Studies in Development Economics and Policy General Editor: Finn Tarp UNU WORLD INSTITUTE FOR DEVELOPMENT ECONOMICS RESEARCH (UNUWIDER) was established by the United Nations University as its first research and training centre and started work in Helsinki, Finland, in 1985. The purpose of the Institute is to undertake applied research and policy analysis on structural changes affecting the developing and transitional economies, to provide a forum for the advocacy of policies leading to robust, equitable and environmentally sustainable growth, and to promote capacity strengthening and training in the field of economic and social policy–making. Its work is carried out by staff researchers and visiting scholars in Helsinki and through networks of collaborating scholars and institutions around the world. UNU World Institute for Development Economics Research (UNU-WIDER) Katajanokanlaituri 6 B, FIN-00160 Helsinki, Finland Titles include: Tony Addison and Alan Roe (editors) FISCAL POLICY FOR DEVELOPMENT Poverty, Reconstruction and Growth Tony Addison, Henrik Hansen and Finn Tarp (editors) DEBT RELIEF FOR POOR COUNTRIES Tony Addison and George Mavrotas (editors) DEVELOPMENT FINANCE IN THE GLOBAL ECONOMY The Road Ahead Tony Addison and Tilman Brück (Editors) MAKING PEACE WORK The Challenges of Social and Economic Reconstruction George G. Borjas and Jeff Crisp (editors) POVERTY, INTERNATIONAL MIGRATION AND ASYLUM Ricardo Ffrench-Davis and Stephany Griffith-Jones (editors) FROM CAPITAL SURGES TO DROUGHT Seeking Stability for Emerging Economies David Fielding (editor) MACROECONOMIC POLICY IN THE FRANC ZONE Basudeb Guha-Khasnobis and George Mavrotas (editors) FINANCIAL DEVELOPMENTS, INSTITUTIONS, GROWTH AND POVERTY REDUCTION Basudeb Guha-Khasnobis, Shabd S. Acharya and Benjamin Davis (editors) FOOD INSECURITY, VULNERABILITY AND HUMAN RIGHTS FAILURE Basudeb Guha-Khasnobis and Ravi Kanbur (editors) INFORMAL LABOUR MARKETS AND DEVELOPMENT Basudeb Guha-Khasnobis (editor) THE WTO, DEVELOPING COUNTRIES AND THE DOHA DEVELOPMENT AGENDA Prospects and Challenges for Trade-led Growth Aiguo Lu and Manuel F. Montes (editors) POVERTY, INCOME DISTRIBUTION AND WELL-BEING IN ASIA DURING THE TRANSITION
George Mavrotas and Anthony Shorrocks (editors) ADVANCING DEVELOPMENT Core Themes in Global Economics George Mavrotas and Mark McGillivray (editors) DEVELOPMENT AID A Fresh Look George Mavrotas (editor) DOMESTIC RESOURCE MOBILIZATION AND FINANCIAL DEVELOPMENT Mark McGillivray (editor) ACHIEVING THE MILLENNIUM DEVELOPMENT GOALS Mark McGillivray (editor) HUMAN WELL-BEING Concept and Measurement Mark McGillivray (editor) INEQUALITY, POVERTY AND WELL-BEING Robert J. McIntyre and Bruno Dallago (editors) SMALL AND MEDIUM ENTERPRISES IN TRANSITIONAL ECONOMIES Vladimir Mikhalev (editor) INEQUALITY AND SOCIAL STRUCTURE DURING THE TRANSITION E. Wayne Nafziger and Raimo Väyrynen (editors) THE PREVENTION OF HUMANITARIAN EMERGENCIES Machiko Nissanke and Erik Thorbecke (editors) THE IMPACT OF GLOBALIZATION ON THE WORLD’S POOR Transmission Mechanisms Machiko Nissanke and Erik Thorbecke (editors) GLOBALIZATION AND THE POOR IN ASIA Matthew Odedokun (editor) EXTERNAL FINANCE FOR PRIVATE SECTOR DEVELOPMENT Appraisals and Issues Amelia U. Santos-Paulino and Guanghua Wan (editors) THE RISE OF CHINA AND INDIA Impacts, Prospects and Implications Laixiang Sun (editor) OWNERSHIP AND GOVERNANCE OF ENTERPRISES Recent Innovative Developments Guanghua Wan (editor) UNDERSTANDING INEQUALITY AND POVERTY IN CHINA Methods and Applications Studies in Development Economics and Policy Series Standing Order ISBN 978–0333–96424–8 hardcover Series Standing Order ISBN 978–0230–20041–8 paperback (outside North America only) You can receive future titles in this series as they are published by placing a standing order. Please contact your bookseller or, in case of difficulty, write to us at the address below with your name and address, the title of the series and the ISBNs quoted above. Customer Services Department, Macmillan Distribution Ltd, Houndmills, Basingstoke, Hampshire RG21 6XS, England
The Rise of China and India Impacts, Prospects and Implications Edited by
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Guanghua Wan
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This book comprehensively analyses the sources, trends and consequences of the rise of China and India as global, economic and political powers. The volume focuses, inter alia, on the role of productivity, labour, private investment, trade specialization, energy and women’s participation in economic activities. It also explores new paradigms for global development and the implications for policy and for developing countries. The chapters in this volume echo the spectacular economic performance of China, and the increasing prominence of India in global technology, innovation and international trade. We conclude that growth (and development) strategies are challenged by the multiplicity of institutional arrangements needed for reform to succeed and to achieve desirable ends.
Contents List of Illustrations
ix
List of Abbreviations and Acronyms
xiii
Acknowledgements
xv
Preface
xvi
Foreword
xviii
Notes on Contributors
xx
Part I The Rise of China and India: Development Strategies and Lessons for Developing Countries 1
2
Globalization and the Developing World: The Difference that China Makes Jeffrey Henderson China, South Africa and the Lewis Model John Knight
3 India’s Economic Growth: Lessons for the Emerging Economies Suparna Chakraborty
Part II 4
27 39
Economic Structure and Growth
Beyond Industrialization: New Approaches to Development Strategy Based on the Service Sector Peter Sheehan
5
The Service Sector Revolution in India: A Quantitative Analysis Rubina Verma
6
China’s Development Strategy and Energy Security Haider A. Khan
7
Identification of Regional Fundamental Economic Structure (FES) of India: An Input–Output and Field of Influence Approach Sudhir K. Thakur
8
3
Comparing Regional Development in China and India Yanrui Wu vii
61 84 108
138 173
viii
9
Contents
The Gains from Growth for Women and Informal Workers in India: A SAM-Based CGE Analysis Anushree Sinha and Haider A. Khan
Part III 10
187
Trade and Regional Development
Firm Location Choice in Cities: Evidence from China and India Kala Seetharam Sridhar and Guanghua Wan
203
11
China’s Global Growth and Latin American Exports Rhys Jenkins
220
12
Component Trade and China’s Global Economic Integration Kunwang Li, Ligang Song and Xingjun Zhao
241
13
The Rise of China and India: Lessons and Implications for Global Development Amelia U. Santos-Paulino and Guanghua Wan
Index
262
271
Illustrations Tables 2.1 2.2 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 5.1 5.2 5.3 5.4 5.5 5.6 5.7 6.1 6.2 6.3 6A1 6A2 7.1 7.2
7.3
The labour force in China: Its level, distribution and change, 1990–2000 The labour force in South Africa: Its level, distribution and change, 1995–2003 Share of employment in industry in total employment, selected countries, 1820–2005 Sectoral growth rates in GDP, China, 1978–2006 Sectoral shares of real GDP, five-year periods, China, 1979–2006 Global manufacturing exports, by selected countries and regions, 1990–2005 Shares of global manufacturing trade, by selected countries and regions, 1990–2005 Distribution of real manufacturing value added, by major country grouping Role of industry and services in India and China Structure of Indian real GDP and GDP growth, 1950–1 to 2007–8 Growth accounting – baseline results Growth accounting – GTAP factor shares Growth accounting – capital share of one-third Calibrated parameters Results for the two steady states Counterfactual experiment Growth rates of GDP per capita in low-income countries Primary energy consumption in China (quadrillion Btu) Input–output decomposition of changes in China’s energy use Primary energy production in China (quadrillion Btu) Simplified schematic social accounting matrix Schematic representation of endogenous and exogenous accounts in a SAM Sector classification for regional economies in India: 1965 Summary of percentage of statistically significant cells at 95% confidence level for regression models with 24 sectors: Indian regional economy Distribution of Fundamental Economic Structure (FES) cells across the Primary (P), Secondary (S) and Tertiary (T) sectors, partitions in the Indian regional economy: 1965 ix
31 35 65 68 68 71 74 74 77 78 91 94 95 98 99 101 105 110 111 112 125 127 145
147
149
x
Illustrations
Summary of adjusted R 2 values for the Indian regional economies with cell sizes as dependent variable 7.5 Input stability in the Indian regional economy: 1965 7.6 Top 15 most important cells in the Indian regional economy: 1965 7.7 Weak, moderate and strong fundamental economic structure (FES) cells in India 7.8 Deviation among actual and predicted (RAS) tables for Punjab economy: 1983–4 7.9 Typology of space-time fundamental economic structure (FES) 7A1 List of industries and states for 1965, 1975, 1985 and 1995 7A2 Regional technical coefficient matrices of states and union territories in India, 1965 8.1 Ranking of Chinese and Indian regional economies 8.2 Estimation results of beta-convergence 8.3 Estimation results 8.4 Sensitive analysis 9.1 Modular composition of the SAM 9.2 Simplified schematic social accounting matrix 9.3 Impact of trade reforms on wages and consumption 9.4 Percentage change in real earnings of female and male workers 9.5 Sector-wise factor intensity distinguished by gender 9.6 Percentage change in average wage rate by skill (real) and welfare 9.7 Employment and real wages (percentage change from baseline) 10.1 Size distribution of cities in China and India 10.2 Location of surveyed firms, by city size 10.3 Sectoral distribution of surveyed firms 10.4 Multinomial estimates of locational choice of firms in China 10.5 Multinomial estimates of locational choice of firms in India 11.1 Indices of competitive threat from China, 2001, 2006 11.2 ICT(1) for total trade, 1996, 2001, 2004, 2006 11.3 ICT(1) for manufactured goods trade, 1996, 2001, 2004, 2006 11.4 Loss of exports in the USA to China, 1996–2001, 2001–2006, 2004–2006 (as % of country’s total exports to the USA) 11.5 Loss of manufactured exports in the USA to China, 1996–2001, 2001–2006, 2004–2006 (as % of country’s manufactured exports to the USA) 12.1 China’s trade in parts and components, 1992–2004 12.2 Shares of component imports trade in China, by region, 1992–2005 (per cent) 12.3 Share of exports of parts and components from China, by region, 1992–2005 (per cent) 7.4
150 152 156 158 162 164 168 168 175 178 181 183 189 190 192 193 194 195 196 209 210 211 213 215 228 230 231 234
235 244 247 248
Illustrations xi
12.4
Unit labour costs across different countries, 1992–2004 (US$ of unit labour per hour) 12.5 Estimation results of equation 1: Imports 12.6 Estimation results of equation 1: Exports 12.7 Estimation results of equation 2: Export shares in total manufacturing exports
250 256 256 258
Figures 3.1 3.2 3.3 3.4 3.5 3.6 3.7 4.1 4.2 4.3 5.1 5.2 5.3 6.1 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.1 8.2 12.1
GDP per capita detrended at 1.5% Realized efficiency or productivity wedge Labour wedge Output per capita: Data and model outcomes Capital-Output ratio: Data and model outcomes Direct tax rates Indirect tax rates Value added shares, labour productivity and employment shares, by major sector, China, 1978–2006 Value added shares by major sector, India, 1950–71 to 2008–9 Share of services in GDP (at factor cost) and deviation from trend, India, 1950–1 to 2008–9 Shares of sectoral exports, 1980–2003 Shares of sectoral imports, 1980–2003 Sectoral TFP levels, 1980–2003 Global pollution and the Chinese situation, 1999 Regionalization in India Regional mix of industries in India: 1965, 1975, 1985 and 1995 Pattern of predictability of cells with log total sector as independent variable and transaction as dependent variable Regional stability of input variability in India, 1965 Most stable cells for the Indian regional economy: 1965 Top 25% of the largest field of influence for the average reference table for India: 1965 Top 15 of the most important cells in the Indian regional economy: 1965 Actual regional transaction matrix for Punjab: 1983–84 Predicted regional transaction matrix for Punjab using FES: 1983–84 Regional ranking and growth rates Sigma-convergence of regional income China’s imports and exports of parts and components: 1992–2005 ( billion US$)
46 47 48 49 50 52 53 69 77 79 86 87 93 113 141 142 148 152 153 155 157 161 161 174 177 243
xii Illustrations
12.2 12.3 12.4 12.5 12.6
Share of parts and components trade in manufacturing trade: 1992–2005 (%) Number of civil aviation airline routes in China, 1986–2005 Scale of seaport trade in China, 1985–2005 (10,000 tonnes) Number of telephones per 1,000 people in China China’s utilized FDI (US$100 million)
243 252 252 253 254
Boxes 6.1 East China Sea dispute between China and Japan 6.2 Dateline of China–Japan energy issues
121 122
Abbreviations and Acronyms AAE ADB ANC AOTS ASEAN BCA CEI CGEM CIBS CMIE CNOOC CNPC COE CSO CV DPA EIA EIU FACS FDI FES FPM FYP GAE GDP GNP GoPRC GPP GRP GTAP HYV IADB ICS IFC ILO IMF IRDA JICA LAC
Anglo-American era Asian Development Bank African National Congress Association for Overseas Technical Scholarship Association of South-East Asian Nations Business cycle accounting China Economic Information Network Computable general equilibrium model China, India, Brazil and South Africa Center for Monitoring Indian Economy China National Offshore Oil Corporation China National Petroleum Corporation Compensation of employees Central Statistical Organization Coefficient of variation Directly productive assets Energy Information Administration Economist Intelligence Unit Firm analysis and competitiveness surveys Foreign direct investment Fundamental economic structure Fixed price multiplier Five-year plans Global-Asian era Gross Domestic Product Gross National Product Government of the People’s Republic of China Gross provincial product Gross regional products Global Trade Analysis Project High yielding programme Inter-American Development Bank Investment climate surveys International Financial Corporation International Labour Organization International Monetary Fund Insurance Regulatory and Development Authority Japan International Cooperation Agency Latin America and the Caribbean xiii
xiv Abbreviations and Acronyms
MAD MAP MD MDGs MNCs MPE MVA NAFTA NBSC NCEUS NFES NGOs NICs NIEs NIS NPLs NSDP NSSO OAW ODA OECD OLS OS PICS PIM R&D RCA RMSE RPED SAM SCO SMEs SOC STVEs TFP TNCs UNCTAD USAID WB WBES WTO
Mean absolute deviation Means absolute percentage error Mean deviation Millennium Development Goals Multinational corporations Mean percentage error Manufacturing value added North American Free Trade Agreement National Bureau of Statistics China National Commission for Enterprises in the Unorganized/ Informal Sector Non-fundamental economic structure Non-governmental organizations Newly industrialized countries New industrialized economies National innovation system Non-performing loans Net state domestic product National Sample Survey Organization Own account workers Official development assistance Organisation for Economic Co-operation and Development Ordinary least square Operating surplus Productivity and investment climate surveys Perpetual inventory method Research and development Revealed comparative advantage Root mean squared error Regional program for enterprise development Social accounting matrix Shanghai Cooperation Organization Small and medium enterprises Social overhead capital Small town and village enterprises Total factor productivity Transnational corporations United Nations Conference on Trade and Development United States Agency for International Development World Bank World Business Environment Surveys World Trade Organization
Acknowledgements This book is the result of a coordinated effort and significant contributions from numerous colleagues. We would like to express our gratitude to all the participants in the various project meetings, and in the conference held in Helsinki, for their inputs and discussions. We also thank Augustin Fosu, Tony Shorrocks, Finn Tarp and the anonymous referees for helpful guidance, comments and suggestions on this book. We are also indebted to the staff at UNU-WIDER, expressly to Janis Vehmaan-Kreula and Barbara Fagerman for valuable support. Liisa Roponen, Neha Mehrotra and Adam Swallow provided effective assistance during the publication process and the project’s activities. UNU-WIDER gratefully acknowledges the financial contribution to the project by the Finnish Ministry for Foreign Affairs, and the financial contributions to the research programme by the governments of Denmark (Royal Ministry of Foreign Affairs), Finland (Ministry for Foreign Affairs), Norway (Royal Ministry of Foreign Affairs), Sweden (Swedish International Development Cooperation Agency – Sida) and the United Kingdom (Department for International Development). Amelia U. Santos-Paulino Guanghua Wan Helsinki, 2009
xv
Preface The performance of emerging economies, remarkably southern giants such as China and India, has rebalanced global economic dynamics. The rapid growth of China and India, and their weight in international trade and capital markets, have generated significant interest. To a great extent the academic and policy concerns are on their growth prospects and on their faculty to influence global governance. The rise of China and India has affected the challenged conventional theories of international trade relations, affecting the allocation of resources – both domestic and international – and welfare.1 China and India have almost no commonalities in terms of history, culture, religion, language and institutions with most of the Western developing world. Although these diversities make a comparison difficult, studying the impacts and the potential economic and political linkages amongst themselves and other developing countries is inevitable. Importantly, China and India are two of the most populous countries, accounting for nearly 40 per cent of the world’s population. China’s economy is also one of the largest, and, at the onset of the reforms in the late 1970s, China was the 10th economy in the world and by 2005 became the fourth largest – following the United States, Japan and Germany.2 Real GDP per capita growth rate in China and India is significantly higher than that of the world’s rate, and above growth in other developing countries and higher-income groups, particularly in recent decades. Furthermore, in recent years, growth trajectory in both nations has followed a similar pattern to advanced countries and other successful East Asian economies at comparable stages of development. 3 Therefore, the role of China and India as development successes calls for considered academic research. This book results from the UNU-WIDER research project entitled the ‘Southern Engines of Global Growth’, directed by Amelia U. Santos-Paulino and Guanghua Wan. The volume sets out to comprehensively study the trends, sources and consequences of the rise of China and India as economic and political world powers. The chapters were selected from the first project conference held in Beijing in January 2007, and from UNU-WIDER’s annual development conference ‘Southern Engines of Global Growth: China, India, Brazil and South Africa’ held in Helsinki in September 2007. The 13 studies herein explore some of the challenges that the ‘southern engines’ face, and in so doing discuss overarching implications for global development. The book is organized in three parts. Part I endeavours to assess how the rise of China and India, and their development strategies, could serve as paradigms for developing countries. The chapters by Henderson, Knight xvi
Preface
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and Chakraborty focus on the nature of China’s and India’s economic growth and outline relevant policy lessons. Importantly, the studies go beyond established theories of growth and industrialization to examine the new approaches to development strategies bestowed by the largest emerging economies. Part II seeks to explain the relationship between economic structures and growth. The chapters by Sheehan, Verma, Khan, Thakur, Wu, and Sinha and Khan analyse the countries’ development paths, focusing on the role of the service sector’s productivity as the main growth driver, and on other significant issues such as energy security, output structure, income distribution and regional development, and gender imbalances in the process of economic growth. Finally, the international dimensions of China’s and India’s economic accomplishments are studied in Part III. The chapters by Sridhar and Wan, Jenkins, and Li et al. cover emerging themes from the international trade and finance spectra, notably economic geography and foreign investment, competitive and complementary relationships with other developing countries, and the relationship between trade structure and economic integration. And the final chapter (Santos-Paulino and Wan) brings together the key policy messages and conclusions from the volume.
Notes 1. Santos-Paulino, A. U., and G. Wan (2009). ‘FDI, Employment, and Growth in China and India’. Review of Development Economics, 13 (4): 737–9. 2. Jefferson, G. H., A. G. Z. Hu, and J. Su (2006). ‘The Sources and Sustainability of China’s Economic Growth’. Brooking Papers on Economic Activity, Vol. 2. 3. Nayyar, D. (2008). ‘China, India, Brazil and South Africa in the World Economy: Engines of Growth?’. UNU-WIDER Research Paper 2008/05.
Foreword China and India are home to nearly two-fifths of the world’s population, and in 1820 these two countries contributed almost half of world income. Yet, around the time of independence in India in 1947 and liberation in China in 1949 their share of world output was only about one-tenth. Since then they have re-emerged as dynamic global, economic, and political powers. China’s dramatic transition during the past three decades from a centrallyplanned system to an emerging market economy has been a defining characteristic of the global financial system. Likewise, India’s development path displays distinct features, particularly economic modernization through industrialization. On this background this volume poses the following questions: ●
●
●
What are the sources, trends, and consequences of the rise of China and India as global, economic and political powers? What is the role of productivity, labour, private investment, trade specialization, energy and the participation of women in economic activities? What are the new paradigms for global development and the implications for policy?
The volume draws on a set of papers presented at the conference ‘Southern Engines of Global Growth: the role of China and India’ organized by UNUWIDER in Beijing in January 2007, and from UNU-WIDER’s annual development conference ‘Southern Engines of Global Growth: China, India, Brazil and South Africa’ held in Helsinki in September 2007. The chapters in this volume demonstrate that the successful performance of these two giant Asian economies is captivating, and that both policy reform and initial conditions and factor endowments have played a key role in their turnaround. Extensive reform and liberalization efforts have gone hand-in-hand with initial conditions, factor endowments and technology development as well as massive flows of foreign investment and trade. The rapid growth in China and India – and the way in which they have affected global economic and political dynamics – has also triggered a surge in South-South economic, social and political links and is leading to new alternatives and sources of development co-operation. In parallel, fast growing developing countries and multinational corporations have emerged as an important source of investment. A wealth of lessons can be derived from the progress of China and India. They include that:
xviii
Foreword xix ●
●
●
The role of industrialization in economic development, particularly through outward-oriented activities in manufacturing industries and services, remains critical. China’s performance and trade expansion have put other developing countries under competitive pressure, especially economies that share similar specialization patterns and factor endowments. The role of the government in designing and implementing development policies and structural reforms is likely to be effective when done in combination with a pragmatic approach to economic reforms.
While successful in economic terms both China and India are also characterized by underperformance in social and human capital indicators; increasing income and regional inequality; social stratification; labour market failures; and quality of governance. While not specific to China and India these are critical policy challenges which sorely need attention. I am grateful to the authors of this volume for a well-timed contribution to our understanding of how rapid economic growth and political change in China and India are rebalancing the world economy and the enduring impact this process is having on other developing countries. Finn Tarp Director United Nations UniversityWorld Institute for Development Economics Research
Contributors Suparna Chakraborty is assistant professor of economics and a member of the doctoral faculty at Baruch College, City University of New York. She holds a master’s degree in economics from the University of Washington at Seattle and a PhD in economics from the University of Minnesota at Minneapolis. Her interests lie in international finance and macroeconomics and her work has been published in a number of peer-reviewed journals including IMF Staff Papers, Economics Letters and Review of International Economics. Jeffrey Henderson is Leverhulme Research Professor of International Development in the Centre for East Asian Studies, University of Bristol. He has previously taught at the Universities of Birmingham, Hong Kong and Manchester and has held visiting professorships or fellowships at the Universities of California (Berkeley, Santa Barbara and Santa Cruz), Glasgow and Melbourne, and at Kwansei Gakuin University, Kobe, among others. His books include The Globalisation of High Technology Production and States and Development in the Asian Pacific Rim. Rhys Jenkins is professor of development economics in the School of Development Studies, University of East Anglia in Norwich, England. His main area of interest is the dynamics of globalization and its impacts on developing countries. He has carried out research in Latin America, East Asia and South Africa and has published widely on aspects of globalization including the role of transnational corporations and trade liberalization. Haider A. Khan is professor of economics at the JKSIS, University of Denver. He has published 12 books and more than 100 articles in professional journals on economic theory and international economics. His work on China, India and East Asia concentrates on both modelling and institutional analyses of problems in energy, ecology, innovation and finance. John Knight is emeritus professor of economics and fellow of St Edmund Hall, University of Oxford. He has written extensively on the economies of China and several African countries. His most recent books include joint authorship of The Economic Decline of Zimbabwe: Neither Growth nor Equity (Palgrave Macmillan 2002) and Towards a Labour Market in China (OUP 2005). Kunwang Li is professor of economics and deputy dean of school of economics at Nankai University. He specializes in international trade studies, industrial organization and the Chinese economy. He has been awarded a number of prestigious prizes for his research work in economics in China. He teaches xx
Contributors xxi
the courses in theory of international trade, industrial organization and microeconomics and supervises a number of master’s and PhD students at Nankai University. He obtained his PhD in economics from Nankai University in 1994 and is currently the Editor-in-Chief of Nankai Economic Studies. Amelia U. Santos-Paulino is research fellow at UNU-WIDER. Previously, she was a research fellow at the University of Sussex’s Institute of Development Studies and has been a visiting scholar at the University of California, Davis. She specializes in trade, macroeconomics and development issues, and her work has been published in academic journals including the Economic Journal, Manchester School and World Development. She holds a PhD in economics from the University of Kent in the UK. Peter Sheehan is the director of the Centre for Strategic Economic Studies at Victoria University in Melbourne. His research interests centre around the impact of global change and rising knowledge intensity on individuals, firms, industries and communities. He also researches on the new development strategies for the twenty-first century, with special reference to the role of health, low carbon technologies and services generally. Anushree Sinha is senior fellow and a member of the management council at the National Council of Applied Economic Research (NCAER), New Delhi, India. Dr Sinha has carried out postdoctoral research in macro modelling and development economics at the Universities of Pennsylvania and Oxford. She has written (and co-authored) a number of papers for professional journals, book chapters and books. Dr Sinha has taken up various international fellowships and also has been a senior consultant with the United Nations Economic Commission (UNECA) in their initiative in mainstreaming gender in African countries. Dr Sinha’s research interest mainly lies in analysing the impact of macro policies on the informal and women workers and on issues pertaining to their productivity. Ligang Song is associate professor at Crawford School of Economics and Government, and director of China Economy and Business Programme at the Australian National University. His research interests focus on applied international trade studies, economic development and transition economics. He teaches graduate courses in the Chinese Economy and Development Economics at the Australian National University and supervises a number of PhD students. Kala Seetharam Sridhar is senior research fellow, Public Affairs Centre (PAC), Bangalore, India, where she heads the Public Policy Research Group. Prior to this, she was Ford Public Affairs Fellow at PAC, and Fellow, National Institute of Public Finance and Policy, New Delhi, India. Kala has taught at the Indian Institute of Management (IIM), Lucknow, India. She has a PhD in public policy and management from the Ohio State University.
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Contributors
Sudhir K. Thakur is an assistant professor in decision sciences at the College of Business Administration, California State University Sacramento. He holds a PhD from The Ohio State University. He previously taught at the University of North Dakota, Grand Forks. His research interests are in the areas of economic geography and regional science, technological change and regional development, GIS and spatial statistics. Rubina Verma is assistant professor at the school of business administration at Instituto Tecnológico Autónomo de México (ITAM), Mexico City, Mexico. She completed her PhD in Economics from the University of Southern California, Los Angeles in May 2008. Her research interests include topics in macroeconomics, international economics, international trade and economic growth, especially as witnessed in low-income countries. Guanghua Wan is a senior economist of the Asian Development Bank. Previously he was senior research fellow and project director at UNUWIDER. Guanghua Wan is honorary professor at several leading universities in China, and a prolific researcher with over 50 analytical papers in refereed journals and more than 10 book volumes. He is a pioneer in developing regression-based decomposition techniques for inequality and poverty accounting. Yanrui Wu is professor in economics at UWA Business School, University of Western Australia. His research interests include the Chinese and Asian economies, productivity analysis, economic growth, resource and environmental economics. He has published extensively in these fields, including several books. Xingjun Zhao received his PhD in economics from Nankai University in 2007. His research interests include international economics, resources economics and corporate finance. Dr Zhao visited University of Western Australia (UWA) and Australian National University (ANU) for academic exchange in 2006. Dr Zhao has several publications in both Chinese and international journals. He has been working for China Sinosteel Corporation since 2007.
Part I The Rise of China and India: Development Strategies and Lessons for Developing Countries
1 Globalization and the Developing World: The Difference that China Makes Jeffrey Henderson
Introduction The re-emergence of China as an economic and political ‘driver’ of the global political economy is becoming one of the defining moments of world history. While there is a growing literature (which includes the current volume) that seeks to analyse the consequences of China’s rise for development elsewhere, the majority of it examines the impact of China’s trade, investment, geopolitical interests and so forth on particular countries or world-regions. While some of it is concerned with international governance, energy security, environmental sustainability and so on, and thus with ‘rising China’s’ global impacts, even this work tends to treat China’s interface with the rest of the world in a conceptually unmediated fashion. What seems to be largely missing is a literature that reflects on the possible consequences of China’s growing economic and political power for the nature of globalization.1 Yet, in its own right, this is a potentially significant issue. This is so because China’s increasing global engagement across the economic and political spectra is changing the rules of the game. As a consequence, when China engages with any particular country or worldregion (sub-Saharan Africa, Latin America etc.), its companies, work forces, migrants, diplomatic personnel and (perhaps ultimately) its military carry with them characteristics that are not only specifically Chinese (in terms of culture, values, priorities etc.), but attributes that arise from the shifting power relations, organizational forms and their associated expectations at the global level. In an attempt to stimulate debate around these questions, this chapter focuses on what the rise and international expansion of China may mean for the nature of globalization. Empirically it attends to the developing rather than the developed world. Given that as social scientists we can never predict the future, the best we can do is to try to identify the trajectories 3
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Jeffrey Henderson
of the possible. As a consequence, the discussion and argument advanced here are couched in terms of a ‘working hypothesis’. Specifically, I suggest that we may be on the verge of a new form of globalization: a ‘Global–Asian Era’ (GAE). While China is currently the principal driver of an emergent GAE, it is likely that in the next two decades or so, if the GAE is to become reality, China will be joined by ‘co-drivers’ such as India and possibly other high-performing Asian – specifically East Asian – political economies. The overarching proposition advanced here is that it may be in the nature and dynamics of a GAE that many of the origins of the economic and political futures of the developing world are likely to be found. The chapter begins by outlining the earlier form and phases of globalization out of which the GAE may be beginning to emerge. It then sketches some of the GAE’s likely key dimensions. In so doing, it marks the reasons why the GAE is likely to be significantly different from the preceding form of globalization, even though it has continuities with that form’s most recent, neoliberal, phase. The chapter proceeds by suggesting that a GAE is likely to throw the developing world into a particular form of ‘crisis’: one in which both opportunities and dangers for development may be inherent in the new global political–economic realities. The chapter continues by highlighting a number of vectors along which a GAE might be seen to be impacting on the developing world and makes a preliminary attempt to identify the trajectories of crisis that movement along three of those is beginning to produce. The chapter concludes by briefly posing the question of whether China’s rise is as secure a prospect as many assume, and thus whether a GAE is indeed likely to displace the current phase of globalization.
Forms and phases of globalization In order to get a handle on what the rise of China may mean for globalization, we need to place current and likely future developments in their historical contexts. This is necessary because, as Fernand Braudel (1984: 20) has reminded us, ...the present after all (is) in large measure the prisoner of a past that obstinately survives, and the past with its rules, its differences and its similarities (is) the indispensable key to any serious understanding of the present. In applying this methodological proposition, as all science adequate to the study of contemporary social phenomena should, it becomes clear that the widely accepted notion of globalization as a set of predominantly economic processes of relatively recent vintage (30–40 years) is historically and intellectually untenable. Viewed more reasonably as transnational flows of capital, labour, power, knowledge, cultural forms and imagery (cf. Castells 2000–3)
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that are organized and driven by firms, states and other agencies and interests, it is clear that the myriad processes for which the shorthand is now ‘globalization’ have been with us for far longer. Globalization in this sense has been a political, economic and cultural phenomenon at least since the Portuguese fleet under the command of Bartolomeu Dias rounded the Cape of Good Hope in 1488, or Cristobal Colon and his Spanish compatriots arrived in the Caribbean in 1492. Since that time, globalization has been realized in a number of different forms and phases and, as a result, has had different economic, political and social consequences that have depended on the articulation of the prevailing form and phase of globalization with the nature of the political economies and societies that have been subject to it. Taking its cue from this historiographic reasoning, this section first presents a categorization of the form of globalization – and its three phases – that has been evident for nearly two centuries: the ‘Anglo-American Era’ (AAE). It goes on to suggest that, while a ‘Global–Asian Era’ (GAE) will have important continuities with the AAE’s current phase, at the same time it is likely to represent a significant break from it. Subsequently this section sketches some of the contours of this new form of globalization. Anglo-American era From the early nineteenth century through to the present period we can conceive of a distinct form of globalization into which the regions of the world have been progressively absorbed. While for analytical purposes this form can be divided into three phases, the continuities inherent in these phases have been much more evident than their discontinuities. Consequently it is reasonable to comprehend them as variations on a seamed, but relatively coherent, form of globalization: the Anglo-American Era. While the motorforce of the AAE and its evolution has been the particular rhythms and organization of accumulation as well as the flows of capital, labour, power and imagery associated with them, it is important to mark that the geopolitical balance of power after 1815 (subsequent to the Napoleonic Wars) and again after 1918 (subsequent to World War I) was central to the emergence of first Britain and then the United States of America as the predominant global powers (cf. Kennedy 1989).2 Anglo-American era I (circa 1815–1919) This first phase was characterized by the continued industrialization of Britain and, by the second half of the period, the emergence of Germany and the USA as major industrial powers. Capital accumulation was associated with competitive forms of capitalism on the one hand and classical imperialism on the other. The principal national beneficiary was Britain, which emerged as the dominant economic and political power. A combination of inappropriate forms of corporate governance and state policies that privileged finance and commercial over industrial capital resulted
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in a systematic underinvestment in technological upgrading, which led, ultimately, to declining competitiveness vis-à-vis Germany and the USA. Interstate rivalries, partly associated with the onset of Britain’s economic decline, and particularly with Germany, culminated in World War I (see, variously, Hobsbawm 1975, 1987; Hutton 1996; Kennedy 1989; Lazonick 1991: chapter 1). Anglo-American era II (circa 1919–74) The second phase of the AAE was associated with the continued rise, and ultimately the economic and geopolitical dominance, of the USA after its emergence as the principal beneficiary of World War I. With the USA in the forefront, the most dynamic centres of capital accumulation globally (particularly Germany and Japan towards the end of the period) were associated with oligopolistic forms of capitalism, while productivity reached historically unprecedented levels during this period. After World War II – from which the USA again emerged as the principal beneficiary – Fordism, as the dominant regime of accumulation during the period, reached its zenith, delivering unprecedented levels of generalized prosperity in the core countries. Throughout the period the core countries of the AAE II (with the principal exception of Japan) witnessed persistent internal challenges to prevailing relations of wealth and power from both organized and ‘unorganized’ labour (see, for instance, Aronowitz 1973; Braverman 1974) and external challenges – militarily and ideologically – from militaristic nationalist and fascist regimes (Germany, Japan and Italy) and from various state socialist regimes (principally the USSR, China and North Korea). In many parts of the developing world, economic and political power associated with US neoimperialism were confronted – sometimes successfully – by popular resistance movements mobilized, in part, by various forms of socialism (Cuba and Vietnam among many others). Anglo-American era III (circa 1975 to the present) The contemporary phase of globalization has been characterized by a deepening and geographic dispersal of the dynamics that began to emerge in the last 15 years or so of the AAE II. Three of these seem to have been particularly important: the Information and Communication Technologies (ICT) revolution and the subsequent transformation of economic transactions, politics, work and everyday life through the global expansion of cybernetworks (a dynamic captured best in Castells’s trilogy: Castells 2000–3); the rise and expansion of neoliberalism as an economic, political and social project and as the principal ideological ‘glue’ of the AAE III (Harvey 2005; Henderson 2010); and the emergence of a new regime of accumulation – ‘financialization’ – associated with the progressive subjection of all other forms of capital to the particular interests and rhythms of finance capital (Boyer 2000; Glyn 2006; Williams 2000). The USA has continued to be the dominant political
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economy throughout this period – not least because it has been the principal source of many of the innovations associated with these dynamics – and geopolitically its dominance has been expressed, in recent years, through a new form of imperialism grounded in neoliberal globalization on the one hand and a militarized foreign policy on the other (Harvey 2003; Johnson 2004). Underlain by deepening structural weaknesses in the US economy (Brenner 2003, 2005), the consequence of these dynamics is that the world economy and its geopolitics have become increasingly unstable. Transformational dynamics With the benefit of hindsight, it is legitimate to talk of there having been a ‘transition’ from the AAE I to the AAE II and on to the AAE III. This is so because (at least in English) the notion of ‘transition’ refers to a process that has both a known start and a known end point. For contemporary processes, however, where we ourselves are living through the period of historical change, the notion of transition is an inappropriate (and thus scientifically inadequate) concept through which to analyse the dynamics involved. More appropriate is the notion of ‘transformation’, for that term signals a process of change that has a known starting point, but an unknown end point (Henderson 1998a: 4–9). Thus, while we can now grasp the fact that the transition from the AAE I to the AAE II and AAE III was relatively uncontentious in the sense that there was an absence of economic, political or military conflagrations between the two dominant powers, the USA and Britain,3 this may not be the case when we come to analyse the transformation from the AAE III to a GAE. Among the reasons for this are the following. China is a dramatically different sociopolitical formation from the countries that dominated the AAE With few signs, as yet, that the Communist Party’s hold on power is weakening, China remains, essentially, a neo-Stalinist state. While there has been an expansion of grass-roots political participation at various sub-provincial levels, the Party continues to formally absorb the state apparatuses,4 and the state remains fundamentally authoritarian and repressive. Though change is afoot, China still lacks a vibrant civil society that could act to moderate its authoritarianism (Nolan 2004a; Pei 2006). Although Britain, the USA and other major global players were nowhere near as democratic as early, or as deeply, as their national mythologies suggest (Chang 2002: chapter 3), and from the Peterloo massacre of 1819,5 through the ‘labour wars’ of the late nineteenth and early twentieth centuries, to the McCarthyite ‘witch hunts’ of the 1950s, their governments could certainly be repressive (see, for instance, Brown 1977; Dawley 1991: part II; Lens 1974; Thompson 1963: part 3), the repression there was never as systematic or as prolonged as it has been in China.6 In a nutshell, as Hutton (2007) argues, the Chinese
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Government does not subscribe to ‘enlightenment’ values. While, again, the extent to which Britain and the USA have themselves subscribed to enlightenment values has been uneven and patchy, depending on political expediency and prevailing perceptions of the ‘national interest’,7 the key difference relative to historic (since the revolution of 1911) and contemporary China is again the dramatic differences in the development of their respective civil societies and thus in their relative capacities for the mobilization of political opposition in support of enlightenment values. To the peoples of the dominant AAE countries (and the ‘Occident’ more generally), China represents the ‘great other’8 By the time the first maritime colonial power, Portugal, had made contact with Chinese merchant ships (in Melaka – now part of Malaysia – in 1511), China was at the zenith of its power as the regional hegemon in East Asia. While it was to slide into economic and geopolitical decline in the following centuries,9 China began to be perceived by Europeans and, subsequently, Americans and other peoples initially of European origin as the ‘great other’ (Jullien 1999; Zhao 2007). While part of this perception was undoubtedly positive, in terms of an appreciation among the middle and upper classes of China’s technological achievements and the aesthetic representations of its culture, elements of it were profoundly negative. China came to be perceived as the polar opposite of European cultures. The size, wealth and cultural sophistication of China, coupled with the fact that it had been controlled by a unified state since the third century BC, began to transmute, by the late nineteenth century, into a sense of China as a major potential threat to European and Eurocentric ‘civilizations’. With the triumph of the People’s Liberation Army in the civil war of the 1930s and 1940s and the founding of the ‘communist’ state in 1949, China began to be perceived – particularly in the USA – as not merely the cultural great other, but, ideologically, as profoundly alien. US and European racism, of course, was a central component of this perception. While Japan, at least from the mid-nineteenth century, was also viewed culturally in Eurocentric societies in similar ways to China, with the predominant exception of the 1930s through to 1945, it was never perceived as an ideological and military threat. On the contrary, with the exception of that period, the Japanese state, from the Meiji Restoration of 1867 through to the present, has specifically linked its political fortunes (arguably in a subservient form) to those of the prevailing dominant power: Britain during the AAE I and the USA subsequently (Murphy 2006). With the size of the temporary and permanent migrations from China that have been associated with the AAE III and the ‘ideological/spiritual pollution’ that has been a result of the absorption of China into the global economy and, particularly, its cybernetworks and cultural forms, it is possible that the negative perceptions in the Occident of China, and in
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China of the Occident,10 are being moderated. Whether that moderation has penetrated the power centres of the AAE III and their equivalents in Beijing, not to mention the vast majority of their respective populations, remains to be seen. The geopolitical context within which China is the principal ‘rising power’ is arguably more fragile than at any time since the Second World War If the leading analyst of the relation of war to the economic decline of the ‘great powers’, Paul Kennedy (1989), and the world’s greatest living historian, Eric Hobsbawm (1994), are in pessimistic agreement that we live in perilous times, then we all need to take note.11 In both their cases, as in the work of many ‘global historians’, there is a sense that the contemporary period is becoming remarkably reminiscent, in terms of economic instability and interstate rivalry, of the decade or two preceding the First World War. As I have indicated above, there is a sense that the US economy, in its structural fabric, is beginning to come apart. We must add to this the facts that finance capital, with all its inherent instabilities, has been ‘unleashed’ internationally (Glyn 2006), the USA and some of its allies are militarily overstretched, Russia and Japan seem to be on the verge of remilitarization,12 poverty in the developing world (with the significant exception of China), continues to increase (Davis 2006) and global warming threatens to deliver devastating climatic shifts. Into this maelstrom, China has now stepped. It thus seems axiomatic that China is re-entering the global political economy at a moment when geopolitical relations are more fragile than at any time since World War II, and thus probably in recorded history. Global–Asian era We are now in a position to sketch some of the key elements of what may emerge as a new form of globalization: a Global–Asian Era. As with the phases of the AAE, the nature of a GAE will initially be associated with the ‘peculiarities’ of China’s political economy and social formation: its history and current condition. Arising from these, two elements could be decisive. The sources of China’s competitive dynamic There are at least two factors that are important here: that China has a ‘giant’ economy and that its economy is constituted as an unusual form of capitalism. ‘Big country’ effect. While its various indices of economic growth are not dissimilar to those of Japan, South Korea or Taiwan in earlier periods (Winters and Yusuf 2007: 9), with a population of over 1.3 billion, a lowwage labour force matched historically in size only by contemporary India, and with increasing skills and innovative potential in abundance, the difference between China and the other East Asian late industrializers (or, indeed,
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any other country that has industrialized) is the sheer scale at which these economic and social dynamics are being brought into play. The following data will serve to underline this comment. In terms of shares in the growth of world exports, for instance, whereas China was second to the USA between 1995 and 2004, with 8.9 per cent relative to the latter’s 10.7 per cent of the total, its share in the growth of world exports between 2005 and 2020 is projected to be 15.4 per cent compared with the USA’s 9.9 per cent (and Japan’s 6.3 per cent and Germany’s 3.8 per cent) of the total (Winters and Yusuf 2007: 15). With regard to the structure of exports, China is now the world’s largest producer of electronic and ICT products (Winters and Yusuf: 42) and, in terms of higher skill and technology-intensive products, China’s performance is beginning to approximate that of South Korea (extrapolated from Winters and Yusuf 2007: figure 2.1). This capacity to move into higher valued-added, more technology-intensive products – at a far earlier stage in the industrialization process than other late industrializers – is underpinned by a dramatic expansion in the graduate labour force, with over 600,000 people now graduating annually in engineering (McGregor 2006).13 Add to this a competitive position in innovative technologies that one would not expect to find in a developing country at this stage of its development – such as nanotechnology, where nearly 1,200 scientists at the Chinese Academy of Sciences alone act as principal researchers (Appelbaum et al. 2006; Appelbaum and Parker 2007) – and we can see the reasons for the World Bank’s conclusion in its report on China and India that ‘even though China is not the dominant force in the world economy, the shock it is administering to the world is unprecedented’ (Winters and Yusuf 2007: 11). Form of capitalism. Far from being a monadic economic system, capitalism is extraordinarily diverse in its organizational composition, state–business relations, managerial priorities and so on (from a vast literature see, for instance, Coates 2000; Hall and Soskice 2001; Henderson 1998a; Lane and Myant 2006; Lazonick 1991; Whitley 1999). One of the implications of this reality, as Chang (2007) argues, for the developing world is that, far from there being one ‘royal road’ to development, as neoliberalism in its various guises would have us believe, there are a multiplicity of potentially successful routes to high-performing economies capable of delivering generalized prosperity. China’s form of capitalism may well be one of these. If it is, and if China becomes a dominant economy, then it will be a very odd form of capitalism to have constituted the core of a new form of globalization. While Chinese capitalism is a complex, hybrid phenomenon (see, for instance, Henderson 1998b: 377–82; Nee and Opper 2007; Nolan 2004b), sharing some similarities with the partially ‘state-orchestrated’ capitalisms of Japan, Taiwan and, particularly, South Korea,14 it is unique in two ways. First, it is the only significant globalizing form of capitalism to have at its
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core a neo-Stalinist state that continues to emphasize its links – though now largely rhetorically – to some of the principles of socialism. Relatedly, it is the only political economy where state-owned companies are at the cutting edge of globalization. As a consequence, it is reasonable to assume that the managerial priorities, time horizons for returns on investment, and so on, of such enterprises are not only very different from US, European, Japanese or South Korean firms, as Nolan’s (2001, 2004b: chapter 2) work implies, but that their primary concerns are not merely with profits (though they are about that), but with China’s national development strategy. If the considerations behind particular investments in particular places are not just commercial, but are driven by the Chinese state’s strategic interests, then, almost by definition, the logic of globalization during a GAE, and its consequences for countries absorbed into it, could be dramatically different from the phases of AAE globalization. China’s unusual nationalism China is the first potentially hegemonic economic–political power, other than the USA, to have been subject to colonization, and, relatedly and importantly, the first to be populated by non-white peoples. The legacies of colonialism, and perhaps issues of race, are consequently likely to influence China’s bilateral and multilateral relations, certainly with the USA, the European powers and Japan, and – in different ways – those with the developing world. Central to the issue (and unlike most other ‘major’ nationalisms) seems to be a sense of historical grievance associated with a perceived ‘one hundred years of humiliation’.15 Roughly from 1842 (the end of the first ‘opium war’ with Britain) to 1949 (the socialist revolution), China was subject to imperialist incursions by Britain, France, Germany and other European countries, the USA and, particularly, Japan, which seem to have left deep scars on the national psyche (Chang 2001; Gries 2005).16 With the decline of Maoism as the ideological ‘glue’ of Chinese society, the key legitimations for the continued rule by the Communist Party have become its ability to deliver sustained economic growth, coupled, as the occasion arises, with its determination to right the perceived historic wrongs perpetrated against China by foreign powers. In that the latter seems to be increasingly associated with a concern to reassert its supposed ‘rightful’ place in the global economic and political order, Chang’s assessment (2001: 175) that one of Deng Xiaoping’s key contributions to China’s development was to transform Maoism into a developmental nationalist ideology, seems valid. While Chinese nationalism has often been seen as a ‘top-down’ affair – essentially as a propaganda strategy designed to legitimate Party rule in the face of the deepening contradictions and rising conflicts associated with the ‘beast’ – capitalism – that the Party itself unleashed (for instance, Shirk 2007: 6–9) – events over the past decade (for example, the US bombing of
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the Chinese embassy in Belgrade in 1999, the periodic mobilizations against Japan’s refusal to apologize for its suppression of China in the 1930s and 1940s, and the ‘anti-Western’ response to international protests over the suppression of Tibetan dissidents in 2008), arguably point to the emergence of a grass-roots ‘popular nationalism’ (Gries 2005: chapter 7). Whatever the sources of China’s ‘new’ nationalism, however, it seems to be infused with a deep sense of victimization and, if Chang (2001) and Gries (2005) are correct, this has contributed to a mass psychology of insecurity. Given that China has territorial ambitions in East Asia (Taiwan most obviously, but also with regard to control over the potentially oil and gas-rich Paracel and Spratley Islands in the South China Sea; islands also claimed by Vietnam, Malaysia and The Philippines), and its self-perception seems to be as much a product of ‘cultural ethnocentrism’ (Taylor 2006; and more directly, Dikotter 1992) as that of the dominant powers of the Anglo-American Era, it seems likely that a future GAE could be marked by less than pragmatic responses on China’s part – as on the part of its potential adversaries – to some of the geopolitical tensions that will inevitably arise. As a consequence of these dynamics, China’s global economic and political footprints are likely to be markedly different from those that have been the products of previous global powers. With a GAE, therefore, we can expect an economic and political world order markedly different from that established under US/British/European hegemony. For instance, with a GAE we might expect less emphasis on the ideal of democratic governance systems, or on human rights and humanitarian issues (even rhetorically). As Jacques (2009) argues, we might expect the communication of a radically different notion of modernity vis-à-vis China’s Eurocentric counterparts. Also – and this could be positive for the developing world – the ‘demonstration effect’ of China’s development may encourage others to further question neoliberal policy agendas. In order to explore some of these questions in a little more detail, I turn, momentarily, to a discussion of some of the vectors along which a GAE might develop. First, however, I need to explain why this discussion is couched in relation to a particular notion of crisis.
China, the Global–Asian Era and the crisis of the developing world There seems to be widespread agreement among academic, media and political commentators alike that we live in a world of turbulence and uncertainty unprecedented in recent times. In such periods, the concept of crisis has often been invoked as a means of grasping and expressing our predicament. The multifaceted and dynamic crisis that we now confront, however, should not be understood in the largely negative, medical sense, evident in European languages: a decisive moment in a potentially terminal illness. Rather, it needs to be understood in the more optimistic sense evident
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in Chinese: as a dialectic between ‘danger’ and ‘opportunity’ (Henderson 1998b: 356). Following the logic of this notion of ‘crisis’, this section proceeds with an examination of the crisis tendencies that seem to be emerging along a number of vectors where an emergent GAE may already have begun to reconfigure the possibilities for development. Vectors of crisis There are a number of vectors along which crisis tendencies associated with the emergence of a GAE could be assessed. Given space constraints, I can deal only with three of these here, and in a schematic way at that. While this section does not tackle (except obliquely) the potentially vital questions of (for example) the environmental impacts of a GAE, the issues of global governance and the geopolitical implications of the GAE’s emergence, it does attend to questions of trade, energy security, and aid and human rights. Even in themselves, however, these vectors, taken both separately and collectively, are clearly ones on which much of the future of the developing world will depend and with regard to energy security, in particular, they are vectors around which geopolitical tensions are already reverberating. Trade Imports The sheer scale of the dramatic and sustained growth of the Chinese economy has massively expanded China’s dependence on the import of primary commodities from elsewhere in the developing world. Thus with the continuing reduction in agricultural land (consequent on urbanization) and the flow of peasants into the cities (as a result of the rising demand for industrial workers on the one hand and rural poverty on the other – though by 2009, as a result of global recession, these flows, if anything, were being reversed), agricultural productivity in China seems to have stagnated. Although China is becoming an important exporter of some types of agricultural produce – certain fruits, for instance – the country has become increasingly dependent on the import of basic foodstuffs. Thus by 2004, China’s trade deficit in foodstuffs amounted to US$3.7 billion (Kaplinsky 2005: 206). While much of this – in grains, for instance – was composed of imports from the developed world (and the USA in particular), some of the shortfall has been sourced from the developing world. Wellknown examples include the import of soya and oil seeds from Brazil and Argentina. Thus, for instance, China’s share of Brazil’s total oil seed exports rose from 20 per cent in 2001 to 30 per cent in 2004 (Gottschalk and Prates 2005). Similarly, China’s increasing demand for metals to feed its industrial revolution has had an impact on developing world exports. Thus increasing proportions of Zambia’s copper exports are heading for China, as are the Congo’s exports of cobalt and copper, Zimbabwe’s exports of cobalt,
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and Ghana’s and Jamaica’s exports of aluminium. China’s share in Chile’s and Peru’s exports of copper, for instance, increased respectively from 9 and 8 per cent of the total in 2001 to 17 and 19 per cent by 2004 (Gottschalk and Prates 2005). Although most of these metals exports continue to be in the form of ores rather than the higher value-added processed metals, it is still the case that the rising prices that they have helped induce are, in principle, beneficial to the economic welfare of the countries concerned. The fact that many of these benefits may not be reaching the workers in these industries, nor the bulk of the populations of the countries concerned, is not directly a product of a GAE, but rather of the inequalities of wealth and power evident in those countries themselves. There is, however, at least one primary commodity where China’s import demands are having a detrimental effect. With its burgeoning furniture and pulp industries, China is now one of the world’s largest importers of timber. Unfortunately, it has become the world’s largest importer of illegally logged timber, particularly from the rain forests of Burma, Malaysia, Thailand, Indonesia and Papua New Guinea (Rapa 2007). As a consequence, some of China’s demand for timber is having adverse environmental implications and at the same time is having few positive welfare implications for the countries concerned (in terms of poverty reduction, for instance). With regard to China’s rising demand for manufactured commodities, the picture, for the moment, is more clearly positive than mixed. Given the nature of China’s industrial economy – with its predominant emphasis on textiles and garments, electronics and engineered metal products (for example, automobiles) – the principal regional source of manufactured imports from the developing world is the industrializing (and recently industrialized) countries elsewhere in East Asia. Thus, with regard to textiles – though not garments (see below) – there is evidence to suggest that China has been increasing its imports from other parts of East Asia and from South Asia (see, for example, Lau 2007). With regard to electronic products, most of China’s component imports have come from South Korea, Taiwan, Singapore, Malaysia and Thailand (and, of course, from Japan, which by definition does not figure in the current analysis). So far, this dynamic has had a positive impact in terms of employment, wages and other economic benefits for the countries concerned. What is unclear, however, is how long such import demands will continue. With the reconfiguration of electronics production networks in East Asia, in consequence of rising skills, but lower production costs in China, investment strategies of the transnational corporations (TNCs) and rising costs elsewhere in the region, it seems likely that production of some components (at least those that embody mediumlevel technologies, such as hard drives and some other computer peripherals) is likely to shift to China itself. Evidence of this is beginning to emerge, for instance, from Malaysia (Henderson and Phillips 2007; Phillips and Henderson 2009) and Taiwan (Yang 2006).
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Exports If the implications for the developing world of China’s demand for imports are mixed (with both dangers and opportunities involved), the consequences of the fact that it is a manufacturing and exporting powerhouse seem more clear-cut. China’s capacity in labour-intensive manufactured exports seems to be helping to drive a ‘race to the bottom’ in parts of the developing world. With regard to textiles, a recent World Bank study (Winters and Yusuf 2007: 89–92) reports that, while exports to China have thus far benefited East Asian producers, by 2020 the situation is likely to have reversed, with Indonesia and Vietnam, for instance, suffering contractions in their respective industries of around 9 per cent. The study’s projections suggest that garments industries will be hit more heavily, with declines of around 19 per cent in Vietnam and North Africa (principally Egypt), for instance. Other research reports the consequences of China’s exports to the USA, EU, and so on for garments industries elsewhere in the developing world. Subsequent to the end of the Multi Fibre Agreement, Lesoto’s garments industry has been all but wiped out, while South Africa’s supply of garments to its domestic market has declined from 80 to about 25 per cent (Kaplinsky and Morris 2008). Furthermore, recent estimates suggest that, between 2003 and 2005, 130,000 workers were laid off in Sri Lanka’s garments industry as a direct result of Chinese competition in third markets (Perera 2006). With further regard to Chinese competition in exports to third markets (for example, the USA), including in higher-technology products such as electronics and auto components, much recent research on Latin America has been equivocal about the extent to which China poses a threat to their indigenous or foreign-owned manufacturing industries, in spite of evidence in the media that suggests that Mexican-based producers, for instance, have been badly damaged with growing unemployment in the maquiladoras bordering the USA. Jenkins’s work (Chapter 11), however, is more consistent with media concerns. He shows that, with regard to Latin Americanmanufactured exports to the USA, Chinese competition is likely to have more serious consequences than has been recognized in the scholarly literature. With regard to East Asian manufacturing industries, work by Lall and Albaladejo (2004) suggests that low and medium-technology industries there are seriously at risk from Chinese competition. This includes not only recently industrialized and industrializing countries such as Malaysia and Thailand respectively (Yusuf and Nabeshima 2009), but also more mature industrial economies such as South Korea. Aid and human rights China’s foreign aid derives largely from four central government, and a variety of provincial government, agencies. The former consist of the Ministry of
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Commerce, the China Export–Import Bank (Exim), the China Development Bank and a variety of less significant actors (which can be grouped as the fourth agent), such as the Ministries of Health, Education, and so on. Of these the Ministry of Commerce (which contains the Department of Foreign Aid) and the Export–Import Bank are currently the most important, though the Development Bank is reputed to have substantial reserves. In 2005, Exim alone had a larger investment portfolio in Africa – US$15 billion – than any of its equivalents from the USA, Japan or Britain (Alden 2007: 24). The latter have also become significant actors in their own right, with the governments of Anhui and Sichuan, for instance – via their stateowned construction firms – having direct links with particular provincial governments in Angola, and Sichuan is currently cooperating with the government of Ogun State, Nigeria, to build a US$50 million pharmaceutical plant (Alden 2007: 29). With this multiplicity of funding sources and the diverse (indeed, given provincial involvement, competing) interests associated with them, it is probably a misreading of the situation to assume that there is a unified ‘strategy’ when it comes to China’s aid programme. That question notwithstanding, there seem to be at least two issues worthy of comment here: the consequences of China’s aid regime and the potential human rights issues as Chinese firms begin to expand their investment in other parts of the developing world. Given its historic – official – commitment to non-interference in the internal affairs of other countries (Taylor 2006), it is, perhaps, unsurprising that China’s overseas aid programme is effectively circumventing the political conditionalities now associated with ‘western’ aid, whether the latter comes from international or national agencies. Judged against the dubious lending standards of the World Bank – or particularly USAID, in earlier periods, for instance – China’s current aid programme (which, among others, includes grants and loans to countries with dubious human rights records such as Burma, Turkmenistan, Uzbekistan and Sudan) seems relatively unproblematic. If there is a problem, however, it is that international lending standards (at least formally, if not informally) have moved on. Thus, while Chinese loans and other forms of assistance provide a politically easy route to development finance for cash-strapped regimes, they release those regimes (some of them highly corrupt and, indeed, predatory on their populations) from the pressures they might otherwise be under to drive through political reform. Unlike their international counterparts, it is a matter of course, for instance, for Chinese aid agencies to refuse to engage with NGOs concerned with the human rights and governance implications of their policies. To take one Chinese example, the China Export–Import Bank consistently fails to conduct environmental and social impact analyses prior to investing in given projects (Alden 2007: chapter 1). One example of recent Chinese loan activity will suffice to underline the general problem.
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China, as with USAID, and many other national aid programmes, lends money predominantly in accord with its commercial and strategic interests. For instance, given its involvement in Angola’s oil industry (referred to below), it is not a surprise that the Angolan regime has been a recipient of China’s largesse. Angola is a country where around 25 per cent of state revenues ‘disappear’ year on year and one that has a president who is believed to be implicated in these ‘disappearances’. In 2005, China’s Exim Bank delivered US$2 billion in aid to the Angolan regime. Whereas much of this has been invested in much-needed transport and infrastructure, the government partners in receipt of aid were not the relevant ministries, as is usually the case, but the president’s office, raising questions as to what proportion of the loan in fact ended up as productive investment. Other sources of potential concern are the types of labour processes and working conditions that are likely to result from Chinese foreign direct investment or, indeed, outsourcing in the developing world. It is in some African countries that these problems are likely to be experienced initially, as Chinese firms are now the leading foreign players in the development of transport infrastructure there and are increasingly prominent in telecommunications, mining, logging and, of course, energy extraction. Alden (2007: 14), for instance, reports that more than 800 Chinese companies are now operating in 49 African countries, with 480 of these involved in joint ventures with African firms. While these investments are undoubtedly positive in terms of economic development, their implications for social development are less clear. Notwithstanding the fact that many of these companies generally prefer to bring (indentured) Chinese workers with them – thus leaving local Africans unemployed – it is likely that working for such companies may leave much to be desired. While the working conditions in the subcontracted plants of the US and European companies that drive, for instance, many of the garments and footwear production networks in the developing world remain highly problematic, this should not absolve the activities of Chinese firms. While there is little evidence available from the developing world on which to base robust conclusions about working conditions in Chinese-invested operations, the treatment of rural migrants in Chinese industrial cities such as Dongguan and Guangzhou (which have working and living conditions that would have been familiar to Engels in 1840s Manchester),17 and across the country’s urban–industrial landscape, does not augur well for the treatment of workers in, say, Pakistan or Nigeria, Indonesia or South Africa, where China’s ‘cultural ethnocentrism’ is likely to further influence management attitudes to the workforces. Energy security Of all the vectors discussed in this chapter, it is China’s search for energy security that is probably attracting the most attention, if not from researchers, then certainly from those concerned with international security policy.
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Among the countries where China already has exploration and/or supply contacts are Kazakhstan, Iran, Saudi Arabia, Sudan, Nigeria, Angola and Venezuela for oil; and Turkmenistan, Burma, Bolivia and Algeria for gas (Boekestein and Henderson 2005). While, in principle, Chinese demand is likely to bring economic welfare benefits to such resource-rich countries, in practice that is only true where the revenues are redistributed and/or invested in productive projects (only in Venezuela are revenues currently redistributed). Beyond the question of the economic – and broader welfare – benefits that may be derived from China’s search for secure supplies of oil and gas, at least two other issues arise that are politicizing developments along this vector. The first of these concerns China’s bilateral relations with some very dubious regimes. In 2003, for instance, 41 per cent of Sudan’s exports and 23 per cent of Angola’s went to China (Jenkins and Edwards 2006: 25). In both cases, these exports were almost totally made up of oil shipments. Like the Sudan, Angola is also an example of a country with an authoritarian, corrupt, predatory regime. Unfortunately the majority of oil-rich countries have regimes that are variations on this theme, so China cannot be faulted for allowing its (state-owned) oil companies to do business with them, just as there has been no attempt by US and European governments, for instance, to constrain the activities of many of ‘their’ oil companies in countries where respect for human rights verges on the non-existent. In spite of the ether of hypocrisy surrounding pronouncements by politicians and the media in the USA and EU on China’s role in some oil-rich countries, the charge that does seem to stick against the Chinese government is the nature of its relations with Sudan. Given the Sudanese government’s complicity in the genocide taking place in the Darfur region and its export dependence on China, as indicated above, China, in principle, must have significant leverage over the regime in Khartoum, particularly given the inability of the regime to sell its oil (at least legally) to other major consumers.18 That this leverage has just begun to be exercised seems to be indicative of two things.19 While the lack of respect for human rights in China is now a matter of (a very long) record, given the fact that since 1949 this has rarely involved the suppression of its own minorities (though there remain issues with regard to Tibet and with the Uighurs), a reluctance to lever the Sudan regime is unlikely to be a product of the fact that the Chinese government is oblivious to human rights issues. For the moment, it is more likely to be a consequence of China’s traditional bilateral relations with African countries, which going back to the 1950s have emphasized (at least officially) non-interference in the internal operations of other states (Taylor 2006). Second, the relative absence of a popular concern in China with the human rights implications of China’s external operations (the significant exception to this general rule is Hong Kong) is, once again, a reflection of tightly controlled media (with the absence of a tradition of investigative journalism)20 and the underdevelopment of an autonomous civil society.
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The second question concerns the interface between China’s search for oil and gas security and the geopolitical ramifications of this. Among an array of issues that could be picked up here, only two will be touched upon: Shanghai Cooperation Organization (SCO) and the ‘string of pearls’. Founded in 2001 on the basis of a Chinese initiative, the membership of SCO now includes China, Russia, Kazakhstan, Tajikistan, Uzbekistan and Kyrgyzstan, while India, Pakistan, Iran and Mongolia have observer status. Pakistan and Iran are currently seeking to become full members. While Chinese motives in SCO are many and diverse, it is likely that the principal reason for the Chinese initiative was its concern to wean the central Asian states from their economic and political reliance on Russia. Beyond that, the development of stable political relations with the potentially major oil and gas suppliers in the region has been high on the list (Boekestein and Henderson 2005). Although the physical logistics of supply from the various former Soviet republics are potentially problematic (though, for instance, China is currently constructing a pipeline to supply gas from a non-SCO member, Turkmenistan), intergovernmental relations via SCO should help to secure supplies, should problems arise in the ‘bottlenecks’ of China’s oil supply lines from the Persian Gulf: the Straits of Hormuz and Melaka (see below). More recently, SCO members have begun to conduct joint military exercises, and this has prompted speculation that SCO could develop into a military alliance similar to NATO (Alexandroni 2007). Consistent with my earlier point about physical ‘bottlenecks’ turning into geopolitical ones, China seems to be engaged on a number of initiatives in the Indian Ocean that, at least in part, could constitute the early stages of a strategy to protect its oil supply lines from the Middle East and Africa. Currently composed of various initiatives to construct port and other logistical facilities in Burma, Pakistan, Sri Lanka and Bangladesh, this ‘string of pearls’ is beginning to prompt speculation that these could, should security circumstances in the region require it, form the basis of Chinese naval bases in Southeast and South Asia. Of these, the Chinese construction of port facilities at Gwadar in Pakistan – close to the Iranian border – and Kyauk Phyu in Burma are, for obvious reasons, becoming sources of particular interest, not least for the US military (see Niazi 2005; Pehrson 2006). With an oil pipeline under construction from Ramree Island (Burma) to the Chinese city of Kunming and a pipeline mooted from Iran to Gwadar, the infrastructure necessary for avoiding military blockages of Chinese oil shipments through the Straits of Hormuz and Melaka seems to be well advanced.
Conclusion I have briefly identified a series of ‘crisis’ dynamics that could well arise along three vectors associated with a future GAE. Consistent with the Chinese ‘reading’ of crisis employed here, it seems clear that, for the moment at least,
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an emergent GAE is likely to carry dangers as well as opportunities for economic, social and political development in various parts of the developing world. This is confirmed by other work, for instance, on the role of China in the World Trade Organization (WTO) and other institutions of global governance (Bergsten 2008; Gu et al. 2008) and by the important – and seemingly largely positive – developments that seem to be flowing from China’s recent investment and aid programme in the Congo (Komesaroff 2008). The key point of this part of the chapter, however – both analytically and in terms of policy responses – is that, rather than ‘read-off’ the likely consequences of a GAE on the basis of our assumptions, hunches or prejudices, we need to be guided, on a case by case basis, by the empirical realities that a GAE may be beginning to generate. These are diverse and this needs to be appreciated. Only subsequently will we be able to develop an aggregated picture of a future GAE’s consequences, and only then will some sort of theoretical closure become possible. Having said this, a further note of caution is in order. Throughout this chapter I have assumed that an initially China-driven form of globalization will come to fruition. While from our current historical vantage point that seems possible, there are a number of dynamics in train that could delay or even derail the process. While this is not the context to deal with these in any detail, a couple of issues can be flagged. First, calculations by the Asian Development Bank suggest that the Chinese economy is 40 per cent smaller than it is currently assumed to be (Keidel 2007).21 If these data are credible (and they have been given the World Bank’s seal of approval), then clearly the emergent phase of a GAE is likely to be longer than would otherwise have been the case. Second, there remain worrying signs in China of a future major crisis (in the ‘European’, not Chinese, sense this time). Some of these derive from China’s substantial contribution to the overaccumulation crisis that underpins the current world recession (Glyn 2006; and, on China, Hung 2008), while others focus on instabilities in China’s financial system. In the latter case, for instance, at a time (2006) when the Chinese Government was admitting that its financial institutions (they are state-owned) were burdened with non-performing loans (NPLs) totalling US$133 billion, the US accountancy transnational, Ernst and Young, calculated that, in fact, the NPLs amounted to a staggering US$911 billion, or about 40 per cent GDP equivalent on the basis of the then-current calculations (Chan 2006).22 Around 35 per cent of the NPLs were bound up with real estate speculation. As the principal internal trigger in some of the East Asian crisis economies in the late 1990s – specifically Thailand, Malaysia, Indonesia and Hong Kong – was real estate speculation (Henderson 1999), this raises the spectre of an East Asian-style crash in China, though on a far larger and more dangerous scale – both internally in China and externally – than was previously the case.
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These questions of the longer-term growth prospects and viability of China’s development project, however, are for the future and, in any case, their pursuit at this point would take us some way from the focus of our current discussion. For the moment we remain poised on the verge of a new episode in global history: one that may see, as Frank (1998), Arrighi (2007) and others suggest, the return of China to its historically central role in the global political economy. If this does turn out to be the case, then, as I have argued in this chapter, we can anticipate that the global political economy could well be absorbed into, and reconstructed by, a new form of globalization. If it is, then it seems clear that this new form will be dramatically different from what has gone before and will have dramatically different consequences for the developing and developed worlds alike.
Notes I am grateful to the Leverhulme Trust for funding the research (grant F/00120/AZ) on which this chapter draws. 1. Though for a partial exception see Pieterse (2008). 2. Periodization in historiography, while conceptually crucial, is sometimes problematic and often imprecise. The periodization of the Anglo-American era offered here and, specifically, the beginnings of its phases are grounded in key symbolic moments: for AAE I, the Congress of Vienna and the allied victory at the Battle of Waterloo in 1815, settling the accounts of Britain and other European powers with Republican and Napoleonic France; for AAE II, the end of World War I and the Treaty of Versailles in 1919, which implicitly acknowledged US power vis-à-vis Europe; for AAE III, the bankruptcy of New York City in 1974–5 and the economic and social agenda that followed, which became the first experiment in neoliberal reform. On the last point see Harvey (2005: 44–8). 3. Though, obviously, there was major opposition from rising powers, particularly Germany and Japan. 4. Even though formally, and increasingly informally, provincial and local state agencies have developed a degree of autonomy, particularly with regard to economic governance (Wang 2009). 5. Caused by a military attack on an unarmed crowd attending a pro-democracy rally in Manchester. 6. Political authoritarianism and repression in China pre-dates the revolution of 1949, having been a feature of ‘modern’ China since the republican revolution of 1911 (repression in Imperial China is not relevant to this discussion). Taiwan is the only Chinese society so far to have evolved a democratic polity based on liberal– representative principles. 7. In over a century replete with instances where enlightenment values have been sidelined, witness, for instance, the US overthrow of the nascent nationalist government in The Philippines in 1898 (Miller 1984), the bombing and gassing of civilians by the British in Mesopotamia (now Iraq) in the 1920s (Glancey 2003), or the overthrow of democratically elected governments in Iran in 1953 – by the British and Americans (Halliday 1979) – Guatemala in 1954 or Chile in 1973, both with the active assistance of the US government (Globalsecurity 2007a, 2007b).
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8. I use the term ‘Occident’ in preference to the ideologically loaded, Cold War term ‘The West’, with its geographical and cultural confusions (are Japan and South Korea, for instance, part of ‘The West’?). 9. For disparate explanations of China’s decline, see, amongst others, Qian (1985); Mokyr (1990); Frank (1998); Pomeranz (2001). 10. On the latter issue see Dikotter’s (1992) important book on issues of race in Chinese culture. 11. For Hobsbawm, if not for Kennedy, the appropriate role for pessimism in the current period would probably mirror Gramsci’s famous dictum: ‘pessimism of the intellect, but optimism of the will’. 12. In May 2007, the Japanese government approved a referendum on whether the Constitution should be rewritten to remove the clause that currently limits military expenditure and participation to defensive purposes only (Onishi 2007). Though participation in the various multilateral forces that are a significant feature of contemporary global politics was the immediate context for this development, the principal reason was undoubtedly security concerns occasioned by the rise of China. 13. Though large proportions of these are apparently considered unemployable by TNCs (McGregor 2006). 14. South Korea from the mid-1960s to the late 1980s, in terms of the developmentally crucial relationship between the authoritarian state and the huge business conglomerates – the chaebol – probably comes closest (but still not that close) to China’s model of capitalism (see Amsden 1989; Kim 1997). 15. The closest parallels here are perhaps with the contemporary nationalisms of some Central and Eastern European countries: Poland, Serbia and Croatia, for instance. 16. ‘Particularly’ Japan, because of the brutality of its colonial occupation. On a personal note, in June 1988 I was with two Hong Kong Chinese colleagues in a hotel bar in Nanjing. Realizing that many of the bar’s clientele were Japanese, one of my colleagues – whom I had previously taken to be apolitical – asked the singer to perform an anti-Japanese song well known in China. She immediately agreed. Fortunately the song was sung in Putonghua and none of the Japanese in the audience seemed to be aware of its meaning or significance. 17. Fieldwork notes, April 2005 and April 2006. 18. Chinese oil companies are not the only ones dealing with Sudan. The stateowned Malaysian company, Petronas, for instance, is still operating there. 19. In 2007 China sent military personnel to Darfur as part of a UN force. This was widely assumed, in the US and European media, to have been a decision driven by the government’s desire to avoid a boycott of the Olympic Games in 2008. 20. This is not unique to China, but is a general problem with the media throughout East Asia. The principal exceptions are Japan and The Philippines. 21. And those in abject poverty (living on less than 1 US dollar a day) are, at 300 million, three times more numerous than earlier estimates suggest. 22. Under Chinese government pressure, the Ernst and Young report was rapidly withdrawn. At the time, Ernst and Young were tendering for a major contact from the Beijing government (Chan 2006).
References Alden, C. (2007). China in Africa. London: Zed Books. Alexandroni, S. (2007). ‘NATO’s Rival in the East’. New Statesman, 20 August.
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Amsden, A. H. (1989). Asia’s Next Giant: South Korea and Late Industrialization. New York: Oxford University Press. Appelbaum, R. P., and R. A. Parker (2007). ‘China’s Bid to Become a Global Nanotech Leader: Advancing Nanotechnology through State-led Programmes and International Collaboration’. Santa Barbara: Center for Nanotechnology and Society, University of California. Mimeo. Appelbaum, R. P., G. Gereffi, R. Parker, and R. Ong (2006). ‘From Cheap Labour to High Technology Leadership: Will China’s Investment in Nanotechnology Pay Off?’. Paper presented at the Annual Conference of the Society for the Advancement of Socio-Economics, 30 June–2 July. Trier, Germany. Aronowitz, S. (1973). False Promises: The Shaping of American Working Class Consciousness. New York: McGraw-Hill. Arrighi, G. (2007). Adam Smith in Beijing: Lineages of the Twenty-First Century. London: Verso. Bergsten, C. F. (2008). ‘China and the Collapse of Doha’. Foreign Affairs, 27 August. Available at: www.foreignaffairs.org. (accessed 1 November 2008)] Boekestein, B., and J. Henderson (2005). ‘Thirsty Dragon, Hungry Eagle: Oil Security in Sino-US Relations’. IPEG Papers in Global Political Economy, 21. Newcastleupon-Tyne: British International Studies Association. Boyer, R. (2000). ‘Is a Finance-led Growth Regime a Viable Alternative to Fordism? A Preliminary Analysis’. Economy and Society, 29 (1): 111–45. Braudel, F. (1984). Civilization and Capitalism, 15th – 18th Centuries: The Perspective of the World. London: Fontana. Braverman, H. (1974). Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century. New York: Monthly Review Press. Brenner, R. (2003). The Economics of Global Turbulence. London: Verso. Brenner, R. (2005). The Boom and the Bubble: The US in the Global Economy. London: Verso. Brown, G. (1977). Sabotage: A Study in Industrial Conflict. Nottingham: Spokesman Books. Castells, M. (2000–3). The Information Age: Economy, Society and Culture, vols I, II, III. Oxford: Blackwell. Chan, J. (2006). ‘Massive Bad Debt Highlights China’s Financial Instability’. Asian Tribune, 18 May. Available at: www.asiantribune.com (accessed 7 July 2007) Chang, H.-J. (2002). Kicking Away the Ladder: Development Strategy in Historical Perspective. London: Anthem Press. Chang, H.-J. (2007). Bad Samaritans: Rich Nations, Poor Policies and the Threat to the Developing World. London: Random House Business Books. Chang, M. H. (2001). Return of the Dragon: China’s Wounded Nationalism. Boulder, CO: Westview Press. Coates, D. (2000). Models of Capitalism. Cambridge: Polity Press. Davis, M. (2006). Planet of Slums. London: Verso. Dawley, A. (1991). Struggles for Justice: Social Responsibility and the Liberal State. Cambridge, MA: Harvard University Press. Dikotter, F. (1992). The Discourse of Race in Modern China. Hong Kong: Hong Kong University Press. Frank, A. G. (1998). ReOrient: Global Economy in the Asian Age. Berkeley and Los Angeles: University of California Press. Glancey, J. (2003). ‘Our Last Occupation: Gas, Chemicals, Bombs: Britain Has Used Them All Before in Iraq’. The Guardian, 19 April.
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Globalsecurity (2007a). ‘Guatemala Coup’. Globalsecurity.org. Available at: www. globalsecurity.org/military/world/war/guatemalacoup.htm. (accessed 1 November 2008) Globalsecurity (2007b). ‘Allende’s Leftist Regime’. Globalsecurity.org. Available at: www.globalsecurity.org/military/world/war/allende.htm. (accessed 1 November 2008) Glyn, A. (2006). Capitalism Unleashed: Finance, Globalization and Welfare. Oxford: Oxford University Press. Gottschalk, R., and D. Prates (2005). ‘Macroeconomic Challenges of East Asia’s Growing Demand for Primary Commodities in Latin America’. Sussex: Institute of Development Studies, University of Sussex. Available at: www.ids.ac.uk/ ids/global/ pdfs/RGMacro%20Challenge.pdf (accessed 7 July 2007) Gries, P. H. (2005). China’s New Nationalism: Pride, Politics and Diplomacy. Berkeley and Los Angeles: University of California Press. Gu, J., J. Humphrey, and D. Messner (2008). ‘Global Governance and Developing Countries: The Implications of the Rise of China’. World Development, 36 (2): 274–92. Hall, P. H., and D. Soskice (eds) (2001). Varieties of Capitalism: The Institutional Foundations of Comparative Advantage. New York: Oxford University Press. Halliday, F. (1979). Iran: Dictatorship and Development. London: Penguin Books. Harvey, D. (2003). The New Imperialism. Oxford: Oxford University Press. Harvey, D. (2005). A Brief History of Neoliberalism. Oxford: Oxford University Press. Henderson, J. (1998a). ‘On Appropriate Models for Transformation in Eastern Europe’. In J. Henderson (ed.), Industrial Transformation in Eastern Europe in the Light of the East Asian Experience. London: Macmillan, 3–21. Henderson, J. (1998b). ‘Danger and Opportunity in the Asia-Pacific’ in G. Thompson (ed.), Economic Dynamism in the Asia-Pacific: The Growth of Integration and Competitiveness. London: Routledge, 356–84. Henderson, J. (1999). ‘Uneven Crises: Institutional Foundations of East Asian Economic Turmoil’. Economy and Society, 28 (3): 327–68. Henderson, J. (2010). ‘Neoliberal Globalization’ in N. Young (ed.), The Oxford International Encyclopedia of Peace, Vol .III. New York: Oxford University Press, 117–22. Henderson, J., and R. Phillips (2007). ‘Unintended Consequences: Social Policy, State Institutions and the “Stalling” of the Malaysian Industrialization Project’. Economy and Society, 36 (1): 78–102. Hobsbawm, E. (1975). The Age of Capital: 1848–1875. London: Weidenfeld and Nicolson. Hobsbawm, E. (1987). The Age of Empire: 1875–1914. London: Weidenfeld and Nicolson. Hobsbawm, E. (1994). Age of Extremes: The Short Twentieth Century, 1914–1991. London: Michael Joseph. Hung, H.-F. (2008). ‘Rise of China and the Global Overaccumulation Crisis’. Review of International Political Economy, 15 (2): 149–79. Hutton, W. (1996). The State We’re In. London: Vintage Books. Hutton, W. (2007). The Writing on the Wall: China and the West in the 21st Century. London: Little and Brown. Jacques, M. (2009). When China Rules the World: The Rise of the Middle Kingdom and the End of the Western World. London: Allen Lane. Jenkins, R. (2007). ‘China’s Global Growth and Latin American Exports’. Paper presented at the UNU-WIDER conference on Southern Engines of Global Growth: China, India, Brazil, and South Africa, 7–8 September. Helsinki.
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Jenkins, R., and C. Edwards (2006). ‘The Asian Drivers and Sub-Saharan Africa’. IDS Bulletin, 37 (1): 23–32. Johnson, C. (2004). The Sorrows of Empire: Militarism, Secrecy and the End of the Republic. New York: Metropolitan Books. Jullien, F. (1999). ‘A Philosophical Use of China: An Interview with Francois Jullien’. Thesis Eleven, 57: 113–30. Kaplinsky, R. (2005). Globalization, Poverty and Inequality: Between a Rock and a Hard Place. Cambridge: Polity Press. Kaplinsky, R., and M. Morris (2008). ‘Do The Asian Drivers Undermine Export Oriented Industrialization in SSA?’. World Development, 36 (2): 254–73. Keidel, A. (2007). ‘The Limits of a Smaller, Poorer China’. Financial Times, 14 November. Kennedy, P. (1989). The Rise and Fall of the Great Powers: Economic Change and Military Conflict from 1500 to 2000. London: Fontana Press. Kim, E. M. (1997). Big Business, Strong State: Collusion and Conflict in South Korean Development, 1960–1990. Albany: State University of New York Press. Komesaroff, M. (2008). ‘China Eyes Congo’s Treasures’. Far Eastern Economic Review, April: 38–41. Lall, S., and M. Albaladejo (2004). ‘China’s Competitive Performance: A Threat to East Asia’s Manufactured Exports?’. World Development, 32 (9): 1441–66. Lane, D., and M. R. Myant (eds) (2006). Varieties of Capitalism in Post-Communist Countries. London: Palgrave Macmillan. Lau, B. K. F. (2007). ‘A Tale of Two Regions: How China Affected the Textiles and Clothing Exports of Other Asian Economies’. Paper presented to the UNU-WIDER conference on Southern Engines of Global Growth: China, India, Brazil, and South Africa, 7–8 September. Helsinki. Lazonick, W. (1991). Business Organization and the Myth of the Market Economy. New York: Cambridge University Press. Lens, S. (1974). The Labor Wars: From the Molly Maguires to the Sitdowns. New York: Anchor Books. McGregor, R. (2006). ‘Up for the Job?: How India and China Risk Being Stifled by a Skills Squeeze’. Financial Times, 20 July. Miller, S. C. (1984). Benevolent Assimilation: The American Conquest of The Philippines, 1899–1903. New Haven: Yale University Press. Mokyr, J. (1990). The Lever of Riches: Technological Creativity and Economic Progress. New York: Oxford University Press. Murphy, R. T. (2006). ‘East Asia’s Dollars’. New Left Review, 40: 39–64. Nee, V., and S. Opper (2007). ‘On Politicized Capitalism’. In V. Nee and R. Swedberg (eds), On Capitalism. Stanford: Stanford University Press, 93–127. Niazi, T. (2005). ‘Gwadar: China’s Naval Outpost on the Indian Ocean’. Association for Asian Research. 28 February. Available at: www.asianresearch.org/articles/2528. html (7 July 2007) Nolan, P. (2001). China and the Global Economy: National Champions, Industrial Policy and the Big Business Revolution. London: Palgrave Macmillan. Nolan, P. (2004a). China at the Crossroads. Cambridge: Polity Press. Nolan, P. (2004b). Transforming China: Globalization, Transition and Development. London: Anthem Press. Onishi, N. (2007). ‘Japan Sets Referendum on Pacifist Constitution’. New York Times, 14 May. Pehrson, C. J. (2006). ‘String of Pearls: Meeting the Challenge of China’s Rising Power across the Asian Littoral’. Carlisle: Strategic Studies Institute, US Army War
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College. Available at: www.strategicstudiesinstitute.army.mil/pdffiles/PUB721.pdf (7 July 2007) Pei, M. (2006). China’s Trapped Transition: The Limits of Developmental Autocracy. Cambridge, MA: Harvard University Press. Perera, Q. (2006). ‘Sri Lanka Should Strive to Gain Additional Garment Export Markets’. Asian Tribune, 11 May. Available at: www.asiantribune.com (7 July 2007) Phillips, R., and J. Henderson (2009). ‘Global Production Networks and Industrial Upgrading: Negative Lessons from Malaysian Electronics’. Journal Entwicklungspolitik (Austrian Journal of Development Studies), 25 (2): 38–61. Pieterse, J. V. (2008), ‘Globalization, the Next Round: Sociological Perspectives’. Futures, 40 (8): 707–20. Pomeranz, K. (2001). The Great Divergence: China, Europe and the Making of the Modern World. Princeton: Princeton University Press. Qian, W. Y. (1985). The Great Inertia: Scientific Stagnation in Traditional China. London: Croom Helm. Rapa, A. (2007). ‘The Socioeconomic Impacts of Logging in Papua New Guinea’. Manchester: School of Environment and Development, University of Manchester. MSc Dissertation. Shirk, S. L. (2007). China: Fragile Superpower. New York: Oxford University Press. Taylor, I. (2006). China and Africa: Engagement and Compromise. London: Routledge. Thompson, E. P. (1963). The Making of the English Working Class. New York: Vintage Books. Wang, C.-I. (2009). ‘The Changing Nature of China’s Economic Governance’. Manchester: Manchester Business School, University of Manchester. PhD thesis. Whitley, R. (1999). Divergent Capitalisms: The Social Structuring and Change of Business Systems. Oxford: Oxford University Press. Williams, K. (2000). ‘From Shareholder Value to Present-Day Capitalism’. Economy and Society, 29 (1): 1–12. Winters, L. A., and S. Yusuf (eds) (2007). Dancing with Giants: China, India and the Global Economy. Washington, DC: The World Bank and Singapore: Institute of Policy Studies. Yang, Y.-K. (2006). ‘The Taiwanese Notebook Computer Production Network in China: Implications for Upgrading of the Chinese Electronics Industry’. CRITO Working Paper 381. San Diego: Center for Research on Information Technology and Organization, University of California. Yusuf, S., and K. Nabeshima (2009). Tigers Under Threat: The Search for a New Growth Strategy by Malaysia and its Southeast Asian Neighbors. Washington, DC: The World Bank. Zhao, H. (2007). ‘Contesting Confucius’. New Left Review, 44: 134–42.
2 China, South Africa and the Lewis Model John Knight
Introduction The Lewis model (Lewis 1954) provides a good framework for explaining the ways in which the fruits of economic development are spread. Within a competitive market system, it is only when the economy emerges from the first, labour-surplus, classical stage of the development process and enters the second, labour-scarce, neoclassical stage that real incomes necessarily begin to rise generally. Up to that point the benefits of economic growth can accrue in the form of the absorption of surplus labour and not necessarily in the form of generally rising real incomes. In this chapter we examine labour market aspects of two of the countries included in the UNU-WIDER research project Southern Engines of Global Growth – China and South Africa. These are both countries on which I have conducted research in separate research projects. A comparison is potentially fruitful; the two economies have both interesting similarities and interesting differences. China is a labour-surplus economy par excellence and South Africa, although historically labour-scarce, has become a labour-surplus economy. Both have a large rural–urban divide; both have had similar policies towards rural–urban migration; in both countries rural–urban migration has increased and is in the process of changing form. Both have formal sector wages well above the level that would be determined by labour market forces, although for somewhat different reasons. They differ, however, in their rates of growth of the economy and of the labour force, and hence in the extent to which surplus labour is being productively absorbed. The progress of each economy is assessed against the stages of development outlined in the Lewis model.
The Lewis model The Lewis model is too well known to require formal elaboration here. Recall that the turning point comes through two possible mechanisms. One 27
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concerns the marginal physical product of labour in the rural (or agricultural, or informal) sector. As labour leaves this sector, so the ratio of land and natural resources to labour eventually improves sufficiently for the marginal product of labour to rise. The second mechanism is the possible improvement in the terms of trade between agriculture and industry as the supply of marketed food falls or the demand for it rises, or both, causing the value of the marginal product of labour in agriculture to rise. The supply price of rural labour is related to the marginal product or the average product of labour, depending on migrant objectives; Lewis assumed that the average product would be relevant until the marginal product exceeded it. A rising marginal product thus directly or indirectly increases the supply price of rural labour, and this is reflected in an eventually upward-sloping supply curve to the urban sector. Accordingly, further transfer of labour to the urban sector raises the market-determined real wage in that sector. The process described above assists broad understanding of the way in which several of the currently developed market economies, and also some recently successful industrializing economies such as South Korea and Taiwan, achieved generally rising living standards. However, as a description of the development process of currently poor economies, the Lewis model requires several qualifications or amendments. First, there is unlikely to be a clear-cut distinction between the classical and the neoclassical stages, for two reasons: spatial heterogeneity and imperfect labour mobility mean that some areas experience labour scarcity before others; and the supply price of rural labour is more likely to rise gently than to jump sharply, so that the supply curve to the urban sector will curve upwards gradually. The second qualification is that in many cases it is not possible to equate the agricultural sector with the rural sector or the informal sector, nor industry with urban or formal. Rural industry can be an important source of employment, and the urban informal sector can be an important store of surplus labour. Third, there can be capital accumulation and technical progress in the rural sector, which raises the average product and hence possibly the supply price of rural labour before the labour outflow itself has its effect on the supply curve. Fourth, the formal sector real wage may be determined by non-market forces at a level that is above the market-clearing wage. The efficiency wage, labour turnover, and profit-sharing theories of wages, as well as institutional or bargained wage determination, are all contenders. This wage may either be set independently of the market-determined wage or bear some positive relationship to it. Fifth, the development of the urban, or industrial, or formal sector can itself lead to the creation of pressure groups and swing the balance of power
The Lewis Model
29
towards those in that sector, to the detriment of those remaining outside it. This urban bias in economic policies can harm the rural sector and thus delay its benefiting from the fruits of economic growth (Knight and Lenta 1980; Knight et al. 2006). Sixth, the growth rate of the urban, or industrial, or formal demand for labour may be inadequate in relation to the growth rate of the labour force. If the difference between the labour force and formal sector employment increases, the economy moves away from the turning point instead of towards it.
Trends in the Chinese labour market This section is based on my research on the Chinese labour market over more than a decade and which culminated in the book with Lina Song, Towards a Labour Market in China, published in 2005. Much of the book is concerned with the transition from the former centrally planned and controlled labour system towards the creation of a labour market. Here I concentrate on the development aspects rather than the transition aspects of this evolution. Under central planning China was compartmentalized by an ‘invisible Great Wall’ between rural and urban areas. Despite its having been a peasant-led revolution, there was a large rural–urban divide in incomes. The disbanding of the communes and the restoration of incentives raised peasant incomes but, as urban reforms advanced, the ratio of urban to rural household income grew, and it now stands at a record level of over 3 to 1. This reflects the unbalanced nature of political influence, however latent it might be (Knight and Song 1999; Knight et al. 2006). The Chinese economy has been a labour-surplus economy par excellence. Even at the time of liberation almost all the arable land was in use, and since then the rural population and labour force have more than doubled. In the 1980s the increase was absorbed mainly by rural industrialization, but as the urban reforms progressed the main absorption of the growing rural labour force was rural–urban migration. The remarkable growth of the Chinese economy – averaging nearly 10 per cent per annum over the last quarter century – and in particular the growth of the urban economy required a great inflow of labour into the cities and towns. This need was accentuated by the slow growth of the urban-born labour force. The draconian one-child family policy, introduced in the late 1970s, began to slow down the growth of the urban-born labour force from the mid-1990s onwards. There are now probably over 100 million rural– urban migrants in China. The phenomenon has been referred to as ‘the greatest migration in human history’. However, rural–urban migration has taken a peculiar form. In principle, the large gap between urban and rural income per capita provides a great
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incentive for migration, and there is apparently no shortage of would-be migrants. However, the Chinese government has controlled and curbed the inflow of migrants into the cities, partly to protect the privileged urban people against labour market competition and partly to avoid the ills of excessive rural–urban migration that are evident in parts of the developing world. Rural–urban migrant flows are regulated to meet the urban demand for migrant labour, and migrants have been allowed into the cities only on a temporary basis. Urban settlement of migrants, and conferment of the rights of urban people, is made very difficult, although it is increasingly occurring, especially in the smaller cities. In the late 1990s the policy of retrenchment of urban workers from the state-owned enterprises – a policy that was forced on government by increased loss-making, and the consequent fiscal costs – produced a slowdown in the inflow of rural–urban migrants. Many millions of urban workers lost their jobs, and open unemployment became a major problem for the first time. The true rate of urban unemployment rose from 4.2 per cent in 1990 to at least 11.5 per cent in 2000 (Knight and Xue 2006). City governments, trying to protect their residents, responded by curbing the employment of migrants (Knight and Song 2005: chapter 6). Rural–urban migrants are at great and discriminatory disadvantage in the urban labour market. They have to take the least attractive jobs – the jobs that urban-born people do not want. Their wages are lower, and they have few of the rights that urban residents normally possess, such as rights to pensions, to health and unemployment insurance, and so on (Knight and Song 2005: chapter 5). Despite these disadvantages, millions of rural workers see migration as the way to improve their incomes. However, most of them return home permanently after an urban spell, and those who keep coming back retain close links with their rural households. Table 2.1 provides summary information on the main trends in the Chinese labour market between the census years 1990 and 2000. The labour force grew by 86.5 million over that decade, or by 1.3 per cent per annum. We see that the increase was very largely absorbed into the urban economy. The urban labour force grew by 74.2 million, or by 3.6 per cent per annum, whereas the rural labour force grew by only 14.1 million, or by 0.2 per cent per annum. In fact, the rural labour force peaked in 1995 and then began to fall gently. The table shows the sharp rise in urban unemployment. The 3.1 per cent per annum growth in urban employment masks a sharp fall (by 3.0 per cent per annum) in state and collectively owned enterprise employment, and a huge rise (by 15.7 per cent per annum) in private sector employment, much of it informal. There was also a redistribution of economic activities in the rural areas. Employment in rural industry grew by 3.3 per cent per annum, private and individual enterprise employment by 9.8 per cent per annum, and the number of household workers (essentially farmers) fell by no less
The Lewis Model 31
Table 2.1 The labour force in China: Its level, distribution and change, 1990–2000 1990
2000
Change 1990–2000
% of % of % of Million total Million total Million total Labour force 651.3 Urban 174.2 Unemployed 3.8 Employed 170.4 Formal 139.0 Informal 31.4 Rural 471.4 TVE employment 92.7 Private and individual 16.0 enterprises Household workers 368.4
100.0 737.8 26.7 248.4 0.6 16.9 26.2 231.5 21.4 96.0 4.8 135.5 73.3 489.3 14.2 128.2 2.5 40.7 56.6
320.4
% change p.a.
100.0 865 33.7 74.2 2.3 13.1 31.4 61.1 13.0 −43.0 18.4 104.1 66.3 12.2 17.4 35.5 5.5 24.7
100.0 85.8 15.1 70.6 −49.7 120.3 14.1 41.0 28.6
1.3 3.6 16.1 3.1 −3.0 15.7 0.2 3.3 9.8
43.3 −48.0
−55.5
−1.4
Source: Knight and Song (2005); tables 2.2 and 2.3, from official statistics.
than 55.5 million, or by 1.4 per cent per annum. In summary, Table 2.1 shows a rapid reallocation of labour away from agriculture and towards the urban sector. In the decade of the 1990s China was rapidly redeploying its surplus rural labour. These trends continued over the five years between the 2000 population census and the 2005 1 per cent population sample survey (National Bureau of Statistics 2006). Whereas rural employment as a whole declined by only 4.4 million, rural household workers fell in number by 81.1 million, and by 2005 they comprised only 44 per cent of the rural total. Urban employment rose by 41.8 million, equivalent to a growth rate of 3.4 per cent per annum. Moreover, urban private sector employment grew by 64.8 million, as the state and urban collective sectors continued to decline. Because the rate of growth of urban-born workers was very slow, much of the additional urban employment reflected rural–urban migration. Has the remarkable growth of the Chinese economy propelled it into the second, labour-scarce, stage of the Lewis model? Recall the prediction that, for an economy with free labour mobility and market-clearing, when labour becomes scarce, both urban real wages and rural real incomes will begin to rise more rapidly. Of course, household real incomes have risen in recent years, in rural as well as in urban China. However, the growth in real income per capita in rural China – at 5.8 per cent per annum over the period 1989–2005 – has been well behind the corresponding annual growth in real GDP per capita (8.7 per cent). By contrast, the growth of urban real wages has been rapid – by 8.0 per cent per annum over those 16 years – but this is probably not the result of growing labour scarcity.
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Urban workers continue to be protected against competition from rural– urban migrants, and we have evidence of profit-sharing in the (predominant) state-owned sector. As profitability has been restored, and has grown, in the state-owned enterprises, so wages have risen. Wages are sensitive to the profitability of the employer. This can be explained by a variant of efficiency wage theory. The culture of the Chinese danwei is such that workers expect to share in its success and will reduce their effort if they do not share it (Knight and Shi 2005). The benefits of economic growth are thus unevenly spread. For this and other reasons, labour market inequalities are growing (Knight and Song 2005: chapters 3, 4 and 7). If the rising wages of urban residents cannot be taken as an indicator of labour shortage, we must look to the wages of rural–urban migrants. The issue remains clouded by the lack of a reliable index of migrant wages, at both province and national levels. Over the period 2001–5 the hourly real wages of migrants in a five-city survey rose by 32 per cent, and by 22 per cent in the case of those with no more than middle school education (Park et al. 2007). However, this is not conclusive evidence: according to the official national household surveys, the real income per capita of rural households rose on average by 36 per cent over the same four years (deducting income transfers makes no difference). It is possible that this increase in rural incomes was an endogenous response to outmigration. However, the growth of rural incomes could have been exogenous, on account, for instance, of more supportive agricultural policies such as the recent abolition of the agricultural tax, and this would have raised the supply price of rural– urban migrants. An inconclusive literature on the Lewis turning point is emerging. For instance, Cai et al. (2007), Cai (2007, 2008) and Wang (2008) argue that the turning point has been reached in China, whereas Meng and Bai (2007) provide contrary evidence from a small case study of migrants employed in Guangdong. Both Kwan (2008) and Minami and Ma (2008) examine China’s agricultural sector and conclude that the national rate of surplus labour there has fallen but remains high. We still have too little evidence to overturn the previously conventional view that there is much surplus labour in rural China, especially in the interior provinces. Nevertheless, the surplus is declining: we know that the number of people working in rural areas began to fall in the mid-1990s, and that the fall in farm labour was dramatic. If China’s remarkable economic growth continues, the labour scarcity that has already been felt in a couple of the growth points will gradually spread across China. Moreover, as migrants move up the job skill ladder in urban enterprises, so the economic need for them to be stable, long-term workers will grow, and this will be recognized by employers and government. The current predominant pattern of temporary or oscillatory migration will increasingly give way to permanent urban settlement, as it has in other parts of the developing world (Knight and Song 2005: chapter 5).
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33
Trends in the South African labour market It is arguable that the main problem facing South Africa is unemployment and its rise. Its economic, social and political consequences threaten the country’s future economic growth. Developments in the labour market hold the key to South Africa’s prosperity or penury. South Africa’s transition to democracy took place in 1993, with the transfer of power to the African National Congress (ANC). Before that time, the economy had been characterized by labour market discrimination on racial lines. Most Africans lived in the rural areas but the majority of livelihoods were obtained in the urban areas. In the late nineteenth century, as the mines and farms were developed, South Africa was characterized by unskilled labour shortage. Indeed, this led to the importation of Indian and Chinese labour. However, by the late twentieth century, the lands allocated to Africans were heavily populated and had become labour reserves. The reserves contained much underemployment, and they supplied migrant labour to the capitalist sector (Knight and Lenta 1980). Restrictions on urban settlement meant that much rural–urban migration was temporary and oscillatory – rather like China! With the advent of democracy, however, rural–urban migration accelerated and increasingly involved urban settlement. Also like China, there is in South Africa a large rural–urban income divide. Relatively unskilled wages in the formal sector are far above market-determined levels, although the reasons for this are different from those in the Chinese case. The emergent African trade union movement was important in the liberation struggle and, when it won power, the ANC formed an alliance with COSATU, the trade union coalition. The government introduced labour market policies which were favourable to organized labour with respect to, for example, bargaining rights, minimum wages, and employment protection. Trends in the labour market are reviewed by Kingdon and Knight (2007, 2009) and are summarized in Table 2.2. There are two concepts, and measures, of the labour force: the broad and the narrow. The broad measure includes all persons who are working for income or report that they want to work; the narrow measure (the normal ILO measure) excludes from the labour force those persons who, although they report that they want to work, were not actively seeking work in the previous reference period, say a week or a month. There are equivalent measures of broad and narrow unemployment. Whichever definition of the labour force is used, the labour force grew rapidly over the period 1995–2003, by 4.2 per cent per annum on the narrow measure and by 4.8 per cent per annum on the broad. There are three possible reasons for this remarkable growth: immigration, natural increase, and increased labour force participation. Net in-migration is difficult to measure, much of it being informal and even illegal. The adult population grew by 2.7 per cent per annum. The labour force participation
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rate rose sharply, for example, from 56 to 67 per cent over the eight years. The rise was greatest among African women, and is associated with rural– urban migration, rising education and changing family structures. The figures look less daunting for the period 2000–3, when the broad labour force grew by 2.6 per cent per annum. The post-2000 slowdown is associated with the effects of HIV/AIDS. The net effect of this epidemic on the labour market is unclear, however, as it may well also have retarded the growth of output and employment. Wage employment grew relatively slowly over the period, by 1.8 per cent per annum. Those not obtaining wage jobs entered self-employment or unemployment. Self-employment did indeed grow rapidly, by 5.1 per cent per annum, but it did so from a small base. Reflecting historical discrimination, the efficiency of the formal sector, and the inhospitable policies of central and local governments, the self-employment and the non-agricultural informal sectors have been, and remain, tiny by international comparison, and only part of the informal sector is characterized by freedom of entry. Most of the increment to the labour force went into unemployment. The narrow unemployment rate rose from 17 to 28 per cent over the period, and the broad from 29 to 42 per cent. Which measure is the more appropriate in the South African context? Kingdon and Knight (2006a) devised three tests and concluded from their analysis that the broad unemployment is the better measure: many of the unemployed found it not worthwhile to search and became ‘discouraged workers’. Is the unemployment of a voluntary or involuntary nature? Kingdon and Knight (2004), in their investigation of this issue, concluded that the great majority of unemployment is indeed involuntary. It appears that the limited scope for entering the informal sector pushed many people into unemployment. If unemployment is high and rising, the prediction is that there will be downward pressure on market-determined wages. However, in much of the formal sector relatively unskilled wages are determined by collective bargaining and institutional arrangements, and such wages are maintained well above market-determined levels. Kingdon and Knight (2006b) have shown the existence of a ‘wage curve’ across different areas of South Africa, that is, a negative effect of unemployment on wages for a wide range of unemployment rates. This implies that there is some partial flexibility of wages to unemployment, but it does not extend to the most organized sectors of the economy. An annual survey of medium- and large-scale enterprises shows that between 1995 and 2003 real wages rose by 1.8 per cent per annum. For wage employment as a whole, however, Table 2.2 shows a gentle decline of 1.6 per cent per annum. By contrast, the fall in real earnings of the selfemployed was dramatic (−11.4 per cent per annum). As workers crowded into the free-entry part of this sector, it bore the brunt of labour market pressures.
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Table 2.2 The labour force in South Africa: Its level, distribution and change, 1995–2003 1995 OHS 2003 LFS Change 000 Change % p.a. Labour force, narrow (000) Labour force, broad (000) Wage employment (000) Self employment (000) Unemployment, narrow (000) Unemployment, broad (000) Unemployment rate, narrow (%) Unemployment rate, broad (%) Real earnings in wage employment, 2000 prices Real earnings in selfemployment, 2000 prices
11,628 13,667 8,231 1,421 1,976 4,015 17 29 3,191
16,192 19,954 9,509 2,111 4,570 8,332 28 42 2,805
4,564 6,287 1,278 690 2,584 4,317 11 13 −386
4.2 4.8 1.8 5.1 11.0 9.6 – – −1.6
6,866
2,610
−4,256
−11.4
Source: Kingdon and Knight (2007), table 1, from official household surveys (the October Household Survey of 1995 and the Labour Force Survey of 2003).
Would greater labour market flexibility have solved the problem of rising unemployment? The rapid divergence between the supply of and demand for labour – by about 3 per cent per annum over the period under study – placed a heavy burden of adjustment on the labour market, which would have put great strain even on the most flexible of labour markets. In fact, it appears that major segments of the South African labour market are not flexible. This imposes an exceptional burden on the more flexible segments, the consequence of which has been rising unemployment and associated poverty. Between 2003 and 2007 the number of unemployed in South Africa actually declined and the unemployment rate fell by over 5 percentage points, to 36 per cent on the broad measure and to 23 per cent on the narrow measure. Has unemployment turned the corner? The question is posed by Kingdon and Knight (2009). In order to judge whether this is a sustainable new trend, we need to understand the reasons for the change. On the supply side, over that four-year period the annual growth rates of the broad and narrow labour force were only 1.2 and 2.0 per cent respectively. This remarkable fall presents a puzzle that is accentuated by the large influx of Zimbabwean workers as economic conditions in that country deteriorated. The slowdown in the growth of the labour force and consequent reduction in unemployment were mainly due to the spread of HIV/AIDS among South Africans. Yet no comfort can be drawn if one great scourge is merely replaced by another. There was some improvement also on the demand side of the labour market. With GDP growth accelerating to 5 per cent per annum over the period 2003–7, total employment grew by no less than 3.7 per cent per annum.
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Inevitably, South Africa’s economy suffered from the world economic recession that began in 2008. However, if those engines of global growth, China and India, were to grow rapidly over the coming years, and thus to increase their demand for raw materials, that would augur well for South Africa’s longterm growth. There is some prospect that South Africa will move towards, rather than away from, the Lewis turning point.
Conclusions The Lewis model is a helpful framework for analysing labour market trends in both China and South Africa. However, it requires modification: in neither country does the evidence correspond well to the theoretical model. In neither does the relative price mechanism that is hypothesized in the model operate: changes in agricultural prices have been determined more by reduced government price interventions and by trade liberalization. In both countries we find powerful urban bias in economic policies and formal sector wages well above the market-clearing level. Their labour markets are characterized by segmentation and inflexibility. Underlying the differences in their recent labour market performance is the disparity in the growth rates of the Chinese and South African economies. China’s rapid economic growth is the result of a combination of many factors, including the continued elimination of inefficiency associated with central planning, the employment of previously underemployed resources, including labour, the opening up of the economy to trade so as to exploit China’s comparative advantage in unskilled-labour-intensive activities, and the investor confidence which is self-reinforcing while the virtuous circle of growth continues. South Africa’s relatively slow growth rate over the last decade is associated with its having a relatively mature economy, with resources other than unskilled labour being fully employed, and low investor confidence – on account, perhaps, of the social instability and crime that stem from high unemployment and concern about the extent of labour protection. Its comparative advantage in natural-resource-intensive activities such as gold, diamonds and minerals has not provided scope for the rapid expansion of exports. There is a danger that low business confidence and inadequate investment make things worse in the labour market, which by various processes of cumulative causation feeds through into self-fulfilling pessimism about the economy. In both countries formal sector workers are well protected against competition from rural people and the unemployed. In China, however, the slow growth of the labour force, in particular the urban-born labour force, has provided great scope for rural people to be absorbed productively into the urban economy. There is as yet little sign that migrant wages in general are rising on account of emerging labour scarcity, that is, that China has
The Lewis Model 37
now reached the Lewis turning point. However, the trend is in the right direction. In South Africa the rapid growth of the labour force in relation to the slow growth of formal sector employment means that the growing residual labour force has gone into low-income self-employment activities or into unemployment. Much of the rural–urban migration has not led to productive absorption into the urban economy. There are good reasons for optimism about the South African economy. These include competent macroeconomic policymaking, a strong entrepreneurial class, sound infrastructure and buoyant prospects for world mineral markets. However, South Africa’s dismal labour market trends, with their implication of rising numbers in poverty, pose a threat to the success of the economy as a whole. They are South Africa’s Achilles Heel. It would be interesting to examine labour market trends in the other two countries included in the UNU-WIDER project on Southern Engines of Global Growth – India and Brazil. A comparative assessment of all four countries within the framework of the Lewis model would help to indicate in what ways, how widely, and for what reasons the fruits of economic growth are being shared within these economies.
References Cai, F. (ed.) (2007). Reports on China’s Population and Labor No. 8: The Lewisian Turning Point and the Challenge of Policies. Beijing: Social Sciences Academic Press (in Chinese). Cai, F. (ed.) (2008). Reports on China’s Population and Labor No. 9: Linking up Lewis and Kuznets Turning Points. Beijing: Social Sciences Academic Press (in Chinese). Cai, F., Y. Du, and C. Zhao (2007). ‘Regional Labour Market Integration since China’s WTO Entry: Evidence from Household-level Data’. In R. Garnaut and S. Ligang (eds), China: Linking Markets for Growth. Canberra: Asian Pacific Press, 133–50. Kingdon, G., and J. Knight (2004). ‘Unemployment in South Africa: The Nature of the Beast’. World Development, 32 (3): 391–408. Kingdon, G., and J. Knight (2006a). ‘The Measurement of Unemployment when Unemployment Is High’. Labour Economics, 13: 291–315. Kingdon, G., and J. Knight (2006b). ‘How Flexible are Wages in Response to Local Unemployment in South Africa?’. Industrial and Labor Relations Review, 59 (3) April: 471–95. Kingdon, G., and J. Knight (2007). ‘Unemployment in South Africa, 1995–2003: Causes, Problems and Policies’. Journal of African Economies, 16 (5) November: 813–48. Kingdon, G., and J. Knight (2009). ‘Unemployment: South Africa’s Achilles’ Heel’. In J. Aron, B. Kahn and G. Kingdon (eds), South African Policy Under Democracy. Oxford: Oxford University Press. Knight, J. B., and G. Lenta (1980). ‘Has Capitalism Underdeveloped the Labour Reserves of South Africa?’. Oxford Bulletin of Economics and Statistics, 42 (3): 157–202. Knight, J., and J. Xue (2006). ‘How High is Urban Unemployment in China?’. Journal of Chinese Economic and Business Studies, 4 (2): 91–107.
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Knight, J., and L. Shi (2005). ‘Wages, Firm Profitability and Labour Market Segmentation in China’. China Economic Review, 16 (3): 205–28. Knight, J., and L. Song (1999). The Rural-Urban Divide. Economic Disparities and Interactions in China. Oxford: Oxford University Press. Knight, J., and L. Song (2005). Towards a Labour Market in China. Oxford: Oxford University Press. Knight, J., L. Shi, and L. Song (2006). ‘The Rural-urban Divide and the Evolution of Political Economy in China’. In J. Boyce, S. Cullenberg, P. Pattanaik, and R. Pollin (eds), Human Development in the Era of Globalization. Essays in Honor of Keith B. Griffin. Northampton, MA: Edward Elgar. Kwan, F. (2008). ‘Labour Productivity and the Incidence of Surplus Labour: Experience from Chinese Agriculture during Reform’. Paper prepared for the Chinese Economic Association (UK) Annual Conference, Cambridge University, April. Lewis, W. A. (1954). ‘Economic Development with Unlimited Supplies of Labour’. The Manchester School, 22 (May): 139–92. Meng, X., and N. Bai (2007). ‘How Much Have Wages of Unskilled Workers in China Increased?’. In R. Garnaut and S. Ligang (eds), China: Linking Markets for Growth. Canberra: Asian Pacific Press, 151–75. Minami, R., and X. X. Ma (2008). ‘The Turning Point of Chinese Economy: Compared with Japanese Experience’. Paper prepared for a meeting on the Chinese Labour Market, Tokyo University, December. National Bureau of Statistics of China (2006). China Statistical Yearbook 2006. Beijing: China Statistics Press. Park, A., F. Cai, and Y. Du (2007). ‘Can China Meet Her Employment Challenges?’. Paper prepared for a conference on the Chinese Economy, Stanford University, November. Wang, D. (2008). ‘Lewisian Turning Point: Chinese Experience’. In F. Cai (ed.), Reports on Chinese Population and Growth No. 9: Linking up Lewis and Kuznets Turning Points. Beijing: Social Sciences Academic Press (in Chinese).
3 India’s Economic Growth: Lessons for the Emerging Economies Suparna Chakraborty
Introduction During the last two decades, India’s name has figured prominently amongst the list of emerging economies. After decades of slow growth, India entered into a new phase of economic reconstruction during the mid-1980s and the 1990s. In comparison to a measly 1 per cent growth rate in the years following independence from the British Raj in 1947, Indian GDP per capita growth rate reached 1.5 per cent in the 1980s, still not quite up to the benchmark but not dismal either. The picture drastically changed in the 1990s, when the growth rate of GDP per capita reached an average of 5 per cent, much beyond the benchmark and a small miracle on its own. This economic transition has been attributed to many factors, among which the development of the software sector and a pro-liberalization approach of the government, as opposed to the more centrally planned socialist approach of yesteryear, have been hailed as the primary drivers of growth. There are quite a few papers that agree with the contribution of the Indian information technology (IT) sector as the catalyst of economic growth. On the one hand, we have Singh and Srinivasan (2004), who argue in favour of the important role played by the Indian IT sector in promoting growth. This view, perhaps not surprisingly, finds great support among the IT pioneers of India. NR Narayana Murthy, chairman of Infosys, one of the fastest-growing IT companies originating in India, hails the changing climate in India by arguing that ‘...the economic reforms of 1991 changed the Indian business context from one of state-centered, control orientation, to a free, open market orientation – especially for hi-tech companies. It allowed Indian companies to start competing effectively on a global scale.’1 At the other end of the spectrum, we have Subramanian and Rodrick (2004), who investigate ‘...a number of hypotheses about the causes of this growth – favourable external environment, fiscal stimulus, trade liberalization, internal liberalization, the green revolution, public investment – and find them wanting’. They argue that ‘...growth was triggered by an attitudinal 39
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shift on the part of the national government towards a pro-business (as opposed to pro-liberalization) approach’. In a previous paper (Chakraborty 2006) we examined the role of technology or productivity growth in bringing about the economic miracle in India using a neoclassical framework following the growth accounting procedure of Kydland and Prescott (1982), where technology is treated as an external shock. However, in addition to merely identifying the macroeconomic fundamentals2 that have played a role in Indian economic development, another issue of interest is ‘how’ they affect the economy. Thus a study numerically accounting for Indian economic growth is incomplete unless, in addition to identifying the primary forces that were particularly successful in generating economic growth in India, we also identify the ‘transmission channels’ through which these factors worked. In this chapter, we concentrate on this second issue, and this leads us to examine the growth of India during the 1980s and the 1990s through the lens of business cycle accounting. We further extend our analysis to see whether changes in taxes played any role in generating increased growth by comparing the time series of tax rates in India with the results of our business cycle accounting. The business cycle accounting (henceforth BCA) procedure, a relatively new procedure,3 is based on the fundamental observation that there are primarily three channels or ‘wedges’ through which any external friction or policy can affect an economy: through an impact on productivity, through an impact on the labour market, and through an impact on the investment market. For example, if we believe that economic liberalization and free market policies were the primary forces behind Indian economic growth, two obvious channels through which these policies affected the economy seem to be the productivity channel and the investment market channel, where the frictions that made it costly for Indian firms to gain access to funds were considerably lessened, if not completely eradicated. Economists also consider government spending to be an important channel through which government can directly affect the economy. There are a couple of crucial points that one needs to keep in mind while conducting a BCA analysis for any economy. In an ideal world with no frictions, the economy would be able to achieve the first best outcome, or grow at a balanced growth rate as assumed in real business cycle literature. However, if an economy is subject to market frictions, these frictions would prevent the economy from achieving the first best outcome and the economy would move away from a balanced growth path. In a real business cycle model, on which the BCA procedure is based, these frictions or ‘wedges’ would show up as distortions in the first order conditions. What BCA tells us is that there are primarily three ways (four if you consider the government expenditure channel) through which external frictions affect an economy. If we know the numerical value of the frictions exactly, then, if we feed in all the frictions jointly in a benchmark real business cycle model, we should be able to replicate the data
India’s Economic Growth 41
exactly. The question we are interested in is: which of these frictions play a major role in the economy? Here the trick is to allow for the frictions one by one to assess their importance by evaluating how well a friction on its own can replicate the data. This exercise is called ‘decomposition’. From a methodological perspective, to apply the BCA procedure to India, we take a neoclassical growth model and extend it to include the timevarying efficiency or productivity wedge that is the Solow residual, labour wedges that are modelled as labour income taxes, investment wedges that resemble taxes on investment expenditure, and government wedges that are actually government spending. The solution of the model involves two parts: since the wedges represent external market frictions, there is no data available on them, and we need to use the results of our model to calculate the wedges (this step is somewhat like back-calculating the value of frictions). The primary idea here is that the wedges summarize all possible frictions that affect the economy. Hence in theory the data on output, investment or labour that we observe is a function of these wedges. Since we know the data from national income accounts, we can use them and the policy functions from our model to calculate the wedges. Next, we feed in the time series of our model-generated wedges one by one and in various frictions and check how well they match up with the observed output. Our decomposition results show that primitives affected the Indian economy primarily by causing changes in productivity. Labour market frictions or the investment market channel were not particularly important. Increasing government expenditure that propped up demand also supported the growth in productivity. If one compares the Indian experience with that of Japan in the reconstruction period after the Second World War, the common channel of growth that one can identify is a productivity increase. Note that we are not saying that the primitives, in the form of increased impetus towards liberalization and pro-business policies, were not important. In fact we are not commenting on the primitives at all at this point. Our premise is that, whatever means (or primitives as we call them) any developing nation decides to implement, they will best serve to jump-start growth if they are directed to improve productivity. At least, that is the lesson that the Indian miracle seems to teach us. Note that the wedges or frictions that affect the first order conditions of our model, at least at face value, resemble taxes. In other words, the impact of these frictions is similar to the impact of changing taxes, though the frictions represent much more than just taxes. They embody all forms of distortions that can affect an economy. For example, the labour wedge, at face value, resembles a tax on labour income. An increase in the labour wedge is associated with an economic depression just as an increase in labour income tax would be. However, it is entirely possible that the labour income tax rates of the economy do not change much, but other factors affecting the labour market do. For example, union bargaining for higher wages would
42
Suparna Chakraborty
result in a slow growth and would be captured by the ‘labour wedge’ channel. The effect is thus similar to what would have happened had the labour income tax rates changed. In this context, it is worthwhile to note that, while the tax rates in an economy are not the be-all and end-all of wedges, they do comprise one of the important factors that show up as wedges in our model and can have far-reaching consequences for the economy. In addition, they are the only market friction for which we have reliable data as provided by the government of a country. Hence, one natural curiosity is to see to what extent changing tax rates affect an economy. For example, if we notice from our decomposition results that the labour wedge is important in accounting for fluctuations in an economy, the natural question would be to what extent it is due to changes in labour income tax rates. For India, note that neither the labour wedge nor the investment wedge turns out to be important in accounting for the economic fluctuations. Does this imply that labour and investment taxes did not change? Or is it that the effect of changing labour and investment taxes were mitigated by opposing forces that show up in our model as the labour and the investment wedge? To answer this question, we compute the effective labour and investment taxes for the Indian economy and find that, while investment taxes did not change much, there was a steady increase in labour income taxes that by itself would have discouraged growth, but was obviously overwhelmed by positive developments in the economy that kept the labour wedge in our model from worsening. The rest of the chapter is organized as follows: in the next section we outline our model, followed by a discussion of the methodology and the results. Next we present the results of comparing the wedges with the taxes, and the last section concludes the chapter.
Business cycle accounting BCA procedure uses a standard growth model with four stochastic variables or wedges: the efficiency wedge At, which appears like time-varying productivity; the labour wedge τnt, which acts like a time-varying tax on labour income; and the investment wedge τxt , which acts like a tax on investment expenditure. Further, per capita government expenditure g t is also considered as a ‘government wedge’, which can have a significant impact on the economy. Each of the wedges represents the overall distortion to the relevant first order conditions. Theoretical model We assume that in every period the economy comprises a set of Nt identical and infinitely lived agents, who are endowed with one unit of time that can be used for work and leisure. The economy also consists of
India’s Economic Growth 43
measure one of identical firms that own the production process. For purposes of analysis, we assume that population grows at a constant rate every period and is exogenously determined. We assume that there is one output that is produced and consumed in the economy. There is a government that collects income and investment taxes and uses the proceeds to finance government expenditure and transfers in such a fashion as to balance the public government budget every period. Given the structure of the economy, we can summarize the problems facing the agents of the economy as follows. Representative consumer’s problem The representative consumer in the economy chooses per period consumption ct and labour lt to maximize present discounted value of lifetime utility. The consumer receives income from two sources: labour income and rental income from capital. In addition, the consumer also receives some transfers from the government. The proceeds of the income and transfers are used to finance consumption and investment expenses. Further, the consumer has to pay income (τnt ) and investment taxes (τxt ) to the government at an exogenously determined rate. Thus the representative consumer’s problem can be written as: ∞
Max E 0 ∑ t u(c t , 1 − l t ) t =0
subject to: c t + (1 + t xt )xt ≤ wt lt (1 −
nt
) + rt kt + Trt
k t+1 ≤ (1 − )k t +x t nonnegativity constraints where kt denotes per capita capital stock, xt denotes per capita investment, after-tax labour income is wtlt (1τnt ) and rental income is rtkt where wt is the wage rate and rt is the rental rate on capital stock, b is the discount factor, d is the depreciation rate on capital stock, and Trt denotes transfers from the government received at period t. Representative firm’s problem Every period, the representative firm produces a single output using labour and capital to maximize profits. Output is subject to an exogenously given production technology. Hence the representative firm’s problem every period is given by: Max y t w tlt rtkt subject to: y t F (kt, Atlt)
44
Suparna Chakraborty
where yt denotes per capita output and At denotes productivity. For my analysis I assume that the production technology is labour-augmenting. The long-run rate of technical progress is denoted by (1 gz). Equilibrium Equilibrium is defined by a vector of price functions { wt,lt }∞ t0 and a vector of such that the price and allocation funcallocation functions {ct,lt, kt1, yt}∞ t0 tions satisfy the following equations every period: (1 )kt g t yt
ct
kt
yt
F( kt , At lt )
1
ult ( ct , lt ) uct ( ct , lt )
(1 W nt )Flt ( kt , At lt )
Et uct 1( ct 1 , lt 1 )( Fkt 1( kt 1 , At 1lt 1 ) (1
)(1 W xt 1 )) (1W xt )uct ( ct , lt )
(1)
(2)
(3)
(4)
where notations like uct, ult, Flt, Fkt etc. denote the first derivative of the utility function and production function with respect to different arguments like consumption, labour and capital. Equation (1) represents the resource constraint faced by the economy every period and is the output market clearing condition. Equation (2) shows that output every period is subject to the production technology. Equation (3) equates the marginal rate of substitution between consumption and leisure to the after-tax marginal return to labour, where, in equilibrium, the marginal return to labour or the wage rate is equal to the marginal product of labour. Equation (4) is the inter-temporal equation taking into account the fact that, in equilibrium, rental rate on capital is equal to the marginal product of capital. The four equations outlined above summarize the equilibrium conditions of the economy every period. Note that the time-varying productivity and taxes on labour income and investment expenditure distort the first order conditions and keep the economy from achieving the first best outcome. Application to India We want to apply the BCA technique to India to account for fluctuations in output during the period from 1982 to 2002. The steps involved in BCA accounting are twofold: 1. Given the parameter values and the first order conditions, we derive the time series of the wedges.
India’s Economic Growth 45
2. Once we have the realized values of the wedges, we feed in the wedges one by one and in various combinations to see to what extent our model matches the data.4 This exercise is called decomposition. In this chapter, we simplify the original method of BCA by assuming that the investment wedge τxt does not vary over time and let only productivity At, the labour wedge τnt and the government consumption wedge g t vary. The logic of our exercise is described in the technical appendix, but the basic idea is that we assume initially that the investment wedge is constant at its steady state value for simplicity. This assumption makes calculating the time series of the productivity and labour wedges easier as we do not have to first estimate how agents form expectations over time, which is necessary to get the investment wedge. Assuming investment wedges to be constant circumvents this problem. Once we get the productivity and labour wedge series, we feed them into our model. If efficiency and labour wedges jointly can well replicate the data, it means that investment wedges did not play an important role and we were right in holding them constant at their steady state value. On the other hand, if, even after feeding in efficiency and labour wedges, the model falls short of explaining the data, we argue that investment wedges (which our model assumes does not change) must in reality have varied and must have played a major role in the economy. In the technical appendix, we discuss this method in greater detail and also explain how we derive the time series for the wedges. For our exercise we need to specify the utility and the production functions and take into account the population growth rate while deriving the first order conditions. We assume a Cobb–Douglas production function and a standard monotonically increasing and strictly concave utility function represented by: u( ct , lt )
yt
( ct (1 lt )1 )1 , when z 1 1 a log ct (1 ) log(1 lt ), when 1
kt ( At lt )1
(5)
(6)
The functional forms that we use are the same as those used by Chari et al. (2002) as well as by Prescott and Hayashi (2002). Note that, on a balanced growth path, the variables ct, kt1, yt and g t grow at a rate (1+gz). The model does not allow us to calibrate for the parameter values, as we do not know the steady state value of the wedges. So our first step for solving the model is to pick parameter values from literature. We assume or the rate of time preference to be 0.95, as is commonly used in business cycle literature.
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Suparna Chakraborty
We take to be 0.8251 and to be 0.36 from Chakraborty (2006) and we assume a depreciation rate =0.25, which we derive from the Indian tax code that allows non-residential corporations to claim tax relief for depreciated capital stock at a maximum depreciation rate of 25 per cent. Once we have our parameter values, we can calculate the wedges from the first order conditions of the model and the data. Given the time series of the realized wedges, we are interested to get an intuitive idea of whether they look promising in generating economic growth in India. Let us begin by graphically demonstrating the evolution of GDP per capita over the period 1982 to 2002 with respect to a long-term time trend of 1.5 per cent. Figure 3.1 graphically demonstrates the index of detrended GDP per capita. The way we arrive at this figure is by first detrending GDP per capita during 1982 to 2002 at the rate of 1.5 per cent, which is the long-term trend growth rate in India that we derived by taking the average growth rate of GDP per capita from 1960 to 2002. Then, we take the value of detrended GDP per capita in 1982 as 100 and recalculate the detrended GDP per capita in the following years with respect to 100. This gives us the index of detrended GDP per capita and is quite useful in charting how GDP per capita has performed over the years. From Figure 3.1, we can summarize that GDP per capita has consistently grown above the trend growth rate in the 1980s and the 1990s, and the rate of growth has accelerated since 1991. 160
GDP per capita with respect to long term trend y(t)
140
Index (1982=100)
120 100 80 60 40 20 0 1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Years Figure 3.1 GDP per capita detrended at 1.5% Note: We plot the GDP per capita after detrending it by the long term growth rate of 1.5%. The GDP per capita of 1982 is assumed to be 100 and we plot the index.
India’s Economic Growth 47
Next, we are interested to see whether the efficiency or labour wedge could have played a role. We can also look at the government consumption wedge, which we are not plotting here for the sake of brevity. Figure 3.2 plots the index of efficiency wedge, where we take the value of At in 1982 to be 100. We find that productivity also grew from 1982 to 2002 with respect to the long-term trend, which at least intuitively is conducive to economic growth. Figure 3.3 plots the labour wedge, τnt. From Equation (3) note that an increase in labour wedge, or an increase in τnt , is associated with labour becoming costlier and would therefore cause a drop in output. Given Figure 3.3, note that, except for the period 1982 to 1983 when we find the labour wedge declining, the labour wedge has not changed much over the last two decades. Now, keeping in mind that labour wedges represent labour market frictions that keep the economy from achieving the first best outcome, we should expect economic growth to be associated with a decline in value of the labour wedge or a decline in labour market frictions. So, if the labour wedge does not show any such decline, intuitively it could not have played a role in bringing about economic growth in India. As for the dramatic decline in 1982 to 1983, we attribute it to a data misspecification,
160
Efficiency wedge
140
Index (1982=100)
120 100 80 60 40 20 0 1982 Figure 3.2
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Realized efficiency or productivity wedge
Note: We plot the efficiency wedge as a Solow residual. Once again, the effciency wedge in 1982 is assumed to be 100.
48
Suparna Chakraborty 0.15
Labor wedge
0.1
Tax rate
0.05
0 1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
−0.05
−0.1
−0.15
Years
Figure 3.3
Labour wedge
Note: We plot the labour wedge as calculated from the data and our model.
as we find that labour data shows a dramatic shift in 1982, which cannot be attributed to any drastic shifts in labour market policy. Our belief is that labour market data reporting, which has been consistent only since the mid1980s, could have somehow contributed to this big jump, and so we do not take this seriously. Decomposition In this section we show the model outcomes generated by feeding in the realized values of the wedges one by one and in various combinations in our decision rules and evaluate how well they can approximate the macro data. We solve for the decision rules of our model using the log-linearization techniques of King et al. (1988). The decision rules are derived as: ª kt « « yt « ¬lt
1
º » » » ¼
PP * kt
ª At º « » QQ * «W nt » « g » ¬ t¼
where PP is a 3×1 matrix and QQ is a 3×3 matrix of coefficients where the log deviation of a variable zt from its steady state value z is denoted by z˜t. The only exception in this specification is τnt, which is equal to τnt − τ n, where we
India’s Economic Growth 49
follow Chari et al.’s (2002) specification. Note that given decision rule for capital k˜t1, output y˜t and labour ˜l t we can implicitly derive the decision rule for consumption c˜t using the market clearing condition for final output. We begin by first stating the correlations between output, efficiency wedge and labour wedge during the period from 1982 to 2002. The correlation between GDP per capita and efficiency wedge is 0.98 and that between GDP per capita and labour wedge is 0.1. Given our model, we expect a positive correlation between productivity or efficiency wedge and output, and that is supported by correlation figures. However, the correlation between output and labour wedge is positive, though small, which indicates that frictions in labour market and output per capita moved in the same direction. This suggests that output increased despite of labour market frictions, not because of a decline in labour frictions, in which case the observed correlation would have been negative. This begets the question: was the increase in productivity enough to account for the magnitude of increase in output? To answer this question, we graphically depict the model outcomes, feeding in various realizations of wedges in our model and comparing them with data. As Figure 3.4 depicts the model outcome with the efficiency wedge alone can very well replicate the output per capita observed in India; 160 140
y(t) y(t)-model y(t)-eff y(t)-taxn
Index (1982=100)
120 100 80 60 40 20 0 1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Year Figure 3.4
Output per capita: Data and model outcomes
Note: We plot the GDP per capita from data and as generated by our model by feeding in efficiency wedge and labour wedge respectively (the legend reads y(t)-eff and y(t)-taxn)). Next, we feed in efficiency wedge, labour wedge and government consumption wedge jointly in our model and plot it (legend reads y(t)-model).
50
Suparna Chakraborty
however, the model outcome feeding in labour wedges cannot explain any of the observed data on output per capita. If we feed in efficiency, labour and government wedges jointly in our model we can almost wholly account for observed output per capita in the data, which leads us to conclude that investment wedges played a limited, if any, role in the Indian economy during the 1980s and the 1990s. This result is consistent with the view that economic growth in India was a result of a sharp increase in productivity. Government expenditure, which continued to increase over the 1990s, also aided growth by propping up demand. Labour market rigidities, on the other hand, were still prevalent and tempered growth to some extent. To verify our analysis we also look at an alternative macro variable, the capital–output ratio (Figure 3.5). The results are pretty similar to what we saw for output per capita. With efficiency wedges alone the model well replicates the data on capital–output ratio, but by feeding in labour wedges alone we cannot account for the observed capital–output ratio. However, if we feed efficiency, labour and government wedges jointly into the model, the
0.8 0.7 0.6
Ratio
0.5 0.4 0.3 0.2 0.1 0 1982
k(t)/y(t) k(t)/y(t)-model k(t)/y(t)-eff k(t)/y(t)-taxn 1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Years Figure 3.5
Capital-Output ratio: Data and model outcomes
Note: We plot the capital output ratio from data and as generated by our model by feeding in efficiency wedge and labour wedge respectively (the legend reads k(t)/y(t)-eff and k(t)/y(t)-taxn)). Next, we feed in efficiency wedge, labour wedge and government consumption wedge jointly in our model and plot it (legend reads k(t)/y(t)-model).
India’s Economic Growth 51
model outcome closely replicates the data, which supports our previous conclusion that economic development in India was a handiwork of increased productivity and increased government spending. Taxes and wedges In the introduction, we mentioned that the labour and investment wedges in business cycle accounting, at least at face value, resemble time-varying labour income taxes and investment taxes respectively. We also noted that, though market frictions other than policy-induced changes in tax rates also affect the wedges, time-varying tax rates by themselves can also affect the wedges and move the economy away from a balanced growth path. While it is difficult to get data for most frictions and we need to resort to using our model and available national income accounts data to back out the value of the wedges, we can independently get the data for tax rates and compare that with the model-generated wedges. This analysis helps us with two issues: (1) it helps us answer to what extent policy-induced changes in tax rates are responsible for the time-varying wedges and the resulting effect on the economy; (2) if the patterns of time-varying taxes and wedges do not match up, it indicates a need for us to turn our attention to other possible sources of the frictions that have the potential to affect the wedges and hence the economy. The results of our decomposition show that productivity wedges by themselves can almost wholly account for output increases in India during the period from 1982 to 2002. This result itself tells us that the role of labour wedges or investment wedges is limited. In fact, the labour wedge (Figure 3.3) shows little fluctuation, except during 1982–3, which we chalk up to data misspecification. Next, we plot the labour income tax rates from the Indian economy. The data is provided by the Reserve Bank of India.5 In our model, we do not have heterogeneous agents, where one group owns labour while the others are entrepreneurs, so we do not have a distinction between labour income taxes and corporate taxes. In our model, the representative agent owns all the income and is responsible for all the income taxes. While we do not have the effective tax rates per se, we need to calculate the time-varying tax rates from the data on tax revenues and output. The data is split between central government revenue and the state government revenue. To calculate the labour income tax rates, we add the revenue from personal income taxes and the corporate taxes of both the central and state government and divide it by the output. The result is plotted in Figure 3.6. The effective labour income tax rates according to our measure have gone up steadily throughout the last two decades. This, by itself, would have put a damper on economic growth, but, when we compare this with the labour wedge in Figure 3.3, we find that the labour wedge does not show many fluctuations. This result indicates that, while income tax rates in India might have been steadily increasing throughout the 1980s and 1990s, there
52
Suparna Chakraborty 8
Direct tax rate
7
In percentage
6 5 4 3 2 1 0 1982
Figure 3.6
1984
1986
1988
1990
1992 Years
1994
1996
1998
2000
2002
Direct tax rates
Note: We plot the labour income taxes from the data. It is measured as the ratio of the direct tax revenue of the central as well as state government to output.
were other changes in the economy that acted to counter the effect of rising labour income taxes and kept the labour wedge from deteriorating. Hence, even though the tax rates were increasing, the Indian economic growth continued unabated. Next, we turn our attention to investment taxes. Now, our decomposition exercise also points to a limited role of investment wedges. Does this indicate that investment taxes did not change much, or did other frictions overwhelm the impact of investment taxes? While India does not have explicit taxes on investment expenditure, from real business cycle literature, we know that we can use taxes on consumption expenditure as a proxy for investment taxes. To this end, we again turn to national income accounts data to calculate the effective tax rates. We take the indirect taxes net of subsidies as a proxy for our investment taxes and divide them by the private consumption expenditure to get the effective tax rates. We plot the result in Figure 3.7. Note that the effective tax rates on investment do not show any significant trends, except for a mild decline since 1994 that would have been conducive to economic growth. Thus, given the time series of taxes, we can conclude that, while labour income taxes were rising, they did not cause a decline in the growth rate,
India’s Economic Growth 53 16
Indirect tax rate
14
In percentage
12 10 8 6 4 2 0 1982
Figure 3.7
1984
1986
1988
1990
1992 Years
1994
1996
1998
2000
2002
Indirect tax rates
Note: We plot the investment taxes from the data. It is measured as the ratio of the indirect tax revenue net of subsidies of the economy to the personal consumption expenditure.
possibly due to overwhelming effects of other positive developments in the economy. The investment tax rates did not change much; thus it is consistent with the fact that we see a limited role of investment wedges, except since 1994, when a decline in investment tax would have been conducive to growth.
Conclusion After decades of unremarkable growth following independence, the Indian economy took off in the 1980s and continued to grow well into the 1990s. While a number of economists have suggested theories to explain this growth phenomenon, empirical studies that attempt a numerical growth accounting of India are at best limited. In an earlier paper (Chakraborty 2006), we had attempted to understand the role of technological progress, held by many as the primary reason for Indian growth, using a neoclassical growth analysis. In this chapter, our attempt is to apply a comparatively new procedure of growth accounting called ‘business cycle accounting (BCA)’ to India. Our objective is to figure out not the macro fundamentals or ‘primitives’ that brought about economic growth in India but the ‘transmission channels’
54
Suparna Chakraborty
through which the primitives played a role. For example, if we establish that liberalization policies in the 1980s and 1990s generated the economic revolution, did liberalization policies act by increasing productivity, did they reduce frictions in the labour market, thus encouraging growth, or did they break down investment market barriers? This chapter helps us answer such questions. The BCA procedure is particularly suited for this job as it is based on the key observation that most primitives affect the economy through productivity, or the labour market or the investment market and neoclassical model can be used to study these frictions, as the frictions resemble taxes, at least at face value. Thus, by solving the neoclassical growth model and by inserting the frictions one by one and in various combinations we can decipher which frictions affected the economy the most and thereby understand the most important transmission channel of the primitive forces that affected the economy. Our results show that primitives affected the Indian economy primarily by causing changes in productivity. Labour market frictions and the investment market channel were not particularly important. Increasing government expenditure that propped up demand also supported the growth in productivity in generating the economic boom. The lesson from the Indian growth experience, therefore, seems to be that targeting productivity would help jump-start growth for any emerging country that is in the same boat today as India was a decade ago. This lesson is not a radical one. It was also corroborated by the Japanese experience during the reconstruction period following the Second World War, and the Japanese fall from grace during the 1990s. Prescott and Hayashi (2002) show us that it was the drop in total factor productivity that knocked Japan off the steady growth path that had been a consistent feature in its almost three decades of growth. Note that, in this chapter, we are not identifying primitives that help jump-start growth. Instead, we focus on the often-neglected channel of growth. Our argument is that government policies and regime changes that will be most successful in initially jump-starting growth will be those that work by increasing productivity. At least that is what the Indian and Japanese experience seems to teach us. Looking back at the effective tax rates, we conclude that labour income taxes are increasing over time, and by themselves would have slowed Indian growth rates, though investment taxes do not show many changes. It is worthwhile, therefore, to explore other changes in the economic climate that affected the Indian economy through the labour wedge channel and kept the negative effect of increasing labour income taxes from overwhelming the economy. A decline in union bargaining activities would be a possible direction to look at, particularly in the private sector. In our view, future research should concentrate on the primitives that might have caused increases in productivity. It would also be interesting to
India’s Economic Growth 55
look at micro data and provide evidence of technical progress that in turn would help us target the improvement areas at a micro level. Recent works by Islam, Dai and Sakamoto (2006) and Vandenbroucke and Dekle (2009) have attempted to deconstruct the sources of TFP (Total Factor Productivity) growth in China. A similar study of India’s TFP transformation, in particular the role of development of human capital as a source of TFP growth, seems timely. In recent years, Indian growth has also been fuelled by outsourcing of technical jobs from the industrialized countries to India, particularly in the area of software development and IT. The service sector has also responded to this influx of IT business and a construction boom has been noted in the major Indian cities, including the technical hubs of Hyderabad, Bangalore and the greater Noida area in the capital of Delhi. Micro evidence of how these developments contributed to overall TFP growth would be of further use in deconstructing the sources of productivity increase in India that led to the growth spurt.
Appendix The first order conditions of the model outlined in Equations (1) to (4) can be simplified to the following three equations by substituting the value of consumption ct from Equation (1) and replacing it in Equations (3) and (4). Taking into account the population growth rate , and discounting the model variables with respect to their long-term trend (1 gz), the fundamental equations of our model reduce to: yt
F( k t , l At lt )
(7)
ult ( c t ( y t , xt , gl t )) (1 Wnt )Flt ( k t , l At lt ) u ( c t ( y , xt , gl )) ct
(8)
t
t
Et uct 1( c t 1( y t 1 , xt 1 , lg t 1 )Fkt 1( k t 1 , l At 1lt 1 ) (1 G)(1 W xt 1 )) l (1 W )u ( c t ( y , xt , g ))(1 g ) xt
ct
t
t
z
(9)
where I denote a variable zt detrended by the long-term growth rate of tech nological development (1 gz)t as z˜t, where z t
zt
(1 g z )t .
Given the wedges At,τnt,τxt and g t, Equations (7) to (9) solve for output, investment and labour in terms of the wedges. The BCA procedure involves feeding in the wedges one by one and in different combinations to see which wedges or combinations of wedges can best replicate the data. The accounting procedure has two parts: first we need to estimate the wedges from the data and then we feed the wedges into our model to generate output, labour
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Suparna Chakraborty
and investment. This latter procedure is called decomposition. Note that, by construction of the BCA procedure, if we feed in efficiency, labour, investment and government wedges in the model all together, then we will get back the data. Taking into account the population growth rate, and the functional forms outlined in Equations (5) and (6), Equations (7) to (9) reduce to: yt
( k t )u ( l At lt )1
§ 1 a · § yt ¨ ¸¨ © a ¹ ¨©
(1
gz )
(1 W xt )
yt yt
g z )k t 1 (1 G)k t lg z · ¸ ¸ 1 lt ¹
(1
(1 u)(1 Wnt )
Et
(10)
u
1
yt lt
(11) (1
(1
g z )k t 1 (1 G)k t lg t g )k t 2 (1 G)k t 1 lg z
(u t 1
yt 1 (1 G)(1 W xt 1 )) kt 1 (12)
where the value of σ is taken as 1. Note that given parameter values we can solve Equations (10) to (12) and get decision rules for output yt, labour lt, and capital stock next period kt+1 in terms of productivity or efficiency wedge At, labour wedge τnt, investment wedge τxt and government consumption wedge g t. Once we get the decision rules, we can plug in the time series of the wedges one by one in our decision rules while holding other wedges constant at their steady state values and thereby account for the contribution of each wedge in generating the macro variables. The problem here is that we do not have time series data available on productivity At, labour wedge τnt and investment wedge τxt, as they represent market frictions and are therefore intangible. So we need to use data from national income accounts and our equations to back out the values of these wedges. The job is relatively simple for efficiency wedge At and labour wedge τnt, which we can derive given Equations (10) and (11) and the time series data on output yt , labour lt and capital stock next period kt+1. The job is not so easy for calculating investment wedges τxt as it involves knowing not only the time series of aggregate macro data but also the expectations that people form about the future, as Equation (12) highlights. Researchers have used many variations to get around this problem. In their exposition, Chari et al. (2002, 2007) hold efficiency and labour wedges constant at their steady state values and let investment wedges be whatever they should be so that they can replicate the investment data exactly to get an approximate idea for the series, and then they iterate such that their model outcomes (feeding in all the wedges) can replicate the data exactly. What we
India’s Economic Growth 57
do in our analysis is to hold the investment wedges constant at their steady state value. We then ascertain how much of the observed data we can generate with efficiency, labour and government wedges jointly. If, after feeding in efficiency, labour and government wedges, we have a large part of the observed data still unexplained, then we can say that investment wedges must have played a significant role, as, by construction, the model is supposed to replicate the data exactly when all four wedges are fed in. Thus, with our simplification, the equations reduce to: ( k t )u ( l At lt )1
yt
§ 1 a · § yt ¨ ¸¨ © a ¹ ¨©
u
(1 u)(1 W nt )
E (1
gz )
Et
g z )k t 1 (1 d)k t lg t 1 lt
h(1
(13)
yt lt
(14)
h(1
yt yt
· ¸ ¸ ¹
h(1
1
g z )k t 1 (1 d)k t lg t g )k t 2 (1 d)k t 1 lg z
(u t 1
yt 1 (1 d)(1W x )) kt 1
(15)
(1 W x )
where − τ x is the steady state value of investment wedge τxt . Rearranging the terms, the equations can be written as:
l At
§ ylt ¨¨ T 1 © ( k t ) (lt )
Wnt
1
W x 1
T
·1 ¸¸ ¹
1
T
lt § 1 a · § y t ¨ ¸¨ (1 T ) y t © a ¹ ¨© 1
T 1 g z
y k
(16)
(1
g z )k t 1 (1)k t lg z · ¸ ¸ 1 lt ¹
(17)
(18)
1G
where Equation (18) is the steady state variation of Equation (15) and helps us to get the steady state value of the investment wedge, − τ x. Of course, one can easily get the steady state value of the efficiency wedge and the labour wedge that we denote by A and − τ n respectively from the steady state version of Equations (16) and (17). Note that, given the data from National Income Accounts, Equations (16) and (17) give us the time series of productivity At and that of the labour wedge τnt. The government consumption wedge g t is taken from National Income Accounts.
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Suparna Chakraborty
Notes I thank Dr Sugata Marjit and CSSSC, Kolkata for hosting me during the preliminary analysis of this paper. All remaining errors are mine. 1. Quotation from speech delivered at the Indian Economy conference at Cornell in 2002. 2. In technical jargon, the external forces or macro fundamentals that affect an economy causing a change in output is also referred to as ‘primitives’. 3. Two seminal papers in this area are by Mulligan (2002) and Chari et al. (2002, 2007). 4. For details, interested readers can refer to the technical appendix available from the authors. 5. Data is available at the following link: http://www.rbi.org.in/scripts/Statistics. aspx.
References Chakraborty, S. (2006). ‘Technology as a Channel of Economic Growth in India’. India Macroeconomics Annual, 1: 69–87. Chari, V. V., P. J. Kehoe, and E. R. McGrattan (2002). ‘Accounting for the Great Depression’. AER, Papers and Proceedings, 92 (2): 22–7. Chari, V. V., P. J. Kehoe, and E. R. McGrattan (2007). ‘Business Cycle Accounting’. Econometrica, 75 (3): 781–836. Islam, N., E. Dai, and H. Sakamoto (2006). ‘Role of TFP in China’s Growth’. Asian Economic Journal, 20 (2): 127–59. King, R., C. Plosser, and S. Rebelo (1988). ‘Production, Growth, and Business Cycles: The Basic Neoclassical Model’. Journal of Monetary Economics, 21 (2): 195–232. Kydland, F. E., and E. C. Prescott (1982). ‘Time to Build and Aggregate Fluctuations’. Econometrica, 50 (6): 1345–70. Mulligan, C. B. (2002). ‘A Dual Method of Empirically Evaluating Dynamic Competitive Equilibrium Models with Market Distortions, Applied to the Great Depression and World War II’. NBER Working Paper 8775. Cambridge, MA: National Bureau of Economic Research. Narayana Murthy, N. R. (2002). ‘The Impact of Economic Reforms on the Hi-Tech Industry in India: A Case Study of Infosys’. The Indian Economy Conference, Cornell University, 19–20 April. Prescott, E. C., and F. Hayashi (2002). ‘The 1990s in Japan: A Lost Decade’. Review of Economic Dynamics, 5 (1): 206. Singh, N., and T. N. Srinivasan (2004). ‘Indian Federalism, Economic Reform and Globalization’. Public Economics 0412007, Economics Working Paper Archive EconWPA. Subramanian, A., and D. Rodrick (2004). ‘From “Hindu Growth” to Productivity Surge: The Mystery of the Indian Growth Transition’. IMF Staff Papers. Washington, DC: IMF. Vandenbroucke, G., and R. Dekle (2009). ‘A Quantitative Analysis of China’s Structural Transformation’. Working Paper, University of Southern California and University of Iowa.
Part II Economic Structure and Growth
4 Beyond Industrialization: New Approaches to Development Strategy Based on the Service Sector Peter Sheehan
Introduction Most of the countries that are now developed achieved that status in large part through a process of industrialization, involving a substantial shift of capital and labour into industrial activity, and a rapid increase in the share of industrial value added in GDP during the development process. As a result industrialization occupies a central place in the rich tapestry of development theory and practice, although that place has varied as those theories and practices have changed over time. Many of the writings of the so-called ‘high development theory’ period of the 1940s and the 1950s were concerned with the conditions for industrialization, and Kaldor wrote (1966: 54) that ‘there can be little doubt that the kind of economic growth which involves the use of modern technology and which eventuates in high real income per capita, is inconceivable without industrialization.’ Thorbecke (2007) provided a valuable survey of the evolution of development doctrine since the heydays of the ‘industrialization-first’ era of the 1950s, showing how the prevailing doctrines responded not only to intellectual trends but even more to the major crises in the world economy. He argues that in the 1960s and 1970s there was a growing reaction against the emphasis on industry-first and the GNP measure of outcomes, with renewed focus on agriculture, on poverty alleviation, on employment strategies and on a broad range of measures to promote human capital and welfare. During the 1980s much stress was placed on stabilization in the wake of problems for developing countries arising from the oil shocks, two global recessions and the Mexican crisis of 1982. But, as that decade proceeded, development doctrine, reflecting broader economic trends, began to emphasize free market themes, including a stress on outward orientation intended to encourage exports and industrialization in labour-intensive consumer goods. This theme was given impetus by the World Bank’s 1993 report 61
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Peter Sheehan
The East Asian Miracle, which stressed the role of strong growth in industrial investment, exports and output, supported by investment in human capital and in infrastructure and by a high savings rate, in driving rapid economic growth. Strong expansion of exports into open markets in key economies has been central to East Asian growth, so that the industrialization model is closely linked to the benefits of free trade and comparative advantage. In the current decade Thorbecke sees less emphasis on ‘big ideas’, important progress in technical areas, a clearer realization that human development is the ultimate goal of economic development, and an imperative to shape development strategies in the light of the reality of globalization. This need to shape development strategies in the light of the reality of globalization – and especially in the light of China’s extraordinary expansion over the past two decades and the emerging impact of its trade on other developing countries – provides the motivation in this chapter for revisiting the role of industrialization in the contemporary development process. At the level of development theory, many studies have addressed aspects of the way in which a self-sustaining transition from a traditional to an industrial economy can be achieved. These include, for example, Young (1928), Rosenstein-Rodan (1943), Lewis (1954) and Kaldor (1967). As Krugman (1992) points out, some of these models have been formalized using the analytical resources embodied in endogenous growth theory and new trade theory. One such paper, on which I draw here, is that of Murphy et al. (1989), who provide a formalization of Rosenstein-Rodan’s model of big-push industrialization. In thinking about industrialization, it is important to remember that economic development is a process of sustained increase in average living standards or overall human welfare in a given country. It is thus, in principle, an open question whether industrialization is the most appropriate means for a particular country, or for most countries at a given time, to achieve increased average living standards. It is also an open question whether, in a given case, current statistical measures (such as real GDP per person) provide a reasonable proxy for living standards and human welfare. Furthermore, it is clear that there are personal, social and environmental costs associated with industrialization, so that the net impact on human welfare is also an important issue. The case I will argue is that it is becoming increasingly difficult for most developing countries to achieve more rapid growth through industrialization, and especially through export-oriented activities, and this difficulty is likely to increase further. But the key mechanisms seen as driving the industrial take-off in the paper noted above and in much of the related literature (internal increasing returns, transfer of labour into higher-value activities and pecuniary externalities) are alive and well, and do not relate only to the industrial sector. Furthermore, China is actively trying to move from a strategy based on industrialization to one based much more on agriculture
Approaches to Development Strategy and the Service Sector
63
and services, as the costs of the current pattern of industrialization become prohibitive, and India has demonstrated that rapid growth based primarily on the service sector is possible. Thus more attention needs to be given to strategies based on the expansion of the agricultural and service sectors, and to the ways in which better services in rural areas and higher rural output can combine to achieve rapid growth and improved human welfare in poor countries. In what follows I develop this argument by briefly considering four building blocks: models of industrialization and the experience of the advanced countries; the nature of China’s recent development path; some constraints on development through industrialization; the search for a new strategy based on agriculture and services in China and the reality of service-driven growth in India.1
Industrialization, development and the East Asian model A model of industrialization Murphy et al. (1989) employ a two-sector model, with a cottage production sector, operating in competitive conditions at constant returns to scale, and a factory sector, which operates advanced technology, with increasing returns to scale due to the presence of fixed costs internal to the firm. With fixed costs there is imperfect competition, and they assume a single monopolist for each industry. Higher wages are paid in the factory sector than in the farm sector, to compensate for the disutility of farm work. The productivity gain from using the advanced technology is assumed to exceed this compensating wage differential, and this assumption is critical to achieving the key results. Both types of workers have the same unit elasticity of demand for manufactures across all products. Thus there are potential pecuniary externalities for expanded manufacturing production: increased production (and wages) in one industry will increase the demand for the products of other industries, other than through profit distribution. In each industry there is a cottage sector available and there will be a monopolist operating if demand is sufficient. If he enters, the monopolist is forced to adopt limit pricing, to meet the price charged by the cottage sector. The model then shows two equilibria, one in which no sectors industrialize and the other in which all sectors industrialize. Which situation occurs will depend on the size of the fixed costs, relative to productivity gains, incurred in using the increasing returns technology and on the level of demand. For some levels of fixed costs, both equilibria will be possible and in these cases the economy is capable of a ‘big push’, that is, sustained growth, as all of the surplus labour is absorbed in the factory sector. The reason for this is the pecuniary externality arising from the fact that the wage premium paid by the manufacturing firm adds to demand for the products of other sectors, even if the firm itself is not profitable. Murphy et al. (1989) consider other
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Peter Sheehan
versions of this model, including ones in which increased activity by some firms generates higher future profitability for other firms and in which it helps to defray fixed infrastructure costs. In these models the source of the multiple equilibria is the pecuniary externalities generated by imperfect competition with large fixed costs, in the context of unlimited supplies of labour at the going industrial wage rate. On the policy side, Murphy et al. infer that a government programme ensuring that many sectors industrialize simultaneously can boost income and welfare substantially, even when investment in any one sector appears unprofitable. This is especially so for an economy whose access to foreign markets is restricted by high transportation costs or trade restrictions. They cite South Korea as an economy that has successfully implemented such a coordinated programme of industrialization. There are many other models within the endogenous growth literature that bear on the issue of development. For example, many follow Romer (1990) in building models based on the idea of external increasing returns due to specialization that goes back to Young (1928), to show how endogenous investment in R&D and technology can drive growth. Durlauf (1993) uses technological rather than pecuniary externalities with internal increasing returns to replicate the ‘leading sectors’ theory of Hirschman (1958). Azariadis and Drazen (1990) have replicated a ‘stages of growth’ path similar to that of Rostow (1960), in a model with human capital externalities linked to a training regime in which private rates of return on human investment depend positively on the existing average quality of human resources. There is also the question of trade. The Murphy–Sheifler–Vishny (1989) model is for a closed economy, and they argue its relevance by pointing to the major role for domestic demand in most markets. In a more general sense, one central theme of many endogenous growth models is that, while the benefits of trade in terms of global growth are enhanced relative to the standard competitive model, these benefits do not necessarily flow through to individual countries. When growth is driven by innovation, learning by doing or other externalities and these effects are geographically concentrated, initial conditions can generate major long-term differences between countries in comparative advantage and in growth potential. If the industries in which a country is specialized are deficient in relevant respects (for example, have a lower capacity for learning by doing, have lower returns to or lower capacity for R&D or have lower levels of other externalities) then free trade will inhibit the growth of the small country, because it will tend to concentrate activity in areas of comparative advantage with lower capacity to generate growth. Returning to the Murphy–Sheifler–Vishny model of industrialization, the three key requirements for self-sustaining industrialization are: ●
fixed costs and increasing returns in industrial production, internal to the firm;
Approaches to Development Strategy and the Service Sector ●
●
65
surplus labour in the traditional sector, and a willingness of that labour to shift to the industrial sector at the going industrial wage rate; and the resulting pecuniary externalities, as the demand arising from higher wage incomes increases the demand for products of all industries.
While recognizing the many complexities touched upon above, I will concentrate on these requirements for the rest of this chapter. Employment shares in industrialization It may be worth commenting briefly on the relevance of these requirements to the history of key developed countries. The relevance of increasing returns to both scale and scope, internal to the firm, to the development of the USA, the UK and Germany has been amply demonstrated by Chandler (1990) among others, and he shows that these increasing returns apply to distribution as well as production. The existence of pecuniary externalities is also widely acknowledged, with authors such as Murphy et al. (1989) noting that these externalities can be accentuated by urbanization, which might be associated with a high elasticity of demand in a wide variety of manufactures. Here we focus on the third requirement, the shift of surplus labour from agriculture into industry. The industrial revolution originated in the UK, which was the first country in modern times to build an industrial economy. This process began in the eighteenth century, and was well advanced by 1820. By that year the share of total employment in British agriculture had already fallen to 37.6 per cent, and nearly one-third of all employment was in industry (Table 4.1). As the growth in industry continued to drive the expansion of the economy Table 4.1 Share of employment in industry in total employment, selected countries, 1820–2005 (Per cent of total employment)
1820 1870 1913 1929 1950 1970 1985 2005
UK
Germany
USA
Japan
Korea
China
32.9 42.3 44.1 45.2 44.9 39.5 26.2 20.5
na 28.7 41.1 na 43.0 na 37.8 31.0
15.0 24.4 29.7 29.4 33.6 28.5 23.3 17.0
na na 17.5 20.9 22.6 35.3 34.6 27.5
na na na na na 16.3 34.7 26.9
na na na na na 10.2 20.8 23.8
Sources: 1820–1950: Maddison (1995: 39, 253); 1970–93 (excluding China): OECD STAN Database 2002; 2005: OECD Statistics (//stats.oecd.org) for Germany, Japan and Korea; NBSC (2006) for China; data for 2005 for USA and UK estimated by the author from national sources.
66
Peter Sheehan
through the nineteenth and early twentieth centuries, the share of total employment located in industry reached 45.2 per cent in 1929, immediately prior to the depression, and by that time the share of agriculture in total employment had fallen to 7.7 per cent. These proportions remained fairly stable through to the 1950s. But after about 1970, as the structure of the world economy began to change, industry began to give way rapidly to services in terms of employment – by 2005 industry provided only 20 per cent of all jobs, and nearly 80 per cent of total employment in the UK was in services. This broad pattern, of a strong expansion in industrial employment fed by a transfer from agriculture but sooner or later displaced by surging employment in services, is apparent in one way or another in the development process of all the major advanced economies. Germany followed a similar path to Britain a little later, with the industry share of employment reaching 41 per cent in 1913 and 43 per cent in 1950, before falling to 31 per cent by 2005. The service sector was important much earlier in the USA, but the industry share rose from 15 per cent in 1820 to nearly 30 per cent by 1913, and peaked at close to 35 per cent in the early 1950s. But by 2005 only about 17 per cent of US jobs were in industry. Japan’s development process began later, and in 1913 60 per cent of employment was in agriculture and only 17.5 per cent in industry. But the industry share rose rapidly during the first three quarters of the twentieth century, peaking at about 37 per cent in 1973 before declining gradually to 27.5 per cent in 2005. It is interesting to consider the case of Korea in the prime decades of its rapid development, between 1970 and 1990. In these two decades Korea not only grew very rapidly (real GDP growing by 8.1 per cent per annum) but experienced rapid structural change. In 1970, 51.5 per cent of Korean employment was in agriculture (a figure comparable to that of China in 2000), with only 16.3 per cent in industry and 32.1 per cent in services. By 1990 the employment share in agriculture had fallen nearly two-thirds to 18.3 per cent, with more than half of that decline being reflected in rising manufacturing jobs. By 1990, 35 per cent of Korean jobs were in industry, while the services share had risen to 47 per cent. In other words, even as late as the 1980s Korea was following the traditional development path in an accelerated fashion, with a pronounced shift of jobs from agriculture to secondary industry, and also to services. Over a period of only two decades, the share of jobs in manufacturing more than doubled, absorbing more than half of the movement of jobs out of agriculture. But in the 1990s the trend reversed equally sharply, with the manufacturing share falling from 35 per cent in 1990 to 27 per cent of all jobs by 2005. By that year, with about 8 per cent still in agriculture, about 65 per cent of all Korean jobs were in the service sector. These data are supportive of Kaldor’s proposition (1967) that rapid growth in manufacturing is a characteristic of an intermediate stage of development,
Approaches to Development Strategy and the Service Sector
67
from immaturity to maturity. But they also bring out how early this maturity was reached in the advanced countries (some 50 years ago for the USA, the UK and Germany), the extent of the shift to services that has taken place in those countries since that time and the degree of shift to services over the past decade even in such a late entrant to industrialization as Korea. The implications of these trends for contemporary development paths remain to be explored. But they do suggest the hypothesis that pursuing a development strategy based on industrialization in the early years of the twenty-first century may be a much more difficult task than even three decades ago, as sustained rapid growth in industrial employment is difficult to achieve in an open world economy.
The nature of China’s growth, 1979–2006 In the wake of the achievements of Japan, South Korea, and other countries in East Asia, China’s remarkable growth since the ‘opening to the market’ in 1979 is often seen as a prime example of the East Asian model at work, and as a contemporary example of rapid growth through industrialization. China’s growth, it is said, has been driven by rapid expansion in industrial output, supported by high levels of investment, strong growth in exports, a high level of savings and proactive government at both central and local levels. But before we conclude that China is the classic case of development through industrialization there are certain matters that deserve further examination. Table 4.2 provides a summary of the key sectoral growth rates of real GDP since China’s opening to the market in the late 1970s. During the immediate post-reform decade, to about 1988, both agriculture and services grew rapidly, as a loosening of controls in these sectors led to strong expansion. Naughton (2007) graphically describes how the early move to a contracting system in agriculture in 1979, and the de facto reinstatement of the household farming system, generated sharp increases in the production of grain and other farm products. Given that most of industry was embodied in large state-owned firms where a contract system had more limited effects, the impact of reform in industry was much more gradual, and over the decade to 1988 services grew more rapidly than industry (12.9 per cent per annum in comparison with 11 per cent per annum). But from about 1988 the more familiar paradigm applies, with the average growth rate of industrial value added over 1978–2006 (11.5 per cent) being about two percentage points higher than that of services and of the economy as a whole. Nevertheless, the overall growth rates of agricultural and service value added were 4.6 per cent and 10.6 per cent respectively over nearly three decades (1978–2006). This constitutes a striking achievement. Reflecting the rapid expansion of industry in the command economy period from 1949 to 1978, by 1978 the Chinese economy was already
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Peter Sheehan
Table 4.2
Sectoral growth rates in GDP, China, 1978–2006 (%) Agriculture
Industry
Services
Total
5.6 4.1 4.6
11.0 11.5 11.3
12.9 9.4 10.6
10.1 9.5 9.7
1978–1988 1988–2006 1978–2006
Note: These data are based on annual real growth rates expressed in ‘comparable prices’. For details see text. Source: NBSC (2006, 2007).
Table 4.3 Sectoral shares of real GDP, five-year periods, China, 1979–2006
Industry
Services
Agriculture
Industry
Services
Agriculture
Industry
Services
1979 1989 1997 2001 2006
At 2005 values, %
Agriculture
At opening values (each period), % At 1988 values, %
31.0 20.3 12.4 10.1 7.6
47.1 51.1 62.2 63.4 66.5
21.9 28.6 25.4 26.5 25.9
38.0 25.2 16.2 13.3 10.8
40.4 43.7 55.9 57.3 60.4
23.8 31.1 29.0 30.4 30.6
41.3 28.2 18.8 15.5 12.0
29.7 32.9 43.7 44.9 48.3
29.0 38.9 37.5 39.5 39.7
Source: NBSC (2006, 2007).
industrialized, at least in the sense that industry provided 48 per cent of GDP at current prices in that year. But this was an economy with administered quotas and prices and with few exports, with neither the quality nor the price of the output tested in competitive markets. This fact, combined with the strong fall in the relative price of manufactured goods that took place after the mid-1980s, makes it difficult to provide realistic estimates of sectoral contributions to the change in China’s GDP. The National Bureau of Statistics of China (NBSC) publishes data for real growth rates in GDP (in total and for the sectors) in ‘comparable prices’, which involves valuing year two’s net value added in terms of year one’s prices, and so on, on a rolling basis, as well as indices compiled from these growth rates. These series can be used to create ‘volume’ measures by multiplying the index numbers by the current price values for any chosen base year. But the choice of base year will influence both the level and the change in the resulting series. Data drawn on three base years are reported in Table 4.3, one for five-year periods using the values for the opening year of each period as the base, one
Approaches to Development Strategy and the Service Sector A
69
Value added shares (2005 values) 60.0 50.0 40.0 30.0 20.0 10.0
Agriculture B
Industry
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
0.0
Services
Labour productivity (index 1988=100)
450 400 350 300 250 200 150 100 50
Agriculture
Industry
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
0
Services
Employment shares
C 40.0
75 70
30.0
65 60
20.0 55 50
10.0
45
Industry (LHS)
Services (LHS)
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
40 1978
0.0
Agriculture (RHS)
Figure 4.1 Value added shares, labour productivity and employment shares, by major sector, China, 1978–2006 Source: NBSC (2006, 2007).
70
Peter Sheehan
for an intermediate year (1988) and one for 2005. Following the argument of Naughton (2007) that the latest year prices are likely to be closest to world market prices, figures on a 2005 base are used in Figure 4.1. While the estimates using different base years differ markedly, they each show four distinct periods, with the falling share of agriculture common to them all. Using the 2005 values as illustrative, from 1979 to 1989 the share of services rose by 10 percentage points while the share of industry was virtually flat; from 1989 to 1999 the share of industry rose by nearly 11 percentage points while the share of services fell; from 1997 to 2001, a period of subdued industrial growth in China, the services share rose more rapidly than that of industry; finally, rapid growth in the industrial share resumed over 2001–6. Overall, and in spite of these data issues, it is fair to say that industry has been the central contributor to China’s growth after about 1990, but that services have also grown very rapidly. This emphasis on the periods before and after 1990 is highlighted by the real value added per employee data in panel B of Figure 4.1. Up to 1990, labour productivity in the three sectors grew at similar, modest rates – 2.8 per cent, 3.6 per cent and 3.3 per cent per annum for agriculture, industry and services respectively. Since 1990 all growth rates have been more rapid, but that in industry has been, at 10.3 per cent per annum, twice that in agriculture and industry (5.1 per cent and 5.3 per cent, respectively).2 All these rates of productivity increase are impressive, but in industry output growth of 12.6 per cent per annum over 1990–2006 was achieved with a growth of only 2.1 per cent in employment. Thus in employment terms (panel C) the story since 1978 is primarily one of services rather than industry. Between 1978 and 2006 the agricultural share of employment fell by 28 percentage points, from 70.5 per cent to 42.6 per cent. Most of the falling share went into services, the share of which rose by 20 percentage points (from 12.2 per cent to 32.2 per cent), and 8 percentage points went into industry. Most of the increase in the industrial share took place before 1986, and for nearly two decades between 1986 and 2004 there was no increase in the share of industrial employment. This is hardly the traditional industrialization model at work.
Some constraints on development through industrialization In this section some of the factors that place constraints on greater involvement by the developing countries in manufacturing trade are reviewed, and their recent position in global trade in manufactures is noted. Intensified competition in world manufacturing trade Perhaps the most obvious factor is the greatly increased competition in world trade, with rapid growth in exports from China and the transition economies of Eastern Europe adding to strong competition from the newly
Approaches to Development Strategy and the Service Sector
71
Table 4.4 Global manufacturing exports, by selected countries and regions, 1990–2005 Manufactured exports (US$billion)
OECD countries
Rate of growth of manufactured exports (% per annum)
1990
2000
2005
1990–2000
2000–5
1990–2005
2,041.2
3,676.4
5,339.0
6.1
7.7
6.6
129.9 46.0 16.2 192.2
389.4 222.3 74.4 686.2
546.1 702.6 169.4 1,418.1
11.6 17.1 16.5 13.6
7.0 25.9 17.9 15.6
10.0 19.9 16.9 14.3
16.1 12.5 8.3 93.1
31.8 34.6 20.2 238.1
61.6 69.8 30.7 334.8
7.0 10.7 9.3 9.8
14.1 15.1 8.7 7.1
9.3 12.1 9.1 8.9
Rapid export growth regions SE Asian NICs China Transition economies Total Other developing countries Brazil India South Africa All other countries Total World
114.0
293.0
435.2
9.9
8.2
9.3
2,347.4
4,655.6
7,192.3
7.1
9.1
7.8
Note: Data for China exclude exports from Hong Kong, but include China’s exports to Hong Kong. The world total excludes exports from Hong Kong that are re-exports of imports from other countries, notably China. Source: WTO (2006).
industrialized countries (NICs). As Table 4.4 shows, by 2005 the manufactured exports of this group totalled over US$1.4 trillion, having increased by 14.3 per cent per annum (double the rate of such exports from all other countries) over 1990–2005, and by 15.6 per cent per annum in the world trade boom since 2000. Growing competition from these sources of supply must put increased pressure on the exports of developing countries, while also leading to increased import penetration. By far the most important case is that of China, whose longer-term export orientation has been given new impetus by its entry into the WTO in 2001. Between 2001 and 2005 China’s total merchandise exports grew by 30 per cent per annum, from US$266 billion in 2001 to US$762 billion in 2005; as a result, China’s share of world trade (excluding Hong Kong) nearly doubled from 3.9 per cent in 2000 to 7.3 per cent in 2005 (WTO 2006). This rapid growth has continued since 2005, with exports increasing by 27.2 per cent in 2006 and by 25.8 per cent in 2007. China’s share of world merchandise trade could reach 15 per cent by 2015, and continue to rise thereafter, and a very high proportion of China’s exports are manufactured goods. But China’s growth has also led to a rapid growth in its imports (which increased by 27 per cent per annum, from US$244 billion in 2001 to US$792 billion in 2006). These trends have been affected, but probably only just interrupted,
72 Peter Sheehan
by the global financial crisis of 2008–9, which saw China’s exports fall significantly, on a year-on-year basis, in the early months of 2009. Such surging trade growth must inevitably have major impacts on other countries, both developed and developing. Given the nature of China’s growth and trade, there is a theoretical expectation that this impact will vary sharply across countries (Ianchovichina and Walmsley 2005). In Japan and the NICs of Asia (such as South Korea and Taiwan), which produce intermediate and capital goods for use in China, the boost to exports to China should more than offset increased competition in their export markets. A similar effect is evident in Australia, where induced investment in resources seems to have more than offset increased competition in manufactured goods. But in poorer countries, competing with China in world markets for consumer goods but with more limited ability to supply China’s import needs, the impact could be sharply negative. In one of the few empirical studies of these issues, Eichengreen et al. (2007) find support for this differential impact in an econometric study of 13 Asian economies. They find a general tendency for China’s exports to crowd out exports from other Asian countries, but that this is largely confined to consumer goods. For example, a 10 per cent increase in China’s exports of textiles leads on average to a 4 per cent fall in textile exports from other Asian countries. They also find a strong tendency for increased exports from these countries to China, but this is mainly in the market for capital goods. Thus China’s rapid growth has positive effects on its high-income neighbours but negative effects on the less developed countries in the region. Their findings for Indonesia are mixed, with a positive effect through intermediate goods (defined as including energy supplies) broadly offsetting substantial crowding out of consumer goods exports. This study uses data only up to 2003 and a simple capital/intermediate/consumer goods classification, but nevertheless provides some confirmation for concerns that China’s rapid trade growth could have adverse effects for some poorer developing countries. Fragmentation and increasing technological requirements in manufacturing A wide range of changes in the nature of global manufacturing that have taken place over the last 10–15 years also make it more difficult for developing countries outside East Asia to expand manufactured exports. One such important change is the rise of so-called fragmentation trade, whereby parts, components and finished goods are often assembled in different places under the overall control of a global integrator. Such fragmented or networked trade may take different forms in different industries – such as production of components to agreed specifications in the ICT and motor vehicles industries, or subcontracting by tender of final production to a predetermined design and specification in the textiles, clothing and footwear industries – but is now very important overall. For example, in a study of trade in machinery and transport
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equipment, Athukorala (2006) found that the share of parts and components in total machinery and transport imports globally was 44 per cent, and that for the countries of East Asia as a whole it was just on 50 per cent. These and related factors mean that other developing countries face two challenges to rapidly expanding exports: gaining access to the networks that control a rising share of world manufacturing trade (and doing so at a price that is profitable) and achieving the technological level for production to the exacting quality standards now required in world markets. Again it seems likely that the demands for quality and performance, and for the availability of the production technology that can meet these demands, will increase further, for example as a new round of performance specifications are imposed to reduce energy use and to make products more environmentally sustainable. The position of manufacturing in poorer countries In spite of these constraints, developing countries outside East Asia have achieved some growth in exports of manufactures, as indicated in Tables 4.4 and 4.5. India, Brazil and South Africa have each increased their share of global manufacturing exports over 1990–2005, India substantially (from 0.5 per cent to 1.0 per cent) and Brazil and South Africa more modestly (from 0.7 per cent to 0.9 per cent and from 0.35 per cent to 0.43 per cent, respectively). The share of manufactured exports captured by all other developing countries also rose from 4.0 per cent in 1990 to 4.7 per cent in 2005, with the major contributors being machinery and transport and ICT exports from developing countries in Southeast Asia, and chemicals and clothing exports from other developing countries. But after 2000, as the impact of China began to intensify, trends in these countries diverged sharply. Over 2000–5 exports of manufactures from both Brazil and India continued to grow at rates above the global figure, and their shares increased. But growth was below the global average for South Africa, and the share of all other developing countries in world manufactured exports fell from 5.1 per cent in 2000 to 4.7 per cent in 2005. As a result, the share of all developing countries outside the rapid export growth regions fell over this five-year period. Another perspective on the role of manufacturing is the distribution of real manufacturing value added (MVA) across countries, and Table 4.6 summarizes some recent estimates. Outside the industrialized countries, China and the NICs, all developing countries accounted for only 3.5 per cent of world MVA in 2006. While this is a significant increase on their share in 1996, the rate of increase has slowed since 2001 and is small in relation to the growth of China. On these estimates China captured an increased 2.2 percentage points of world MVA between 2001 and 2006, while the rise for all other developing countries was only 0.2 percentage points.
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Table 4.5 Shares of global manufacturing trade, by selected countries and regions, 1990–2005 Share of manufactured exports (%) 1990
2000
2005
87.0
79.0
74.2
Rapid export growth regions SE Asian NICs China Transition economies Total
5.5 2.0 0.7 8.2
8.4 4.8 1.6 14.7
7.6 9.8 2.4 19.7
Other developing countries Brazil India South Africa All other countries
0.7 0.5 0.4 4.0
0.7 0.7 0.4 5.1
0.9 1.0 0.4 4.7
OECD countries
Total World
4.9
6.3
6.1
100.0
100.0
100.0
Note: Data for China exclude exports from Hong Kong, but include China’s exports to Hong Kong. The world total excludes exports from Hong Kong that are re-exports of imports from other countries, notably China. Source: WTO (2006).
Table 4.6 Distribution of real manufacturing value added, by major country grouping (% of world total)
1996 2001 2006
Industrialized countries
Newly industrialized countries
China (excluding Hong Kong)
All other countries
79.6 77.5 73.3
13.0 13.1 14.8
4.6 6.1 8.3
2.8 3.3 3.5
Source: UNIDO database.
These data clearly require closer and more detailed analysis, but they appear to support the view that, while manufacturing will continue to play an important role in the growth of many countries, in current conditions ‘big-push’ industrialization is unlikely to drive the growth of any new developing countries outside East Asia in the foreseeable future. Indeed, when enhanced import competition is also included, the existing manufacturing sectors in some countries could be under threat.
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The global financial crisis The global financial crisis, which developed in US financial markets from late 2007 and led to a sharp slowdown in world growth from the second half of 2008, is also affecting the position of developing countries in manufacturing trade. World trade has fallen sharply, being 31 per cent lower in the first quarter of 2009 than a year earlier (WTO 2009). There is evidence that the reduction in world trade has been particularly focused on motor vehicles, other durables and capital goods, which are directly reliant on finance and which represent major but discretionary commitments for individuals and firms. This means that those countries heavily specialized in these impact areas (such as Japan, Germany, Singapore, Korea and Taiwan) have felt the trade impacts particularly severely. But as the recession deepened in most developed countries the decline in trade volumes has extended into consumer and light manufactured goods of special interest to developing countries. Competition in global markets for these products is likely to be intense for some years to come.
Development strategies based on agriculture and services The final building block of my argument concerns the role of alternative strategies based on agriculture and services in the two major developing countries of the world: China is urgently seeking to develop such a strategy and India has, in fact, been implementing one. Towards a new development strategy in China In the Eleventh Five Year Plan (2006–11) China decided to pursue fundamental changes in its development strategy in the light of emerging problems. These problems – including heavy energy use and serious pollution, uncontrolled fixed asset investment, limited benefits being delivered to many in the population, and growing inequality (especially in access to health and education) – have led the government to search for policies to create a ‘balanced and harmonious society’ as the key national priority. Many of the issues with the expansion path have been recognized by the Chinese government, and are being actively addressed in the process of implementing the Eleventh Plan. In his March 2006 Report on the Work of the Government, Premier Wen Jiabao said of the issues arising from the Tenth Plan period (2001–5): The main problems were an unbalanced economic structure, weak capacity for independent innovation, slow change in the pattern of economic growth, excessive consumption of energy and resources, worsening environmental pollution, serious unemployment, imbalance between investment and consumption, widening gaps in development between urban and rural areas and between regions, growing disparities between certain
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income groups, and inadequate development of social programmes. We need to work hard to solve all these problems. It is also widely recognized that more serious social unrest than at present is possible if these issues are not resolved. As one observer has written, China’s Eleventh Five Year Plan proposals for the period 2006–10 are remarkable: There emerges from this Plan document a rich and comprehensive vision of a sustainable development process in China, and a glimpse of the kind of government role that would be required by this development process. The vision is of a society that is more creative, more focused on human resource development, and treads with a lighter and more environmentally benign step. (Naughton 2006: 9) But it is one thing to outline a vision of a sustainable economy and a harmonious society and quite another to define and implement a detailed set of programmes to give effect to this vision. This is especially so in such a diverse, vibrant and internationally engaged society as contemporary China. The forces shaping the current growth pattern – from the role of local governments and the limited power of the central government, the strong influence of foreign companies and investors and the level of the exchange rate to the popular desire for a strong China and a better life – are complex and interrelated, and it will take a major effort to realign them. To some extent these forces have been strengthened by the government’s aggressive response to stimulate growth in the wake of the financial crisis. But achieving this redirection remains a central commitment of the Chinese government, and the stimulus package contains important programmes to support innovation, to strengthen the agricultural and service sectors and to assist the environment. Rapid growth led by the service sector in India The second case is that of India, which has recently achieved growth in GDP of just over 9 per cent per annum in the five years to 2007–8, in a services-oriented economy. While India is striving to increase the role of manufacturing and of goods exports, the fact remains that the Indian acceleration has been driven by services, and by service exports, rather than by the industrial sector. There are many distinctive features of the Indian growth model, especially in comparison with that which has become common in East Asia. These include gradual rather than sharp acceleration; a reliance on services and domestic consumption rather than on industry and exports; an emphasis on high-technology and ICT services rather than on low-cost labour inputs to manufactured exports; growth driven by local private entrepreneurs as
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Table 4.7
Role of industry and services in India and China
Agriculture Industry Services Total
77
Share in GDP, 2005 (%)
Real growth rate (% per annum)
Share of GDP growth, 2000–5 (%)
China
India
China
India
China
India
12.6 47.5 39.9
19.5 26.4 54.1
3.9 10.7 10.0
2.8 7.5 8.6
6.1 52.3 41.6
8.7 27.9 63.4
100.0
100.0
9.5
7.0
100.0
100.0
Notes: For China 2005 values are used as the base. Data for India are for the year 2005–6 (the year ending 31 March 2006) and for the five years to 2005–6, and are in constant 1999–2000 prices. The growth rate shown is for the five years to 2005 for China and to 2005–6 for India. Source: NBSC (2006); GoI (MOSPI) (2009).
60.0 50.0 40.0 30.0 20.0 10.0
1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
0.0
Agriculture
Figure 4.2
Industry
Services
Value added shares by major sector, India, 1950–71 to 2008–9
Source: GoI (MOSPI) (2009).
government withdraws rather than by government agencies and enterprises or foreign investors; low reliance on foreign direct investment; and, more generally, more emphasis on increased productivity than on a rapid increase in the factors of production (capital and labour). Perhaps the most striking of these features is the heavy reliance on services. Table 4.7 illustrates the central role of the service sector as the driver of growth in India, in stark contrast with the current position in China. In 2005, industry (defined as including mining, manufacturing, energy
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Table 4.8 Structure of Indian real GDP and GDP growth, 1950–1 to 2007–8
Components of the service sector Trade Hotels and restaurants Railways Other transport Storage Communications Banking and insurance Real estate and ownership of dwellings Public administration and defence Other services Total services Total services ex communications Memorandum items Agriculture Industry GDP at factor cost
Average annual growth rate (% per annum)
Share of GDP (%)
1950–1 to 1983–4
1983–4 to 1996–7
1996–7 to 2002–3
2002–3 to 2007–8
1983–4
5.0 4.9 4.1 6.6 4.9 6.9 7.2 3.4
6.7 8.0 3.9 7.3 2.5 9.2 10.2 7.3
7.3 8.7 4.9 6.7 0.6 22.9 9.4 6.9
9.4 12.4 8.2 9.8 4.2 26.1 12.0 8.6
10.7 0.8 1.5 3.7 0.1 0.7 2.8 5.6
14.3 1.6 1.2 5.2 0.1 5.7 7.1 7.5
6.2
5.5
7.3
4.4
5.7
5.2
3.4 4.7 4.7
6.2 6.9 6.8
6.8 7.8 7.3
7.7 10.1 8.9
7.1 38.6 37.9
8.0 55.7 50.1
2.6 5.4 3.8
3.2 6.2 5.5
0.7 4.7 5.2
4.9 9.4 8.8
37.1 24.3 100
17.8 26.5 100
2007–8
Source: GoI (MOSPI) (2009).
production and water, and construction) amounted to 26.4 per cent of GDP in constant 1999–2000 prices in India, in comparison with 47.5 per cent in China (see Table 4.7). In terms of growth contribution the difference is also marked: the service sector provided 63.4 per cent of growth in real GDP in India over 2000–5, in comparison to 41.6 per cent in China; industry provided 52.3 per cent of growth over this period in China, but little over half that in India (27.9 per cent). This is a stark difference between the two economies, and India’s recent growth can truly be described as driven by the service sector. Figure 4.2, which is based on data on sectoral value added at 1990– 2000 prices recently provided by the Indian Ministry of Statistics and Programme Implementation, places this trend in the broader context of post-independence India. These data make it clear that the dominance of services in India is by no means a recent phenomenon. In 1950–1, on these estimates, the services share of GDP at factor cost was just double the industrial share of only 15 per cent. As Singh (2008) points out, the
Approaches to Development Strategy and the Service Sector
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60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0
1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
20.0
Service share
Trend 1950–1983, projected to 2008
Figure 4.3 Share of services in GDP (at factor cost) and deviation from trend, India, 1950–1 to 2008–9 Source: GoI (MOSPI) (2009) and estimates of the author.
centrepiece of India’s post-independence development strategy was modernization through industrialization, with a strong emphasis on the role of public policy and public enterprises. In the two decades after independence the industrial share of GDP increased strongly, reaching 23.3 per cent in 1966–7. But this could not be sustained in the 1960s, as policy became more restrictive to private and foreign enterprises (Panagariya 2008), and the industrial share of GDP remained virtually unchanged for 40 years, being only 25.9 per cent in 2008–9. The other key fact evident from Figure 4.2 is the acceleration of the rise in the service sector share that has taken place in two stages over the past decade or so, once after 1983–4 and again after 1996–7. To make this clearer, Figure 4.3 shows the service sector share for the full period, together with a trend line calculated over 1950–1 to 1983–4 and then projected to 2008–9. The increasing divergence of the service sector share from the earlier trend in these two stages is evident, and provides another illustration of the extent to which services have driven accelerating growth in modern India. This highly distinctive growth pattern raises important questions for development theory. Why did services-led development take hold in India? What does it look like, in more specific terms? Is it a sustainable option, or will India need to turn to the industrial sector to continue rapid growth? There is a substantial literature developing on these issues (see, for example, Bosworth and Collins 2007; Kochhar et al. 2006; Singh 2006, 2008). It is beyond the scope of this paper to address them here, and I make just two
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points. First, data on real value added for 10 components of the service sector (Table 4.8) indicates that, while most components other than public administration and storage have grown rapidly, most of the increase in the share of services in GDP since 1983–4 is due to four industries: communications, banking and insurance, trade and real estate. Especially since 1996–7, the growth of real value added in communications has been in excess of 20 per cent per annum, and communications accounted for over one-quarter of the rise in the services share since 1983–4. But rapid growth is much more widespread than communications, although India’s rising competency in these areas may be contributing to growth in other services areas. Second, this may indeed be ‘big-push’ growth driven by services, as there is every reason for supposing that many such activities (communications, banking, trade and real estate) are associated with the key conditions of fixed costs and increasing returns, increasing use of labour at higher productivity levels and pecuniary externalities. At the very least, this hypothesis is worthy of much closer examination than has been possible here.
Conclusion There are a number of theories on the link between industrialization and development, but one simple yet robust model, Murphy et al. (1989), captures many of the elements of earlier work. In this two-sector model the key elements are increasing returns, internal to the firm, in a modern sector; a substantial surplus labour in the traditional sector willing to move, with a wage premium, to the modern sector; and pecuniary externalities between industries within the modern sector, whereby expansion in one sector increases the demand for the output of other sectors even if it is individually unprofitable. There is evidence that these conditions were met by the industrial sector during the development of key advanced countries, although in latecomers such as Korea the shift of labour into industry was reversed at a relatively early stage of development. But there is nothing about these conditions that inherently relates to industry or manufacturing – any ‘modern’ sector meeting the conditions of increasing returns, labour shift to higher productivity uses and pecuniary externalities could drive sustained growth. These conditions could be met by many service industries. China’s development is a particularly interesting and important case in point. The initial phase of China’s growth after 1979 was driven more by agriculture and services than by industry, but from about 1990 industry has been the major contributor to China’s growth. In this sense China has experienced a process of industrialization over the past two decades. But in terms of employment the predominant shift has been from agriculture to services between 1978 and 2006, and the share of industrial employment was flat for nearly two decades to 2004. Thus this period cannot be regarded as big-push industrialization as characterized by Murphy et al. (1989). But, if many parts
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of China’s service industries (such as communications, transport, wholesale trade, and distribution and business services) meet their three conditions, there is no reason why this experience should not be described as a bigpush driven in part by the service sector. Further, India’s rapid growth may be plausibly regarded as a service-led big-push, as services provided nearly three-quarters of the increase in real GDP in India over the past decade. I suggest that a central challenge for development theory and practice now is to understand and implement rapid growth based on services, and on a closer link between services and the rural sector. Little is understood about how to stimulate service growth in a developing country, as China’s difficulties in changing strategies suggest. But industrialization as it used to be understood is no longer a realistic option for most developing countries, and they need to find ways of participating in the growth of the modern service sector, which can directly improve the living standards of their people.
Notes 1. For a recent discussion of some similar themes see Dasgupta and Singh (2007). 2. There are many complexities to be addressed in interpreting these data. There is a break in the Chinese employment data in 1990, although this has little impact on the trends discussed here. The employment shake-out in state-owned manufacturing enterprises after about 1996 was massive, and must be a significant contributor to the trends noted here. Finally, since 2004 the official data report a more rapid growth in employment in industry, with a slowing of productivity growth to about 5 per cent, and a sharper decline in employment in agriculture, implying annual gains in rural value added per employee of about 10 per cent per annum in recent years. These trends are also being affected by the global financial crisis, which has led to a curtailment in the growth of industry relative to services in China during 2008 and 2009.
References Athukorala, P. (2006). ‘Singapore and ASEAN in the New Regional Division of Labour’. RSPAS Working Paper 2006–11. Canberra: Australian National University. Azariadis, C., and A. Drazen (1990). ‘Threshold Externalities in Economic Development’. Quarterly Journal of Economics, 105 (2): 501–26. Bosworth, B., and S. M. Collins (2007). ‘Accounting for Growth: Comparing China and India’. NBER Working Paper 12943. Cambridge, MA: National Bureau of Economic Research. Chandler, A. (1990). Scale and Scope: The Dynamics of Industrial Capitalism. Harvard: The Belknap Press. Dasgupta, S., and A. Singh (2007). ‘Manufacturing, Services and Premature Deindustrialization in Developing Countries: A Kaldorian Analysis’. In G. Mavrotas and A. Shorrocks (eds), Advancing Development: Core Themes in Global Economics. Basingstoke: Palgrave Macmillan for UNU-WIDER. Durlauf, S. (1993). ‘Nonergodic Economic Growth’. Review of Economic Studies, 60 (203): 349–66.
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Eichengreen, B., Y. Rhee, and H. Tong (2007). ‘China and the Exports of Other Asian Countries’. Review of World Economics, 143 (2): 201–7. GoI (Government of India) (2009). ‘Ministry of Statistics and Programme Implementation’. New Delhi. Available at: www.mospi.nic.in
accessed 20 May 2010. Hirschman, A. (1958). The Strategy of Economic Development. New Haven: Yale University Press. Ianchovichina, E., and T. Walmsley (2005). ‘Impact of China’s WTO Accession on East Asia’. Contemporary Economic Policy, 23 (2): 261–77. Kaldor, N. (1966). The Causes of the Slow Rate of Growth in the United Kingdom. Cambridge: Cambridge University Press. Kaldor, N. (1967). Strategic Factors in Economic Development. Ithaca: Cornell University. Kochhar, K., U. Kumar, R. Rajan, A. Subramanian, and I. Tokatlidis (2006). ‘India’s Pattern of Development: What Happened, What Follows?’. IMF Working Paper WP/06/22. Washington, DC: IMF. Krugman, P. (1992). ‘Towards a Counter-Counterrevolution in Development Theory’. Proceedings of the World Bank Annual Conference on Development Economics. Washington, DC: World Bank. Lewis, W. A. (1954). ‘Economic Development with Unlimited Supplies of Labour’. Manchester School of Economic and Political Studies, 22 (2): 139–91. Maddison, A. (1995). Monitoring the World Economy 1820–1992. Paris: OECD. Murphy, K., A. Sheifler, and R. Vishny (1989). ‘Industrialization and the Big Push’. Journal of Political Economy, 97 (October): 1003–26. Naughton, B. (2006). ‘The New Common Economic Program: China’s 11th Five Year Plan and What It Means’. China Leadership Monitor, 16. Naughton, B. (2007). The Chinese Economy: Transitions and Growth. Cambridge, MA: MIT Press. NBSC (National Bureau of Statistics China) (2006). China Statistical Yearbook 2006. Beijing: NBSC. NBSC (National Bureau of Statistics China) (2007). ‘Statistical Communiqué on the 2006 National Economic and Social Development’, 28 February. Beijing. Available at: www.stats.gov.cn/english/newsandcomingevents/t20070301_402388091.htm< http://www.stats.gov.cn/english/newsandcomingevents/t20070301_402388091. htm> accessed 20 May 2010. OECD (Organisation for Economic Cooperation and Development) (2009). ‘Statistics Portal’. Paris. Available at: http://www.oecd.org/statistics accessed 20 May 2010. Panagariya, A. (2008). India: The Emerging Giant. New York: Oxford University Press. Romer, P. (1990). ‘Endogenous Technological Change’. Journal of Political Economy, 98 (5 part 2): 71–102. Rosenstein-Rodan, P. (1943). ‘Problems of Industrialization of Eastern and SouthEastern Europe’. Economic Journal, 53: 202–11. Rostow, W. (1960). The Stages of Economic Growth. Cambridge: Cambridge University Press. Singh, N. (2006). ‘Services-Led Industrialization in India: Assessment and Lessons’. In D. O’Connor (ed.), Industrial Development for the 21st Century: Sustainable Development Perspectives. New York: UN-DESA, 235–91. Singh, N. (2008). ‘India’s Development Strategy: Accidents, Design and Replicability’. MPRA Paper No.12453, Munich Personal RePEc Archive. Available at: http://mpra. ub.uni-muenchen.de/12453/ accessed 20 May 2010.
Approaches to Development Strategy and the Service Sector 83 Thorbecke, E. (2007). ‘The Evolution of Development Doctrine, 1950–2005’. In G. Mavrotas and A. Shorrocks (eds), Advancing Development: Core Themes in Global Economics. Basingstoke: Palgrave Macmillan for UNU-WIDER. Wen, J. (2006). ‘Report on the Work of the Government 2006’. Report delivered at the Fourth Session of the Tenth National People’s Congress on March 5. Beijing: People’s Republic of China. Available at: http://english.gov.cn/official/2006-03/14/ content_227248.htm accessed 20 May 2010. WTO (World Trade Organization) (2006). International Trade Statistics 2006. Geneva: WTO. WTO (World Trade Organization) (2009). ‘Quarterly World Merchandise Export Developments Since 2005’. Geneva. Available at: www.wto.org/english/res_e/ statis_e/quarterly_world_exp_e.htm accessed 20 May 2010. Young, A. (1928). ‘Increasing Returns and Economic Progress’. Economic Journal, 38: 527–42.
5 The Service Sector Revolution in India: A Quantitative Analysis Rubina Verma
Introduction An empirical analysis of contemporary developed and some developing nations reveals significant differences in their growth patterns. For most industrialized nations, historical data reveal that, at low levels of per capita income, the agricultural sector dominated the composition of output and employment. As these nations embarked on a path of rapid and sustained economic growth, resources were transferred from the agricultural sector to the manufacturing sector. Once the economy matured and reached the status of a high-income nation, the contribution of the service sector became more pronounced. Today, for some low-income industrializing nations, this process of sectoral reallocation of economic activity, also known as structural transformation, looks different. In these countries, even at low levels of per capita income, the service sector is a significant source of growth and accounts for much of the economy’s output and employment. Therefore, the role of the service sector has become more prominent at early stages of growth and development. I conduct an empirical exercise to identify the set of low-income, rapidly growing economies that exhibit the pattern of services-led growth. A lowincome country is defined as a country with a level of GDP per capita less than US$825 in 1980.1 Following this criterion, I identify 42 low-income countries in 1980 and calculate their average growth rates of GDP per capita during the period 1980–2004. Table 5.7 in the appendix lists these countries in descending order of their growth rates, together with their respective GDP per capita in 1980. The average growth rate for the entire sample is 0.51 per cent, owing to a large number of countries that witnessed negative growth rates during this time period. Amongst these countries, 17 countries experienced negative growth rates, while 11 countries grew at an average rate of 0–1 per cent and three countries witnessed growth rates between 1 and 2 per cent. My interest lies in choosing the rapidly growing countries which witnessed average annual growth rates of GDP per capita in excess 84
The Service Sector Revolution in India
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of 2 per cent, which was the secular growth rate of the US economy in the twentieth century.2 The US economy was the industrial leader throughout the twentieth century and hence the growth performance of the rapid growers is measured relative to the US economy. I refer to these 11 countries as Rapid Growers. These countries include China, Thailand, Bhutan, India, Indonesia, Sri Lanka, Chad, Lesotho, Pakistan, Bangladesh and Nepal. Next, I examine the performance of the three sectors, namely, agriculture, industry and services, in contributing to aggregate growth of output in these economies. It is well recognized that, as an economy grows and witnesses structural transformation, growth proceeds at an uneven rate from sector to sector. Following Syrquin (1988), I examine the relation between aggregate and sectoral growth by differentiating with respect to time the definition of total output, V = Vi and expressing the result in growth terms: g v = ∑ ri g v i i
where g v and g vi are the growth rates of V and Vi, respectively, and the weights are sectoral output shares, ri = Vi/V. The above equation expresses the contribution of each sector to aggregate GDP growth measured in terms of the average share of total GDP accounted by this sector, weighted by the growth rate of GDP in this sector. For each of the 11 Rapid Growers, I decompose the growth rate of aggregate GDP using growth rates of sectoral output and shares of the sectoral output in GDP. Following this decomposition, I identify those low-income, fast-growing countries that have witnessed service sector-driven growth. Specifically, in these economies, the service sector has made the highest average contribution to aggregate growth during the 1980–2004 period. I classify them as service sector-dominated countries. This set of countries includes India, Sri Lanka, Pakistan, Bangladesh and Nepal. Notably, all these countries have initiated economic and trade liberalization reforms. Amongst all these service sector-led countries, India has witnessed the most rapid growth in GDP and in GDP per capita during the 1980–2004 period.
The Indian economy During the period 1980–2003, the average annual growth rate of the total output of the Indian economy was 5.6 per cent while that of the service sector alone exceeded it at 7 per cent. Specifically, the service sector accounted for about 38 per cent of Indian output and 20 per cent of Indian employment in 1980, and its share increased to account for 52 per cent of total output and 28 per cent of total employment by 2003. Over the same time period, per capita income increased from 220 constant 2000 US dollars to about 510 constant 2000 US dollars, but India is still classified as a lowincome country. As a result, the share of services in India’s GDP has come to
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resemble that of a high-income country, even though its per capita income remains that of a low-income country. The role of trade became more prominent in the Indian economy in the 1990s. A balance of payments crisis in 1991 forced India to undertake a formal liberalization. Indian data reveals that total trade as a percentage of GDP nearly doubled from 16 per cent in 1980 to about 31 per cent in 2003. Figures 5.1 and 5.2 depict the evolution of agricultural, manufacturing and services exports and imports respectively, as measured by their share in GDP. A look at the graphs reveals that both agricultural exports and imports were small shares of GDP and were never in excess of 2 per cent of GDP. Industrial exports show a clear increasing trend, growing from 3 per cent of GDP in 1980 to about 9 per cent of GDP in 2003. Similarly, industrial imports also increased from about 3 per cent of GDP in 1980 to about 7 per cent of GDP by 2003. In the service sector, exports were growing slowly till about the mid-1990s, but following 1995 one observes a clear upward trend, increasing sharply from about 2 per cent of GDP in 1995 to about 4 per cent of GDP in 2003. Service imports also rose steadily until the early
0.09 Pre liberalization
Post liberalization
0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1980
1985
1990
1995
2000
Year Agriculture Figure 5.1
Industry
Services
Shares of sectoral exports, 1980–2003
Source: Computed using World Trade Organization Trade Statistics.
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0.08 Pre liberalization
Post liberalization
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1980
1985
1990
1995
2000
Year Agriculture Figure 5.2
Industry
Services
Shares of sectoral imports, 1980–2003
Source: Computed using World Trade Organization Trade Statistics.
1990s; thereafter their growth accelerated during the late 1990s and started slowing a little by 2000. In order to measure how much of the growth in real output can be attributed to accumulation of factors of production, and how much of this increase can be attributed to growth in total factor productivity (TFP), I conduct growth accounting for the Indian economy at the sectoral level. The data for this procedure have been collected and compiled to form a sectoral database for India for the 1980–2003 period and are described in detail below. Two empirical facts emerge from the Indian data analysis: first, growth accounting reveals that changes in TFP were the largest source of service sector value added growth; and second, trade statistics show a sharp acceleration in services trade following liberalization in 1991. Motivated by these findings, in this study I build a simple three-sector growth model with two main inputs: growth in sectoral TFP and trade in industry and services. The model is calibrated to the Indian sectoral data across two steady state years, 1980 and 1999, during which trade is balanced. The performance of the model is assessed on how closely it can replicate the sectoral composition
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of GDP and the allocation of sectoral labour across two steady state years, 1980 and 1999. In addition, the importance of each of the two exogenous changes is tested by conducting a counterfactual experiment in which one change is allowed to operate while the other change is shut down. The results from the counterfactual indicate that productivity growth versus trade has a more important role in capturing the sectoral composition of GDP in India. The rest of the study is organized as follows. The next section discusses the growth accounting methodology, data and results, followed by the next, which describes the model, while the calibration procedure and results are explained in the next two sections respectively. The counterfactual experiment and its results are discussed in the penultimate section. The last section concludes and discusses possible extensions and ideas for future research.
Sectoral growth accounting To gain further insight into the sources of growth in service sector value added, I conduct a growth accounting of value added for each of the sectors – agriculture, industry and services, for the 1980–2003 period. This exercise involves decomposing changes in value added into the portions due to changes in factor inputs and the portion due to changes in efficiency with which these factors are used, measured as total factor productivity (TFP) of a sector. To summarize, the results indicate that changes in TFP are significant in accounting for value added growth in the service sector. Also, the growth of agricultural value added is largely accounted for by TFP growth. By contrast, the growth of industrial output is largely driven by the growth of factor inputs, primarily due to growth in capital. Additionally, I find that TFP growth rate in the service sector is the highest across the three sectors for the entire time period, primarily because it has grown at a very rapid rate after economic liberalization in 1991. Methodology This section describes the model of value added by sector used in the growth accounting procedure. The methodology for constructing the factor shares is described in the following subsection. I follow the standard methodology of growth accounting, which involves decomposing output growth into TFP growth, capital growth and labour growth.3 The production function in each sector is assumed to be Cobb–Douglas with constant returns to scale. In particular, the function is described by: v
1v j
Yjt = Ajt K jtj Njt
j 僆 {industry, services}
where vj and 1 vj represent the share of rental payments to capital and share of wage payments to labour in the total income of sector j, respectively. The
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agricultural production function has an additional input of land. The production function is accordingly modified as: va ga 1v ga Yat Aat Kat Lat Nat a
The factor income shares in this sector are va – capital income share, ga – share of rental income from land and 1vaga – labour income share. By differentiating the production function with respect to time, t, and dividing by Yj, the growth rate of total factor productivity growth in sector j = {a, i, s} can be estimated as: dAj dt
dYj dt
Aj
Yj
vj
dK j dt Kj
(1 v j j )
dN j dt Nj
dLj dt j
Lj
(1)
In industry and services, gj 0 since land is not a factor of production in these sectors, and accordingly then the share of labour income is 1vj. Data In order to conduct growth accounting, data are collected for the three sectors – agriculture, industry and services – for the 1980–2003 period. Real GDP Data for sectoral real GDP are taken from the Business Beacon, Center for Monitoring Indian Economy (CMIE). Agriculture includes forestry, logging and fishing; Industry consists of manufacturing, mining, electricity, gas and water supply, and construction; while Services include trade, hotel, transport, communication, finance, insurance, real estate, business services and social and personal services. All data are measured in constant 1994 Indian Rupees. Capital stock The capital stock series are constructed using the Perpetual Inventory Method (PIM), where investment is measured using the gross fixed capital formation series and a constant depreciation rate of 5 per cent. In each sector, the initial capital stock is the sectoral gross fixed capital stock in 1952. Using the PIM, the entire capital stock series for all sectors are constructed from 1952 to 2003. For my purpose, I use the capital stock series for the 1980–2003 period. All sectoral capital stock data are measured in constant 1994 Indian Rupees and are obtained from the Central Statistical Organisation (CSO) of India. Employment India does not report the number of labour hours worked in each sector. Hence, I measure employment as the number of people working in each
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sector. Sectoral employment numbers are calculated using the definition of employment on a current daily status (CDS) basis.4 These data are constructed with the help of annualized growth rates of sectoral employment reported by Gupta (2002). In particular, this report presents sectoral employment numbers for the years 1983, 1987–8, 1993–4, 1999–2000 and 2001–2 as well as the average annual growth rates for the intermittent years. Using these growth rates, I construct sectoral employment series for the 1980–2003 period. Land An estimate of land used in the agricultural sector is needed. Data series on gross sown area are used for this purpose. Gross sown area is defined as the sum of area covered by all individual crops including the area sown under crops more than once during a given year. It is also referred to as gross cropped area. These data are obtained from Business Beacon (CMIE) from 1980 to 2001. For 2002 and 2003, gross sown area data have been taken from the Statistical Pocket Book, 2005, available from CSO, India.5 Factor income shares I follow Gollin (2002) and calculate factor shares by adjusting for income of the self-employed. For the 1980–2003 period, CSO reports factor incomes from different sub-sectors, which comprise Compensation of Employees (COE) and Operating Surplus (OS). In each sub-sector, the COE and OS are further divided into two components, one part accruing from the organized sector and the second part originating in the unorganized sector. I consider OS of the unorganized sector as Operating Surplus of private unincorporated enterprises (OSPUE). Then, using the second adjustment method followed by Gollin,6 I compute labour income shares for different sub-sectors. Using the share of each sub-sector’s output in the output of the agricultural, industrial and service sectors as weights, I construct weighted labour shares for these three sectors. The shares of capital income in the industrial and service sectors are deduced as residuals. The share of rental income from land in agricultural income is taken to be 0.2 (average over the period 1980–99) as reported by Sivasubramonian (2004). Consequently, the labour and capital shares are rescaled to sum to 1 minus the share of land. I also conduct a sensitivity analysis of the growth accounting results by using two alternate sets of factor shares. The first set consists of sectoral labour shares computed using Global Trade Analysis Project (GTAP) data, as reported by Terry Roe.et al. (2010). The second set assigns the customary value of one-third as the share of capital income and treats the residual as the share of labour income in the industrial and service sectors. For the agricultural sector, the capital income and labour income shares of one-third and two-thirds are rescaled so that they sum to 1 minus the share of land, where the share of land is taken as 0.2.
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Results Table 5.1 reports the decomposition of average annual growth in real value added due to change in capital, labour, land and TFP in each sector. These results have been obtained using ‘baseline’ factor shares, calibrated from the CSO data. I refer to these results as ‘baseline’ results. For the agricultural sector, the labour income share is 0.58, the share of land is 0.2 and the share of capital is determined residually as 0.22. The contribution of each factor is measured as the product of the factor share with the growth rate of the factor. During the 1980–2003 period, agricultural real value added grew at an average annual rate of 3.08 per cent. The contributions of capital, labour and TFP were 19, 24 and 56 per cent, respectively. Land made a negligible contribution of 1 per cent during the entire period. In the pre-liberalization period, 1980–90, real value added was growing at 4.27 per cent, of which TFP growth accounted for 52 per cent. After TFP, the contribution of labour was next largest at 29 per cent, followed by capital, which accounted for about 16 per cent. Land made a Table 5.1 Growth accounting – baseline results Agriculture
Industry
Services
Factor share capital labour land Growth rate (%) 1980–2003 real value added capital labour land TFP
0.22 0.58 0.2
0.55 0.45 –
0.44 0.56 –
3.08 2.64 1.25 0.16 1.73
6.18 6.85 3.11 – 0.98
7.06 4.36 3.42 – 3.11
Growth rate (%) 1980–1990 Pre-liberalization real value added capital labour land TFP
4.27 3.08 2.10 0.72 2.19
6.78 7.42 4.28 – 0.74
6.63 3.39 4.12 – 2.73
Growth rate (%) 1991–2003 Post-liberalization real value added capital labour land TFP
2.01 2.37 0.44 −0.45 1.33
5.54 6.39 2.33 – 0.96
7.57 5.13 2.83 – 3.60
Source: Author’s computation.
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small contribution of 3 per cent. In the post-liberalization period, growth in real value added decreased to about 2.01 per cent and the contribution of TFP increased to account for 66 per cent of real value added growth. Capital and labour accounted for 26 and 13 per cent of growth, respectively, whereas the contribution of land was small and negative at minus 5 per cent. With respect to the industrial sector, the calibrated capital and labour shares are 0.55 and 0.45, respectively. Real value added in industry grew at 6.18 per cent during the entire 1980–2003 period. The contribution of capital was the largest at 61 per cent while that of labour was about 23 per cent. TFP in industry made a relatively small contribution of 16 per cent during this period. In the pre-liberalization period, real value added was growing at 6.78 per cent, of which capital made a significant contribution of 61 per cent. The contribution made by labour was 28 per cent, followed by TFP, which accounted for a relatively small proportion, 11 per cent. In the post-liberalization period, growth of industrial real value added slowed down to 5.54 per cent. Again, the contribution of capital was largest, accounting for about 64 per cent of growth in real value added, followed by labour, which made a contribution of 19 per cent. In this period, the contribution of TFP increased to account for about 17 per cent of real value added growth in this sector. For the service sector, the shares of capital and labour income are calculated to be 0.44 and 0.56, respectively. During the 1980–2003 period, real value added grew at 7.06 per cent, of which TFP accounted for 44 per cent, followed by capital and labour, which each accounted for about 28 per cent of services’ value added growth. In the pre-liberalization period, real value added grew at 6.63 per cent. The contributions of capital and labour were 23 and 35 per cent, respectively, while that of TFP was about 42 per cent. In the post-liberalization period, service sector real value added grew at 7.57 per cent. The contribution of capital increased to 49 per cent while the contribution of labour decreased to about 21 per cent in this period. TFP’s contribution increased, and TFP growth alone in this period accounted for 48 per cent of real value added growth. Bosworth et al. (2007) conduct sectoral growth accounting for the Indian economy and find similar sectoral TFP growth rates for the 1980–2004 period. Their estimates of TFP growth rates in agriculture, industry and services are 1.1, 1 and 2.9 per cent respectively. They do not calibrate factor shares, but assume a capital share of 0.4 in industry and services. For agriculture, the factor shares are 0.5, 0.25 and 0.25 for labour, capital and land respectively. They have another factor input of human capital (education) in each sector. In spite of this additional input, my estimates of TFP growth rates are similar to their numbers, suggesting that education has not played a very significant role in contributing to the growth of sectoral real value added.
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From Table 5.1 one observes that the service sector in India has witnessed very rapid TFP growth, which exceeds TFP growth in the agricultural and industrial sectors for the 1980–2003 period, primarily because of the high growth it experienced in the 1991–2003 period. Figure 5.3 depicts the evolution of sectoral TFP from the initial time period, 1980 (the levels in all sectors have been normalized to unity), to 2003. It is further evident from the graph that the service sector witnessed the fastest rate of TFP growth throughout the sample period. In addition, the rate of TFP growth in services increased after 1991. In the Indian case, the finding of high TFP growth in services does not depend on the values of factor shares. I report results using two other sets of factor shares. Table 5.2 reports the results using the Global Trade Analysis Project (GTAP) computed sectoral factor shares (as reported by Terry Roe) and Table 5.3 presents the results using capital share values of one-third in the sectors. These results validate the finding that, amongst the three sectors, TFP growth is highest in the service sector for the entire sample period, especially due to the high growth observed in the post-liberalization period. 2.2 Pre liberalization
Post liberalization
2 1.8 1.6 1.4 1.2 1 0.8 1980
1985
1990
1995
2000
Year Agriculture Figure 5.3
Industry
Services
Sectoral TFP levels, 1980–2003
Source: Computed using Real GDP data from Business Beacon, CMIE.
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Table 5.2 Growth accounting – GTAP factor shares
Factor share capital labour land Growth rate (%) 1980–2003 real value added capital labour land TFP Growth rate (%) 1980–1990 Pre-liberalization real value added Capital Labour Land TFP Growth rate (%) 1991–2003 Post-liberalization real value added capital labour land TFP
Agriculture
Industry
Services
0.21 0.41 0.38
0.61 0.39 –
0.5 0.5 –
3.08 2.64 1.25 0.16 1.93
6.18 6.85 3.11 – 0.76
7.06 4.36 3.42 – 3.05
4.27 3.08 2.10 0.72 2.45
6.78 7.42 4.28 – 0.56
6.63 3.39 4.12 – 2.77
2.01 2.37 0.44 −0.45 1.50
5.54 6.39 2.33 – 0.72
7.57 5.13 2.83 – 3.46
Source: Factor shares from Roe et al. (2010); author’s computation.
The model The set-up of the model is very similar to Stokey (2001). There are three final goods, consisting of agricultural goods, industrial goods, and services; three primary factors – capital, labour and land (in agriculture); and trade consisting of exports and imports of industrial goods and services. In addition, there is total factor productivity (TFP) growth in each sector and it is assumed that this growth rate is constant over the sample period. The production function for each final good displays constant returns to scale and is assumed to be Cobb–Douglas. Technology The model is set up in terms of per capita quantities for simplicity. Agricultural goods are produced using capital ka, land la, and labour na as inputs; industrial goods and services are produced using capital and labour, ki, ni, ks and ns respectively. Time is discrete and on a per capita basis the production functions are: y at bat katu latg nat1ug
(2)
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Table 5.3 Growth accounting – capital share of one-third
Factor share capital labour land Growth rate (%) 1980–2003 real value added capital labour land TFP Growth rate (%) 1980–1990 Pre-liberalization real value added capital labour land TFP Growth rate (%) 1991–2003 Post-liberalization real value added capital labour land TFP
Agriculture
Industry
Services
0.24 0.56 0.2
0.3 0.7 –
0.3 0.7 –
3.08 2.64 1.25 0.16 1.69
6.18 6.85 3.11 – 1.88
7.06 4.36 3.42 – 3.24
4.27 3.08 2.10 0.72 2.17
6.78 7.42 4.28 – 1.49
6.63 3.39 4.12 – 2.63
2.01 2.37 0.44 −0.45 1.28
5.54 6.39 2.33 – 1.94
7.57 5.13 2.83 – 3.92
Source: Author’s computation.
yit bit kata nit1a
(3)
y st bst kstf n1stf
(4)
where bjt is the TFP level in sector j = {a, i, s}. The parameters u, g, a, w 僆 (0, 1) and u + g ≤ 1. It is assumed that all firms behave competitively in all markets. There are three market clearing conditions for produced goods: cat yat
(5)
cit kt+1 (1d)kt xit yit iit
(6)
cst xst yst ist
(7)
where cjt is the consumption level in sector j = {a, i, s} and ijt and xjt are the imports and exports in sector j = {i, s} respectively. These conditions imply that, while agricultural consumption is met entirely from domestic
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production, the sum of domestic output and imports of services equals the sum of domestic consumption and exports of services to the rest of the world. In the industrial sector, domestic output and imports of industrial goods together equal the sum of domestic consumption, investment and exports of industrial goods to the rest of the world. There are also three market clearing conditions for primary inputs: kat kit kst kt nat nit nst 1 lat 1 where labour supply per capita is normalized at unity and where lat is the supply of land per capita, also normalized to unity. There is a role for foreign trade. When the model is calibrated, industrial net imports (niit iit xit) are fixed at a level chosen to match the data, and net exports of services (nxst xst ist) are assumed to adjust. I make a simplifying assumption of trade being balanced in the steady state. Hence this implies that the net exports of services needed to pay for net imports of industrial goods are: pstnxst pitniit
(8)
where the price of the industrial good, pi, is given and assumed it to be unity at all dates. Then let {rkt, Rlt, wt, pat, pst} denote the rental prices for capital and land, the wage rate, the price of the agricultural good, and the price of the service good, at date t, respectively. Preferences There is an infinitely-lived representative household endowed with one unit of time in each period. The lifetime utility function for the household is given by: t 0
btU ( cat , cit , cst )
where cj is the consumption of good j (j = a, i, s) in period t and b is the discount factor. The per period utility function is given by: U ( cat , cit , cst ) ln( with < 1 and
vj
c
a at
a ,i , s
c
i it
s st
c )
1
1. Thus, the elasticity of substitution between ca, ci and cs is given by 1 1 .
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The parameter, , plays an important role in generating structural change in models with differential TFP growth across sectors. Specifically, if consumption goods are complements, then, in the presence of differential TFP growth across sectors, resources are transferred to the sector experiencing the lowest TFP growth. But, if consumption goods are substitutes, then resources are allocated to the sector witnessing highest TFP growth. The underlying reasoning is that the sector witnessing highest TFP growth also experiences the most rapid decline in the price of the good that it produces. If the goods are substitutes, the household increases its share of consumption expenditure on this relatively cheap good, and reduces the share of expenditure on the other goods. The household then demands more of the cheap good and reduces the demand for the relatively expensive good. As a result, when the two goods are substitutes, labour shifts into the sector where TFP growth is the highest. The converse is true when goods are complements. Since the growth accounting results reveal TFP growth to be largest in the service sector for India, and the data show that the output and employment of this sector have grown, I assume < 1 and therefore assume that the three goods are substitutes in consumption. The representative household faces the following maximization problem in each period: max
t 0
btU ( cat , cit , cst )
subject to pat cat cit pst cst kt1 (1d)kt rkt kt wt Rlt lat pst nxst niit given k0, all prices, the net import level of industrial goods ni. The above equations, together with assumptions that firms maximize profits and markets are perfectly competitive, provide a complete description of the model. Calibration The two years considered as steady states are 1980 and 1999. In these two years, the value of net imports of industrial goods (as a share of GDP) was approximately equal to the value of net exports of services (as a share of GDP). In other words, trade balance as a share of GDP was roughly small (–0.2 per cent in 1980 and 0.7 per cent in 1999) and hence I assume balanced trade in these two years.7 To calibrate the model, I fix the level of net imports of industrial goods from the data and solve for the level of net exports of services, by using the balanced trade condition. Factor shares for each sector have been constructed as explained above. The TFP growth rates for each sector have been taken from the baseline growth accounting exercise8. The subjective
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discount factor, , is calibrated to match the real interest rate in 1980 and the depreciation rate is set at 5 per cent. The remaining parameters (TFP levels in the initial period – ba0, bi0, bs0; the weight on the agricultural and industrial good in the utility function – a, i; and the parameter dictating the elasticity of substitution between the three goods – ) are calibrated to minimize the sum of squared differences between the data and the model with respect to six targets in the initial steady state. These six targets are: the share of output in agriculture, the share of output in services, the share of employment in agriculture, the share of employment in services, the share of consumption expenditure on services and the relative price of the service good, all in 1980. Specifically, ˆ 0, ˆps0 are the model’s prediction for the six targets and ya0, ˆa0, n ˆs0, CS if yˆa0, yˆs0, n p ys0, na0, ns0,CS0, s0 are the actual observations in the data, then I solve the following problem: ˆa0 n ˆa0)2 {ba0, bi0, bs0, va, vi, v} arg m in {(yˆa0 ya0)2 (yˆs0 ys0)2 (n {x,y,z} 2 2 2 ˆ ˆ ˆ (ns0 ns0) (CS 0 CS 0) (ps0 ps0) }
(9)
In order to calibrate the above parameters, I need data on private final consumption expenditure as well as relative prices of service goods. CMIE reports disaggregated data for private final consumption expenditures. To
Table 5.4
Calibrated parameters
Parameter
ba0 bi0 bs0 gat git gst
a i s
1/(1 – ) nii (1980) nii (1999)
Description capital share in agriculture land share in agriculture capital share in industry capital share in services initial TFP level in agriculture initial TFP level in industry initial TFP level in services growth rate of TFP in agriculture growth rate of TFP in industry growth rate of TFP in services discount factor depreciation rate weight on agricultural good weight on industrial good weight on service good elasticity of substitution share of net industrial imports in GDP share of net industrial imports in GDP
Source: Author’s computation.
Value 0.22 0.2 0.55 0.44 3.9 1.1 1.9 0.0215 0.0078 0.0302 0.98 0.05 0.44 0.19 0.37 4.3 0.0019 −0.015
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construct sectoral consumption expenditure, I group the disaggregated final consumption expenditures under the three sectors, following Echevarria (1997).9 Since the industrial good is assumed to be the numeraire in the model, relative prices for the service goods are got by dividing the GDP deflator series for services with that of industry. The parameter values are listed in the table 5.4. Results Table 5.5 reports the results for the two steady state years, 1980 and 1999. The model’s predictions for the composition of output are good. The model predicts that 38 per cent of output is attributable to agriculture in 1980, which is equivalent to what is observed in the data. In the industrial sector, the model slightly over-predicts the output share at 29 per cent, while in the data the corresponding share is 24 per cent. The share of output accounted by services is about 38 per cent in the data and the model’s prediction for this share is at about 33 per cent for the initial steady state. By 1999, agriculture’s share of output reduces to about 28 per cent, measuring closely to the share of 26 per cent seen in the data. The model allocates about 29 per cent of output in the second steady state to industry, roughly equal to the 27 per cent observed in the data. With respect to services, the share of output accounted by this sector is about 42 per cent as predicted by the model, a little less than the 46 per cent seen in the data. With respect to the allocation of labour in 1980, the model allocates the largest share of labour to agriculture, similar to what is observed in the data, although the model estimates this share at 41 per cent while in the data the share is larger at 65 per cent. The model allocates about 25 per cent of labour to industry, more than the corresponding share of 15 per cent observed in the data. With respect to the share of labour in the service
Table 5.5 Results for the two steady states 1980 Data 1980 Model 1999 Data 1999 Model Composition of GDP Share in agriculture Share in industry Share in services
0.38 0.24 0.38
0.38 0.29 0.33
0.26 0.27 0.46
0.28 0.29 0.42
Allocation of labour Share in agriculture Share in industry Share in services
0.65 0.15 0.21
0.41 0.25 0.34
0.57 0.18 0.26
0.3 0.25 0.45
−0.0003
0.002
−0.007
−0.023
Share of service net exports in GDP
Source: Model simulated results; author’s computation.
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sector, the model predicts the share to be 34 per cent in 1980, higher than the 21 per cent observed in the data. Although the model cannot precisely capture the quantitative shares observed in the data, it does replicate the qualitative pattern of labour allocation in 1980, that is, the largest share of employment is accounted by agriculture, followed by services and then industry. By the final steady state, the share of labour accounted by the agricultural sector decreases to about 30 per cent; less than its data counterpart of 57 per cent. In the industrial and service sectors, the share of labour increases to 25 and 45 per cent respectively, although these shares are higher than their data counterparts – 18 per cent in industry and 26 per cent in the service sector. With respect to foreign trade, the model predicts the level of services’ net exports to be small and slightly positive at 0.2 per cent of GDP in 1980; in the data this share is small and negative at −0.03 per cent of GDP. By the final steady state in 1999, the model predicts net exports of services to be negative and large, at 2.3 per cent of GDP; the corresponding share observed in the data is about −0.7 per cent of GDP.
Counterfactual experiment The objective of this study is to analyse the relative importance of two factors observed in the data with respect to service sector growth: the increase in services trade that occurred after 1991, versus high TFP growth in the service sector. In this respect, I conduct two counterfactual experiments. The first experiment allows TFP growth to take place in all three sectors but does not allow any trade to occur in industry and services. The second experiment allows trade to take place in the industrial and service sectors but shuts down the growth of TFP in each of the sectors. In each experiment I examine the model’s prediction for the change in the composition of GDP as well as the change in labour allocation in the second steady state 1999. The results are displayed in table 5.6. For ease of comparison, the data values and the original model’s predictions for 1999 are reproduced in the table. A comparison reveals that labour allocation, as well as composition of GDP, does not change as one moves from an environment of trade to no trade. Hence, one observes that the absence of trade does not alter the sectoral composition of GDP, or the labour allocation in each sector, as compared with the results obtained from the original model. The above inference does not apply in the case where TFP growth in each sector ceases while trade takes place in the economy. In 1999, the model predicts a much larger share of output accruing in the agricultural sector at the expense of the service sector; that is, in the absence of productivity growth, the share of output in agriculture is about 38 per cent, higher than the data value of 26 per cent. With respect to services, the share of output accounted by this sector is about 33 per cent, falling short of the 47 per cent observed
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Table 5.6 Counterfactual experiment 1999 Data
1999 Original Model
No Trade
No TFP Growth
Composition of GDP Share in agriculture Share in industry Share in services
0.26 0.27 0.47
0.28 0.30 0.42
0.28 0.30 0.43
0.38 0.29 0.33
Allocation of labour Share in agriculture Share in industry Share in services
0.57 0.18 0.26
0.3 0.25 0.45
0.3 0.25 0.45
0.41 0.25 0.34
Share of service net exports in GDP
−0.007
−0.02
−0.02
Source: Model simulated results; author’s computation.
in the data and the 42 per cent predicted by the original model. The absence of TFP growth affects the industrial sector relatively less. Industrial share of output is about 29 per cent as compared with the data value of 27 per cent. The model allocates the largest share of labour to agriculture at 41 per cent, followed by services (34 per cent) and then industry (25 per cent). The model predicts the same value of net exports of services (as a share of GDP), at −0.02 per cent, as that seen in the original model; this is higher than the share of –0.007 per cent observed in the data.
What explains productivity growth? The quantitative results in the previous section highlight the importance of productivity growth vis-à-vis trade in accounting for the large share of services in GDP. An important issue, therefore, relates to the source of the high TFP growth in services for the sample period, and particularly following liberalization in the economy. Verma (2008) has a thorough discussion on the various explanations trying to analyse the source of productivity. Here, for the sake of brevity, I present a brief summary of the argument in Verma (2008), which finds that the liberalization reforms in 1991 account for the rapid rise of productivity and output growth in services. The economic liberalization of 1991 involved many crucial reforms. There was a big effort directed towards liberalizing trade, opening up to foreign direct investment, deregulation and privatization in some important industrial and service sub-sectors, and so on. In addition to the policy reforms, there is a view that education and the large number of people speaking fluent English, particularly in information technology and business services, could explain the increase in services productivity and services output in
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India. Among all these explanations, I find that deregulation and privatization in the banking, insurance, business and communication sectors were the crucial changes resulting in rapid services growth. The trade liberalization in 1991 involved simplification of tariff rates, reduction in export controls, and removal of exports and imports licensing, as well as special trade quotas. However, it is still true that the Indian economy is relatively closed and tariff rates still remain high. The World Bank Report (2004) states that the average tariff rate in India (inclusive of customs duties and other general and selective protective levies) in 2002–3 was still high, at 35 per cent. With respect to exports of services, there is no refutation of the fact that, as a share of service sector GDP, these exports grew following trade liberalization. However, one can observe from Figures 5.1 and 5.2 that both sectoral exports and imports continue to be dominated by the industrial sector. By the end of 2003 the service sector exports amounted to a little more than 4 per cent of total GDP; clearly a small percentage compared with what is observed in the Indian manufacturing sector. With respect to foreign direct investment in Indian services, Verma (2008) finds that there has been an absolute increase in FDI (Foreign Direct Investment) in services; however, when one considers the cumulative share of FDI in the total service sector output over the 1991–2002 period, the number is a mere 0.3 per cent. In light of this fact, the argument that an increase in FDI accounts for growth of services is difficult to support. Since services are assumed to be relatively skill-intensive, one could argue that education has a big role to play in driving growth in this sector. Bosworth et al. (2007) conduct growth accounting for each of the three sectors (agriculture, industry and services) for the Indian economy between 1960 and 2004 and explicitly take into account the role of human capital (education). For the 1980–2004 period, they report that TFP in services grows at an average annual rate of 3 per cent, similar to my findings. Their results indicate that the average annual growth of education as a factor of production in the service sector is small, at 0.4 per cent, and accounts for 14 per cent of services’ output growth. Moreover, when one looks at the educational attainment statistics of Indian workers aged between 15 and 64, the percentage of workers with graduate education and above is small – 6 per cent. Given how small this number is, one cannot support the hypothesis that education is the driver of TFP growth in services. Prior to liberalization, the service sector had been subject to heavy government intervention. There was a conspicuous dominance of the public sector in the key sectors of insurance, banking and telecommunications. Following liberalization, there was an active deregulation of some sectors, and entry of private firms was allowed in the service sector. Prior to 1991, insurance was a state monopoly. In 1999, the Indian Parliament passed the Insurance
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Regulatory and Development Authority (IRDA) Bill, which established an Insurance Regulatory and Development Authority and permitted private sector participation in the insurance sector. The banking sector too saw major policy reforms after 1991, but prior to liberalization the scene looked quite different. Following the nationalization of banks in 1980, quantitative loan targets were imposed on these banks to expand their networks in rural areas and they were directed to extend credit to priority sectors, as well as banks were being used to finance fiscal deficits. Although non-nationalized private banks and foreign banks coexisted with public-sector banks, their activities were highly restricted through entry regulations and strict branch licensing policies, and hence their activities remained negligible. Following the recommendations of the Narasimhan Committee in 1991–2, the banking sector was opened up to allow private banks to operate. Also, there was a shift of banking sector supervision from intrusive micro-level intervention over credit decisions toward: (a) a prudential regulations and supervision; (b) a reduction of the CRR (Cash Reserve Ratio) and SLR (Statutory Liquidity Ratio); (c) interest rate and entry deregulation. Another sector that witnessed massive growth in its output was telecommunications. Until the early 1990s, this sector was a state monopoly, but with the creation of the National Telecommunications Policy in 1994 the doors were opened to provide for cellular, as well as basic and valueadded, telephone services by the private sector. The Handbook of Industrial Policy and Statistics 2003–2005 lists the share of public sector investment in commodity-producing enterprises in 2003 as 60.36 per cent, while the corresponding share in enterprises-rendering services was much less, at 35.6 per cent. Hence, having presented the possible hypotheses, I conclude that deregulation and privatization in the key sectors of banking, insurance and telecommunications were crucial in accounting for the gains in TFP and output witnessed by the Indian service sector.
Conclusion Following economic liberalization in India, the service sector has gained prominence in the economy, as it accounts for the largest share of GDP, and in addition the share of this sector in GDP has been growing very rapidly. Empirical data reveal two significant trends in the service sector following liberalization in 1991: growth in service sector productivity and growth in services trade. The objective of this study is to build a simple three-sector quantitative model that can capture the increase in the share of service sector in GDP after liberalization. In particular, the model is assessed on how closely it can replicate the composition of output and allocation of labour for the three principal sectors of the Indian economy, namely, agriculture, industry and services, across two steady state years. Within the context of
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the model, there are two exogenous changes that occur across the two steady states: growth in sectoral TFP and trade in industrial and service sectors. A steady state is defined as a year in which the trade balance as a share of GDP is closest to zero. The two years that meet this criterion are 1980 and 1999. The model developed here is successful in replicating the shares of sectoral output and the change in this sectoral composition across the two steady states. It can correctly capture the direction of structural change as the economy transforms from a situation where the agricultural sector dominates the GDP to a situation where the service sector gains primary importance. It cannot capture the quantitative shares of labour allocation in the two years. Although the model predicts a much larger level of net exports of services, it does estimate the level of net exports as a share of GDP to be negative; implying that the economy is importing more services than it is exporting, this is similar to what is observed in the data. The second focus of my analysis is to identify the relative importance of TFP growth versus trade in industry and services. This is done by shutting down one source of exogenous change and letting the other operate solely on its own. The results from the counterfactual reveal that shutting down sectoral TFP affects the ability of the model to capture the data trends, whereas the absence of trade has a negligible effect on the results. The mechanical method of modelling trade limits the model’s ability to address trading opportunities as a source of growth and is an avenue to be explored in the future. However, the simple quantitative model does show that TFP growth can replicate the sectoral composition of output and hence is a better candidate to examine the model’s dynamic performance.10 A brief discussion of the source of TFP growth reveals that the liberalization reforms of 1991, in particular the deregulation and privatization in Indian banking, insurance and telecommunications, explain the increase in productivity and output gains observed in the Indian service sector.
Data Appendix 1. Classification according to current daily status approach (CDS): The activity pattern of people, particularly in the unorganized sector, is such that a person might be pursuing more than one activity during a week, and sometimes even during a day. In the current daily status, up to two activity statuses were assigned to a person on each day of the reference week. The unit of classification was thus half a day in the CDS. In assigning the activity status on a day, a person was considered as working for the entire day if he had worked four hours or more during the day. If he had worked one hour or more but less than four hours, he was considered as working (employed) for half a day and seeking/available for work (unemployed) or not available for work (not in the labour force) for the other half day, depending on whether or not he was seeking/available for work. On the
Table 5.7 Growth rates of GDP per capita in low-income countries Countries
1980 GDP per capita less than 825 constant 2000 US$
Average annual growth rate (%) of GDP per capita 1980–2004
Rapid growers: growth rate greater than 2% China 186.44 Thailand 804.48 Bhutan 263.65 India 222.05 Indonesia 396.63 Sri Lanka 441.86 Chad 147.26 Lesotho 309.65 Pakistan 327.43 Bangladesh 240.51 Nepal 140.08 Countries with growth rate greater than 1% but less than 2% Sudan 274.22 Mozambique 179.01 Burkina Faso 191.69 Countries with growth rate greater than 0% but less than 1% Kiribati 435.41 Mauritania 361.8 Guyana 819.41 Ghana 233.56 Senegal 405.53 Benin 292.44 Mali 220.22 Solomon Islands 597.09 Cameroon 638.19 Papua New 582.54 Guinea Gambia 327.21 Countries with growth rate less than 0% Kenya 435.24 Malawi 161.7 Guinea-Bissau 144.44 Nigeria 425.32 Comoros 404.63 Rwanda 280.35 Burundi 126.36 Zimbabwe 598.68 Zambia 450.51 Central African 313.57 Republic Togo 346.28 Madagascar 341.81 Niger 245.5 Haiti 802.62 Sierra Leone 310.4 Congo, Dem. 251.12 Rep. Liberia 744.48 All countries: average annual growth rate 0.51% Source: World Development Indicators (2003), World Bank.
8.51 4.58 4.12 3.76 3.5 3.29 2.34 2.34 2.31 2.16 2.11 1.93 1.8 1.08 0.84 0.79 0.79 0.74 0.53 0.47 0.302 0.26 0.15 0.15 0.12 −0.08 −0.23 −0.23 −0.24 −0.29 −0.48 −0.78 −1.12 −1.21 −1.37 −1.45 −1.66 −1.87 −2.57 −2.82 −4.29 −7.02
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other hand, if a person was not engaged in any work even for one hour but was seeking or available for work for four hours or more, he was considered as unemployed for the entire day. If he was available for work for less than four hours only, he was considered unemployed for half a day and not in the labour force for the other half of the day. A person who neither had any work to do nor was available for work even for half of the day was considered not in the labour force for the entire day and was assigned one or two non-economic activity status codes. The aggregate of person days classified under the different activity categories for all the seven days gave the distribution of person days by activity category during an average week over the survey period of one year. 2. Expenditure on agriculture goods includes food, beverages, pan and intoxicant, tobacco and its products. Expenditure on industry includes clothing and footwear, gross rent, fuel and power, furniture and household, personal transport equipment and operation of personal transport equipment. Expenditure on services includes other services in furniture and so on, medical care and health services, equipment, recreation, education and cultural services, miscellaneous goods and services, hotels and restaurants, and transport and communication minus the sum of personal transport equipment and operation of personal transport equipment.
Notes 1. In 2004, The World Bank defined a low-income country as a country which had a level of Gross National Income per capita less than US$825. 2. Following Kehoe and Prescott (2002), they calculate the average growth rate of output per working-age person in the US economy to be 2 per cent in the twentieth century. 3. Gross output by sector would also be analysed, but data is unavailable. 4. Details of the CDS approach are provided in the data appendix. 5. Note that this is incomplete – land is also used for cattle and large animals, and so on, but no estimates of these data are available. 6. Labour income share = Compensation of Employees/(Compensation of Employees+Operating Surplus of Incorporated Enterprise+Consumption of Fixed Capital). 7. Agricultural trade as a share of GDP was relatively small for the sample period and hence I assume no trade takes place in this sector. 8. TFP growth rates used here are for the period 1980–99. 9. Details of the classification methodology are provided in the data appendix. 10. See Verma (2008) for a thorough exposition of the model’s dynamic performance.
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References Bosworth, B., S. M. Collins, and A. Virmani (2007). ‘Sources of Growth in the Indian Economy’. NBER Working Paper 12901. Cambridge, MA: National Bureau of Economic Research. Center for Monitoring Indian Economy Private Ltd. (2005). ‘Business Beacon Version 2.0’. Bombay. CSO (Central Statistical Organisation) (2008). ‘National Accounts Statistics’. Ministry of Planning and Programme Implementation. New Delhi: Government of India. Echevarria, C. (1997).’Changes in Sectoral Composition Associated with Economic Growth’. International Economic Review, 38 (2): 431–52. Gollin, D. (2002). ‘Getting Income Shares Right’. Journal of Political Economy, 110 (2): 458–74. Gupta, S. P. (2002). ‘Report of the Special Group on Targeting Ten Million Employment Opportunities per year over the Tenth Plan Period’. Delhi: Planning Commission, Government of India. Kehoe, T., and E. C. Prescott (2002). ‘Great Depressions of the Twentieth Century’. Review of Economic Dynamics, 5 (1): 1–18. Roe, T. L., R. B. Smith, and D. S. Saracoglu (2010). ‘Multisector Growth Models: Theory and Application’. Berlin: Springer Publishing Company. Sivasubramonian, S. (2004). The Sources of Economic Growth in India 1950-1 to 1999-2000. Oxford: Oxford University Press. Stokey, N. L. (2001). ‘A Quantitative Model of the British Industrial Revolution, 1780-1850’. Carnegie-Rochester Conference Series on Public Policy, 55 (1): 55–109. Syrquin, M. (1988). Patterns of Structural Change, vol. 1. The Handbook of Development Economics. Amsterdam: Elsevier Science Publishers B.V. Verma, R. (2008). ‘Productivity Driven Services led Growth’. ITAM Working Paper. Available at: http://allman.rhon.itam.mx/~rubina.verma/research.html World Bank (2004). ‘Trade Policies in South Asia: An Overview’. Report No. 29949. Washington, DC: World Bank.
6 China’s Development Strategy and Energy Security Haider A. Khan
Introduction This chapter analyses both global and regional approaches to solving problems of energy security and ecological imbalance by specifically addressing the problems of China’s energy security. The growing energy dependence of the People’s Republic of China (PRC) has become a major concern for both economic and national security policymakers in that country. The ambitious goal of modernization of the economy along the lines of the other newly industrialized economies (NIEs) of Asia has succeeded only too well, and it is difficult to reorient economic priorities. China’s remarkable growth during the last 25 years has led to a rapid increase in energy demand, and its hunger for energy is apparently insatiable. There are, of course, both economic and ecological aspects of this insatiable demand for energy. There are also security issues that exercise the Chinese leadership increasingly. In this chapter, I examine the measures that China is taking to achieve energy security and the motivations behind these measures. I also look at China’s investment in overseas oil exploration and development projects, interest in transnational oil pipelines, plans for a strategic petroleum reserve, expansion of refineries to process crude supplies from the Middle East, development of the natural gas industry, and gradual opening of onshore drilling areas to foreign oil companies. The key question is: can China hope to achieve both equitable growth and energy security over the next two decades? I suggest the kind of mathematical modelling and political economy analysis that may be necessary to answer this two-pronged question rigorously. China faces at least two pressing sets of energy policy challenges. The first is a problem of short-run efficiency and therefore relates to the immediate need to improve management and coordination of the nation’s energy supply. During the last five years economic growth has been more than 8 per cent per annum. At the same time, energy demand grew by about 15 per cent annually while oil imports grew at 30 per cent per year. In today’s China, electrical power shortages are widespread, and transport bottlenecks constrain the ability of the industry to move both coal and oil to where they are needed. 108
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There are also longer-term energy policy challenges. These concern the continuing inability of China’s government to formulate a coherent energy policy which could provide the basis for the effective management of the energy sector and its environmental consequences for the next 50 years and beyond. China’s entry into the WTO has already had, and in the future will have, diverse consequences for its economy and energy. The best overall assessment from the Chinese perspective is that, although the short-run adjustment costs – for example, the increase in unemployment in the agricultural sector – may be high, the long-run economic benefits from integration into the world trading system are likely to be considerable. In particular, it is assumed that the export-led growth will continue and will also lead to the modernization of the economy along the lines of the other NIEs of Asia. If examined rigorously, such an assumption can be seen to entail the goal of creating further technological capabilities. In particular, China seems to be firmly committed to the creation of a largely self-sustaining innovation system as part of a knowledge-based economy of the future (Lu 2000; Simon 1996; Simon and Goldman 1989). Such innovation systems, called positive feedback loop innovation systems or POLIS (Khan 1998, 2003, 2004a), have been created by advanced countries, and NIEs such as South Korea and Taiwan are proceeding to create these as well.1 Can China do the same? And will China’s entry into the WTO help or hinder such efforts? While the answers here are not clear, it is reasonable to say that its WTO membership2 will open the way towards diversification of energy import sources. However, I will argue that much depends on the US–China bilateral relationship also. Furthermore, as I will show, the current strategy of indiscriminate growth alone without regard to sustainability or equity will make China’s growth deeply problematic even in an open economy context of rules-based trading in the WTO framework. In what follows, I will begin by identifying the most important aspects of the energy demand and supply in PRC in order to ascertain China’s energy dependence. This will enable us to look at both economic welfare and sustainability issues, as well as security issues, before discussing the China– Japan relations in the field of energy against a broader background of cooperation and conflict. I also illustrate some of the problems that China and other potential partners for regional cooperation will need to face and solve. In conclusion, I draw attention to the real dilemma faced by the ambitious Chinese leaders and suggest that multilateralism and regional cooperation are the best strategic responses available to China.
China’s energy dilemma: Demand, supply and distributional issues China is now the world’s second largest consumer of energy, accounting for some 12 per cent of global energy demand, but its rate of increase of
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demand is some four to five times that of the rest of the world. So what happens in China’s energy sector will affect the rest of the world as well. Table 6.1 gives a picture of China’s actual and projected energy consumption till the year 2015. China’s energy sector displays continuing dependence on coal. China is the world’s largest consumer of coal, accounting for more than 30 per cent of global coal consumption. Further, coal provides approximately 64 per cent of China’s primary energy demand. While such dependence on coal is not necessarily a huge problem, it has two unfortunate consequences. The first problem is that the use of coal is characterized by low energy efficiency. The second problem is that extensive coal usage without clean technologies creates pollution on a large scale. The intrinsic energy value of a unit weight of coal is known to be less than that for oil and gas. The recovery rates for many of China’s coal mines are also low, meaning that much of the country’s coal resource is left in the ground, never to be recovered. Table 6.1 confirms this. Furthermore, the efficiency of appliances that use coal in China continues to be substantially lower than the average in OECD countries. Progress has been slow in enhancing the efficiency of consumer electrical appliances and implementing building codes that reduce heat losses. Finally, the continuing low level of end-user prices has failed to provide consumers with incentives to save energy. From Table 6.1, it is clear that by the year 2015 China’s energy consumption will be four times what it was in 1980. The share of oil during the same period will be on the increase also. China shifted from being a net oil exporter to a net oil importer in 1993. What explains the increasing
Table 6.1 Primary energy consumption in China (quadrillion Btu) Year
Coal
1980 1985 1990 1995 2000 2005 2010 2015
12.5 16.9 20.7 27.5 22.7 26.4 33.3 40.1
Natural gas Petroleum 0.6 0.5 0.6 0.7 1.2 2.2 3.4 5.3
3.8 4.0 4.9 7.2 9.7 11.2 14.2 17.9
Total 17.3 22.2 27.0 35.2 36.7 43.2 55.3 69.1
Notes: Totals may not sum because of other fuels and rounding. Figures for 2005–15 are EIA projects, reference case. Source: EIA (2002).
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dependence on oil? Table 6.2 is an input–output decomposition of China’s energy use. Scrutinizing Table 6.2, we can see that it is intimately related to final demand shift. Within the final demand shift, both household and investment needs are responsible for the lion’s share. Thus, China’s industrialization strategy and the increasing prosperity of households can explain its increasing dependence on energy imports. This is not likely to change in the near future. Although domestic production figures have not been projected till 2015 in Table 6.3, simple extrapolation would show that import dependence is here to stay. Thus China’s import dependence will be a policy problem at least till 2025, if not for even a longer period. The vulnerability from potential disruption of supplies can be extensive. Scenarios run by the EIA and the Rand Corporation in the US show the effects on GDP when supply is disrupted because of unforeseen geopolitical events or natural disasters. These effects are in the 3–4 per cent range when prices shift unfavourably, affecting supply and production, and can be larger when the shocks are unpredictable and much substitution out of imported fossil fuels may not be feasible. In the Appendix, I outline a model that looks
Table 6.2
Input–output decomposition of changes in China’s energy use Coal
Total change
Oil and gas Electricity Petroleum
Coke
5.78
14.09
36.55
12.65
37.24
Technical change
−30.42
−38.55
−42.24
−46.21
−48.64
Energy technical change
−40.16
−47.84
−55.91
−48.97
−59.29
9.75
9.29
13.67
2.77
10.65
Final demand shift
36.19
52.63
78.78
58.86
85.88
Level
61.35
61.35
61.35
61.35
61.35
Non-energy technical change
Distribution Composition
6.84
0.18
4.26
−3.62
−29.9
4.73
−15.55
17.26
−6.75
28.15
20.0
24.39
39.0
22.67
40.42
Sources of final demand shift Household consumption Government consumption Investment
2.7
4.86
3.4
5.2
2.28
15.0
35.96
42.0
36.9
52.25
Export
23.4
28.0
22.4
42.93
Import
−23.6
−45.8
12.87
−32.0
−38.17
−33.68
Others
2.6
2.4
1.7
Source: Author’s calculations based on GoPRC (various years).
1.9
1.6
112 Haider A. Khan Table 6.3
Primary energy production in China (quadrillion Btu)
Year
Coal
Natural gas
Petroleum
Total
1980 1985 1990 1995 2000 2005 2010 2015
12.6 17.7 21.9 28.3 18.4e na na na
0.6 0.5 0.6 0.7 1.1e na na na
4.5 5.4 6.0 6.4 7.0 6.7 6.6 6.5
18.1 24.3 29.4 35.4 27.8e na na na
Notes: Totals may not sum because of other fuels and rounding. Petroleum includes crude oil, natural gas liquids and refinery processing gain. Figures 2005–15 are EIA projects, reference case. Total is primary energy only. e = estimated; na = not available. Source: EIA (2002).
at the distributional impacts of such disruptions in a linear approximation scenario. In particular, poverty impacts are derived. The preliminary results suggest strongly that almost all of China’s growth-induced poverty reduction gains can be destroyed through disruptions in the energy markets alone. In addition, the ecological consequences of the current fossil fuel-dependent development strategy are already proving to be serious. The following global pollution map for the year 1999 makes this point clear. The most developed parts of China are also among the most polluted areas in the world. A global map of nitrogen dioxide (NO2) in the atmosphere from Germany has revealed the most precise view yet of pollution hotspots around the world (Figure 6.1). The instrument used was the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), developed by John Burrows at the University of Bremen, Germany. A set of 18 months’ worth of satellite data shows very high levels of NO2 above major European and North American cities and across much of North-East China.3 The Chinese cities are revealed to be the largest cluster with the most population. Coal mining, in particular as it is practised in China, is an environmentdegrading process. In order to minimize degradation of water, soil, air and human health, it is extremely important to formulate strict rules, regulations, acts and laws keeping local socioeconomic, geological and environmental settings in mind. The usual principles documents, such as the Equatorial Principles and World Bank Standards, are written in generalized terms and are not enforceable or applicable to a particular country. The Equatorial Principles are suggested by lenders to borrowing companies as recommended sets of rules which encourage an environmental assessment
Source: From maps and data compiled by the UN statistical offices database for global environment and Heidelberg University using data from Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), developed by John Burrows at the University of Bremen, Germany.
Figure 6.1 Global pollution and the Chinese situation, 1999
114 Haider A. Khan
and corporate social responsibility before a lender can loan out a huge sum of money. These principles also emphasize the need for participation by local stakeholders in projects carried out by private companies that borrow money from a financial institution like Asian Development Bank (ADB). What China needs is to formulate a strong legal framework similar to or, given the fragility of her environment and dense population, even stronger than those in India or the USA in order to control environmental degradation, monitor compliance, and enforce rules and laws applicable during all phases of coal mining. Some distributional issues By any measure – even discounting by a few percentage points to satisfy the sceptics – China’s economic growth over the last two decades has been impressive.4 However, particularly since 1995, the distributional situation has deteriorated. This has happened both spatially and in almost a fractal way within provinces and nationally. The Gini index and other measures of inequality all bear this out. Although rapid growth has led to a large reduction in absolute poverty, there are questions as to how much more mere growth-induced poverty reduction can succeed in the future. According to some observers, China has large pockets of chronic poverty that will be hard to tackle by high growth alone. The model of Appendix 1, which is a short- to intermediaterun model, yields growth–poverty elasticities in the range of 0.6 to 0.85. Furthermore, according to my preliminary calculations using this model, the severity of poverty cannot be diminished by growth alone. In fact, in some instances, for example among the rural poor households in Gansu province, growth actually worsens poverty severity. This is not an argument against growth; but it does point to using a more distributionally sensitive and inclusive approach to growth and redistribution. In particular, increasing both physical assets, including land, and the social capabilities of the poor will be crucial if the current administration’s declared goal of a harmonious society is to be achieved. For China’s policymakers all the above issues pose serious strategic questions. What options are available to them? What can be realistically done over the short and medium term? Fortunately, a distributionally sensitive strategy which is ecologically sustainable is available. In the next section, I review and assess the steps taken and suggest in broad outlines a strategy most likely to succeed. If there is serious interest, the sectoral and regional details can be worked out within the course of a few months by an international team of competent researchers with heavy Chinese participation. Policy inputs to Chinese policymakers from such a group of experts can then lead to rapid implementation of energy, infrastructure and other sectoral policies along with poverty reduction and capabilities enhancement policies.
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China’s energy dilemma: Strategic moves China’s energy dilemma has renewed concerns about long-term energy security. Several decades ago, the same concerns existed but the solution was also clear and simple – to increase domestic production. Our analysis indicates that this is no longer a viable option.5 Discovery of large reserves is unlikely. Thus oil is seen as a source of vulnerability in Beijing. Since plans for building a POLIS depend crucially on oil, it could make China vulnerable to foreign pressures. Many years of debate within the state council have not resolved this difficult problem. Policymakers have been divided over whether domestic production should be increased or explored for oil abroad. In practice both the paths are being followed. I will argue that neither is going to reduce vulnerability to a large extent soon. In the medium to long run, however, there is a third option available; but it will require both rethinking development priorities at home and reorienting diplomacy towards greater regional cooperation. But let us look at the existing divided strategy first. The centrepiece of the current strategy seems to be an ambitious scheme of investment in overseas oil projects together with continuing domestic exploration. The state-owned oil companies of China are the key players in both arenas. During the 1980s China launched three large oil companies. The China National Offshore Oil Corporation (CNOOC) has controlled most of the offshore oil businesses since its founding in 1982. The China National Petrochemical Corporation (Sinopec) was founded in 1983 and is responsible for refining and marketing. The China National Petroleum Corporation (CNPC) was founded in 1988 by the Ministry of Petroleum Industry. In 1998 the government reorganized CNPC and Sinopec and created two vertically integrated oil companies. However, CNPC is still China’s dominant upstream oil company and Sinopec the main downstream company in the industry. Initially, the government maintained a two-tiered pricing policy that required CNPC to sell to its industrial customers at a fraction of the market price. The resulting cash flow constraint led to limited exploration. From the users’ perspective, artificially low price of oil led to inefficient energy use for industrial production and transportation in particular. In 1993, the government relaxed the pricing policy, increasing the first tier crude price. CNPC was the main beneficiary, as indeed was the intention. CNPC officials knew that unless the money was quickly invested it would be confiscated. The coincidental appointment of Zhou Yongkang, who favoured Chinese investment abroad, led to rapid overseas expansion of investment.6 Since this time, views of top CNPC officials have mattered a great deal in China’s foreign energy investments. However, they are chosen ultimately by the Communist Party leadership. Hence it can be rightly said that the Party is the final arbiter.
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The importance accorded to foreign investment in the energy sector is shown by the fact that CNPC has invested in energy from Canada to Kazakhstan. In 1997 it pledged over US$8 billion for oil concessions in Canada, Venezuela, Iraq and the Sudan. Such activities underline the high priority attached to these projects. They can also lead to increasing tension between China and the US. For example, in 1997, CNPC acquired a 60 per cent stake in Kazakhstan’s Aktyubinskmunaigaz Production Association. CNPC beat out Texaco, Amoco and Russia’s Yujnimost by agreeing to pay a US$320 million bonus to the Kazakh government and to conduct a feasibility study on the construction of an 1,800-mile pipeline to Western China. This was estimated to cost US$3.5 billion. In September 1997, CNPC followed similar tactics to defeat Petronas, Unocal and Amoco to win a controlling interest in Uzen, Kazakhstan’s second largest oilfield, with reserves of 1.5 bb. In June 1997, a consortium of Chinese oil companies signed a 22-year production sharing contract with Iraq to develop half of the Al-Ahdab field after the lifting of UN sanctions. Al-Ahdab is the country’s second largest oilfield. Prior to the US invasion of Iraq, CNPC was negotiating for rights to develop three other Iraqi fields. These are Halfaya, Luhais and Suba. However, the US invasion of Iraq and the subsequent occupation have put these plans in great jeopardy. The global energy situation is now marked by even more uncertainty and the Chinese must feel more vulnerable than ever. It is always possible that US imperialism will fail, like all imperialist powers, to ultimately control the Middle Eastern oil or even the sea routes permanently. But the realists in China must know that the current situation is one of US dominance. Apart from its domestic snafus, reflected in such facts as the shelving of the Kazakhstan–China pipeline plans in 1999, the US pre-eminence as a military power and its willingness to use this power for various purposes including protecting control over overseas oil, China’s energy dependence seems logically to lead to dependence on the US hegemony. What is the way out? I suggest that rethinking development strategy and increasing regional cooperation offer the best hope for China. Strategically, the Chinese development path has been so far marked by the East Asian model. According to this model, heavy state involvement, together with the promotion of market-led industrialization through export promotion, is the best strategy for development. As an industrialization strategy, this can succeed up to a point. However, as the Asian financial crisis demonstrated, there are risks as well (Khan 1997, 1998, 2004a, b). The Chinese strategy has led to high growth and a lopsided distribution together with energy dependence. Following this path further without qualification, as the current leadership seems to have grasped, will exacerbate the negative aspects with the inevitable growth slowdown. However, there is an
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alternative which I have developed in detail elsewhere. Here I will merely sketch the main outline. In my development of an alternative strategy (Khan 1982a, b, 1983, 1997, 1998, 2002a, b, 2003, 2004a, b; Khan and Sonko 1994), based among others on an extension of Schumpeterian theory of innovation and Sen’s capabilities, the key is a distributionally sensitive approach with clear recognition of ecological constraints. The main idea is to moderate the growth rate and hence short-term dependence on fossil fuels and minimize consequent ecological problems. With a proper poverty reduction strategy – called ‘growth plus’ strategy7 – the adverse distributional consequences from moderate growth can be avoided. In fact, with a properly moderate ‘growth plus’ strategy, the capabilities of the great majority of the population can be enhanced along with the productive capacity. This will lead to the creation of a domestic market for commodities that are produced in a much more ecologically sustainable manner. Furthermore, the goal of a transition to a relatively non-fossil fuel-based knowledge and information economy will be facilitated and the transition will occur more quickly and smoothly under the proposed POLIS strategy. Although the move away from fossil fuels can only be gradual, a beginning must be made not in a makeshift way, nor for the purpose of window-dressing, but in a genuinely strategic manner at the highest policymaking level. Therefore, serious attention must be paid to the scope and necessity for developing and harnessing alternative energy sources, including renewable ones. Starting from the realistic assessment that in the short and medium run fossil fuels will still be the predominant ingredients in China’s energy mix, serious and credible plans will need to be formulated for increasing the share of alternative energy sources. For example, China has rich potential for solar, wind and tidal energy. China may also consider the potential of modern biofuels. Given the importance of agriculture in the economy even with rapid growth and industrialization, it may be possible to find a win–win solution by choosing an appropriate crop mix that also enhances the country’s energy security. Biogas energy can also play an important part in this mix. In addition, there is a pressing need for reducing system loss, and improving energy efficiency in buildings and transportation sectors. The Chinese government should also be cognizant that coal is a fossil fuel and coal-fired power plants are one of the primary sources of global warming. China’s future energy choices must be mindful of the impacts of climate change for the sake of the future generations in China and the world. A strategic step consistent with the POLIS strategy suggested here that will signal credibility is for the Chinese government to initiate a public consultation process for formulation of a national energy strategy, so that all the relevant issues can be discussed and resolved in an open, participatory and transparent manner. Creation of a domestic market system in
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a harmonious society, which is the goal of the current regime, will in fact require such a move. The question of domestic market creation leads also to considerations of cooperation in Asia. For reasons of regional financial stability, reciprocal exporting and energy security, greater cooperation in each of these spheres is an urgent necessity. China, along with the other countries in Northeast Asia, must proceed to diffuse tensions and build up regional institutions. Given the historic hostilities, this will be no easy task. Yet the enlightened self-interests of the countries, together with imperatives of regional peace and prosperity, lead logically towards cooperation. There is much here to learn from the European integration experience, particularly about the role of enlightened leadership. No doubt, there are special Asian features and special Asian roadblocks that might require particular Asian approaches to negotiation and conflict resolution at times. But China’s energy dilemma is one compelling reason for that country to take the initiative and start a dialogue. There is no time to lose. In particular the dialogue with Japan is of great significance. The special treatment of China–Japan relations here in no way implies that for China other bi- or multilateral relationships, such as China–US or China–Africa relations, are not important. However, in the Asian context, this is clearly salient in a bilateral context. More importantly, the future of regional cooperation in Asia to create regional energy security in particular and to foster regional economic development in general may well depend on the ability of China and Japan to cooperate in broad functional areas. Therefore, I now turn to a brief analysis of the problems of and prospects for cooperation between China and Japan in the near future.
Conflict and cooperation in China–Japan energy policy in the Asian context The current relations between China and Japan in the energy security area continue a historic dialogue which, at times, has been difficult. The most recent moves, on the surface at least, seem to signal increased willingness to cooperate on the part of both the countries in some crucial areas of mutual concern. On 11 April 2007, the Japan–China Joint Press Statement was announced in Tokyo, Japan, during the time Chinese Prime Minister Wen Jiabao visited Japan. In the statement, the content related to the energy sector in particular is as follows: (2) Reinforcement of mutually beneficial cooperation (2.i) Energy and environmental cooperation Both sides welcomed the announcement of the ‘Joint Statement by Japan and the People’s Republic of China on the Further Enhancement of
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Cooperation for Environmental Protection’. Both sides affirmed their sincere efforts at tackling the global environmental issues affecting the two countries. Furthermore, both sides confirmed that they would focus their cooperation on areas such as prevention of water pollution in vital waters, including the Bo Hai and Huang Hai regions and the Yangtze basin, building recycling societies, air pollution prevention measures to combat climate change, prevention of drifting marine litter, and measures for tackling acid rain and yellow sand. Both sides welcomed the holding of the ‘first energy ministerial policy dialogue’ and the announcement of a joint statement concerning energy cooperation enhancement. Both sides agreed that they would strengthen their cooperation focusing on energy-related issues such as energy conservation, coal and nuclear power, and so on. Such cooperation will also include model projects for the promotion of energy conservation and environmental businesses. Furthermore, attempts will be made to bolster cooperation within multilateral frameworks for the promotion of energy conservation in the Asian region. Both sides supported the activities of the Japan–China Board of Assistance to Greening Activities. They confirmed that they would further promote reforestation cooperation programmes carried out in China by Japanese private entities, and that the two countries would cooperate toward sustainable forest management. Starting in the 1980s, contrary to belligerent political rhetoric at times, China and Japan have cooperated economically in a number of areas. However, there were and still are many potential areas of conflict. It may be recalled that China has received a great deal of Japanese aid in the past. The section below gives a brief overview of this relation, which may have paved the way for cooperation in the areas of energy and environment. Overview of official development assistance (ODA) to China Official development assistance (ODA) to China began in 1979, and, from that time to the present, approximately 3.1331 trillion yen in loan aid (yen loans), 145.7 billion yen in grant aid, and 144.6 billion yen in technical cooperation have been implemented. Grant aid is financial assistance that is extended to recipient countries without imposing an obligation of repayment. Loan aid involves the provision of loans under relaxed conditions (low interest, long repayment period) to recipient countries. These are in principle untied loans. Technical cooperation involves the technologies being provided to recipient countries to spread the use of technology among people in developing countries and improve technical levels. Past ODA projects in China included large-scale economic infrastructure projects, including the building of roads, airports and power stations, as well as infrastructure projects in medical and environmental areas. These
120 Haider A. Khan
projects have played a significant role in the realization of China’s current economic growth. For example, through Japanese loan aid (yen loans), 5,200 km of railway lines were electrified, and in the area of seaports approximately 60 large-size berths capable of taking ships in excess of 10,000 tons were built. In addition, the China–Japan Friendship Hospital, which was established through grant aid, is one of the major medical institutions in the Beijing metropolitan area, treating approximately 3,000 patients each day. Assistance includes more than infrastructure projects. In the area of technical cooperation, the Japan International Cooperation Agency (JICA) has accepted trainees from China for the purpose of providing assistance to cultivate administrative personnel. As of FY2003 JICA had accepted a cumulative total of over 15,000 trainees, and the Association for Overseas Technical Scholarship (AOTS) had accepted more than 22,000 trainees to nurture the human resources required for industrial promotion. JICA has also dispatched 5,000 experts to China. Appendix 4 describes some specific types of aid to China. Below, I briefly discuss energy sector-related loans in particular. Energy loans The State Department of China authorized the Bank of China as the delegate to negotiate with the Japan Import and Export Bank. In May 1979, the two sides signed a memo for an energy loan, the amount of which reached 420 billion yen. And, in 1984 and 1992, Japan Import and Export Bank provided two other energy loans to Bank of China, which reached 580 billion and 700 billion yen, respectively. Thus, the total amount of energy loan Japan provided to China is 1.7 trillion yen. It is clear, however, from Boxes 6.1 and 6.2 that the relation between China and Japan has been fraught with some tension. The East China Sea dispute between China and Japan (Box 6.1) is one example. In 2007, during a visit to Japan, the Prime Minister of China had talks with his Japanese counterpart about the East China Sea dispute. They agreed on the acceleration of future talks on this dispute. On energy policy coordination in particular, the first minister-level energy policy dialogue between China and Japan was held in Tokyo in April 2007. Both sides signed an agreement in which the two nations committed to cooperation on developing energy resources and building nuclear power plants in China. Both parties agreed that energy dialogue will be held annually. Such dialogue is expected to address energy conservation and other related issues in the future. They also decided to hold a China–Japan energy-saving and environment protection symposium in Beijing. Thus, in the Asian context, China seems to have realized the need for cooperation in developing a joint energy strategy with Japan. There seems
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Box 6.1 East China Sea dispute between China and Japan 1969 1970 1982 May 2004 June 2004 June 2004
July 2004
Oct 2004 Apr 2005
May 2005 July 2005 Aug 2005 Sept 2005
Mar 2006 May 2006
July 2006 Mar 2007 Apr 2007
Four corporations, including Teikoku Oil in Japan, applied to the Japanese government for oil gas development rights. China started to explore oil and gas resources in the East China Sea. Japan proposed the principle of ‘midline’ to China, for division of the water area of the East China Sea between the two countries. China started to develop the Chunxiao Gasfield in the East China Sea. China proposed that the two countries should cooperatively develop the resources in this area. The Japanese government set up the ‘sea right related cabinet minister conference’, encouraging Japanese enterprises to explore resources in the East China Sea. Japan began to conduct seabed resource surveys to the east of their unilateral ‘midline’ in the East China Sea, an area disputed between the two countries. Japan and China held the first round of talks on the East China Sea issue. The Japanese government started to accept applications from enterprises for East China Sea resource development and exploration. Japan and China held the second round of talks on the East China Sea issue. The Japanese government granted Teikoku Oil the right to explore gas in the East China Sea. Teikoku Oil completed all the government-required procedures. Japan and China held the third round of talks on the East China Sea issue. Japan accepted China’s proposal of cooperative development in this area. Japan and China held the fourth round of talks on the East China Sea issue. Japan and China held the fifth round of talks on the East China Sea issue. The two sides reached an initial intent of cooperative development. Japan and China held the sixth round of talks on the East China Sea issue. Japan and China held the seventh round of talks on the East China Sea issue. The Prime Minister of China visited Japan, during which he had talks with his Japanese counterpart about the East China Sea dispute. They agreed the acceleration of the future talks on this dispute, and a basic timetable for the dispute resolution has been reached.
Source: Asahi Shimbun, Tokyo, Japan, various issues.
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Box 6.2 Dateline of China–Japan energy issues 27 Nov 1972 Jan 1974
Feb 1978 Aug 1978
28 May 1980 29 May 1980 15 Dec 1980
23–24 Jun 1981 15–16 Dec 1981
18 Feb 1982
5 Sep 1983
17 Dec 1984 Mar 1989
1990
Japan–China Association on Economy & Trade was founded in Tokyo. The Foreign Minister of Japan, Masayoshi Ohira, visited China, during which the two countries signed the China–Japan Trade Agreement in Beijing. Signing of the China–Japan Non-governmental Long-term Trade Resolution in Beijing. The Foreign Minister of Japan, Tadashi Sonoda, visited China. Signing of the Sino-Japanese Peace and Friendship Treaty in Beijing. Scientific Cooperative Agreement between People’s Republic of China and Japan was signed and became effective in Tokyo. Agreements between China and Japan on cooperative oil exploitation in South and West Bohai Bay were signed in Tokyo. Bohai Oil Corporation and Chengbei Oil Development Corporation (CODC) of Japan signed the Chengbei Oilfield Development Contract. The total investment capital reached US$270 million. It was put into operation in 1986, and is the first Chinese–Japanese joint venture in this regard. The China–Japan Scientific Cooperation Committee held the first meeting in Beijing. The second China–Japan government member meeting was held in Tokyo, during which the two parties signed a summary about the capital cooperation of Daqing oil chemical industry and Baogang Steel first stage construction. China and Japan signed a cooperation agreement on coal exploration in the Liuzhuang coal mine zone, in Anhui Province of China. It is the first project in which the Chinese geological industry cooperated with a foreign country. Petroleum companies from China, Japan, the UK and the USA signed a contract in Beijing to cooperatively explore the oil in the South China Sea. China and Japan signed an agreement on atomic energy between the two governments. Japanese Export and Import Bank provided a loan of about 72.6 billion yen to the Bank of China to be used in the exploration of China’s land oil fields such as Liaohe oil field in Northern China. Bozhong 28–1 oil field jointly developed by China and Japan put into operation. Bozhong 34–2/4E oil field jointly developed by both countries put into operation.
China’s Development and Energy Security
18 May 1993 Feb 1994 3 May 1994 29 Aug 1995 1996 17 July 1996
2006
11–13 Apr 2007 11 Apr 2007 12 Apr 2007
Apr 2007
123
The China–Japan Energy Communication Association is founded in Beijing. According to Chinese customs statistics of 1993, Japan had become the largest trade partner of China. China–Japan Nuclear Safety Agreement signed in Beijing. Because of China’s nuclear experiment, Japan announced the suspension of interest-free loans and aid provided to China. The Diaoyu Island dispute became the hot point in the China– Japan relationship. Japan claimed that Diaoyu Island belonged to Japan. The debate restrained economic cooperation between the two countries. A Joint Statement of Energy Ministers of China, India, Japan, Republic of Korea and the United States was announced in Beijing, China. Chinese Prime Minister Wen Jiabao visited Japan. Japan–China Joint Press Statement announced in Tokyo, reinforcing their agreement on energy cooperation in the future. The first minister-level energy policy dialogue between China and Japan was held in Tokyo. Both sides signed an agreement in which the two nations committed to cooperate on developing energy resources and building nuclear power plants in China. Both parties agreed that energy dialogue will be held annually, and this is expected to address energy conservation and other issues. They also decided to hold a China–Japan ‘energysaving[Query: please check opening quote has been added in the correct place] and environment protection symposium’ in Beijing in September. China–Japan energy cooperation symposium held. About 180 companies and research institutions, in total more than 600 entrepreneurs, from the two countries attended. They were divided into four groups: electricity, oil and gas, coal, and new energy, for discussion. Six cooperation accords and/ or agreements were also signed in various areas including electricity and gas. The China National Petroleum Corporation and the Nippon Oil Corporation of Japan agreed on cooperation in the area of overseas oil and gas development. Nippon is to provide oil refining techniques and oil chemical techniques to China, and it agreed to promote more technique cooperation on new energy development such as biofuel. At the same time, both sides agreed to greater cooperation in energy safety and environment protection. The China National Offshore Oil Corporation (CNOOC) and Mitsui & Co. Ltd of Japan also signed an accord on liquefied natural gas spot trading on the same day.
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Box 6.2 Continued The China Datang Corporation (CDT), one of the five largescale power generation enterprises in China, and Kyushu Electric Power and Sumitomo Corporation of Japan signed a framework agreement on cooperation in renewable energy. The three companies will construct a wind farm in Neimenggu Province of China. Source: Compiled by the author from Japan’s National Broadcasting System (NHK).
to have been the beginning of a reciprocal understanding on Japan’s part as well.
Conclusions and future directions In this chapter I have sketched the energy dilemma for China in this century. As long as the current geopolitical situation persists, the pursuit of China’s present development strategy will further increase its energy dependence. For both political and economic reasons, China needs to rethink its development strategy. I have sketched such an alternative strategy that relies much less on fossil fuels and emphasizes regional cooperation. This POLIS strategy will ultimately lead to a sustainable economy based on growth with equity. A transition to a non-fossil fuel-based knowledge and information economy will also be easier to effect under the proposed strategy. However, time is of the essence. Given the path dependence of development, unless strategic disengagement from the existing path followed by a strategic engagement with the alternative strategy is begun within the next five years, it may well be too late. The stakes are indeed very high. A more detailed strategy paper based on the key ideas from the alternative strategy outlined here with concrete quantitative scenarios and feasibility studies along the lines of models sketched in the Appendix (and other, more detailed models) will go some distance towards giving the appropriate analytical foundations for the policymakers. The preliminary results confirm the predictions regarding fossil fuel-based energy shortage and lead towards a serious consideration of alternative energy sources. Achieving the twin goals of energy security and ecological balance is challenging but not impossible for China. Serious policy research can be used effectively if there is the political will to do so. The goal of regional cooperation is also achievable if patient negotiations in good faith can start in earnest. In particular, cooperation with other Asian economies, particularly Japan, Indonesia, Vietnam and India, will be crucial. This chapter has sketched out the complexities of cooperation and conflict between China and Japan.
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Future work will address the problems of regional cooperation for China in the East, South and South Asian context as well as in the context of Africa and Latin America.
Appendix 1: Growth impacts of the energy sectors – a simple SAM-based model Fixed price modelling in a SAM-based framework In this section the social accounting matrix (SAM) is presented as a data-gathering framework as well as an analytical tool for studying the effects of the energy sectors on growth. Appendix 2 presents the methodology for estimating the impact of growth generated by the energy sectors on poverty alleviation. The origins of social accounting can be traced as far back as Gregory King’s efforts in 1681, but more recent work stems from the attempts by Richard Stone, Graham Pyatt, Erik Thorbecke, and others.8 In the methodological framework of this study, the SAM is used for mapping production and distribution at the economy-wide level. In this section, first a general SAM is described. Then it is shown how the method for studying the effect of growth within this framework follows logically from its structure. The model used is a simple version of a class of SAM-based general equilibrium models.9 It summarizes succinctly the interdependence between productive activities, factor shares, household income distribution, balance of payments, capital accounts, and so on, for the economy as a whole at a point in time. Given the technical conditions of production, the value added is distributed to the factors in a determinate fashion. The value added accrued by the factors is further received by households
Table 6A1 Simplified schematic social accounting matrix EXPENDITURES Endogenous accounts Exogenous Total 1
2
3
4
5
T1.3
x1
y1
Endogenous accounts Factors Households RECEIPTS Production activities
1
0
0
2
T2.1
T2.2
0
x2
y2
3
0
T3.2
T3.3
x3
y3 yx
Exogenous accounts Sum. of other accounts
4
l1
l2
l3
t
Total
5
y1
y2
y3
y x
Source: constructed by Haider A. Khan.
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according to their ownership of assets and the prevailing wage structure. In the matrix form the SAM consists of rows and columns representing receipts and expenditures, respectively. As an accounting constraint receipts must equal expenditures. As is elaborated further in Khan and Thorbecke (1988), the SAM framework can be used to depict a set of linear relationships in a fixed coefficient model. For deciding the question of determination, the accounts need to be divided into exogenous and endogenous ones. For instance, in the China SAM, there are three endogenous accounts. These are factors, households and production activities, leaving the government, capital and the rest of the world accounts as exogenous.10 Looking at Table 6A2, which represents a SAM, we can see immediately that ynx
(1)
y1t
(2)
Now if we divide the entries in the matrix Tnn by the corresponding total income (that is, yn), we can define a corresponding matrix of average expenditure propensities. Let us call this matrix A. We now have: y n x Ay x
(3)
y (1 A)1 x Mx
(4)
M has been called the matrix of accounting multipliers by Thorbecke, for these multipliers, when computed, can account for the results (for example, income, consumption, and so on) obtained in the SAM without explaining the process that led to them. Let us now partition the matrix A in the following way (Khan and Thorbecke). 0 A = A21 0
0 A22 A32
A13 0 A33
(5)
Given the accounts factors, household and the production activities, now we see that the income levels of these accounts (call them y1, y2, y3, respectively) are determined as functions of the exogenous demand of all other accounts. In this respect, what we have is a reduced-form model which can be consistent with a number of structural forms. This is quite satisfactory as far as tracing the effects of a certain injection in the economy is concerned
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Table 6A2 Schematic representation of endogenous and exogenous accounts in a SAM EXPENDITURES Endogenous
RECEIPTS
Sum
Exogenous
Sum
Totals
Endogenous
Tnn
N
Injections Tnx
x
yn
Exogenous
Leakages Txn
1
Residual balances Txx
t
yx
Totals
y n
y x
Source: Khan and Thorbecke (1988).
or for prediction purposes when the structural coefficients are more or less unchanged. One limitation of the accounting multiplier matrix M as derived in equation (4) is that it implies unitary expenditure elasticities (the prevailing average expenditure propensities in A are assumed to apply to any incremental injection). A more realistic alternative is to specify a matrix of marginal expenditure propensities (Cn below) corresponding to the observed income and expenditure elasticities of the different agents, under the assumption that prices remain fixed. The Cn matrix can be partitioned in the same way as the A matrix above. The most important difference between the two partitioned matrices is that C32 ≠ A32. Expressing the changes in income (dy) resulting from changes in injections (dx), one obtains dyn Cndyn dx (I Cn)1 dx Mc dx
(6) (7)
Mc has been called a fixed price multiplier matrix and its advantage is that it allows any non-negative income and expenditure elasticities to be reflected in Mc. In particular, in exploring the macroeconomic effects of exogenous changes in the output of different product-cum-technologies on other macroeconomic variables, it would be very unrealistic to assume that consumers react to any given proportional change in their incomes by increasing expenditures on the different commodities by exactly that same proportion (that is, assuming that the income elasticities of demand of the various socioeconomic household groups for the various commodities were all unity). Since the expenditure (income) elasticity is equal to
128 Haider A. Khan
the ratio of the marginal expenditure propensity (MEP i) to the average expenditure propensity (AEP i) for any given good i, it follows that the marginal expenditure propensity can be readily obtained once the expenditure elasticity and the average expenditure propensities are known, that is, y i MEPi AEPi
(8)
MEPi y i AEPi
(9)
and
∑ MEP = 1
(10)
i
i
Thus, given the matrix A32 of average expenditure propensities, and the corresponding expenditure elasticities of demand, yi, the corresponding marginal expenditure propensities matrix C32 could easily be derived.11
Appendix 2: Energy sectors, growth, distribution and poverty Multiplier decomposition, growth and poverty alleviation sensitivity Since poverty in the present context is measured by identifying a poverty line in monetary terms, incomes of the various household groups are the crucial variables. In particular, sectoral growth generated by the energy sectors must be linked to incomes of the various households in order to determine the exact extent of the alleviation of poverty through growth. The exact effect of income growth on poverty, of course, depends on the sensitivity of the adopted poverty measure to income. In this paper the Foster–Greer–Thorbecke (FGT) (Foster et al. 1984) Pa class of additively decomposable poverty measure is selected for this purpose. For a 0, 1, n this measure becomes the headcount ratio, the poverty gap and a distributionally sensitive measure that gives specific weights to each poor person’s shortfall, respectively. If we apply Kakwani’s (1993) decomposition to the Pa measure for specific sectors and households i and j respectively, the change in Paij can be written as follows: dPaij
∂Paij ∂y i
∂Paij
∑ ∂u k1
ijk
duijk
(11)
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Here Paij is the FGT Pa measure connecting sector j to household group i, yi is the mean per capita income of household group i, and ijk is the income distribution parameter. Under the unrealistic but simplifying assumption of distributional neutrality: dPaij Paij
dy h ai i yi
(12)
where ai is the elasticity of Pij with respect to the mean per capita income of each household group i resulting from an increase in the output of sector j. d y−i on the right hand side is the change in mean per capita income of household group i. This can be written as (by considering the fixed price multiplier matrix): dyc mij dxj
(13)
where dxj is the change in the output of sector j on a per capita basis for group j. We can now rewrite the average change in poverty measure as: dx j h a i mij yi
dPa ij Pa ij
(14)
By aggregating across the household groups we can arrive at the overall poverty alleviation effect: d Paj
d Paij h i Paij h 1
m
∑
Paj
i
d Paij Paij Paij Paj 1
m
∑ i
(15)
Since we are considering a P measure d Paj
d Paij ∑ ((z Paij ∑ ((z 1
m
∑
Paj
i
a yk ) z ) a y1 ) z )
(16)
where qi is the number of poor in the ith group and the total number of m poor q ∑ qi i 1 Let s i be the poverty share of household group i (naturally ∑ i 1 sai 1 ) m
sai =
qi
z yk ∑ z k 1
a
q
z yl ∑ z l 1
a
(17)
130 Haider A. Khan
We can further rewrite the expression for the average change in overall poverty alleviation: d Paj
d Paij sai Paij 1
m
∑
Paj
i
(18)
Combining equations 14 and 18, we now have: d Paj
∑s h ai
Paj
ai
d xj mij yi
(19)
Thorbecke and Jung (1996) separate the income increase via the modified multiplier effect from the sensitivity of the poverty measure formally in equation (19) by defining the following two entities: maij saij mij gives the modified multiplier effect in terms of income of a poor group. qaij ai (dxj y−i ) represents the sensitivity of the poverty index to the change in income. I adopt their terminology and call this the poverty sensitivity effect. But each multiplier mij can be further decomposed: mij j dij
(20)
where nj gives the (closed loop) interdependency effects and dij the distributional effects of a change in demand for the product of sector j on household group i. Thus: d Paj Paj
m
=
∑ m′
=
∑ (r )(s
a ij
m
aij
(21)
qaij
aij
)( )
dij q aij
(22)
The dij on the right hand side can be further decomposed by multiplicatively decomposing the total distributive effects. Given the structure of Cn matrix: D D3D2 D1 where D3 (I C22)1; D2 C21C13, and D1 (I C33)1 The particular element for each household i and sector j can be selected from these three matrices.
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Thus the contribution of an increase in output of a particular sector i to poverty alleviation can be decomposed multiplicatively into its two components: (i) the contribution due to the change in mean income of the poor across all groups and (ii) the sensitivity of the particular poverty measure to this change in average income of the poor.
Appendix 3 Joint Statement of Energy Ministers of China, India, Japan, Republic of Korea and the United States, 16 December 2006 1. The energy ministers of China, India, Japan, Republic of Korea and the United States, countries which together account for nearly half of world oil consumption, met in Beijing, China on 16 December 2006 to discuss ways to promote energy security, stability, and sustainability. Energy is crucial to sustainable economic and social well-being in developed and developing countries. Our common challenge is to ensure sufficient, reliable and environmentally responsible supplies of energy with reasonable prices, as well as more efficient utilization of energy. 2. In recent years, fluctuations and increases in international oil prices have exerted a negative impact on the world economy, in particular for the developing countries. It is in this context that the energy ministers of the five countries held this meeting to review the world energy situation and the implications of increased oil prices on the global economy and to discuss cooperative efforts to advance: sufficient energy investment, energy conservation and efficiency, development of alternative energies and utilization of clean and efficient energy technologies, including those related to nuclear energy and clean coal and establishment of oil stockpiles. Our collective efforts are of great significance for the stability of the international oil and other energy markets and for enhancing global energy security. 3. We recognize that the reasons behind the oil price volatility are complex and manifold, including growing demand for oil, concerns over the adequacy of investment in oil production capacity in the long-term, current low levels of spare production capacity, refining capacity bottlenecks, prolonged political instability in some oil producing regions and market speculation. 4. We recognize that current high oil prices are affecting energy demand and investment. We affirm that market-based pricing helps send the appropriate signals for energy conservation and efficiency, and investment in conventional and alternative energies. We encourage investment based on market principles in oil and gas exploration and production so as to increase the supply capacity of oil and gas.
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5. We recognize that China, India, Japan, Republic of Korea and the United States share common interests in the energy area. Our policies to accelerate the development and deployment of new energy technologies and improve energy efficiency will significantly enhance our energy security as well as global energy security. In order to strengthen our collective energy security, we agree to enhance our cooperation in the following areas: i) Diversifying our energy mix to make wider use of clean and alternative energy, such as clean coal, nuclear energy and renewables, including in the transport sector. To this end, we will expand collaboration in research, development and deployment of alternative energy technologies such as clean coal, nuclear energy, renewables, hydrogen, bio-fuels, hybrid vehicle technologies and carbon sequestration. ii) Improving energy conservation and efficiency, which provides costeffective solutions to enhancement of energy security, reduces greenhouse gas emissions and helps attain sustainable development. iii) Strengthening cooperation on strategic oil stocks to promote international energy security. Past experience has shown that international coordination, such as through the International Energy Agency (IEA), in response to significant supply disruptions benefits world energy markets. iv) Improving transparency of data in the market through better sharing of information to enhance oil market stability. In this context, we welcome implementation of the Joint Oil Data Initiative (JODI) and will continue to provide timely market data on oil. v) Encouraging extensive and in-depth cooperation among the business sectors of the five countries in areas including energy efficiency, alternative energies, and transportation. In this context, we welcome the progress of the Asia-Pacific Partnership on Clean Development and Climate (APP) and the collaborative efforts it has catalyzed between the business and public sectors in diffusing clean energy technologies and best practices in energy intensive sectors. 6. We call on all countries of the international community to enhance global energy security through the following measures: i) Open, transparent, efficient, and competitive energy markets to encourage investment in the whole energy supply chain, especially in oil and gas exploration and production, including transparent and effective legal and regulatory frameworks. ii) Diversification of energy supply and demand and energy sources. iii) Promotion of energy conservation and energy efficiency measures as well as development and deployment of environmentally sustainable energy technologies.
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iv) Cooperative energy emergency response through strategic oil stocks. v) Safeguarding critical energy infrastructure and sea route security for transportation of oil and gas. vi) Improved quality and timeliness of energy data made available to the market.
Appendix 4 Large-scale economic infrastructure projects (loan aid) Airports ●
● ● ● ●
Shanghai Pudong International Airport Construction Project (40.0 billion yen) Beijing Capital Airport Terminal Area Expansion Project (30.0 billion yen) Lanzhou Zhongchuan Airport Expansion Project (6.3 billion yen) Wuhan Tianhe Aerodrome Construction Project (6.3 billion yen) Xi’an Xianyang International Airport Terminal Expansion Project (3.09 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 111.6 billion yen. Railway lines ● ● ● ● ●
Beijing–Qinhuangdao Railway Expansion Project (87.0 billion yen) Guiyang–Loudi Railway Construction Project (30.0 billion yen) Chongqing Urban Railway Construction Project (27.1 billion yen) Beijing Subway Construction Project (19.7 billion yen) Datong–Qinhuangdao Railway Construction Project (18.4 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 641.8 billion yen. Roads ● ● ● ● ●
Hangzhou–Quzhou Expressway Construction Project (30.0 billion yen) Liangping–Changshou Highway Construction Project (24.0 billion yen) Xinxiang–Zhengzhou Highway Construction Project (23.5 billion yen) Guiyang–Xinzhai Highway Construction Project (15.0 billion yen) Heilongjiang Heihe–Beian Road Construction Project (12.6 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 195.1 billion yen.
134 Haider A. Khan
Seaports ● ● ● ●
●
Qinhuangdao Port Expansion Project (67.4 billion yen) Qingdao Port Expansion Project (59.7 billion yen) Huanghua Port Construction Project (15.4 billion yen) Shenzhen Dapeng Bay Yantian Port 1st Phase Construction Project (14.7 billion yen) Dalian Port Dayao Bay 1st Phase Construction Project (6.7 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 272.6 billion yen Power stations ● ● ● ● ●
Tianshengqiao Hydroelectric Power Project (118.0 billion yen) Jiangxi Jiujiang Thermal Power Plant Construction Project (29.6 billion yen) Wuqiangxi Dam Construction Project (25.2 billion yen) Shanhe Thermal Power Plant Construction Project (24.6 billiion yen) Beijing Shisanling Pumped Storage Power Station Construction Project (13.0 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 488.2 billion yen. Fertilizer plants ● ●
●
Weihe Chemical Fertilizer Plant Construction Project (26.9 billion yen) Inner Mongolia Chemical Fertilizer Construction Project (21.4 billion yen) Jiujiang Chemical Fertilizer Plant Construction Project (21.4 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 106.3 billion yen Miscellaneous projects ● ● ●
●
●
●
Shanghai Baoshan Infrastructure Improvement Project (31.0 billion yen) Environment Protection Prevention of Air Pollution Environment Model City Project (Guiyang, Chongqing, Dalian) (30.7 billion yen) Forestation Ningxia Afforestation and Vegetation Cover Project (8.0 billion yen) Sewage System Xiang River Basin Hunan Environment Improvement Project (3.1 billion yen) Beijing Sewage Treatment Plant Construction Project (2.6 billion yen)
Cumulative total of aid in this area excluding the above-mentioned projects: 857.8 billion yen.
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Human resources development projects ● ●
●
Inland Higher Education Project (88.8 billion yen) Infrastructure Projects in Medical and Environmental Areas The Project for Construction of the China–Japan Friendship Hospital (Grant aid: 16.430 billion yen) The Project for Construction of the China–Japan Friendship Environment Protection Centre (Grant aid: 10.499 billion yen).
Notes 1. A formal and complete description of POLIS as an innovation system, and contrasts with NIS (national innovation system) of which POLIS is both an extension and an extended critique, is outside the scope of this paper. Khan (2004a) gives a formal description and two existence theorems in topological spaces. Technically, non-linearities and multiple equilibria are at the heart of a formal proof of POLIS and its properties. Khan (2003) presents both a conceptual and a concrete critique of NIS by comparing and contrasting the national innovation system (NIS) with POLIS in the context of Taiwan. 2. For welfare aspects of China’s entry into WTO see Khan (2002b). 3. Data for the map were obtained between January 2003 and June 2004 from the European Space Agency’s Envisat satellite, which scans the globe every six days. 4. The quality of Chinese statistics has been questioned. However, even critics such as Rawski (2002) accept that the long-term trend growth rate is impressive. 5. See also Christoffersen (1998) for an analysis of the situation particularly with respect to China’s relations with Russia and Central Asia in historical perspective. 6. For a detailed description, see Lieberthal and Oksenberg (1998). 7. This is discussed in detail in Khan and Weiss (2007: chapter 1). 8. For a description of SAM as a data-gathering device, see Pyatt and Thorbecke (1976). Khan (1997) also has a chapter on this alone. 9. In Walrasian general equilibrium models the flexible price vector determines the equilibrium. In a Keynesian (dis)equilibrium model in the short run the quantities vary while the price vector remains fixed. 10. See Khan and Thorbecke (1988: chapter II) for more theoretical details and empirical examples. The presentations here follow the cited work closely. 11. See Khan and Thorbecke (1988) for some examples. See also Pyatt and Round (1979: 861).
References Christoffersen, G. (1998). ‘China’s Intentions for Russian and Central Asian Oil and Gas’. The National Bureau of Asian Research Analysis, 9 (2): 11–12. EIA (Energy Information Administration) (2002). International Energy Database. Washington, DC: EIA. Foster, J. E., J. Greer, and E. Thorbecke (1984). ‘A Class of Decomposable Poverty Measures’. Econometrica, 52: 761–6.
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GoPRC (Government of the People’s Republic of China) (various years). Input-Output Tables. Beijing: PRC. Kakwani, N. (1993). ‘Poverty and Economic Growth with Application to Côte d’Ivoire’. Review of Income and Wealth, 39 (2): 121–39. Khan, H. A. (1982a). ‘Energy, Technology and Income Distribution: A Social Accounting Matrix for Energy Modelling’. Paper prepared for the Applied Simulation and Modelling Conference, Calgary. Khan, H. A. (1982b). ‘Choice of Technology in the Energy and Textiles Sectors in Korea’. World Employment Programme Working Paper. Geneva: ILO. Khan, H. A. (1983). ‘Choice of Technology, Energy and Income Distribution: A Macroeconomic Framework’. Ithaca: Cornell University. Unpublished PhD dissertation. Khan, H. A. (1997). Technology, Energy and Development: The South Korean Transition. Cheltenham: Edward Elgar. Khan, H. A. (1998). Technology, Development and Democracy: The Limits of National Innovation Systems in the Age of Postmodernism. Cheltenham: Edward Elgar. Khan, H. A. (2002a). ‘Managing Global Risks and Creating Prosperity: The Role of the IMF and Regional Financial Architectures’. Tokyo: Graduate School of Economics, University of Tokyo. Available at: www.e.u-tokyo.ac.jp/cirje/index.htm Khan, H. A. (2002b). ‘China’s Entry into the WTO: ICT Sectors, Innovation, Growth and Distribution’. CIRJE F-series. Tokyo: CIRJE, University of Tokyo. Available at: www.e.u-tokyo.ac.jp/cirje/research/dp/2002/2002cf157.pdf Khan, H. A. (2003). ‘Creating Social Capabilities in POLIS’. In T. Misa, P. Brey and A. Feenberg (eds), Technology and Modernity. Cambridge, MA: MIT Press. Khan, H. A. (2004a). Innovation and Growth in East Asia: The Future of Miracles. Basingstoke: Macmillan. Khan, H. A. (2004b). Global Markets and Financial Crisis in Asia: Towards a Theory for the 21st Century. Basingstoke: Macmillan. Khan, H. A., and K. Sonko (1994). ‘A Further Extension of Adjustment Models: The Environment and Equity’. In G. Shepherd and K. Sonko (eds), Economic Justice in Africa: Adjustment and Sustainable Development. Westport: Greenwood Press, 189–201. Khan, H. A., and E. Thorbecke (1988). Technology Choice and Diffusion in a Macroeconomic Framework. Aldeshot: Gower. Khan, H. A., and J. Weiss (eds) (2007). Poverty Strategies in Asia: Growth Plus. Cheltenham: Edward Elgar. Lieberthal, K., and M. Oksenberg (1998). Policy Making in China: Leaders, Structures, and Processes. Princeton: Princeton University Press. Lu, Q. (2000). China’s Leap into the Information Age. Oxford: Oxford University Press. People’s Republic of China. ‘Authorized Release: The Renewable Energy Law’. Available at: www.climate-policy-map.econsense.de/l egalbasis_download/ china/ renewable_energies_law.pdf Pyatt, G., and J. I. Round (1979). ‘Accounting and Fixed Price Multipliers in Social Accounting Matrix Framework’. Economic Journal, 89: 850–73. Pyatt, G., and E. Thorbecke (1976). Planning Techniques for a Better Future. Geneva: ILO. Rawski, T. G. (2002). ‘Measuring China’s Recent GDP Growth: Where Do We Stand?’. Pittsburg: University of Pittsburg. Available at: www.pitt.edu/ tgrawski Simon, D. F. (1996). ‘From Cold to Hot – China’s Struggle to Protect and Develop a World Class Electronics Industry’. China Business Review, Nov–Dec: 8–16.
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Simon, D. F., and M. Goldman (1989). Science and Technology in Post-Mao China. Cambridge, MA: Harvard University Press. Thorbecke, E., and H.-S. Jung (1996). ‘Multiplier Decomposition Method to Analyze Poverty Alleviation’. Journal of Development Economics, 48 (2): 279–301. World Bank (1985). China: The Energy Sector. Washington, DC: IBRD.
7 Identification of Regional Fundamental Economic Structure (FES) of India: An Input–Output and Field of Influence Approach Sudhir K. Thakur
Introduction The Indian economy is large, with over a billion in population in 2001 (Census of India 2001), and the various regional divisions are characterized by a high degree of heterogeneity. There has been a growing concern over the rising regional imbalances in the Indian economy. The study of regional disparity is important to policymakers since with an increase in regional disparity, other things being equal, imbalances at the national level increase, along with economic and social imbalances. This has drawn the attention of policymakers since such disparities often intertwine with political and ethnic tensions and thus lead to political instability (Kanbur and Venables 2005). Thus policymakers need to understand the structure of the regional economic system and analytical tools for evaluating and formulating balanced regional development strategies. Berry et al. (1966) and Berry (1972) studied the Indian regional structure with respect to commodity flows and regional interdependence using economic transactions. Berry’s analysis confirmed the general theory of polarized development in India. Reed (1967) in a microanalytical study of commodity flows in the Bengal–Bihar region concluded that commodity trades in the area were inversely related to the distance shipped and directly to the demand and supply conditions in the Indian economy (Berry et al. 1966; Chattopadhyaya and Raza 1975; Reed 1967). Raza and Aggarwal (1986) conducted a substantive study of the Indian regional structure and concluded that: (1) the Indian economy was dominated by the distorted role of metropolitan cities, (2) the urban system was less integrated with the regional system due to lack of transport infrastructure, and (3) the urban system was characterized by a significant primary, weak secondary and bloated tertiary sector. 138
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Chakravorty (2000, 2003) examined the impact of structural reforms policies on the regional dimension of the Indian economy. He concluded that the pattern of industrial location has changed between the pre-reform and post-reform periods to that of promoting ‘interregional polarization’ followed by ‘intraregional dispersal’ in the leading regions of growth. Further, Lall and Chakravorty (2005) examined the causes of spatial inequality at the firm level in India. They observed that industrial diversity leads to cost savings at the firm level in regional economies. They also observed that private sector tended to locate away from ‘inland towns’ and ‘lagging regions’. The reason for this was lack of social infrastructure, which distanced firms from the ‘coastal towns’ and ‘urban clusters’. Thakur (2007) examined the impact of development policies on regional growth patterns in India. He observed that the Northwestern states performed better than Eastern states in the agriculture sector, and in the industrial sector Western states performed better than Eastern states. The Indian policymakers implemented the tenth Five Year Plan (2002–7) (Government of India 2002), which focuses upon ‘growth, equity and sustainability’. This poses the question whether the Indian regional economic structure lends itself to a sustainable economic development in the long run. The growing regional differences raise several policy questions. Will the economic reform process make the prosperous regions more successful and lagging regions more impoverished? Will economic reforms boost the growth rates of lagging regions and lead to economic convergence across regions? Are the geographical differences large in India, and would that prevent regional equality? Which economic activities shape the Indian regional structure? Which economic activity is the engine of growth? Is it agriculture-led activities, manufacturing activities, urban-type service activities, business-intensive, finance, insurance and real estate-intensive activities, information-intensive activities or a combination of these activities? Is there a core set of economic activities that is minimally required for the sustenance of Indian regional economies? To provide answers to such questions, economic analysts require an understanding of the composition, magnitude and interrelationships among economic sectors for evaluating and formulating balanced regional development strategies. The current study, in response to previous studies, addresses the problem of whether there are identifiable patterns of relations between various macro aggregates and the regional economic structure as revealed via regional input–output tables. Would the identification of such patterns allow regional analysts to predict regional development and change?
Regional economies of India The Indian economy is characterized by unequal distribution of natural resource endowments, and misallocation of resources across sectors and states
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(Kumar 2000; Lefeber 1964; Mathur 1983; Prasad 1988; Shaw 1999), imperfect mobility and indivisibility in production factors, imbalance in infrastructure supply (Ghosh and De 1998; Lall 1999) and an unequal growth profile of regions (Das and Barua 1996; Dhalokia 1994; Saha 1988, 1993; Shand and Bhinde 2000), leading to an uneven regional growth in India. Sachs et al. (2002) have shown a partial economic convergence for the regional economies in India between the periods 1980 and 1998. The rich states experienced a higher degree of convergence and the poor states showed divergence. This observation prompts the questions whether geographical differences are large and whether this difference would be a hindrance to economic convergence in India. Indian planners until the 1990s had adopted the policy of economizing the use of scarce resources and have located economic activities in selected points in space, inducing regional concentration. The economic reforms measures implemented in 1991 have alleviated this inclination. Kant (1999) argued that ‘spatial efficiency’ has increased at the cost of ‘spatial equity’, thereby increasing inter-regional inequality in India (Chakravorty 2000; Jha 2000). A cursory glance at the map of India reveals the spatial pattern of development for the Indian economy. The Western region is industrialized and prosperous; the Northwest is agriculturally prosperous and East moderately prosperous; the South and Southeast are high-tech regions; the Southwest is characterized by high human and social development; and Central states such as the BIMARU states (Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh) are known as the sick states and are a drag on development (Figure 7.1). There are strong physical contrasts in the distribution of population in India. The highest densities occur along the river Ganges in the Northern Indian plains, and around the coastal plains of the peninsula. These areas of high density contrast markedly with the low population densities of the dry and mountainous regions. Economic and social well-being in India is increasingly a matter of east–west and north–south contrasts. The states of Punjab, Haryana (Northwest) and Gujarat, and Maharashtra (West) experienced faster economic development than the Eastern states of Bihar and Orissa. Similarly, Karnataka, Tamil Nadu and Andhra Pradesh in the South have experienced higher growth as compared with Madhya Pradesh and Uttar Pradesh in the North. Figure 7.2 shows the changing mix of Indian industries at the regional levels for four different time periods (1965, 1975, 1985 and 1995). The list of industries and states depicted in Figure 7.2 is shown in Appendix Table 7A1. The economy was highly agricultural in the mid-1960s, and most of the states showed high shares of primary sector and relatively low shares of secondary and tertiary sectors. This trend changed and the shares for secondary and tertiary sectors increased during the periods 1975, 1985 and 1995. The share of agriculture as a proportion of total gross state domestic product was more than half till the mid-1980s for Bihar, Uttar Pradesh, Madhya Pradesh, Rajasthan, Orissa, Punjab, Haryana and Himachal Pradesh. During the post-1980s the
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No Data North West (High agricultural region)
BIMARU region (Lagging region) Eastern region (Low agricultural region)
Western region (Industrialized region)
South East (High Tech region)
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E S South (High human development)
0
Figure 7.1
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SudhirThakur
Regionalization in India
Source: Prepared by the author.
share of secondary sector, especially in the areas of mining, manufacturing, electricity and construction, increased. Similarly, in the tertiary sector industries such as trade, hotels and restaurants, banking and insurance, real estate and public administration increased significantly across states in India.
Types of fundamental economic structure (FES) The economic structure of a region is defined as the composition and patterns of various components of the regional economy, such as: production,
Sudhir K. Thakur 70
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Andhra Pr. Assam Bihar Delhi Gujarat J&K Kerala Karnataka Maharashtra Madhya Pr. Orissa Punjab Rajasthan Tamil Nadu UP West B. Himachal Pr. Goa Daman Diu Tripura Pondicherry A & N Is.
Andhra Pr. Assam Bihar Delhi Gujarat J&K Kerala Karnataka Maharashtra Madhya Pr. Orissa Punjab Rajasthan Tamil Nadu UP West B. Himachal Pr. Goa Daman Diu Tripura Pondicherry A & N Is.
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Figure 7.2
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Andhra Pr. Assam Bihar Delhi Gujarat J&K Kerala Karnataka Maharashtra Madhya Pr. Orissa Punjab Rajasthan Tamil Nadu UP West B. Himachal Pr. Goa Daman Diu Tripura Pondicherry A & N Is.
Andhra Pr. Assam Bihar Delhi Gujarat J&K Kerala Karnataka Maharashtra Madhya Pr. Orissa Punjab Rajasthan Tamil Nadu UP West B. Himachal Pr. Goa Daman Diu Tripura Pondicherry A & N Is.
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Regional mix of industries in India: 1965, 1975, 1985 and 1995
Source: Prepared by the author with data from ‘Domestic Product of States in India, 1960–61 to 2000–01’, EPW Research Foundation, Mumbai.
employment, consumption, trade, savings, investment, taxes, subsidies, regional product and expenditure, and regional domestic product. The regional economic structure can be examined over a given period of time for sub-national economies and at a given period of time. Broadly, two approaches have been used to study structural change. The first seeks to identify statistically certain universal relationships between economic growth and change in economic structure using international cross-section data or time-series data for national economies. Syrquin and Chenery (1989) in their study identified similarities in structural change during the process of economic growth for a 100-nation sample in an attempt to provide a ‘general theory of structural change’. A few similar characteristics are a shift from agricultural to industrial production, steady accumulation of human and physical capital, changes
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in consumer demand and shift from food and basic necessities to manufactured goods and services, growth of cities and urban industries due to migration of people from rural to urban areas, and increase in the demand of information-intensive goods and services and products produced in foreign countries (Smith and Todaro 2003). The second approach focuses on historical change and experience as economies with similar initial conditions develop over time. The understanding provided by Lewis’s dual sector theory (1954), Myint’s vent for surplus theory (1958) and Todaro’s rural–urban migration theory (1969) provides insights into the process of structural change during economic development. Syrquin (1988) identified three stages of structural transformation in the regional economic evolution process: during stage one the focus remains on primary production; in the second stage focus shifts to manufacturing sector; and during the third stage the shift focuses to services and exports. Further, Malecki (1997) includes the quaternary and quinary as the new tertiary sector activities upon which policymakers focus their growth. An important variant and advance in structural change studies is the taxonomic approach to classifying economic activities (Jensen et al. 1987). Simpson and Tsukui (1965) discovered the concept of fundamental structure of production. This concept was reformulated and extended to form the notion of fundamental economic structure (FES) (Jensen et al. 1987). The concept of FES includes various other economic activities beyond the production, such as households, imports, exports and government expenditure (Jensen et al. 1987). The concept of FES embraces people-related activities, as these were assumed to be the common denominator for all economies. This belief led to the contemplation of a ‘partitioned approach’ to the identification of FES. The approach classified each cell in an input–output table as either fundamental or non-fundamental. The second type of FES, the tiered approach, is based on the concept that the input–output tables could be separated into two layers, one fundamental and the other non-fundamental (Jensen et al. 1991). The partitioned and tiered FES approaches were inherently an expression of a spatial FES. The third type of FES is the ‘temporal FES’. It is that component of an economy which is predictable over time. Jensen et al. (1988) studied the regional economic structure of Queensland economy in Australia. The analysis identified regularities and patterns in cell behaviour for the Queensland economy. The term cell behaviour implies change in value, rather than regularity in value relationships. Further, empirical regularities in certain cell values pertain to the relationships of economic size and cell values. Similarly, Van der Westhuizen (1992), Imansyah (2000) and West (2000, 2001) identified FES for the South African, Indonesian and Australian economies, respectively. These studies are an exercise in positive economics as they provide empirical evidence of the existence of spatial and temporal FES.
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The above studies claim that structures of regional economies are more similar than different at various levels of aggregation. If the core economic structures are similar, then, this information can be utilized to predict economic structure at similar levels of development. Although structure of economies varies across regions, some economic activities are common to all regions, and this common part is called regional FES. Thus, regional FES is conceptualized as those economic activities that are consistently present or inevitably required in economies at statistically predictable levels. These ‘core’ sets of economic activities are represented by transactions in input–output tables and are a function of the size of economies measured by aggregate economic indicators of the region. It is postulated that economic transactions and the region size are related and this functional relationship can be estimated using suitable indicators of size as independent variables and transactions as dependent variables. A regional economic structure can be classified into FES and non-FES (NFES) cells in the input–output tables. The FES cells are the core and remain the same while the non-FES cells are the unpredictable component. The FES component is predictable since it comprises those sets of economic activities that are similar in all sample tables over time or across space and is extracted from the common characteristics of the economic systems. In the process of identifying regularities and FES patterns, a tentative natural order of sectors has been suggested based on the economic logic of the ‘primary– secondary–tertiary’ continuum (Jensen et al. 1988). Can a regional FES be identified from a set of sub-national economies within a nation to extend the applicability of the theory of structural change? This research is novel and is the first attempt to identify and interpret the regional economic structure of the Indian economy using the FES approach. The author in an earlier study identified a temporal FES for the Indian economy for the period 1968–90 (Thakur 2008). This research addresses the following questions: (1) Does the regional FES exist at predictable levels and can it be identified for the period 1965? (2) What proportions of the cells are predictable in a statistical sense? (3) Can the regional FES be used to predict the 1983–4 Punjab regional input–output table? (4) Does the regional FES manifest an improved understanding of the Indian regional structure?
Research methodology Regional models capture the real world as a simplified version of reality or an abstraction from the complex collage of regional and inter-regional interdependencies in economic interaction. If the purpose of a model is forecasting, then predictive performance is an important attribute of a model. In this context Friedman (1953) posited predictive performance as the most important criterion of the validity of a model, and Samuelson (1947) argued that realism of assumptions and power of the model in explaining the real
Regional Fundamental Economic Structure of India 145
world were the most important attributes of a model. The overall objective of this chapter is to demonstrate the existence of a regional FES and use the identified regional structure to predict the intermediate component of the Punjab regional input–output tables (RIOT) for 1983–4. In the implementation of that process a regional analytical methodology has been utilized for the identification of a regional FES, analysis and prediction, and so the methodology is technical. To identify the FES at the regional level, 21 regional input–output tables were assembled from the Artha Vijnana (Economic Science) journal published by the Gokhale Institute of Politics and Economics in their special edition titled Regional Input-Output Matrices, India (Venkatramaiah et al. 1979) (Appendix Table 7A2). These regional tables represent the regional economic structure of both state and union territories in India. These tables are consistent and comparable and pertain to the same base year, 1965. These tables are the only set of consistent matrices available at the regional level for the Indian economy. An assumption is made in the identification of regional Table 7.1 Sector classification for regional economies in India: 1965 Number
Sector name
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Food crops Oilseeds, fibres and sugarcane Plantation and other crops Livestock Fisheries and forestry Coal, ferrous and crude petroleum Stone, clay and bauxite Fertilizer, chemicals, metal and non-metal mining Food manufacturing and beverages Textiles, apparel and footwear Sawmills, wood and furniture Paper, printing and publishing Rubber and leather products Fertilizers, chemicals, oils and paints Misc. chemicals, petroleum and coal Clay, cement, glass and chinaware Metals, non-metals and iron steel Machinery Transport equipment Scientific, unspecified and other industries Construction Electricity and gas Rail, road transport and repair services Trade and excise
Source: Compiled by the author.
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FES. Although the table is dated it still reflects the regional structure during the late 1980s, since the Indian regional structure did not change due to the adoption of a command economy approach during 1965–85. The regional tables are of varying dimensions and have been made comparable after sector aggregation. Table 7.1 shows the sector classification. The sectors have been aggregated to 24 and, hence, the RIOT are comparable with the dimension of 24 × 24 intermediate transaction cells. Three economic variables characterizing the region sizes have been selected: population, net state domestic product (NSDP), and total sector output for 1965 for the sample regions. These economic variables were obtained from the report titled Domestic Product of States in India (Economic and Political Weekly Research Foundation 2003) and the total sector output from the regional tables (Venkatramaiah et al. 1979). Two regression models, linear and logarithmic, are run in statistical package for social sciences (SPSS) with one independent variable (that is, economic size) and another dependent variable (that is, transaction size).
Identification of regional FES in India Pattern analysis This section identifies the fundamental economic structure using the tiered approach (West 2000, 2001). The fundamental and non-fundamental components are determined by implementing the regression analysis and analysing the pattern of regression estimates. This is done by examining the significance tests of the beta parameters at the 5 per cent significance level and examining the adjusted R2 results. Similarly, stability pattern and importance are examined by applying coefficient of variation and field of influences tests. The three patterns together characterize the fundamental economic structure. The intersection and union of the three sets establish a weak, moderate and strong fundamental and non-fundamental economic structure at the regional level. Predictability The fundamental economic structure is characterized by a predictability element, which can be measured by regression analysis. It is hypothesized that a systematic pattern in transactions can be recognized by applying the FES notion that selected characteristics of an economy will vary predictably with region size, as measured by net state domestic product, population and total gross output or other measures of region size. It is well understood that as regions develop the transaction size changes or becomes complex, such that economic transaction represented by the intermediate transaction table becomes intricate and also tends to be more secondary and tertiary-oriented (Jensen et al. 1988; Leontief 1963). The economic activities are urban-oriented or people-oriented activities. To test the statistical relationship, two regression
Regional Fundamental Economic Structure of India 147
models are run to establish the relationship between the variables – economic transactions and region size. The dependent variable is intermediate transaction, and independent variables are net state domestic product, population and total sector output representing the region size. The two models are linear and logarithmic regression models and can be written as: Xij (r) = X (r)
(1)
Log Xij (r) = log X (r)
(2)
Where Xij(r) = is the economic transaction from industry i to industry j for the rth region (i, j = 1 ... k) X(r) = is the independent variable for the rth region (population, net state domestic product, total sector output) = is the constant term = are the coefficients of regression r = is the number of regions (1 ... n) k = is the number of sectors after aggregation Subsequent to sector aggregation the regional input–output tables are consistent and comparable with 24 sectors and 576 cells. Of these 576 cells, 183 cells have zero values and 393 cells have positive transaction values. Thus, the statistical analysis is based upon 393 cells as the dependent variables and the three independent variables used one at a time independently as a single predictor. This precludes any multicolinearity amongst the independent variables. Of the six regression models, three are linear and three logarithmic with three independent variables. The highest proportion of significant cells is predicted by the variable log total sector output; that is, 243 cells out of 393 were significant at the 5 per cent significance level. This amounts to 61.7 per cent of the cells being statistically predictable (Table 7.2). Table 7.2 Summary of percentage of statistically significant cells at 95% confidence level for regression models with 24 sectors: Indian regional economy Independent variables Model/ variable/ cells Linear Logarithmic Total cells predicted
Net state domestic product %
No. of significant cells
20.80 61.20
Source: Compiled by the author.
82 241 393
Population %
16.20 28.40
No. of significant cells 64 112 393
Total sector output % 33 61.70
No. of significant cells 190 243 393
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The second best predictor is log net state domestic product, with 241 cells predictable at the 5 per cent significance level. This makes 61.2 per cent of the dependent variables predictable. The linear regression models (that is, net state domestic product and population) do not perform well as a predictor. The pattern of predictable cells with total sector output and log total sector output perform better than the linear regression models with population and NSDP as independent variables. The predictable pattern of cells with log total sector output is shown in Figure 7.3. It can be observed that predictable cells are located in the secondary–secondary, primary–secondary, primary–primary and secondary–primary partitions of the intermediate transactions table. This pattern is unlike that for the Australian regional economies, in which the fundamental cells were located predominantly in the secondary, tertiary and trade sectors (Jensen et al. 1988, 1991; West 2000). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Z Z
Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z
Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z
Z Z Z
Z
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Z
Z
Z
Z
Z
Z Z Z
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Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z
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Z Z Z
Z Z
Z Z Z Z
Z
Z Z Z Z
Z
Z
Z Z Z Z Z
Z
Z
Z Z Z Primary sector
Secondary sector
Z Tertiary sector
Figure 7.3 Pattern of predictability of cells with log total sector as independent variable and transaction as dependent variable Notes: Unpredictable (blank cells), predictable (shaded cells), zero (Z). Source: Compiled by the author.
Regional Fundamental Economic Structure of India 149
Table 7.3 Distribution of Fundamental Economic Structure (FES) cells across the Primary (P), Secondary (S) and Tertiary (T) sectors, partitions in the Indian regional economy: 1965
Partition
Zeros
P–P P–S P–T S–P S–S S–T T–P T–S T–T Total
40 42 11 51 35 2 0 1 1 183
Unpredictable cells 7 17 4 28 34 17 15 25 3 150
Predictable cells 17 53 1 33 127 9 1 2 0 243
Total
% share in partition
64 112 16 112 196 28 16 28 4
70.8 75.7 20.0 54.1 78.9 34.6 6.3 7.4 0.0
Source: Compiled by the author.
The predictable cells in the Indian regional economy are located in the secondary and primary sectors predominantly (Table 7.3). The Indonesian study established that predictable cells were located in secondary, tertiary and primary sectors as well, thus expanding the domain of FES cells (Imansyah 2000). The cell pattern observed in the Indonesian study is closer to the Indian case, in which the primary sector constitutes a part of the fundamental economic structure, unlike the Australian regional FES. These economic activities are urban and people-oriented activities (Jensen et al. 1988; West 2001) and also include primary activities located in rural regions. Stoner (1968) observed that Indian cities during the periods 1951 and 1961 required greater than expected non-basic employment representing city-serving employment or the minimum required services for population in Indian cities. This observation lends support to the notion of fundamental activities as the basic and driving force regulating the regional distribution of economic activities. Several reasons explain why the primary sector in the Indian regional economy is a component of the regional FES. First, during 1965, regional economies in India had over one-third to two-thirds of the industrial origin from the primary sector, especially agriculture. Second, in the period prior to 1965 the five-year plans (FYP) expressed emphatically the importance of assigning the highest priority to overcoming the food crisis in India (Misra and Puri 1996). This policy was later translated to the adoption of the high yielding variety (HYV) programme, also known as new agriculture technology, for enhancing agricultural productivity in the various regions. Third, agriculture in the rural sector is a subsistence farming activity and the output is meant for self-consumption – a hedge against monsoon failure in a future season, thus making it a basic activity. The rural sector is
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heavily dependent upon farming activities, which are also the main source of employment for the majority of the population. Fourth, several regions endowed with mineral resources were heavily dependent upon such resources for revenues through mining, forestry and logging activities. Therefore, substantial investments were made in the exploitation and mining of these resources. This was especially true in Eastern India, establishing the dominant role of the primary sector as an important component of basic activity. These explanations provide plausible suggestions for the primary sector in India’s regional economy as a component of the regional FES. In comparing the three linear regression models with the three logarithmic regression models, one encounters a comparison problem. In comparing the models, the dependent variables ought to be in the same measuring unit, since three models are in logarithmic form and the other three in non-logarithmic form. The following steps will resolve the problem and make it comparable. First, if the dependent variable is in logarithmic form, then, after estimating the model, the next step is to reckon the dependent variables in logarithmic form using the regression estimates; second, transform the estimated values of the dependent variable in antilog form; third, recalculate the adjusted R2 based on these transformed values. These three transformation steps will make the models comparable with dependent variables with the same measuring units. Table 7.4 shows the comparison of adjusted R2 for the various regression models. The logarithmic regression model with log population as independent variable shows that more than half (52.25 per cent) the cells have an Table 7.4 Summary of adjusted R 2 values for the Indian regional economies with cell sizes as dependent variable
Total sector output Net State Domestic Product (NSDP) Population Log sector output Log NSDP Log population
Zeros
Less than 0.7
More than 0.7
Nonzero cells
Total cells
Number of cells Percentage Number of cells
183 31.8 183
329 57.6 289
64 11.1 104
393
576
393
576
Percentage Number of cells Percentage Number of cells Percentage Number of cells Percentage Number of cells Percentage
31.8 183 31.8 183 31.8 183 31.8 183 31.8
50.2 321 55.7 278 48.3 265 46.0 92 15.97
18.1 72 12.5 115 20 128 22.2 301 52.25
393
576
393
576
393
576
393
576
Source: Compiled by the author.
Regional Fundamental Economic Structure of India 151
adjusted R2 value of 0.7. The second best predictor is log total sector output, with 20 per cent of cells having adjusted R2 values of more than 0.7. Stability The term stability in input–output literature implies consistent economic interaction among industries over a period of time or across a range of regions. In other words, it implies no change in the coefficients over time or across regions. The concept of stability deals with structural or technical change in inter-industry analysis (Miller and Blair 1985). Thus, if a sample of RIOT is examined, the variation of coefficients across the regions will be expected to be minimal, and thus can be used to ascertain the stability or minimal change in the technological coefficients. Gaiha (1980) showed that changes in the input–output coefficients were nominal for the Indian economy for a limited number of tables he examined for India. He also stated that the assumption of stability of input–output relationships is rationalized, although the accuracy of intermediate demand projections can be enhanced if changes in crucial relationships are incorporated from time to time. The coefficient of variation (CV) measure is utilized to ascertain the degree of stability in the regional economies of India. The calculation of CV can be expressed as standard deviation divided by mean of the technological coefficients for the sample regions in India: ( aij CVaij
aij )2 N aij
(3)
a_ij = coefficient of regional input–output tables a ij = mean of coefficients of regional input–output tables N = number of regions (21) Of the 576 cells in the regional table, 183 cells, or 31.6 per cent of all cells, have a coefficient of variation of zero. The mean of the cell values with coefficient of variation distribution is 1.74, which is also assumed to be the threshold. There are 119 cells with coefficient of variation values less than 1.74 but more than zero; 274 cells have coefficient of variation values more than 1.74 (Table 7.5). Most of the stable cells with less than the threshold value of 1.74 are located in the primary–primary, primary–secondary, secondary–secondary and tertiary–secondary partitions of the intermediate transactions table (Figure 7.4). Figure 7.5 shows that 24 of the most stable cells are those with coefficient of variation values between 0.8 and 1.1. These cells are located in the following industries: tobacco, tea and coffee, livestock, construction, rail road
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Table 7.5 Input stability in the Indian regional economy: 1965 Coefficient of variation (CV) Zero <1.74 >1.74 Total
Number of cells 183 119 274 576
Per cent 31.60 20.66 47.74 100.0
Source: Compiled by the author.
5.0
3.0 S22 S19 S16 S13 S10 S7
2.0 1.0
stry
Coefficient of variation
4.0
S23 S21
Figure 7.4
S19 S17 S15 S13 S11 S9 Industry S7
Indu
0.0
S4 S5
S3
S1 S1
Regional stability of input variability in India, 1965
Source: Compiled by the author.
transport and repair services. The cells with minimum variance imply that these cells across all regions are part of the core activities and are the foundation of the regional economies. The cells representing these economic activities are basic activities necessary for the sustenance of the economy and also necessary for the average household consumption. These cells represent a constituent of the fundamental economic activities of a region and would remain situated even if the economy were to undergo a downswing or an upswing. The non-fundamental economic activities would envisage the more variable-type economic activities that rise or fall due to change in
Regional Fundamental Economic Structure of India 153
1.2
1.0
0.6
0.4
S15 S13 S11 S9 S7 S5 S3 S1
0.2
S1
S3
S5
S9
S7
S11
S13
S15
S17
S19
S21
S23
0.0
stry
S23 S21 S19
Indu
Coefficient of variation
0.8
Industry Figure 7.5
Most stable cells for the Indian regional economy: 1965
Source: Compiled by the author.
economic environment, taste, technology, and consumer preferences. This will also imply that, if the economy were undergoing a business downturn, the non-fundamental economic activities would be affected more and the fundamental activities would be the least affected. Importance The notion of technological change can be analysed by measuring the extent and magnitude of coefficient change by a method called the field of influence. In a series of research papers, Hewings et al. (1988), Sonis and Hewings (1989), Hewings et al. (1989), Sonis and Hewings (1992), Sonis et al. (1996) and Okuyama et al. (2002) have developed the mathematical formulation and application of the concept of field of influence. The approach proposes a methodology of measuring the largest field of influence due to a small change in the input–output coefficients. Suppose there is a small change ( or epsilon) in the direct input coefficients, then, the concomitant change in the components of Leontief inverse can be
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ascertained by the following mathematical formulation (Hewings et al. 1988): aij aij(t 1) aij(t)
(4)
The term aij is the direct input coefficients, and the change in the coefficients can be represented by equation (4). The parameter that generates the transformation from aij(t) to aij(t 1) can be expressed as equation (5): aij() aij(t) aij
(5)
where is the transfer parameter and the value remains between 01. Further, the matrix A () = aij() and the associated Leontief inverse can be 1 written as C() [I A ()] . If = 0, then, the matrix: A(0) aij(t) This is the matrix of direct input coefficients at time t with Leontief inverse expressed as: 1
C(0) [I A (t)]
Also, when 1, then, A (t 1) aij (t 1) is the matrix of the direct input coefficients at time (t+1). The associated Leontief inverse can be expressed 1 as C (t 1) I A (t 1)] . If the direct input coefficient is changed by perturbing the matrix with a small then the field of influence can be measured by the following equation: G (t 1, t) = [C () C (0)]
(6)
The outlined approach can be applied to ascertain the most important cells in the input–output tables. First, the average of the sample regions is calculated from the regional input–output coefficient tables for 1965. This is called the average reference table. Second, all the cells in the average reference table are perturbed by a =10 per cent increase in cell values. The product of this approach is shown below as the top 25 per cent of the cells, which experience the maximum field of influence and hence are most important. Of the 576 cells in the average regional economy, 144 (25 per cent) cells are critical with respect to maximum connectivity and importance to the rest of the regional economic system (Figure 7.6). Of these critical cells, the top 15 most important cells for the year 1965 are shown in Table 7.6 and Figure 7.7. The value in the column shows the largest field of influence and denotes
Regional Fundamental Economic Structure of India 155
2.0 1.5 1.0
s2 s213 s19 s1 s157 s13 s11 s9 s7 s5 s3 s1
y
Coefficient change
2.5
Industr
0.5
s1
s3
s7
s5
s9
s13
s11
s17
s15
s19
s23
s21
0
Industry Figure 7.6 Top 25% of the largest field of influence for the average reference table for India: 1965 Source: Compiled by the author.
the difference in the Leontief inverse before and after a 10 per cent increase in each of the cells. These cells were identified to have maximum change, hence signifying maximum importance in the regional economic system. A unit change in the multiplier value of these cells will have the maximum ripple effect within the regional economies. The three cells associated with the transport sector are: rail, road, and repair services in transport. The transport sector plays an important role in the regional economic development process. This sector is a social overhead capital (SOC), which is needed in the production of other directly productive assets (DPA). This type of economic infrastructure is immobile, labour-intensive, indivisible and open-access and has economy-wide impacts. Also, transport infrastructure plays three important roles in the process of regional development (Rietveld 1989). First, it is utilized as a production factor as land, labour, capital or entrepreneurship; second, as a location factor influencing the spatial decision-making of private and public investment and employment generation; and third, it has an impact on enhancing inter-regional trade flows. Regional development analysts argue that economic infrastructure investment, such as transport, telecommunication, public utilities and social community facilities, are some of the most essential social facilities for the economic development of a region. The argument is that infrastructure is a precondition for economic development and, hence, should be provided
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Table 7.6 Top 15 most important cells in the Indian regional economy: 1965 Most important cells Water and electricity – rail, road transport and repair services Trade and excise – rail, road transport and repair services Metals, non-metals and iron steel – rail, road transport and repair services Trade and excise – transport equipment Textile, apparel and footwear – textile, apparel and footwear Metals, non-metals and iron steel – transport equipment Scientific, unspecified and other industries – transport equipment Trade and excise – construction Trade and excise – textile, apparel and footwear Scientific, unspecified and other industries – construction Scientific, unspecified and other industries – textile, apparel and footwear Metals, non-metals and iron steel – machinery Trade and excise – machinery Metals, non-metals and iron steel – metals, non-metals and iron steel Trade and excise – sawmills, wood and furniture
Largest field of influence 2.488 1.589 1.077 1.017 1.015 0.954 0.907 0.804 0.792 0.649 0.629 0.580 0.550 0.543 0.538
Source: Compiled by the author.
before development in the form of excess capacity or in the form of directly productive assets. The second option will lead to bottlenecks due to lack of social overhead capital and, hence, delay development. The First and Second Five Year Plans (FYP) encompassing the periods 1951 through 1961 placed a strong emphasis upon overcoming the transport infrastructure bottleneck. The goal was to attain heavy industrialization, and provision of transport infrastructure was thus a necessity. The transport sector has a gestation period between investment and returns and, thus, the impacts are realized over a substantially long period of time. This sector is important and has been used as an input in every industry in the regional economies in India. D’Souza (1986) has econometrically estimated the extent of infrastructural linkages for railway, power and coal and has shown that changes in the quantity of coal produced brings about most significant changes in the output of freight services via the demand side. Similarly, other important sectors such as construction, transport equipment, metals, non-metals and iron and steel, and textile, apparel and footwear are important industries
Regional Fundamental Economic Structure of India 157
1.600 1.400
Coefficient change
1.200 1.000 0.800 0.600 s22 s19 s16 s13 s10 s7 s4 s1
Industry
0.400 0.200
s1
s3
s7
s5
s9
s11
s15
s13
s17
s21
s19
s23
0.000
Industry Figure 7.7 Top 15 of the most important cells in the Indian regional economy: 1965 Source: Compiled by the author.
where the linkages with other industries are important, and so a change in the magnitude of these industries will produce a sizeable impact on others via inter-industry relationships. A significant observation is that most of the critical cells are located in the primary–primary, primary–tertiary, secondary–secondary and secondary–tertiary sectors. This pattern is different from the one observed for the Australian and South African economies. The FES in Australia and South Africa, and to some extent Indonesia, constituted secondary, tertiary and trade sectors that are essentially urban-oriented and people-type economic activities. Union and intersection of FES characteristics After examination of the FES characteristics (predictability, stability and importance), a union and intersection of the sets of predictable, stable and important cells can give an estimate of the number of cells that can be ascertained to be weak, moderate or strongly fundamental (Table 7.7). A weak fundamental structure implies that the transactions are predictable, or stable or important. A moderate fundamental structure implies that the cells are characterized by predictability and stability, or predictability and importance,
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or stability and importance. A strong fundamental structure implies that the transactions have all three properties of predictability, stability and importance. A fundamental cell is one which is weak, moderate or strong, that is, predictable (P), stable (S), important (I), predictable and stable (PS), predictable and important (PI), stable and important (SI) and/or predictable, stable and important (PSI). The regional tables of India have almost 30–35 per cent of the cells with zero values at the 24 sector aggregation. A third of the cells have zero values, and thus the estimation of the economic interaction utilizing the fundamental economic structure approach becomes less expensive, since only the other two-thirds of the cells need to be estimated. Using the predictability criterion, 243 cells (61.8 per cent) can be estimated using regression analysis; 119 cells (30 per cent) can be estimated using the stability property of the fundamental economic structure; and 144 cells (25 per cent) can be ascertained to be important with critical links with the rest of the regional economic system (Table 7.7). There are 54 cells that are predictable and stable, 38 cells that are predictable and important, and 14 cells that are stable and important, and 106 cells share the properties
Table 7.7 Weak, moderate and strong fundamental economic structure (FES) cells in India Weak FES
%
Predictable (P) Stable (S)
243 119
61.8 20.6
Important (I)
144
25.0
Predictable and stable (PS) Predictable and important (PI) Stable and important (SI) Total (PS, PI and SI) Predictable, stable and important (intersection) Predictable, or stable or important (union) Source: Compiled by the author.
Moderate FES
%
54
13.7
38
9.7
14
3.6
106
27.0
Strong FES
%
44
11.2
Fundamental cells
%
302
76.8
Regional Fundamental Economic Structure of India 159
of predictability and stability, predictability and importance and stability and importance. A total of 44 cells are strongly fundamental of the 576 cells. There are 302 cells that are fundamental, since these cells are either predictable (P), stable (S), important (I), predictable and stable (PS), predictable and important (PI), stable and important (SI) and/or predictable, stable and important (PSI). Thus, 52.6 per cent of cells are fundamental, 15.1 per cent are non-fundamental and 31.8 have zero values. Of the total cells 90 are nonfundamental. Regional FES table for Punjab, 1983–4 So far, by utilizing the characteristics of FES and the methodology outlined in the research methodology section, the predictable, stable and important cells for the average regional economy have been determined. If it is conceivable to demonstrate the existence of regional FES based on the 21 regional input–output tables, then it is possible to use the information extracted to predict the RIOT for the Punjab economy for 1983–4 using the total sector output as a predictor. A total of 243 cells out of 576 cells are statistically predictable using the logarithmic regression model with log total sector output as the predictor. In addition, the stability property determined 119 cells to be stable, with values less than 1.74, which is the threshold. Applying the field of influence method, 144 cells were ascertained to be important and critical. A common feature of all the three characteristics is that the pattern of the predictable cells corresponds to the pattern of stable and important cells. All the cells that are predictable, stable and important are located in the primary–primary, secondary–secondary and secondary–tertiary partitions. This implies that fundamental economic structure is not only predictable, but stable and important. Three different methods (regression, coefficient of variation, and field of influence) have been utilized to examine the fundamental economic structure and the results show similar patterns of fundamental economic structure. The three results can be combined to ascertain and construct the regional FES table, which can be used to construct the Punjab regional table. The State of Punjab is the most developed agricultural economy in India. The rationale for the success of Punjab’s economy is the risk-taking entrepreneurs and the successful implementation of the seed–fertilizer technology during the 1960s in the cultivation of wheat and rice. The Punjab development model suggests that rapid agricultural growth stimulated growth in other sectors via input, output and consumption linkages and, thus, made it possible to transform the economy into a modern and developed region (Bhalla 1995). The Punjab input–output table for 1983–4 has 78 sectors and has 79×86 dimensions (Saluja 1990). The regional table has been aggregated to 24×24 for the intermediate transactions component. To predict the Punjab regional table using the regional FES properties, the following steps
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are followed. First, the logarithmic value of the total sector output for the Punjab regional table is used, along with the regression coefficients from the predictability analysis for estimating 393 cell values, which include both fundamental and non-fundamental cells; second, the locations of the stable and important cells are known a priori; third, the estimated values are substituted in 243 of the predictable cells. The remaining 150 cells are stable, important or non-fundamental cells. The cell locations of the stable and important cells are already known. Thus, if the cells are stable, the average coefficient sizes of regional tables are taken and then multiplied by the average of the column totals of transactions to ascertain the cell size. Also, if the cell is not predictable and unstable, it is checked for importance. If the cell is important, the regression estimate is used to ascertain the value of the cell. For any cell that is not predictable, stable or important but is non-fundamental, the regression estimates are still used to determine the cell size; and, further, if the cell had a zero value in the original table, that value is kept in the predicted table. The actual regional table for Punjab for 1983–4 is shown in Figure 7.8. Following the above steps, the regional table for Punjab’s economy is compiled and shown in Figure 7.9. The bi-proportional technique has been used to reconcile the predicted and original Punjab tables. Verification and validation A model is an idealized and structured representation of the real world. The degree of accuracy in formulating a problem into a model is known as verification. Validation is the process of substantiating and accepting the model for the intended use, provided it meets a specified performance requirement (Rykiel 1996). In other words, the term validation of a model implies whether or not it behaves as expected. Thus validation is not a test that is reported, but a practice where researchers examine and state the degree of validity of the model in question (Jensen et al. 1991). In the process of compiling input–output tables, errors may be introduced due to factors such as inter-regional variations in prices, consumption patterns, inter-regional trade relationships, and spatial variations in technology, among others (Jackson 2001). Modellers often report validity by examining error patterns. After compilation of intermediate transactions tables based upon ordinary least square estimates and the associated FES characteristics (predictability, stability and importance), the next step involves model verification and validation. In the current study validity can be determined by comparing the predicted regional intermediate transactions tables with the actual intermediate transactions tables. In the quest for maximizing accuracy in compiling intermediate transactions tables it is important to bear in mind the distinction between partitive and holistic accuracy. Jensen (1980) distinguishes between these two terms in the context of deriving synthetic tables. A partitive accuracy focuses on cell-by-cell precision in a statistical sense
Regional Fundamental Economic Structure of India 161
Transaction size (Rupees)
12000000 10000000 8000000 6000000 4000000
ust
ry
2000000
s23 s21 s19 s17 s15 s13 s11 s9 s7 s5 s3 s1 Ind
Industry Figure 7.8
s5 s3 s1
s9 s7
s23 s21 s19 s17 s15 s13 s11
0
Actual regional transaction matrix for Punjab: 1983–84
Source: Compiled by the author.
7000000 6000000 5000000 4000000 3000000 2000000
ry
1000000
Ind
s5 s3 s1
Industry
s9 s7
s23 s21 s19 s17 s15 s13 s11
0
Figure 7.9
s23 s21 s19 s17 s15 s13 s11 s9 s7 s5 s3 s1 ust
Transaction size (Rupees)
8000000
Predicted regional transaction matrix for Punjab using FES: 1983–84
Source: Compiled by the author.
within input–output tables, while holistic accuracy centres on identifying the main features of the economy in a descriptive sense and preserves the importance of these features in an analytical sense. Also, partitive accuracy maintains that the table will be holistically accurate; but holistic accuracy does not guarantee a high degree of partitive accuracy (Jensen 1980).
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The identification of FES can provide a holistic, as opposed to partitive, accuracy in the process of updating and compiling regional input–output tables. This accuracy can be measured by examining the degree of deviation between actual and predicted matrices and analysing the error patterns. To implement the error analysis five measures of deviation are utilized and results reported to compare the actual and predicted intermediate transactions table based upon the FES methodology. These measures of deviations are: mean deviation (MD), mean absolute deviation (MAD), mean percentage error (MPE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The predicted intermediate transactions table is assembled utilizing the marginal totals for the regional table from 1983–4. A bi-proportional adjustment technique has been used to reconcile the predicted table with the actual table for the Punjab (1983–4) economy. The error patterns for the Punjab table are reported in Table 7.8. The results are based upon implementing the tiered approach for identifying the regional FES and the associated characteristics. The error patterns for fundamental and non-fundamental economic structure components are reported separately to contrast how well the two components are predicted for the Punjab economy. For the Punjab economy the three deviation measures (mean deviation, mean absolute deviation, and root mean square error) show the non-fundamental component to depict relatively lower errors as compared with the fundamental component. The mean absolute percentage error and root mean square error show that the fundamental component has relatively lower error as compared with the non-fundamental component (Table 7.8). This implies that, although the non-fundamental cells are unpredictable in a statistical sense, these cells are fundamental since they might be characterized by stability and importance properties. The OLS estimates can still be used to predict the non-fundamental cells. The overall matrix error is a weighted sum of the FES and non-FES components. However, compared with the other measures of error, the mean percentage error is high for the Punjab economy. Table 7.8 Deviation among actual and predicted (RAS) tables for Punjab economy: 1983–4 Variable
ATV (RAS)
Intermediate transactions 69,323.15 matrix Fundamental Economic 69,323.15 Structure (FES) component Non-Fundamental Economic 69,323.15 Structure (NFES) component Source: Compiled by the author.
MD
MAD
MPE
MAPE
RMSE
0.0364 0.0129 (−)241.5
240.6
0.0259
0.0426 0.0139 (−)239
238.6
0.032
0.0085 0.0097 (−)249.2
250.2 0.0289
Regional Fundamental Economic Structure of India 163
FES: Interpretation and implications Interpretation This research addressed the question of whether regional economies are similar in economic structure and, if they are similar, whether it can be statistically estimated using the regional FES approach. The analysis has shown that the regional FES approach provides a new methodology for compiling the intermediate component of the regional table for the Punjab economy from the Indian regional FES. The identification of a regional FES for India is an important milestone in understanding the regional evolution of the Indian economic structure. A vast amount of information exists for economies at various geographical scales in the form of time series and cross-section data on economic interaction between economic sectors. Most nations in the world compile national and regional input–output tables that portray the size of inter-industry transactions at the national and regional levels. The input–output table describes the direct and indirect relationships among industries in terms of sale and purchase of inputs and final output at the national, regional and inter-regional scales. This framework is useful in its alternate forms for assessing the impact of changes in regional economic activities, targeting industries for retention, closure or expansion, and formulating recruitment policies (Jackson 1985). Economic activities at the national scale are more aggregated than economic activities at the regional levels. Regional tables show detailed snapshots of economic interaction at various geographical levels. It would be useful to utilize this information to provide a classification of economic activities and make generalizations of the behaviour of regional economic systems. It is postulated that a degree of similarities exists between the economic snapshots of a national economy at different points in time, and between different sub-national economies within a nation at a given time period. These similarities can be identified using the notion of an FES. An FES consists of those economic activities (cells in the intermediate component of regional tables) that are consistently present across various regional economies. These economic activities vary relative to region size as measured by: total gross output, population, regional domestic product or any other measure of economic size. The FES is based on the assumption that economies are more similar than dissimilar in the presence of economic activities across different geographical scales. The FES concept suggests that the size of the uniform and predictable component is larger than the nonuniform and unpredictable components (Jensen 1990). Thus, if the core set of economic activities is similar, this information can be utilized to predict the economic structure at similar levels of development (Thakur 2008). A typology of space-time FES can be conceptualized showing a classification of economic activities into a fundamental and a non-fundamental
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Table 7.9 Typology of space-time fundamental economic structure (FES) Space-Time FES
FES
Non-FES (NFES)
Regional Temporal
Regional FES Temporal FES
Regional NFES Temporal NFES
Source: Thakur (2008).
component at different geographical scales (Table 7.9). The temporal FES is that component of an FES that is predictable over time. This concept is broader, since it includes economic activities that are aggregated at the regional scale. A regional FES would demonstrate the presence of those economic activities that are consistently present across regions at statistically predictable levels. The temporal non-FES is the unpredictable component of the FES at the national level. The regional non-FES is the unpredictable component at the regional level due to geographical differences in natural resource endowments such as agriculture, tourism, and mining activities. It will be interesting to examine the economic activities that constitute regional and temporal FES as well as regional NFES and temporal NFES. The economic activities within each group of the typology could be similar, overlapping, common or different. The set of economic activities in the various FES–NFES categories will manifest an improved understanding of the spatial–temporal evolution of economic activities in India. Implications The identification and existence of an Indian regional FES suggests the presence of some economic activities or a group of economic activities that are inevitably present at predictable levels in all types of regional economies in India. Since this regional regularity has been ascertained over a range of geographical economies from the smaller agriculture-based rural economies to more developed and diversified economies, it can be asserted with some confidence that a given level of total activity, such as gross output in a region, will almost inevitably require reasonably predictable levels of economic activity in identifiable groups within the intermediate transaction component of the RIOT. This implies that as the regional economies grow over time the size of the economic activities (cell sizes in RIOT) can be statistically predicted using aggregate measures of region or economic size, such as gross output, state domestic product, and regional expenditure. The identification of a regional FES has several implications in terms of table compilation and regional investment decisions. The first implication is for the compilation of the intermediate transaction component of the regional table. Regional tables are difficult to compile at
Regional Fundamental Economic Structure of India 165
smaller geographical scales due to manpower, money and time constraints. The regional FES methodology identified 76.8 per cent of the economic transactions as fundamental, that is, a total of 302 cells as fundamental cells. The regional FES has three characteristics: ‘predictability’, ‘stability’ and ‘importance’. Predictability is defined as that portion of the regional economic structure that will be dependent upon the aggregate economic size of the regional economies. The highest proportion of significant cells is predicted by the variable log total sector output; that is, 243 cells out of 393 were significant at the 5 per cent significance level. This amounts to 61.7 per cent of the cells being statistically predictable. This suggests that the regional table can be compiled for any region in India using the information on the cells within the FES component, which is based on a common economic structure for all regional economies. The cell sizes of the non-fundamental component can be determined using superior data such as government publications or actual surveys. The stable cells are those cells in the regional economies that are present across a range of regional tables and show minimum changes in the technological coefficients. This implies that these economic activities are required for the sustenance of the regional economies. Of the 576 cells, 119 cells have coefficient of variation values less than 1.74 but more than zero; and 274 cells have coefficient of variation values more than 1.74. Most of the stable FES cells are located in the primary–primary, primary– secondary, secondary–secondary and tertiary–secondary partitions of the intermediate transactions component. An economic implication of the existence of stable cells is that these activities will be present even during economic depression and booms. The non-fundamental component will be affected adversely during upswings and downswings of the economic cycle. The important cells are those elements in the fundamental component that are considered to be critically significant. These are those economic activities whose change would in all probability create the maximum potential for region-wide changes (Jensen et al. 1987). Of the 576 cells, 144 fundamental cells have been determined to be important. The important cells have an implication for policymaking. The most important cells consist of the industries associated with the transport sector in India. Thus, if regional governments invest in transport-related industries the possibilities of growth can be enhanced due to their maximum linkage with the rest of the economic system.
Conclusions This research is a significant departure from Berry’s (1966, 1972) analysis of the spatial structure of the Indian economy. The FES is defined as that component of the regional economic system which consists of
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transaction cells in input–output tables consistently present at a statistically predictable level across a range of regional economies. A regional FES for the Indian economy exists and has been identified for the period 1965 using the 21 regional input–output tables for 21 representative states of India. The highest proportion of predictable cells has been identified using log total sector output as the predictor resulting in 61.7 per cent of the cells being statistically predictable at the 5 per cent significance level. These cells are located in secondary–secondary, primary–secondary and secondary–primary partitions of the intermediate transactions table. The logarithmic regression models have outperformed the linear regression models. Using the FES characteristics of predictability, stability and importance, 20.6 per cent of the cells were found to be stable, with coefficient of variation values less than average; 25 per cent of the cells were found to be important. The stable cells with values less than the threshold of 1.74 are located in the primary–primary, primary–secondary, secondary–secondary and tertiary–secondary partitions of the intermediate transactions table. Further, the important cells are located in the primary–primary, primary–tertiary, secondary–secondary and secondary–tertiary partitions of the intermediate transactions table. These patterns are unlike the Australian regional economic structure since the fundamental components are located in the secondary, tertiary and trade sectors of the intermediate transactions table. This study has extended and modified the notion of FES. Utilizing the characteristics of FES, fundamental cells have been classified to be weak, moderate and strong. A weak fundamental structure implies that transactions are predictable, stable or important. A moderate fundamental structure implies that cells are characterized by predictability and stability (13.7 per cent), predictability and importance (9.7 per cent) or stability and importance (3.6 per cent). A strong fundamental structure implies that transactions are characterized by predictability, stability and importance (11.2 per cent). A fundamental cell is one which is weak, moderate, or strong (76.8 per cent). Utilizing the regional FES properties, the regional input–output table for the Punjab economy (1983–4) has been compiled for a 24-sector classification. The predicted table shows a similarity in the regional economic structure as compared with the actual table. This research is a significant contribution to the FES literature, since the only other study identifying regional FES is that for the Australian regional economies (Jensen et al. 1988, 1991). Compiling a regional input–output table is expensive, manpower-intensive and time-consuming. The FES methodology provides an alternative approach in constructing the regional tables using a hybrid approach. The FES characteristics of predictability, stability and importance could be utilized to ascertain an intermediate transaction matrix.
Regional Fundamental Economic Structure of India 167
The cells that are not fundamental can be ascertained using superior data such as published and survey data from government departments. The application of such an approach will make the tables more accurate and realistic. The FES is a conceptual notion which provides an improved understanding of the regional economic structure. The FES methodology can be utilized to measure, interpret, understand and predict economic structure at various geographical scales. This methodology is a challenge to regional analysts to test, modify, refute, provide alternative hypotheses and explanations of the study of regional economies, and strengthen the notion of a proposed general theory of FES. The identification of regional FES has implications for regional table compilation and regional investment decision-making. This study encountered five limitations in implementing the regional FES methodology to identify the Indian regional economic structure. First, the identification of a regional FES is based upon the assumption of the availability of a large number of regional input–output tables (RIOT) for different regional economies. It is difficult to ascertain the predictability characteristics when sample regional tables are limited in number. This study utilized 21 regional tables to identify FES at the regional levels and its associated FES characteristics. Second, the 1965 regional data have been utilized, as they are the only consistent RIOT available for the identification of the regional FES. No system-wide regional tables are available for a current time period except for sporadic RIOT for individual states for non-overlapping years. Third, this study used a sector aggregation scheme of grouping 65 to 75 sectors to 24 sectors for identifying the stylized facts and patterns of development. The study could have used other levels of aggregation for identifying regional FES. However, analysis of disaggregated regional tables would not have depicted the expected cell behaviour and predictable patterns of regional FES. Fourth, almost 30–35 per cent of the cells across the aggregated intermediate transaction tables have zero values or missing information. This implies that either the value of the economic transaction between industries was zero or goods and services were exchanged via barter trade. Fifth, the estimation of the proportion of highest predictable cells utilized simple linear and logarithmic regression models. A multivariate regression model could have been utilized by including all the independent variables in the model simultaneously to determine the highest proportion of predictable cells, or a principal component analysis could have been used, and/ or a structural equation approach could have been the alternate estimation technique.
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Appendix Table 7A1 List of industries and states for 1965, 1975, 1985 and 1995 S. No.
Industries
States
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Agriculture Forestry Fishing Mining Manufacturing Construction Electricity, gas and water supply Transport, storage and communication Railways Other commerce and transport Storage Communication Trade, hotels and restaurants Banking and insurance Real estate, ownership of dwellings and business services Public administration Other services
Andhra Pradesh Assam Bihar Delhi Gujarat Jammu and Kashmir Kerala Karnataka Maharashtra Madhya Pradesh Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh
16 17 18 19 20 21
West Bengal Himachal Pradesh Goa Daman Diu Tripura Pondicherry Andaman Nicobar Is
Source: ‘Domestic Product of States of India, 1960–61 to 2000–01’, Economic and Political Weekly Research Foundation, Mumbai.
Appendix Table 7A2 Regional technical coefficient matrices of states and union territories in India, 1965 States
Union territories
Andhra Pradesh Assam Bihar Gujarat Jammu and Kashmir Kerala Madhya Pradesh Madras/Tamil Nadu Maharashtra Mysore/Karnataka Orissa Punjab Rajasthan Uttar Pradesh West Bengal
Andaman and Nicobar Islands Delhi Goa, Daman and Diu Himachal Pradesh Pondicherry Tripura
Source: Venkatramaiah et al. (1979).
Regional Fundamental Economic Structure of India 169
References Berry, B. J. L. (1972). ‘The Interdependence of Spatial Structure and Spatial Behavior: A General Field Theory Formulation’. In W. K. D. Davies (ed.), The Conceptual Revolution in Geography. Totowa, NJ: Rowman and Littlefield, 394–416. Berry, B. J. L., R. Vening, and D. Ranajit (1966). ‘Essays on the Commodity Flows and the Spatial Structure of the Indian Economy’. Research Paper 111. Chicago: Department of Geography, University of Chicago. Bhalla, G. S. (1995). ‘Agriculture Growth and Industrial Development in Punjab’. In W. Mellor (ed.), Agriculture on the Road to Industrialization. Baltimore: International Food Policy Research Institute, Johns Hopkins University Press. Chakravorty, S. (2000). ‘How Does Structural Reform Affect Regional Development? Resolving Contradictory Theory with Evidence from India’. Economic Geography, 76 (4): 367–94. Chakravorty, S. (2003). ‘Industrial Location in Post-Reform India: Patterns of Interregional Divergence and Intraregional Convergence’. The Journal of Development Studies, 40 (20): 120–52. Chattopadhyaya, B., and M. Raza (1975). ‘Regional Development: An Analytical Framework’. Indian Journal of Regional Science, 7 (1): 11–34. Das, S. K., and A. Barua (1996). ‘Regional Inequalities, Economic Growth and Liberalization: A Study of the Indian Economy’. Journal of Development Studies, 32 (3): 364–90. Dhalokia, R. H. (1994). ‘Spatial Dimension of Acceleration of Economic Growth in India’. Economic and Political Weekly, 27 August: 2303–9. D’Souza, A. (1986). ‘A Model of Infrastructural Inter-Linkages for India’. Indian Economic Review, 21 (2): 115–48. Economic and Political Weekly Research Foundation (2003). Domestic Product of States of India: 1960–61 to 2000–01. Mumbai. Friedman, M. (1953). Essays in Positive Economics. Chicago: Chicago University Press. Gaiha, R. (1980). ‘On Testing the Stability of Input-Output Relationship in the Indian Economy’. Journal of Development Economics, 7 (2): 273–82. Ghosh, B., and P. De (1998). ‘Role of Infrastructure in Regional Development: A Study over the Plan Period’. Economic and Political Weekly, 21 November: 3039–48. GoI (Government of India) (2002). Tenth Five-Year Plan. New Delhi: Planning Commission, Government of India. Hewings, G. J. D., M. Sonis, and R. C. Jensen (1988). ‘Fields of Influence of Technological Change in Input-Output Models’. Papers Regional Science Association, 64: 25–36. Hewings, G. J. D., R. C. Jensen, G. R. West, M. Sonis, and R. W. Jackson (1989). ‘The Spatial Organization of Production: An Input-Output Perspective’. Socio-Economic Planning Sciences, 23 (1–2): 67–86. Imansyah, H. (2000). ‘The Development of a Horizontal Hybrid Method for Constructing Input-Output Tables: A Fundamental Economic Structure Approach to Indonesia’. Australia: University of Queensland. Unpublished Doctoral Dissertation. Jackson, R. W. (1985). Input-Output Analysis: Assessing Regional Economic Impacts. Columbus: Department of Geography, Ohio State University. Jackson, R. W. (2001). ‘Assessing the Spatial Variations in US Technology’. In M. Lahr and R. E. Miller (eds), Regional Science Perspectives in Economic Analysis. Amsterdam: Elsevier Science, 323–43.
170 Sudhir K. Thakur Jensen, R. C. (1980). ‘The Concept of Accuracy in Regional Input-Output Models’. International Regional Science Review, 5 (2): 139–54. Jensen, R. C. (1990). ‘Construction and Use of Regional Input-Output Models: Progress and Prospects’. International Regional Science Review, 13: 9–25. Jensen, R. C., G. J. D. Hewings, and G. R. West (1987). ‘On Taxonomy of Economies’. The Australian Journal of Regional Studies, 2: 3–24. Jensen, R. C., G. R. West, and G. J. D. Hewings (1988). ‘The Study of Regional Economic Structure Using Input-Output Tables’. Regional Studies, 22: 209–20. Jensen, R. C., J. H. Dewhurst, G. R. West, and G. J. D. Hewings (1991). ‘On the Concept of Fundamental Economic Structure’. In J. H. Dewhurst, R. C. Jensen and G. Hewings (eds), Regional Input-Output Modeling: New Development and Interpretations. Sydney: Avebury, 228–49. Jha, R. (2000). ‘Reducing Poverty and Inequality in India: Has Liberalization Helped?’. WIDER Working Paper 204. Helsinki: UNU-WIDER. Kanbur, R., and A. J. Venables (2005). ‘Rising Spatial Disparities and Development’. Policy Brief 3/2005. Helsinki: UNU-WIDER. Kant, S. (1999). ‘Spatial Implications of India’s New Economic Policy’. Tijdschift voor Economische en Sociale Geogrefie, 90 (1): 80–96. Kumar, T. R. (2000). ‘Public Investment and Regional Disparities’. Economic and Political Weekly, 35 (41): 3683–7. Lall, S. (1999). ‘The Role of Public Infrastructure Investments in Regional Development: Experience of Indian States’. Economic and Political Weekly, 34 (12): 717–35. Lall, S. V., and S. Chakravorty (2005). ‘Industrial Location and Spatial Inequality: Theory and Evidence from India’. Review of Development Economics, 9 (1): 47–68. Lefeber, L. (1964). ‘Regional Allocation of Resources in India’. In J. Friedman and W. Alonso (eds), Regional Development and Planning: A Reader. Cambridge, MA: MIT Press, 642–53. Leontief, W. (1963). ‘Structure of Development’. Scientific American, September: 148–66. Lewis, A. (1954). ‘Economic Development with Unlimited Supplies of Labor’. The Manchester School, 22: 139–91. Malecki, E. (1997). Technology and Economic Development: The Dynamics of Local, Regional and National Competitiveness, 2nd edn. Addison Wesley Longman. Mathur, A. (1983). ‘Regional Development and Income Disparities in India: A Sectoral Analysis’. Economic Development and Cultural Change, 31(3): 475–506. Miller, R. E., and P. D. Blair (1985). Input-Output Analysis: Foundations and Extensions. Englewood Cliffs, NJ: Prentice Hall. Misra, S. K., and V. K. Puri (1996). Indian Economy. Delhi: Himalayan Publishing House. Myint, H. (1958). ‘The Classical Theory of International Trade and the Underdeveloped Countries’. Economic Journal, 68 (270): 317–37. Okuyama, Y., G. J. D. Hewings, M. Sonis, and P. Israilevich (2002). ‘Structural Change in the Chicago Economy: A Field of Influence Analysis’. In G. J. D. Hewings, M. Sonis, and D. Boyce (eds), Trade, Networks and Hierarchies: Modeling Regional and Interregional Economies. Berlin: Springer, 201–24. Prasad, P. (1988). ‘Roots of Uneven Regional Growth in India’. Economic and Political Weekly, 23 (33): 1689–701. Raza, M., and Y. Aggarwal (1986). Transport Geography of India: Commodity Flows and the Regional Structure of the Indian Economy. New Delhi: Concept Publishing House.
Regional Fundamental Economic Structure of India 171 Reed, W. (1967). ‘Areal Interaction in India: Commodity Flows of the Bengal-Bihar Industrial Area’. Research Paper 110. Chicago: Department of Geography, University of Chicago. Rietveld, P. (1989). ‘Infrastructure and Regional Development: A Survey of Multiregional Economic Models’. Annals of Regional Science, 23: 255–74. Rykiel, E. J. Jr. (1996). ‘Testing Ecological Models: The Meaning of Validation’. Ecological Modeling, 90: 229–44. Sachs, J. D., N. Bajpai, and A. Ramiah (2002). ‘Understanding Regional Economic Growth in India’. Asian Economic Papers, 1 (3): 32–62. Saha, S. K. (1988). ‘Industrialization and Interregional Disparities in Postcolonial India: Towards a New Regional Policy’. Tijdschift voor Economische en Sociale Geogrefie, 81 (2): 93–109. Saha, S. K. (1993). ‘Uneven Development of Engineering Companies in India: Spatial Dimensions of Competitive Advantage’. Economic and Political Weekly, 28 (4): M122–36. Saluja, M. R. (1990). ‘Structure of Punjab Economy Inter Industrial Flows and Patterns of Final Demand: 1983–84’. Margin, October–December 23 (1): 58–85. Samuelson, P. (1947). Foundation of Economic Analysis. Cambridge, MA: Harvard University Press. Shand, R., and S. Bhinde (2000). ‘Sources of Economic Growth: Regional Dimensions of Reforms’. Economic and Political Weekly, 34 (16–17): 3747–57. Shaw, A. (1999). ‘Emerging Patterns of Urban Growth in India’. Economic and Political Weekly, 17–24 April: 969–78. Simpson, D., and J. Tsukui (1965). ‘The Fundamental Structure of Input-Output Tables, An International Comparison’. Review of Economics and Statistics, 47: 434–46. Smith, S. C., and M. P. Todaro (2003). Economic Development, 8th edn. Boston, MA: Addison Wesley. Sonis, M., and G. J. D. Hewings (1989). ‘Error and Sensitivity Input-Output Analysis: A New Approach’. In R. E. Miller, K. R. Polenske, and A. Z. Rose (eds), Frontiers in Input-Output Analysis. Oxford: Oxford University Press, 232–44. Sonis, M., and G. J. D. Hewings (1992). ‘Coefficient Change in Input-Output Models: Theory and Applications’. Economic Systems Research, 4 (2): 143–57. Sonis, M., G. J. D. Hewings, and J. Guo (1996). ‘Sources of Structural Change in Input-Output Systems: A Field Influence Approach’. Economic Systems Research, 8 (1): 15–32. Stoner, E., Jr. (1968). ‘A Comparative Analysis of the Urban Economic Base: The Employment Structure of Indian Cities, 1951–1961’. Economic Geography, 44 (1): 71–82. Syrquin, M. (1988). ‘Patterns of Structural Change’. In H. B. Chenery and T. N. Srinivasan (eds), Handbook of Development Economics, vol. 1. Amsterdam: North Holland, 203–68. Syrquin, M., and H. B. Chenery (1989). ‘Patterns of Development, 1950 to 1983’. WB Discussion Paper 41. Washington, DC: World Bank. Thakur, S. K. (2007). ‘Structure and Structural Changes in India: A Regional Analysis’. In B. Thakur, G. Pomeroy, C. Cusack, and S. K. Thakur (eds), City, Society and Planning: Essays in Honor of Professor A. K. Dutt, vol. 3. New Delhi: Concept Publishing Company, 137–64. Thakur, S. K. (2008). ‘Identification of Temporal Fundamental Economic Structure (FES) of India: An Input-Output and Cross-Entropy Analysis’. Structural Change and Economic Dynamics, 19 (2): 132–51.
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Todaro, M. P. (1969). ‘A Model of Labor Migration and Urban Unemployment in Less Developed Countries’. American Economic Review, 59 (1): 138–49. Van der Westhuizen, J. M. (1992). ‘Towards Developing a Hybrid Method for InputOutput Table Compilation and Identifying a Fundamental Economic Structure’. Philadelphia: University of Pennsylvania. PhD Dissertation. Venkatramaiah, A., R. Kulkarni, and L. Argade (1979). ‘Regional Input-Output Matrices, India 1965’. Artha Vijnana, 21 (3–4): 273–67. Poona: Gokhale Institute of Politics and Economics. West, G. R. (2000). ‘Updating Input-Output Tables with the Help of a Temporal Fundamental Economic Structure’. Australian Journal of Regional Studies, 6 (3): 429–49. West, G. R. (2001). ‘Structural Change and Fundamental Economic Structure: The Case of Australia’. In M. L. Lahr and E. Dietzenbacher (eds), Input-Output Analysis: Frontiers and Extensions. London: Palgrave Macmillan, 318–37.
8 Comparing Regional Development in China and India Yanrui Wu
Introduction Since the early 1990s, the world has witnessed rapid economic transformation and growth in China and India. This has subsequently triggered a boom in comparative studies of the two Asian giants.1 This chapter adds to the literature by focusing on regional growth in the two countries. In particular, it attempts to explore how regional economies in the two vast countries have performed during the period of high growth and assess whether or not regional disparity has deteriorated as economic liberalization deepens. It is found that substantial regional disparity exists in China and India. This disparity has shown an increasing trend in both countries during the period of rapid economic growth, in particular since the early 1990s. However, the increase in regional disparity in both countries largely reflects the enlarging gap between the super-rich regions and the rest of the economy within each country. In other words, economic growth has not led to catch-up effects in the relatively poor regions as postulated by the new growth theories (Abramovitz 1986). This chapter also shows that variations in infrastructure development and the levels of urbanization are the main sources of regional disparity in both countries. In China, the export sector also plays a role in affecting regional development. In India, human capital development in recent years may also affect regional disparity.
Regional growth in China and India During 1980–2005, China and India achieved phenomenal economic growth at the average rate of 9.7 and 5.9 per cent, respectively.2 These growth rates are unprecedented in the history of the two countries. However, there are substantial variations in growth across the regions of the two economies. Figure 8.1 presents the distribution of real gross regional products (GRP) per capita and GRP growth rates in China and India in 2001. 3 Several observations can be made. 173
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First, in both countries, the regions (China’s provinces and special municipalities and India’s states and union territories) can be divided into two groups: the most urbanized areas, including Shanghai, Beijing, and Tianjin in China and Chandigarh, Goa and Delhi in India, and the rest of the economies. As expected, the most urbanized regions have the highest per capita income in both economies. Thus, one could conclude that the level of urbanization is positively associated with the level of economic development among the regions. Second, the gap between the rich and the poor is much bigger in China than in India. For instance, the ratio of per capita GRP in China’s richest region (Shanghai) over that in the country’s poorest region (Tibet) in 2001 was 13.1, much greater than the same ratio (8.1) in India, that is, the ratio of per capita GRP in Chandigarh (the richest) over that in Bihar (the poorest). Third, if the super-rich regions (three municipalities in China and four states in India) are excluded, regional disparity appears less severe in the two countries. Without the super-rich, the income ratio of the richest region over the poorest region was 5.0 in China and 4.4 in India in 2001. These are still high but seem to be close to those in economies at similar stages of development. For example, the ratio of per capita income of the richest state over the poorest state was 5.8 in the USA in 1900, 3.0 in Italy in 1950, 2.4 in France in 1950, 2.8 in Japan in 1955 and 2.2 in the USA in 1990.4 As for the rates of regional growth, they are very diverse in both countries. According to Figure 8.1, as expected, the average rate of growth is higher in China than in India. China’s most developed regions tend to grow faster
35000
Chandigarh Shanghai
14
30000 Indian mean income Rupees, Yuan
16
12
25000
10
20000
8 15000
Chinese mean income
10000
4
5000 0
%
6
2 Indian regions
Chinese regions
0
Figure 8.1 Regional ranking and growth rates Notes: Ranking is based on the value of 2001 gross provincial product (GPP) per capita in China and 2001/2 net state domestic product (NSDP) per capita in India. Growth rates are the average rates during 1994–2001. In 2001, US$1=47.186 rupees and 8.277 yuan. Source: Data are drawn from NBSC (various issues) and Reserve Bank of India (2005).
Regional Development in China and India 175
than the country’s least developed regions. In India, the fastest-growing regions appear to be the ‘middle classes’, that is, those states with per capita income ranked between the super-rich and the poorest states. Perhaps due to these variations in the rates of growth, the ranking of the regions between 1980 and 2001 changed considerably in China and modestly in India (Table 8.1). In China, only five of the top 10 regions in 1980 remain in the top-10 group in 2001. The major winners in China are such coastal regions as Zhejiang, Jiangsu, Guangdong, Fujian and Shandong. These regions are also the beneficiaries of China’s economic reforms, as
Table 8.1
Ranking of Chinese and Indian regional economies
Chinese regions
1980
2001
Shanghai Beijing Tianjin Zhejiang Jiangsu Guangdong Liaoning Fujian Shandong Heilongjiang Hebei Jilin Hubei Xinjiang Inner Mongolia Shanxi Anhui Hunan Qinghai Guangxi Henan Jiangxi Sichuan Ningxia Shaanxi Yunnan Gansu Guizhou
1 2 3 12 11 15 4 19 17 5 8 9 20 14 13 10 27 21 7 22 25 23 24 6 16 26 18 28
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Indian regions Goa Delhi Pondicherry Andaman and Nicobar Islands Punjab Maharashtra Haryana Gujarat Tamil Nadu Nagaland Karnataka Himachal Sikkim Kerala Andhra Pradesh West Bengal Tripura Meghalaya Arunachal Pradesh Rajasthan Madhya Pradesh Jammu and Kashmir Manipur Assam Orissa Uttar Pradesh Bihar
1980 2 1 3 5 4 6 7 8 16 20 14 11 13 15 18 10 23 19 12 26 21 9 17 24 22 25 27
2001 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Note: China’s Hainan, Chongqing and Tibet are excluded from the ranking exercises due to missing data. So are India’s Chandigarh, Jharkhand, Chhattisgarh and Uttaranchal. Ranking is based on the value of gross regional product (GRP) per capita. Indian ranking is based on the 1980/1 and 2001/2 financial year statistics. Source: Data are drawn from the NBSC (various issues) and Reserve Bank of India (2005).
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they were offered special policy initiatives under the concept of ‘letting some get rich first’. Thus, economic policies have played an important role in affecting regional disparity in China in the past two decades. In India, between 1980 and 2001, eight of the top 10 regions maintained their top positions. Only two states, Tamil Nadu and Nagaland, moved into the top-10 category.5 In addition, the ranking of the top eight regions in India has remained the same, with the exception of Goa and Delhi swapping positions. Thus, with regard to changes in regional disparity over the past two decades, China and India are very different. One may argue that India’s democratic system makes it difficult for the country to pursue an unbalanced development strategy such as the one China has adopted over the period. The deteriorating regional disparity in China is hence partly attributed to the country’s economic policies, while in India it may be more related to non-institutional factors such as historical and geographical reasons. For example, Rao et al. (1999) argue that India’s more developed regions with relatively better infrastructure, human resources and accessibilities to markets have been able to exploit the opportunities offered by economic liberalization better than the relatively poor states. Sachs et al. (2002) claim that Orissa’s vulnerability to floods and devastation from tropical cyclones is partly attributed to its poor performance in agriculture and hence the overall economic conditions in the state.
Regional convergence or divergence In the growth literature, two types of convergence have been defined and applied, that is, sigma-convergence and beta-convergence (Barro and Salai-Martin 1995). In this chapter, sigma-convergence is measured by the standard deviation of the logarithm of the GRP per capita. Two sets of samples are employed for each country. That is, one set contains all regions in each country and the other excludes the super-rich regions according to the 2001 ranking of per capita GRP. The estimation results are presented in Figure 8.2. In comparison, Chinese regions have shown greater dispersion than Indian regions according to Figure 8.2, which depicts the two curves for China above the Indian ones. The chart also presents evidence of modest regional convergence in China in the 1980s, particularly during the first half of the 1980s. This observation is consistent with findings reported in other studies (for example, Démurger et al. 2002; Raiser 1998; Wu 2004). However, since the early 1990s, Chinese regions have shown the tendency of divergence, which is mainly reflected in the widening gap between the three large municipalities and other provinces. Figure 8.2 shows clearly that the standard deviation across regions excluding the three municipalities hardly changed in the 1990s, implying that income differences were relatively stable.
Regional Development in China and India 177 0.45 0.40 0.35
SD
0.30 0.25 0.20 0.15 0.10 0.05 0.00 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 India Figure 8.2
China
India-4
China-3
Sigma-convergence of regional income
Note: SD stands for the standard deviation of the logarithm of gross provincial product per capita in China and net state domestic product per capita in India. India-4 is calculated excluding the four super-rich regions (Chandigarh, Goa, Delhi and Pondicherry) and China-3 is based on all regions but three super-rich (Shanghai, Beijing and Tianjin). Source: Computed by the author.
In the case of India, there was little change in regional disparity in the first half of the 1980s, but regional divergence has taken place since 1984, when Rajiv Gandhi took office and initiated the first wave of economic reforms. However, the trend of regional divergence seems to have originated from different sources in the late 1980s and since the early 1990s. In the late 1980s, divergence occurred mainly between regions excluding the four super-rich states and territories (Chandigarh, Goa, Delhi and Pondicherry). This is clearly demonstrated in Figure 8.2, where the two curves for India are almost parallel to each other during the 1980s. But, since the early 1990s when more comprehensive reforms were introduced in the aftermath of the 1991 balance of payments crisis, regional divergence seems to be driven by the widening gap between the superrich states and the rest of the economy. In the absence of the super-rich, regional disparity remains unchanged in the 1990s (that is, almost a horizontal line with a value of 0.15, as shown in Figure 8.2). This is similar to the observation in China in the 1990s. That is, with the exclusion of the super-rich states, regional disparity has remained almost at the same level in both economies since the early 1990s while the gap between the superrich and the rest of the economy has become larger. These findings are in sharp contrast to popular perceptions about regional inequality in China
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and India during the reform periods (Jian et al. 1996; Rao et al. 1999).6 Thus, in many existing studies, regional disparity may be inflated due to the impact of several highly urbanized economies among the regions (the three municipalities in China and Chandigarh, Goa and Delhi in India). To gain a better understanding of regional disparity, one should treat the super-rich regions separately. The observed sigma-convergence or divergence is mirrored in the estimation results of beta-convergence, which are summarized in Table 8.2. The estimated values of beta and t-ratios in parentheses are derived from the following non-linear regression: log yiT log yi0 A 1 ebT log yi0 «i
(1)
where yiT and yi0 are income per capita at periods T and 0, respectively, and «i is the standard white noise. In the case of India, all scenarios considered here show evidence of regional divergence during 1980–2001. In China, the estimation results in Table 8.2 demonstrate the trend of regional convergence in the first half of the 1980s. This reflects, however, a fall in the gap between the three largest cities (Shanghai, Beijing and Tianjin) and the rest of the country. Without the three municipalities, the findings in Table 8.2 do not show significant evidence of convergence among the regions in China. To sum up, the results from the analyses of both sigma-convergence and beta-convergence illustrate that, during the period of high economic growth, the regions in China and India have shown the tendency of divergence.
Table 8.2 Estimation results of beta-convergence Group I beta (t-ratios)
Group II n
beta (t-ratios)
n
China 1980–7 1987–2002 1980–2002
0.0067 (1.982)* 28 −0.0020 (−0.676) 30 0.0013 (0.486) 28
0.0044 (0.760) 0.0003 (0.037) 0.0034 (0.680)
25 27 25
India 1980/1–1995/6 1995/6–2001/2 1980/2–2001/2
−0.0428 (−1.071) −0.0035 (−0.310) −0.0083 (−1.719)
−0.0435 (−0.742) 0.0158 (1.303) −0.0045 (−0.606)
24 27 24
27 31 27
Notes: Group I includes all regions for which data are available in the two countries. Group II excludes China’s Shanghai, Beijing and Tianjin, and India’s Chandigarh, Goa, Delhi and Pondicherry, respectively. These regions are the city states or provinces in the two countries. * indicates significance at the level of 5 per cent. The numbers under ‘n’ represent the size of the samples used. Source: Author’s own calculation.
Regional Development in China and India 179
The latter has mainly been driven by the enlarging gap between several highly urbanized regions and the rest of the economy in the two countries. This trend of divergence seems to be relatively more severe in India than in China in recent years, though China’s regional disparity in absolute terms is much worse than India’s. In both countries, it seems that regional disparity has been relatively stable if several super-rich regions (provinces or states) are excluded from the analysis. This is contradictory to popular perception that regional disparity has deteriorated seriously in both China and India since the early 1990s.
Sources of regional disparity: A regression analysis The results discussed in the preceding sections indicate the existence of regional disparity in both China and India. There is, however, no evidence of regional convergence, although both economies have experienced phenomenal growth over the past decades. This section attempts to provide an explanation of the sources of regional disparity. A regression approach is employed. To introduce this approach, consider the following income equation: y b0 bixi «
(2)
where y represents a measure of income, for example, income per capita, and xi (i =1, ... , n) are a list of variables that affect income. Equation (2) can be estimated using the ordinary least square (OLS) technique. Given the ^ ^ b ^ x . Morduch estimates, bi , the prediction of income is y^ ^b0 y i b 0 i i and Sicular (2002) show that the proportional contribution of the ith component to inequality measured using the variance or squared coefficient of variation is given as follows:7 i i Scv Svar cov(y i, y) var(y)
(3)
To estimate the system of Equations (2) and (3), the following empirical model is considered: log(y) ƒ (inf, hum, rub, ind, z) «
(4)
where y, inf, hum, urb, ind and z represent real GRP per capita, infrastructure, human capital, urbanization, industrialization and a control variable. The selection of these variables and their definitions are very much dictated by the availability of regional data in the two countries. For the same reason, Equation (4) is estimated for each country using two 1-year crosssectional datasets representing the 1990s and the current period, respectively.8 The variables identified in Equation (4) are also popular candidates in growth analyses.9 A more detailed description of the variables is provided as follows.
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Infrastructure The condition of infrastructure development plays an important role in economic development. Well-developed infrastructure such as roads and telecommunications can help reduce business costs and improve efficiency, and hence is a key prerequisite for attracting domestic as well as foreign investment. The level of infrastructure development is expected to be related positively to income. For the India model, access to drinking water is employed as the proxy of infrastructure development, and the information is available for each state for the years 1991 and 2001.10 Another indicator that may also capture the activities of infrastructure development is investment in transport in each state.11 This variable is included in the Indian model as a control variable. For the Chinese model, as access to drinking water is very much universal in China, telecommunication density across the regions is employed as a proxy of the level of infrastructure development.12 Human capital The role of human capital in economic growth is highlighted in the new growth theory (Benhabib and Spiegel 1994). The measurement of human capital stock is, however, difficult and controversial.13 This chapter simply employs regional literacy ratios as an indicator of the level of human capital development among the regions in China and India. Urbanization Preliminary analysis in the preceding section provides clear evidence about the relationship between the level of income and the degree of urbanization in both China and India. Thus, a variable reflecting the progress of urbanization is included in Equation (4) so that the contribution of urbanization to regional disparity is analysed. Industrialization Regional disparity could be driven by structural differences between the regional economies in each country. More industrialized regions are expected to be more developed and hence have higher per capita income. For example, Bhattacharya and Sakthivel (2004) find that India’s more industrialized states grew much faster than the less industrialized states during the reform period. To examine the effect of economic structure on regional disparity, the share of the manufacturing sector in GRP is employed as a proxy of the level of industrialization in the empirical models. International trade Openness to trade and investment is expected to play an important role in economic development.14 This is confirmed by the recent development experience of both China and India. The two economies, in particular
Regional Development in China and India 181
China, have benefited substantially from foreign investment and access to foreign markets through exports and imports. While regional trade figures are not available in India, the shares of the value of exports over GRP for the Chinese regions are included as a control variable. The estimation results are presented in Table 8.3. The table shows that the chosen variables can explain a large proportion of the variation (between 70 to 90 per cent) in regional income per capita. Another important conclusion can also be drawn from the table. That is, variations in infrastructure development and urbanization are the main sources of regional disparity in both countries. These two factors account for the bulk of the shares in regional disparity. This finding is consistent with the conclusions about the role of infrastructure by Nagaraj et al. (2000) and urbanization by Sachs
Table 8.3 Estimation results 1991 Indian model
^ b i
Intercept Infrastructure Human capital Urbanization Industrialization Transport 2 R 2 adjust-R Sample size
6.4987 (0.2258)* 0.0067 (0.0027)** 0.0055 (0.0033) 0.0086 (0.0034)** 0.0058 (0.0070) 9.8615 (2.7220)* 0.8064 0.7554 25
2001 Shares, %
24.5 8.1 25.5 5.3 16.0
^ b i 6.8447 (0.3470)* 0.0042 (0.0021)*** 0.0226 (0.0046)* 0.0099 (0.0026)* 0.0099 (0.0040)** 1.4260 (1.3281) 0.8619 0.8305 28
1990 Chinese model
^ b i
Intercept Infrastructure Human capital Urbanization Industrialization Export 2 R 2 adjust-R Sample size
6.7894 (0.2356)* 0.0859 (0.0340)** 0.0013 (0.0036) 0.0139 (0.0045)* 0.0046 (0.0047) 0.6489 (0.2497)** 0.8757 0.8498 30
Shares, %
6.6 36.1 34.2 8.5 0.9
2001 Shares, %
30.4 1.4 37.2 6.7 11.9
^ b i 7.4887 (0.3377)* 0.0480 (0.0117)* 0.0370 (0.4396) 0.0059 (0.0054) 0.0010 (0.0049) 0.4319 (0.3078) 0.9129 0.8954 31
Shares, %
65.7 0.2 15.7 0.5 9.2
Notes: All four regressions are tested for heteroscedasticity. The null hypothesis of homoscedasticity for India in 1991 is rejected and hence the weighted least square approach is employed to re-estimate the model. Standard errors are reported in the parentheses. *, ** and *** represent significance at the level of 1%, 5% and 10% respectively. Source: Author’s own calculation.
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et al. (2002). The former examined various forms of infrastructure (social, physical and economic) and their impact on long-term regional growth potentials in India. The authors particularly highlighted six states (Jammu and Kashmir, Madhya Pradesh, Rajasthan, Orissa, Himachal Pradesh and Assam) where improvement in infrastructure would exert a higher growth impact, and the overall regional disparity in India. Sachs et al. (2002) also employed Indian state statistics to show that a 10 percentage point increase in the rate of urbanization is linked with a 1.3 percentage point increase in the rate of growth per year. In addition, human resource development tends to play a key role in affecting regional disparity in recent years in India. According to limited statistics, regional disparity in human capital development (measured using the literacy ratios here) hardly changed between 1981 and 1991, with the standard deviation of regional literacy ratios at 14.8 per cent in 1981 and 14.7 per cent in 1991.15 However, the standard deviation of the same data series dropped to 10.9 per cent in 2001. During the same periods, the average literacy ratio grew from 47.8 per cent in 1981 to 58.5 per cent in 1991 and to 69.7 per cent in 2001. Thus, during the decade (1991–2001) the overall human capital development in India improved. In the meantime, regional disparity was reduced too. In the case of China, international trade or openness also plays a role in influencing regional development. There exists a huge gap between the Chinese regions in terms of economic openness. The latter, measured using the ratio of export value over GRP, ranges from as little as 2–3 per cent in Sichuan and Henan to as high as 70–80 per cent in Guangdong. Between 1990 and 2000, there was hardly any change in regional disparity in this aspect. Regional disparity in economic openness remained high in China in the 1990s. As for the level of industrialization, it is not the dominant contributor to regional disparity.16 In fact, surprisingly, none of the variables representing the level of industrial development is statistically significant. Thus, there may be some room for improvement, for example, the use of alternative variables. These findings imply that, to reduce regional disparity, governments in China and India should seriously deal with regional differences in the level of infrastructure development and urbanization. As argued by Nagaraj et al. (2000), public investment in infrastructure could be targeted at specific states/regions and, in this way, would generate better long-term returns in terms of improved growth performance and hence reduced regional disparities. While the findings in Table 8.3 provide important implications, they are subject to several qualifications, such as the problems of endogeneity and outliers. The regression results from OLS may be biased due to endogeneity, but lack of data makes it impossible to apply alternative approaches such as the two-stage least square method. The impact of outliers can, however, be assessed and the results are presented in Table 8.4.17 The outliers for
Regional Development in China and India 183 Table 8.4
Sensitive analysis
Contributing factors
1991
2001
1990
2000
Infrastructure Human capital Urbanization Industrialization Transport/export Total
10.3 6.5 41.6 9.0 25.4 92.7
17.4 30.1 37.1 3.1 1.0 88.6
30.3 1.4 35.5 8.9 11.5 87.6
59.2 24.3 9.7 93.2
Notes: The numbers represent percentage contributions. Three outliers are identified and excluded for the 1991 model (India), one for the 1990 model (China), two for the 2000 model (China) and three for the 2001 model (India). For the 2000 Chinese model, human capital and industrialization variables have insignificant coefficients and are excluded from the final regression. Source: Author’s own calculation.
each dataset are identified by examining the residuals from the regressions in Table 8.3.18 The regressions excluding the outliers are rerun. Table 8.4 shows that the explaining power improves after the exclusion of the outliers in each case. The decomposition of the sources of regional disparity is, in general, consistent with the results in Table 8.3. That is, infrastructure and urbanization variables still account for the major shares of regional disparity.
Concluding remarks This chapter presents some preliminary findings comparing regional economic growth, disparity and convergence in China and India. It is found that regional economic development in China and India is unbalanced. Relatively more developed regions in both countries have forged ahead with no evidence of catch-up by the backward regions. As a result, regional disparity has become worse though the two countries enjoyed unprecedented growth over the past two decades. In particular, the gap between the super-rich regions and the rest of the economy in both countries has widened since the early 1990s. While regional disparity is in general more severe in China than in India, in recent years it has risen faster in India than in China. The Chinese government is now fighting hard to reduce regional disparity and build a harmonious society. India will face the same problem if the current trend of rising disparity continues. India can learn from the Chinese experience and try to avoid repeating China’s mistakes. This chapter also shows that variations in urbanization and infrastructure development are major contributors to regional disparity. While both countries have to build more infrastructure and speed up urbanization, they should also aim for a more balanced strategy among the regions. In
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addition, it seems that human resource development has also played a role in affecting regional disparity in India in recent years, while in China the export sector is found to be a positive contributor to regional disparity. These findings, though subject to qualifications, may point the direction for policy responses by governments in China and India in the near future.
Appendix: Sources of data Chinese statistics are drawn mainly from the Statistical Yearbook of China compiled by the National Bureau of Statistics China–NBSC (various issues). Indian data come from several sources including the Handbook of Statistics on the Indian Economy published by the Reserve Bank of India (2005), the Handbook of Urban Statistics compiled by the National Institute of Urban Affairs, Department of Urban Development and Five Year Plans, Planning Commission, Government of India.
Notes 1. Recent comparative studies of China and India include Swamy (2003) and Srinivasan (2004) on reforms and economic performance, Srinivasan (1994) and Balasubramanyam and Wei (2005) on international trade, Kehal (2005) on foreign investment, Singh (2005) and Wu and Zhou (2006) on bilateral relationships, and Wu (2007) on the service sector, to cite a few. 2. These rates are the author’s own estimates using official statistics of China and India published by the National Bureau of Statistics (various issues) and Reserve Bank of India (2005), respectively. 3. The 2001 data are employed here, as there are too many missing data in more recent statistics for Indian states. 4. These statistics are based on data reported in Barro and Sala-i-Martin (1995). Tokyo is excluded from the comparison. 5. In fact, Chandigarh is excluded from the ranking due to missing data. Given that Chandigarh has been the richest region in India, Tamil Nadu is probably the only region which moved into the top 10. 6. In this chapter, ‘regional inequality’ refers to inequality between provinces (and states) in China (and in India). Inequality within the provinces/states is not addressed here but becomes increasingly important in both countries. For empirical studies, see Knight et al. (2004) and Kanbur and Zhang (2005) on China, and Bhanumurthy and Mitra (2004) on India. 7. Recent applications of similar technique include Zhang and Zhang (2003) and Wan (2004). 8. The choice of one single year’s data is due to the unavailability of data in particular Indian regional statistics. 9. These variables are also included in the 60 variables identified by Sala-i-Martin (1997). 10. The sources of data are listed in the appendices. 11. Canning (1998) presents a detailed study of measuring the stock of infrastructure in the world.
Regional Development in China and India 185 12. The same data for India are not available, unfortunately. 13. See, for example, Benhabib and Spiegel (1994), Pritchett (1997) and Temple (1998). 14. There is a large body of literature on the relationship between economic development and trade. Readers may refer to Edwards (1992) and Harrison (1996). 15. Author’s own calculations. 16. Mitra (1992) and Ravallion and Datt (1996) show that industrial growth had nominal impacts on rural and urban poverty in India. 17. Only the decomposition results are presented. 18. It should be pointed out that the choice of outliers is based on an ad hoc method which determines the number of outliers to be excluded and in the meantime maintains a reasonable size for the new data sample after the exclusion of the outliers in each case.
References Abramovitz, M. (1986). ‘Catching Up, Forging Ahead, and Falling Behind’. Journal of Economic History, 46 (2): 385–406. Balasubramanyam, V. N., and Y. Wei (2005). ‘Textile and Clothing Exports from India and China: A Comparative Analysis’. Journal of Chinese Economic and Business Studies, 3 (1): 23–37. Barro, R. J., and X. Sala-i-Martin (1995). Economic Growth. New York: McGrawHill Inc. Benhabib, J., and M. Spiegel (1994). ‘The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data’. Journal of Monetary Economics, 34 (2): 143–74. Bhanumurthy, N. R., and A. Mitra (2004). ‘Economic Growth, Poverty, and Inequality in Indian States in the Pre-reform and Reform Periods’. Asian Development Review, 21 (2): 79–99. Bhattacharya, B. B., and S. Sakthivel (2004). ‘Regional Growth and Disparity in India: Comparison of Pre- and Post-reform Decades’. Economic and Political Weekly, 6 March: 1071–7. Canning, D. (1998). ‘A Database for the World Stocks of Infrastructure, 1950–95’. World Bank Economic Review, 12: 529–47. Démurger, S., J. Sachs, W. T. Woo, S. Bao, G. Change, and A. Mellinger (2002). ‘Geography, Economic Policy and Regional Development in China’. Asian Economic Papers, 1 (1): 146–97. Edwards, S. (1992). ‘Trade Orientation, Distortions and Growth in Developing Countries’. Journal of Development Economics, 39 (1): 31–57. Harrison, A. (1996). ‘Openness and Growth: A Timeseries, Cross-country Analysis for Developing Countries’. Journal of Development Economics, 48 (2): 419–47. Jian, T., J. D. Sachs, and A. M. Warner (1996). ‘Trends in Regional Inequality in China’. China Economic Review, 7 (1): 1–21. Kanbur, R., and X. Zhang (2005). ‘Fifty Years of Regional Inequality in China: A Journey through Central Planning, Reform, and Openness’. Review of Development Economics, 9 (1): 87–106. Kehal, H. S. (ed.) (2005). Foreign Investment in Rapidly Growing Countries: the Chinese and Indian Experiences. Basingstoke and New York: Palgrave Macmillan.
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Knight, J., S. Li, and R. Zhao (2004). ‘Divergent Means and Convergent Inequality of Incomes among the Provinces and Cities of Urban China’. WIDER Research Paper 2004/52. Helsinki: UNU-WIDER. Mitra, A. (1992). ‘Growth and Poverty: the Urban Legend’. Economic and Political Weekly, 28 March: 659–65. Morduch, J., and T. Sicular (2002). ‘Rethinking Inequality Decomposition with Evidence from Rural China’. The Economic Journal, 112 (476): 93–106. Nagaraj, R., A. Varoudakis, and M. A. Veganzones (2000). ‘Long-run Growth Trends and Convergence across Indian States’. Journal of International Development, 12 (1): 45–70. NBSC (National Bureau of Statistics China) (various issues). China Statistical Yearbook. Beijing: China Statistics Press. Pritchett, L. (1997). ‘Where Has all the Education Gone?’. WB Policy Research Working Paper 1551. Washington, DC: World Bank. Raiser, M. (1998). ‘Subsidizing Inequality: Economic Reforms, Fiscal Transfers and Convergence across Chinese Provinces’. Journal of Development Studies, 34 (3): 1–26. Rao, M. G., R. T. Shand, and K. P. Kalirajan (1999). ‘Convergence of Income across Indian States: A Divergent View’. Economic and Political Weekly, 27 March–2 April. Available at: www.epw.org.in.(accessed November 2008). Ravallion, M., and G. Datt (1996). ‘How Important to India’s Poor Is the Sectoral Composition of Economic Growth’. The World Bank Economic Review, 10 (1): 1–25. Reserve Bank of India (2005). Handbook of Statistics on the Indian Economy. Available at: www.rbi.org.in/scripts/publications.aspx.(accessed November 2008) Sachs, J. D., N. Bajpai, and A. Ramiah (2002). ‘Understanding Regional Economic Growth in India’. Asian Economic Papers, 1 (3): 32–62. Sala-i-Martin, X. (1997). ‘I Just Ran Two Million Regressions’. American Economic Review (Papers and Proceedings), 87: 178–83. Singh, S. (2005). ‘China-India Economic Engagement: Building Mutual Confidence’. CSH Occasional Paper 10. New Delhi: French Research Institutes in India. Srinivasan, T. N. (1994). Agriculture and Trade in China and India: Policies and Performance Since 1950. San Francisco: ICS Press. Srinivasan, T. N. (2004). ‘China and India: Economic Performance, Competition and Cooperation: An Update’. Journal of Asian Economics, 15 (4): 613–36. Swamy, S. (2003). Economic Reforms and Performance: China and India in Comparative Perspective. New Delhi: Konark Publishers Pvt. Ltd. Temple, J. (1998). ‘Robustness Tests of the Augmented Solow Model’. Journal of Applied Econometrics, 13 (4): 361–75. Wan, G. (2004). ‘Accounting for Income Inequality in Rural China: A Regressionbased Approach’. Journal of Comparative Economics, 32 (2): 348–63. Wu, Y. (2004). China’s Economic Growth: A Miracle with Chinese Characteristics. London and New York: Routledge Curzon Press. Wu, Y. (2007). ‘Service Sector Growth in China and India: A Comparison’. China: An International Journal, 5 (1): 137–54. Wu, Y., and Z. Zhou (2006). ‘Bilateral Trade between China and India’. Journal of Asian Economics, 17 (3): 509–18. Zhang, X., and K. Zhang (2003). ‘How Does Globalization Affect Regional Inequality within a Developing Country? Evidence from China’. Journal of Development Studies, 39 (4): 47–67.
9 The Gains from Growth for Women and Informal Workers in India: A SAM-Based CGE Analysis Anushree Sinha and Haider A. Khan
Introduction The main purpose of this chapter is to examine how economy-wide modelling of both gender and the informal sector in the economy can facilitate understanding of the role these important aspects play in driving the growth trajectory in a developing country such as India (Harriss-White and Sinha 2007). In the wider context, we feel that there are similarities with other countries (China, Brazil and South Africa – the other countries in CIBS) in the role of the informal sector. Moreover, a large share of women workers is involved in informal activities without any significant policy addressing them explicitly. This could lead to unintended developments that may hamper desirable growth with equity; an important objective for the government of India as well as the United Nations’ Millennium Development Goals (MDGs). Both the informal sector and gender-related developmental issues have prompted serious debate. But the absence, first, of the necessary data and, second, of the appropriate gender and informal sector-related macroeconomic analytical tools has prevented rigorous quantitative analyses. More generally, it must be recognized that there are few instruments that can relate macroeconomic policy and microeconomic behaviours. In this context, the structuralist macroeconomic frameworks, such as the social accounting matrix (SAM), the fixed price multiplier (FPM) and the computable general equilibrium model (CGEM), are tools that can address these concerns (see, for example, Jung and Thorbecke 2001; Khan and Thorbecke 1989; Taylor 1990; Thissen 1998). More importantly, developing these frameworks drives the extraction of relevant data from various economy-wide data sources and forces a consistent framework, satisfying the national accounting equalities for an economy. Such models have been applied to a range of policy questions in a number of economic fields in recent years. They include public 187
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finance and taxation issues, international trade policy questions, evaluation of alternative development strategies, and the implications of macroeconomic policies for the distribution of resources. The base date set for developing either a FPM or a CGE model is generally a SAM. The roots of SAMs go back to the pioneering work in social accounting by Gregory King in 1681. However, modern social accounting is largely inspired by the work of Sir Richard Stone in connection with the Cambridge growth model in the 1950s and 1960s. Stone’s work with the UN System of National Accounts (SNA) project gave further impetus to developing a disaggregated household sector description. Pyatt, Round and Thorbecke (1977 and 1976) developed the work by applying the idea of a SAM to developing countries. The work by Khan (1982a, b, 1983), Khan and Thorbecke (1989) and others led to the desegregation of technologies and the inclusion of the informal sector separately within a SAM. The SAM summarizes succinctly the interdependence between productive activities, factor shares, household income distribution, balance of payments, capital accounts, and so on, for the economy as a whole at a given point in time. Given the technical conditions of production, the value added is distributed to the factors in a determinate fashion. The value added accrued by the factors is further received by households according to their ownership of assets and the prevailing wage structure. In the matrix form, the SAM consists of rows and columns representing receipts and expenditures, respectively. As an accounting constraint, receipts must also equal expenditures in a SAM. The SAM can be structured to include various types of households to facilitate examining poverty profiles – which are particularly important in connection with gender and informal sector aspects (see Sinha et al. 2007). Therefore, in any country one particular set of accounts assumes special importance: the household accounts. The proper flow of income and expenditure needs to be recorded for these accounts if an accurate picture of poverty through adequate measures of income and consumption is to emerge from a given SAM. For this reason, the classification of households needs special care. There are at least six aspects that need careful attention: 1. Classification of households by socio-economic characteristics; 2. Understanding the income generation process by which households receive their incomes; 3. Pinpointing the distribution mechanisms; 4. Understanding household consumption patterns; 5. Linking household income and consumption to social capabilities and functioning; and 6. Estimating the resource-generating capacity and resource absorption capacity of the households.
Imports of final consumer goods
Factor income paid abroad
Total factor income paid
Rest of the world
Total
Source: Authors.
Indirect taxes on final consumption
Indirect taxes
Total expenditure of the institutions
Domestic savings module
Capital account
Income redistribution module
Domestic consumption module
Income distribution module
Institutions
Production activities
Institutions
Factors of production
Factors of production
Table 9.1 Modular composition of the SAM
Total supply
Imports of intermediate consumer goods
Indirect taxes on intermediate consumption
Industrial transactions module
Income generation module
Production activities
Total gross investments
Imports investment goods
Indirect taxes on investment goods
Domestic investment module
Capital account
Total net indirect taxes
Total net indirect taxes
Indirect taxes
Total receipts from abroad
Balance of payments deficits
Exports
Transfers received from abroad
Factor income received from abroad
Rest of the world
Total payments abroad
Total net indirect taxes
Total savings
Total demand
Total disposable national income
Total factor income received
Total
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If the above six items can be investigated systematically by combining economic and social modes of inquiry in a SAM, proper policy intervention for poverty reduction will become a more tractable exercise than it is at present.
Informal sector and gender SAMs: Recent examples in CGE analysis for India A SAM (see Table 9.2) is developed with the use of an input–output (IO) table. In India, the recent two IO tables produced by the CSO for 1993–4 and later for 1999–2000 have been used to develop SAMs incorporating the informal sector as well as gender distinctions by Sinha and Sangeeta (2000) and Sinha, Sangeeta, and Siddiqui (2003), respectively. In both the exercises the 114 sectors of the Indian IO tables are aggregated into a lower number of sectors for sharper analysis, with informality and gender aspects driving the aggregation of sectors. The National Sample Survey Organisation (NSSO) employment and expenditure surveys of 1993–4 provide information on the characteristics of the surveyed households’ employment status and consumption expenditure. Workers are distinguished as casual, regular, own account workers (OAW) or employers, and, for the latter study, as home-based workers. The authors have characterized informal parts of the industry as having a lower output–labour ratio and also hiring only casual labour. Casual labour attracts lower wages compared with regular labour, Table 9.2 Simplified schematic social accounting matrix Expenditures
Receipts Endogenous accounts
Exogenous
Factors 1 Households 2
Endogenous accounts
Exogenous
Technology production Factors Households activities
Sum of other accounts
Totals
1
2
3
4
5
0 T2.1
0 T2.2
T1.3 0
x1 x2
y1 y2
Production activities
3
0
T3.2
T3.3
x3
y3
Sum. of other accounts
4
111
112
113
t
yx
Totals
5
y11
y12
y13
y1x
Source: Khan and Thorbecke (1988).
Gains of Growth for Women and Informal Workers in India 191
and is a part of distinct household categories. Another major assumption has been that the informal sector does not pay any production tax to the government. However, such firms can both import and export and are thus exposed to external shocks and trade reforms. The findings of our study depend on the major characteristics adopted while formulating the model. Both the SAMs have also been used as a database for developing CGE models.1 The CGE models help through simulations to examine the impact of reforms (Dervis et al. 1982) on the economy. The gender-focused CGE model (GENCGE) developed for the Indian economy (see Sinha and Adam 2000; Sinha and Sangeeta 2003) extended the standard CGE model to examine the impact of external shocks on informal workers and also on women workers. The augmented GEN-CGE model developed is of the type discussed by Devarajan et al. (1996), which are widely used trade-focused models for developing countries. The GEN-CGE results give information about the possible consequences of changes in trade policy for the distribution of income between the formal and informal factors and distinguished by gender and across a variety of household types. The other important feature of past studies is the distinction of various types of households with expenditure classification as well. As noted, the latter version of the gender model (Sinha et al. 2003b) incorporated information on home-based workers. Moreover, capitalists are also distinguished by gender. Formal and informal capital owners are distinguished very carefully, keeping the CSO definition of formal enterprises in view. It is to be recognized that the breakdown of the various parts of the SAM and the gender households is the starting point in the building of a model. The authors have results that indicate the majority of women workers are involved in the informal sector, so the authors recognize that informal sector analysis is important to get an accurate perspective on the situation of women in India; and, we suspect, in China, Brazil and South Africa. In the paper by Sinha and Sangeeta (2000), the authors distinguished various factors of production by gender and informal and formal categories by using National Sample Survey Organization (NSSO) data (1993–4) as given below: 1. 2. 3. 4. 5. 6.
Female casual (informal) labour Male casual (informal) labour Female regular (formal) labour Male regular (formal) labour Own account workers (informal capital owners) Employers (formal capital owners)
The initial probe into such a macro analysis in this paper shows that the female workforce constitutes only 26 per cent of the total labour force, and that they are mainly working in the informal sector. Of the total number of female workers, including helpers or unpaid workers, about 92 per cent
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are involved in informal activities. Casual agricultural workers make up 36 per cent of the total female workforce and another 36 per cent are actually unpaid helpers, whereas 22 per cent of the male workers are casual agricultural workers and only 12 per cent are unpaid helpers. A sectoral classification further shows that females are mostly occupied in agricultural and related sectors. Within formal activities, women have the highest representation in the education, scientific and research service sector, with 31 per cent of the total of non-agricultural regular women workers occupied in this sector, mostly as teachers. The findings reveal that, apart from the usual agriculture and livestock-related activities, a substantial informal share in production activities is in textile production, wood and wood products, other manufacturing, manufacture of miscellaneous metal products, construction and services. The share of informal workers in sectors such as agriculture, construction, mining, manufactured food products, gur and khandsari (by-products of sugar mostly used in rural India), beverages, wood products and leather is higher than that of formal workers. Most importantly, the study reflects (as expected) the fact that households are poorer within the informal category. This paper developed two versions of the CGE model with a gender distinction. A static version of the model was used for a comparative static analysis for simulations 1 and 2. A dynamic version was developed to capture the longer-run fiscal consequences of trade reforms for simulation 3. In simulation 1, the import tariff of the manufacturing sector was reduced by 50 per cent; in simulation 2, the import tariff of the capital goods sector was reduced by 17 per cent; and in simulation 3 a 50 per cent reduction in tariff rates of all tradable sectors was imposed. In simulation 2, the average wage rate of male labour rises less sharply. In simulation 3 both male and female regular wage rates increase, with male rates rising more sharply than female rates (see Table 9.3). In all cases the regular wage rates of male workers rise more sharply. In the case of casual wage rates, female casual wage rates fare better than male casual wage rates. The earnings of households change as a result of the wage rate changes.
Table 9.3 Impact of trade reforms on wages and consumption Sim 3 BASE
Sim 1
Sim 2
Av. Real Wage rate Casual female 71.47 Casual male 98.57 Regular female 191.97 Regular male 254.63
−1.57 −2.72 0.57 1.52
0.15 0.01 0.37 0.47
Source: Sinha and Sangeeta (2003).
P1
P6
−1.32 −2.77 1.31 2.45
−4.81 −6.72 −1.84 −0.89
Gains of Growth for Women and Informal Workers in India 193
Table 9.4
Percentage change in real earnings of female and male workers Simulation 3 Simulation 1
Simulation 2
P1
P6
Household Type
Female
Male
Female
Male
Female
Male
Female
Male
Rural Formal1 Rural Formal2 Rural Formal3 Urban Formal1 Urban Formal2 Urban Formal3 Rural Informal1 Rural Informal2 Rural Informal3 Rural Informal4 Urban Informal1 Urban Informal2 Urban Informal3
3.11 1.49 1.21 2.20 3.31 3.05 1.26 1.50 1.22 2.12 1.15 1.26 1.15
3.78 4.28 4.32 3.64 3.87 4.27 0.63 2.65 3.19 1.09 2.45 1.63 4.33
0.29 0.13 0.10 0.20 0.31 0.29 0.10 0.13 0.10 0.19 0.09 0.10 0.09
0.36 0.41 0.41 0.34 0.36 0.41 0.03 0.24 0.29 0.08 0.22 0.13 0.41
3.77 1.78 1.43 2.65 4.01 3.69 1.51 1.80 1.45 2.56 1.37 1.50 1.36
4.57 5.19 5.24 4.40 4.68 5.18 0.68 3.18 3.84 1.26 2.93 1.92 5.25
0.69 −1.57 −1.96 −0.59 0.96 0.60 −1.88 −1.55 −1.95 −0.69 −2.04 −1.89 −2.04
1.25 1.94 1.99 1.06 1.37 1.93 −3.11 −0.31 0.43 −2.47 −0.59 −1.72 2.00
Total
2.01
2.58
0.18
0.23
2.42
3.09
−0.85
−0.42
Note: The earnings refer to wage earnings, as capital earnings have no male–female variation. Source: Sinha and Sangeeta (2003).
The table below shows that male earnings increase more than female earnings for nearly all types of households, except for rural poor and non-agriculture informal households (see Table 9.4). This is because of the worker composition of these households and the change in wage rates due to the simulations. The percentage of casual male workers in rural informal poor and non-agriculture households is very high compared with other informal households, and the share of regular male labour is very small. The slow rise in the casual male wage rate compared with the female casual wage rate results in lesser benefits accruing to males in rural poor and non-agriculture households. However, in all other types of households the earnings of male workers improve in comparison to earnings of female workers as a result of the simulations. The latter SAM and CGE (Sinha et al. 2003b) distinguished workers by gender and also as home-based and non-home based The data collected by NSSO in 1999–2000 was used for this latter version of the gender model. The factor intensity in the base level is presented in Table 9.5. The table shows that male workers are more intensive in most sectors, except in the agro-processing informal sector, where home-based female workers have a higher share than their male counterparts. The table on factor intensity above shows that female workers are less intensive in all sectors, except in the agro-processing informal sector, which has a higher share of only home-based female workers. Whenever there is a
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Table 9.5 Sector-wise factor intensity distinguished by gender
AGRIC AGROPRF AGROPRIF RGMF RGMIF OTMGF OTMGIF CONST OTSERF OTSERIF PUB
Labour casual female
Labour casual male
Labour home-based female
Labour home-based male
Labour regular female
Labour regular male
0.420 0.214 0.307 0.076 0.168 0.098 0.195 0.111 0.042 0.151 0.005
0.580 0.236 0.422 0.321 0.676 0.360 0.564 0.889 0.135 0.687 0.025
0.000 0.000 0.153 0.000 0.040 0.000 0.083 0.000 0.000 0.027 0.000
0.000 0.000 0.119 0.000 0.116 0.000 0.158 0.000 0.000 0.136 0.000
0.000 0.116 0.000 0.159 0.000 0.039 0.000 0.000 0.178 0.000 0.100
0.000 0.434 0.000 0.444 0.000 0.504 0.000 0.000 0.644 0.000 0.870
Source: Sinha et al. (2003b).
decline in a sector, the demand for more intensive factors of production in that sector contract relatively more than demand for less intensive ones. To analyse the impact of trade policy and certain pro-poor domestic policy changes, two simulations are designed using the CGE model. In this work, only a static version of the CGE model is used, and hence the results are somewhat different than the earlier work (Sinha and Sangeeta 2000). The two simulations used are: a decrease in tariffs in the manufacturing sectors by 50 per cent; and an increase in direct taxation of formal rich households to compensate for the decrease in tariff reduction as in simulation 1. The latter simulation allows government revenue to remain unchanged. In the model, lower tariff and the resultant lower import prices would change the relative demand for domestic goods to imports in each sector. These changes depend on the reduction of tariff rates and the elasticities of substitution. However, given common elasticities for all sectors, the import share and tariff rates will play the main role in variation in sectoral relative prices and hence sectoral production. Moreover, it is obvious that the non-tradable sector would not benefit through tariff reduction. The authors present the findings on average wages by gender. In the case of simulation 1, there is contraction in sectors such as manufacturing as well as in construction, which are more intensive in regular male labour. Employment demand leads to wage rate changes in the case where there is no supply constraint. Hence, in simulation 2 richer households are taxed and the urban non-poor formal households have a higher tax burden. This household category experiences a decline in consumption and hence in welfare. The decline in consumption leads to interaction within sectors, leading to a decline in overall demand and hence in domestic output, and relative remuneration of regular female labour
Gains of Growth for Women and Informal Workers in India 195
rises at a relatively lower rate in simulation 2. However, in both the policy packages as carried out in the simulations, there are overall positive impacts on welfare, as market distortions are removed through trade liberalization. Sinha and Adam (2006) introduced labour market dichotomy and developed a CGE model incorporating workers with informal distinction. The model built in wage rigidity in the formal labour market. This study could examine how trade reforms cause the wages of casual workers to increase with full flexibility in both the labour markets specified in the study (namely formal and informal). However, with wage rigidity in the formal labour market, the results differ. Specifically, when the demand for formal labour declines in these circumstances, some formal labour is laid off. These workers then seek employment in the casual sector, swelling the ranks of casual workers, thus driving down their wages. The study shows that the very bindings that result in greater expansion in the informal sector, namely wage rigidity, cause informal sector wages to contract. This dual labour market model is used to conduct a comparative static analysis of trade reforms in India under a range of assumptions. The assumed trade reform combines over the base year of 1999–2000: (i) the effect of a 60 per cent reduction in import tariffs across the board for all products; and (ii) a corresponding reduction in Quantitative Restriction imposed on trade (QRs). (where they exist). First the simulations are carried out with a labour market that is fully flexible. In this case, the decline in the demand for labour in the formal sector due to a policy shock leads to a decline in formal sector real wages (Table 9.7). At the same time, though demand for casual labour declines in formal sectors, it rises in informal sectors. Hence, the real wages of casual labour rise. The authors introduce labour market rigidity and examine the impact of the same two experiments under the assumption that wages in the formal sector are sticky downwards; these then are simulations 3 and 4. Now, there may be formal incipient unemployment in equilibrium. This unemployment is hidden, however, since unemployed formal sector workers are Table 9.6 Percentage change in average wage rate by skill (real) and welfare BASE LCF LCM LHBF LHBM LRGF LRGM TOTAL Percentage change in welfare measure Source: Sinha et al. (2003a).
1495.3700 3234.4200 808.7500 1970.9000 8443.8400 8865.5200 24818.8000 29.984651
Simulation 01 0.3293 0.2997 0.3095 0.3155 0.3959 0.2985 0.3354 0.0075
Simulation 02 0.1318 0.1730 0.2500 0.3486 0.5560 0.4685 0.4228 0.0030
Casual labour Agriculture Agro processing (formal) Agro processing (informal) Manufacturing goods (formal) Manufacturing goods (informal) Capital goods (formal) Construction (informal) Other services (formal) Other services (informal)
Employment by sector and skill (millions)
Variables
196.5 137.8 0.9 3.2 7.3 9.7 0.3 9.5 2.0 21.2
Base (millions) 0.00 −0.26 −0.53 1.27 −0.45 1.06 −3.43 2.20 −5.53 0.80
Simulation 1 0.00 0.41 1.63 4.12 −1.38 −1.88 −23.56 −5.67 −4.01 1.15
Simulation 2
Tariffs and Quantitative Restrictions (QRs) reduced Tariffs reduced by 60% by 60%
Flexible labour markets
Table 9.7 Employment and real wages (percentage change from baseline)
0.18 0.08 2.17 −27.14 2.91 2.51 2.06 3.52 −1.78 1.86
Simulation 3
Tariffs reduced by 60%
0.24 −2.93 9.03 3.58 13.34 8.23 0.38 7.57 7.63 7.69
Simulation 4
Tariffs and QRs reduced by 60%
Real wage rigidity in formal sector
Source: Sinha and Adam (2006).
Government services (formal) Regular labour Agriculture Agro processing (formal) Agro processing (informal) Manufacturing goods (formal) Manufacturing goods (informal) Capital goods (formal) Construction (informal) Other services (formal) Other services (informal) Government services (formal) Total Total casual labour (formal) Total casual labour (informal) Average consumption wage rate by skill (real) Casual wage labour Regular wage labour 3.39 0.21 −1.96 0.00 0.00 −1.04 0.08
7.5 1.1 15.0 17.1 238.2 15.0 181.5
2.56 −1.17
2.97
1.0
2,632 8,657
0.00 0.00
4.6 41.7
6.76 2.85
0.00 0.00 −1.53 0.12
−0.36
−20.66
2.78
6.68
0.00 0.00
−0.09 1.45
0.00 0.00 1.36 0.08
−3.28
0.51
1.45
0.63
0.00 −0.87
−2.26 8.99
0.00 0.00 8.06 −0.41
−3.48
−9.99
2.12
−1.43
0.00 −1.15
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assumed to join the informal sector. In simulation 3, the demand for regular labour declines, leading to retrenchment of skilled labour at prevailing wages. With nominal wages preserved in the formal sector, the decline in consumer prices results in a rise in real formal sector consumption wages. However, as shown in Table 9.7, the decline in demand for regular labour is reflected in an increase in the supply of informal labour as labour laid off from the formal sector becomes informalized. This increased supply of informal labour serves to drive down average real wages in the informal sector. This result is magnified in simulation 4. Thus, the last two simulations show that, when regular labour market rigidities are in place, the ‘cost’ of wage adjustment is overwhelmingly borne by casual workers (the old and the new entrants to this market). Though tentative, the model constructed delivers a number of important insights, most of which are consistent with the existing literature. The model findings indicate that informalization has been growing with reforms in India. It is important to note here that these findings are supported by the latest report of the National Commission for Enterprises in the Unorganised/Informal Sector (NCEUS), which was made public in August 2007. This report estimated that the total number of informal workers in India for the year 2003–4 had risen by 17 per cent (to a share of 92.6 per cent) over 1999–2000. Moreover, the report submitted that the majority of the informal workers were economically vulnerable, as 75 per cent of self-employed and 90.5 per cent of casual workers within total informal workers have a consumption expenditure roughly between Rs. 9 and 15 per capita per day (for comparison, Rs. 48/40 = 1 USD).
Concluding remarks and future work The CGE analyses clearly show that explicitly incorporating gender differentiation of informal sector and informal workers in future work on economic reforms is critical in understanding growth trajectories in developing countries. As a large section of the workers is being absorbed in informal activities during the process of economic reforms, it is essential to understand how such workers would lose out to or benefit from new policy changes and thereby contribute (or otherwise) to the growth process. Such information could help in designing flanking policies that would help mitigate any negative influence of liberalization on informal workers and hence on the economy itself. The CGE model findings show that the nature of the functioning labour market is highly significant in casualization of the labour force and depressing their wages in the process. However, as a consequence of reforms, nearly all households improve real consumption wages (which themselves reflect the downward pressure on the relative price of imports and import substitutes). The gain is more favourable for regular workers when wage rigidity is introduced in the segmented labour market and for casual workers when there is no
Gains of Growth for Women and Informal Workers in India
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such rigidity. It can be said that, as women function more as casual workers, wage rigidity has a relatively adverse impact on the female labour force. In future research, the social and human development impact of macroeconomic policy must look at how choice sets have been altered and how alterations have affected women and men and the more disadvantaged within these groups. The policy analyses should help in understanding the changes confronted by various types of workers, and ultimately the changes in welfare of both genders. We demonstrate that the analytical tool could examine how public policy changes will directly and also indirectly impact on the welfare of women, men and the more marginalized workers. The differential impact on men and women could be examined, for instance, in the area of health facilities. Moreover, it is important also to recognize the contribution of non-market work in a gender analysis, and this aspect needs to be introduced in a formal modelling framework such as the CGE to better understand the policy impacts on the welfare of women. For example, by improving rural water supplies, the time used by women to fetch water could be significantly reduced by reducing non-market work in the care sector, and this time could be used in other productive or quality activities (learning new skills, and so on). Further, this could enhance the welfare of women by providing them either with market power or with adequate rest and hence better health. Younger women could enhance their vocational skills and/or improve their level of education. Thus, macro quantitative frameworks can be rigorous and make the impact of policies on different groups of sectors and individuals more visible to policymakers in developing countries.
Note 1. The SAM and CGE model specifications can be requested from Anushree Sinha: [email protected].
References Dervis, K., J. de Melo, and S. Robinson (1982). General Equilibrium Models for Development Policy. Cambridge: Cambridge University Press. Devarajan, S., S. Robinson, and J. Lewis (1996). Getting the Model Right: Applied General Equilibrium Modelling. Cambridge: Cambridge University Press. Harriss-White, B., and A. Sinha (eds) (2007). Trade Liberalization and India’s Informal Economy: Macro meets Micro. New Delhi: Oxford University Press. Jung, H.-S., and E. Thorbecke (2001). ‘The Impact of Public Education Expenditure on Human Capital, Growth, and Poverty in Tanzania and Zambia: A General Equilibrium Approach’. IMF Working Paper 01/106. Washington, DC: International Monetary Fund. Khan, H. A. (1982a). ‘Energy, Technology and Income Distribution: A Social Accounting Matrix for Energy Modelling’. Applied Simulation and Modelling. Calgary: ACTA Press.
200 Anushree Sinha and Haider A. Khan Khan, H. A. (1982b). ‘Choice of Technology in the Energy and Textiles Sectors in Korea’. WEP Working Paper. Geneva: World Employment Programme, ILO. Khan, H. A. (1983). ‘Choice of Technology, Energy and Income Distribution: A Macroeconomic Framework’. Ithaca: Cornell University. Mimeo. Khan, H. A., and E. Thorbecke (1988). Macroeconomic Effects and Diffusion of Alternative Technologies Within a Social Accounting Matrix Framework: The Case of Indonesia. Aldershot: Gower. Khan, H. A., and E. Thorbecke (1989). ‘Macroeconomic Effects of Technology Choice: Multiplier and Structural Path Analysis within a SAM Framework’. Journal of Policy Modelling, 11 (1): 131–56. National Commission for Enterprises in the Unorganized Sector (NCEUS) (2007). Report on Conditions of Work and Promotion of Livelihoods in the Unorganised Sector. Delhi: NCEUS, Government of India. NSSO (The National Sample Survey Organisation). National Sample Survey 50th Round (1993–94). Schedule 1.0 on Consumer Expenditure and Schedule 10.0 on Employment and Unemployment. New Delhi: NSSO. NSSO (The National Sample Survey Organisation). National Sample Survey 55th Round (1999–00): Schedule 1.0 on Consumer Expenditure and Schedule 10.0 on Employment and Unemployment. Data processed at NCAER. NSSO (The National Sample Survey Organisation). National Sample Survey 50th Round (1997). ‘Employment and Unemployment in India, 1993–94’. Fifth Quinquennial Survey NSS Fiftieth Round. Report No. 409. New Delhi: NSSO. Pyatt, G., and J. Round (1977). ‘Social Accounting Matrices for Development Planning’. Review of Income and Wealth, 23 (4), 339–64. Pyatt, G., and E. Thorbecke (1976). ‘Planning Techniques for a Better Future’. Geneva: ILO. Sinha, A., and C. Adam (2000). ‘Trade Policy Reforms and the Informal Sector in India’. Paper presented at the 13th International Conference on Input-Output Techniques, Marcerata. Sinha, A., and C. Adam (2006). ‘Reforms and Informalization: What Lies Behind Jobless Growth in India’. In B. Guha-Khasnobis, and R. Kanbur (eds), Informal Labour Markets and Development. Basingstoke: Palgrave Macmillan. Sinha, A., and N. Sangeeta (2000). ‘Macro Economic Analysis: An Attempt in CGE Modelling’. New Delhi: NCAER. Mimeo. Sinha, A., and N. Sangeeta (2003). ‘Gender in Macroeconomic Framework’. In S. Mukhopadhaya, and M. Sudarshan (eds), Tracking Gender Equity under Economic Reforms: Continuity and Change in South Asia. Ottawa: Kali for Women and IDRC. Sinha, A., K. A. Siddiqui, and P. Munjal (2007). ‘A SAM Framework of the Indian Informal Economy’. In B. Harriss-White, and A. Sinha (eds), Trade Liberalisation and India’s Informal Economy. New Delhi: OUP. Sinha, A., N. Sangeeta, and K. A. Siddiqui (2003a). ‘Informal Economy: Gender, Poverty and Households’. In R. Jhabvala, R. M. Sudarshan, and J. Unni (eds), Informal Economy Centrestage – New Structures of Employment. New Delhi: Sage. Sinha, A., K. A. Siddiqui, P. Munjal, and S. Subudhi (2003b). ‘Impact of Globalization on Indian Women Workers: A Study with CGE Analysis’. New Delhi: NCAER. Mimeo. Taylor, L. (1990). ‘Structuralist CGE Models’. In L. Taylor (ed.), Socially Relevant Policy Analysis. Cambridge, MA: MIT Press. Thissen, M. (1998). ‘A Classification of Empirical CGE Modelling’. SOM Research Report 99C01. Groningen: University of Groningen.
Part III Trade and Regional Development
10 Firm Location Choice in Cities: Evidence from China and India Kala Seetharam Sridhar and Guanghua Wan
Introduction It is well known that the economies of China and India have performed remarkably well recently. To some extent, the growth of the global economy is critically dependent on the rate of growth in China and India. These economies are now having major effects on the global economy. Along with their rising economic power, these countries are also forging political alliances between themselves, with other developing countries and with Europe. They are increasingly active and vocal on the world stage, especially in the context of trade with the developed countries. It is easy to assess that the rise of China and India will eventually have profound implications for international governance, and developments in other countries, not only in the economic sphere, but in other spheres as well. It is instructive to note the significance and contribution of the private sector to capital accumulation, employment and industrial growth in India and China. The private sector in China has grown exponentially in the past two decades and now contributes almost as much to the economy as the state sector. Initially allowed only on the fringes of the economy, the private sector now accounts for about a third of gross domestic product and is officially recognized as an important component of the economy. We concur with IFC (2000), which recognizes that there are rather limited statistics on the private sector in China. In India, according to the Central Statistical Organization, aggregate employment in the private large industries increased by an annual compound rate of 0.9 per cent from 1993–4 to 2001–2, whereas it declined by 0.4 per cent in the public sector (Sridhar 2005). The private sector’s share in the country’s organized sector employment increased from 25.6 to 27.6 per cent over this period, whereas the public sector’s share declined from 71 to 69 per cent in 2001–2. While the larger share of employment growth in India’s private sector was accounted for by the service industry (finance, real estate and insurance), India’s manufacturing sector also witnessed increased 203
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employment growth during the period 1993–4 to 2001–2, at an annual rate of 0.6 per cent, raising its share in total employment from 16.9 to 17.9 per cent. In contrast, employment in public sector manufacturing fell by 3.5 per cent annually during the same period. In both China and India, the industrial sector constitutes a large component of the national GDP, ranging from 28 per cent (India) to 46 per cent (China). Given the significance of industrial growth in these economies and their relevance for the global economy, it is critical to understand what factors determine locational choice of firms within these countries.
Background and research objective It is worth noting the importance of urbanization in China and India. Thirty per cent of India’s and 37 per cent of China’s population was urbanized as of 2001. For China, the urbanization rate is 10 percentage points lower than its industrialization level. At present, cities in China account for 70 per cent of the nation’s GDP, and the corresponding contribution by cities in India is about 50 per cent, which is expected to reach 65 per cent by 2011. Overall, there are 667 cities in China (Anderson and Ge 2005), and India has over 5,000 cities and towns as of 2001. So the scale of urbanization in China and India is too large to be ignored. In both these countries, major reforms and changes have taken place, which are bound to affect their urban areas significantly. Wu et al. (2007) argue how the market has been created in post-reform (post-1978) cities of China, challenging state regulation with respect to land and housing development. In India, too, the landmark 74th Constitutional Amendment Act of 1992 recognized cities, or urban local bodies, as the third tier of government. Given the above, a careful examination of locational choice of firms in cities of these countries can help highlight specific problem areas of the business environment, which has direct impact on the sustainability of their economic growth. While location of domestic firms and that of multinational corporations (MNCs) are driven by different considerations, the preconditions necessary for location of domestic firms would represent the bottom line for MNC. This provides the motivation to model location choice of firms in these countries. To be more specific, in this chapter we attempt to explore determinants of locational choice of firms between large millionplus cities and other smaller cities. Towards this objective, we model the locational choice of firms in cities in each of these countries using large data sets – Investment Climate Surveys (ICS) – of firms surveyed by the World Bank. Such a comparative analysis may uncover problems and lessons that China and India can learn from each other, as well as providing lessons for other developing economies to promote industrial growth. Further, it is possible that factors that influence firm location decisions are different in
Firm Location Choice in Cities
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these countries. A comparison of these differences, particularly in terms of city-level economic geography factors, has important implications for urban policies to attract new firms and for the sustainability of industrial growth in these countries.
Literature review The earliest explanation of the existence of cities and industry is provided by Lösch’s (1954) central place theory. As summarized by Parr (2002), the theory assumes that firms locate in such a way as to maximize profits. Krugman (1995) extended the central place explanation to include market size, agglomeration, and localization economies. In addition to such market-based factors, policy-related factors such as favouritism towards certain regions can also explain location of industry (Markusen 1999). Henderson et al. (2000) offer a review on agglomeration. Recent theory on international trade predicts that increased globalization is associated with increased locational concentration of particular economic activities, and hence increased specialization of national and regional economies. Resmini (2003) finds that border regions in Central and Eastern Europe did indeed increase their shares of national employment and had been changing their patterns of specialization, the determinants of these relocation processes being foreign direct investment (FDI) and proximity to the EU. However, Forslid and Wooton (2003) provide a counter-example to this familiar result. Egger and Falkinger (2003) present a model in which final goods producers outsource intermediate input production. They find that national public infrastructure investment has a positive effect on both the number of intermediate input producers and the return to the immobile factor in the home country, which is consistent with other literature. There are a number of empirical studies on firm locations in China and India. Head and Ries (1996) find that in post-liberalization China foreign firms located in cities where other foreign firms had located earlier, after controlling for fiscal incentives and infrastructure, highlighting the importance of agglomeration economies. Sridhar (2005), based on an anecdotal survey of India’s firms, argues that infrastructure is an important determinant of firm location in the growth centres of India. Without the infrastructure (power, telecom, roads and banking), many firms (even some representing local entrepreneurship) would not have located there. This is consistent with Rajaraman et al. (1999), who reported that abundant power was an important factor attracting investment into a major Indian state during the 1980s. According to Mani et al. (1996), power availability (rather than its price), reliable infrastructure and factors of production played a significant role in firm location decisions across major Indian states. Tulasidhar and Rao’s (1986) analysis of a large number of medium- and large-scale industries in an Indian state indicated that the sales tax incentive, whichever
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way designed, was not the appropriate instrument to raise the level of investment or spread this to backward areas. Lall et al. (2003) analyse the influence of economic geography on the cost structure of manufacturing firms in India. They find that industrial diversity is the only economic geography variable that has a significant, consistent and substantial cost-reducing effect, particularly for small firms. This finding calls into question the fundamental assumptions regarding localization economies, and raises further concerns over the industrial development prospects of lagging regions in developing countries. Most recently, Sridhar (2006a) examines locational choice of firms using the ICS, concluding that India’s highly efficient firms are much more adaptable and efficient than their Chinese counterparts. Sridhar (2006b, 2007) finds that Indian firms tend to follow people, and hence supply of labour could be the constraint in reducing unemployment rates in developing countries. A study by the World Bank and Confederation of Indian Industry (2002) examines investment climate and the bottlenecks that deter private investment and productivity growth in India, drawing upon the World Bank’s Firm Analysis and Competitiveness Surveys (FACS).1 It states that, while China and India are both low-inflation countries, interest expenses occupy a higher share of costs for Indian firms (12.3 per cent, relative to 5.9 per cent for Chinese firms). Meanwhile, freight as a percentage of traffic units is a mere 5 per cent in India compared with 79 per cent in China, highlighting the much poorer utilization of freight infrastructure in India. As may be clear from the above literature survey, firm-level studies and city-level data regarding firm locations are sparse. One such study is by Byrnes et al. (1999) on the USA. Another is by Sridhar (2006a) on locational choice of firms using the ICS, for India and China. This is surprising, as firm-level data are authentic for assessing investment climate or highlighting crucial aspects of city-level governance and policies, which can thwart or promote their ability as engines of national economic growth in these countries. This motivates the modelling and comparison of firm location in urban areas across China and India. This chapter contributes to the industrial location literature and examines why industry locates or refrains from locating in large urban areas, using invaluable firm-level data across three fast-growing economies of the world. This enables comparison of the factors that determine firm location across the two countries in the context of a common framework, which has important implications for urban and industrial policy.
Model and data Model One technique to gather empirical evidence on investment climate is primary surveys of firms (Bartik 1991). Sridhar (2003) takes this approach.
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Another technique is empirical modelling of firm location choices. Given the invaluable firm-level data available in the ICS, we use the latter approach in this chapter. Let i index firm and j index city size where the firm is located. Our model, a multinomial choice equation, can be written as: Lij f (Gij, Iij, Bij, Fij, Eij) (j 1, 2, or 3)
(1)
where Lij represents the location choice of the i-th firm in city size j (j 1, 2, and 3 corresponding to large, medium and small city, respectively). Gij represents firm-level economic geography variables, Iij refers to infrastructure and services in city size j, and Bij represents factors governing business–government relationships at the firm level. Fij represents firm-level characteristics such as size, labour intensity, post-reform and export status. Eij represents the efficiency of the ith firm located in city size j. While information on economic geography factors such as proximity to market and agglomeration are available for China’s firms at the city level in the ICS, they are not available for India’s firms at such a disaggregated level (they are available at the state level). In the interests of model consistency we use proximity to inputs for firms, an indicator directly available from ICS, as our measure of economic geography (Gij) in all countries. The expectation is that the greater the extent to which inputs are accessible in the city or state, the higher would be the odds of firms locating there. Iij represents city-level characteristics such as electricity cost, existence of paved roads and telephone lines. While the ICS contains data on these variables, they are available only for the current year, making them endogenous with firm location choice. Thus, we use a dummy for a capital city as a proxy for these city-level characteristics. It is widely understood that capital cities attract firms for various services and agglomeration economies, notably infrastructure of national/international standards such as highways, mass transport, high tele-density, international airport, relatively large international community and culture, and world-renowned cultural institutions and universities. As argued by Henderson et al. (2000), national governments’ favouritism frequently allocates local public services in favour of national capitals, where decision-makers live. As a measure of Bij, we use the optimum employment as a proportion of the firm’s existing labour force to indicate the extent of labour regulation. As is known, regulation affects the location and functioning of businesses. Panagariya (2006), for instance, argues that labour market rigidities are one reason why India lags behind China. Specifically, the greater the extent of restrictions on labour hire and fire, the lower would be the likelihood of firm location. As measured in the ICS, a value of Bij greater than 100 indicates that a firm would have liked to expand if there had been no restrictions on labour recruitment. The reverse holds if Bij is less than 100.
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Other firm characteristics (Fij) include labour intensity of firm, firm size and exporting status, proportion of government or private ownership, and a dummy indicating year of its establishment. Firm size is represented by its number of employees, as is conventionally done in Glancey (1998), Hall (1986), Kim et al. (2004) and Kumar et al. (1999). While we expect exporting firms to locate in large cities (for the ease of logistics and availability of infrastructure), and larger firms to locate in larger cities, the impact of labour intensity on firm location cannot be determined a priori. This would depend on the relative advantages of the availability of skills and a labour pool in cities of different countries, and matching skill availability with requirement. For instance, different types of skills are required for steel firms than those required by water treatment plants.2 If there is a good match of skill availability with skills required by a firm, then labour intensity will have a positive impact on firm location. If not, the impact would be negative. It is important to control for a firm’s ownership structure. This is because government-owned firms might be required by law or policy to locate in cities of a certain size or certain regions (considered industrially backward). Sridhar (2006b) discusses one such policy initiative in India, in which firms that locate in industrially backward regions are provided with infrastructure incentives. As argued by Dollar et al. (2004) and Lall and Mengistae (2005), inherently more efficient firms could tolerate more inhospitable business environments (for example, restrictive labour regulations) or more adverse geography (for example, difficult access to inputs) and locate in such cities. Therefore, it is appropriate to control for firm efficiency in the firm location choice model. We measure efficiency by the net sale value (total revenue from sales minus total production costs) as a proportion of total costs (consisting of labour, material, energy and other costs). To ensure its exogeneity, this variable is lagged by one year before being included in equation (1). Finally, given sweeping economic reforms that have taken place in each of these countries, a dummy for post-reform establishment of firms is included to account for the impact of nationwide reforms on the investment climate in cities of different sizes. Data The World Bank’s ICS covers more than 1,000 firms in over 50 countries, providing data on many aspects of conducting business. Two types of data set exist for each survey: the country data set and the core data set. The former consists of all variables for each country while the latter contains questions asked in all countries. For purposes of comparison, we use the core data sets in this chapter. Data are collected from China in 2002 and 2003, and from India in 2000, 2002 and 2005. The business establishment rather than the firm per se is the sampling unit. Each country survey covers a minimum number of sectors that are
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common to all or most countries. Within each country, major export and/ or growth industries are adequately represented. Due to budget considerations, coverage in the ICS is limited to areas of minimum concentration of activity. Probability sampling rules are used within each stratum defined by locations and sectors of activity chosen on the basis of those criteria. The ICS contains data on infrastructure and services, business–government relations, labour relations and firm-level characteristics. The firm-level information on government–business relations pertains to restrictions on recruitment and retrenchment of workforce. For some countries, such as China, the ICS provides information on city-level economic geography factors such as proximity to inputs and markets, and the existence or otherwise of competitors. For each establishment, the ICS records city of location, year of establishment, exporting status, ownership structure, major constraints on successful operation, and other characteristics. It is noted that the location of a firm is a stock variable. That is, firms locate in these cities at a single point in time. Even in the case of India and China, where the survey was conducted for several years, the same firms were not followed. So it is not possible to track the mobility of footloose firms. Table 10.1 describes the size distribution of cities in China and India. It was not possible to use the same classification of city sizes in our models Table 10.1 Size distribution of cities in China and India Size of city
Number
China Population over 1 million Population 500,000–1 million Population <500,000
164 273 230
All cities
667
India Population >5 million Population 1–5 million Population >100,000 Population 50,000–99,999 Population 20,000–49,999 Population 10,000–19,999 Population 5,000–9,999
6 29 379 503 1,391 1,558 1,040
All cities*
5,161
Note: *This category includes cities with population <5,000 in addition to those in the city sizes mentioned above. Source: Anderson and Ge (2005) for China, data current as of 1999; Census of India, 2001 for India.
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Table 10.2 Location of surveyed firms, by city size Number of firms and percentage distribution
Population of city Less than 1 million 1–2 million More than 2 million All firms
India 2000, 2002 and 2005
China 2002 and 2003
1,167 (23%) 1,222 (24%) 2,613 (52%)
200 (5%) 700 (18%) 3,048 (77%)
5,002 (100%)
3,948 (100%)
Note: The total number of firms surveyed is 5,008 for India and 3,948 for China. Not all firms are included in the table as some industry categories are omitted to ensure comparability across countries. Source: World Bank (2002), and authors’ computations.
for both countries, given that firm distribution across city sizes in the two countries was quite varied. Based on Table 10.1, India had 35 cities with million-plus population in 2001, and China several times more. Given this size distribution of cities, the location choice of firms among the ‘million plus’ and other large cities is intriguing. Table 10.2 describes the location of surveyed firms in China and India. A majority of firms in India (52 per cent) and in China (77 per cent) were located in cities with population of 2 million or more. Comparisons of the profile of firms across countries Overall, 5,008 firms in India and 3,948 firms in China were surveyed by the ICS over different years. In India, almost all the surveyed firms (99 per cent) were manufacturers. In the case of China, roughly 29 per cent of firms were in services (accounting, marketing, advertising and business services), with the remaining being in manufacturing. Table 10.3 shows the sectoral distribution of the surveyed firms within manufacturing and suggests that a majority of China’s firms are high-tech (that is, in electronics manufacturing), when compared with firms in India. As far as employment size is concerned, India is characterized by a large number of small firms, whereas most of China’s firms are medium, big or very big. On average, the number of employees was 568 for China’s firms, and 156 for India. Based on this, it does seem that in China the small town and village enterprises (TVEs) are excluded from the ICS and our data set.3 Regarding ownership, almost all the firms in India were privately owned, whereas in the case of China, on average, government ownership was about 22 per cent. Nearly 47 per cent of firms were exporters in India. This percentage is only about 23 per cent for China.
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Table 10.3 Sectoral distribution of surveyed firms Sector Textiles Leather** Garments** Food Metals and machinery Electronics Chemicals and pharmaceuticals Wood and furniture Auto and auto components All firms
India (2000, 2002, 2005)
China (2002, 2003)
707 (14.12%) 139 (2.78%) 783 (15.63%) 330 (6.59%) 661 (13.20%) 316 (6.31%) 603 (12.04%) 16 (0.32%) 472 (9.42%) 5,008*
NA 465 (11.78%) NA 290 (7.35%) 178 (5.27%) 953 (24.14%) 102 (2.58%) NA 431 (10.92%) 3,948*
Notes: * The number of firms may not total to 5,008 or 3,948 as some industry categories have been omitted in the interests of comparability across countries. ** In the case of China, garments and leather products are combined. Source: World Bank (2002), and authors’ computations.
The year of establishment or initial operation for a typical firm surveyed is 1985 for India’s firms, and much more recent for those in China, being 1987. Thus, a large proportion (81 per cent) of surveyed firms started operations in the post-reform (that is, post-1978) period in China, whereas only 40 per cent of India’s firms (that is, post-1991) were post-reform firms. Speaking of the importance of capital cities emphasized by Henderson et al. (2000), we observed from the data that 87 per cent of China’s surveyed firms were located in capital cities, whereas only 44 per cent of India’s firms were located in capital cities. In terms of labour intensity, 22 per cent each of the total costs of production in Indian and China’s firms was on labour. Input linkages In the ICS, information exists on economic geography factors for firms such as availability of inputs and proximity to markets. In the case of China, there exists more information on input linkages. For example, in terms of average expenditure, 31 per cent of suppliers of Chinese firms were located outside the same district, but within the same city. In the case of India, proximity to input suppliers is measured only at the level of state, not at the city level as in China. Almost 50 per cent of surveyed Indian firms were located in the same state where they obtained their supplies of key natural resources, such as raw materials. It appears that economic geography factors measured by proximity to inputs do play an important role in firm location in both countries. Labour regulation On average, the optimal level of employment as a proportion of the current workforce is below 100 in both countries. This implies that these
212 Kala Seetharam Sridhar and Guanghua Wan
firms would have liked to retrench some of their employees if restrictions had not been imposed on compensation, pension and so forth. To be more precise, this optimal level of employment as a proportion of the existing workforce was respectively 91 and 95 for firms in China and India. Clearly, labour force regulations are more restrictive in China than in India, as Chinese firms would have liked to retrench nearly 9 per cent of their workforces if restrictive policies had not been in place. Clearly, firm location decisions are expected to be negatively impacted by such regulation. Firm-level efficiency In this chapter, efficiency is indicated by firms’ net value of sales as a proportion of their total costs. This is an output-based measure of efficiency. An input-based approach could focus on capacity utilization, defining firms as being efficient if they utilized a large proportion of their capacity in terms of equipment and labour shifts. However, such firms need not be efficient in their turnover, production, and sales as a proportion of their costs of production. Yet another approach is to take advantage of the frontier functions or data envelopment analysis (Chapelle and Plane 2005; Goaïed and Ayed-Mouelhi 2000).
Location choice of firms In the following, we will discuss city classification and model estimation results for China and India. Location choice of firms in China All cities in which surveyed ICS firms are located will be classified into three groups: large, medium and small. For China, small cities are defined as those with less than 2 million population, mid-sized cities with population between 2 and 5 million, and large cities with population greater than 5 million.4 The classifications were defined after examining the distribution of firms across cities to ensure roughly equal distribution of firms among the three city groups. Model (1) is fitted to data from China. Table 10.4 summarizes the multinomial logit estimation results. Several findings are noteworthy. First, postreform firms are more likely to locate in larger (medium and large) cities. This is possibly attributable to the fact that markets are mostly created in major cities in post-reform China (Wu et al. 2007). Also, large cities have become the engines of growth, with better infrastructure such as national highways, mass transport, and well developed ports. The result is consistent with the significant coefficient estimate for the dummy variable of capital cities, indicating high odds for firms to locate in capital cities. This is particularly
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Table 10.4 Multinomial estimates of locational choice of firms in China Medium city Variable Constant Whether firm established in post-reform period (1 = yes; 0 = no) State ownership of firms (%) Whether capital city (1 = yes; 0 = no) Proximity to inputs (% of suppliers located in the same city) Firm size (average number of employees previous year) Whether exporter (1 = yes; 0 = no) Labour intensity of firms Optimum employment as a size of existing labour force Firm efficiency
Large city
Estimate
Z value
Estimate
Z value
−1.33 (0.25)*** 0.55 (0.15)***
−5.23 3.79
−1.78 (0.29)*** 0.74 (0.15)***
−6.05 4.91
0.00 (0.00) 2.18 (0.15)***
0.14 14.57
−0.00 (0.00) 2.96 (0.18)***
−1.60 16.50
0.01 (0.00)***
2.60
0.01 (0.00)***
4.56
0.00 (0.00)
0.79
0.00 (0.00)
1.34
0.44 (0.15)***
2.99
1.08 (0.15)***
7.40
−0.73 (0.25)*** −0.00 (0.00)
−2.91 −1.71
−1.19 (0.26)*** −0.01 (0.00)***
–4.54 3.87
0.01 (0.00)***
2.49
0.01 (0.00)***
2.52
Notes: Dependent variable: whether located in a medium or large city. Number of observations = 3,142. Base outcome: whether firm located in a small city. Standard errors in parentheses. *** indicates statistically significant at the 1% level. Source: World Bank (2002), and authors’ computations.
true for medium and big firms. It is noted that the capital dummy is used to capture effects of public services and infrastructure. Second, the variable reflecting proximity to inputs is found to be significant. In other words, firms with a greater share of suppliers from the same city are likely to locate in medium and large cities. This confirms the importance of geographical factors in determining firm location decisions in China. Third, the probability is significantly higher for exporting firms to locate in medium and large cities. This is reasonable because exporting firms require good transport and logistic infrastructure, which is better in larger cities. Fourth, labour intensity has a significant and negative impact on the likelihood that a firm will locate in a medium or large city. This is not surprising because wages, training costs and attrition rates are higher in large cities, and employees in large cities tend to be more aware of labour regulations and their rights than those in small cities.
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Fifth, the optimum employment as a proportion of a firm’s existing workforce is used as an indicator of restrictive labour regulation. This factor exerts a significant and negative impact on the probability that a firm will locate in a large city, but no significant impact on firm location in medium-sized cities. Thus, the more a firm plans to recruit, the more likely it is to move away from very large cities, implying more restrictive labour regulation and enforcement in large cities in China. This finding is also in line with the negative coefficient of the labour intensity variable. Finally, more efficient firms are found to prefer large- and medium-sized cities, perhaps for larger markets and good infrastructure. This is contrary to earlier speculation that these firms can overcome an inhospitable business environment. Rather, they continue to seek a favourable business climate to locate. Location choice of firms in India As with China, the cities under study are classified into three groups by ensuring roughly equal distribution of firms across the three groups. Small cities are defined as those with less than 1 million population, mid-sized cities with 1–3 million population, and large cities with population over 3 million. Table 10.5 summarizes the multinomial log odd estimates of a firm locating in a medium or large city in India. India and China share a few similarities and differences as far as the results are concerned. The differences are in the impacts of year of establishment and proximity to inputs. Post-reform firms in India are averse to locating in medium or large cities. This is likely because of the higher costs of doing business (real estate, skills attrition, and so on) in larger cities in post-reform India (see Sridhar 2006b). It seems that larger cities in post-reform China have done a better job in attracting firms when compared with their Indian counterparts. Another difference from China is that such firms in India are less likely to locate in a larger city even if the state has key raw materials as production inputs. For medium cities, the availability of inputs in the state does not have a significant impact on location choice. This could be an artefact of the data, since city-level information on proximity to inputs is unavailable in the ICS in the case of India’s firms. Since a state represents a much larger geographical area than a city, the mere availability of inputs, if in a remote or distant part of the state, does not ensure a firm’s location in the city. Even if raw materials and other inputs were to be readily accessible within a state, it could well be the case that the city has some offsetting locational disadvantages. A final difference from China is that big firms in India are likely to choose large cities. This is likely due to the ready availability of skills, infrastructure and other support networks in large cities in India.
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Table 10.5 Multinomial estimates of locational choice of firms in India Medium city Variable Constant Whether firm established in post-reform period (1 = yes; 0 = no) Firm’s private ownership (%) Whether capital city (1 = yes; 0 = no) Proximity to inputs (whether state has raw materials firm uses as inputs) Firm size (average number of employees previous year) Whether exporter firm (1 = yes; 0 = no) Labour intensity of firm Optimum employment as a size of existing labour force Firm efficiency
Estimate
Z value
Large city Estimate
Z value
1.40 (0.44)*** −0.22 (0.09)***
3.16 −2.53
−0.77 (0.62) −0.38 (0.12)***
−1.25 −3.27
−0.01 (0.00) 0.76 (0.13)***
1.45 5.73
0.01 (0.01) 3.68 (0.14)***
1.27 26.47
−0.11 (0.09)
−1.33
−0.33 (0.11)***
−2.88
−0.00 (0.00)
−1.38
0.00 (0.00)***
4.30
0.11 (0.09)
1.20
0.24 (0.12)
1.94
−0.76 (0.21)*** −0.00 (0.00)
−3.58 −1.45
−0.62 (0.27)** −0.01 (0.00)***
−2.29 −3.81
0.00 (0.62)***
3.11
0.00 (0.00)***
3.11
Notes: Dependent variable: whether located in a medium or large city. Number of observations = 3,363. Base outcome: whether firm located in a small city. Standard errors in parentheses. *** statistically significant at the 1% level; ** statistically significant at the 5% level. Source: World Bank (2002), and authors’ computations.
Other findings are similar to what we find for China. These include: 1. More labour-intensive firms tend to refrain from locating in mediumsized cities relative to smaller cities in India, possibly because labour cost has increased to a high level in those cities. A firm’s labour intensity is a significant factor deterring firms from locating in the large cities of India as well. This indicates that labour costs must be a significant factor in determining firm location choices. In other words, labour-intensive firms are attracted to small cities for their low costs of training and low attrition rates. 2. Indian firms find capital cities attractive. This reinforces the idea that public investments are biased in favour of capitals where policymakers live (Henderson et al. 2000). 3. Exporting firms prefer larger cities because the product value chain is better integrated in larger than in smaller cities. 4. The labour regulation indicator has a significant and negative impact on the odds of a firm locating in a large city in India. This is consistent with
216 Kala Seetharam Sridhar and Guanghua Wan
Besley and Burgess (2004) and Lall and Mengistae (2005), both showing that regional differences in labour regulation are part of the reason why some of India’s states have lagged behind in industrial development. 5. Firm efficiency has a significant positive impact on the log odds of a firm locating in the large cities of India. This implies that more efficient firms prefer to locate in medium and large cities, a fact concurring well with reality, which again dispels the hypothesis of efficient firms being able to overcome inhospitable environments.
Summary and policy implications It is important to identify factors that determine firm location in cities of China and India, as economic growth and its sustainability in these countries depend to a significant degree on urban development. Firm locations can have long-lasting impacts on the sustainability of employment they create in cities, due to their hysteresis effects.5 Further, location of domestic firms provides strong signals to prospect MNCs, showcasing the area as a good place for businesses, their employees and households to locate. Fitting multinomial logit models to ICS data from China and India, we find that capital cities in all countries are attractive for firms to locate. In India and China, labour-intensive firms tend not to locate in medium or large cities, perhaps due to higher wage, training and attrition costs. In both China and India, labour regulations deter firms from locating in the larger cities and exporter firms prefer to locate in large cities. While the size of a firm has no impact on its location decision in China, in India big firms prefer to locate in large rather than mid-sized cities. Regarding economic geography factors, proximity to inputs within the city has a positive impact on firm location. However, availability of raw materials in the same state does not have a significant impact on firm location in India. Firms established after 1978 in China tend to locate in large cities, whereas post-1991 firms in India refrain from locating in the mid-sized or large cities. This vividly indicates that, in post-1991 India, large cities have not been successful in creating a pro-business environment. They are faced with many infrastructure constraints, as Sridhar and Mathur (2009) highlight. However, municipal governments in post-reform China have formulated various competitive strategies to promote business development, such as reconstruction of city centres, creating university towns, science parks and ‘urban villages’ (Wu et al. 2007). Although special economic zones are initiated in India to enable firms to overcome bureaucratic delays and regulation, they are currently mired in controversies of land acquisition. The major findings uncovered in this chapter have important policy implications for urban development. First, there is little need for capital cities to package various incentives for firms to locate, since firms are attracted to
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these cities due to their amenities, agglomeration effects and political clout. Second, economic geography factors matter at the city level. For example, if an iron ore mine exists around a city, it makes sense for the city/local government to attract a car/tractor/aircraft or related equipment manufacturing plant, not a software or high-tech firm. In such instances of forward or backward integration, it is unnecessary to offer extra incentives to the firm, other things being equal. Third, efforts are needed for large cities of China and India to increase the supply of skilled labour so as to narrow down the gap in labour cost between large and small cities. Meanwhile, labour regulation must be reformed to make hiring and firing easier. Finally, small cities in China and India need to do more to attract efficient firms, perhaps by providing them with infrastructure incentives (Sridhar 2006b). After all, small cities face limited market size and often mean higher transportation costs, even to efficient firms.
Notes 1. All countries in China and India are now covered under the PICS (Productivity and Investment Climate Surveys) project, a new World Bank initiative that combines surveys carried out since the mid-1990s under the Firm Analysis and Competitiveness Surveys (FACS), the World Business Environment Surveys (WBES) and the Regional Program for Enterprise Development (RPED). 2. While the type of skills required would be different for manufacturing and service firms as well, only manufacturing firms are covered by the ICS in the set of core surveys used here. 3. Thus it should be noted that the Chinese sample excludes small and even medium firms, most of which are private. One reason why firm size is larger in China than in India is that only large cities are included in the Chinese sample. Very few prefectural and county-level cities, where most firms locate, were selected by the ICS in China. This shows that both small cities and small and medium enterprises (SMEs) are under-represented in the Chinese sample. 4. The list of cities in which ICS firms are located is available from the authors. 5. Hysteresis is a term borrowed from physics to explain how the electromagnetic properties of certain materials are completely changed due to the temporary application of certain magnetic fields, and is frequently applied to the labour market.
References Anderson, G., and Y. Ge (2005). ‘The Size Distribution of Chinese Cities’. Regional Science and Urban Economics, 35 (6): 756–76. Bartik, T. J. (1991). Who Benefits From State and Local Economic Development Policies?. Kalamazoo, MI: W. E. Upjohn Institute for Employment Research. Besley, T., and R. Burgess (2004). ‘Can Labour Regulation Hinder Economic Performance? Evidence from India’. Quarterly Journal of Economics, 119 (1): 91–134. Byrnes, P., M. Marvel, and K. Sridhar (1999). ‘An Equilibrium Model of Tax Abatements: City and Firm Characteristics as Determinants of Abatement Generosity’. Urban Affairs Review, 34 (6): 805–19.
218 Kala Seetharam Sridhar and Guanghua Wan Chapelle, K., and P. Plane (2005). ‘Technical Efficiency Measurement within the Manufacturing Sector in Côte d’Ivoire: A Stochastic Frontier Approach’. Journal of Development Studies, 41 (7): 1303–24. Dollar, D., M. Hallward-Driemeier, and T. Mengistae (2004). ‘Investment Climate and Firm Performance: in Developing Economies’. Washington, DC: World Bank. Mimeo. Egger, H., and J. Falkinger (2003). ‘The Role of Public Infrastructure for Firm Location and International Outsourcing’. CESifo Working Paper 970. Munich: CESifo. Forslid, R., and I. Wooton (2003). ‘Comparative Advantage and the Location of Production’. Review of International Economics, 11 (4): 588–603. Glancey, K. (1998). ‘Determinants of Growth and Profitability in Small Entrepreneurial Firms’. International Journal of Entrepreneurial Behaviour and Research, 4 (1): 18–27. Goaïed, M., and R. B. Ayed-Mouelhi (2000). ‘Efficiency Measurement with Unbalanced Panel Data: Evidence from Tunisian Textile, Clothing and Leather Industries’. Journal of Productivity Analysis, 13 (3): 249–62. Hall, B. H. (1986). ‘The Relationship between Firm Size and Firm Growth in the US Manufacturing Sector’. NBER Working Paper 1965. Cambridge, MA: National Bureau of Economic Research. Head, K., and J. Ries (1996). ‘Inter-city Competition for Foreign Investment: Static and Dynamic Effects of China’s Incentive Areas’. Journal of Urban Economics, 40 (1): 38–60. Henderson, V. J., Z. Shalizi, and A. J. Venables (2000). ‘Geography and Development’. Journal of Economic Geography, 1 (1): 81–105. IFC (International Financial Corporation) (2000). China’s Emerging Private Enterprises: Prospects for the New Century. Washington, DC: IFC. Kim, J., S. J. Lee, and G. Marschke (2004). ‘Research Scientist Productivity and Firm Size: Evidence from Panel Data on Inventors’. SUNY Discussion Paper 04–06. Albany: Department of Economics, University at Albany. Available at: http://ideas. repec.org/p/iek/wpaper/0708.html.(accessed 12 march 2010) Krugman, P. (1995). Development, Geography, and Economic Theory. Cambridge, MA: MIT Press. Kumar, K., R. Rajan, and L. Zingales (1999). ‘What Determines Firm Size?’. NBER Working Paper 7208. Cambridge, MA: National Bureau of Economic Research. Lall, S. V., and T. Mengistae (2005). ‘The Impact of Business Environment and Economic Geography on Plant-Level Productivity: An Analysis of Indian Industry’. WB Working Paper S3664. Washington, DC: World Bank. Lall, S. V., J. Koo, and S. Chakravorty (2003). ‘Diversity Matters: The Economic Geography of Industry Location in India’. WB Policy Research Working Paper 3072. Washington, DC: World Bank. Lösch, A. (1954). The Economics of Location, 2nd edn, trans. W. H. Woglom with the assistance of W. F. Stolper. New Haven: CT. Mani, M., S. Pargal, and M. Huq (1996). ‘Does Environmental Regulation Matter? Determinants of the Location of New Manufacturing Plants in India in 1994’. WB Working Paper 1718. Washington, DC: World Bank. Markusen, A. R. (1999). ‘National Contexts and the Emergence of Second-Tier Cities’. In A. R. Markusen, Y.-S. Lee, and S. DiGiovanna (eds), Second-Tier Cities: Rapid Growth Beyond the Metropolis. Minneapolis, MN: University of Minnesota Press. Panagariya, A. (2006). ‘Trade and Foreign Investment: Comparing India and China’. Paper presented at the Pan Asia Conference, Stanford University, 1–3 June. Palo Alto.
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Parr, J. B. (2002). ‘The Location Of Economic Activity: Central Place Theory and the Wider Urban System’. In P. McCann (ed.), Industrial Location Economics. Cheltenham: Edward Elgar. Rajaraman, I., H. Mukhopadhyay, and N. Bhatia (1999). ‘Fiscal Industrial Incentives of the Government of Madhya Pradesh: Costs and Benefits’. New Delhi: NIPFP. Mimeo. Resmini, L. (2003). ‘Economic Integration and Regional Patterns of Industry Location in Transition Countries’. Paper presented at the 43rd ERSA European Conference, 27–30 August. Jyväskylä. Sridhar, K. S. (2003). ‘Firm Location Decisions and Impact on Local Economies’. Economic and Political Weekly, 38 (39): 4121–30. Sridhar, K. S. (2005). Incentives for Regional Development: Competition among SubNational Governments. Basingstoke: Palgrave Macmillan. Sridhar, K. S. (2006a). ‘Location Choice of firms: Recent Evidence from India and China’. Paper presented at workshop on The Rapid Development of China and India: Domestic and International Consequences, 31 March – 1 April. Antwerp. Sridhar, K. S. (2006b). ‘Local Employment Impact of Growth Centres: Evidence from India’. Urban Studies, 43 (13): 2585–98. Sridhar, K. S. (2007). ‘Density Gradients and Their Determinants: Evidence from India’. Regional Science and Urban Economics, 37 (3): 314–44. Sridhar, K. S., and O. P. Mathur (2009). Costs and Challenges of Local Urban Services: Evidence from India’s Cities. New Delhi: Oxford University Press. Tulasidhar, V. B., and M. G. Rao (1986). ‘Cost and Efficacy of Fiscal Incentives: The Case of Sales Tax Subsidy’. Economic and Political Weekly, 21 (41): 1799–806. World Bank (2002). ICS – Investment Climate Surveys. Washington, DC: The World Bank Group (for the PICS surveys). World Bank and CII (2002). Improving the Investment Climate in India. Washington, DC: World Bank in collaboration with the Confederation of Indian Industry (CII). Wu, F., J. Xu, and A. G. Yeh (2007). Urban Development in Post-Reform China: State, Market, Space. New York: Routledge.
11 China’s Global Growth and Latin American Exports Rhys Jenkins
Introduction The spectacular growth of China and its increased openness since the beginning of the economic reforms in the late 1970s have led to it becoming a major player in the global economy. It is now the world’s fourth largest economy in terms of GDP and the third largest in terms of merchandise trade. Between 1995 and 2005, real GDP grew at 9 per cent and the volume of exports increased by 19 per cent per annum (WTO 2007). Although China’s import growth (16 per cent per annum between 1995 and 2005) has lagged behind that of its exports, and it has run a large trade surplus in recent years, it has provided a growing market for exporters and contributed to a price boom for primary commodity exporters. This has helped boost growth in a number of developing countries in recent years. However, China’s global expansion has also led to concerns amongst other developing country exporters that they will be displaced by Chinese competition in their export markets. These concerns were intensified by China’s accession to the WTO in 2001 and subsequently with the phasing out, and final elimination on 1 January 2005, of import quotas for textiles and garments under the WTO Agreement on Textiles and Clothing. In Latin America reports from Mexico claimed that maquiladoras on the US–Mexican border were relocating to China, with significant negative effects on employment (Santos-Navarro 2005). In Central America textile mills and clothing factories were reported to be laying off workers and closing down as a result of Chinese competition in the US market, according to the New York Times (Thompson 2005). Recently the International Herald Tribune reported that Colombian textile manufacturers have seen their exports drop as a result of stronger competition from China, and the president of the National Foreign Trade Council in Washington is quoted as saying ‘The least developed countries in Latin America are scared to death’ (Murphy et al. 2007). In contrast to the pessimistic view that pervades media and business discussion of the impacts of China on Latin America, the majority of academic 220
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studies and reports from international institutions, such as the World Bank, the Inter-American Development Bank and the OECD, see China much more as an opportunity than a threat for the region – an ‘angel’, not a ‘devil’, in the words of one much quoted paper (Blázquez-Lidoy et al. 2007). With the exception of Mexico, China is not a significant threat to Latin American countries’ exports to third markets. The economies of the region are seen as being complementary to China in terms of their productive structure and there is a tendency for this to increase over time. They are therefore well placed to take advantage of the growing market that China represents and to benefit from expanding global production networks. This chapter considers these contrasting views. It focuses specifically on the impact of China on Latin American exports to third markets and sets out to answer three related questions.1 First, to what extent do the Latin American countries face competition from China in their export markets and which countries within the region are most affected? Second, has the competition faced by the Latin American economies increased over time, particularly since China became a member of WTO, or is the threat from China receding? Finally, how large has the displacement effect of Chinese competition been for the exports of the various Latin American countries in recent years?
Previous studies of the impact of China on Latin American exports This section considers the answers given to these three questions in the existing literature on the impact of China on Latin American exports. The focus here is on regional studies that identify individual Latin American countries and are based on disaggregated product data. There have been a number of global CGE modelling exercises, which have generated predictions on the likely impact of China on Latin America as a whole and in some cases separate out the largest Latin American countries, but these are highly aggregated and will not be discussed here.2 There are also a growing number of studies of individual Latin American countries, particularly of Mexico 3 and Brazil,4 but these too will not be covered here since the emphasis is on the experience of the region as a whole. The extent of competition from China There is a widespread consensus in the literature that competition from China is not a major threat to Latin American exports. Several studies have found that the structure of Latin American exports is quite different from that of China, suggesting that there is little competition between them (Blázquez-Lidoy et al. 2007; Meller and Contreras 2003; Schott 2004). Asian countries covered in these studies are found to have a much greater similarity of export structures to China than has Latin America. Lall and Weiss
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(2005) analysed changes in the world and US market shares of China and the Latin American countries, coming to similar conclusions: The direct threat to exports to third country markets appears small: Latin America and the Caribbean’s (LAC’s) trade structure is largely complementary to that of China. (p. 163) At the level of individual nations there is general agreement that Mexico is the only country in the region that has a level of export similarity with China comparable to those found in a number of Asian countries. It is therefore seen as the only major Latin American country that could face a serious competitive threat from China. Argentina, Chile and the Andean Group countries are regarded as largely unaffected by Chinese competition, while Brazil may be partially affected. Some authors recognize a potential threat to the smaller Central American and Caribbean countries (Freund and Ozden 2006; López-Cordóva et al. 2007), although other studies do not show a great export similarity with China in terms of the conventional indices.5 Is Latin America facing increased competition from China over time? The popular perception that Latin American countries are facing increasing competition from China in third markets is based on a number of factors. First, the accession of China to the WTO in December 2001 improved its access to developed country markets. While China had been granted normal trade relations with the USA even before it joined the WTO, this had to be renewed annually by Congress. Membership therefore gave China much greater security of market access (Rumbaugh and Blancher 2004: 10). Second, the ending of the quota system, which had regulated textile and clothing trade since the 1950s, at the start of 2005 was also predicted to increase competition from China. In 2002, the US International Trade Commission estimated that the export tariff equivalent of the quota for Chinese apparel exports to the USA was 21 per cent (quoted in LópezCórdova et al. 2007: 122), so that the removal of quotas would lead to a significant increase in Chinese competitiveness in this sector. One estimate predicted a tripling of the Chinese share of the US apparel market from 16 per cent to 50 per cent following the elimination of quotas (Nordas 2004). Third, low wage levels, massive scale of production and increasing productivity have led to falling prices and increased competition over time for many of the goods exported by China (Kaplinsky 2005: chapter 6). In contrast, productivity growth in Latin America has lagged behind (Lora 2007: table 1.1). In the light of these trends, it is paradoxical to find that the dominant view in the literature on the impact of China on Latin America is that, far from facing increased competition in export markets from China, the Latin
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American economies are becoming more complementary to the Chinese economy and are thus likely to benefit more over time from Chinese expansion. For example, Lederman et al. (2006b: 16–17) conclude that: The specialization pattern of LAC economies – with the exception of Mexico – has been moving in opposite direction to the trade specialization pattern of China and India. This indicates that LAC’s trade specialization pattern is becoming more complementary to the specialization pattern of China and India. Evidence presented by Moreira (2007: figures 18 and 19) also shows a decline in the similarity of export structures between Latin American countries, apart from Mexico and, to a lesser extent, the Central American Common Market, and China between 1992 and 2004. Using their alternative methodology, Lall and Weiss (2005: tables 6 and 7) also find that the proportion of Latin American exports that face a competitive threat from China, both in the world market as a whole and in the US market, declined between 1990 and 2002. A slightly different view is provided by the Inter-American Development Bank report, which shows the export similarity between Latin America and Asia increasing over time in both the world and the US markets (2004: table 5.5 and figure 5.1). On closer inspection, however, it is clear that in the period from 1991 to 2001 this was driven largely by a significant increase in the similarity between Mexican and Chinese exports to the world and the US market, while the Export Similarity Index between all the other Latin American countries (apart from Brazil) and China declined during the period (IADB 2004: table 5.6 and figure 5.1). Thus the consensus that emerges from the literature is that the Latin American economies (apart from Mexico) have over time become more complementary to China’s in terms of their export structure. The impact of China on Latin American exports to third markets Much of the literature on China and Latin America has been concerned with identifying the future threat that China’s growth might represent for Latin America. This reflects the fact that it was initially sparked off by concerns over the impact that China’s WTO accession and the ending of textile quotas might have on the region. However, a few studies have attempted to quantify the extent to which China’s growth has negatively affected Latin American exports in the past. These studies use a number of different methodologies, including an extension of constant market share analysis, the gravity model of trade flows, and econometric estimation of elasticities of substitution. With one exception, they all find evidence of Chinese growth having had some negative impact on Latin American exports to third markets. The outlier is a
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study by Lederman et al. (2007), which finds that, far from Chinese exports crowding out Latin American exports to third markets, they in fact tend to promote them, a result that is attributed to the creation of production networks.6 Although there is general agreement in the literature that China has had some negative effect on Latin American exports, the dominant view tends to downplay the severity of this impact. An overview of research by the World Bank concludes that: There is some evidence of substitutability between LAC exports and Chinese exports to third markets within industries, but these effects are limited to a few countries (mainly Mexico and, to a minor extent, Central America) and a few manufacturing sectors. (Lederman et al. 2006a: 26) The scale of the negative impact on Latin American exports found in most of the studies is relatively modest. Moreira (2007: 366), for instance, estimates that the loss to China in world markets between 1990 and 2004 was only 1.7 per cent of the region’s total exports at the end of the period. Hanson and Robertson (2006) have larger estimates for the four countries that they studied (Argentina, Brazil, Chile and Mexico), but still conclude that other factors are more important than Chinese competition in explaining the declining share of Latin American countries in world markets. Although in aggregate terms a fairly consistent picture emerges from these studies, this is not as clear-cut when looking at the results in terms of the countries or sub-regions that have been most severely affected or of the sectors that have suffered most. While Freund and Ozden (2006) and Hanson and Robertson (2006) both identify Mexico as severely affected, Moreira (2007: figure 13) estimates that the largest losses of market share to China have been suffered by the Andean countries and Mercosur, while López-Córdova et al. (2007: table 4.1) see the South American countries and Mexico as facing similar impacts. In terms of sectors, while López-Córdova et al. (2007) identify traditional labour-intensive sectors as being particularly affected (textiles, clothing and leather), Freund and Ozden (2006) see the main threat from China in high-wage industries, and Moreira (2007) emphasizes that Latin America is facing competition across a wide range of manufacturing sectors.7
The competitive threat from China to Latin America Methodology Two main approaches have been used in the literature discussed above to estimate the extent of the competitive threat that China poses for the Latin American countries’ exports to third markets. The most common approach involves analysing the extent to which China and specific Latin American
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countries have a comparative advantage in the same products. The greater the similarity in their comparative advantage, the greater is likely to be the competition between two countries. Within this approach, two types of methodologies have been used. The first uses specific indices of export similarity such as Finger and Kreinin’s Export Similarity Index, the Coefficient of Conformity, and the Index of Export Competition.8 These are all based on specific formulae that use the share of each product in the two countries’ exports to calculate an index of the extent to which their export structures overlap. Where there are no common products exported by the two countries, the value of the index is zero, while where they have identical export structures the value is one. The absolute level of exports is irrelevant, as is the share of each country’s exports in world markets. All that matters in determining the extent of competition is the product structure of exports. The second methodology, derived from comparative advantage theory, is based on estimating the correlation between the export structures of two countries. In some cases this involves correlating product export shares, while others correlate an index of Revealed Comparative Advantage (RCA).9 Again it is only the structure of each country’s exports that matters, irrespective of their absolute level. Since, by definition, countries always have a comparative advantage in something, a fall in these indices is seen as reflecting less competition from China as Latin American countries’ comparative advantage shifts over time. This is consistent with the Hecksher– Ohlin model of trade, where neither the size of new entrants nor the rate of export growth matters (Lall and Weiss 2005: 165). The alternative approach adopted by Lall and Weiss (2005) explicitly rejects a neoclassical model of trade based on perfectly competitive markets. Recognizing the role of scale economies, differentiated products, market power, externalities and agglomeration effects, endogenous technical change and cumulative learning, among other factors, they argue that Krugman (1994: 44) was wrong to claim that ‘competitiveness is a meaningless word when applied to national economies’. They therefore develop an alternative approach based on the business literature in which relative market shares are used as a common measure of competitive performance. Thus their approach emphasizes ‘competitive advantage’ as opposed to ‘comparative advantage’. In contrast to the latter approach, where the relative growth rates of two countries’ exports has no bearing on the measure of competition between them as long as the structure of exports remains unchanged, changing relative shares in world markets are a key measure of competitive threat in the Lall and Weiss (2005) approach. This involves classifying products according to the combination of changes in world market shares for China and the Latin American countries. Two categories of threat are identified. A ‘direct threat’ exists where China gains market share and the Latin American country is
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losing market share. There is also an ‘indirect threat’ in those products for which, although Latin America is gaining market share, it is doing so less quickly than China. There is no threat to Latin American exports in products for which its exports are increasing faster than China’s or in those products where China’s share of the market is falling. They then calculate the share of each country’s exports that is either directly or indirectly threatened. Implicit in this approach is that there are two dimensions to the competitive threat faced by a country. One is the extent of the threat, as represented by the share of a country’s exports that fall into the threatened categories. The second is the intensity of that threat, which is measured by the significance of the direct as opposed to the indirect threat.10 This, however, is a very crude measure of the intensity of the threat, since it only distinguishes between those products where Latin America’s export share is falling and those where it is increasing. A more comprehensive measure of the competitive threat from China should combine a measure of the extent of the threat and its intensity. One approach to this would be to weigh the share of each product in a country’s exports by a measure of the intensity of Chinese competition in that product. The intensity of Chinese competition could reasonably be measured either by the share of China in world exports of a product or the increase in its share over time. Thus a new index of the competitive threat which a country faces from China could be calculated, in which: ICT(1) xt Hi*kt Ci
(1)
where xt Hi Xt Hi / Mt H (the share of product i in total imports from country H by the destination market at time t, measuring the extent of competition faced by country H) and ktCi = XtCi / Mti (the share of China in total imports of product i by the destination market at time t, measuring the intensity of competition from China). Alternatively, the intensity of competition could be measured by the change in China’s market share, so that Equation (1) would become ICT(2) xt 1
Hi
*∆k
Ci
(2)
The value of the index derived in Equation (1) could range from zero, when a country has no exports of products which are exported by China, to one where the country’s exports are made up entirely of products in which China’s exports account for the entire market.11 In practice the value of the index is unlikely to be anywhere approaching unity. Changes over time will, however, indicate whether competition from China is increasing or falling, and comparisons across countries will reveal which countries are most likely to be affected by Chinese competition.
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Unlike ICT(1), ICT(2) can take negative values when a country specializes mainly in products in which China’s market share is falling. Theoretically the limits are 1 and 1, with the former occurring when a country exports products of which China was initially the sole supplier and from which it has over the period withdrawn completely, while the latter would arise in the case when China has gone from not being a supplier to being the sole supplier. These are limiting cases, and in practice values are likely to fall within a much narrower range around zero. An empirical analysis of the Chinese competitive threat to Latin America in the US market The analysis presented here focuses on the US, since this has been the most significant market in terms of competition between China and Latin America.12 The data comes from the US International Trade Commission (http://dataweb.usitc.gov/) and covers imports from 18 countries in the region and from China and Hong Kong. Since Hong Kong officially became part of China in 1997 and a significant part of Hong Kong’s exports originate from China, a more complete picture of the competition between China and other countries can be obtained by including imports from Hong Kong. Product data at the five-digit level of the SITC (Standard International Trade Classification) (Rev.3) were used. It was important to have a high level of disaggregation in order to ensure that the products being compared were close substitutes for each other. For example, at the three-digit level SITC 841 includes coats, jackets, suits, trousers, shirts and underwear for men and boys made from woven fabrics. At the five-digit level, each of these products is treated separately and, in the case of coats and suits, according to whether or not they are woollen. Clearly, at the threedigit level, there is a danger that a country exporting anoraks would be treated as though it competed with another country that might be exporting underwear. The data were collected for four key years, 1996, 2001, 2004 and 2006. 1996 represents the situation some time before China became a member of the WTO; 2001 is immediately prior to WTO accession in December 2001; 2004 is the last year before the final removal of quotas on textiles and garments on 1 January 2005;13 and 2006 is the latest year for which data are available. The use of these four years makes it possible to analyse how competition between China and Latin America has evolved over time in response to these changes in the trade regime. Table 11.1 presents the two indices of competitive threat, ICT(1) and ICT(2), for two years, 2001 and 2006. ICT(1) uses the share of China in US imports by product in 2001 and 2006 as the measure of the intensity of competition, while ICT(2) uses the increase in the share of Chinese imports between 1996–2001 and 2001–6 for 2001 and 2006 respectively.
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Table 11.1 Indices of competitive threat from China, 2001, 2006 ICT(1)
Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
ICT(2)
2001 (in %)
2006 (in %)
2001 (in %)
3.7 11.2 8.0 2.6 2.8 7.9 12.2 2.1 12.3 13.0 11.3 7.7 9.7 4.3 7.6 6.1 6.6 0.7
7.0 11.3 9.9 4.1 5.1 10.5 16.7 1.5 17.0 16.0 16.7 13.2 13.4 5.5 17.8 5.6 4.2 0.6
1.3 4.2 2.0 1.1 0.0 0.1 −1.6 0.6 −4.0 −3.8 −2.9 2.0 −3.7 1.5 2.7 −0.6 1.5 0.0
2006 (in %) 3.6 −1.0 4.6 1.4 2.3 4.7 6.4 0.6 6.8 5.3 5.9 7.1 4.7 2.0 8.5 1.0 2.3 0.2
Source: Own elaboration from USITC data.
ICT(1) shows that four of the five top countries in terms of the competitive threat from China in both 2001 and 2006 were in Central America and the Caribbean (El Salvador, Guatemala, Honduras and the Dominican Republic). According to this index, these countries faced greater competition from China in both years than did Mexico, which is normally regarded as the country facing the greatest threat. In 2006, the country most threatened by Chinese competition according to ICT(1) was Paraguay.14 The Andean countries, apart from Bolivia, face only limited competition from China, while Argentina, Brazil and Costa Rica occupy an intermediate position. Although it has not been possible to compare the degree of competition faced by the Latin American countries with other regions, the fact that several of the countries included in Table 11.1 rank above Mexico suggests that the problem of Chinese competition is more widespread in the region than has been recognized in the literature up to now. Turning to the second index, ICT(2), the pattern in 2006 is broadly similar to that using ICT(1). Mexico rises from seventh to second place and Bolivia falls from eighth to eighteenth, but otherwise there is little difference from the broad pattern described above. However, the ICT(2) for 2001 shows a completely different ranking from ICT(1) for the same year. Specifically, the Central American countries (El Salvador, Guatemala, Honduras and
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Nicaragua) and the Dominican Republic have significant negative coefficients, indicating that they were specialized in products where China had been losing market share in the preceding five years (1996–2001). The fact that they ranked amongst the top countries in terms of ICT(1) shows that they were specialized in products in which China had a significant share of the US market in 2001. The data suggests that this result was driven by developments in textiles and clothing, in which the Chinese market share in the USA fell in the period prior to WTO accession. These were precisely the sectors that Central America and the Dominican Republic were expanding through Export Processing Zones in the 1990s.
Changes in the competitive threat over time The dominant view amongst researchers is that the Latin American economies (apart from Mexico) are evolving in different directions to become more complementary in terms of their trade structures, so that the competitive threat posed by China is decreasing. As mentioned earlier, this runs counter to the popular perception of increased Chinese competition following WTO accession and the phasing out of quotas on textiles and clothing, reflected in falling prices of goods produced in China and growing market penetration. One explanation of the contrast between the findings of academic studies and this latter view can be found in the nature of the indices that have been used to measure the degree of competition from China. As was pointed out earlier, these are based solely on the composition of the exports of the two countries being compared. This is true of both the various export similarity measures and the use of correlation coefficients. They only measure the extent of competition from China and not its intensity within each product. They therefore miss a key dimension of what has been happening in global markets, namely, the increased global share of China across a wide range of products. They assume that a decline in the share of a given product in China’s total exports over time indicates a loss of comparative advantage in that product, and, if this is a product exported by Latin America, a decline in the competition that the latter faces. It ignores the fact that a decline in the share of a particular product in China’s exports can be accompanied by an increase in China’s share of the world market in that product, since China’s exports are growing so rapidly. This apparent loss of comparative advantage in fact hides an increase in China’s competitive advantage in the product. Since the new indices developed in the previous section measure both the extent and the intensity of competition, they provide a better measure of what is happening to the threat faced by Latin American countries from China over time. Do these indicators support the view that Chinese competition has increased in recent years?
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Table 11.2 ICT(1) for total trade, 1996, 2001, 2004, 2006
Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
1996 (in %)
2001 (in %)
2004 (in %)
3.1 9.5 8.0 1.8 2.9 9.9 13.7 1.3 15.2 11.4 12.2 5.4 8.8 2.5 5.3 4.6 6.5 0.7
3.7 11.2 8.0 2.6 2.8 7.9 12.2 2.1 12.3 13.0 11.3 7.7 9.7 4.3 7.6 6.1 6.6 0.7
4.8 11.7 9.1 4.3 4.2 9.0 14.7 1.7 14.1 13.4 13.0 11.0 11.2 5.8 13.4 5.5 3.6 0.6
2006 (in %) 7.0 11.3 9.9 4.1 5.1 10.5 16.7 1.5 17.0 16.0 16.7 13.2 13.4 5.5 17.8 5.6 4.2 0.6
Source: Own elaboration from USITC data.
Table 11.2 shows that over the decade from 1996 to 2006 competition from China increased in all countries apart from Uruguay and Venezuela. In the case of the latter it was negligible throughout the period. If the period is subdivided, several countries saw a decline in competition from China in the period before it joined the WTO. These were mainly Central American countries (Costa Rica, El Salvador and Honduras) and the Dominican Republic. However, this trend was reversed after 2001, and particularly after the ending of the Agreement on Textiles and Clothing at the start of 2005. Ecuador, Peru, Uruguay and Venezuela were the only countries to face less competition from China in 2006 than they did in 2001. Since China competes mainly in manufacturing, Table 11.3 calculates the same index of competitive threat but only includes manufactured goods. If anything, the picture of increased competition is even clearer than in Table 11.2, with Uruguay being the only country facing less competition in 2006 than in 1996.15 It is worth noting that the reason why the present approach gives such different results from previous studies is that it takes into account not just the structure of China’s exports, but also their share in world markets. Thus it captures the increased penetration of the US market by China in recent years, which measures of export similarity fail to take into account. By incorporating the intensity of competition from China in this way, it presents a more realistic picture of the competitive threat faced by Latin American countries.
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Table 11.3 ICT(1) for manufactured goods trade, 1996, 2001, 2004, 2006
Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
1996 (in %)
2001 (in %)
2004 (in %)
1.3 5.3 7.6 1.0 2.3 9.5 13.5 0.5 15.0 11.0 12.0 5.1 8.2 1.8 4.1 3.8 4.4 0.2
2.0 6.1 7.6 1.1 2.1 7.5 12.0 0.8 12.2 12.7 11.0 7.4 9.0 1.9 4.1 4.7 4.6 0.3
3.0 9.2 8.4 1.5 3.3 8.5 14.4 0.6 13.9 13.0 12.7 10.6 10.7 2.6 6.2 4.0 2.3 0.3
2006 (in %) 3.9 8.5 9.0 1.3 4.0 9.8 16.4 0.6 16.6 15.5 16.4 12.7 13.0 2.8 10.3 4.2 2.4 0.3
Note: Manufactured goods are defined as SITC 5–8 (excluding 68). Source: Own elaboration from USITC data.
Estimating the loss of market share to China This section estimates the extent to which Latin American countries have lost market share in the United States to China in recent years. This will throw further light on a number of key issues raised in the chapter. First, how significant has the impact of Chinese competition been on Latin American exports? Second, which countries within the region have been most severely affected by Chinese competition? Finally, has the impact of China on the region’s exports increased over time, particularly following China’s accession to the WTO and the removal of quotas on textiles and garments? Methodology The methodology used to estimate the loss of market share to China is an extension of Constant Market Share (CMS) analysis developed by Chami Batista (2008). CMS decomposes the changes in the overall share of an exporting country in a particular market over a given time period into a competitiveness effect (that is, changes in the share within each product category) and a product composition effect. ∆k H XtHi*∆mi Mit1*∆k Hi
(3)
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where: k H is the share of country H in total imports of the destination market XH is the total exports of country H to the destination market mi is the share of product i in the total imports of the destination market Mi is the total imports of product i by the destination market Superscripts t and t 1 represent the initial and final years of the period. The first term on the right hand side of Equation (3) represents the product composition effect and the second term the competitiveness effect. In a two-country world, one country’s gain in market share is the other country’s loss. In reality, however, there are many countries exporting to a given market, some of which will be gaining competitiveness and increasing their market shares, while others will be losing market share. The question then is how to distribute one country’s loss of market share among those countries that are gaining market share. In other words, if Mexico is losing market share in the USA, and China and some other countries are gaining market share, how much of the loss in Mexico’s market share should be attributed to China and how much to other countries that are increasing their share of the US market? One approach used in Moreira (2004), based on an earlier methodology developed by Chami Batista and Azevedo (2002), distributes a country’s loss of market share between those countries that have gained market share in proportion to their share in the total gain in market share. More recently, however, Chami Batista (2008) has pointed out a limitation of this approach, since it does not take into account the relative rates of growth of exports from different countries. He proposes an alternative formulation in which the gains (losses) of competitiveness between countries are related to their relative growth rates. In other words, countries gain from those countries whose exports are growing more slowly and lose to those that are growing faster than their own. The loss of market share by a country (H) to China (C) in a particular product i is defined as: ∆k HCi ∆k Hi*ktCi ∆ kCi* ktHi
(4)
Summing over all products gives the aggregate loss of market share to China: ∆k HCi ∆k Hi*ktCi ∆ kCi* ktHi
(5)
Summing over all exporters to the destination market gives the total competitiveness effect, which is the same as the last term on the RHS (right hand side) of Equation (3). It can be shown that this formulation satisfies four desirable properties.16 Country H cannot lose or gain from itself. A gain for exporter H from exporter C is equal to the loss of exporter
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C to exporter H. The sum of the gains and losses of any country to all its competitors is equal to the total gain or loss of market share by that country. Finally, the gain (loss) of a country to another country is a function of, and has the same sign as, the difference between the growth rates of their exports.17 There is a close relationship between this measure of loss of exports to China and the Index of Competitive Threat, ICT(1), developed earlier in this chapter. It is possible to rewrite Equation (4) as: ∆k HCi (∆k Hi / ktHi ∆ kCi / ktCi)* ktHi* ktCi since and ktHi
(6)
xtHi XtHi / MtH ktHi XtHi / Mti xtHi * MtH / Mti
(7)
Substituting into (6): ∆k HCi (∆k Hi / ktHi − ∆ kCi/ktCi)* ktCi *xtHi * MtH / Mti
(8)
and (Mti/MtH)*∆k HCi (∆k Hi/ktHi ∆ kCi/ktCi)* ktCi *xtHi
(9)
Summing over all products i gives: (1 / MtH)* Mti ∆k HCi (∆k Hi / ktHi ∆ kCi / ktCi)* ktCi *xtHi
(10)
The LHS (left hand side) of Equation (10) gives the loss of market to China by country H as a share of its initial exports to the destination market. On the RHS, the term within the brackets is the difference between the proportionate changes in the market share of Country H and of China over the period. This is negative when China’s market share is increasing faster than that of country H.18 The term outside the bracket on the RHS is the index of competitive threat, ICT(1). Thus the scale of the loss of exports as a result of competition from China is positively related to the index that was developed earlier to measure the competitive threat from China. The actual impact on the exports of a given country will, however, vary depending on the relative growth rates of exports in different industries. Empirical estimation of the impact of China on Latin American exports The loss of market share to China is estimated with the same data set of US imports that was used in the previous section. Three periods will be
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Table 11.4 Loss of exports in the USA to China, 1996–2001, 2001–2006, 2004–2006 (as % of country’s total exports to the USA) 1996–2001 (in %) Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela Latin America
−1.8 −10.4 −4.1 −1.3 −0.5 −1.3 −2.4 −1.0 6.5 6.2 3.8 −1.1 6.4 −2.4 −6.9 2.0 −5.8 0.0 −1.0
2001–2006 (in %) −5.1 1.6 −7.7 −3.0 −2.3 −7.8 −13.0 −1.1 −12.3 −10.5 −7.7 −11.4 2.3 −2.4 −5.7 0.5 −9.3 −0.7 −9.3
2004–2006 (in %) −1.6 −1.3 −3.3 −1.3 −1.7 −1.6 −6.1 0.0 −10.5 −8.7 −6.0 −4.5 −0.8 −1.1 −5.5 −1.4 −1.6 −0.5 −3.8
Source: Own elaboration from USITC data.
analysed: pre-WTO accession (1996–2001), post-WTO accession (2001–6) and post-ATC (2004–6). The first point that stands out from Table 11.4 is the sharply increased impact of China on Latin American exports to the USA in the period since it became a WTO member. Whereas between 1996 and 2001 the aggregate effect on the region as a whole amounted to US$1.3 billion (1 per cent of 1996 exports), over the next five years the impact came to over US$18 billion (9.3 per cent of exports in 2001). This is further evidence to support the view that, far from decreasing over time, the impact of Chinese competition on Latin America has been on the increase. Looking at the experience of individual countries, it can be seen that only two countries (Nicaragua and Peru) have not lost exports in the USA to Chinese competition over the whole period 1996–2006. As well as Nicaragua, several other Central American countries (El Salvador, Guatemala and Honduras) were able to gain market share from China in the period before the latter became a WTO member. However, in all three countries, the gains were more than offset by losses after China joined WTO. These losses were particularly concentrated in the period after 2004, when even Nicaragua lost exports to China. In the period from 1996 to 2001, the countries most severely affected were Bolivia, Paraguay and Uruguay, whose estimated losses to China represented
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more than 5 per cent of their total exports to the US. Between 2001 and 2006, those worst hit were the Dominican Republic, El Salvador, Mexico and Guatemala, all of which lost over 10 per cent. As might be expected, between 2004 and 2006, the countries which lost most in the aftermath of the phasing out of the ATC were the Dominican Republic and the Central American countries, which rely heavily on textile and clothing exports to the USA. The countries that have been least affected in the post-2001 period have been the Andean group countries, whose exports to the USA are mainly of minerals and oil and have therefore not faced significant Chinese competition. Since Chinese competition is particularly centred on manufactured goods, it is also worth considering the extent to which exports of manufactures to the USA have been affected. Indeed, one of the concerns raised by the global expansion of China is that it will make it increasingly difficult for other countries to diversify into manufactures and upgrade the technological content of their exports. Table 11.5 presents the impact of loss of market share to China on the exports of manufactured goods to the USA for the 18 Latin American countries. As might be expected, the impact on manufactures is even more severe
Table 11.5 Loss of manufactured exports in the USA to China, 1996–2001, 2001–2006, 2004–2006 (as % of country’s manufactured exports to the USA)
Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela Latin America
1996–2001 (in %)
2001–2006 (in %)
−4.7 −9.7 −6.0 −9.8 −3.4 −2.0 −3.0 −6.0 8.0 12.2 5.4 −1.5 13.4 −9.2 −8.1 5.2 −9.8 −1.6 −1.6
−8.7 −4.4 −10.7 −6.7 −8.4 −12.0 −15.0 −20.0 −13.3 −15.3 −9.4 −13.6 3.8 −13.2 −13.6 −1.0 −13.3 −6.3 −13.0
Note: Manufactured goods are defined as SITC 5–8 (excluding 68). Source: Own elaboration from USITC data.
2004–2006 (in %) −4.5 −5.6 −4.5 −3.6 −9.0 −2.3 −7.3 −11.8 −11.7 −13.2 −7.4 −5.8 −1.3 −5.2 −9.3 −6.0 −6.2 −3.8 −5.8
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than on total exports. Again the overall impact was relatively small between 1996 and 2001, but increased markedly to 13 per cent in 2001–6, with almost half of that impact being felt in the past two years. Some countries lost over 15 per cent of their manufactured exports in the five years from 2001 to 2006, and three countries lost over 10 per cent in just two years from 2004 to 2006. Andean countries such as Chile, Colombia, Ecuador and Venezuela, which have not suffered much in overall terms, saw a significant loss of market share in manufactures to China. Thus, while they are protected from serious losses in their overall export earnings by their specialization in minerals and oil, they may find it more difficult to diversify into exports of manufactured goods as a result of Chinese competition.
Summary and conclusion The chapter has developed new measures of competitive threat, which, it is claimed, give a more realistic indication of the extent of competition that countries face from China than any of the indices used in previous studies. They explicitly take into account the intensity of Chinese competition in different products, as well as the share of a country’s exports that competes with Chinese goods. As a result, they are better able to capture the effect of increased penetration of world markets by China than the measures based on comparative advantage, particularly when making comparisons over time. The two indices, ICT(1) and ICT(2), were applied to data for US imports from 18 Latin American countries. The results were more supportive of the popular perception of a Chinese threat to Latin American exports than the more sanguine view propagated by the international institutions and previous academic studies. First, the competitive threat in Latin America is not confined to Mexico. On this evidence, it is even more severe for Central America and the Dominican Republic. This is not that surprising, since one of the arguments used to garner support for the Dominican Republic– Central American Free Trade Agreement (DR-CAFTA) was that it would give countries a critical advantage in competing with Asia, particularly China (USTR 2005). It was also shown to be a problem for some South American countries, particularly Bolivia, Brazil and Paraguay. Second, the competitive threat increased quite sharply over time with China’s accession to the WTO and the phasing out of the Agreement on Textiles and Clothing. There is no evidence to support the view that Chinese competition is becoming less of a problem for Latin America. The chapter also presented new estimates of the impact of China on the value of Latin American exports to the USA over the past decade, using an extension of Constant Market Share analysis. While it is necessary to be cautious about assuming a direct causal relationship between the growth of Chinese exports and the loss of market share, the results are broadly in line with those from the competitive threat analysis. First, most Latin American
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countries have lost significant market share to China since 2001. Second, the trend has been for losses to increase over time. Third, losses, as would be expected, have been particularly severe in markets for manufactured goods. The chapter has only presented a partial analysis of the impact of China on Latin America, since it has been confined to a study of exports to the USA. It is true that for some countries China has been a significant and growing market for exports, and this has offset the negative impacts on exports to third markets. However, the evidence presented here shows that the Latin American countries need to take seriously the challenge posed by increasing Chinese competition in the US market. The observed changes in market shares reflect a loss of competitiveness vis-à-vis China in a number of manufacturing industries, and should not be dismissed as simply being a result of shifts in comparative advantage.
Notes 1. The growth of bilateral trade between China and Latin America has been discussed by the author in another paper (Jenkins et al. 2008). 2. See, for example, Dimaranan et al. (2007); Ianchovichina and Martin (2004); IMF (2004: chapter 11); Yang (2003). 3. Dussel Peters (2005); Hummels (2006); Neme Castillo (2006). 4. Abreu (2004); Machado and Ferraz (2006). 5. There are two possible reasons for this. First, the conventional indicators of similarity tend to underestimate the extent of competition faced by a small country from a much larger exporter (see Jenkins 2008). Second, some Central American countries do not include exports from export processing zones in their statistics for exports of goods, and, since these are predominantly of clothing, which does compete with Chinese exports, this would create a further downward bias. 6. The study provides no evidence that production networks involving Latin American and Chinese exports to third countries are in fact significant, and this in fact contradicts the evidence that, in contrast to East Asia, Latin American countries have not been significantly integrated into global production networks. 7. In an earlier version of the paper Moreira (2004) found that resource-based manufactures were the most severely affected group of products in the period 1990–2002. 8. The Export Similarity Index has been used by Calderón (2006), IADB (2004) and Schott (2004), the Coefficient of Conformity by Blázquez-Lidoy et al. (2007) and the Index of Trade Competition by Meller and Contreras (2003). 9. Correlations of export shares between China and Latin American economies have been presented in Lall and Weiss (2005), Meller and Contreras (2003) and Moreira (2007), while correlations of RCAs can be found in Lederman et al. (2006b). 10. Lall and Weiss (2005: 178) point to the decline in the share of exports from Latin America under direct threat from 30 per cent in 1990 to 11 per cent in 2002 and comment that ‘The intensity of the Chinese threat decreases significantly over time’ (italics in the original).
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11. This is of course a logical impossibility, since, if China accounted for the entire market for a product, no other country could also be an exporter. 12. An emerging issue is the increased competition with China in the Latin American market, but this is relatively recent and will not be analysed here. For a discussion of Chinese competition with Brazil in the Argentine market, see Sica (2007). 13. Although it should be noted that subsequently the USA imposed new restrictions on Chinese textile and clothing imports. 14. Paraguay is usually regarded as one of the countries least affected by Chinese competition, with the lowest export similarity of the Latin American countries considered by Blázquez-Lidoy et al. (2007: table 2.5) and by IADB (2004: table 5.6). However, Lall and Weiss (2005: figure 2) also find that in 2002 Paraguay was the country with the highest proportion of its exports to the US market threatened by China. 15. The figure is lower for manufactures than for total trade, since it is a subset of total trade. An alternative approach would be to calculate the shares of each product in manufactured exports rather than total exports, which would give different results. 16. See Chami Batista (2008) for the mathematical proof that Equation (5) satisfies these requirements. 17. One should note, however, that the decomposition is based on accounting identities, and should therefore be careful in making any causal inferences from it. 18. This is similar to the criteria used by Lall and Weiss (2005) to classify what proportion of a country’s exports are under threat from China.
References Abreu, M. (2004). ‘Implications of China’s Emergence in the Global Economy for Latin America and the Caribbean Region. The Case of Brazil’. Background paper prepared for the Inter-American Development Bank Report on China. Washington, DC: IADB. Blázquez-Lidoy, J., J. Rodriguez, and J. Santiso (2007). ‘Angel or Devil? China’s Trade Impact on Latin American Emerging Markets’. In J. Santiso (ed.), The Visible Hand of China in Latin America. Paris: Development Centre Studies, OECD. Calderón, C. (2006). ‘Trade, Specialization and Cycle Synchronization: Explaining Output Co-movement between Latin America, China and India’. Background paper for the Office of the Chief Economist for Latin America and the Caribbean Regional Study, Latin America Respond to the Growth of China and India. Washington, DC: World Bank. Chami Batista, J. (2008). ‘Competition between Brazil and other Exporting Countries in the US Import Market: A New Extension of Constant-Market-Shares Analysis’. Applied Economics, 40 (19): 2477–87. Chami Batista, J., and J. P. Azevedo (2002). ‘NAFTA and the Loss of US Market Share by Brazil, 1992–2001. CEPAL Review, 78: 167–82. Dimaranan, B., E. Ianchovichina, and W. Martin (2007). ‘Competing with Giants: Who Wins, Who Loses?’. In L. A. Winters, and S. Yusuf (eds), Dancing with Giants: China, India and the Global Economy. Washington, DC: World Bank, 67–100. Dussel Peters, E. (2005). Economic Opportunities and Challenges Posed by China for Mexico and Central America. Bonn: DIE/GDI. Freund, C., and C. Ozden (2006). The Effect of China’s Exports on Latin American Trade with the World. Background paper for the Office of the Chief Economist for Latin
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America and the Caribbean Regional Study. Latin America Respond to the Growth of China and India. Washington, DC: World Bank. Hanson, G., and R. Robertson (2006). ‘China and the Recent Evolution of Latin America’s Exports’. Background paper for the Office of the Chief Economist for Latin America and the Caribbean Regional Study. Latin America Respond to the Growth of China and India. Washington, DC: World Bank. Hummels, D. (2006). ‘Understanding the Extent and Nature of Recent Changes in the Structure of Mexico’s Comparative Advantage’. Paper prepared for the InterAmerican Development Bank. IADB (2004). The Emergence of China; Opportunities and Challenges for Latin America and the Caribbean. Washington, DC: Inter-American Development Bank, Integration and Regional Programs Department/Research Department. Ianchovichina, E., and W. Martin (2004). ‘Economic Impacts of China’s Accession to the World Trade Organization’. World Bank Economic Review, 18 (1): 3–28. IMF (International Monetary Fund) (2004). World Economic Outlook, April. Washington, DC: IMF. Jenkins, R. (2008). ‘Measuring the Competitive Threat from China for Other Southern Exporters’. The World Economy, 31 (10): 1351–66. Jenkins, R., E. Dussel Peters, and M. M. Moreira (2008). ‘The Impact of China on Latin America and the Caribbean’. World Development, 36 (2): 235–53. Kaplinsky, R. (2005). Globalization, Poverty and Inequality: Between a Rock and a Hard Place. Cambridge: Polity Press. Krugman, P. (1994). ‘Competitiveness: A Dangerous Obsession’. Foreign Affairs, 73: 28–44. Lall, S., and J. Weiss (2005). ‘China’s Competitive Threat to Latin America: An Analysis for 1990–2002’. Oxford Development Studies, 33 (2): 163–94. Lederman, D., M. Olarreaga, and G. Perry (2006a). ‘Latin America and the Caribbean Response to the Growth of China and India: Overview of Research Findings and Policy Implications’. Washington, DC: World Bank, Office of the Chief Economist – Latin America and the Caribbean. Draft version. Lederman, D., M. Olarreaga, and E. Rubiano (2006b). ‘Latin America’s Trade Specialization and China and India’s Growth’. Washington, DC: World Bank, Chief Economist Office for Latin America and the Caribbean. Lederman, D., M. Olarreaga, and I. Soloaga (2007). ‘The Growth of India and China in World Trade: Opportunity or Threat for Latin America and the Caribbean?’. Policy Research Working Paper 4320. Washington, DC: World Bank. López-Cordóva, E., A. Micco, and D. Molina (2007). ‘Competing with the Dragon: Latin American and Chinese Exports to the US Market’. In J. Santiso (ed.), The Visible Hand of China in Latin America. Paris: Development Centre Studies, OECD. Lora, E. (2007). ‘Should Latin America Fear China?’. In J. Santiso (ed.), The Visible Hand of China in Latin America. Paris: Development Centre Studies, OECD. Machado, J., and G. Ferraz (2006). Comércio Externo da China: Efeitos sobre as Exportacões Brasileiras, Texto para Discussão No. 1182. Brasilia: IPEA. Meller, P., and G. Contreras (2003). La competitividad de las exportaciones chinas en los mercados de Estados Unidos y Japon. CIEPLAN Serie Estudios Socio/Económicos No. 16, Santiago. Moreira, M. (2004). ‘Fear of China: Is there a Future for Manufacturing in Latin America?’. Background paper for IADB. Moreira, M. (2007). ‘Fear of China: Is there a Future for Manufacturing in Latin America?’. World Development, 35 (3): 355–76.
240 Rhys Jenkins Murphy, H., C. Swann, and M. Drajem (2007). ‘Economic Outlook: Competition from China Squeezes Developing Countries’. International Herald Tribune, 2 April. Neme Castillo, O. (2006). La competencia entre México y China: la disputa por el Mercado de Estados Unidos. Mexico D.F.: Miguel Angel Porrúa. Nordas, H. K. (2004). ‘The Global Textile and Clothing Industry Post the Agreement on Textiles and Clothing’. Geneva: WTO. Mimeo. Rumbaugh, T., and N. Blancher (2004). ‘China; International Trade and WTO Accession’. IMF Working Paper 04/36. Washington, DC: IMF. Santos-Navarro, G. (2005). ‘China’s Competitive Threat to Mexico: Is the Future of Maquiladoras in Mexico Threatened by the Emergence of China?’. Norwich: School of Development Studies, University of East Anglia. MA Dissertation. Schott, P. K. (2004). ‘The Relative Competitiveness of China’s Exports to the United States vis-à-vis Other Countries in Asia, the Caribbean, Latin America and the OECD’. New Haven: Yale School of Management. Mimeo. Sica, D. (2007). ‘Algunos elementos de análisis sobre la presencia de China en la relácion Argentina-Brasil’. abeceb.com economía on line.(date accessed Feb 1 2007). Thompson, G. (2005). ‘Mill Closings Hit Hard in Central America’. New York Times, 25 March. USITC (United States International Trade Commission). Interactive Tariff and Trade Data Web. Available at http://dataweb.usitc.gov/.(date accessed 23 July 2007). USTR (Office of the United States Trade Representative) (2005). ‘The Case for CAFTA: Growth, Opportunity and Democracy in Our Neighborhood’. CAFTA Policy Brief, February. Washington, DC: Office of the United States Trade Representative. WTO (World Trade Organization) (2007). ‘Trade Profiles 2006’(p.38) published in Geneva, with an electronic version at http://www.wto.org/english/res_e/booksp_e/ anrep_e/trade_profiles06_e.pdf (date accessed 26 March 2010). Yang, Y. (2003). ‘China’s Integration into the World Economy: Implications for Developing Countries’. IMF Working Paper 03/245. Washington, DC: IMF.
12 Component Trade and China’s Global Economic Integration Kunwang Li, Ligang Song and Xingjun Zhao
Introduction A symbol of China’s economic integration into the global economy is its progressive engagement in international commodity trade, with an increasing scale and intensity. From 1992 to 2005, the average growth rate of China’s total trade was 18.5 per cent per annum – twice as high as the growth of gross domestic product (GDP) in the same period. China’s total trade reached US$1.76 trillion in 2006, with a trade dependency ratio (defined as the ratio of total trade over its GDP) rising to about 68 per cent by the national account measures (UNCTAD 2006). Among this increasing trade, China’s engagement in the so-called international fragmentation of production – namely, ‘cross-border dispersion of component production/assembly within vertically integrated manufacturing industries’ (Feenstra 1998; Jones and Kierzkowski 2005) – has become an increasingly important form of its economic integration into the regional as well as the global economy. The development of component trade is significant for the following reasons (Jones et al. 2005). First, it has exhibited a dynamism exceeding that of trade in final goods, as breaking down the integrated process into separate stages of production opens up new possibilities for exploiting gains from specialization (Jones and Kierzkowski 2001). Second, despite its being a type of trade increasingly paramount in advanced stages of globalization, it opens up an important role for developing economies (unlike intra-industry trade, which has been conducted mainly among industrialized countries). Third, it is more closely related to the development and liberalization of the service sector than to final good trade, particularly with respect to transportation, communication, banking and insurance, and other connecting service links. This is because increasing returns to scales tend to be associated with the development of these service sectors, which could in turn reduce the costs associated with the fragmentation process. The global total trade in parts and components increased from US$400 billion in 1992 to US$1.25 trillion in 2005. The share of component trade 241
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in the total trade of manufacturing goods increased from 21 per cent to 26 per cent. Such a trend is especially pronounced in East Asia, which has accounted for more than half of the trade in parts and components. East Asia is progressively replacing the United States and the European Union as the world’s largest region for trade in parts and components, forming a large integrated network for production (Athukorala 2006). China has been playing an increasingly important role in this development. This chapter presents the recent trend of trade in parts and components between China and its main trading partners. It explores how China’s pattern of trade in parts and components is being determined in terms of the shares of this trade in its total exports and imports, and their respective shares in total manufacturing trade. The analytical approach adopted is an adjusted gravity modelling method incorporating relative labour costs, foreign direct investment (FDI) and telecommunications in estimating the regression models. Policy implications with respect to the role of technology, labour productivity, further development, and liberalization of the service sectors are then discussed.
China’s increasing trade in parts and components The rise of China’s trade has been an important part of the changing pattern of international production segmentation in East Asia. On the import side, the share of parts and components already accounted for more than one-third of the total imports in China in 2002. Among the intermediate goods trade, imports of parts and components increased more significantly than any other intermediate goods and the share of parts and components in total imports increased from 19 per cent in 1997 to 27 per cent in 2002. On the export side, final goods played a dominant part in total exports– accounting for more than 60 per cent of the total in 2002. Meanwhile, China’s intermediate goods trade has been concentrated mainly in Asia: more than 80 per cent comes from Asia and more than 60 per cent of exports of parts and components goes to other Asian economies (Gaulier et al. 2005). In general, importing intermediate goods, then processing and exporting the final goods characterizes China’s trading relations with its main trading partners. To analyse and compare China’s trade in parts and components with its main trading partners, we select 219 kinds of parts and components taken from the United Nations’ Standard International Trade Classification (SITC Version 3) numbers SITC-7 and SITC-8, consisting of 2,800 commodities, following Yeats (2001) and Athukorala (2006).1 Altogether, there are 25 trading partners: the United States of America, Canada, Mexico, the United Kingdom, France, Germany, Italy, Australia, Japan, Korea, Hong Kong, Singapore, Malaysia, the Philippines, Thailand, Indonesia, Vietnam, Myanmar, Cambodia, Brunei, Laos, India, Brazil, Pakistan and South Africa.2
Component Trade and China’s Economic Integration 243
Since the early 1990s, trade in parts and components between China and its trading partners has been increasing rapidly. In 1992, China’s imports and exports of parts and components were worth US$12.1 billion and US$4.3 billion respectively (Figures 12.1 and 12.2). By 2005, China’s 200.00 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00
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Figure 12.1 China’s imports and exports of parts and components: 1992–2005 ( billion US$) Source: Calculated using the data from UN COMTRADE Data set (2006).
40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0
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97 19
19
95 19
94 19
93 19
19
92
0.0
imports
Figure 12.2 Share of parts and components trade in manufacturing trade: 1992–2005 (%) Source: Calculated using the data from UN COMTRADE Data set (2006).
244
Kunwang Li, Ligang Song and Xingjun Zhao
imports and exports of parts and components had increased to US$176 billion and US$110 billion – 15 times and 26 times that in 1992, respectively (Figure 12.1). Growth of imports of parts and components is continuing to outpace that of exports. At the same time, the share of imports of parts and components in total manufacturing imports increased dramatically from 16 per cent in 1992 to 35 per cent in 2005 (Figure 12.2). The fact that component trade accounted for more than one-third of total manufacturing imports illustrates China’s increasing role in international fragmentation of production. To further analyse the increasing trend of trade in parts and components, one needs to look at the distribution of component trade by sector as well as by destination. The former tells how component trade concentrates on certain sectors and the latter shows with whom China has engaged in developing component trade.
The pattern of component trade by sectors In 1992, China’s exports of parts and components for machinery and transportation (SITC-7) were worth US$3.4 billion, which accounted for 80 per cent of the total exports of parts and components and 5.1 per cent of manufacturing exports (Table 12.1). In the same year, its exports of parts and components for other manufacturing goods were worth US$800 million, accounting for 19 per cent of total exports of parts and components and
Table 12.1
China’s trade in parts and components, 1992–2004 SITC-7 Value
Share in
SITC-8 Share in
Value
Share in
Share in
(US$ P&C manufacturing (US$ P&C manufacturing billion) (per cent) (per cent) billion) (per cent) (per cent) Exports 1992 1996 2000 2004 Imports 1992 1996 2000 2004
3.4 10 28 87 10 20 51 148
80 87 92 94
5.1 7.8 12.4 15.9
0.8 1.5 2.4 5.5
19.1 13.2 8.0 5.8
1.2 1.2 1.1 1.0
87 92 96 97
15.8 17.7 29.1 33.4
1.6 1.7 2.1 4.1
13.1 8.1 4.0 2.7
2.4 1.1 1.2 1.0
Note: SITC-7 denotes parts and components for machinery and transport equipment, and SITC-8 represents miscellaneous manufacturing commodities. Source: Authors’ calculations based on UNCTAD (2006).
Component Trade and China’s Economic Integration 245
1.2 per cent of manufacturing exports. Exports of parts and components accounted for 6.3 per cent of total manufacturing exports. In 2004, China’s exports of parts and components for machinery and transportation were worth US$87.5 billion, accounting for 94 per cent of the total exports of parts and components and 15.9 per cent of total manufacturing exports. Its exports of parts and components for other manufacturing goods were worth US$5.5 billion, accounting for 5.8 per cent of the total exports of parts and components and 1 per cent of manufacturing exports. Total exports of parts and components accounted for 16.9 per cent of total manufacturing exports. The annual growth rate of exports of parts and components for machinery and transport in this period was 31 per cent, while that of other manufacturing was 17.1 per cent. The annual growth rate of total exports of parts and components was 29.3 per cent. China’s imports of parts and components for machinery and transportation were worth US$10 billion in 1992, accounting for 87 per cent of total imports of parts and components and 15.8 per cent of total manufacturing imports (Table 12.1). China’s imports of parts and components for other manufacturing goods were worth US$1.6 billion, accounting for 13 per cent of total imports of parts and components and 2.4 per cent of total manufacturing imports in the same year. Total imports of parts and components accounted for 18.2 per cent of total imports of manufactured products. In 2004, China’s imports of parts and components for machinery and transportation increased to US$148 billion, accounting for 97 per cent of total imports of parts and components and 33.4 per cent of total manufacturing imports. China’s imports of other parts and components climbed to US$4.1 billion, accounting for 2.7 per cent of total imports of parts and components and 1 per cent of total manufacturing imports. Total imports of parts and components accounted for 34.3 per cent of manufacturing product imports. The annual growth rate of imports of machinery and transportation products was, on average, 24.7 per cent and that of imports of other parts and components was 8.2 per cent in this period. The annual growth rate of total imports in parts and components was 23.4 per cent, on average, in this period. Machinery and transportation products increased more quickly than other products in component trade. In particular, office machinery and equipment (SITC-75), telecommunications and recording equipment (SITC-76) and electrical machinery and parts (SITC-77) played dominant roles, accounting for nearly 80 per cent of the total component trade. As a result, the share of machinery and transportation in the total trade of parts and components is increasing while that of other manufacturing products is decreasing over time. This finding is consistent with the fact that the composition of China’s exports has been moving quickly towards those capital and technology-intensive products in recent years.
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Kunwang Li, Ligang Song and Xingjun Zhao
China has been running component-trade deficits, caused mainly by rapid increases in its import of parts and components for machinery and transportation products. This trade gap tends to increase further as China’s capacity for producing these products increases, so China will continue to rely heavily on imported parts and components. On the other hand, China has become a net exporter of parts and components relating to labour-intensive manufacturing products since 2000. The growth rates of China’s component trade have been higher than the growth rates of manufacturing sectors as well as the growth rates of total trade. This suggests that the pattern of the international division of labour in which China has participated has been deepened to cover finer international fragmentation of production. The further development of such patterns means that countries involved will gain from trade in parts and components rather than from trade in final goods. Such development will cement even closer trading relationships among countries involved in such a network of production. This point can be illustrated by looking at China’s trade in parts and components by partners.
China’s trade in parts and components by partners Although the regional distribution of China’s imports of parts and components varies over time, the main sources of China’s imports of parts and components seem to concentrate in Northeast Asia, which accounted for more than 50 per cent of China’s total imports of components (Table 12.2). Japan’s share was increasing between 1992 and 1997, but started to fall after 1998, due possibly to the impact of the East Asian financial crisis. Japan, however, together with Korea (whose share increased rapidly throughout the period), ranked the highest in terms of their shares of China’s imports of components. The shares of the United States and Canada reached their peak about 2000 and started to decline thereafter. Hong Kong’s share decreased from 38.7 per cent to 2.15 per cent, reflecting the fact that it was no longer a major supplier of parts and components to China. The shares from four EU countries (Germany, France, the United Kingdom and Italy) also decreased to low levels in 2005. Imports of parts and components from five Association of South-East Asian Nations (ASEAN) countries (Malaysia, the Philippines, Indonesia, Singapore and Thailand) increased rapidly from less than 1 per cent in 1992 to 18 per cent in 2005. The data show that China has been relying less and less on the major industrialized countries in Europe and North America to provide parts and components for its part in the international fragmentation of production. In contrast, the newly industrialized economies and ASEAN countries have become important sources of China’s imports of parts and components. There is also a strong tendency for Korea and large ASEAN countries to replace Japan and Hong Kong in providing China with parts and components.
Component Trade and China’s Economic Integration 247 Table 12.2 Shares of component imports trade in China, by region, 1992–2005 (per cent) Region/country
1992
1993
1996
1997
2000
2001
2004
2005
World a NAFTA United States Canada Mexico EU (4) Germany France United Kingdom Italy Northeast Asia Hong Kong Japan Korea ASEAN (5) Indonesia Malaysia Philippines Singapore Thailand Australia Other
100 10.1 9.6 0.6 – 13.9 8.2 2.4 1.3 2.1 61.5 38.7 20.2 1.8 0.8 – 0.1 – 0.6 0.1 0.1 14.2
100 10.7 10.3 0.4 – 13.7 8.7 1.6 1.7 1.6 54.5 16.2 32.1 3.9 2.2 – 0.3 – 1.6 0.1 0.2 20.9
100 10.7 9.8 0.9 – 10.2 6.4 1.4 1.3 1.2 58.8 9.5 35.1 7.5 6.7 0.1 1.5 0.3 3.7 1.1 0.2 19.3
100 11.9 10.9 0.9 0.1 10.8 6.3 1.8 1.7 0.9 57.6 7.6 32.3 8.4 9.3 0.2 2.1 0.2 5.4 1.4 0.2 19.4
100 10.7 8.5 1.6 0.6 10.9 5.7 2.7 1.7 0.9 52.7 5.4 25.9 9.3 12.1 0.4 4.5 1.9 2.8 2.4 0.1 25.3
100 11.4 9.4 1.2 0.8 11.2 6.4 2.3 1.5 0.9 51.1 5.1 24.2 8.4 13.4 0.5 5.4 2.2 2.7 2.6 0.1 25.8
100 7.0 5.8 0.5 0.6 7.6 5.2 1.2 0.6 0.6 52.9 2.6 20.8 12.6 16.8 0.5 6.6 4.4 3.1 2.2 0.1 32.1
100 6.5 5.5 0.4 0.6 6.1 3.7 1.2 0.6 0.5 53.4 2.1 17.9 15.3 18.0 0.4 6.9 5.2 3.4 2.1 0.1 33.7
Note: a North American Free Trade Agreement. Source: Authors’ calculations based on UNCTAD (2006).
The rapid development of component trade between China and its neighbouring economies in East and Southeast Asia indicates that these regional economies have become increasingly integrated through the international fragmentation of production. Total exports of parts and components from China to industrialized countries have been increasing steadily, while those to East Asia – in particular, to Hong Kong and Japan – have been falling (Table 12.3). Despite this trend, East Asia’s average share of China’s exports to the world was about 55 per cent compared with 17 per cent for the North American Free Trade Agreement (NAFTA) countries and 7 per cent for the European Union in 2005. Exports to Korea have increased from 1.3 per cent to 5 per cent and those to six ASEAN countries (Singapore, the Philippines, Indonesia, Malaysia, Thailand and Vietnam) have also increased – from 4.1 per cent in 1992 to 12 per cent in 2005. Korea, Japan and the six ASEAN countries have become important sources of China’s imports of parts and components, accounting for more than 50 per cent of China’s total imports of these products (Table 12.3). As the shares
248
Kunwang Li, Ligang Song and Xingjun Zhao
Table 12.3 Share of exports of parts and components from China, by region, 1992–2005 (per cent) Region/country
1992
1993
1996
1997
2000
2001
2004
2005
World NAFTA United States Canada Mexico EU (4) Germany France United Kingdom Italy Northeast Asia Hong Kong Japan Korea ASEAN (5) Indonesia Malaysia Philippines Singapore Thailand Australia Other
100 7.2 6.8 0.3 0.1 2.2 1.0 0.3 0.7 0.2 77.1 62.3 9.1 1.2 4.1 0.6 0.8 0.3 1.9 0.5 0.2 14.2
100 17.2 16.1 0.9 0.2 9.1 4.6 1.8 2.1 0.7 56.7 30.1 17.4 2.4 6.4 1.2 1.2 0.3 2.6 0.8 0.3 20.9
100 16.0 15.2 0.7 0.1 5.8 2.6 0.9 1.7 0.6 63.6 26.7 20.5 5.5 10.7 1.4 2.3 0.7 5.1 0.8 0.4 19.3
100 16.0 15.0 0.7 0.3 6.2 2.6 1.1 1.9 0.5 62.5 26.4 19.6 4.9 11.2 1.6 2.2 1.3 4.9 1.0 0.4 19.4
100 16.1 14.4 1.0 0.6 7.4 3.1 1.2 2.3 0.8 59.5 26.6 15.4 4.8 12.4 1.1 2.0 1.3 5.7 2.0 0.5 25.3
100 16.6 14.9 0.7 1.0 6.6 2.6 0.9 2.2 0.8 59.6 27.0 15.0 4.6 12.7 1.1 3.4 0.7 5.1 2.1 0.5 25.8
100 17.2 15.5 0.6 1.0 7.1 3.2 1.1 2.0 0.7 57.4 28.0 11.7 5.3 12.3 0.9 3.3 1.4 4.8 1.4 0.5 32.1
100 17.2 15.4 0.7 1.0 7.1 3.7 0.9 1.9 0.7 55.7 28.4 10.5 4.9 11.8 0.9 3.2 1.2 4.8 1.4 0.5 33.7
Source: Authors’ calculations based on UNCTAD (2006).
of Japan and Hong Kong are decreasing, those of Korea and ASEAN countries have been increasing. This implies that there have been some substitution effects between Japan, Hong Kong and other Asian countries in providing parts and components to China. Despite this substitution effect, China’s continuing strong demand for parts and components from the region will be conducive to the economic growth of regional economies. About 55 per cent of China’s exports of parts and components have been to Northeast and Southeast Asia, with China’s major exporting markets being Hong Kong, Japan, Korea, Singapore, Malaysia and Thailand. This trend is likely to be strengthened further. At the same time, China’s export shares to North America and Europe have been steadily increasing over time. Overall, China’s relatively high export and import shares of parts and components to East Asia supports the view that China has engaged more deeply with East Asian economies in forging this new type of international specialization in production. China’s export shares of parts and components to Australia have been generally higher than its import shares, but both shares have been relatively low compared with East Asian economies.
Component Trade and China’s Economic Integration 249
Determinants of China’s component trade In explaining the phenomenon of component trade or international fragmentation of production, Jones (2007) postulates the following theories. The first is a Ricardian one, which says that workers have different skills across different areas of a country, and, when the skills required in each production block (fragments) differ, a dispersion of activity according to comparative advantage could lower marginal production costs. The second theory is a Heckscher–Ohlin type of difference, namely, the production blocks differ from each other in the proportions of different factors that are required. This suggests that labour-intensive production blocks are better when located in regions in which labour is relatively inexpensive compared with productivity. The third theory is that fragmentation introduces new costs, namely, the costs of connecting the two or more production blocks by service links such as transportation and communication. Therefore, ‘the movement towards deregulation, especially of services, and the technological changes that have drastically reduced the costs of communication as well as making transportation less expensive have all encouraged a fragmentation of previously vertically integrated production processes’ (Jones and Marjit 2001: 365). Furthermore, it is argued that the optimal degree of fragmentation depends on the size of the market, so economic growth in general encourages fragmentation and trade in parts and components (Jones et al. 2005). We examine in this section the main determinants of China’s trade in parts and components based on these theories. The theory of comparative advantage has been consistent with China’s trading pattern: China produces and exports a large range of mainly labourintensive goods, such as textiles and clothing, shoes, toys, travel goods and furniture. The question is whether the theory of comparative advantage can be applied also in analysing intra-product trade based on vertical specialization of production such as component trade. China tends to focus on producing parts and components related to labour-intensive products due to its abundant supply of labour. For example, the production of personal computers requires a lot of components, including central processing units, mainboards, sound cards, display cards, screens and so on. Although China has no comparative advantage in producing some of the key components, it can assemble and export a large quantity of personal computers since it has low-cost production for the assembly of those components. One way to examine the relationship between comparative advantage and the production and trade of parts and components is to compare labour-cost differences across different countries. Wages are a good index as they reflect the supply and demand of the labour market as well as the quality of labour. When a country’s wage levels are relatively low, it could indicate that the
Source: EIU.
1995
16.0 16.4 16.8 17.2 17.6 16.9 16.3 16.5 ....................................1.1 25.2 23.9 25.3 30.1 17.3 16.5 17.1 19.3 14.3 12.5 13.0 13.8 18.9 15.4 15.4 15.7 13.2 12.6 14.1 15.4 16.2 18.9 21.0 23.6 5.2 5.6 6.4 7.3 3.9 4.3 4.6 4.8 4.9 5.2 6.2 7.6 0.6 0.7 0.7 0.8 0.3 0.3 0.4 0.4 1.2 1.3 1.2 1.4 1.9 2.0 2.2 2.5 n.a. n.a. n.a. n.a. 0.5 0.4 0.5 0.5 0.2 0.3 0.3 0.3
1994
United States Canada Mexico Germany France United Kingdom Italy Australia Japan Korea Hong Kong Singapore Philippines Indonesia Thailand Malaysia Brazil India China
1993
1992 17.8 17.0 1.0 29.6 19.0 14.1 17.1 17.0 20.5 8.2 5.1 8.2 0.8 0.5 1.5 2.7 5.8 0.5 0.4
1996 18.2 16.8 1.1 26.0 17.1 15.7 16.1 16.7 19.1 7.8 5.4 8.0 0.8 0.4 1.3 2.6 5.8 0.5 0.5
1997 18.6 15.9 1.2 25.2 17.3 17.0 15.8 15.1 17.7 5.7 5.4 7.4 0.7 0.1 1.1 1.9 5.6 0.5 0.5
1998 18.9 16.1 1.3 24.6 17.0 17.3 15.5 15.9 20.5 7.4 5.4 7.0 0.7 0.2 1.2 2.0 3.5 0.6 0.5
1999 19.7 16.5 1.6 22.7 15.5 16.7 13.8 14.4 22.0 8.2 5.4 7.2 0.7 0.3 1.1 2.1 3.6 0.6 0.6
2000 20.6 16.2 1.9 22.5 15.6 16.7 13.6 13.3 19.4 7.7 5.7 7.0 0.7 0.3 1.0 2.1 3.0 0.6 0.7
2001
Unit labour costs across different countries, 1992–2004 (US$ of unit labour per hour)
Country
Table 12.4
21.4 16.7 1.9 24.2 17.1 18.2 14.7 15.4 18.6 8.8 5.7 6.7 0.7 0.4 1.1 2.2 2.7 0.7 0.8
2002 22.3 19.4 1.9 29.6 21.1 21.2 18.1 19.8 20.3 10.0 5.5 7.2 0.7 0.5 1.1 2.2 2.8 0.8 0.9
2003
22.9 21.4 1.9 32.5 23.9 24.7 20.5 23.1 21.9 11.5 5.5 7.4 0.7 0.5 1.2 2.2 3.4 0.9 1.0
2004
Component Trade and China’s Economic Integration 251
country has comparative advantage in producing labour-intensive products, including parts and components. In the period 1992–2004, labour costs in China were far lower than those in most industrialized countries, including the United States, Japan, Canada, Germany and the United Kingdom (Table 12.4). They were also relatively lower than in many developing economies in Asia, such as Korea, Hong Kong, Singapore, Malaysia and Thailand. With respect to the impact of economy of scale on component trade, it is usually the case that rapid economic growth requires a large market to accommodate the products being produced. The process of trade liberalization in the past few decades – enhanced through multilateral, bilateral or unilateral means – provides China with an external environment in which rising trade from China has been accommodated rather smoothly. The enlarged markets for products made in China have created an enormous economy of scale in production. It is known that the degree of specialization of production tends to be intensified when there are economies of scale in production. As economy of scale is enlarged, the production and trade of parts and components within and across different countries tend to be enhanced. On the other hand, China has a large domestic market, implying that there is relative thickness in the market in those competitive sectors (meaning that there are relatively more suppliers of intermediate goods for a given sector). This is especially true in sectors such as textiles and clothing, shoes, computers and automobiles. As Grossman and Helpman (2003) note, this characteristic reduces the search costs for multinational companies investing in overseas markets. At the same time, continuing domestic reforms in China provided an improved legal and regulatory environment in which foreign investments have flourished, deepening the process of international specialization in production. China’s transportation and telecommunications industries have experienced rapid growth since the 1990s, providing the necessary infrastructure for China’s participation in international specialization. Figures 12.3 and 12.4 show the recent development of China’s transportation industry. The number of civil aviation airline routes increased from 287 in 1986 to 1,257 in 2005 – increasing by 3.4 times – and international airline routes also increased, from 108,000 km to 856,000 km. The capacity of seaports also increased – from 300 million tonnes in 1985 to 2.9 billion tonnes in 2005. China’s telecommunications industry has developed rapidly. The number of fixed lines per 1,000 people increased from fewer than 10, on average, in 1992 to 241 in 2004 (Figure 12.5). Along with the development of the telecommunications industry, call fees are declining. For example, in 1997, the average cost of calling the United States was US$6.70, but this reduced to US$2.90 in 2004. Meanwhile, the number of mobile phones in China has reached 480 million by March 2007 – making it the largest mobilephone market in the world – and the number of people accessing the
252 Kunwang Li, Ligang Song and Xingjun Zhao 1,400 1,200 1,000 800 600 400 200
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
0
Figure 12.3 Number of civil aviation airline routes in China, 1986–2005 Source: Data from CEI.
350,00 300,00 250,00 200,00 150,00 100,00 50,000
Figure 12.4
2005
2004
2003
2002
2001
2000
1995
1990
1985
0
Scale of seaport trade in China, 1985–2005 (10,000 tonnes)
Source: Data from NBSC (2006).
Internet reached 144 million by March 2007, making China the second largest country using the Internet after the United States. 3 These facts suggest that the rapid development of China’s transportation and telecommunication industries could be another factor influencing the pace and depth of
Component Trade and China’s Economic Integration 253 300 250 200 150 100 50
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
Figure 12.5 Number of telephones per 1,000 people in China Source: Data from World Bank (2006).
China’s participation in international fragmentation of production. This is mainly because the developments in these service links tend to reduce the costs of transactions with considerable benefits from the economies of scale associated with such developments. FDI has been considered an important determinant of trade in parts and components in the literature because multinational corporations are an important channel for conducting trade in intermediate goods through intrafirm trade. Multinational firms can choose to invest in host countries and make use of the local comparative advantage to produce their intermediate goods. For example, Kaminski and Smarzynska (2001) studied the impact of FDI on Poland’s internationalization process and found that the large FDI inflow helped Poland achieve international specialization and increased the country’s trade. Cheng et al. (2001) studied the relationship between FDI and international specialization by using a partial equilibrium model to examine trade between Hong Kong and Guangdong Province, incorporating FDI. They found that FDI was an important factor for production specialization and trade in parts and components in China. China has absorbed a large amount of FDI since the 1990s and has now become the leading country in the world for the receipt of FDI inflows (Figure 12.6). The rapid increase of FDI inflows into China promotes the development of processing trades (Spencer 2005). The share of the FDI-related manufacturing sector in the total manufacturing sector has been increasing
254 Kunwang Li, Ligang Song and Xingjun Zhao 700 600 500 400 300 200 100
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
0
Figure 12.6
China’s utilized FDI (US$100 million)
Source: Data from CEI
rapidly, and the share of total processing trade increased from 35 per cent in 1988 to 57 per cent in 2003, surpassing the share of ordinary trade. Based on the theories surveyed and the background information, we adopted an adjusted gravity model to examine the determinants of trade in parts and components between China and its trading partners. The basic formula is introduced by Egger and Egger (2005) as follows: LnPCj.China.t
C0
1 3
LnAGDPj.China.t
LnRWAGEj.China.t
2 4
LnDISTj.China.t
LnFDI j.China.t
LnTeleline j.China.t
(1)
where PC represents either the export or import of parts and components between China and its trading partners, AGDP is the average real GDP of China and its trading partners, DIST is the economic distance between China and its trading partners, RWAGE is the relative labour cost of China and its trading partners, FDI is the foreign direct investment, and Teleline is the average number of fixed telephones in China and its trading partners. According to Jones and Kierzkowski (2005), these factors can affect the trade in parts and components as well as the final goods. Thus, Equation 1 can be revised as follows: LnSPCj.China.t
C0
1
LnAGDPj.China.t
3 LnRWAGE j .China.t
2
LnDISTj.China.t
4 LnFDI j .china.t
LnTeleline j.China.t
(2)
Component Trade and China’s Economic Integration 255
where SPC is the share of exports of parts and components in the total trade in manufactured products. A fixed effect is introduced in both equations while conducting the panel data regressions. The variables used in the regressions are defined as follows: to capture the impact of economy of scale, we use the average GDP of China and its trading partners. AGDPj.China
GDPj ⫹ GDPChina 2
(3)
For the distance variable, we use the weighted-average economic distance to represent the transportation costs, in which DISTf is the physical distance between countries. DISTChina.f
DISTf * GDPf 20
(4)
∑ GDP
f
f 1
To reflect the impact of comparative advantage, we use the relative wage (RWAGE) to represent the unit costs of China and its trading partners. RWAGEj.China ⫽
Average cost of unit labour per hourj
(5)
Average cost of unit labour per hourChina
In order to examine the impact of FDI on trade in parts and components, we simply use the share of FDI over that of GDP. To capture the impact of telecommunications, we use the number of telephone lines per 1,000 people to represent the index for telephone lines. The data on trade in parts and components between China and its trading partners are taken from UNCTAD (2006), covering 219 products. The time period runs from 1992 to 2004 and the sample comprises 20 countries, as introduced in the last section. GDP for each country is taken from the Penn World Table 6.2. The indexes we have chosen are ‘CGDP’ and ‘POP’ and their products are used as the GDP variable. The FDI data are taken from the China Statistical Yearbooks for 1993–2005 (NBSC various issues). The data for economic distance are taken from Haveman (2004). The data of telephone lines per 1,000 people are taken from the World Development Indicators (World Bank 2006). The data of average labour unit costs per hour are taken from EIU Country Data (EIU 2007). The empirical results are reported in Tables 12.5 and 12.6, and show the determinants of China’s imports and exports of parts and components respectively. In each table, there are two groups of results: one contains no
Table 12.5 Estimation results of equation 1: Imports LNIMPORT Variable C LNAGDP LNDIST LNFDI LNRWAGE LNTELELINE Fixed effect 2 Adjusted R SE F-statistic Number of observations
(1) –83.84*** (–14.27) 4.83*** (16.42) –0.96*** (–11.75) 0.19*** (3.85) 0.71*** (5.39) – – Yes 0.74 1.14 36.73 221
(2)
–57.32*** (–12.51) 3.53*** (15.51) –0.74*** (–11.12) 0.26*** 6.60 0.64*** 4.98 – – No 0.73 1.13 147.32 221
–85.10*** (–15.45) 4.70*** (16.31) –0.90*** (–10.69) 0.16*** (3.43) 0.31** (2.36) 0.99*** (3.88) Yes 0.76 1.10 38.15 221
–43.69*** (–5.72) 2.76*** (6.59) –0.58*** (–5.84) 0.28*** (7.16) 0.32** (2.17) 0.55* (2.40) No 0.74 1.12 122.31 221
Note: *** 1 per cent significant level, ** 5 per cent significant level, * 10 per cent significant level. T-statistics are in parentheses. Source: Authors’ own estimations.
Table 12.6 Estimation results of equation 1: Exports LNEXPORT Variable C LNAGDP LNDIST LNFDI LNRWAGE LNTELELINE Fixed effect2 Adjusted R SE F-statistic Number of observations
(1) –70.70*** (–9.79) 4.19*** (11.37) –0.68*** (–8.08) 0.24*** (4.32) 0.13 (1.63) – – Yes 0.86 0.64 81.32 221
–47.49*** (–15.42) 3.05*** (18.54) –0.48*** (–9.65) 0.30*** (7.19) 0.05 (0.89) – – No 0.85 0.65 311.78 221
(2) –71.42*** (–8.54) 4.12*** (9.63) –0.65*** (–6.90) 0.22*** (3.72) –0.16* (–1.71) 0.56*** (7.56) Yes 0.87 0.62 83.35 221
–41.76*** (–7.45) 2.73*** (8.71) –0.41*** (–5.29) 0.31*** (6.66) –0.09 (–0.78) 0.23* (1.72) No 0.85 0.65 253.01 221
Notes: *** 1 per cent significant level, ** 5 per cent significant level, * 10 per cent significant level. T-statistics are in parentheses. Source: Authors’ own estimations.
Component Trade and China’s Economic Integration 257
variable of LNTELELINE and the other contains this variable. Both results show good fitness to our model specification, since the R-squares for the two model estimations are 0.7 and 0.8 respectively. The results show that the coefficients of LNAGDP in all regressions are positive and statistically significant at the 1 per cent level, implying that China’s size and economies of scale and its trading partners have a positive impact on China’s imports of parts and components. That is, the larger the economies of scale, the greater the trade in parts and components. Furthermore, comparing this result with that from the regression using Equation 2, we found that the impact of economies of scale on trade in parts and components was larger than that of final goods. This is consistent with findings obtained in Jones and Kierzkowski (2005). The coefficients of LNDIST – as the measurement of the impact of transport and other costs on bilateral trade flows – are negative and statistically significant, as expected. Despite the fact that the rapid development of telecommunications techniques leads to the ‘death of distance’ (Cairncross 1997), the estimated coefficient of economic distance is still negative and statistically significant. This implies that trade in parts and components in China relative to its trading partners is still sensitive to the changes in transportation costs and the physical distance between them. As expected, FDI has a positive impact on China’s imports and exports of parts and components and it is statistically significant at the 1 per cent level. The regression results show that the impact of FDI on exports of parts and components is similar to that of imports, and the magnitudes of the estimated coefficient are within the range of 0.2–0.3. This finding implies that a 1 per cent increase in FDI could lead to a 0.2–0.3 per cent increase in China’s exports of parts and components. The purpose of FDI is twofold: one is to sell products to China’s domestic market and the other is to make use of China’s comparative advantage of low-cost labour. Both motives could lead to increased trade in parts and components. Tables 12.5 and 12.6 also show the results, and LNTELELINE is incorporated as the measurement to capture the impact of telecommunications on component trade. As is expected, the telecommunications service and its coverage have a significant positive impact on international specialization through trade in parts and components. The results of LNRWAGE show significant differences between the regressions of imports of parts and components, which are consistent with the likely prediction of the theory of comparative advantage. For China’s imports of parts and components, the coefficients of foreign countries’ labour costs are positive and statistically significant at the 5 per cent level (Table 12.5). The results imply that the relatively low cost of labour in China is one of the most important determinants of the rapid development of trade in parts and components between China and its trading partners – since China’s comparative advantage in labour costs attracts greater imports of parts and components for assembly.
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The estimated results show, however, that there is no significant difference for the variable of LNRWAGE (Table 12.6) in determining China’s exports of parts and components. The insensitivity of wage differentials in determining China’s exports of parts and components remains a puzzle, which requires further study. Since all the factors included in the regressions would have similar effects on trade in final products, we can also run the regression with the share of trade in parts and components in total manufacturing trade as the dependent variable in both equations. The estimation results are reported in Table 12.7. Comparison with the previous results shows that economies of scale have a significantly positive impact on trade in parts and components, while transportation costs have a significantly negative impact. The findings are consistent with those reported in Tables 12.5 and 12.6. Furthermore, the impact of FDI on exports in parts and components is not statistically significant compared with the results in Table 12.6. The impact of labour costs is significantly negative in the export shares in total manufacturing exports, which is in contrast with the positive correlation found in China’s imports of parts and components (Table 12.5). This suggests that the theory of comparative labour cost applies more to Table 12.7 Estimation results of equation 2: Export shares in total manufacturing exports LNSEXPORT Variable C LNAGDP LNDIST LNFDI LNRWAGE LNTELELINE Fixed effect Adjusted R 2 S.E. F-statistic Number of observations
(1) –19.37*** (–5.97) 1.09*** (6.87) –0.34*** (–12.25) –0.02 (–1.04) –0.12*** (–4.46) – – Yes 0.70 0.39 29.44 221
–19.00*** (–10.30) 1.07*** (11.93) –0.33*** (–16.43) –0.01 (–0.54) –0.14*** (–5.11) – – No 0.67 0.40 110.79 221
(2) –19.46*** (–6.16) 1.09*** (7.06) –0.34*** (–12.94) –0.02 (–1.15) –0.15*** (–6.24) 0.07* (1.77) Yes 0.70 0.40 27.69 221
–17.14*** (–9.88) 0.97*** (11.33) –0.31*** (–16.76) 0.00 (–0.33) –0.18*** (–5.85) 0.08** (2.54) No 0.67 0.40 88.81 221
Note: *** 1 per cent significant level, ** 5 per cent significant level,* 10 per cent significant level, T–statistic in parentheses. Source: Authors’ estimation.
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China’s imports of parts and components than to its exports of them. Since China’s imports of parts and components are more concentrated on capital- and technology-intensive goods such as machinery and transportation (Table 12.1), the finding of positive correlation of labour cost differentials may suggest that foreign firms investing in China are more sensitive to labour cost differences between China and elsewhere in those more sophisticated parts and components. This is understandable, as labour costs in producing these kinds of intermediate goods in developed countries are much higher than those in China (Table 12.4), whereas the labour cost differentials between industrialized and developing economies in producing less sophisticated intermediate goods tend to be narrower. Since China’s exports of parts and components concentrate more on those less sophisticated labour-intensive products, both foreign firms and domestic firms producing such intermediate goods tend to become less sensitive to labour cost differentials. Under this circumstance, market size, economies of scale and service links rather than FDI and labour cost differentials will play more important roles in determining the export pattern of parts and components. Finally, consistently with the findings reported in Tables 12.5 and 12.6, the development in telecommunications is also correlated positively with the shares of exports of parts and components in total manufacturing exports.
Conclusion The empirical results reveal the following points. First, China’s rapid economic growth, increasing market size, and economies of scale are important factors in explaining the rapid increase of bilateral trade in parts and components with its trading partners. Second, the spatial distance and transportation costs have significant negative impact on China’s trade of parts and components. This suggests that the reduction in transportation costs by technological innovation and investment could enhance trade in parts and components, and thereby deepen the process of international specialization involving China and its main trading partners. Third, FDI has a positive impact on China’s participation in international fragmentation of production, taking advantage of China’s comparative advantage in low labour costs in assembling of intermediate goods and producing final goods for domestic sales and exports. Finally, the development of the telecommunications and related service sector helps intensify the degree of international specialization by reducing transaction costs, which will encourage more domestic and foreign firms to be involved in international fragmentation of production. Along with the trend of globalization, China has become an important player in the international production chain through its increasing share of trade in parts and components with its main trading partners. China has now been incorporated into the regional production network consisting of Korea, Japan, Hong Kong, Taiwan and six ASEAN countries and is engaged
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in trading relationships with North American and European economies for parts and components. China has achieved tremendous dynamic gains from trading in parts and components, especially with respect to its industrial upgrading through technological learning associated with processing trade (Amighini 2004). For the East Asian economies, the accession of China into such a regional production network has not only encouraged the further development of regional trade liberalization, including service trade liberalization; it has also provided an enlarged market for all the regional economies. As a result, all parties involved will gain from participating in this dynamic process. Given the prospects of the rapid growth of the Chinese economy, its current and planned massive investment in research and development and in infrastructure, including transportation and telecommunications, its continual policies in attracting FDI and its rapid move towards liberalizing its service sector, the scope for China and its trading partners to benefit from the process of international fragmentation of production is tremendous.
Notes 1. SITC Version 3 was adopted in 1992, so the data series used in this chapter began in that year. 2. In selecting partnering countries, consideration includes country representation and availability of data. 3. See ‘Internet in the People’s Republic of China’ on Wikipedia (available online at http://www.wikipedia.com).
References Amighini, A. (2004). ‘China in the International Fragmentation of Production: Evidence from the ICT Industry’. The European Journal of Comparative Economics, 2 (2): 203–19. Athukorala, P. C. (2006). ‘Multinational Production Networks and the New GeoEconomic Division of Labour in the Pacific Rim’. Paper for the thirty-first Pacific Trade and Development Conference, Guadalajara, Mexico. Cairncross, F. (1997). The Death of Distance. Boston: Harvard Business Press. CEI (China Economic Information Network) (2007). Available at: www.cei.gov.cn (accessed March 2007). Cheng, L., L. D. Qiu, and G. Tan (2001). ‘Foreign Direct Investment and International Fragmentation of Production’. In S. Arndt, and H. Kierzkowski (eds), Fragmentation: New Production Patterns in the World Economy. New York: Oxford University Press, 165–86. Egger, H., and P. Egger (2005). ‘The Determinants of EU Processing Trade’. World Economy, 28 (2): 147–68. EIU (Economist Intelligence Unit). CountryData. Available at: www.eiu.com (accessed March 2007). Feenstra, R. (1998). ‘Integration of Trade and Disintegration of Production in the Global Economy’. Journal of Economic Perspectives, 12 (4): 31–50.
Component Trade and China’s Economic Integration 261 Gaulier, G., F. Lemoine, and D. Unal-Kesenci (2005). China’s Integration in East Asia: Production Sharing, FDI and High-tech Trade. CEPII Working Paper 2005–09. Paris: Centre D’Etudes Prospectives et D’Informations Internationales. Grossman, G. M., and E. Helpman (2003). ‘Outsourcing vs FDI in Industry Equilibrium’. Journal of the European Economic Association, 1: 317–27. Haveman, J. (2004). ‘Industry Concordances’. Available at: www.macalester.edu/ research/economics/PAGE/HAVEMAN/Trade.Resources/TradeConcordances. html#gravity (accessed March 2007). Jones, R. W. (2007). ‘Production Fragmentation and Outsourcing: General Concerns’. Rochester: University of Rochester. Mimeo. Jones, R. W., and H. Kierzkowski (2001). ‘A Framework for Fragmentation’. In S. Arndt, and H. Kierzkowski (eds), Fragmentation: New Production Patterns in the World Economy. Oxford: Oxford University Press, 17–34. Jones, R. W., and H. Kierzkowski (2005). ‘International Fragmentation and the New Economic Geography’. North American Journal of Economics and Finance, 16 (1): 1–10. Jones, R. W., and S. Marjit (2001). ‘The Role of International Fragmentation in the Development Process’. American Economic Review, 91 (2): 363–6. Jones, R. W., H. Kierzkowski, and L. Chen (2005). ‘What Does Evidence Tell us About Fragmentation and Outsourcing?’. International Review of Economics and Finance, 14: 305–16. Kaminski, B., and B. K. Smarzynska (2001). ‘Foreign Direct Investment and Integration into Global Production and Distribution Networks: The Case of Poland’. WB Policy Research Working Paper 2646. Washington, DC: World Bank. NBSC (National Bureau of Statistics China) (various issues). China Statistical Yearbook. Beijing: China Statistical Press. Penn World Tables, Centre for International Comparisons at the University of Pennsylvania, Philadelphia. Spencer, B. J. (2005). ‘International Outsourcing and Incomplete Contracts’. NBER Working Paper 11418. Cambridge, MA: National Bureau of Economic Research. UNCTAD (United Nations Conference on Trade and Development) (2006). Trade and Development Report. New York and Geneva: United Nations. World Bank (2006). World Development Indicators. Washington, DC: World Bank. Yeats, A. J. (2001). ‘How Big is Global Production Sharing?’. In S. Arndt and H. Kierzkowski (eds), Fragmentation: New Production Patterns in the World Economy. Oxford: Oxford University Press.
13 The Rise of China and India: Lessons and Implications for Global Development Amelia U. Santos-Paulino and Guanghua Wan
Introduction This book comprehensively analyses the sources, trends and consequences of the rise of China and India as global, economic and political powers. The volume focuses on the role of productivity, labour, private investment, trade specialization, energy and women’s participation in economic activities. It also explores new paradigms for global development and the implications for policy. In what follows we will attempt to generalize and place in a common context the key messages emanating from the book, particularly on the successful experiences of China and India and the implications for development and developing countries.
Implications for development China’s and India’s development strategies: Lessons for developing countries The first part of the book focuses on the role of China’s and India’s strategies, and provides useful lessons for development policy. Chapters 1–3 present a systematic background to the volume, by examining the legacy of China and India as global, economic and political engines. The role of globalization in shaping China’s and India’s path to growth and prosperity is a fundamental corollary from the studies in this collection. But, at the same time, the Asian engines of growth are leading to a more globalized world. China and India have significantly imprinted global economic and political dynamics, and have brought new alternatives and sources of development assistance for developing countries, in both the economic and the political spheres. China is regarded as an economic and political driver of the global economy. China’s role and global impact transcend the areas of international 262
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trade in goods and services, energy security, and environmental sustainability. This volume goes beyond the current literature in analysing the potential effects of China’s growing economic and political power for the nature of globalization (Chapter 1). The economic engagement of China with developing countries and regions such as sub-Saharan Africa and Latin America entails interactions in the areas of labour, human rights, international relations, and security, conveying China’s inherent values and priorities, and hence affecting power relations and organizational forms at the global level. But the emergence of China poses challenges as well as opportunities for global economic, social and political development. This is particularly the case in its participation in the global trade arena (especially after joining the World Trade Organization, WTO) and other institutions of global governance. China’s growing influence is also reflected in the increased flows of investment and aid to developing economies, particularly Africa. The potential threats are mostly associated with trade and financial flows (mostly aid), and with the social and political implications of China’s financial outflows. Nevertheless, in the midst of the recent global economic crises, emerging countries proved to be a cushion to the declining flows of resources from advanced nations. But these are still emerging issues, which require further empirical assessment and regularity, and will still pave avenues for research. Another set of lessons for developing economies emanates from the internal dynamics of successful emerging countries. The labour market peculiarities, particularly in post-reform China (that is, in a competitive market framework), are a key element in understanding how the fruits of economic growth can accumulate in the form of the labour surplus absorption, not necessarily from increasing real incomes (Chapter 2). This contrasts well with what has been happening in South Africa, where labour and migration dynamics, as in China’s case, have moulded each stage of the country’s development. Chapter 2 offers a comparative study of the two countries’ progress, assessed against the stages of development as demonstrated by the classical Lewis Model. That is, surplus labour from traditional agricultural sector has shifted to the progressive industrial sector, whose growth over time is supposed to absorb the labour, promote industrialization and stimulate sustained development. China and South Africa share various binding characteristics related to the labour markets, mostly their segmentation and inflexibility. These include an extensive rural–urban inequity, rapid rural–urban migration despite various restrictions, and high and rising real wages in the formal sector. China and South Africa differ, however, in the dynamism of their formal sector growth of output and employment, and in the growth of their labour forces. China, a labour-surplus economy, is rapidly developing scarcity, whereas South Africa – which historically has suffered worker shortages – is increasingly evidencing labour surplus in the form of open unemployment.1 But the standard Lewis
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model does not respond to the outlined cases: in neither China nor South Africa does the relative price mechanism that is hypothesized in the model operate. That is, changes in agricultural prices have been determined by government interventions and by other political economy influences, such as trade liberalization. In both countries there is a strong urban bias in economic policies and formal sector wages well above the market-clearing level. A fundamental difference between China’s and South Africa’s labour market performances is the remarkable growth rate of the Chinese economy. China’s comparative advantage in unskilled-labour-intensive activities – alongside a manifest upscaling of its production and export baskets – has iterated with improvement in the investment climate, in tandem with a continuous virtuous circle of growth (Feenstra and Wei 2010 Guariglia and Santos-Paulino 2007; Rodrik 2006). In contrast, South Africa’s relatively slow growth rate is associated with its relatively mature economy, with resources other than unskilled labour being fully employed, and low investor confidence – on account, perhaps, of the social instability and crime that stem from high unemployment and concern about the extent of labour protection. Its comparative advantage in natural-resource-intensive activities, such as gold, diamonds and minerals, has not provided scope for the rapid expansion of exports, and therefore the process of cumulative causation has not unfolded into higher growth. Despite competent macroeconomic policies, a strong entrepreneurial sector, sound infrastructure, and buoyant prospects for world mineral markets, South Africa’s labour market trends, with their implication of rising numbers in poverty, pose a risk to the success of the economy as a whole. Another set of lessons is offered by India’s tale of productivity and economic growth, fuelled by a significantly advanced labour force and technology. India’s experience proffers an unparalleled paradigm of the role of technological progress, and the transmission channels through which macroeconomic fundamentals can explain the country’s economic success. Empirical evidence shows that macroeconomic fundamentals affected the Indian economy primarily by inducing changes in productivity, evidenced in unprecedented increases, particularly in the early 1990s (Chapter 3). Research further indicates that changes in labour market antagonisms and investment market frictions (such as taxing labour income) did not play a significant role, though increased government consumption aided growth by inducing demand. The Indian experience in targeting productivity evokes that of other successful Asian economies such as Japan in a similar stage of development, or during the take-off process. The economic structures of China and India and their impact on growth The successes of China and India, notably their rapid economic growth, reinforce the role of industrialization in economic development, particularly
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through outward-oriented activities in manufacturing industries, services and energy (Chapters 4, 5 and 6). But the traditional elements of industrial policy have had to evolve together with global and internal economic dynamics. The traditional mechanisms driving the industrial take-off include increasing internal returns, transfer of labour into higher value activities, and pecuniary externalities (Chapter 4). But China is definitely trying to step from a strategy based on industrialization to one based more on services, as the current pattern of industrialization becomes more costly. India’s experience further demonstrated that rapid growth based primarily on the service sector is possible. Thus, more attention needs to be given to strategies based on the expansion of the service and possibly agricultural sectors, and to the ways in which better services in rural areas and higher rural output can combine to achieve rapid growth and improved human welfare in less developed countries. Following the economic liberalization in India, the service sector has gained prominence in the economy, and currently accounts for the largest share of GDP (nearly 54 per cent, according to figures of the Reserve Bank of India). The liberalization reforms of 1991, in particular the deregulation and privatization of banking, insurance and telecommunications, explain the increase in productivity and output gains observed in the Indian service sector. Furthermore, empirical data reveal two significant trends in the service sector following liberalization in 1991: growth in service sector productivity and growth in service trade (Chapter 5). Particularly, productivity growth in the service sector rather than quantitative increase in service trade explains the value added growth observed in the service sector. China’s increasing reliance on energy for productive activities, and the environmental consequences, are a major concern for economic policy as well as for national security. The findings herein confirm the predictions regarding fossil fuel-based energy shortage and lead towards a serious consideration of alternative energy sources (Chapter 6). A key characteristic of China’s development path is the commitment to the creation of a largely self-sustaining innovation system as part of a knowledge-based economy, like other newly industrialized countries (NIEs) in a similar developmental stage. This policy further contributes to an increase in China’s energy burden and dependence. Achieving the goals of energy security and ecological balance is challenging. Despite the potential conflicts, regional cooperation, particularly with other Asian economies (for example, Indonesia, Vietnam and India), could be crucial in establishing an ecologically sustainable development path. Despite their overall economic success, the unequal distribution of welfare is a challenge for both China and India. Regional disparities tend to mingle with political and social tensions, leading to economic and political instability. This volume has addressed the issue of the spatial structure
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of the Indian and Chinese economies with respect to India’s regional economic structures (Chapter 7) and China’s regional development (Chapter 8). The quantitative assessments reported show that, in the case of India, there are fundamental economic activities that have important implications for policymaking, as far as regional development is concerned. These are industries associated with the infrastructure sector (mostly transport). Therefore, if regional governments invest in infrastructure and related industries the possibilities of growth can be enhanced due to their linkages with the rest of the economic system, particularly trade. Substantial regional disparity prevails in both China and India. This inequality has shown an increasing trend in both countries during the period of rapid economic growth and post-economic reforms and liberalization, in particular since the early 1990s. The increase in regional inequality in China and India reflects the enlarging gap between the coastal areas in the case of China and the technology-intensive metropolis in the case of India, in contrast to the more backward hinterlands (Chapter 8). That is, economic growth has not led to catch-up effects in the relatively poor regions as postulated by theory. Unequal regional development is also tainted by variations in infrastructure development and the level of urbanization. In China, the export sector plays a role in affecting regions, whereas in India human capital development in recent years is shown to affect regional disparity. That is, in order to address regional imbalances and spatial inequality, policymaking should comprehensively focus on a similar set of policy areas similar to those applied for national development. These include: human capital and labour, infrastructure, urbanization, industrialization and international trade. Unequal distributions of welfare are also present between male and female labourers. Importantly, a large share of women workers are involved in informal activities in countries like India without any significant policy addressing them explicitly (Chapter 9). This could lead to unintended outcomes that may hinder a desirable and equitable growth trajectory. The empirical results of Chapter 9 suggest that gains from policy reform, foremost trade liberalization, are more favourable for regular workers in the presence of wage rigidity in the segmented labour market and for casual workers when there is no such rigidity. Because women usually perform as casual workers, wage rigidity has a relatively adverse impact on the female labour force, and hence introduces gender disparities into the production process. This is an important area of inquiry and policy analysis, not just in the case of gender inequality, but in the case of other marginalized and vulnerable workers. It is well known that women in developing countries lag behind men in many domains: access to education, labour market opportunities, political representation and legal rights (Dufflo 2005). Therefore, reducing gender inequality and improving the status of women may contribute to higher rates of economic growth and greater macroeconomic stability (Stotsky
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2006). It is also important to acknowledge the contribution of women to non-market activities in formal economic analysis, to better understand policy impacts on welfare. The role of investment and trade Private sector development and private investment have become major pillars in China’s and India’s economic performance, and have significantly contributed to capital accumulation, employment and industrial growth in both countries (Santos-Paulino and Wan 2009). A defining phenomenon has been the expansion of the private firms and multinational corporations (MNCs), mostly in countries with highly urbanized metropolises, such as China, India and Brazil. Particularly, capital cities are attractive for firms to locate to. In India and China, labour-intensive firms tend not to locate in mid-sized or large cities, when compared with smaller ones, due to higher wages, training and attrition costs (Chapter 10). For example, although labour regulations in China and India deter firms from locating in the larger cities, exporting firms prefer to locate in large cities. Proximity to inputs within the city has a positive impact on firm location. These findings have important policy implications for urban governance, infrastructure, labour and environmental policies, which are key issues for growth and development. At the global scale, China’s performance and trade expansion have led to concerns about the competition pressure on other developing countries, especially in economies that share similar specialization patterns in medium technology and labour-intensive manufactures. A commonly cited case is the threat to Latin American economies. The empirical research shows that, contrary to earlier predictions, China has had a significant impact on the exports of a number of Latin American countries and that this has increased since China joined the WTO in 2001 with the phasing out of the Agreement on Textiles and Clothing (Chapter 11). This impact is more acute on middle-income economies such as Central America and the Dominican Republic, and some South American countries (for example, Bolivia, Brazil and Paraguay). Similarly, Latin American countries have lost significant market share to China since 2001 in the United States, particularly in markets for manufactured goods. These findings echo a loss of competitiveness in relation to China in a number of manufacturing industries, and are not precisely the result of shifts in comparative advantage. This is a crucial issue for middle-income economies, which feature a high concentration of production and exports. Finally, China’s engagement in the so-called international fragmentation of production – namely ‘cross-border dispersion of component production/ assembly within vertically integrated manufacturing industries’ – has become an increasingly important form of its economic integration into the regional as well as the global economy (Chapter 12). The featured research shows that China’s rapid economic growth, increasing market size and economies of
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scale, foreign direct investment and infrastructure development, including transportation and telecommunications, are important factors in explaining China’s rapid increase of bilateral trade in parts and components with its trading partners. This relates to the other findings in this volume, which highlight the role of both domestic policy reforms and engagement with the rest of the world via trade and capital flows. Moreover, given China’s prospects of continuing rapid economic growth, a significant expansion of investment in research and development (R&D) and in infrastructure is foreseen, alongside the country’s manifested policies in attracting FDI and its rapid move towards liberalizing its service sectors, including its financial sectors. Therefore, the scope for China and its trading partners to benefit from the process of international fragmentation of production is sizeable. The higher investment in high-technology and knowledge-related activities also reflects the changes evidenced in global production and investment patterns, determined by the economic performance of developing countries such as China, India, and Brazil, and their engaging in more sophisticated production activities (Santos-Paulino et al. 2008). These phenomena challenge the conventional assumption that knowledge creation is mainly the domain of advanced economies, as recent evidence suggests.
Concluding remarks China’s and India’s economic success has been largely interpreted as the result of thriving economic and political reforms, which have allowed them to increasingly engage in, and in some instances dominate, global trade and financial activities. The unparalleled performance of China and India, and their influence on the world economy, have been larger and faster than implied in earlier research, notably the Goldman Sachs report ‘Dreaming with the BRICs’ (Wilson and Purushothaman 2003), as well as its follow-up research on the role of Brazil, Russia, India and China. All chapters in this volume echo the spectacular economic performance of China, and the increasing prominence of India in global technology, innovation and international trade. However, the political economy view of such phenomena cannot be overlooked, particularly in the case of China. Therefore, it is pertinent to emphasize the role of the government in designing and implementing successful development policies and structural reforms. First, a key lesson from China’s experience is the adoption of a pragmatic approach to economic reforms (which was the turning point in China’s economic development), and the adaptive capacity of the countries’ economic agents to this process. Second, industrial policy has been at the heart of development policies and strategies in developing countries, although not particularly so in India. As in the case of other strategies and economic reforms, policy implementation produced varied outcomes, and different levels of success. Third, trade and the liberalization of commercial policies have played a primary role in
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growth success in China and India, where both countries have effectively opened to global markets. The interface of trade liberalization and domestic reforms has contributed to their success, akin to developing and transition country role models. The substantial restructuring of state-owned enterprises is another area of policy accomplishment in China and India. Also, the formulation of economy-wide development strategies should be a balanced outcome of the government and private agent decisions and choices, reflecting at the same time the country’s – evolving – comparative advantages. These policies and processes should also adjust to the continually changing global economy. The chapters in this book have shown that growth (and development) strategies are challenged by the multiplicity or non-uniqueness of institutional arrangements needed for reform to succeed and to achieve desirable ends. Many generations of reforms have led an international agenda, but the lessons provided by sui generis experiences such as the Asian giants – China and India – and other successful emerging economies such as Brazil might prove to be more inspiring and generate more positive spillovers for other developing countries due to their autonomous and uncompromised nature.2
Notes 1. South Africa’s labour market structure is affected both by the rural-urban migration as well as from inflows of foreign workers. 2. See Rodrik (2005).
References Dufflo, E. (2005). ‘Gender Equality in Development’. BREAD Policy Paper No. 011, December. Feenstra, R., and S.-J. Wei (eds) (2010). China’s Growing Role in World Trade. Chicago: The University of Chicago Press. Guariglia, A., and A. U. Santos-Paulino (2007). ‘Export Productivity, Finance and Economic Growth: Are the Southern Engines of Growth Different?’. UNU-WIDER Research Paper No. 2008/2. Rodrik, D. (2005). ‘Rethinking Growth Strategies’. In G. A. Cornia, M. Pohjola, and A. Shorrocks (eds.), WIDER Perspective in Global Development. Palgrave Macmillan, chapter 5, 201–23, Basingstoke: England. Rodrik, D. (2006). ‘What’s so Special About China’s Exports?’. NBER Working Paper 11947, January. Santos-Paulino, A. U., and G. Wan (2009). ‘FDI, Employment, and Growth in China and India’. Review of Development Economics, 13 (4): 737–91. Santos-Paulino, A. U., M. Squiciarini, and P. Fan (2008). ‘R&D (Re)location: A Bird’s Eye (Re)view’. UNU-WIDER Research Paper No. 2008/100. Stotsky, J. G. (2006). ‘Gender and its Relevance to Macroeconomic Policy: A Survey’. IMF Working Paper WP/06/233. Wilson, D., and R. Purushothaman (2003). ‘Dreaming with BRICs: The Path to 2050’. Goldman Sachs Global Economics Paper No. 99.
Index accumulation 6 acid rain 119 Africa, impact of China on 16, 17, 18, 19, 118, 125, 263 African National Congress (ANC) 33 Agreement on Textiles and Clothing (WTO) 220, 230, 236, 267 agriculture China 13, 31, 32, 67–70, 76, 77, 80, 109 and development strategy 61, 62–3, 265 and growth patterns 84, 88, 91–5 India 77, 86, 91–5, 98, 99–100, 101, 104, 140, 149–50 Lewis model 28 new technology in 149, 159 Punjab 159 in rapid growing economies 85 shift to industry from 142, 146 South Korea 66 women in 192, 193 agro-processing 193–4 aid programme, China 16–17, 263 airline routes, China 251, 252 airports 133 Algeria 18 Andean countries, threat of Chinese exports to 222, 224, 228, 235, 236 Anglo-American era of globalization 5–7 Angola 16, 17, 18 Argentina 222, 224, 228 ASEAN 246, 247, 248, 259 Asia-Pacific Partnership on Clean Development and Climate (APP) 132 Asian Development Bank (ADB) 20, 114 Asian energy cooperation 118–24, 131–3 Association for Overseas Technical Scholarship (AOTS) 120 Australia component trade 248 investment in resources 72 regional economies 143, 148, 149, 157, 166
authoritarianism 7, 18 Bangladesh 19, 85 Bank of China 120, 122 banking, India 80, 102–3, 104, 265 BCA see business cycle accounting Beijing 174, 178 beta-convergence 176, 178 Bhutan 85 Bihar 174 biofuels 117, 132 biogas energy 117 Bolivia 18, 228, 234, 236, 267 Braudel, Fernand 4 Brazil economic growth 37, 269 export threat from China 221, 222, 223, 224, 228, 236, 267 informal sector 187, 191 investment in technology 268 manufactured exports 73, 74 multinational corporations in 267 Britain see United Kingdom buildings, energy efficient 110, 117 Burma 16, 18, 19 Burrows, John 112, 113 Business Beacon, Centre for Monitoring Indian Economy (CMIE) 89, 90 business cycle accounting (BCA) 40–1 application to India 44–53 theoretical model 42–4 Canada Chinese oil concessions in 116 component trade 246 labour costs 251 capital cities, and location of firms 207, 211, 212, 215, 216–17, 267 capital stock 89 capital–output ratio 50 capitalism, Chinese form of 10–11 carbon sequestration 132 care sector 199 Caribbean, Chinese export threat to 222, 228
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Index
Cash Reserve Ratio (CRS) 103 casual workers 190–1, 192, 198, 199, 266 catch-up effects 173, 266 Central America, Chinese exports threat to 220, 224, 228, 229, 230, 234, 235, 236 Central American Common Market 223 central place theory 205 Central Statistical Organization (CSO) (India) 89, 90, 203 CGE see computable general equilibrium Chad 85 Chandigarh 174, 178 Chile 222, 224, 236 China agriculture 13, 31, 32, 67–70, 76, 77, 80, 109 aid programme 16–17, 263 airline routes 251, 252 business development 216 civil war 8 communist regime 6, 7–8, 11, 115 component trade 241–60 cooperation in Asia 118–24 and developing countries 263 development strategy 62–3, 67, 75–6, 80–1, 114, 115, 116, 124, 262–4, 268–9 economic growth 3, 36, 67–70, 85, 108, 109, 114, 173–6, 203, 220, 241, 259, 260, 262, 264, 268 economic reforms 29, 67, 175, 204, 208, 211, 212, 216, 220, 251, 263, 266, 268–9 education 75, 135 energy security 17–19, 108–25, 131–3, 263, 265 environmental issues 112–14, 117, 119, 120, 134, 135, 263 exports 10, 15, 70–2, 73–4, 109, 173, 181, 182, 184, 220–37 financial system 20 food production 13 foreign investment in 16, 251, 253–4, 255, 257, 258, 259, 260, 268 form of capitalism 10–11 GDP 67, 68, 77, 220, 241, 254, 255 and global development 262–9 global economic integration 241, 267 as the ‘great other’ 8–9 historic wrongs 11
human rights 12, 16, 18, 263 imports 13–14, 71, 220 industrial growth 67–70, 80, 204, 265–6 industrial sector 14, 77, 78, 204, 263 industrialization 67–70, 265–6 inequality 75, 114 informal sector 187, 191 international relations 11, 18, 19, 203, 263 labour market 27, 29–32, 80, 263–4 and Lewis model 27, 29–32, 36–7, 263–4 living conditions 17 loan aid infrastructure projects 133–5 location of firms 205, 208–14, 216–17 long-term growth prospects 21 manufacturing trade 210, 235, 237, 244 migrations from 8 national security 108 nationalism 11–12 and nature of globalization 3, 20, 21, 259, 262–3 ODA projects in 119–24 political reforms 16 poverty 112, 114, 174 private sector 203, 267 real incomes 31–2, 263 regional development 173–84, 266 regional disparity 173–84, 266 regional production network 259–60 relations with Japan 118–24 relations with US 109 rise of 262 rural-urban divide 29–32, 75, 263 scale of economic and social dynamics 9–10 seaports 251, 252 service sector 67, 68–70, 76, 77, 78, 80–1, 210, 265 sociopolitical formation 7–8 telephones 251, 253, 254, 255 territorial ambitions 12 threat to Latin America 220–37, 267 and Tibet 18 trade 13–15, 71–2, 182, 203, 220, 241 treatment of minorities 18 unemployment 30, 31, 109 urban economy 29–32 urbanization 204 value added shares 68–70 wages 222
Index China – continued Western view of 8 working conditions 17 WTO accession 20, 109, 220, 222, 223, 227, 229, 230, 234, 236, 263, 267 China Datang Corporation (CDT) 124 China Development Bank 16 China Export–Import Bank (EXIM) 16 China National Offshore Oil Corporation (CNOOC) 115, 123 China National Petrochemical Corporation (Sinopec) 115 China National Petroleum Corporation (CNPC) 115, 116, 123 China–Japan Energy Communication Association 123 China–Japan Non-governmental Longterm Trade Resolution 122 China–Japan Nuclear Safety Agreement 123 Chinese Academy of Sciences 10 cities location of firms 209–10, 212–14, 215–17, 267 reconstruction of centres 216 city-level governance 204, 206 climate change 119 clothing see garments industries coal 110, 111, 122, 132 and environmental degradation 112, 114 Cobb–Douglas production function 45, 88 Coefficient of Conformity 225 Colombia 236 Colon, Cristobal 5 colonialism 11 commodities, domestic market for 117 communications 80, 102 comparative advantage 225, 229, 236, 237, 249, 264, 267, 269 Compensation of Employees (COE) 90 competition from Chinese exports 220–37, 267 intensity of 226, 229, 230 in manufacturing trade 64, 70–2 competitive advantage 225, 229 component trade by partners 246–8 by sectors 244–6 China 241–60, 267–8 determinants of 249–59
273
fragmentation of production 72–3, 241, 249, 253, 259, 267 global 241–2 high-technology 15, 72, 249 import versus production 14 computable general equilibrium (CGE) model 187–8, 192–9, 221 computers, components 249, 251 Congo 20 conservation 132, 134, 135 Constant Market Share (CMS) analysis 223, 231, 236 construction boom, India 55 construction industry 192 consumer goods 72, 117, 143 consumer prices 198 consumption expenditure 52 convergence 140, 176–9 corporate taxes 51 corrupt regimes 18 COSATU 33 Costa Rica 228, 230 cottage sector 63 crisis concept of 12–13 vectors of 13–19 Cuba 6 cultural ethnocentrism 12, 17 cybernetworks 6 Darfur 18 decomposition 41, 48–51 Delhi 174, 176, 178 democracy less emphasis on ideal of 12 South Africa 33 US and British 7 Deng Xiaoping 11 deregulation 101–2, 103, 104, 265 developing world China’s demonstration effect 12 Chinese export competition threatens 220 Chinese imports boosts growth in 220 Chinese workers and unemployment in 17 development lessons from China and India 262–9 exports 71 impact of China and GAE on 3–4, 12–19, 72, 263
274 Index developing world – continued inequalities of wealth and power in 14 manufacturing in 73–4 rapid growing economies 84–5 development strategies based on agriculture and services 75–80 based on industrialization and exports 60–3, 80, 81, 109, 116 East Asian model 116 Indian regional 138, 139, 164, 165, 167 infrastructure investment 155–6 new approaches to 61–3, 117, 124 path dependence 124 development theory 61 Diaoyu Island dispute 123 Dias, Bartolomeu 5 direct tax rates 52 directly productive assets (DPA) 155 divergence 176–9 diversification 236 domestic consumption 76, 96, 194 domestic firms 204, 216 domestic market creation 117–18, 251 Dominican Republic 228, 229, 230, 235, 236, 267 Dominican Republic–Central American Free Trade Agreement (DR-CAFTA) 236 Dongguan 17 East Asia, component trade 242, 246, 247, 248, 260 East Asian financial crisis 20, 246 East Asian model 116 East China Sea dispute 120, 121 Eastern Europe, competition from 70 ecology, and energy needs 108, 112, 117, 265 economic convergence 140, 176–9 economic development spreading fruits of 27 stages of 27 economic geography 206, 207, 209, 211, 216, 217 economy of scale 251, 255, 257, 258, 259 Ecuador 230, 236 education China 75, 135 India 92, 101, 102, 199 women 199 effective tax rates 51, 52, 54
Egypt 15 El Salvador 228, 230, 234, 235 elasticity of substitution 98, 223 electrical efficiency 110 electronic products 10, 14 employment trends 203–4 endogeneity 182 Energy Information Administration (EIA) 111 energy security alternative energy sources 117, 124, 131, 132 China 17–19, 108–25, 263, 265 China–Japan energy policy 118–24 demand, supply and distribution 109–14 domestic energy production 112, 115 energy loans 120 energy use 75, 109–10 energy wastage/saving 110, 117, 120, 123, 132 international cooperation 131–3 English language, fluency in 101 enlightenment values 8 environmental impact analyses 16 environmental issues, China 112–14, 117, 119, 120, 134, 135, 263 Equatorial Principles 112 equilibrium 44 European Union, component trade 246, 247 exogeneity 208 export controls 102 Export Processing Zones 229 Export Similarity Index 223, 225 exports China 10, 15, 70–2, 73–4, 109, 173, 181, 182, 184, 220–37 Chinese parts and components trade 241–60 expansion of 62, 67, 70–1 and FES 143 India 73, 74, 86, 96, 102, 104, 210, 215 Latin America and threat from Chinese 220–37 and location of firms 208, 210, 212, 215 and regional disparity 173, 181, 182, 183, 184 tariffs 222 external shocks 40, 191, 195 factor income shares 90, 94, 95 factor intensity 193–4
Index fascist regimes 6 FDI see foreign direct investment fertilizer plants 134 FES see fundamental economic structure financialization 6 Firm Analysis and Competitiveness Surveys (FACS) 206 firms, location of 203–17, 267 China 205, 208–14, 216–17 efficiency 212, 216 India 205–6, 208–12, 214–17 ownership structure 208, 209 profile comparisons 210–11 Five Year Plan (China) 75–6 Five Year Plans (India) 139, 149, 156 fixed asset investment 75 fixed costs 63, 64, 80 fixed price multiplier (FPM) 187 food crisis, India 149 food production China 13 India 192, 193–4 footwear industry 17 foreign direct investment China 16, 251, 253–4, 255, 257, 258, 259, 260, 268 and component trade 242, 253–4, 255, 257, 258, 259, 260, 268 India 77, 101, 102 and location of firms 205 and regional disparity 181 foreign policy, militarized 7 forestry 119, 150 formal labour 191, 195, 198 fragmentation trade 72, 241, 249, 253, 259, 267–8 France component trade 246 regional disparity 174 free trade 62, 64 freight infrastructure 206 frictions 40–1, 264 Fujian 175 fundamental economic activities 149, 150, 152, 153, 163, 165 fundamental economic structure (FES) concept of 143, 163 identification of Indian regional 146–62 implications 164–5 importance 153–7 pattern analysis 146
275
predictability 146–51 Punjab 159–62 regional 144 spatial and temporal 143, 163–4 stability 151–3 types of 141–4 union and intersection of characteristics 157–9 furniture 249 GAE see Global-Asian Era Gandhi, Rajiv 177 Gansu Province 114 garments industries 15, 17, 220, 222, 224, 227, 229, 235, 236, 249, 251 gas exploration and supply 12, 18, 19, 108, 111, 112, 121, 123, 131, 132 GDP China 67, 68, 77, 220, 241, 254, 255 and energy supplies 111 growth rates in low-income countries 105 India 39, 46, 76, 77, 78, 79, 80, 85, 86, 88, 99, 100, 101, 102, 103–4 and industrial sector 204 and industrialization 61 in rapid growing economies 85, 105 sectoral real 89 and service-led growth 84–5 South Africa 35 South Korea 66 gender-related development 187, 190–9, 266–7 genocide 18 geopolitics and energy security 124 instability of 7, 9, 12 vectors of crisis 13–19 Germany component trade 246 fascism 6 industrialization 5–6, 65, 66, 67 labour costs 251 Gini index 114 global development 262–9 global economy, impact of China and India on 203, 204 global financial crisis 75 Global Trade Analysis Project (GTAP) 90, 93, 94 global warming 9
276 Index Global-Asian Era (GAE) 4, 9–12 and dangers for the developing world 12–20 transformation to 7–9 globalization Anglo-American era (AAE) 5–7 China and nature of 3–4, 20, 21, 259, 262–3 and component trade 241 forms and phases of 4–12 Global-Asian era 9–12 and location of firms 205 neoliberal 7 origins of 5 role in growth of China and India 262 transformation dynamics 7–9 GNP 61 Goa 174, 176, 178 Gokhale Institute of Politics and Economics 145 government expenditure and BCA 40, 42, 43, 49–51 and FES 143 graduate labour force 10 graduates, India 102 gravity model of components trade 242, 254 of trade flow 223 gross regional products (GRP) 173–5, 176, 179, 180, 181 growth models 63–5, 116–17 ‘growth plus’ strategy 117 GRP see gross regional products GTAP see Global Trade Analysis Project Guangdong 175, 182 Guangdong Province 253 Guangzhou 17 Guatemala 228, 234, 235 health care 75, 199 investment in 120, 135 Henan 182 Himachal Pradesh 182 HIV/AIDS 34, 35 Hobsbawm, Eric 9 holistic accuracy 160–2 home-based workers 190, 193 Honduras 228, 230, 234 Hong Kong and Chinese exports 227 component trade 246, 247, 248
and FDI 253 labour costs 251 real estate speculation 20 regional production network 259 households classification of 188 and FES 143 real consumption wages 198 and SAM 188, 190, 191–2 human capital 55, 61, 62, 64, 92, 102, 199, 266 and regional disparity 173, 180, 182, 183, 184 human resource development 135, 182, 184 human rights issues, China and 12, 16, 18, 263 hybrid vehicles 132 hydrogen 132 imperialism 7 imports China 13–14, 71, 220 Chinese parts and components trade 241–60 and FES 143 India 87, 96, 97, 98, 194, 195 quota system 220, 222, 223, 229 see also energy security incentive schemes, and location of firms 216–17 income and industrialization 64 regional disparity 181 see also real incomes increasing returns 62, 63, 64, 65, 80 Index of Competitive Threat 228, 233 Index of Export Competition 225 India agriculture 77, 86, 91–5, 98, 99–100, 101, 104, 140, 149–50 banking 80, 102–3, 104, 265 BCA analysis of 41–2, 44–54 CGE analysis 190–9 construction boom 55 development strategy 63, 76–80, 138, 139, 164, 165, 262–4, 269 economic growth 39–40, 53–5, 76–80, 85–8, 173–6, 203, 262, 264, 268 economic reforms 39, 85, 101–3, 104, 139, 140, 177–8, 180, 191, 192,
Index India – continued 195, 198, 204, 208, 211, 214, 265, 266, 268, 269 economic and social imbalances 138 education 92, 101, 102, 199 energy security 124, 131–3 exports 73, 74, 86, 96, 102, 104, 210, 215 food crisis 149 food production 192, 193–4 foreign direct investment 77, 101, 102 GDP 39, 46, 76, 77, 78, 79, 80, 85, 86, 88, 99, 100, 101, 102, 103–4 and global development 262–9 and Global-Asian Era 4 imports 87, 96, 97, 98, 194, 195 increased government spending 51 increased productivity 51, 54, 101–3 industrial growth 79, 156, 204, 265–6 industrial sector 77, 78, 91, 92–5, 98, 99, 101, 104, 139, 140, 149, 156–7, 168, 204 industrialization 79, 156, 265–6 informal sector 187, 190–9 information technology (IT) sector 39–40, 55, 76 infrastructure 138, 165, 182 insurance industry 80, 102–3, 104, 265 international relations 203 investment in technology 268 labour market 37, 99–100, 101, 103, 104 location of firms 205–6, 208–12, 214–17 manufactured exports 73, 76, 86, 102 manufacturing sector 203–4 metropolitan cities 138, 149, 204 mining industry 150, 164, 192 population 138 poverty 174 private sector 203, 210, 267 real incomes 192–9 regional cooperation 124, 265 regional development 138, 173–84 regional disparity 173–84, 266 regional economies 138–68, 266 rise of 262 service sector 63, 76–80, 81, 85–104, 141, 203, 265 and Shanghai Cooperation Organization 19 software development 39, 55 tax rates 40, 51–3, 194
277
telecommunications 103, 265 TFP growth 55, 87–8 trade 80, 86, 96, 97, 100, 101, 102, 104, 203 unemployment 195, 198 urban economies 138 urbanization 204 women workers 187, 191–5, 198–9, 266–7 Indian Ocean 19 indirect tax rates 53 Indonesia component trade 246, 247 rapid grower 85 real estate speculation 20 regional cooperation 124, 265 regional economies 143, 149, 157 textile industry 15 industrial sector China 14, 77, 78, 204, 263 development of 28 and energy supplies 108, 111 growth accounting 88, 91, 92–4 India 77, 78, 91, 92–5, 98, 99, 101, 104, 139, 140, 149, 156–7, 168, 204 input-output tables 163 in rapid growing economies 85 regional distribution 140–1, 142 industrialization Anglo-American 5 China 67–70, 265–6 constraints on development through 70–5 and development strategies 61–3, 80, 81 employment shares 65–7 India 79, 156, 265–6 models of 63–5, 263 and regional disparity 180, 182–3, 266 role in economic development 264–5 inequality, China 75, 114 informal economic sector 187, 190–9 informalization 198 Information and Communication Technologies (ICT) revolution 6 information technology (IT) sector components 72, 249 India 39–40, 55, 76 products 10, 14 infrastructure and component trade 249, 251–3, 268 investment in 62, 119–20, 133–5, 266
278
Index
infrastructure – continued and location of firms 205, 207, 209, 212, 214, 216, 217 and regional disparity 173, 176, 180, 181–3, 266 innovation, and growth 64, 109, 265 innovative technologies 10 input linkages 211, 213, 214, 267 input-output tables 163 insulation 110 insurance industry, India 80, 102–3, 104, 265 Insurance Regulatory and Development Authority (IRDA) (India) 102–3 Inter-American Development Bank 221, 223 interest expenses 206 intermediate goods 242, 251, 259 International Energy Agency (IEA) 132 international governance 203 Internet access 252 investigative journalism, Chinese absence of 18 Investment Climate Surveys (ICS) 204, 206, 207, 208–9, 210, 214, 216 investment market, frictions/ wedge 40, 41, 42, 45, 51, 52, 53, 54, 264 investment, regional 165, 167 investment taxes 52, 53, 54 Iran 18, 19 Iraq, oil supplies 116 Italy component trade 246 fascism 6 regional disparity 174 Jammu and Kashmir 182 Japan component trade 246, 247, 248 economic growth 6, 41, 54, 67, 264 energy policy 118–24, 131–3 historic treatment of China 12 industrialization 65, 66 labour costs 251 regional disparity 174 regional production network 259 remilitarization 9 state-orchestrated capitalism 10, 11 Western view of 8 Japan Import and Export Bank 120, 122
Japan International Cooperation Agency (JICA) 120 Japan–China Association on Economy and Trade 122 Japan–China Board of Assistance to Greening Activities 119 Jiangsu 175 Joint Oil Data Initiative (JODI) 132 Kazakhstan 18, 19, 116 Kennedy, Paul 9 King, Gregory 188 knowledge-based economy 109, 117, 265, 268 Kyrgyzstan 19 labour costs 215, 217, 242, 249–51, 254, 257–9 labour income tax rates 51–2, 54 labour intensity 207, 208, 211, 213, 215, 216, 264 labour market and component trade 242, 249–51 frictions/wedge 40, 41–2, 45, 47–50, 51, 52, 54, 264 import of labour 33 India 99–100, 101, 103, 104 and industrialization 65–7 and location of firms 206, 207 sectoral employment 89–90 segmentation 36, 263, 266 stages in economic development 27, 28, 31 surplus 65, 80, 263 trends in Chinese 29–32, 80, 263–4 trends in South African 33–6, 263–4 labour protection 33, 36, 264 labour regulations 207, 208, 209, 211–12, 213–14, 215–16, 217, 267 land, agricultural use 90 Latin America China an opportunity or threat for 221, 263 China’s impact on exports from 15, 220–37, 267 declining share in world markets 224 trade structure complementary to China’s 222, 223, 224, 229 leather industry 192, 224 Lesotho 15, 85 Lewis model 27–37, 263–4
Index literacy 180, 182 living conditions, China 17 location choice, firms 203–17, 267 McCarthy witch hunts 7 machinery, trade in parts and components 72–3, 245–6, 259 Madhya Pradesh 182 Malaysia 12 component trade 14, 246, 247, 248 labour costs 251 real estate speculation 20 risk from Chinese competition 15 male earnings 192–3, 199 manufacturing trade China 210, 235, 237, 244 competition in 70–1 exports to USA 235–6, 237 fragmentation and technological requirements in 72–3 India 203–4 parts and components 244–5, 246 in poorer countries 73–4 shares of global 74 shift to 143, 146 manufacturing value added (MVA) 73–4 Maoism 11 marginal product 28 marginalized workers 199, 266 Marine litter 119 market liberalization 36, 39, 40, 41, 54, 62, 67, 86, 88, 93, 101–2, 103, 104, 173, 195, 241, 251, 260, 263, 264, 265, 266, 268–9 market share, loss to China 231–6 markets, proximity of firms to 211 Meiji Restoration 8 Melaka 19 Mercosur 224 metals, Chinese import of 13–14 metropolitan cities, India 138, 149, 204 Mexican crisis (1982) 61 Mexico, Chinese exports threaten 15, 220, 221, 222, 223, 224, 228, 232, 235, 236 Middle East, and oil supplies 108, 116 migrations from China 8 rural-urban 27, 28, 29–30, 31, 32, 33–4, 143 temporary or oscillatory 32, 33
279
militaristic regimes 6 mineral resources 150, 236 mining, India 150, 164, 192 Ministry of Commerce (China) 16 minorities, Chinese treatment of 18 mobile phones 251 Mongolia 19 monopolists 63 monsoon failure 149 motor vehicles industry financial crisis and 75 parts manufacture 72 Multi Fibre Agreement 15 multinational corporations (MNCs) investments in overseas markets 251, 253 location of 204, 216, 267 municipalities, and regional disparity 176, 178 MVA see manufacturing value added NAFTA see North American Free Trade Agreement Nagaland 176 nanotechnology 10 Narasimhan Committee 103 Narayana Murthy, N.R. 39 National Bureau of Statistics of China (NBSC) 68 National Commission for Enterprises in the Unorganised/Informal Sector (NCEUS) (India) 199 National Foreign Trade Council (Washington) 220 National Sample Survey Organisation (NSSO) (India) 190, 191, 193 national security, China 108 National Telecommunications Policy (India) 103 nationalist regimes 6 natural disasters 176 natural resources 164 neo-Stalinism 7, 11 neoimperialism 6 neoliberal globalization 7 neoliberalism 6, 10, 12 Nepal 85 net state domestic product (NSDP) 146, 147, 148, 164 newly industrialized countries (NICs) 71, 72, 74, 108, 109
280
Index
Nicaragua 229, 234 NICs see newly industrialized countries Nigeria 16, 17, 18 nitrogen dioxide 112 non-fundamental economic activities 152, 153, 163, 165 non-performing loans (NPLs) 20 North Africa 15 North American Free Trade Agreement (NAFTA) 247 North Korea, communist regime 6 Northeast Asia, component trade 246, 248 nuclear power 120, 122, 123, 131, 132 ODA see official development assistance OECD see Organization for Economic Cooperation and Development official development assistance (ODA), China 119–24 Ohira, Masayoshi 122 oil Andean countries 236 Angola 17 Chinese state-owned oil companies 115 consumption 110–11 exploration and supply 18, 19, 108, 121, 122, 123, 131, 132 and global economy 131 investment in overseas projects 115, 116 strategic stocks 133 vulnerability through dependence on 115, 116 oil crises 61 one hundred years of humiliation 11 Operating Surplus (OS) 90 Organization for Economic Cooperation and Development (OECD) 221 Orissa 176, 182 outliers 182–3 output per capita 49 outsourcing 55 over accumulation crisis 20 own account workers (OAW) 190, 191 Pakistan 17, 19, 85 Paracel Islands 12 Paraguay 228, 234, 236, 267 partitive accuracy 160–2
parts manufacture 72–3 path dependence 124 pecuniary externalities 62, 63, 64, 65, 80, 265 People’s Liberation Army 8 People’s Republic of China (PRC) see China Perpetual Inventory Method (PIM) 89 Persian Gulf 19 Peru 230, 234 Peterloo massacre (1819) 7 Philippines 12 component trade 246, 247 PIM see Perpetual Inventory Method pipelines, transnational oil 108, 116 Poland, foreign direct investment 253 POLIS see positive feedback loop innovation systems pollution 75, 112–14, 119 population distribution of 40 regional 146, 147, 148, 163 Portugal, voyages of discovery 5, 8 positive feedback loop innovation systems (POLIS) 109, 115, 117, 124 poverty 9, 37 alleviation of 61, 112, 117 China 112, 114, 174 and economic growth 114, 128–31, 173 gap between rich and poor 174 India 174 and SAM 188 South Africa 264 power stations 134 power supplies, and location of firms 205, 207 primary commodity exporters, China and price boom for 220 primitive forces 41, 53–4 private entrepreneurs 76 private sector China 203, 267 India 203, 210, 267 private unincorporated enterprises 90 privatization 101–2, 103, 104, 265 pro-business policies 40, 41, 67, 216 production costs 249 production networks 224 productivity economic growth and increased 54, 77, 80
Index productivity – continued frictions/wedge 40, 41, 45, 47, 49–51, 54, 264 industrialization and increased 63 reasons for growth in 101–3, 104 and technology 73 see also total factor productivity Punjab, regional economy 144, 145, 159–63, 166 Queensland (Australia) 143 race to the bottom 15 racial discrimination 8, 33 railways 133 Rajasthan 182 Rand Corporation 111 real estate 80 speculation 20 real incomes 27, 28 China 31–2, 263 India 192–9 South Africa 34, 35, 263 recessions 20, 36, 61 recruitment policies 163 regional convergence and divergence 176–9 regional cooperation 118, 124–5, 265 regional development 173–84, 265–6 regional disparity 173–84, 265–6 regional domestic product 163 regional economies imbalances in 138, 139–40 India 138–68 sector classification 144 similarities between 163 structural transformation 142–3, 144 regional expenditure 164 regional investment 165, 167 regional models 144–5 regression analysis 146, 150, 179–83, 255–9 renewable energy 117, 124, 132 relative price mechanism 36, 264 representative consumer 43 representative firm 43–4 research and development (R&D) 64, 268 Reserve Bank of India 51, 265 retrenchment 30, 198, 209, 212 Revealed Comparative Advantage (RCA) 225
281
rich and poor, gap between 174 roads 133, 180 rural industry 28, 29, 30–1 rural-urban divide 27, 28, 29–33, 36, 75, 263 Russia 19 remilitarization 9 sales tax incentive 205–6 SAM see social accounting matrix Saudi Arabia 18 savings rates 62, 67 Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) 112, 113 Schumpeterian theory of innovation 117 science parks 216 seaports 134 China 251, 252 sectoral growth accounting 88–94 sectoral reallocation 84 security policy, international 17, 263 segmentation, of labour market 36, 263, 266 self-consumption 149 self-employment 34, 37 service sector China 67, 68–70, 76, 77, 78, 80–1, 210, 265 and component trade 259, 260 components of 78, 80 deregulation 102 and economic growth 84–5, 88, 91, 92–5, 97 importance of deregulation and privatization to 102–3, 104 importance of education and fluent English in 101, 102 India 63, 76–80, 81, 85–104, 141, 203, 265 liberalization of 241, 242, 260 in rapid growing economies 85 shift to 143, 146 sewage systems 134 Shandong 175 Shanghai 174, 178 Shanghai Cooperation Organization (SCO) 19 shoes 249, 251 Sichuan 182
282 Index sigma-convergence 176, 177, 178 Singapore component trade 14, 246, 247, 248 labour costs 251 Sino-Japanese Peace and Friendship Treaty 122 skill availability, and location of firms 208, 214, 217 social accounting matrix (SAM) 125–8, 187–91, 193 social impact analyses 16 social overhead capital (SOC) 155 software development, India 39, 55 solar energy 117 Sonoda, Tadashi 122 South Africa garment industry 15 informal sector 187, 191 labour market 33–6, 37, 263–4 and Lewis model 27, 36–7, 263–4 manufactured exports 73, 74 poverty 264 regional economies 143, 157 rural-urban divide 33, 263 transition to democracy 33 South Korea component trade 246, 247, 248 economic growth 67, 109 electronic and ICT products 10, 14 energy policy 131–3 exports to China 72 industrialization 28, 64, 65, 66–7, 80 labour costs 251 regional production network 259 state-orchestrated capitalism 10, 11 Southeast Asia, component trade 246, 247, 248 Soviet Union, communist regime 6 spatial inequality 266 special economic zones 216 specialization 64, 241, 251, 253, 257, 259 Spratley Islands 12 Sri Lanka 15, 19, 85 Standard International Trade Classification (SITC) 227, 242 standard of living 28, 62 state socialist regimes 6 Statutory Liquidity Ratio (SLR) 103 Stone, Sir Richard 188 Straits of Hormuz 19 ‘string of pearls’ 19
structural transformation 84–5, 142–3, 144 stages of 143 sub-Saharan Africa 3, 263 subcontracting 72 subsistence farming 149 Sudan 16, 18, 116 super-rich regions 173, 174, 177 sustainable economy 76, 131, 132, 139, 204, 216 Taiwan Chinese ambitions over 12 component trade 14 exports to China 72 industrialization 28 and POLIS 109 regional production network 259 state-orchestrated capitalism 10 Tajikstan 19 Tamil Nadu 176 tariff rates 102, 194, 195 tax rates as frictions 41–2, 51, 54 India 40, 51–3, 194 technology and component trade 242, 259, 260 and economic structure 151, 153 and production standards 73, 268 telecommunications and component trade 242, 249, 251–3, 254, 255, 257, 259, 268 and economic development 180 India 103, 265 telephones, China 251, 253, 254, 255 textiles 72, 192, 220, 222, 223, 224, 229, 235, 236, 249, 251 TFP see total factor productivity Thailand 15, 20, 85 component trade 246, 247, 248 labour costs 251 third markets, China and Latin American exports to 220–37 Tianjin 174, 178 Tibet 174 Chinese treatment of 18 international protests over 12 tidal energy 117 timber, illegally logged 14 total factor productivity (TFP) 55, 87–8, 91, 92–5, 97, 98, 100, 102, 104 total gross output 146, 147, 148, 163, 164
Index tourism 164 town and village enterprises (TVEs) 210 toys 249 trade China 13–15, 71–2, 182, 203, 241 and economic growth 64 and global financial crisis 75 India 80, 86, 96, 97, 100, 101, 102, 104, 203 policy and informal sector 191, 194, 195 and regional disparity 180–1, 182, 266 trade quotas 102 trade surplus, China 220 trade unions 41, 54 training 64 transmission channels 40, 53–4 transnational corporations 14 transparency 132 transport energy efficient 117, 132 investment in infrastructure 119–20, 180, 251 linking production blocks 249 and regional economic development 155, 156, 165, 180, 183 trade in parts and components 72–3, 245–6, 251–3, 259, 268 travel goods 249 Turkmenistan 16, 18, 19 Uighurs 18 unemployment China 30, 31, 109 India 195, 198 South Africa 33, 34, 35, 36 United Kingdom component trade 246 economic decline 6 industrialization 5, 65–6, 67 labour costs 251 as predominant global power 5, 7, 8 sociopolitical formation 7–8 United Nations Millennium Development Goals 187 United States bombing of Chinese embassy in Belgrade 12 Chinese competition to Latin American exports to 220, 222, 223, 227–9, 230–7, 267
283
component trade 246 economic instability 9 energy policy 131–3 imperialism 6, 7, 116 industrialization 5–6, 65, 66, 67 invasion and occupation of Iraq 116 labour costs 251 as predominant global power 5, 6–7, 85 regional disparity 174 relations with China 109 sociopolitical formation 7–8 university towns 216 unskilled labour 264 UNU-WIDER 27, 37 urban development 216 urban economy, China 29–32 urban villages 216 urbanization 65 China 204 India 204 and regional disparity 173, 174, 178, 180, 181, 182–3, 266 Uruguay 230, 234 US International Trade Commission 222, 227 USAID 16, 17 Uzbekistan 16, 19 value added growth 88, 91, 92 value added shares, China 68–70 Venezuela 18, 116, 230, 236 Vietnam 6, 12 component trade 247 garment industry 15 regional cooperation 124, 265 voyages of discovery 5 wage determination 28, 34 wages Chinese low level 222 and component trade 249–51, 257–8 formal sector 195, 198 industrial 63, 65 informal sector 192–8 water drinking 180 pollution 119 supplies 199 wealth distribution 114, 116, 117, 128–31 wedges see frictions
284 Index welfare and development strategy 61, 62 gender issues 199, 266–7 and industrialization 64 uneven distribution 265, 266 Wen Jiabao 75–6, 118, 123 wind energy 117, 124 women workers, India 187, 191–5, 198–9, 266–7 wood products 192 working conditions, China 17 World Bank 10, 15, 16, 20, 62, 102, 204, 206, 208, 221, 224
World Bank Standards 112 world economy, instability of 7, 9 World Trade Organization, China’s accession to 20, 109, 220, 222, 223, 227, 229, 230, 234, 236, 263, 267 World War I 6 yellow sand 119 Zhejiang 175 Zhou Yongkang 115 Zimbabwe, migration from 35
