST process. The challenge for the South Asian countries is to alter the sectoral share pattern towards manufacturing given its higher productivity and employment growth potentials compared with both agriculture and services. This is necessary to overcome the process of arrested and incomplete industrialisation. For South Asia, the key policy challenge is to achieve economic growth that is both inclusive and driven by productive ST.

3.1 Introduction The development literature considers ST as both the cause and the effect of economic growth. In effect, ST is a process by which (i) the shares of agriculture in GDP and employment decline; (ii) rural–urban migration increases; (iii) agriculture and rural economy shrinks and industrial and urban economy expands; and (iv) a demographic transformation results in low birth and death rates. The dualism disappears over time (Timmer et al. 2012). More specifically, the modern view of economic growth is concerned with changes in the sectoral composition of output and the pattern of employment. As labour productivity rises, labour is released from traditional agriculture for employment in the non-agriculture sector having higher labour productivity and wages. Higher income raises savings and investment; economic growth accelerates inducing further resource reallocations. The process engineers ST in the economy, and economic growth itself is affected by ST. Along with most developed countries, the newly industrialised countries (NICs) of Asia (including China) have followed this traditional pattern of ST. In recent decades, the East Asian countries have moved the most towards the traditional pattern of ST. In Southeast Asia, countries like Malaysia, Indonesia, and Thailand have undergone © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. K. Mujeri and N. Mujeri, Structural Transformation of Bangladesh Economy, South Asia Economic and Policy Studies, https://doi.org/10.1007/978-981-16-0764-6_3

45

46

3 Structural Transformation in South Asia: An Overview

major ST as well, from agrarian countries to middle-income countries with expanding industrial and manufacturing bases. The countries of South Asia, however, have not experienced a truly sustained episode of convergence towards the structural characteristics of the advanced countries. For instance, in South Asia, the progressive decline in agriculture’s share in GDP is accompanied by increases in the services sector’s share while the industry (manufacturing) sector’s share has somewhat stagnated (with minor exceptions). From the traditional growth perspective, this points to the challenge for the South Asian countries to alter the sectoral share pattern towards a greater share of manufacturing, in view of the existing un- and under-realised productivity potential in manufacturing and the prospects of achieving higher employment growth in manufacturing. From this perspective, the South Asian countries suffer from arrested or incomplete industrialisation. For South Asia, however, the key policy challenge is to achieve a form of economic growth that is driven by ST, but that is ‘inclusive’ as well. Historically, the process has been associated with rising inequality, but the countries of South Asia need falling, or at least, unchanged inequality in view of the high incidence of poverty and deprivations that exist in these countries. This chapter examines the nature and direction of ST in six major South Asian countries—Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka—with reference to other countries wherever necessary.

3.2 Structural Transformation in South Asia Although South Asia has largely failed to keep pace with other regions (e.g. East and Southeast Asia) in development, the South Asian countries have made significant progress in ST and socioeconomic development over the past few decades. On average, per capita income has almost trebled in South Asia, life expectancy at birth has risen from less than 50 years (except in Sri Lanka) to around 70 years, and primary school enrolment has become nearly universal (Table 3.1).

3.2.1 South Asian Growth Surprises Economic growth in the region highlights ‘South Asian surprises’ that have succeeded, at least partially, in overcoming some of the binding constraints to growth in other contexts, such as corruption (e.g. Bangladesh, which was identified by Transparency International as one of the most corrupt countries in the world, but has grown at more than 8 per cent per year in recent times), civil conflict (e.g. Sri Lanka which faced a civil war for more than twenty years but could achieve GDP per capita growth at over 3 per cent a year), and high fiscal deficits (e.g. India where GDP grew at more than 6 per cent per year for a few decades with a fiscal deficit of around 10 per cent of GDP).

3.2 Structural Transformation in South Asia

47

Table 3.1 Long-term development in south asian countries 1961–1965

2010–2015

2019

Bangladesh

52.6

157.6

164.7

Bhutan

0.2

0.8

0.8

India

479.4

1,272.0

1,380.0

Population, million

Nepal

10.6

28.0

29.1

Pakistan

48.4

181.7

220.9

Sri Lanka

10.7

20.6

21.4

Bangladesh

2.9

1.2

1.1

Bhutan

2.6

1.6

1.2

India

2.2

1.2

1.5

Nepal

1.6

1.2

1.2

Pakistan

2.5

2.1

2.0

Sri Lanka

2.4

0.8

0.7

Bangladesh

6.9

2.2

2.0

Bhutan

6.7

2.2

2.0

India

5.9

2.4

2.3

Nepal

6.0

2.3

2.1

Pakistan

6.6

3.7

3.3

Sri Lanka

5.1

2.1

2.0

Bangladesh

392

882

1,698

Bhutan

426

2,445

3,243

India

402

1,482

2010

Population growth, % per year

Fertility rate, births per woman

GDP per capita, 2010 USD

Nepal

274

649

1,034

Pakistan

344

1,088

1,482

Sri Lanka

594

3,368

4,103

Bangladesh

47

70

73

Bhutan

35

69

72

India

42

68

70

Nepal

36

68

71

Pakistan

48

65

68

Sri Lanka

59

71

78

Life expectancy at birth, years

(continued)

48

3 Structural Transformation in South Asia: An Overview

Table 3.1 (continued) 1961–1965

2010–2015

2019

Bangladesh

63.6

117.7

116.0

Bhutan

21.7

107.3

100.0

India

65.4

108.5

113.0 144.0

Primary enrolment rate, % gross

Nepal

39.0

139.4

Pakistan

50.8

91.9

94.0

Sri Lanka

94.8

100.7

100.0

Source World Bank, World Development Indicators (WDI) database

The explanation of South Asia’s success goes beyond conventional macroeconomic discourse (Devarajan 2005; World Bank 2008; Mujeri and Mujeri 2020). For example, in Bangladesh, the non-government organisation–microfinance institutions (NGO-MFIs) stepped up to provide basic services such as education, health, and income-generating opportunities and the private sector provided entrepreneurial stimulus (RMGs industry); in Sri Lanka, specific regions took advantage from trade reforms and high literacy rate (e.g. Western Province around Colombo); in Nepal, a sharp increase in remittances created growth opportunities; and India succeeded in keeping inflation and interest rates low despite high fiscal deficits and the debt-to-GDP ratio of nearly 90 per cent.

3.2.2 GDP Growth and its Composition All South Asian countries have witnessed significant growth and structural changes over time. Figure 3.1 shows the changes in the growth of GDP and GDP per capita over the period from 1961 to 2015. It is seen that growth accelerated between the two time periods in most countries, with the sharpest increase taking place in Bangladesh. The pattern of ST in South Asia, as shown by changes in sectoral contribution to GDP, is shown in Table 3.2. The share of agriculture has declined while the share of industry increased in all South Asian countries. However, in manufacturing, the transformation is somewhat modest. In all countries, the manufacturing share is less than 20 per cent of GDP. The share of services has increased rapidly in all countries and exceeds 50 per cent in almost all countries except Bhutan. In the literature, two phases of ST are sometimes distinguished (Kuznets 1971). In the first phase, resource reallocations occur from agriculture into industry and services while, in the second, into services. The consequence is a rapid increase in the share of manufacturing in GDP in the initial stage and that of services in the subsequent stage. Overall, ST in the South Asian countries, except in Bangladesh and Bhutan, is consistent with the second phase. In all countries except Bhutan and marginally for Nepal, the services sector contributes more than 50 per cent to GDP.

3.2 Structural Transformation in South Asia Fig. 3.1 Growth in GDP and GDP per capita in South Asian countries. Source World Bank, World Development Indicators (WDI) database

49

1961–1990

7.1

5

1991–2015

4.9

4.5

3.6 2.7 2.1

2.8

2.7 1.8

1.1 0.1

GDP Per Capita Growth, % per year 10.1

6.6

1961–1990

6.6

5.8

5.4 4.4 2.7

1991–2015

4.4 3.3

4.1

5.4 4.6

GDP Growth, % per year

The GDP per capita has been high especially in recent years in most of these countries. In terms of stability, the services sector growth is most stable while agricultural growth is most unstable. Although the share of services in GDP is the largest, agriculture is the main source of employment in South Asia, absorbing between 40 and 60 per cent of the labour force. In view of the current changes, some analysts argue that the South Asian countries could achieve sustainable high growth largely by-passing industrialisation. The opponents argue that, since there exists a strong relationship between the sophistication of the economy’s production structure and economic growth, this is less likely to happen as most services activities are less

29.7

Sri Lanka

8.6

25.1

35.3

18.2

17.3

16.5

21.8

20.8

10.3

23.6

NA

9.1

Source World Bank, World Development Indicators (WDI) database

68.2

38.0

44.6

India

Pakistan

NA

Bhutan

Nepal

54.7

Bangladesh

Industry 1966–1970

1966–1970

2011–2015

Agriculture

Table 3.2 Sectoral contribution to GDP in South Asian countries

30.8

21.1

15.5

31.0

43.6

27.3

2011–2015

Manufacturing

16.1

15.3

3.6

15.6

NA

5.9

1966–1970

19.2

14.1

6.5

16.8

8.8

17.2

2011–2015

Services

48.5

41.2

21.5

31.8

NA

36.2

1966–1970

60.6

53.8

49.2

50.8

39.1

56.2

2011–2015

50 3 Structural Transformation in South Asia: An Overview

3.2 Structural Transformation in South Asia

51

susceptible to technological changes. Moreover, generating employment in the highskilled sectors, such as ICT, has limits since the vast majority of the labour force is either semi-skilled or unskilled in South Asia.

3.2.3 Changes in Macro Aggregates The growth and ST over the years in South Asia have been associated with considerable changes in other macro aggregates (Table 3.3). The rate of investment has substantially increased in all countries except in Pakistan which probably explains its slower growth performance in recent years. The trade ratio in all countries has risen; with Bhutan emerging as the most open economy of South Asia with a trade ratio of over 100 per cent, due to the country’s overwhelming dependence on India for hydropower exports and consumer goods imports. A significant change in the dependence on foreign assistance has also taken place in all South Asian countries, especially in Sri Lanka and Bangladesh. Another feature of ST is the heavy reliance of the countries on external remittances. Except for Bhutan, remittance inflows are high and have increased sharply as shares of GDP in all countries. In Nepal, remittances amount to nearly 28 per cent of GDP, making it one of the highest remittance-dependent countries by international standards. Thus, the growth and ST in South Asian countries point out a number of special features. In terms of growth, all countries except Pakistan have experienced substantial growth acceleration in the recent decades. The growth acceleration has also been underpinned by rapidly rising investment and increasing trade openness. The dependence on foreign aid has declined substantially as well. The improvements in macroeconomic fundamentals have been sustained by a rapid increase in the flow of workers’ remittances. Moreover, the structures of the economy of the South Asian countries have changed significantly as a consequence of ST that has accompanied growth acceleration. In all countries, there has been a sharp decline in the share of agriculture in GDP, but not necessarily with expected increases in the share of manufacturing. The services sector has raised its share in output rather rapidly in all countries.

3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis Economic growth and ST are closely linked; ST induces allocative efficiency of resource use across sectors and stimulates productivity growth and employment. With economic growth, economies usually diversify and re-specialise, and the stages of diversification may be determined by productivity-driven ST if resources are concentrated in sectors/industries which should not dominate the production structures in

NA

16.5

5.3

16.2

16.7

Bhutan

India

Nepal

Pakistan

Sri Lanka

33.3

14.9

38

35.9

57.4

28.3

46.7

24

14.6

1.2

NA

21.1

Source World Bank, World Development Indicators (WDI) database

11.5

Bangladesh

14.1 30.4

51.1

42.6

3.9

24.1

35.8

1981–1985

2

8.6

12.3

0.4

12.6

5.1

2011–2015

Foreign aid, % of investment

31.5

47.8

3.5

101

45.7

2011–2015

1966–1970

1966–1970

2011–2015

Trade ratio, exports, and imports as % of GDP

Investment rate, % of GDP

Table 3.3 Changes in macro aggregates in South Asian countries

5.4

8.5

1.3

1.2

NA

2.7

1981–1985

8.6

6.5

27.5

3.5

0.8

9.1

2011–2015

Remittances, % of GDP

52 3 Structural Transformation in South Asia: An Overview

3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis

53

the long run. Traditionally, countries have systematically shifted resources from agriculture towards manufacturing industries with rapid productivity growth followed by the services sector indicating a major role of productivity mechanisms for ST. In the case of South Asia, there however remains concern that ST has not been sectorally balanced which can ensure inclusive and resource-efficient growth (IGC 2010; Srinivasan 2013; Mallick 2017). The employment share in agriculture has declined slowly relative to output share; there has been a slower rise in labour productivity and a slower shift from agriculture to manufacturing and tradable service activities in South Asia compared with other regions. Agriculture still remains the largest employer in South Asian countries. Hence, future ST in South Asia should explicitly take into account the role of productive agricultural transformation and facilitate the move towards the next phase of agricultural development through promoting longterm productivity growth in agriculture, facilitating the expansion of agro-enterprises, and linking them with regional and global value chains. Further, the interrelationships between ST, openness, and productivity growth are becoming more important in the South Asian economies over time. For example, in a relatively open economy, domestic comparative advantages influence the production structure and factor reallocations in multiple ways. An increase in net exports of one sector has an impact on labour reallocation in the sector and in other sectors although structural changes in other sectors may remain slow. Further, several studies suggest that the effect of factors such as globalisation on ST depends to a great extent on the policy choices and initial conditions in specific countries (McMillan et al. 2014; Rodrik 2016). In this section, we focus on the growth of labour productivity as the primary measure of aggregate productivity growth in South Asia since labour productivity is a sustainable measure that captures economic dynamics, competitiveness, and ST (Freeman 2008; UNCTAD 2014). Moreover, growth acceleration in South Asian countries largely depends on increasing labour productivity which largely determines economic growth. The empirical literature on whether ST is triggered by productivity growth or the forces work in the opposite directions is somewhat inconclusive (Echevarria 2000; McMillan et al. 2014; Timmer et al. 2012). Also, trade is traditionally considered as one of the sources of ST (Kuznets 1973; Syrquin 1988). Evolutions in trade in both size and composition can trigger resource reallocations in an economy. Three factors are considered important in determining the extent to which ST can contribute to economic growth: (i) revealed comparative advantage; (ii) labour market flexibility; and (iii) the exchange rate; all of which are influenced by the degree of openness of a country (McMillan et al. 2014). In all respects, the effect of globalisation has become pervasive in the South Asian countries.

54

3 Structural Transformation in South Asia: An Overview

3.3.1 A Dynamic Panel Model for South Asia The dynamic panel model (DPM) can address the endogeneity issues in cross-country growth analysis, capture heterogeneities in production, allow for exploitation of the causal direction of the growth process, and trace the reverse causality when the regressors are predetermined (Arellano 2003; Nickell 1981; Islam 1995; Wooldridge 2002; Moral-Benito et al. 2019). For the South Asian countries, we hypothesise that changes in total labour productivity are determined by changes in sectoral productivity and employment share, and other inputs such as capital stock. This implies that lagged total productivity and shifts in sectoral labour composition affect growth in South Asia. Therefore, the growth of total labour productivity depends on its lagged values, initial labour productivity, sectoral labour shares, and relevant structural variables having an influence on aggregate production. Following Karimu (2019), labour productivity growth (LPGit ) in country i at time t is defined as follows: L P G it = l n (L Pit−1 ) = l n (L Pit ) − (L Pit−1 )

(3.1)

The dynamic relationship between growth in labour productivity and structural change is given by L P G it = β1 (L P G it−1 ) + β2 l n(L Pit−1 ) + β3l n(S Hi jt ) + β4 l n(K Sit ) + ηi + ξt + μ,

(3.2)

where LPGit is total labour productivity growth in country i at time t and is calculated by ln (LPit ) – ln ( LPit −1 ); LPit and LPit −1 are the labour productivities at the end and the beginning of the period, respectively; LPGit −1 is the lagged total productivity growth; SHijt is the labour share in sector j of country i at time t; KSit is the total capital stock of country i at time t; ηi and ξt are country and period effects, respectively; and μit is the error term. In order to capture the impact of openness and other exogenous forces, we augment Eq. (3.2) with openness variables as follows: L P G it = β1 (L P G it−1 ) + β2 l n(L Pit−1 ) + β3l n(S Hi jt ) + β4 l n(K Sit ) + β5l n(I Mit ) + β6l n(E X it ) + β7 (F D Iit ) + β8 (G Oit ) + ηi + ξt + μit ,

(3.3)

where IM it and EX it are total imports and total exports, respectively. FDI is the net inflow of foreign direct investment, and GO is the global openness index. As the sectoral shares of labour sum up to unity, these are specified in logarithm to overcome the collinearity problem. Hence, the sectoral share coefficients of labour are the impacts of labour productivity growth to changes in sectoral labour shares.

3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis

55

According to the growth convergence hypothesis, countries having low initial levels of labour productivity tend to grow faster giving an inverse relationship between labour productivity growth and initial productivity. A negative coefficient of the initial productivity level means that countries having low initial levels grow faster, while a positive coefficient means that countries with high initial levels experience higher growth. Hence, the coefficient of the initial labour productivity level in the model captures the likely conditional convergence of productivity growth (Barro 2003; Solow 1956). Since annual data have a very short time interval to analyse ST, we use non-overlapping three-year sub-periods for five South Asian countries (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) covering the period 1991–2017.1

3.3.2 Methodology and Data The generalised least square (GLS) estimator has been widely used to deal with the endogeneity problems associated with DPM (Barro and Lee 1994; Barro and Sala-iMartin 1997). Moreover, the generalised method of moments (GMM) estimator has been the most popular econometric estimation approach for DPM (Bond et al. 2001; Caselli et al. 1996). The most popular of the GMM estimators are the difference GMM and the system GMM estimators (Roodman 2009). For GMM estimator in small samples, the maximum likelihood method has been proposed (Allison et al. 2017; Moral-Benito et al. 2018; Williams et al. 2018). To avoid complexities, we have used the least squares with dummy variables (LSDV) estimator. It is argued that the LSDV estimator is valid for dynamic panels and can be consistent with maximum likelihood estimates (Amemiya 1967; Islam 1995). For the data, Pt −1 is labour productivity at the beginning of the period and Pt is labour productivity at the end of the period. Labour productivity growth is measured as the difference of labour productivity in the beginning and end years of the sub-periods. The other variables—sectoral share of labour, capital stock, net FDI inflow, global openness index, total exports, and total imports—are measured as averages over the three-year sub-periods (Barro and Lee 1994; Islam 1995). With high collinearity among the openness variables, e.g. trade, net FDI inflow, global openness index, and globalisation index, we measure economic openness by exports and imports, net FDI inflow, and global openness. The data are taken from four major sources: UN main economic aggregates, World Bank economic indicators, ILO labour statistics, and the IMF. Total labour productivity is the total value of output per worker in the economy at constant 2005 USD prices. Sectoral labour share is the percentage of total labour employed available from the ILO database for agriculture, industry, and services. The data for GDP and exports and imports are taken from the UN database of main economic aggregates at 1 The

three-year sub-periods have been chosen mainly due to data limitations. For studying ST, Barro and Lee (1994) have used ten-year sub-periods, Levine et al. (2000) five-year sub-periods, while Islam (1995) 25- year period, and Karimu (2019) five-year sub-periods.

56

3 Structural Transformation in South Asia: An Overview

constant 2010 USD prices. The capital stock data are from the IMF and are measured in billions of constant 2011 international dollars while the net inflow of FDI data are from the World Bank database of economic indicators measured as a percentage of GDP. The indices for global openness are from Chinn and Ito (2006, 2008) which are normalised between 0 and 1, where 1 indicates high openness.

3.3.3 Empirical Results and Implications

0

.05

.1

.15

One key issue in cross-country analysis is the growth convergence hypothesis. If the South Asian countries do not differ structurally, then there will be similar steady productivity and capital per person growth for given exogenous technical progress. Otherwise, the absolute convergence hypothesis suggests that the countries having low levels to begin with will grow faster to catch up (Barro and Sala-i-Martin 2004). Figure 3.2 shows the labour productivity growth for the sub-periods and the initial labour productivity on the vertical and horizontal axis, respectively, for the five South Asian countries over the period 1991–2017. The figure shows no strong positive or negative relationship, and the fitted curve is fairly flat. This suggests that the underlining data do not support the absolute convergence hypothesis. The South Asian countries with low initial productivity do not seem to be growing faster than those with high initial productivity. Table 3.4 presents the summary statistics of the key variables in the present analysis. It shows gains in the level of labour productivity between 1991–1993 and 2015–2017. For instance, over the period, the difference between the average values

6.5

7

7.5

8

8.5

9

lnlabor 95% CI diffgrowthoflaborproductivity

Fitted values

Fig. 3.2 Labour productivity growth and initial level of labour productivity in South Asia. Source Authors’ construction based on ILO (2019)

2,340.06

2,471.12

2,696.34

3,081.67

3,477.25

4,014.58

4,490.41

1997–1999

2000–2002

2003–2005

2006–2008

2009–2011

2012–2014

2015–2017

2,942.23

2,671.77

2,160.97

1,794.76

1,518.60

1,349.19

1,277.87

1,239.77

907.29

4,018.76

3,252.64

2,506.84

1,931.53

1,529.43

1,285.01

1,081.27

SD

6,890.48

5,507.44

4,130.16

3,089.13

2,367.94

1,949.06

1,615.10

1,341.00

1,139.58

44.47

46.45

48.78

50.56

52.98

55.86

57.65

58.66

60.33

Mean

Agriculture

Share (%) of labour in SD

13.23

12.87

12.86

12.64

12.73

13.25

14.00

15.25

15.22

21.94

21.25

20.01

18.78

17.48

16.57

15.66

14.76

14.87

Mean

Industry

Note SD = Standard deviation. The data are for five South Asian countries (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) Source Authors’ calculation using ILO (2019), IMF (2019), UN Statistics Division (2019), and World Bank (2019)

2,235.18

1994–1996

776.76

Mean

1,098.04

SD

Mean

2,046.37

1991–1993

Capital stock, billion 2011 international $

Labour productivity, constant 2005 USD prices

Table 3.4 Summary statistics of key indicators

SD

4.51

4.53

4.46

4.58

4.58

5.12

5.98

6.64

7.13

33.59

32.30

31.21

30.66

29.53

27.57

26.69

26.58

24.80

Mean

Services SD

9.22

9.01

9.10

9.17

9.00

8.61

8.40

8.87

8.65

3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis 57

58

3 Structural Transformation in South Asia: An Overview

of labour productivity is USD 2.444 at constant 2005 prices indicating a substantial rise. The standard deviation of labour productivity has increased showing rising differences in productivity levels across the countries. Despite declining average labour share, agriculture still has the largest average share of labour. This is supported by the available statistics that shows that agriculture still dominates in South Asia. The average labour share of industry has also increased but standard deviations have declined indicating suggesting that the gaps across the countries may be reducing. On the other hand, services have experienced a rapid increase in the average labour share. This shows that labour is moving towards industry and services but more so in the services sector activities. Table 3.5 gives the estimation results. Column 1 shows the results with the laggeddependent variable (growth of labour productivity), initial labour productivities, and sectoral labour share. In other columns, capital stock, imports, exports, net FDI inflow, and global openness have been added. The results bring out four major findings: (i) industry and services changes are mostly insignificant and negatively associated with labour productivity growth; (ii) change in agriculture is also statistically insignificant; (iii) openness has a statistically weak relationship; and (iv) the country-level fixed effects are significant. Further, the initial labour productivity levels are negatively and significantly linked with labour productivity growth. The coefficients are relatively stable with regard to the sensitivity of including other explanatory variables. This suggests a trend towards some conditional convergence of labour productivity growth across the countries. While the results may not be robust, it shows that heterogeneities exist in the labour productivity growth paths of the South Asian countries. In the literature, trade is often identified as the future growth frontier for developing countries (UNCTAD 2017). In reality, the effect of trade on productivity growth depends on many determinants, such as terms of trade, nature of traded goods, and characteristics of the country’s trading regime. No doubt, trade can transform production technologies and enhance technological spillovers through several channels, e.g. changes in output composition, consumption structure, and labour-saving technologies. However, the results in Table 3.5 show that although the coefficient of exports is positive, it is statistically insignificant for South Asia indicating a weak effect of trade on labour productivity growth. Overall, the results do not indicate any strong contribution of economic openness to labour productivity growth in South Asian countries. The changes in productivity and income can occur only when trade openness stimulates employment reallocations and changes in output composition (Barro 1996). If these do not change, the effect on productivity growth remains weak. The key will be to transform the trade composition towards more sophisticated goods to create a significant impact of trade on productivity growth in the South Asian countries. For the South Asian countries, the dynamic panel data analysis points to several challenges of ST, especially in the agriculture sector. For instance, the reallocation of labour out of agriculture in these countries has not positively affected the aggregate productivity growth, and this has not led to any significant rise in agricultural labour productivity and capital investment in agriculture. For sustaining higher growth and

3.3 Productivity Growth and ST in South Asia: A Panel Data Analysis

59

Table 3.5 Labour productivity growth, structural transformation, and openness in South Asia: LSDV estimation results Growth of labour productivity 1 Lagged labour productivity growth

–

2 0.86498***

–

3 0.81926***

–

4 0.84132***

–

5 0.74503***

– 0.85474***

Log of initial 0.875566*** level of labour productivity

0.796971***

0.808815***

0.6957***

0.776379***

Log of total employment share in agriculture

0.112871

– 0.06012

– 0.19256

– 0.25136

– 0.31489*

Log of total employment share in industry

0.043843

0.010671

– 0.04324

– 0.04412

– 0.03854

Log of total employment share in services

0.041356

– 0.02173

– 0.09019

– 0.12444*

– 0.1339*

Log of total capital stock

…

0.010238*

0.009848*

0.00701

0.01997*

Log of total exports

…

…

0.002983*

0.037436

0.014323

Log of total imports

…

…

…

– 0.03284

– 0.01108

Net inflow of … FDI

…

…

…

-0.01432

Global openness

…

…

…

…

0.125741

R-squared

0.3484

0.4022

0.4628

0.4902

0.5699

Country fixed effects

Yes

Yes

Yes

Yes

Yes

Period fixed effects

Yes

Yes

Yes

Yes

Yes

Notes Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 Source Authors’ calculation based on ILO (2019), IMF (2019), UN Statistics Division (2019), and World Bank (2019)

60

3 Structural Transformation in South Asia: An Overview

ST, the South Asian countries need to adopt policies for increasing labour productivity growth much beyond their current levels. The aim should be to increase both sectoral and aggregate productivities through using more efficient production technologies, desired intra-sectoral transformations, and scaling up the efficient integration of labour-saving technologies in agriculture. The key has to be sectoral capital deepening (not aggregate capital accumulation alone) for raising the productivity levels. As we have seen, the services sector is emerging as the biggest sector in South Asian countries, which creates both opportunities and challenges for growth and ST. There are wide-ranging opportunities in the digital economy, telecommunications, finance, energy, and other subsectors of services. However, there are also challenges. Employment in services could be limited by the lack of dynamism in other sectors. If the services sector productivity growth is not supported by required growth in aggregate demand, employment growth may even decline which would further expand the existing sectoral productivity gaps (UNCTAD 2014).

3.4 Factors Influencing ST in South Asia Increasing rates of investment and rising trade openness are among the major factors that propel economic growth in the developing countries. Both these factors have played key roles in growth acceleration in the South Asian countries. However, one may also note that all South Asian countries experienced growth deceleration in the 1970s compared with the rates achieved during the late 1960s.2 This, in part, explains why South Asia lagged behind East and Southeast Asia although all these regions started their development journey from a reasonably comparable situation in the early 1960s. There exists a wide literature on the inefficiencies of the restrictive trade and industrial policy regimes; in particular, this is true for the import substitution strategies and the public sector-led development approach that was widely pursued throughout the South Asian region in the 1950 and 1960s (Osmani 2009). However, one must note that there were other factors that worked in the South Asian countries such as wide-scale destruction of infrastructure and productive capacities due to the war of liberation in 1971 and natural disasters and global oil price shock of 1973–74 for Bangladesh; loss of the protected export market in Bangladesh and floods and pest attack affecting agricultural output and depressing effect of oil price shock for Pakistan; and excessive reliance on the policy of import-substituting industrialisation and unsustainably high levels of welfare expenditures for Sri Lanka. Overall, the 2 For

example, compared with the 1966–1970 period, average yearly growth deceleration during 1971–1980 was as follows: 3.4 per cent to 1 per cent for Bangladesh; 4.6 per cent to 3.1 per cent for India; 2.6 per cent to 2.1 per cent for Nepal; 7.1 per cent to 4.7 per cent for Pakistan; and 5.8 per cent to 4.4 per cent for Sri Lanka. However, the growth rates started to rise in the 1980s and, in some cases, in the 1990s. Several authors describe the 1970s and 1980s for some South Asian countries as the ‘dismal’ or ‘lost’ decades for South Asia. See Mujeri and Sen, 2004; Osmani, 2009.

3.4 Factors Influencing ST in South Asia

61

‘lost decade’ of the 1970s (and a part of the 1980s for some countries) in South Asia had a complex origin extending far beyond the protective trade strategy. A series of supply shocks emanating from different sources, including ‘bad’ policies, also played a major role in deepening the economic crisis resulting in slow growth across all countries of South Asia.

3.4.1 Reforms for Transition and Growth The reforms for transition and growth in South Asian countries were pursued throughout the late 1970s and the 1980s, and later on as well. The reform measures were marked by decisive shifts in policy regimes in all countries. The new regimes had many common features, but their sequencing and implementation speed varied across the countries. The new regimes entailed a significant liberalisation of the domestic economy and dismantling of the state control on the economy, allowing more freedom for the market forces to operate in all spheres of the economy. In Sri Lanka, the reforms replaced the quantitative restrictions by tariffs on most import items, reduced tariff rates, devalued the currency, removed control on the current account, withdrew state monopoly over import trade, and adopted other liberalisation measures. In Pakistan, reforms were implemented with the avowed aims of reversing the state control strategy and restoring business confidence of the private sector. To this end, steps were taken to denationalise several industries, liberalise the investment licensing system, and encourage private sector investment in all areas permitted under the policies. In Bangladesh, the new industrial policy (NIP) in 1982 initiated a process of denationalisation. Nepal moved slowly in the 1980s, relying mainly on currency depreciation for boosting exports. In India, the reforms were significant in the 1980s and the Indian economy responded strongly. In particular, the elasticity of growth to reforms in South Asia was high in the 1980s although the responses were somewhat short-lived in the absence of more concerted reforms in later years. The theory of distortions suggests that the deeper the distortion, the greater the initial benefit from its relaxation. The large response to limited reforms in the South Asian countries in the 1980s, therefore, seems to be consistent with the static theory of distortions. As a result, deep-rooted structural problems started to outweigh the stimulus of demand expansion resulting in growth deceleration since the mid-1980s, and most countries undertook a series of structural adjustment programmes (SAPs) initiated by the World Bank and IMF. The SAPs, apart from trying to stabilise demand, involved reforms in both domestic and external sectors. While the nature of the reform was similar in all countries, the outcomes varied due to implementation differences and the political economy of the reforms. The biggest problems were faced by Pakistan and Nepal; Sri Lanka managed well, while Bangladesh enjoyed sustained benefits from the reforms.

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3 Structural Transformation in South Asia: An Overview

In Pakistan, political uncertainty played a major role in generating growth cycles, which created a stagnation of investment. Since the 1980s, Pakistan’s investment– GDP ratio remained the lowest in South Asia, and its growth performance was also the worst. In Nepal, the reforms implemented in the early 1990s yielded some results; the exports–GDP ratio almost doubled from 5 per cent in the 1980s to nearly 10 per cent in the 1990s, and GDP grew at 5 per cent per year in the 1990s. The post-conflict period since 2007 saw the political compact around the new constitution in Nepal which has endorsed democratic elections and political accountability. Sri Lanka’s post-reform growth is marked by fluctuations, and on average the economy grew at around 5 per cent during the 1990s. Several structural bottlenecks inhibit further growth acceleration in Sri Lanka. The high budget deficits of the 1980s led to the unsustainable accumulation of public debt and interest payments burden. Defence expenditure also rose due to the civil war; the inflation rate remained high leading to a persistent appreciation of the real exchange rate and erosion of export competitiveness. The government’s emphasis on social development was reflected in distributive policies and support for economic activities of the rural populations in Sri Lanka. Nevertheless, the peace dividend at the end of the civil war, along with domestic demand-oriented policies, has enabled Sri Lanka to maintain steady growth over a prolonged period. Bangladesh made its transition to a higher growth path in the early 1990s with both agriculture and manufacturing contributing to higher growth. Both growth-enhancing policy interventions and favourable external conditions helped the country to realise better economic performance. In agriculture, input market liberalisation and other market-oriented reforms contributed to higher productivity. The expansion of irrigation coverage, along with a rapid increase in the use of chemical fertilisers and other modern inputs, accelerated agricultural growth in a sustained manner. Manufacturing growth in the country was led by the export-oriented readymade garments (RMGs) industry, which benefited from both policy reforms and other measures. Between the late 1980s and early 1990s, Bangladesh embarked on a remarkable episode of trade liberalisation, reducing the tariff rates much faster than most other developing countries in Asia.3 Reduced protection, along with direct incentives provided to the export-oriented enterprises, substantially reduced the anti-export bias in policies. Export growth was also supported by several external factors such as the MFA, which ensured easy access of Bangladeshi RMGs to the developed country markets. Along with the increased flow of workers’ remittances, the practice of virtually uninterrupted democracy for more than three decades has created a stable political environment, enabling the country to realise the potential benefits of economic reforms. In addition, the NGO-MFIs in Bangladesh has played a major role in relaxing several constraints to growth by providing affordable credit to the rural poor (microfinance presently covers around 60 per cent of the rural poor). The NGO-MFIs (including the Grameen Bank and BRAC) have also softened the credit constraint of 3 The

unweighted protection rate declined from 73 per cent in 1991–92 to 28 per cent in 1995–96. See. Mahmud 2004.

3.4 Factors Influencing ST in South Asia

63

25 21.2 20

South Asia World

18.5 15

15

15.1

10

10.7

7.3

5

6.1

2.2

1.9

0 Bangladesh

Bhutan

India

Maldives

Nepal

Pakistan

Sri Lanka

Fig. 3.3 Income poverty in South Asian countries, 2013. Source Based on World Bank, PavcalNet

the microentrepreneurs, especially in the rural areas. In addition, through various grassroots programmes of women’s empowerment (including microcredit), the NGO-MFIs have engaged the female labour force in income-generating activities. The innovative initiatives for providing informal education, skill training, healthcare, and other social development inputs to the poor of these organisations (led by BRAC—the largest NGO in the world) have helped to improve the level of human capital for the segments in society having the weakest human resources.

3.5 Poverty and Human Development4 Over the years, South Asia’s economic transformation was also accompanied by rapidly falling poverty and rising human development. Although global poverty has declined from 94 per cent in 1820 to 10.7 per cent in 2013 (Bourguignon and Morrison 2002), South Asia’s share of the global poor has increased from 27.3 per cent to 33.4 per cent between 1990 and 2013. In absolute terms, however, the number of poor people living in South Asia has actually fallen by 249 million during 1990–2013. Further, the poverty headcount index of South Asia has dropped from 44.6 per cent in 1990 to 15.1 per cent in 2013. Moreover, the poverty headcount indices of the South Asian countries are extremely heterogeneous (Fig. 3.3).5 In India, 21.2 per cent of the population lives below the international poverty line of $ 1.90 and, with 270 million poor, it has the largest number of the poor. In Bangladesh, 18.5 per cent of the population lives below the poverty line while the poverty rates are 15.0 per cent in Nepal, 6.1 per cent in 4 Some

parts of the analysis draws from the earlier work of the authors, Mujeri and Mujeri 2020.

5 At the international level, poverty measured at the international poverty line of $1.90 a day is used

to monitor progress towards meeting the target of reducing the share of people living in extreme poverty to zero by 2030 of SDG1. See Ravallion, Chen, and Sangraula, 2008.

64

3 Structural Transformation in South Asia: An Overview

93.8

91 73.7 60.6

32.6

20.2

85.5

59.8

51.9 33

80

26.7 11.4 2.6

Change in poverty, %

Rate of poverty reduction, %

Fig. 3.4 Changes in poverty in South Asian countries. Source Based on World Bank, PavcalNet

Pakistan, and 7.3 per cent in the Maldives. Sri Lanka’s poverty rate is 1.9 per cent while Bhutan has a rate of 2.2 per cent. The highest poverty reduction in South Asia has occurred in Nepal where poverty incidence has declined from 81.0 per cent to 7.3 per cent over the period of 1984– 2010 (Fig. 3.4). Pakistan and Bhutan have also recorded a rapid reduction in poverty. Bhutan’s poverty rate has dropped from 35.2 per cent to 2.2 per cent within nine years driven by rising agricultural productivity and trade, extensive road and other infrastructure development, and spillover benefits from hydroelectricity. In contrast, both poverty reduction and the rate of poverty reduction are low in the Maldives and Sri Lanka.

3.5.1 Multidimensional Poverty in South Asia Afghanistan has high multidimensional poverty (66.2 per cent), while the index is 53.7 per cent in India and 44.2 per cent in Pakistan (Fig. 3.5).6 In contrast, Sri Lanka has only 4.0 per cent multidimensionally poor people, the lowest in South Asia. 6 The

multidimensional poverty index (MPI) shows both the incidence and the average intensity of their poverty. A person is identified as poor if he/she is deprived in at least one third of the weighted indicators. See OPHI/UNDP (2019).

3.5 Poverty and Human Development 70

65

66.2

60

57.8

53.7 50

44.2

41.3 40 30

38.0

28.6

27.2

Latin America & the Caribbean

30.9 Europe & Central Asia

20

21.5 19.4

10

9.1

0

World South Asia Sub Saharan Africa

5.2

4

2.1

East Asia & the Pacific Arab States

Fig. 3.5 Multidimensional poverty in South Asia. Source Based on data from OPHI

However, multidimensional poverty in South Asia is high relative to the world and other regional averages (OPHI/UNDP 2019).

3.6 South Asia: A Region of Growing Inequality Although the standard inequality indicators may indicate that South Asia has modest levels of inequality, there is a growing inequality throughout all South Asian countries (Rama et al. 2015). In recent years, rapid economic growth has produced the largest number of billionaires in South Asia. The highly unequal access to resources, jobs, markets, infrastructure, and development benefits results in a disproportionate incidence of poverty in South Asia relative to most other regions of the world. Further, the unequal participation of different groups (e.g. women and the poor) in roles, functions, decisions, rights, and opportunities in society persistently reinforces these trends. The rapidly growing middle-class and unprecedented urbanisation in the South Asian countries also contribute to the growing income gap and unemployment, rural–urban migration, and proliferation of slum dwellings, without access to the basic amenities of life. Although the South Asian countries are more prosperous today compared with any time in history, the fact remains in which prosperity exists alongside extreme impoverishment, growth alongside declining living standards, and extreme concentration of wealth alongside abysmal poverty.

66

3 Structural Transformation in South Asia: An Overview

3.6.1 Income and Wealth Inequality The Gini coefficient, a widely used inequality measure, shows a worsening general level of income inequality in South Asia (Table 3.6). The table shows that inequality has increased in almost every country over the last decade (Credit Suisse 2017). The Gini coefficient has risen between 2009–2011 and 2015–2017, implying that the region as a whole is moving towards greater inequality. The Palma ratio, on the other hand, compares the incomes of the richest 10 per cent with the bottom 40 per cent of the population. In South Asia, the bottom 40 per cent mostly lives in poverty and deprivations. Ideally, the Palma ratio should not exceed unity. As Table 3.7 shows, the total income of the lower 40 per cent is marginally above half of the top 10 per cent in Sri Lanka, Bhutan, and the Maldives while it is around two-thirds in India. More importantly, the inequality is rising except in Nepal. Another inequality measure, the quintile ratio, gives the ratio of the total income accrued to the richest 20 per cent of the population (top quintile) to the total received by the bottom quintile (Table 3.8). In the Maldives, the top quintile earns seven times Table 3.6 Gini coefficients of income in South Asian countries 2009

2010

2011

2012

2013

2014

2015

2016

2017

Afghanistan

…

…

Bangladesh

…

0.321

…

0.278

0.278

…

…

0.310

…

…

…

…

…

…

0.324

…

Bhutan

…

…

…

0.388

…

…

…

…

0.374

India

…

…

0.351

0.339

0.336

…

0.352

…

…

Maldives

0.384

…

…

…

…

…

…

…

…

Nepal

…

0.328

…

0.328

…

0.329

…

…

…

Pakistan

…

0.298

0.309

0.300

0.307

…

0.335

…

…

Sri Lanka

0.364

…

…

0.392

…

…

0.392

0.398

…

Source UNDP, Human Development Reports, 2009–2017

Table 3.7 Palma ratio in South Asian countries 2010–2017

Income growth of bottom 40% relative to top 10%

Bangladesh

1.3

– 0.19 (2010–2016)

Bhutan

1.8

– 0.05 (2012–2017)

India

1.5

– 0.49 (2004–2011)

Maldives

1.3

3.58 (2003–2010)

Nepal

1.7

NA

Pakistan

1.2

– 1.53 (2010–2015)

Sri Lanka

1.9

– 0.48 (2012–2016)

Source UNDP, Human Development Reports and World Bank, WDI databank

3.6 South Asia: A Region of Growing Inequality

67

Table 3.8 Quintile share ratio in South Asian countries 2012

2013

2015

2010–2017

Afghanistan

3.99

3.99

…

…

Bangladesh

4.60

4.36

4.69

4.8

Bhutan

6.80

6.81

6.84

6.9

India

5.01

5.01

5.31

5.3

Nepal

5.01

5.01

4.99

5.0

Maldives

6.80

6.80

7.08

7.0

Pakistan

4.16

4.12

4.36

4.4

Sri Lanka

5.78

5.78

6.58

6.8

Source UNDP, World Bank, Poverty and Equity Data Portal

more than the bottom quintile followed by Bhutan (6.9 times) and Sri Lanka (6.8 times). While Nepal has a relatively stable quintile ratio during 2012–2015, it is rising in India. Both Bangladesh and Pakistan also have similar trends. Overall, the quintile ratios are rising in the South Asian countries and, to reverse the trend, the income of the bottom quintile needs to increase at much faster rates than the other quintile groups.7 Thus, South Asia is rapidly emerging as one of the most unequal regions of the world. The population in South Asia has grown by 29 per cent (from 1.39 billion to 1.79 billion) between 2000 and 2017, while the GDP in absolute terms has grown by almost five and a half times (from USD 622.37 billion to USD 3.35 trillion) during the same period. As such, rising inequality need not be an inevitable consequence of economic growth in South Asia.

3.6.2 Landlessness and Rising Inequality In the predominantly agrarian societies in all South Asian countries, land, water, and other natural resources are essential ingredients for the livelihoods of the majority of the population, especially in the rural areas. However, unequal distribution of land and other natural resources perpetuates widespread rural poverty in South Asia. Bangladesh has the lowest land–man ratio in the world (0.06 hectares per person in 2013), and 13 per cent of the rural households have no land ownership at all, including the homestead. Among the indigenous communities, landlessness is also a 7 Further,

although South Asia is rapidly advancing towards a more prosperous future, the incremental wealth is accruing mostly to a small section of the population. In Nepal, during 1996 to 2011, the income share of the top quintile rose by almost five percentage points while the income share decreased for the rest four quintiles. In Pakistan, over the past 30 years, the bottom quintile has seen a decline in its share of national income while the top quintile experienced a steady increase. In India, the top 5 per cent earn as much as the remaining 95 per cent taken together. In 2017, 73 per cent of the created wealth has accrued to the richest 1 per cent. See Credit Suisse, 2017.

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3 Structural Transformation in South Asia: An Overview

common feature. In recent years, landlessness is also related to rising river erosion, land grabbing and illegal land acquisition by the elites, and development interventions of the government. In most South Asian countries, inadequate land reforms could not reduce the inequality in land distribution. Further, as land is the biggest incomegenerating asset in the rural areas, the landless people have no productive asset. Inequality of land ownership also reproduces inequality of wealth and income. One of the important indicators of wealth inequality in Pakistan is the distribution of farmland among the rural households. The top 20 per cent of Pakistani society owns 69 per cent of the country’s farmland. Nepal faces historic land inequality due to the absence of any meaningful land reforms. The top 7 per cent of the households own about 31 per cent of arable land and 1.3 million households (29 per cent of the population) do not own any land. It is true that the problem of land inequality has historically plagued the countries of South Asia. Despite several episodes of land reforms, the lack of any genuine redistributive agenda continues to fuel rising inequality in all aspects of rural lives including incomes and access to basic services thereby making land inequality as a strong determinant of rural poverty in South Asia. Equitable access to land is necessary to ensure both livelihoods and dignity and social status closely linked with economic independence, social equality, and economic and social justice in the South Asian region.

3.6.3 Gender Inequality The South Asian countries have ratified the convention on the elimination of all forms of discrimination against women (CEDAW), but still one in every two women in South Asia faces violence at home. There exists significant discrimination between women and men in all aspects of economic, social, and cultural life. In South Asia, social norms and values are dominated by the patriarchal system, where women are deprived of their basic human rights. They are compelled to work in very hazardous conditions and receive unequal wages. Women face the ‘double burden’ of bearing the exclusive burden of productive work and household chores. Further, the privatisation of the health system has made it difficult for the poor women to get access to basic health services. Other aspects of the vulnerability of women include society’s control over sexuality and human trafficking. In the case of gender income inequality, South Asia has the world’s most skewed gender wage gap. Further, the labour force participation gap between women and men is wide. The gender gap index of South Asia shows an extremely unequal society relative to other regions (Table 3.9). On an average, women earn about a half (or less than a half) than men in South Asia. In fact, South Asia is behind Sub-Saharan Africa and is just ahead of the Middle East and North Africa in gender discrimination with most women involved in lowpaid informal jobs and unpaid family work. In India, women undertake almost six hours of unpaid care and household activities per day compared with only half an hour for men. In general, labour markets in South Asia are marked by gender-based

143

109

Pakistan

Sri Lanka

0.669

0.546

0.669

0.664

0.669

0.638

0.719

0.521

0.309

0.641

0.599

0.376

0.622

0.465

0.55 0.63

0.42

…

0.58

0.62

0.79

0.57

F/M ratio

Wage equality for similar work

0.30

0.73

0.94

0.35

0.80

0.54

Source World Economic Forum, Global Gender Gap Report, 2016

111

106

108

India

Maldives

124

Bhutan

Nepal

47

Bangladesh

F/M ratio

Score

Rank

Score

Labour force participation

Global Gender Gap Index, Economic 2016 participation and opportunity

Table 3.9 Gender gap index in South Asia

6,491

1,610

10,501

1,963

2,424

6,226

2,364

Female

18,599

8,695

15,256

3,030

10,428

10,967

4,776

Male

Estimated income (USD, PPP)

0.35

0.19

0.69

0.58

0.23

0.57

0.50

F/M ratio

0.97

0.28

1.09

0.42

0.34

0.49

0.41

F/M ratio

Professional and technical workers

3.6 South Asia: A Region of Growing Inequality 69

70

3 Structural Transformation in South Asia: An Overview

disparities (ILO 2015, 2018; Huynh 2016). Women form the vast majority of the lowest paid wage labour, but only a tiny fraction of the highest wage-earners. This means that women are poorly represented in the high-paid jobs as well as face a wide gender pay gap at low-paid jobs. Female labour force participation is negligible in Afghanistan, both in rural and urban areas. In Bangladesh, the gender gap in employment rates in 2010 is 32 percentage points. The RMGs industry in Bangladesh, where more than 80 per cent of the workers are women, is known for low wages and high rates of exploitation. In Pakistan, the gender wage gap persists in almost all sectors of the country. Although rural women, who mostly work in farming and livestock rearing, play a key role in the agriculture sector, they are not paid fair wages. Further, due to restrictive gender and cultural norms, women in South Asia face severe constraints in their choice to seek paid employment. Deeply rooted in the socioeconomic structures of the region, lies a culture where men are considered to have the ‘natural rights’ to dominate, oppress, and exploit women. Violence against women is widely persistent across the region. This reinforces their sub-ordination to men as women work in low-paid domestic work, in the informal sector, or as migrant labourers. The process and forces of globalisation have further accentuated gender inequality and women’s poverty in South Asia. As homemakers, women bear the exclusive burden of globalisation resulting from commercialisation of basic services, such as drinking water, basic health, and other services, and privatisation of education. The neoliberal market-oriented development paradigm, currently in practice in the South Asian countries, has further deepened the traditional discrimination against women. Women’s exclusive contribution to the ‘care economy’—reproducing, cooking, cleaning, bearing, and caring for the child, elderly, and other family members—is also ignored in the formal economy. In the context of South Asia, achieving gender equality requires a wider socioeconomic and cultural transformation covering the value system and other dimensions of the social paradigm beyond limited policy prescriptions. Pro-women legislations and policies are no doubt helpful and important in initiating such a transformation process. Such legislations would eliminate biases both within the formal and informal structures in society. However, the critical factor is the implementation of policies to eliminate all gender biases, make the education and value system gender-sensitive, and empower women in all walks of life.

3.6.4 Rising Informality in Employment In South Asia, relatively high economic growth in all countries has also contributed to faster growth of informal or unorganised employment. Apart from widening wage and income inequality, rising labour market informality leads to a proliferation of precarious job environment leading to health and social hazards (ILO 2010). With the rising migration of labour from agriculture to industry and services sectors, these

3.6 South Asia: A Region of Growing Inequality

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sectors have witnessed significant flexibilisation and have adopted extensive use of precarious forms of employment—uncertain, unstable, and insecure employment practices. The formal sector is also witnessing rapid informalisation, limiting the scope for collective bargaining as flexibilisation weakens the labour unions and limits their capacity. The rapid expansion of precarious and informal sector activities is one of the drivers of economic inequality in South Asia. As wages are a major component of household incomes, rising wage inequality is a primary cause of higher inequality in total household incomes in the South Asian countries. In fact, informality affects as high as around 90 per cent of all workers in Bangladesh, India, and Nepal. It is therefore not surprising that the high economic growth in the region is being achieved at the cost of widespread use of precarious jobs, built on the widespread use of uncertain, unstable, and insecure employment practices. In addition, employment in the modern or formal sector in these countries is growing slowly. This implies that a small minority of the workers have access to reasonable benefits, relative security, and collective bargaining. Moreover, even this small formal sector is undergoing rapid casualisation and informalisation along with persistent growth of the informal sector. Workers, especially women and those belonging to the poor and disadvantaged groups, are mostly engaged in precarious employment in the informal sector. The incidence of vulnerable and precarious employment has remained largely unchanged in recent years, affecting around 72 per cent of the total workforce in South Asia, and this remains more pervasive among women (Table 3.10). No doubt, increasing visibility of women in the formal sector is accompanied by growing empowerment in terms of livelihood choices and social mobility, but this is also associated with increasing marginalisation in terms of wages and labour conditions in South Asia. This is observed in the RMGs industry especially in Bangladesh, India, Pakistan, and Sri Lanka. In these predominantly conservative societies, the shift of women from the rural informal sector to the modern industrial sector no doubt in itself is a major change in societal norms and attitude. These young women live mostly in the city slums, undertake transactions with financial institutions to meet their daily needs, and use cell phones and other digital technologies. However, they are also subjected to inhumane conditions for livelihoods at workplaces and in society. Table 3.10 Vulnerable employment in South Asia

2017

2018

Unemployment, million

29.5

29.7

30.2

Vulnerable employment rate, %

72.1

72.0

71.9

Vulnerable employment. million

498.7

Source ILO (2018)

505. 7

2019 (projected)

512.6

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3 Structural Transformation in South Asia: An Overview

The RMGs industry in Bangladesh has achieved a phenomenal growth in less than three decades, mainly due to its competitive labour costs. Lower investment in working conditions and safety measures may have contributed towards making RMG exports competitive, but this has probably been achieved at high social costs and well-being and security of the workers at the workplaces (e.g. Rana Plaza tragedy near Dhaka city where the factory building collapse in 2013 resulted in deaths of about 1,200 persons mostly RMGs workers who were women). No doubt, the neglect of workers’ safety issues and labour exploitation overshadow the sector’s ‘shining achievements’, at least to a certain extent. Nearly 81 per cent of all employed persons in India make a living by working in the informal sector, and similar shares are 90.7 per cent in Nepal, 60.6 per cent in Sri Lanka, and 77.6 per cent in Pakistan. Among the 63.7 million employed persons in Bangladesh in 2017, 36.6 million work in vulnerable employment conditions. Informality is also widespread in the non-agricultural sectors, such as construction, wholesale and retail trade, and accommodation and foodservice industries. Currently, more than 65 per cent of the population belongs to the working-age group but the job growth has been the slowest during the last two decades. The Bangladesh Labour Force Surveys show that the country could add only 1.4 million jobs between 2013 and 2016, while 4 million jobs were added between 2010 and 2013. The challenge of unemployment is particularly acute among the youths; despite gains in educational attainment (the percentage of youth completing secondary education increased from 9 per cent in 1990 to 35 per cent in 2010) in Bangladesh. Further, unemployment increases with educational attainment. In 2010, the unemployment rate was 14 per cent among youths having secondary education, which was 25 per cent for youths with a postgraduate degree. And despite considerable progress in female education and empowerment, the gender gap in employment rates in 2010 was as high as 32 percentage points. In 2010, the employment rate of men was 90 per cent, while it was 58 per cent for women. In 2017, Pakistan’s informal sector contributed between 30 and 50 per cent to the total GDP, which is above the South Asian average along with the employment of about 60 per cent of the labour force. About 73 per cent of the non-agriculture workers are employed in the informal sector. The economic reforms in Sri Lanka since 1977 have also significantly changed the nature and structure of employment in the country. For ensuring the dignity of labour and reducing income inequalities, the South Asian countries need to implement the ILO Declaration on Fundamental Principles and Rights at Work adopted in 1998. The declaration lays specific guidelines about the core labour standards. The governments across the region need to ensure minimum wages for the entire workforce, universal social security coverage, elimination of informal labour, and increase spending for funding comprehensive welfare and social security systems.

3.7 Inequality in Access to Basic Services

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3.7 Inequality in Access to Basic Services The access to basic services including water supply, sanitation, electricity, telecom, and transport is low in South Asia relative to other regions, probably with the only exception of Sub-Saharan Africa. Meanwhile, high rates of malnutrition along with infant and maternal mortality rates pose a significant health challenge in South Asia (Black et al. 2008). The health expenditure in South Asia in GDP is low at 3.5 per cent although the average stands at more than 10 per cent globally. Similarly, accessing quality education by all is a big development challenge in South Asia. Only about half of the primary-school-aged children get education following the minimum learning standards framework. Moreover, even these students can make only limited progress under the ill-functioning public education system. The quality of South Asian educational systems in terms of student learning is extremely poor (Dundar et al. 2014). The students at all levels suffer from low quality which prevents them from upward mobility based on their educational skills.

3.7.1 Access to Water and Sanitation For a large majority of the South Asian people, access to safely managed water is still far from reality. Most of the people suffer from inadequate access to water as well as contamination of water and these two problems are interrelated (WHO & UNICEF 2017). As Table 3.11 shows, the progress achieved over the last one and a half decade (between 2000 and 2015) is also not very encouraging. In South Asia, although the access has risen from 73 per cent to 93 per cent since 1990, still more than 134 million people do not have access to improved drinking water. Recent estimates show that between 68 to 84 per cent of the sources of water are contaminated (Table 3.11). In such situations, women have to shoulder the responsibility of fetching drinking water including water for domestic uses like cooking and cleaning, sanitation, and other uses such as for livestock rearing. This arduous task takes a large part of their time along with a heavy cost to their health and well-being. Especially in remote rural areas, most women are required to make at least six trips per day for fetching water, sometimes walking 10–12 kms and carrying up to 15 L each trip. Many young girls are also forced to drop out of schools to help their family members to perform these essential household chores. In the case of access to sanitation facilities, Table 3.12 shows severe unavailability of proper sanitation facilities. The situation is abysmal as in many cases the majority is deprived of proper sanitation facilities. At present, most of the world’s open defecators live in South Asia. Open defecation fell from 65 per cent to 34 per cent between 2000 and 2015 with India, Bangladesh, Nepal, and Pakistan achieving more than a 30 percentage point reduction. Despite this, 610 million in South Asia still practice open defecation (over 60 per cent of the total global number). Inadequate drinking water and sanitation facilities in schools

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3 Structural Transformation in South Asia: An Overview

Table 3.11 Access to safe drinking water in South Asian countries Year

Safely managed

Accessible on premises

Accessible when needed

Free from contamination

2000

14

…

…

…

2015

40

…

…

…

2000

56

64

…

56

2015

56

77

…

56

Bhutan

2000

27

67

…

27

2015

34

87

…

34

India

2000

…

38

75

…

2015

…

57

80

…

2000

24

43

74

24

2015

27

61

81

27

Maldives

2000

…

86

65

…

2015

…

95

75

…

Pakistan

2000

38

83

…

38

2015

36

77

…

36

2000

…

58

73

…

2015

…

77

89

…

% of population Afghanistan Bangladesh

Nepal

Sri Lanka

Source WHO & UNICEF (2017)

including improper hygiene behaviour among children reduce the access and efficiency of education across South Asia. Further, poor menstrual hygiene management among young girls in schools is a major factor in school absenteeism and dropouts for girls. These also affect the quality of early childhood development in the region. Further, the poor conditions in health care facilities reduce the quality of healthcare causing high new-born and mothers’ mortality and morbidity in most countries of South Asia.

3.7.2 Access to Health Services South Asia’s socioeconomic inequalities are also manifested in wide gaps in access to healthcare services of the people. In most cases, the largely dysfunctional public health system plagues the health service delivery systems in all countries. The region is lagging in critical health indicators, e.g. life expectancy, child mortality, child malnutrition, and mortality rates. In five South Asian countries (India, Nepal, Bangladesh, Pakistan, and Afghanistan), health indicators are mostly comparable with Sub-Saharan Africa (WHO 2018). The average life expectancy in South Asia

3.7 Inequality in Access to Basic Services

75

Table 3.12 Access to proper sanitation facilities in South Asia Year

Latrines and others

Septic tanks

Sewer connections

% of population using improved sanitation facilities, excluding shared Afghanistan

2000

17

6

2015

30

7

3

Bangladesh

2000

16

7

2

2015

34

9

3

Bhutan

2000

13

14

4

2015

40

45

5

2000

4

13

5

2015

12

23

9

Nepal

2000

9

8

2

2015

9

33

5

Maldives

2000

9

38

31

2015

13

29

54

2000

0

14

17

2015

8

27

23

2000

78

5

2

2015

87

5

2

India

Pakistan Sri Lanka

2

Source WHO & UNICEF (2017)

is below 70 years which is only above Sub-Saharan Africa where life expectancy is 60 years (Fig. 3.6). The infant mortality rates also indicate wide disparities in the region (Fig. 3.7). The quality of the primary health care is poor. Total government expenditure on health care in 2015 was between 0.4 and 2 per cent of GDP, among the lowest in the world. The primary health infrastructure is poor, with little supply of drugs and equipment and acute shortages of qualified health staff. The region has an inadequate number of trained health personnel, especially in the rural areas. The share of births attended by skilled health personnel is low in Afghanistan (50 per cent), Bangladesh (50 per cent), Pakistan (55 per cent), and Nepal (58 per cent). While WHO recommends a minimum of 2.28 health care professionals (doctors, nurses, and mid-wives) for every 1,000 population, South Asian countries reveal acute deficits (Fig. 3.8). Meanwhile, the region has also witnessed privatisation of the health care system over the last three decades, and private health expenditure (PHE) constitutes about two-thirds of the total health expenditure (THE). Further, out of pocket expenses of the people are one of the largest for health care in South Asia (Table 3.13). The commercialisation of the health care system in South Asian countries no doubt drives out the poor from affordable health care facilities. With rising space for the private sector, the dominant outcome of privatisation in the highly unequal societies in South Asia has been to marginalise the access to quality and equitable health care services,

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3 Structural Transformation in South Asia: An Overview

Life expectancy at birth (years) 79

77

76

75

74 69 60

Fig. 3.6 Life expectancy at birth in different world regions, 2017. Source data.worldbank.org

52

Mortality rate, infant (per 1,000 live births) 36

15 6

8

19 13

Fig. 3.7 Infant mortality rates in world regions, 2017. Source data.worldbank.org

especially to the lower middle class and the poor, who constitute 30–60 per cent of the total population in the region. It is also seen that the largest number of chronically malnourished population in the world live in the South Asian region (FAO and others 2014,2018). The underprivileged people in the rural areas and in the urban slums bear the largest burden of

3.7 Inequality in Access to Basic Services Fig. 3.8 Density of health workers in South Asian countries. Source World Health Organisation, 2018

77

No. of physicians

2.1

No. of nurses and midwives 2

1 0.8 0.6

0.5

0.5

0.3

Bangladesh

India

Nepal

Pakistan

No. per 1,000 population during 2007-2016

Table 3.13 Total and private health expenses in South Asian countries Total health spending as % of GDP

Private spending on health as % of all spending

Afghanistan

7.6

88.3

Bangladesh

3.5

66.4

Bhutan

5.2

13.2

India

4.1

70.8

Nepal

6.3

66.8

Maldives

5.5

39.5

Pakistan

2.2

61.5

Sri Lanka

2,9

55.3

Source https://www.ijpediatrics.com

acute malnutrition. It also persists with the children’s stunting and other deficiencies (Table 3.14). Around half of the preschool children in Bangladesh are malnourished. South Asia has a high share of the world’s total stunted children; with India alone having 61 million. Further, India, Pakistan, and Bangladesh have the highest incidence of child malnutrition in the world. About 84 per cent of the pregnant women in India suffer from vitamin D deficiency. Over 50 million children under five are affected by wasting worldwide, and half of them live in South Asia. The global hunger index (GHI) indicates the level of hunger and undernutrition worldwide, measured in terms of four GHI indicators—undernourishment, child stunting, child wasting, and child mortality. The GHI 2020 shows that all South Asian countries are in the lower half of GHI with Afghanistan at the 99th position out of 117 countries (Table 3.15). South Asia has the 2020 GHI score of 26.0, second only

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3 Structural Transformation in South Asia: An Overview

Table 3.14 Child malnutrition in South Asia Under weight (