Neoliberalism in the Emerging Economy of India: The Political Economy of International Trade, Investment and Finance 1000406407, 9781000406405

Neoliberal economic reforms over the last four decades have altered the economic cartography of emerging market economie

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Neoliberalism in the Emerging Economy of India: The Political Economy of International Trade, Investment and Finance
 1000406407, 9781000406405

Table of contents :
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
List of figures
List of tables
Part 1 Finance
Chapter 1 Finance and the real economy: The evolving distance in the context of India
Chapter 2 Capital accumulation and finance capital in the age of finance
Chapter 3 Revisiting “fictitious capital” and the autonomy of finance in the circuit of global capital
Chapter 4 The financial sector in the Indian economy: Some reflections using Hyman Minsky’s lens
Chapter 5 Is priority sector lending responsible for higher NPA in the banking industry?
Chapter 6 An empirical exploration of the Indian stock market: Investigating the interface of return, sentiment and exchange rate
Part 2 Investment
Chapter 7 The dynamics of global demand, investment and trade deficit: A model of India’s external dependence
Chapter 8 India’s recent slowdown and neoliberal regime of accumulation: Is there a link?
Chapter 9 Foreign direct investment and productivity spillovers: Evidence from the Indian pharmaceutical industry
Part 3 International Trade
Chapter 10 Reformatory policies and factor prices in a developing economy with an informal sector
Chapter 11 Impact of trade liberalization on informal employment: A theoretical approach
Chapter 12 Trade potential and WTO issues for West Bengal

Citation preview


Neoliberal economic reforms over the last four decades have altered the economic cartography of emerging market economies such as India, particularly in the context of international trade, investment and finance, and in terms of their effects on the real economy. This book examines the issues of financialization, investment climate and the impact of trade liberalization. By analyzing these three features of neoliberal reform the book is unique, since it accommodates both a mainstream neoclassical approach and a non-mainstream political economy approach. The major questions answered by this book, cover three basic lines of enquiry pertaining to neoliberal reforms. They are (a) how financialization as a new process affects the real economic health of emerging market economies characterized by globalization; (b) how the changing form of international trade in the new regime impacts upon the informal economy, and employment and trade potential in the home country; and (c) how global investment has shaped the real economy in emerging countries like India. The book will be extremely useful for postgraduate students of international economics, particularly development economics and political economy, including researchers with a keen interest in India. Byasdeb Dasgupta is a Professor of Economics at the University of Kalyani of West Bengal, India. He did his PhD in Economics at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. He has published widely in international journals and books on issues pertaining to international economics, finance and development, the political economy of labour in a Marxian perspective, gender studies and development. Archita Ghosh is a Professor of Economics at the University of Kalyani of West Bengal, India. She has published widely in journals and books pertaining to issues on development economics and international economics. Bishakha Ghosh is an Associate Professor of Economics and Head of the Department of Economics at the University of Kalyani of West Bengal, India.


EMERGING BOND MARKETS Shedding Light on Trends and Patterns Tamara Teplova, Tatiana V. Sokolova and Qaiser Munir DESIGNING INTEGRATED INDUSTRIAL POLICIES VOLUME I For Inclusive Development in Asia Edited by Shigeru Thomas Otsubo and Christian Samen Otchia DESIGNING INTEGRATED INDUSTRIAL POLICIES VOLUME II For Inclusive Development in Africa and Asia Edited by Shigeru Thomas Otsubo and Christian Samen Otchia THE ECONOMIC DEVELOPMENT OF BANGLADESH IN THE ASIAN CENTURY Prospects and Perspectives Edited by Quamrul Alam, Atiur Rahman and Shibli Rubayat Ul Islam REGIONALISM IN LATIN AMERICA Agents, Systems and Resilience Edited by José Briceño-Ruiz and Andrés Rivarola Puntigliano ECONOMIC GROWTH AND CONVERGENCE Global Analysis Through Econometric and Hidden Markov Models Michał Bernardelli, Mariusz Próchniak and Bartosz Witkowski NEOLIBERALISM IN THE EMERGING ECONOMY OF INDIA The Political Economy of International Trade, Investment and Finance Edited by Byasdeb Dasgupta, Archita Ghosh and Bishakha Ghosh For more information about this series, please visit: www​.routledge​.com​/ Routledge​-Studies​-in​-the​-Modern​-World​-Economy​/book​-series​/SE0432

NEOLIBERALISM IN THE EMERGING ECONOMY OF INDIA The Political Economy of International Trade, Investment and Finance

Edited by Byasdeb Dasgupta, Archita Ghosh and Bishakha Ghosh

First published 2021 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2021 selection and editorial matter, Byasdeb Dasgupta, Archita Ghosh and Bishakha Ghosh; individual chapters, the contributors The right of Byasdeb Dasgupta, Archita Ghosh and Bishakha Ghosh to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Dasgupta, Byasdeb, editor. | Ghosh, Archita, editor. | Ghosh, Bishakha, editor. Title: Neoliberalism in the emerging economy of India: the political economy of international trade, investment and finance / edited by Byasdeb Dasgupta, Archita Ghosh and Bishakha Ghosh. Description: 1 Edition. | New York: Routledge, 2021. | Series: Routledge studies in the modern world economy | Includes bibliographical references and index. Identifiers: LCCN 2021001607 (print) | LCCN 2021001608 (ebook) Subjects: LCSH: Financialization–India. | India–Economic policy–21st century. | Free trade–India. | Informal sector (Economics)–India. | Unemployment–India. Classification: LCC HG187.I4 N46 2021 (print) | LCC HG187.I4 (ebook) | DDC 330.954–dc23 LC record available at LC ebook record available at ISBN: 978-0-367-67553-0 (hbk) ISBN: 978-0-367-67554-7 (pbk) ISBN: 978-1-003-13176-2 (ebk) Typeset in Times New Roman by Deanta Global Publishing Services, Chennai, India


List of figures vii List of tables x Editors xii Contributors xiii Acknowledgements xvi Introduction




Finance 1


Finance and the real economy: The evolving distance in the context of India




Capital accumulation and finance capital in the age of finance




Revisiting “fictitious capital” and the autonomy of finance in the circuit of global capital




The financial sector in the Indian economy: Some reflections using Hyman Minsky’s lens SUKANYA BOSE



C ontents


Is priority sector lending responsible for higher NPA in the banking industry?




An empirical exploration of the Indian stock market: Investigating the interface of return, sentiment and exchange rate 103 KUNTAL CHAKRABORTY


Investment 7


The dynamics of global demand, investment and trade deficit: A model of India’s external dependence




India’s recent slowdown and neoliberal regime of accumulation: Is there a link?




Foreign direct investment and productivity spillovers: Evidence from the Indian pharmaceutical industry




International Trade


10 Reformatory policies and factor prices in a developing economy with an informal sector



11 Impact of trade liberalization on informal employment: A theoretical approach



12 Trade potential and WTO issues for West Bengal







1.1 Volatile finance and disparate growth. Source: Bombay Stock Exchange 26 1.2 Market capitalization BSE. Source: Bombay Stock Exchange 26 1.3 BSE Sensex: annual averages. Source: Bombay Stock Exchange 27 1.4 GDP growth rates. Source: Economic Survey, Government of India 27 4.1 Worsening NPAs of SCBs. Source: RBI, Database on the Indian Economy. Notes: As on 31 March 74 4.2 Growth in credit disbursed by SCBs (%). Source: RBI, Database on the Indian Economy 74 4.3 The widening gap: quarterly growth rate (%). Source: Bose and Kumar (2018). Notes: Rolling Quarterly Growth Rate 78 4.4 Net receivables (+ve)/payables (–ve) by institutions. Source: RBI, Financial Stability Report, various issues. Note: Urban Cooperative Banks (UCBs), Asset Management Companies – Mutual Funds (AMC-MFs), Non-Banking Financial Companies (NBFCs), Housing Finance Companies (HFCs), Pension Funds (PFs) and All India Financial Institutions (AIFIs) 80 4.5 Total debt to GDP and its composition across sectors (%). Source: Reserve Bank of India (RBI), Central Statistics Office (CSO), Bloomberg, NBFCs/HFCs company reports, CEIC, MOFSL as cited in Ecoscope, Motilal Oswal Group 83 5.1 GNPA and GNPA ratio: 2005–19. Source: Statistical Table Relating to Banks, RBI 92 5.2 Priority sector advances and priority sector advances ratio: 2005–19. Source: Statistical Table Relating to Banks, RBI 93


F igures

7.1 Share of gross capital formation, exports and net exports in GDP (%). Source: Linked GDP series, National Statistical Commission and National Account Statistics, CSO 116 7.2 Nominal growth rates of merchandize imports of the rest of the world and India’s merchandize exports (%). Source: WITS, COMTRADE 117 7.3 Determination of the steady-state investment rate, gross exports and net exports 127 7.4 Impact of higher global demand: (a) case 1 (m  mc) 130 7.5 Simulation of steady-state equilibrium. Note: Simulation made with Matplotlib 133 7.6 Simulation of comparative dynamics. Note: Simulation made with Matplotlib 133 7.7 Actual, fitted and residual values 137 8.1 Average annual growth in pre- and post-reform period. Note that these are growth rates of GDP at factor cost in 2004–05 prices (1980–81 to 2012–13) and gross value added (GVA) at 2011–12 basic prices (2013–14 to 2018– 19). Source: Authors’ illustration using data from the MoSPI 145 8.2 Average annual growth during various sub-periods. Note that these are growth rates of GDP at factor cost in 2004– 05 prices (1980–81 to 2012–13) and GVA at 2011–12 basic prices (2013–14 to 2018–19). Source: Authors’ calculation based on data collected from the MoSPI 145 8.3 Growth performance in the pre-reform era. Source: Authors’ illustration using data from the MoSPI 146 8.4 Growth performance in the post-reform era. Source: Authors’ illustration using data from the MoSPI 146 8.5 Recent growth deceleration. Source: Authors’ illustration using data from the MoSPI 147 8.6 Average annual growth of spending on various private consumption items. Source: Authors’ calculation and illustration based on data collected from the EPWRFITS 153 8.7 Various determinants of private consumption 156 8.8 Growth–inequality nexus in India 164 8.9 Scatterplot of errors and explanatory variables 167 8.10 Scatterplot matrix 167 8.11 Kernel density test for normality of errors. The residuals are normally distributed for both the regressions 168 viii

F igures

11.1 Free market commodity prices, monthly, January 1960– May 2013. Source: UNCTADSTAT 12.1 NTBs facing Indian imports into the United States. Source: Mehta, R (2005), “Non-tariff Barriers Affecting India’s Exports”, Research and Information System for Developing Countries, New Delhi, Discussion Paper No. 97 12.2 NTBs faced by Indian imports into the EU. Source: Mehta, R (2005), “Non-tariff Barriers Affecting India’s Exports”, Research and Information System for Developing Countries, New Delhi, Discussion Paper No. 97






5.1 5.2 5.3 5.A1 5.A2 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 7.1 7.2 7.3 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11

Spearman rank correlation: GNPA ratio and priority sector advances ratio Fixed-effect model Random-effect model List of the public sector banks List of the private sector banks Descriptive test Normality Test Augmented Dickey-Fuller test VAR lag order selection criteria VAR residual serial correlation LM test VAR model stability test VAR Granger causality/block exogeneity Wald tests Johensen’s cointegration test Sign of ∂n*/∂w for different parametric values Augmented Dickey–Fuller tests for unit roots Result of co-integration analysis Sources of growth: Cumulative contribution share Sources of growth: Cumulative growth ratio Growth performance of components of aggregate demand Cumulative contribution share: A disaggregated analysis Growth ratio: A disaggregated analysis Average annual growth: A disaggregated analysis Increasing importance of certain private consumption Regression results The PCSE result List of 15 major states and compatible NSS regions (1993–94 and 2009–10) Summary statistics x

94 98 98 100 101 106 106 107 107 108 108 109 109 132 135 135 143 148 148 149 150 152 153 160 163 165 166


8.12 8.13 8.14 8.15 9.1 9.2 9.3 9.4 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8

Correlation coefficients between residuals and the regressor 167 Result for the link test for model specification error (p-value) 167 Omitted variable test for model specification error 168 Multicollinearity test: Variance inflation factor (VIF) 168 Maximum-likelihood estimates of the stochastic production function for domestic firms 184 Maximum-likelihood estimates of the stochastic production function for the full sample 185 Tests of hypothesis of the stochastic production function for full sample 186 Descriptive statistics of selected variable used in this study 189 Products that have high production value in West Bengal 242 Products with high production levels in West Bengal and high export growth rates from India 242 Products for which ports in West Bengal have a high (>10%) export share 243 Products for which West Bengal has production advantage 244 Important export items passing through ports in West Bengal in which West Bengal has a production advantage 244 Potential of different exportables from West Bengal 245 Products with high import growth rates – India 245 Products for which West Bengal has high production levels/production advantage and that have high import growth rates for India 245



Byasdeb Dasgupta is a Professor of Economics at the University of Kalyani of West Bengal in India. He did his PhD in Economics at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. He has published widely in international journals and books on issues pertaining to international economics, finance and development, the political economy of labour from a Marxian perspective, gender studies and development. A few of his notable books include Unfreedom and Waged Work: Labour in India’s Manufacturing Industry, Non-Mainstream Dimensions of Global Political Economy, External Dimensions of An Emerging Economy, India, The Indian Economy in Transition and “Capital” in the East: Reflections on Marx. Archita Ghosh is a Professor of Economics at the University of Kalyani of West Bengal in India. She did her PhD in Economics at Jadavpur University, Kolkata. She has widely published in journals and books pertaining to issues on development economics and international economics. Bishakha Ghosh is an Associate Professor of Economics at the University of Kalyani of West Bengal in India. She is currently Head of the Department of Economics of the University of Kalyani.



Sukanya Bose is an Assistant Professor at the National Institute of Public Finance and Policy, Delhi. She did her PhD in Economics at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. She has widely published in academic journals and books on issues pertaining to macro-economic aspects of India, public finance and development. Debattom Chakraborty is an Assistant Professor of Economics at Sundarban Hazi Desarat College Pathankhali, South 24 Parganas, West Bengal in India. He pursued his PhD in Economics at the University of Kalyani. Kuntal Chakraborty is a research scholar pursuing his doctoral work at the Department of Business Administration of the University of Kalyani. Saswati Chaudhuri is an Associate Professor of Economics at St. Xavier’s College, Kolkata. She did her PhD in Economics at the University of Calcutta. She has widely published academic papers on issues pertaining to gender and development, international economics and macro-economic subjects relating to India. Byasdeb Dasgupta is a Professor of Economics at the University of Kalyani, West Bengal, India. He has widely published in journals and books on the issues pertaining to labour and development, finance and development, international economics, gender and political economy. He did his PhD at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. He has visited several globally reputed universities and institutes in Europe and China. A few of his reputed publications include Unfreedom and Waged Work: Labour in India’s Manufacturing Industry, Non-Mainstream Dimensions of Global Political Economy, External Dimensions of An Emerging Economy, India, The Indian Economy in Transition and “Capital” in the East: Reflections on Marx. Zico Dasgupta is an Assistant Professor of Economics at Azim Premji University, Bengaluru. He did his PhD at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. He has published in journals and books, xiii

C ontributors

including Economic and Political Weekly, on issues pertaining to macro-economic subjects relating to India. Sujata Ghosh is an Assistant Professor of Economics at Kidderpore College, Kolkata. She did her PhD under the supervision of Dr Biswajit Mandal in the Department of Economics and Politics of Viswa Bharati University, Santiniketan. Biswajit Mandal is an Associate Professor at the Department of Economics and Politics, Viswa Bharati University, Santiniketan, India. He did his PhD in Economics at the Centre for Studies in Social Sciences, Kolkata and Rabindra Bharati University. He has visited globally reputed universities and institutes, including those in Japan. He has published widely on issues pertaining to international trade theory and also on empirical analysis pertaining to external dimensions of the Indian economy. Labanya Pal is an Assistant Professor of Economics at Suri Vidyasagar College, West Bengal, India. He did his PhD in Economics at the University of Kalyani. He has published academic papers pertaining to foreign direct investment in India during the post-reform period. Rajendra N. Paramanik is an Assistant Professor of Economics at the Department of Humanities and Social Sciences of the Indian Institute of Technology, Patna. He pursued his PhD in Economics at the Central University of Hyderabad. He has published academic papers pertaining to macro-economic subjects relating to India. Saumita Paul is a research scholar at the Department of Economics of Jadavpur University, Kolkata. She is pursuing her doctoral work under the supervision of Professor Malabika Roy. Debabrata Roy is an Assistant Professor of Economics at the University of Kalyani. He is pursuing his doctoral work at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. Malabika Roy is a Professor of Economics at the Jadavpur University, Kolkata. She has published academic papers in journals and books pertaining to finance and development and macro-economic subjects relating to India. Satyaki Roy is an Associate Professor at the Institute for Studies of Industrial Development, Delhi. He has widely published in journals and books on issues pertaining to the political economy of development, industrial development in India and macro-economic subjects relating to India. Sunanda Sen is a retired Professor of Economics at the Centre for Economic Studies and Planning of Jawaharlal Nehru University, Delhi. She has visited several globally reputed universities and institutes like Cambridge University in England and FMSH in France. She has widely published in international xiv

C ontributors

journals and books on the issues pertaining to international economics, finance and development, labour, gender and macro-economic subjects, especially with reference to India. A few of her noted publications include Globalisation and Development, Global Finance at Risk, Unfreedom and Waged Work: Labour in India’s Manufacturing Industry, Trade and Dependence, Development on Trial: Shrinking Space for the Periphery and The Changing Face of Imperialism. Saswata Guha Thakurata is an Assistant Professor of Economics at Kanchrapara College, West Bengal, India. He did his PhD at the Central University of Hyderabad. He has published on issues pertaining to macro-economic subjects relating to India.



This book is the result of teamwork. It is a collection of research papers on the political economy of international trade, investment and finance during the neoliberal period, which started in June 1991. We owe a debt to several persons for preparing the manuscript of this book. First of all, we acknowledge the University Grants Commission (UGC) of India for helping the Department of Economics of the University of Kalyani with their Special Assistance Programme, for which a national conference could be held on trade, investment and finance. The idea of this book was shaped by some of the eminent participants in the said conference, although strictly speaking this book is not a conference proceeding. Rather, it is a research monograph where the authors have put down their viewpoints regarding the political economy of trade, investment and finance in the emerging economy of India during the neoliberal period. Second, we remain especially thankful to the anonymous referees at Routledge who helped us in revising the papers to make them of an international standard. Our special thanks go to Kristina and her team at Routledge, who helped us enormously in preparing, submitting and proofreading the manuscript. Finally, we remain grateful to the administration of the University of Kalyani, without whose help we could not bring together so many specialists in the field, and also for providing a wonderful platform for threadbare discussions on the subject matter. We remain solely responsible for all the errors and omissions. Byasdeb Dasgupta Archita Ghosh Bishakha Ghosh


INTRODUCTION Byasdeb Dasgupta1, Archita Ghosh and Bishakha Ghosh

The global economy has been subject to neoliberal economic reforms for the last four decades with emerging economies like India being no exception. One of the basic tenets of this reform in terms of the globalization of (local) national economies is the opening of these economies to the world market. In this context, international trade, investment and finance have evolved in these economies in new ways, keeping in tune with the logic of market-centric neoliberal reforms. The basic premises on which neoliberal economic reforms are based include: (a) market will replace state as far as production and economic activities are concerned; (b) private entrepreneurship (in particular corporate enterprises) will replace state-led enterprises; (c) foreign capital will supplement domestic capital if not totally replace it; and (d) domestic market will open in terms of trade and foreign capital flows to the global market. The era of neoliberal economic reforms has registered the following changes or transitions in national economies (of the emerging market economies in particular): (i) wide financialization across national economies with financial integration with global finance; (ii) institutional changes and/or the installation of a global organization as far as international trade is concerned; and (c) some inherent contradictions in national economies as in India following reforms – the contradictions which are part and parcel of (global) capitalism. This book is all about the political economy of international trade, investment and finance in the emerging economy of India during the above-mentioned neoliberal period. India began its journey of neoliberal economic reforms in 1991 as advocated by multilateral financial institutions such as the International Monetary Fund (IMF) and the World Bank. And these reforms have unleashed paradigm shifts in India’s economy as far as international trade, investment and finance are concerned. This book is about recent changes in international trade, investment and finance in the Indian economy. The book is divided into three broad parts: (i) finance, (ii) investment and (iii) international trade. The unique feature of this book is that it accommodates chapters with differing viewpoints. So, as far as trade, investment and finance are concerned, we have both mainstream neoclassical empirical analysis as well as non-mainstream heterodox analysis. This is quite unique as far as the planning of the research chapters in this book is 1

Dasgupta, Ghosh and Ghosh

concerned. Each of the parts viz. finance, investment and international trade has an overdetermining influence on the other parts. However, for the sake of proper analytical understanding, we have divided the analysis into three parts: (i) finance, (ii) investment and (iii) international trade. One can easily read one part without considering the other two parts, which is another novelty of this book. All three parts taken together provide a holistic understanding or overview of the political economy of finance, investment and international trade in the present neoliberal context of the Indian economy. The first part of the book deals with the question of finance. The political economy of finance in India in recent times cannot be analyzed without considering the financialization of Indian society – a term frequently used today in the existing heterodox literature dealing with finance and the financial process.2 Today, financialization is a process that signifies the unfettered interest of finance in an economy with the real economy shrinking. There is an ever-increasing distance between the financial sector and the real sector in an economy and this distance marks the growing significance of finance capital as distinct from industrial capital and with it the growing importance of the rentier class (or unproductive capitalists in the Marxian sense of the term) in society. Some refer to it as stock market capitalism. This has changed the modus operandi of banking business in emerging economies – a change which periodically erupts as a credit risk crisis of banks. Attempts have been made to solve these problems using market-based reforms, but so far such attempts have been in vain. The most striking example is the global financial crisis of 2007–08 which culminated in a global economic crisis comparable only with the Great Depression of the 1930s. The finance part of the book consists of six chapters, four of which delve into an analysis of financialization as a financial process and its probable impact on the real economy of India. The remaining two chapters deal with an empirical exploration of the Indian banking sector as far as non-performing assets are concerned and also with the emergence of the Indian stock market as a key player in the financial sector in particular and the Indian economy as a whole. The chapter titled “Finance and The Real Economy: The Evolving Distance in the Context of India” by Sunanda Sen deals with, as the title suggests, the ever widening distance between the financial sector and the real sector of the Indian economy in the present context of financialization as effected by the neoliberal economic reforms since 1991, which mainly consist of financial deregulation and thus allow free play of financial forces in Indian society at large. As the chapter shows, the respective growth rates of the real and the financial sectors have generally moved along different trajectories, abandoning the classical role of finance which is to intermediate between savers and borrowers. The chapter attempts to provide a convincing explanation for what has gone wrong, raising two fundamental questions: whether finance no longer offers a stimulus to real growth and whether finance reflects a pure disconnect between financial boom and real growth which are stagnant of late. The chapter seeks to answers some of these questions by providing an assessment of alternate theoretical positions that exist 2


on related issues, including prescriptions for efficient markets in mainstream theories and the alternate positions offered in heterodox economics. The chapter then provides an analysis of the limitations of the mainstream prescriptions at work, relying on evidence available from the workings of the global financial markets. The asymmetric movements of growth in the financial and the real sectors in the current phase of capitalism, as the author indicates, generate volatile and expanding financial activities together with the financial crisis and stagnation in the real economy. The chapter draws attention to the fact that mainstream economic policies for initiating the deregulation of finance have been a major source of financial instability and real stagnation. The major theoretical tool applied by the author relates to the notion of fundamental uncertainty as propounded these days by the post-Keynesian school. The chapter titled “Capital Accumulation and Finance Capital in the Age of Finance” by Byasdeb Dasgupta relies on a Marxian interpretation of finance capital and financialization following a class-focused Marxist approach to explore finance capital accumulation and the role played by finance capital as opposed to industrial capital à la Marx in the present neoliberal context of the Indian economy. The chapter argues that current globalization and global capitalism are characterized by financialization – the latter implying a steep increase in the interest of finance across society. Some also refer to this as stock market capitalism. Financing, insurance and real estate (FIRE) as a percentage of gross value added increased from 8% in 1951–52 to more than 18% in 2010–11 (at 2004–05 constant prices) and from 18.88% in 2011–12 to more than 23% in 2019–20 in India. The Union Budget 2020 has served the interest of finance at the cost of the real economy without considering the ongoing stagnation in the real economy, the steep rise in the rate of unemployment and the informalization of the economy. For example, the Government of India has done away with dividend distribution tax for financial companies and, in 2019, removed surcharges on long- and short-term capital gains earned by high-income taxpayers on transfers of equity shares. Additional taxes introduced in the 2019 budget on the “super-rich” with income slabs over Rs 20 million and beyond Rs 50 million were adjusted downwards. In addition, corporate taxes were subject to significant cuts. Given the pattern of budgetary measures in recent times, questions are bound to be raised on the underlying priorities in recent budgets including the present one. How does one justify that the financial sector and big capital reign supreme in contesting and finally appropriating official policies in India? Why do the voices of the working poor and unemployed remain unheard in terms of remedial measures? The idea of finance capital is related to speculative motives. The circulation of finance remains within the financial sector with no bearing on the real economy. As per Marx, we can think of two circuits of production as M-C-M′ and M-M′; in the first circuit, money capital is transformed into a commodity and then into higher money through market exchange, and in the latter circuit, money capital is directly transformed into higher money through market exchange. In his magnum 3

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opus Capital, Marx did not use the term finance capital. It was first used by Rudolph Hilferding in 1910. Finance capital is based on the second circuit mentioned above. Investment or distribution of part of the surplus in financial assets or instruments such as shares, bonds and derivatives products generally has no impact on the real economy, i.e. on the production of goods and services or in the M-C-M′ circuit. As the empirical evidence suggests, corporate investments today are predominantly in financial assets and not in constant and variable capital. Also, surplus generated in the financial sector is mainly circulated in the financial sector itself, i.e. in the M-M′ circuit. This signifies the increased interest of the rentier class or unproductive capitalists. The circulation of finance capital is within the financial sector itself as the circulation of capital in the economy is understood in terms of Marx’s Capital (Volume 2). In fact, there is a contest between the real sector and other social needs in society on the one hand and finance on the other hand. To understand this, the author has used the class-focused Marxist approach. The masterstroke of Marx is his concept of “surplus labour” which, in the capitalist class process, becomes “surplus value”. Class, as per Marx, is a process of performance, appropriation, distribution and receipt of surplus labour. The site of the economy is disaggregated and decentred in various class processes. The total surplus generated in an entire economy from different capitalist and non-capitalist class processes can be divided between production surplus and social surplus. Production surplus consists of subsumed payments needed to meet the conditions of the existence of the class processes. In the capitalist class process, a part of the surplus is used for capital accumulation. Social surplus targets the socially determined needs of the people (related to poverty, the environment, childhood, unemployment, old age and even entertainment, to name a few) who provide no direct conditions of existence per se to the class processes. Given the empirical evidence of money capital circulating within the financial sector in several financial instruments and also growing corporate investments in financial instruments, we can say that a part of the total surplus, generated at least in all the capitalist class processes, goes to the finance sector which can be dubbed financial surplus. So, is the contest between the production and social surplus on the one hand and the financial surplus on the other hand? A unique example of this contest can be found in this year’s Union Budget (Budget of Government of India, 2020). Issues such as unemployment and underemployment along with the poverty faced by large sections of people and the ongoing recession as well as stagnation in the real economy, while much discussed, are not prioritized in policymaking. The allocation of budgetary funds in the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) has been downsized. There has been little increase in the allocations for the different social sector schemes of the Government of India. All these signify, in class-focused terms, the lowering of the distribution for social surplus. And the contest also lies between the production surplus and the financial surplus with finance capital playing a hegemonic role over real production, thereby adversely affecting real economic growth as shown in the chapter by Sunanda Sen. Finance capital today has emerged as a power bloc that only serves 4


the interest of the rentier class or unproductive capitalists in the financial sector and overpowers industrial capital and thus real economic growth too. In his chapter titled “Revisiting ‘Fictitious Capital’ and the Autonomy of Finance in the Circuit of Global Capital”, Satyaki Roy attempts to reshape the idea of finance capital by taking a clue from Marx’s fictitious capital in his seminal work Capital, and seeks to show the inherent autonomy of finance capital in the circuit of global capital. The relative autonomy of finance in the process of capitalist accumulation has manifested in recent times through the rising share of finance-related sectors in gross domestic product (GDP), the increasing share of profit going to financial companies and the faster growth of profit compared to the growth of investment and employment. Increasing concern for shareholder return has intensified growth-profit trade-off reflected by the predominance of corporate concern over managing balance sheets through short-term financial returns rather than long-term growth. The dominance of finance is also captured by the rising wealth-based or asset-based consumption of people at large, including the rich and the middle class. The new context of financialization, therefore, demands a relook at the question of the production, appropriation and distribution of surplus value. The way that money emerges as capital and circuits of capital, particularly monetary, productive and commodity capital, evolves and mutually constitutes in this phase of capitalist accumulation is the principal focus of this chapter. In the literature, even within the heterodox tradition, the emphasis on the apparent disconnect between the productive and financial sectors assumes immense importance and since the relative share of the profit of the financial sector happens to be comparatively higher there seems to be a tendency to treat these two capitals as separate. Such a perspective helps us to understand how self-augmenting capital assumes a trajectory where the appropriation of capital and the distribution of accumulated capital are disconnected from the act of production; however, in a sense this undermines the process of exploitation and expropriation that is intrinsic to the act of creating surplus value in the realm of production. Using a Marxian frame, Roy’s chapter focuses on the movement of capital in general or “industrial capital”, the circuit of which undergoes a metamorphosis with capital in productive, commodity and various monetary forms changing its role structures. The generality of capital is actualized in the process of financialization where it takes the most general and abstract form, “fictitious capital”, where capital assumes a peculiar form of augmenting value without the act of production, which is the M-M′ circuit. In other words, the production of exchange value or value, in general, does not appear to require the production of new use values. In discussing the conceptual frame of value realization, the chapter essentially attempts to contextualize the process about neoliberal globalization. Rising inequality and the declining share of wages in value added in different countries give rise to a persistent problem of deficient demand for goods and services but concurrently create new demand for financial assets. The demand for financial assets needs to be met by creating a new class of assets or property rights that ensures a future income stream. This, on the one hand, requires transforming erstwhile 5

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state- or community-owned assets into private property and, in this way, innovating financial instruments that commodify risks at various levels. The redefining of property rights entails the use of force and involves a process of creating capital relations through redefining the rules of the game. Globalization involves a process of redefining institutional norms both at the global and local levels that creates an environment conducive to such changes. The author focuses on finance as part of the integrated circuit of global capital. Nowadays, the production and appropriation of surplus value are actualized to a large extent through global production networks. Even though capital is far more globalized in its operation, it is also highly concentrated in terms of ownership. A global division of labour largely determined by gains from labour arbitrage defines the complex structure of value capture through rents, interests and profits appropriated through global trade. It also involves a political process in which the state and particularly supranational institutions play important roles in defining property rights and distributional shares. The appropriated global surplus involves a complex process of distribution particularly subsumed class payments involving commercial capital and interest-bearing capital. The prominence of “fictitious capital” or finance is analyzed in the context of rising uncertainty in terms of global fluctuations in both exchange and interest rates. It is also important to recognize the fact that despite competition and conflict between different forms of capital, such conflicts are subservient to the overarching concern for profit-making and hence secondary to the cause of exploiting labour. Rising interest claims in the age of finance tend to put pressure on the share of profit, which may give rise to severe problems of accumulation, but the preferred way to protect profit is to put pressure on workers’ wages. This clearly explains why financial deregulation has often been followed by labour market deregulation. Finally, the chapter argues that financialization imposes a governance structure by way of reification, making financial values appear as objective guidance in maintaining the reproduction of the neoliberal regime of accumulation. It is the channel by which the life of a firm, as well as that of human beings both rich and poor, is intertwined in the imperatives of global capital. The functioning of the system and capital relations is internalized through this financial norm of governance. The flow of global finance conditions the fiscal space of national governments and, more importantly, with privatized services private consumption expenditure including essential services is routed through insurance and various forms of credit-related financial instruments. The norms of global finance, therefore, tend to hegemonize every aspect of the economy and human life. The chapter titled “Financial Sector in the Indian Economy: Some Reflections using Hyman Minsky’s Lens” by Sukanya Bose, talks about the financial instability of a capitalist economic system such as India from a Minskian perspective. As the author opines, the basic disequilibrating tendencies of capitalist finance will once again push the financial structure to the brink of fragility, argued Hyman Minsky. The financial sector along with real estate and business services has been the fastest-growing sector in the Indian economy, with its share of GDP rapidly 6


rising to around 22% in a relatively short time frame. And yet, this expansion has been fraught with periodic crisis; a growth in finance that is not embedded in the real sector; and exclusionary and inequalizing tendencies across the economy. To fully comprehend the nature of the growth of finance, the Keynes–Minsky writings on the dynamics of finance and investment under uncertainty provide the essential conceptual framework. The Minskian understanding of capitalist finance is applied to look at developments in two sets of institutions in recent years – the transformations in the formal banking system and the rise of the shadow banking system in India. The financial sector, particularly non-bank financial companies (NBFCs), is in a deep crisis that calls for comprehensive reforms. The chapter looks at Minsky’s agenda for reforms that have great significance for the Indian economy. What kind of reforms was Minsky suggesting? Instability that is put to rest by one set of reforms will eventually emerge in a new guise requiring a new era of reforms. There is no possibility that things can be set right once and for all. One can, however, develop a system where the flaws are less evident. One way of addressing the instabilities caused by the shadow banking sector is to have regulation by function rather than institution. If a shadow bank offers a financial product that is subject to regulation when offered by a bank, the shadow bank must also be regulated. This approach would impose similar costs and reduce “the race to the bottom”. Minsky favoured a policy to support small- to medium-sized banks as he believed that a bank’s size is related to the size of the firms with which it does business, and he wanted smaller-sized banks to provide a broad range of services required by their smaller customers, but he didn’t recommend such a structure for large banks and preferred a separation of commercial banking from investment banking as was done through the Glass–Steagall Act in the United States in the 1940s. Downsizing finance is necessary to ensure that the capital development of an economy is successful. By delving into Minsky’s agenda for reforms, this chapter offers a critique of the present policy direction in India. For any modern financial system, banks today play a key role and the problem with Indian banks in the neoliberal era is the proliferation of non-performing assets (NPAs). The chapter by Saumita Paul and Malabika Roy titled “Is Priority Sector Lending Responsible for Higher NPA in Banking Industry?” is an attempt to understand whether the huge increase in NPAs in public sector banks is due to the government policy of priority sector lending, which is gradually waning in the neoliberal period. In the last few years, India’s banking sector has been facing a huge balance sheet stress with mounting loan default. Such loans are overdue for more than 90 days. Most of the literature identifies high NPAs as a reason for this stress. An asset becomes non-performing when it ceases to generate income for the bank as well as being overdue for a pre-defined period. According to staunch believers of neoliberal market economics, it is the compulsory priority sector lending that causes higher NPAs for the banks because neoliberal reforms talk in favour of financial deregulation and hence, no state intervention in banking business. The priority sector lending comprises small- and medium-sized loans 7

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that are directed to the producers of sectors such as agriculture, micro, small and medium enterprises, export credit, education, housing, social infrastructure and renewable energy. As these sectors are vulnerable to different kinds of shocks, it is believed that they face difficulty in repaying their loans; hence, their credit worthiness is low. However, due to their immensely important role in the development of the basic needs of the country, these sectors together can get 40% of adjusted net bank credit from all the scheduled commercial banks at the Reserve Bank of India (RBI)-specified and favourable rate(s) of interest. According to mainstream economics, priority sector borrowers fail to repay these huge loans within the specified period, ultimately leading to high NPAs on banks’ balance sheets. This claim, however, raises doubts because from the existing literature as well as the data used in this chapter, it is evident that loans to different segments defined as the priority sector have actually declined in some years of the post-reform period and, in many cases, the NPA of the priority sector has actually declined and that of the non-priority sector has increased, notwithstanding the different methods that banks use to actually channel the mandatory share of a priority sector’s loan to the non-priority sector. Such paradoxical claims have been the motivation behind the chapter by Saumita Paul and Malabika Roy, who attempt to examine whether this claim has any empirical support. Together with the priority sector lending ratio, they have used the asset size and the management quality of a bank as the other probable factors behind a high NPA. They have taken the gross NPA to gross advances ratio and priority sector advances to gross advances ratio as the two dependent variables. The authors have followed exploratory data analysis such as a graphical representation and a rank correlation test to develop their hypothesis and then applied panel data regression to test the hypothesis that taken as a ratio of gross advances, changes in an NPA are not driven by changes in priority sector lending. Using data from the majority of the public and private sector banks that were operative in India during 2005–2019, they find that it is not actually the changes in the priority sector lending, but the change in the asset size as well as management quality that causes an increase in NPAs. The effect of size is magnified when the bank belongs to the public sector. One of the most significant features of the current financialization of this neoliberal age is the emergence of the stock market as an indicator of the economic fundamentals of a country. India is no exception. India has witnessed a huge increase in the importance of the stock market since 1991. And investment in equities is, to a great extent, guided by investors’ sentiment which determines the market capitalization. In his chapter titled “An Empirical Exploration of Indian Stock Market: Investigating the Interface of Return, Sentiment and Exchange Rate”, Kuntal Chakraborty attempts to explore the interrelationship between investment sentiment and exchange rate with stock market return in recent times in India. The proxy variables for stock market return and investor sentiment and exchange rate are chosen as the Nifty50 return and volatility index (VIX) and the foreign exchange rate of the Indian rupee with respect to the US dollar over the period from April 2009 to March 2019. To explore the nexus between these 8


three underlying variables, initially an unrestricted vector auto regression (VAR) model is used to find the optimal number of lags for defining the equilibrium relationship. Then, the Granger causality test and the Johansen co-integration test are conducted to explore both the short-term and long-term relationships between these variables. It is observed that in the short run, the stock market return along with investor sentiment significantly affects the exchange rate. Although in the long run, the author has not found any significant relationship between the variables. The second part of the book concerns the political economy of investment in the neoliberal era with reference to India. Here, investment is understood in the mainstream macroeconomic sense. Economic growth in macroeconomic theory depends to a great extent on investment. And the nature of investment during the neoliberal era has undergone a sea change from public investment to private investment, more specifically, private investment by corporate firms. The neoliberal era in India that started in 1991 witnessed a high growth period till around 2015, after which growth stagnation began and, as we all know, during the Covid period the growth rate in India has plummeted. The part on investment consists of three chapters of which two relate to India’s recent growth story and the remaining one relates to foreign direct investment (FDI) in one of the major manufacturing sectors of India – the pharmaceutical industry. The neoliberal age is premised on the substitution of domestic investment by foreign investment and in that respect this particular chapter empirically investigates the effectiveness of recent foreign direct investment on the pharmaceutical sector in India. Zico Dasgupta’s chapter titled “The Dynamics of Global Demand, Investment and Trade Deficit in India: A Model of India’s External Dependence” is a theoretical attempt in the heterodox sense to analyze India’s external dependence as far as the recent growth dynamics in the post-reform period are concerned. A particular question raised by the author in this regard is: Does any specific factor exist, the presence or absence of which can demarcate India’s high growth phases from the slowdowns during the liberalization period? The author tries to conceptualize India’s once celebrated growth story in the midst of the present slowdown. Against the backdrop of some stylized facts, the chapter strives to address this question by providing a demand-side theoretical framework that simultaneously explains the high growth phase as well as episodes of slowdown. The primary stimulus for investment and growth is argued to be exogenous changes in global demand, whereas higher global demand in itself is argued to open up the possibility of increasing the trade deficit. The growth story that would emerge out of such an accumulation process would be one of external dependence, where the domestic economy is exclusively dependent on favourable external economic conditions to maintain demand and the growth rate. The author attempts to vindicate the theoretical findings in this regard with the help of a simple econometric exercise and concludes that, of late, India’s growth and investment are shaped by an exogenous factor viz. India’s external dependence on global demand, and not so much on domestic factors such as domestic private and public consumption. The 9

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chapter has serious implications for policy imperatives in India as far as domestic economic growth is concerned. India’s recent (pre-Covid-19) growth downturn has been a matter of serious concern. The chapter titled “India’s Recent Slowdown and Neoliberal Regime of Accumulation: Is There a Link?” by Saswata Guha Thakurata and Rajendra N. Pramanik delves into the problem of India’s recent slowdown in growth rates in the context of the public-private investment structure of the neoliberal period. The severity of the situation is heightened by its potential impact on the lives of millions of Indians, especially those belonging to the lower rungs in income distribution. Despite sustained high growth for about two and a half decades brought about by private corporate domestic investment and foreign investment since the onset of reforms, the Indian economy has not experienced any stylized structural transformation and, more importantly, the high growth has virtually failed to create a satisfactory amount of remunerative and good quality jobs. This, together with other factors, has led to rapidly rising inequality (both in terms of income and wealth) which has marked the entire post-liberalization period. India’s economic growth is infamous for its joblessness. The recent economic slowdown has only aggravated the employment crisis. Analysts have argued that the Indian economy has transited from being characterized by “jobless growth” to “job-loss growth”. Additionally, various unofficial accounts strongly indicate a fall in the real purchasing power of the rural population in the recent past. Debates have already emerged about the nature of the (pre-Covid-19) slowdown – whether it is structural or cyclical – as well as its causes, and the outbreak of the pandemic has aggravated this further. In retrospect, the rapid urban-centric growth that has marked the free-market era has been characterized by a spectacular boom in consumption accompanied by accentuating disparity. During the period of reforms, private consumption expenditure has been one of the key sources of domestic (aggregate) demand. Urban areas have been the main centres of this consumerism spree. The other source of growth in domestic demand has been the steady growth in private corporate investment. In this analysis, the authors consider export growth as exogenous. The period of steady high growth has substantially deepened class cleavage. Notably, even the growth downturn phase has multiplied the fortune of the billionaire in an unprecedented manner. This endeavour engages with the ongoing debate around India’s “great slowdown” from the political economy perspective. Its main objective is to enquire about the possible links between the neoliberal regime of accumulation and India’s recent growth performance primarily from the perspective of demand. The period of analysis ranges from 1991–92 to 2019–20. In this chapter, a demand decomposition and sectoral growth-based analysis is deployed to investigate the nature of India’s growth from a long-term perspective while considering various episodes of growth acceleration and slowdown since the onset of reforms. It is further substantiated with an exploration of the growth–inequality relationship especially from the perspective of consumption and investment growth. The authors use the National Sample Survey Organization that conducted various rounds of household consumer expenditure 10


data to empirically verify the link between consumption and inequality using a cross-sectional analysis. As far as the analysis of investment growth or accumulation is concerned, the authors follow the post-Keynesian characterization of an accumulation regime being wage-led or profit-led. Using the relevant data for various sectors, they deploy a panel data standard error model to explore the relationship between accumulation and profit share, capacity utilization and capacity– capital ratio. The empirical findings of this chapter suggest that there is a positive relationship between consumption expenditure and inequality and that a higher profit share leads to higher accumulation in post-reform India. In other words, the increase in private consumption growth is linked with burgeoning inequality and rising profit share boosted accumulation, in effect deepening the problem of demand deficiency. Under such circumstances, export growth becomes critical, but is, however, dependent on the global economic conditions. Therefore, an intensifying agrarian crisis and detrimental government policies such as demonetization in the context of a global slowdown further dampen the effective demand. In contrast to accounts that argue that this slowdown is an outcome of the financial crisis alone, this chapter emphasizes the critical role of the growth–inequality nexus underlying India’s (post-reform) primarily export and (conspicuous) consumption-driven and profit-led neoliberal accumulation regime. Undoubtedly, this dynamic has amplified the suffering of millions of poor Indians during the current tumultuous time of the Corona pandemic. One of the prime objectives of economic reforms in India since the 1990s has been to make Indian industry competitive and efficient. Apart from changes in industrial policies such as ending the industrial licensing regime, deregulating industries and disinvesting the public sector unit, foreign direct investment policy has also been considerably liberalized. FDI not only provides direct benefits to the recipient country in the form of investment in capital and the generation of employment, but also generates indirect benefits through FDI spillovers and externalities. Such indirect benefits may increase the efficiency and productivity of domestic firms by way of various non-market mechanisms such as labour mobility, demonstration effect and linkage. India took various liberalization measures to increase FDI inflow in different phases. As a result, India became a preferred investment destination and received large FDI inflows which rose from around US$ 6 billion in 2001–02 to almost US$ 53.3 billion in 2016–17. As conventional wisdom goes, along with financial capital, FDI brings technical, managerial and organizational knowledge and this knowledge may spill over to the rest of the economy contributing to the productivity growth of domestic firms. Now, an important question is whether huge FDI inflows bring about any productivity gains in Indian domestic firms. The chapter by Labanya Pal titled “Foreign Direct Investment and Productivity Spillovers: Evidence from Indian Pharmaceutical Industry” attempts to examine whether FDI contributes to any productivity spillovers to Indian domestic firms in the liberalized regime. For the analysis, the author has selected the pharmaceutical industry as this is one of the most high-tech and capital-intensive industry where 11

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100% FDI is allowed. The Indian pharmaceutical industry has attracted considerable FDI inflows since 2000. Additionally, this industry has gone through different policy regimes such as patents, designs and international technology transfer. The author has used the stochastic frontier analysis (SFA) following Battese and Coelli (1995) to examine the impact of spillovers from FDI on productivity gains. Productivity spillovers from FDI are estimated through the relationship between the FDI spillover and technical inefficiency and the SFA model is used to estimate a production function and an inefficiency function simultaneously. Apart from spillovers from FDI, a set of firm-specific factors such as R&D intensity, embodied and disembodied technology intensity, export and import intensity, age and size of the firms and a competition index are also considered to examine their impact on efficiency or productivity change. Two additional inefficiency variables, namely the absorption capacity of firms and market power, have also been used. Competition among firms is measured using the Herfindahl–Hirshman index. The study is based on data provided by the Centre for Monitoring Indian Economy (CMIE) prowess database and the Annual Survey of Industries (ASI) of 132 pharmaceutical firms over the period 2000–01 to 2016–17. The results indicate that there are positive productivity spillovers to domestic firms due to the presence of FDI firms in the Indian pharmaceutical industry. Competition also facilitates spillovers from multinational companies (MNCs) in the industry. Technology intensity also has a significant positive impact. Astonishingly, firms with R&D expenditure cannot gain spillovers from FDI firms signifying that firms with R&D expenditure cannot reap the benefits from foreign firms, rather their efficiency improvement depends on their in-house R&D expenditure. It is also found that firms with higher market power proxies by its size have larger spillovers from a foreign presence. Thus, it can be said that larger domestic firms may gain larger spillover benefits from foreign firms. However, in an industry where the majority of firms are small and where the majority of firms do not engage in R&D or innovative activities, there is practically no spillover benefits from a liberalized FDI policy. Thus, to reap the benefits of a foreign presence in the economy, policies should be directed towards strengthening the absorptive capacity of domestic firms through investing in R&D expenditure, knowledge and human capital formation. The third part of the book pertains to India’s international trade during the neoliberal era and its implications for the Indian economy – especially the informal sector and (informal) employment. International trade in the traditional mainstream neoclassical economic literature is dubbed as the growth engine of a country. However, the question has emerged whether the free trade that the world is witnessing since the inception of the World Trade Organization (WTO) in 1995 is in any way fair for all trading countries of the world particularly the developing countries. Today, the debate regarding this issue is very intense in the theoretical literature, particularly pertaining to employment generation through trade in the home economy and also the impact of (free) trade on the informal economy – which constitutes the major part of the Indian economy. Also, the 12


question is whether the inception of the WTO (which is ideally responsible for instituting free multilateral trade) has created a positive trade potential for an emerging economy such as India. Another question raised is what happens with the factors of production through this trade channel in an emerging economy such as India. Is trade really an engine of growth remains a vital question today in this age of neoliberal reform as far as informal employment generation is concerned and also the benefits of trade accruing to the Indian economy at large because most of the employment generated since 1991 has occurred in the informal sector of India. Economies around the world have witnessed sweeping changes in the area of international trade. Closed economies, especially developing economies, have opened their doors to international trade in goods and services and also to foreign capital. This has ushered in reforms not only in a tariff–subsidy exchange rate regime, but also in the structure of domestic production. For the majority of countries, production goes on in the formal as well as informal sectors. In most of the developing countries in Asia, Africa and South America, around half of the working population finds employment only in the unorganized, informal sector. The last decade of the 20th century witnessed the beginning of globalization and the liberalization of hitherto closed and controlled economies of the world. Liberalization consists of structural reform in the product and input markets as well as in the arena of the trade and financial sectors. The opening up of economies has a deeper implication for labour markets both in the formal and informal sectors in developing countries. The chapter titled “Reformatory Policies and Factor Prices in a Developing Economy with an Informal Sector” by Biswajit Mandal, Sujata Ghosh and Saswati Chaudhuri explores the effects of the liberalization of an economy on the informal sector in terms of a neoclassical trade theoretical model. The chapter focuses on the impact of the labour market and trade policy reforms on factor prices and outputs. The authors consider a small open economy producing goods and services in four sectors using four types of inputs. The agricultural sector (A) producing exportable goods and the urban unorganized manufacturing industry (I) operating in the informal sector. The organized manufacturing sector (M) producing importable goods and the organized service sector (Y) producing exportable services operating in the formal sector. The four inputs are land, unskilled labour, skilled labour and capital. Land is a specific input in sector A and skilled labour is a specific input in sector Y. Capital is a freely mobile input used in sectors I, M and Y. Unskilled labour is an input in sectors A, I and M. Unskilled labour is freely mobile across sectors A and I and earns the same wage there, whereas in sector M they receive a higher formal wage fixed by trade unions. The wage of skilled workers is market determined and is higher than that of unskilled labour. Liberalization policies usher in the deregulation of labour laws with a view to relaxing wage rigidity. The authors show that labour market reform would decrease the gap between formal and informal wages, increase the rents on land and capital, contract informal manufacturing I and the formal service sector Y, 13

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while expanding output in agriculture A and organized manufacturing M. Trade liberalization translated into tariff reductions, on the other hand, induces the exact opposite results. However, a reduction in an ad valorem export subsidy to A and Y lowers the wage gap; contracts the exportable sectors A and Y; and reduces the returns to the specific inputs used in A and Y. The authors also extend the model to introduce the possibility of corruption in the informal sector and examine the effects of reforms. It is found that a decrease in the cost of corruption helps to increase the informal wage along with expanding informal output, but the effect on the number of extortionists remains ambiguous. The chapter titled “Impact of Trade Liberalization on Informal Employment: A Theoretical Approach” by Debabrata Roy presents an elegant neoclassical trade model where production goes on in the formal as well as informal sectors with labour as the only factor of production. Labour may be formal or informal, but there is no difference in wage. Producers in the formal sector may engage only formal labour or may engage informal labour partially and indirectly through subcontracting. Demand uncertainties can lead to the retrenchment of labour and in that case while formal labour gets severance pay, informal labour gets no severance pay. Why then does the formal sector employ formal labour in spite of the retrenchment cost? The author argues that firms in the tradable sectors need to register in order to avail of tax subsidies and other concessions and government supports. Firms in the exportable sector also have to follow institutional bindings and labour laws. The author shows that with the opening up of trade, there will be a rise in demand uncertainty and the formal sector will increasingly employ more informal labour and less formal labour in order to maximize its profits. A country like India varies widely in its landform, natural resources, vegetation and labour skills at the regional level, thereby making each region especially suited to its own specialized products. As a member of the WTO, if India has to utilize the opportunities for freer and increased trade that the WTO platform offers, a slew of reforms has to be initiated both by central government and the state governments to identify the areas of comparative advantage and provide impetus for growth and exports in those products. The chapter titled “Trade Potential and WTO Issues for West Bengal” by Debottam Chakraborty addresses the issue of trade potential for the state of West Bengal. The virtual absence of data or reports on the WTO issues specific to the state of West Bengal has made the author’s work necessary as well as difficult. Further, state-specific data is available for the production of goods, but not for exports. To circumvent the problem, the author has developed an index by using production data to find the commodities in which West Bengal has comparative advantage. The products identified may be categorized into those which appear prominently in the export or import basket and those that have the potential to do so. The products comprise both agricultural and industrial goods. The author goes on to discuss some of the issues that have been debated in the WTO with respect to these sectors. While the WTO promotes freer trade, it has also made provisions for countries to prevent the entry of importable goods by citing the clauses of sanitary and phytosanitary 14


measures (SPS) and technical barriers to trade (TBT). India also faces the issue of the non-reciprocity of geographical indicators (GI) protection in developed countries. Exporters regularly face these barriers to trade, in addition to stiff competition in the world market. The role of central government in safeguarding the interests of domestic exporters and ensuring a fair playing field for them in the world market becomes essential. Handling WTO issues at the national level may not be enough, as different states have different priority sectors. State governments need to sensitize Indian negotiators at the WTO to protect the interests of the state and to pave the way for maximizing the benefits that can accrue in the liberalized trade environment. This book includes researched chapters on the issue of finance, trade and investment with reference to the Indian economy which is considered one of the emerging economies of the world in this neoliberal age. The uniqueness of the book is that it contains political economic debates centred around the question of finance, finance capital and financialization as discussed above. Similarly, it includes researched chapters on trade in emerging economies such as India relating to the question of the informal economy, employment and (positive) trade potentials. Also, some research chapters take up the question of investment in such a global order including foreign direct investment in India – some of which are inalienable from the question of finance in neoliberal global capitalism. As is clear from this discussion, the particular focus of the book is on the Indian economy during the neoliberal age which started in 1991. One striking feature of this book is that in one place it accommodates researched chapters on international trade, investment and finance – some of which follow mainstream neoclassical economic theories and some non-mainstream political economic renditions (in particular Marxian and post-Keynesian stances) as their method of analysis. The book will be useful for researchers and postgraduate students in universities/institutes – particularly in India and the United States – and can be used as a secondary study material for postgraduate students and research scholars. Additionally, it will be of great interest to policymakers.

Notes 1 Corresponding author: byasdeb​@gmail​.c​om. 2 For an analytical understanding of financialization as a financial process see Mader Philip, Daniel Mertens and Natascha van der Zwan (2019), “Financialization: An Introduction” in Philip Mader, Daniel Mertens and Natascha van der Zwan (eds) Routledge Handbook on Financialization. Routledge: London and New York.


Part 1


1 FINANCE AND THE REAL ECONOMY The evolving distance in the context of India Sunanda Sen With respective growth rates in the real and the financial sectors often moving along different trajectories, one needs a convincing explanation of how this happens. Why does finance no longer offer a stimulus to real growth? Does it reflect a pure disconnect or more than that? To take an extreme position, can the splurge in the financial sector be related to the slump in the real economy? Before we seek some explanations of the growing rift between the financial and the real economy in terms of their performances, we look into the situation in India, a country considered as an “emerging economy” in terms of having relatively higher growth rates in recent years and larger flows of overseas capital with the opening of its financial markets. The first section of this chapter provides data that confirms the presence of an asymmetric growth pattern between the real and the financial sectors in India during recent times. Questions, such as those as we ask above, evoke an assessment of the theoretical positions as are available on related issues. These include the efficient market principles offered in mainstream theory and the alternate interpretations that are offered in heterodox economics. We offer, in the second section of this chapter , an outline of the alternate theoretical positions for interpreting the divergent paths of the financial and real activities in de-regulated markets. The third section provides examples which reflect the continuing use of mainstream principles in global financial markets including those relating to India. . The last section provides concluding observations.

Finance and real sector disparity in India A simple way to compare the respective performances of the real and financial sectors can be to compare the aggregate growth rates of GDP and stock market transactions. We provide, in Figure 1.1, the respective growth rates for India’s GDP and the stock index for the Bombay stock market. The stagnation in India’s GDP when compared to the volatile growth rates in stock exchange transactions indicates the relative buoyancy of the financial sector. Figure 1.2 expresses the above fact more explicitly in terms of the massive expansions in the market capitalization of the Bombay Stock Exchange, moving up 19

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nearly 50 times, from a sum of Rs. 323,363 crores in 1991–92 to Rs. 1,51,08,711 crores in 2018–19. (See Appendix I for Figures 1.1 to 1.4.) That an upsurge in financial activities does not necessarily contribute to real activities (in terms of generation of output or employment) can be figured out easily if one recalls the fact that gains realized in the financial sector, say as capital gains obtained on the stock exchange, stands as a mere transfer between two parties. Transacted as the sale of a stock, unlike those with initial primary offers (IPOs), these gains do not generate real activity. Accordingly, neither the rising index of stock market transactions nor the value of capitalization can be expected to have much of an impact on the real economy other than those related to the second-round multiplier effect of expenditures from such gains. However, downswings in financial activity does convey a parallel tendency in the real economy, thus indicating a rather asymmetric relation between the two. Situations of a financial sector collapse followed by similar downturns in the real economy are not difficult to observe, especially after the global financial crisis of 2008/9 which started with the upheavals in the US financial sector and was followed by massive downturns in both the financial and real activities in the world as a whole. The inherent asymmetry between the effects on real activities, of a financial boom as opposed to its collapse opens up further issues concerning the volatility of the financial sector. Thus, while the upsurges in finance have little to offer in terms of an expansionary effect to production and employment, the sequence is very different with shrinking finance since the losses met by the financial institutions affect the customers in the real sector. One has witnessed what the bankruptcy of Lehman Brothers and its contagion effects caused to major banks and their clientele in different parts of the world during the recent clobal financial crisis.. A need thus arises to tame the growth and the volatility of the financial sector, which offers few benefits to the real sector when it’s booming while inflicting sharp real contractions with its collapse. However, with the opening of the markets and the steady pace of financial de-regulation under the dictates of neoliberal theory in most countries, the world is witnessing the worst consequences of the “efficient market” policies in the global economy. .

Methodology behind the choice of investment decisions It remains a fact that since the ascendancy of the marginalist principles and prescriptions of market-led efficiency in the early 1970s, official policies in both advanced as well as in developing countries have continued to be guided by neoliberal economics. A major consequence of the above development has been the spread of financialization in most countries, which include developing area. As it has been aptly put, financialization can be viewed as “the increasing role of financial motives, financial markets, financial actors and financial institutions in the operation of the domestic and international economies” (Epstein, 1995). 20

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The above pattern is reflected in the relative expansions of financial activities, as already mentioned above. The theoretical frame underlying the neoliberal prescriptions that guide investment decisions in the market relies on an assumption relating to “rational choice” on the part of agents in the market – that they have full knowledge of the related developments. These abilities, relying on the positivist methodology of Friedman relies for testing on the only option which is “it’s empirical testability” (Friedman, 1953, 14). Assumption of full knowledge and rational expectations on part of agents, also indicate the capacity, on the part of the agents, to predict the future – by relying on an ergodic probability function. Evidently, such assumptions in effect rule out uncertainty, the sine qua non of the decision-making process. This limitation has been pointed out by several critics of the neoliberal approach from the heterodox school (Crotty, 2013, Davidson, 2003, in Runde and Mizuhara, 2003, 230–31). Mention may be made here of the use of the rational choice assumptions in the call–put option pricing formula, a common procedure for deciding on investments in the stock markets. Considered to predict the margin of gains/losses obtained on bids of calls–puts in option trading, the formula relies, among other things, on its assumption that agents have the ability to exercise “rational choice” in the markets, thus relying on an ergodic probability function. As we already mentioned above, with such assumptions, the future is intractably linked to the past, and weights attributed to past events continue to determine the probabilities of future outcomes, which rules out uncertainty. Incidentally, in models such as above, option prices (and the corresponding premium on calls) are pitched higher, along with increased volatility in stock prices , The latter, by assumption, is subject to a standard normal distribution over time, as in the Black–Scholes–Merton formulation of call–put options. Clearly, the link suggests an underlying interest on part of the financial agents in having instability in the financial markets. As for the critiques of the mainstream approach as above, we may recall here that the ability of agents to calculate probability was first questioned by Keynes (Keynes, 1921). This was at least a decade and a half before his major related critique in his magnum opus, The General Theory of Employment, Interest and Money (Keynes, 1936). Deviating from the on-going frequency theories based on the Benthamite utilitarian and cardinal (or statistical) notions of probability estimation, Keynes in 1921 held that probability relations are logical and rationalobjective. He reformulated it as α = a/h where α represents the degree of belief and “a” and “h” the observations and knowledge (Bateman, 1991, in Bateman and Davis, 1991, 55). Moving further, Keynes in 1936 offered a major alternate formulation of probability relations under uncertainty. This was with a subjective base for probability, as driven by the “animal spirits” of agents in the market. It can be surmised that by reformulating the notion of probability as above, Keynes was relying on uncertainty and on the subjective elements of “animal spirits” as the core of his 21

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argument. The difference of this from the notions of the utilitarian calculations of probability remains amply clear in his statement, “Human decisions affecting the future cannot depend on a strict mathematical expectation, since the basis for making such calculations does not exist” (Keynes, 1936, viii). To make it more explicit, the alternate interpretation of “animal spirits … [is] … the spontaneous urge for action rather than inaction and not … the outcome of a weighted average of quantitative benefits multiplied by quantitative averages” (Keynes, 1936, 161–62). Keynes’s contribution to the probability issue under uncertainty provided the very basis for questioning the mainstream prescriptions for de-regulated finance and capital account liberalization in the interest of financial stability and market efficiency. One can notice the related flows of short-term capital across countries, much of which has been feeding speculatory transactions in the financial markets. There has been a related spread of financialization, with consequences which have hardly been beneficial for the real economy (Sen (2020) in Mader, Mertens and Zwan (ed), 2020). If probability is not predictable under uncertainty, as pointed outby Keynes in 1936 , investment decisions which continue to rely on such probability estimates are also without merit for judging the estimated returns on investments. Contesting the scientific basis of the neoliberal prescriptions which are used in markets to decide on investments, the heterodox school, led by the post-Keynesians, directs attention to alternative positions which are more realistic (Sen, 2019). We draw attention here to attempts, on the part of some post-Keynesians, to conceptualize what can be viewed as “fundamental uncertainty”. Focussing on uncertainty as the primary force behind all changes in the market, one can here conceive of a future which is not only unpredictable but also remains subject to change, and often through the actions of the actors themselves (Dunn, 2001, 578; see also Scazzieri, 2011, in Brandolini, Marzetti and Scazzieri, 2011, 73). As it is pointed out, with “fundamental uncertainty” “the agent does not choose from a given list of possibilities, but actually creates the list” (Carvalho, 1989, 66–81). Putting it differently, the un-knowability of the future amounts to a “lack of determinacy as an ontological property of the universe with imprecise knowledge as an epistemic property of agents in that universe” (Brandolini, Marzetti and Scazzieri, 2011, 73). It does not require much to conclude that Keynes, in the General Theory, was much in line with what was identified later in Post-Keynesian economics as fundamental uncertainty”. As it has been put, Keynes’s concept of uncertainty reflects the future as “transmutable or creative in the sense that future economic outcomes may be permanently changed … by the actions today of individuals, groups and/or governments, often in ways that are not even perceived by the creators of change” (Davidson, 2003, 234). Interpretations as above of the “fundamental uncertainty” faced by investors, somewhat tally with what Keynes famously interpreted as “uncertain knowledge”, by which, “I do not mean merely to distinguish what is known for certain from what is probable … About these 22

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matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know” (Keynes, 1936, 235–36). Thus, in terms of “fundamental uncertainty” precise predictions of the future are ruled out, with new realities coming up as “potential surprises” (Rosser, 2001, 547). To come back to financialization in the context of the methods used in to decide on investments in the market – the former, by generating higher rates of return on financial assets, creates a tendency on part of agents to chase financial rather than real assets. Here comes the question in the context of the un-knowability of the future under fundamental uncertainty, concerning the composition of investment which is likely to emerge. A legitimate point may be whether the rise in financial assets as results – can continue over time with rising degrees of uncertainty. To provide an answer, one needs to follow up the impact of the growing uncertainty on the real–financial divide in investments which responds to the relatively higher rates of return on financial assets in the market. While the respective demand for financial as well as real assets is likely to be higher when their individual return moves up, returns on the short-term financial assets, unlike those on long term real assets, remain susceptible to uncertainty. Moreover, it is a fact that returns on financial transactions can move only with some uncertainty in the market, providing opportunities for either gains or losses.1 However, the rising returns on financial assets, corresponding to higher degrees of risks under uncertainty, can continue only up to a point and then turn negative with degrees of uncertainty crossing a limit – approaching a state of fundamental uncertainty. One finds here a parallel to the Minskian state of Ponzi finance when borrowers fail to reschedule the debt liabilities by borrowing fresh in an uncertain market. The points we have made above can be used to infer the relevance of financialization for the growing divergence of the real and financial sectors under uncertainty. It can be argued that the expanding share and growth of the financial sector in an economy, along with the declining share and relative growth of the real sector, generates a path which at some stage is likely to meet a road-block under excessive degrees of uncertainty. The result is a financial collapse, leading to an overall crisis in the economy. It is not difficult to find examples, especially with the recent global financial crisis and the great recession which followed. Contributions, as above, in line with Keynesian analysis, provide the basis for the critique, both of capital account liberalization and the spread of financialization, as offered by the post-Keynesians in the heterodox tradition, contesting the on-going neoliberal policies based on mainstream economics (Mader, Mertens and Zwan, 2020).

Mainstream prescriptions at work in global financial markets Limitations of mainstream prescriptions for arriving at investment decisions replicate abundantly in contemporary capitalism, where markets are subject to volatility, frequent crises and a growing asymmetry between the financial and the real growth rates. Consistent with their advocacy for free market, mainstream policies 23

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have been responsible for de-regulation in financial markets and the setting of binding limits on fiscal deficits, both to target inflation. For economies like India, the policies initiated austerity with a squeezing of aggregate demand and economic activity. (Sen and Dasgupta 2014 pp. 423–450) An important consequence of financial de-regulation has been the added degree of uncertainty in the markets, contributing much to the risks of conducting business. Uncertainty-related risks in de-regulated markets were responsible for innovating derivative instruments to hedge such enhanced risks. Financial devices, innovated as derivatives, consist of forwards, futures, options, swaps and the like, along with other financial assets in the secondary markets of stocks – all contributing to the financialization process, which entail an institutional change in such economies. Looking at the value of derivatives traded in the global market, a sum as large as $472 trillion of outstanding over-the –counter (OTC) transactions was circulating by the end of the first half of 2010. The sum was more than 7000 times the reported value of the global GDP in 2010 as a whole. Similarly, daily trading of exchange-traded derivatives (options and futures), amounting to $7.64 trillion in a day on average in August 2019, compares favourably to the annual value of global GDP in 2018 at $85.91 trillion. Advocacy of derivatives as hedging instruments can be traced back to the claim in mainstream theory that such instruments, by providing a better allocation of market risks over time, are welfare-enhancing. Derivatives are thus considered to provide an opportunity for the transfer of risks, from risk-averse to risk-neutral agents in the market, thus achieving an efficient allocation of resources over time and space. Accordingly, derivatives are considered to make for efficiency by offering insights into a future which is uncertain. As pointed out by Hicks, “the explosive use of financial derivative products in recent years was brought about by three primary forces: more volatile markets, deregulation and new technologies” (Hicks, 1967). However, the sharp increases in the turnover of derivatives do not speak for efficiency in terms of financial stability in the volatile markets. Thus it does not remain a surprise that derivatives, while failing to achieve the targeted goals of efficiency by transferring risks, result in further uncertainty and instability in those markets. As a consequence, relying on instruments like derivatives does not necessarily contribute to material growth in the real economy by achieving efficiency in the financial sector. Finally, it is important to reiterate the point that the multiplicity of financial instruments which include derivatives as well as other financial assets transacted in stock exchanges or in OTCs, while originating from the same base in terms of specific real activities, do not expand their respective bases. (Those transactions relate to the secondary market alone where shares originally sold in the primary market against the expansion of real activities are resold and exchanged by changing hands.) Instead, these financial transactions in the secondary market (including those in derivatives) amount to a piling up of claims which in turn originate 24

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from the same set of real assets. In terms of the standard convention related to national accounts, capital gains/losses related to such financial transactions are reckoned as pure transfers which are not included in GDP computations.

Concluding observations We have tried, in this chapter, to provide an interpretation of the asymmetry between the financial and the real sector in terms of their respective growth. We draw attention, in particular, to the expansion of financial activities, which came up with the de-regulation of financial markets as prescribed in terms of neoliberal economics. We also draw attention to the volatility and instability of markets, which resulted from the growing uncertainty under de-regulation.. Use of instruments to mitigate risks in the market, while popularly used as a major device to secure investments, do not work in achieving the goals, as can be seen with the continuing fluctuations in such markets. Uncertainty, while creating the requisite space for financial activities by making it possible to earn or even lose under changing circumstances, fail to improve profitability when the degree of the former approaches a level which relates to “fundamental uncertainty”, a realm of complete unknowns. We also highlight the proven failures of de-regulated finance in accelerating real growth via “efficient markets”, as can be witnessed from the working of the global economy since the mid-1970s. The inadequacy as above can be related to the misconceived strategies of investments based on the approximation of probabilities which is untenable with uncertainty. Much of the above relies on neoliberal notions to arrive at returns on investments by calculating probability. Analysis in this chapter, of the divergences between the real and financial sectors, matches the observed tendencies in the global economy as well as in developing countries like India.


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Appendix Disparate growth 100.0 80.0

GDP Stock market capitalisation

Growth rates

60.0 40.0 20.0


1991-92 1993-94 1995-96 1997-98 1999-00 2001-02 2003-04 2005-06 2007-08 2009-10 2011-12 2013-14 2015-16 2017-18



Figure 1.1  Volatile finance and disparate growth. Source: Bombay Stock Exchange.


Rs. Crores

12000000 10000000 8000000 6000000 4000000 2000000 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1990-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19



Figure 1.2  Market capitalization BSE. Source: Bombay Stock Exchange.


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Annual averages BSE SENSEX Index 40000 35000 30000 25000 20000 15000 10000 5000 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1990-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19


Figure 1.3  BSE Sensex: annual averages. Source: Bombay Stock Exchange.

GDP at constant prices : growth rates 10.0 9.0

Growth rates

8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1990-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19


Figure 1.4  GDP growth rates.

Source: Economic Survey, Government of India.

    Note 1 While we do not agree with the mainstream models which interpret the stock market transactions in terms of call–put margins, one notices the role of higher standard deviation in stock prices as a factor inducing the rise in margins of calls, which in effect is the role of volatility in uncertain markets.


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References Bateman, B.W. and Davis, J.B. (ed) (1991), Keynes’s Philosophy: Essays on the Origin of Keynes’s Thought. Basingstoke: Edward Elgar Publishing. Carvalho, C. de (1989), “Keynes on Probability, Uncertainty and Decision Making”. Journal of Post Keynesian Economics 11 (1): 66–81. Crotty, J. (2013), “Realism of Assumptions Does Matter: Why Keynes-Minsky Theory Must Replace Efficient Market Theory as a Guide to Financial Regulation theory”. In M. Wolfson and G. Epstein (edited) The HandBook of the Political Economy of Financial Crisis. Oxford University Press, pp. 133–158. Davidson, P. (2003), “The Terminology of Uncertainty in Economics and the Philosophy of an Active Role of Government Policies”. In J. Runde and S. Mizuhara (edited) The Philosophy of Keynes’s Economics. Oxfordshire: Routledge. Dunn, S. (2001), “Bounded Rationality Is Not Fundamental Uncertainty: A Post-Keynesian Perspective”. Journal of Post Keynesian Economics 23 (4): 567–87. Epstein, G. (1995), Financialisation and the World Economy. Basington: Edward Elgar Publishing. Friedman, M. (1953). “The Methodology of Positive Economics.” in Friedman, M. (ed) Essays in Positive Economics, Chicago: University of Chicago Press. Hicks, J.R. (1967), Critical Essays in Monetary Theory. London: Clarendon Press. Keynes, J.M. (1921), A Treatise on Probability. London: McMillan and Co. Keynes, J.M. (1936), The General Theory of Employment, Interest and Money. London: MacMillan & Co. Mader, P., Mertens, D. and Zwan, N. (2020), The Routledge Handbook of Financialization. London and New York: Routledge. Rosser, J. Barkley (2001). Alternative Keynesian and Post Keynesian Perspective on Uncertainty and Expectations. Journal of Post Keynesian Economics 23 (4): 545–66. Scazzieri, R. (2011), “A Theory of Similarity and Uncertainty.” In Brandolini, D., Marzetti, S. and Scazzieri, R. (edited) Fundamental Uncertainty: Rationality and Plausible Reasoning. London: Palgrave Macmillan. Sen, S. (2019), “Investment Decisions under Uncertainty.” Journal of Post Keynesian Economics 43 (2): 267–80. Sen, S. (2020). “Financialisation, Speculation and Instability” in Philip Mader et al. (ed) International HandBook of Financialisation Routledge, 2020. Sen, S. and Dasgupta, Z. (2014). “Economic Policies in India: For Economic Stimulas, or for Austerity and Volatility?” PSL Quarterly Review, 67 271 423–50.



Byasdeb Dasgupta Presently, globalization and global capitalism are characterized by financialization – where the latter implies a steep increase in the interest of finance throughout society. Some have also referred to it as stock market capitalism. Financing, insurance and real estate (FIRE) as a percentage of gross value-added increased from 8% in 1951–52 to more than 18% in 2010–11 (at 2004–5 constant prices) and from 18.88% in 2011–12 to more than 23% in 2019–20 in India. The present chapter is an attempt to understand the overdetermined relationship between capital accumulation and finance capital in the present context of a developing country like India. There is an abundance of recent literature on finance, finance capital and financialization. But most of these studies contextualize the problem with respect to developed (imperialist) nation-states like the US. Research in the context of the developing South is rare. Our chapter is an attempt to fill that gap. However, before we take up the issue of finance capital and financialization in the context of India there is a need to dwell on what constitutes finance capital, as to date, different authors have had different viewpoints, starting with the seminal works of Hilferding and Lenin in this respect. So, the first section of this chapter will delve into finding a working definition of finance capital, taking a clue from the definition of capital a la Marx in his magnum opus Capital. Once we settle on a definition of finance capital, we will delineate the interrelationship between capital accumulation and finance capital in present-day global capitalism, where there is also a need to describe what is meant by global capitalism in today’s context. No discussion of finance capital can be undertaken without mentioning how it is linked with the international monetary system, as controlled today by supra-national entities like the International Monetary Fund (IMF) and the World Bank. Also, one should not forget the role played by the private international commercial banks in this regard since the breakdown of the Bretton Woods system in 1973.


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It is argued in the existing literature that in today’s context there is a deep connectivity between banking capital and large corporations. The question is, who controls whom? In the theoretical literature on finance capital it is often argued that the large monopoly banks control the corporations. Our question is: Is this the case in India? The third section will decipher the nature of the connectivity between financial companies like banks in India and large corporations. We will offer here empirical facts on this issue from the existing literature with respect to India. And from this, we will try to decipher an analytical standpoint on this connectivity. The current age is characterized by globalization, neoliberal imperialism and finance-capital-led financialization. It has been dubbed the age of finance, as the latter decides the nature of the economic and political in the nation-state and in the international context. And this age is not devoid of periodic crises that afflict capitalism. We will try to understand the role of finance as far as this crisis is concerned with special reference to India.

Finance capital Marx never used the word finance capital in his works. He used words like industrial capital, merchant capital, fictitious capital and interest-bearing capital. It was Hilferding who first coined the term “finance capital” in 1910. Capital according to Marx is not an object or a thing. It is a process that has the capacity for self-valorization or self-expansion in a capitalist class process. A class process can be defined, following Resnick and Wolff (1987), as the performance, appropriation, distribution and receipt of surplus labour where surplus labour is the unpaid labour performed by the labourers (the performers of surplus labour) beyond their necessary labour. In the capitalist class process, surplus labour takes the form of surplus value measured in money form as the commodity produced is exchangeable in the market. In fact, in the capitalist class process, labour power is also a commodity and thus has exchange value like any other commodity. A capitalist class process is one where the surplus value created by the direct producers (the performers of surplus value) is appropriated by the (productive) capitalist(s). A capitalist is one who personifies capital. A capitalist production process follows a M–C–M’ circuit where the initial M is money capital forwarded by the productive capitalist to start the production process, i.e. the initial monetary investment made by the productive capitalist. With this M, both means of production (constant capital in the form of machinery or capital goods or fixed capital and raw materials in the form of circulating capital) and labour power (in the form of variable capital) are bought. Labour power, when applied to the means of labour (machinery and raw materials), produces the commodity which is to be sold in the market. The commodity may or may not be sold at its equilibrium value, which depends on the demand–supply condition in the market. Now, by selling the commodity in the market, the capitalist earns M’ – the gross value in money form. This gross value consists of the value of constant capital 30

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(c), variable capital (v) and surplus value (SV). Note that M’ is greater than the initial M, i.e. M’ – M = SV. SV is not equal to the profit of the capitalist. Rather it is distributed as profit, rent, interest and other payments to be made associated with the production process. A part of SV or M’ – M, is added to the initial M when the second round of production and capital accumulation takes place. This is the case of expanded reproduction as delineated by Marx in Capital. Here, the nature of capital is typically industrial capital, which is a self-valorization process as per Marx. The question is: from where the initial M or the money for the initial investment comes. It may come from the individual savings of the capitalist, from selling shares of the company (in the case of corporates), from selling bonds of the company (which is a loan) or from taking loans from financial institutions like banks. The latter kind of money in the form of bank credit forms what bank capital is, and from the point of view of the banks it is interest-bearing capital as the capital extended by the banks in the form of loans that gets self-valorized by producing interest to this capital as it is a loan to the industrial capitalist. So, bank capital, strictly speaking, is not directly put into real production but indirectly through the industrial capitalist. So, from the point of view of the bank capitalist, bank capital or interest-bearing capital follows the M–M’ circuit where through circulation initial M (in the form of bank capital) gets self-valorized as M’ and M’–M is the surplus value which takes the form of interest in a monetary account. One may argue that the notion of bank capital is the nascent concept of finance capital that was later propounded by Hilferding first and then by Lenin. What is missing in the latter circuit is the labour process. Here, in this circuit, the initial M is not used to purchase constant and variable capital; rather, it gets self-valorized through circulation in M’. Actually, here M’ = M + the interest accrued by bank capital. If we take the notion of bank capital as the starting point of finance capital, then we must declare that the very idea of finance capital is linked to a capitalist class process and not with a non-capitalist class process. So, the emergence of finance capital can be traced back to the emergence of the capitalist production process. Today finance capital and the accumulation owed to finance capital as a process is not just simply due to bank capital. It is fictitious capital, but fictitious in more complex ways than was envisaged by Marx and then by Hilferding and Lenin. Today finance capital includes processes related intrinsically to speculative purposes, serving the interest of short-term capital gains in hybrid financial markets. The nature of fictitious capital today is enigmatic as the circulation processes through which this capital self-valorizes are much more complex than they were in the days of Hilferding and Lenin. The financial oligarchy of a few banks, financial organizations and large corporations is emerging every day as a hybrid system of finance and production, with finance dominating production in the real economy. The classical role of finance in a modern money-using economy is to intermediate between the surplus and deficit units in the economy. However, as finance 31

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has evolved over the last five decades worldwide, this intermediation is now hegemonized by circulation in the financial circuits. Two things merit attention regarding this complex hegemony of finance (which as a process is dubbed financialization). First of all, the complexity of finance, finance capital and the capital accumulation thereof within the boundary of a nation-state is interrelated with the rise of large corporations and the centralization of banking businesses within a nation-state and the continuous emergence of hybrid financial instruments, such as complex derivatives products. Second, the network of finance today is not confined within any nation-state. Rather, its spread is international. So, in many ways, it remains exceedingly difficult to nationally regulate financial profligacy and the associated crisis. Rather, the rise of finance and financialization is led by financial deregulation – one of the basic tenets of free-market neoliberal policy, in vogue since the Bretton Woods system’s breakdown in 1973. In India, the starting point of such policies was 1991. The international monetary system which followed the breakdown of Bretton Woods does not today warrant the backing of national currencies or foreign exchange rates by gold. Rather, at the national level, the international monetary system, especially when a crisis erupts in global capitalism, warrants conservative monetary policy, which implies a downward adjustment of interest rates as far as possible for the availability of easy money for financial investment and the high short-term gains from hybrid financial instruments like asset-backed securities, mortgage-backed securities and other complex varieties of securitization. Along with it, there is a market-based adjustment in exchange rates either in an upward or in a downward direction as the situation demands. So, exchange rates and interest rates have emerged as the most important macro-economic variables to facilitate a smooth circulation of finance capital within the global circuits of finance, as opposed to the global circuits of industrial capital. This is the age of finance today, and this is what finance capital is meant for. And the rendition of finance capital can be capitulated in the very process of financialization that has afflicted every neoliberal economy worldwide.

Global capitalism and finance capital Now, the basic question is: What is the connectivity between finance capital, financialization and global capitalism? Is there any connection between finance capital and industrial capital globally? Is it possible to untangle finance capital without linking it to labour? Or is labour missing when the circulation of finance capital takes place nationally as well as globally? To respond to these questions, we must first try to analyze contemporary global capitalism, and to understand global capitalism as it has unfurled today, we take recourse to the class-focused Marxist theory as propounded first by Resnick and Wolff (1987). To understand today’s global capitalism, let us consider the accounting framework of a capitalist class enterprise. A capitalist class enterprise is an entity consisting of various class and non-class processes. It can generate 32

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revenue from the class as well as non-class processes associated with it. First, it can have revenue in the form of surplus value from various production processes (ΣSV) related to its fundamental class processes where the latter consist of the performance and appropriation of surplus value. Next, revenue can accrue from various subsumed class processes (ΣSSCR) with which it is involved, where the subsumed class process consists of the distribution and receipt of surplus value. Finally, revenue can accrue from various processes (ΣNCR) that are not class processes, and hence we call these non-class processes, following Chakrabarti, Dhar and Cullenberg (2012). For example, suppose an enterprise extends a home loan to its employees, the interest receipts from these loans will form non-class revenue. As the enterprise has revenue, it also has expenditure associated with these class and non-class processes. First of all, it distributes its aggregate surplus value (ΣSV) to all stakeholders with subsumed class positions, providing the necessary conditions of existence to the enterprise; this is an aggregate expenditure in the form of subsumed class payments (ΣSSCP). Next, to earn a subsumed class revenue, it incurs certain expenditure, which we can call the sum of payments made to secure the subsumed class revenue (ΣX). Finally, it incurs certain expenditure for earning non-class revenue, which we call the sum of expenditure made to incur non-class revenue (ΣY). At equilibrium, the total revenue of the enterprise must be equal with the total expenditure incurred by it:


If the left-hand side is greater than the right-hand side of the above equation, then there is a case of over accumulation that may cause a crisis, and when the latter occurs, aggregate expenditure will be greater than the aggregate revenue from both class and non-class processes. A capitalist enterprise will be a global capitalist enterprise when it secures some or all its revenue in one or more locations globally and incurs costs in different locations globally. For example, suppose all its class and non-class revenues are secured in India, whereas its expenditures are incurred, or the revenues are distributed, in the US. So, global capital is a self-valorizing process whose circuits or networks are spread across the globe. The above describes global capital, global capitalist enterprise and circuits of global capital. The circuits of global capital may have connections with local capital, including financial institutions. There are many different financial institutions, including banks. In fact, in the financial system of a modern money-using economy, there are different types of financial institutions and financial instruments, including derivative products. These financial institutions do not create (surplus) value through the labour process. These organizations, including the large corporations, provide the necessary conditions of existence to fundamental class processes in other capitalist enterprises. As opined by Chakrabarti, Dhar and Cullenberg (2012), “the board of 33

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directors” of the financial institutions (especially banks and in the Indian context insurance companies, particularly the Life Insurance Corporation of India), will receive as revenue a part of the surplus value from productive capitalists (SSCR) for the money capital (value) advanced and non-class value from unproductive sources or NCR (such as interest payment from loans advanced for purchase of automobiles, housing, education and so on). By virtue of the fact that additional value is returned against the initial value forwarded, the board members represent unproductive capitalists and with the value flows being global they take the form of global unproductive capitalists. Other kinds of revenue may come from dividend payments against the acquired stocks of industrial corporations, interest return from credit to other banks and financial enterprises and so on. While receiving this revenue to the equivalent of M-M’ from these varied sources, the board of directors will also take the decisions of distributing the revenue for purposes that will activate those processes, which provide the conditions of existence for these revenue generations. These will include payments not only to the internal condition providers such as managers and accountants, but also to external condition providers such as creditors, state, stockholders, landlords and so on. Payments will also be incurred for buying and expanding the pool of necessary means of production. On all these counts, the board makes the decision of distribution of values between X and Y. A financial institute, therefore, may be interlinked today, as far as global capitalism is concerned, with two kinds of enterprise: (a) global and local capitalist enterprises and (b) other financial institutions. In the case of the former, financial institutes provide the necessary conditions of existence to the global and local capitalist fundamental class processes and thereby earn subsumed class revenue (SSCR), whereas in the case of the latter, the financial institutes provide the necessary non-class conditions of existence to the global and local financial institutes, and thereby earn non-class revenue (NCR). But note that in either case the financial institute does not create surplus value by engaging in the labour process, unlike the global and local capitalist enterprises. A financial institute may have two kinds of interlinkage, as mentioned above. Or it may have just one kind of interlinkage. But what is significant in the context of present-day financialization is the fact that the global financial institute is mostly interlinked with other global/ local financial institutes. So, in the circuits of financial capital, such capital may circulate only between the financial institutions. This is the most important facet of the finance capital today as it has evolved since 1973 following the breakdown of Bretton Woods arrangements. In terms of our accounting framework (like the one mentioned above for a global capitalist enterprise), the following may represent a global financial institute:


Capital accumulation and finance capital

Note in the above equation we do not have ΣSV on the left-hand side of the equation as the financial institute here does not create any surplus value involving labour processes, and as such it also does not have ΣSSCP on the right-hand side of the equation. Note that the circuits of global capital in any specific setting, directly or indirectly connected with global capitalist enterprises, that is, all those processes that are in a constitutive relations with processes pertaining to global capital. The circuits of global capital thus have a much wider scope than that specified by the physical reach of all the global capitalist enterprises combined. This is an important point to note since, quite often, capitalism is reduced to the transnational/MNCs/global capitalist enterprises when, in fact, the reach overflows that of the global capitalist enterprises. This is as if a curious state of affairs – it is a curious stage of capitalism – where it is the adulteration, the blemish, the defilement of the noncapitalist enterprise or local capitalist enterprise that provide crucial conditions of existence to global capital. (Chakrabarti, Dhar and Cullenberg, 2012, 150) The same is true today of global financial institutions, including banks, except for the fact mentioned above that a financial institution does not engage in a labour process. What is remarkable or curious about global financial institutes and local financial institutes is that their circuits of finance capital remain restricted between the different financial institutes, and the circulation of financial capital thus effected does not have any positive impact on the circulation of industrial capital. So, the circulation of finance capital through the financial circuits may have a circuit independent of industrial capital and generate value in M–M’ circuits. The question then arises: Is the productive labour which generates surplus value in capitalist enterprises – missing in the circuits of financial capital? The answer is no. To understand this, let us consider a hypothetical example. Suppose there is a financial institute that is linked with a global/local capitalist enterprise in terms of extending loan/credit to the latter. In this case, the financial institute will receive a part of the surplus value (in the form of an interest payment) generated by the capitalist enterprise, and that surplus value is generated by the direct producers or productive labour in that capitalist enterprise. So, here comes the link between productive labour and finance capital. In fact, to increase capital accumulation through the circuits of finance, there may be more and more pressure on direct producers in capitalist enterprises to generate unpaid labour. And hence, the generation of more and more surplus value both in an absolute and relative sense a la Marx. The global circuit of financial capital is thus a complex one. In this circuit, two types of interlinkage are discernible. First, there is the connectivity between 35

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a global/local financial institute and a global/local capitalist enterprise. This connectivity is the source of all finance capital to be circulated and accumulated as finance capital later. Second, there is connectivity between the financial institutions only, which generates and accumulates finance capital. In the second circuit, speculation may play a big role in the short-term generation and accumulation of finance capital. So, the initial money capital to be invested in the circuit of finance capital may be sourced from the surplus value generated in global/local capitalist enterprises, and labour only becomes visible when one traces the circulation and accumulation of finance capital. Since financial institutions and financial capitalists do not appropriate surplus value created by direct producers in capitalist enterprises, they are dubbed unproductive capitalists. One point to note here is that most of the time a surplus entering the global circuit of finance capital does not go back into a real production process to be invested as initial money capital where industrial capital is accumulated. This is one of the most significant features of present-day finance capital, noted by Sen (2004). The reach of the circuits of finance capital today is extremely broad, as the crisis of global capitalism in 2008 shows. A hub of finance capital is constituted by processes flowing from global financial institutes, including international commercial banks, insurance companies, hedge funds, pension funds and like. This hub, like that of global capital, revolves around global financial capital. Some financial institutions are associated with global finance capital and some are away from global capital. Like global capital, the overdetermined processes within the circuits of finance capital may have contradictory effects, which may lead to different conflicts. In this regard, we quote Chakrabarti, Dhar and Cullenberg (2012): conflicts may break out between capitalists within the ambit of global capital – pitting say, the financial capitalists and shareholder capitalists (who want the world to be rid of any protection against movement of capital) against the productive capitalists (who, say, may want some protection for their produced commodities in order to secure their right to appropriate and distribute surplus value). (Chakrabarti, Dhar and Cullenberg, 2012, 153) One may witness similar conflicts between financial capitalists who want to keep a large part of the generated finance capital and the managers in financial institutions who want a large distribution of the financial capital for themselves. So, intense conflicts and contradictions may exist between financial capitalists who are unproductive capitalists, between the financial capitalists and the workers in the financial institutions, between global productive capitalists and global unproductive financial capitalists. When we talk about global capitalism and the hub of global capital what is interesting to note is the emergence of global cities in this age of neoliberal globalization. These cities provide space to global capitalist enterprises and their 36

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hubs (Chakrabarti, Dhar and Cullenberg, 2012). These cities like London, New York, Amsterdam, Singapore, Dubai, Mumbai, Hong Kong, Frankfurt and so on have become mega-cities with a concentration of global capitalist enterprises and their interlinked local companies. The same holds true for financial institutes. These mega-cities have become hubs of finance capital. In India, Mumbai has emerged as one such city where we find a concentration of global and local financial institutes, and where the process of the centralization of acquisition and distribution of value takes place. These global cities, as mentioned in Chakrabarti, Dhar and Cullenberg (2012), “form the hub of concentrated values. … values created all over the world are congregating to such cities” (as in Mumbai in India) at a breathtaking pace and are getting further distributed from there to the different parts of the globe …” In fact, global and local financial institutes which provide necessary conditions of existence to the fundamental class processes pertaining to the industrial capital (in the global capitalist enterprises) are concentrated in these global financial cities. As mentioned earlier, the financial institutes that are not directly connected with global capitalist enterprises and industrial production processes are also congregated in such global financial cities. They too acquire part of the value created by other financial institutions and distribute them worldwide. These cities can be regarded as financial centres where the financial organizations acquire values from other enterprises and disperse them to other financial centres. By concentrating in one city, the financial institutes create what in economics is known as positive externality, which helps them acquire value and distribute value smoothly. Values are therefore flowing in and out of these cities. From a Marxian perspective, the term financial needs to be disaggregated into polymorphous class and non-class form of values flowing in and out of productive capitalists and an array of unproductive capitalists. Different settings such as equity market and other security markets exist in such cities to facilitate the further flow and increment of values which is an addition to the values that congregate from all over the world into the headquarters. From a class perspective, global financial cities are hubs of massive amount of values in a state of flux. (Chakrabarti, Dhar and Cullenberg, 2012, 170) In the context of Mumbai, where the headquarters of most of the Indian financial institutes are located, as well as the branches of various global financial institutes, the flow of values is facilitated by the existence of share markets, foreign exchange markets and other securities markets, including the newly emerging derivatives markets. Mumbai has been transformed over the last three decades from a hub of manufacturing (industrial capital) to a hub of global financial capital with the waning out of the traditional industries in the city like the textile industry. Mumbai has thus emerged as the financial centre of India, which is today considered one of the most important emerging global capitalist economies of the world. 37

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Corporations and banks and other financial institutions in India The Union Budget 2020 served the interests of finance at the cost of the real economy without considering the ongoing stagnation in the real economy, steep rises in the rate of unemployment and informalization of the economy. For example, India’s government did away with dividend distribution tax for financial companies and, in 2019, removed the surcharges on long- or short-term capital gains earned by high-income taxpayers on transfers of equity shares. Additional taxes introduced in the last budget on the “super-rich” with income blocks of over Rs. 2 crore and beyond Rs. 5 crore adjusted downwards. In addition, corporate taxes were subject to significant cuts. Given the pattern of budgetary measures in recent times, questions are bound to be raised on the underlying priorities of the recent budgets, which include the most recent. How does one justify the financial sector and big capital reigning supreme by contesting and finally appropriating official policies in India? Why do the voices of the working poor and unemployed remain unheard in terms of remedial measures? The idea of finance capital is related to speculative motives. Circulation of finance remains within the financial sector with no bearing on the real economy. As per Marx, we can think of the two circuits of production as M–C–M’ and M–M’ while in the first circuit money capital gets transformed into a commodity and then into higher money through market exchange, and in the latter the money capital gets directly transformed into higher money through market exchange. Marx, in his magnum opus, did not use the term finance capital. It was first used by Rudolph Hilferding in 1910. As far as finance capital is concerned, it is based upon the second circuit mentioned above. Investment or distribution of part of the surplus in financial assets or instruments like shares, bonds and derivatives products, generally has no impact on the real economy, i.e. in the production of goods and services or in the circuit M–C–M’. As indicated by Sen and Dasgupta (2018), corporate investments today are mostly in financial assets, not in constant and variable capital. The surplus generated in the financial sector is mostly circulated in the financial sector itself, i.e. in the M–M’ circuit. This suggests the increased interests of the rentier class or unproductive capitalists. The basic question is whether in India it is the banks and other financial institutes that facilitate the concentration of industrial and financial capital in a few hands? Or is it the large corporations that provide the necessary impetus for a concentration of financial and industrial capital in only a few hands? To answer these two questions, we refer to the work on financialization and corporate investments by Sen and Dasgupta (2018). In the Indian context, we find that the banks and other financial institutes invest in the securities (mainly short-term) of large non-financial corporates (henceforth, NFC). Similarly, there has been a rising pattern of investments in financial assets by the NFCs in India over the last two decades (Sen and Dasgupta, 2018). The financial corporates, including banks and the NFCs, are investing in short-term


Capital accumulation and finance capital

financial assets, including equities of the corporates in the secondary market. This has been guided by the speculation of a short-term return on these assets at the cost of real growth within the NFCs over the last two decades, as the study of Sen and Dasgupta (2018) indicates. So, in the Indian context, both large financial corporates and large NFCs are involved in the process of financialization and accumulation of finance capital, where financialization is defined as the significant rise in financial assets (particularly short-term assets including equities in the secondary markets and mutual funds and derivatives products) in the total portfolio of assets in the country since the inception of neoliberal economic reforms in 1991. In fact, as the Reserve Bank of India dataset indicates, the share of physical assets in the portfolios of financial institutes as well as the NFCs have been falling unabated during the first two decades of the twenty-first century. Within the physical assets, the share of buildings and like is much higher than the share of machinery, plants, and equipment. The latter adversely affects the real growth of the industrial sector, as Keynesian theory tells us. As indicated in Dasgupta (2013), and also indicated at the onset of this chapter, the share of FIRE in GDP in the Indian economy, as in the advanced economies of the world, has been increasing significantly since 1991 at the cost of the share of primary and secondary sectors, respectively. Cross-border daily foreign exchange transactions (which are mostly related to global financial transactions in the form of foreign direct investment, foreign portfolio investment and external credit, including credits from international commercial banks) are worth more than 2 trillion dollars. And these flows are rarely related to the real economic activities in the receiving economy. India is no exception in this regard. And this also concerns the relation between financial markets and the large NFCs. We quote here from Crotty (2003) in the context of financialization and the accumulation of finance capital on a larger scale than industrial capital in both advanced and emerging economies like India: The first is a shift in the beliefs of financial agents, from an implicit acceptance of the Chandlerian view of the large NFC as an integrated combination of illiquid real assets – that is, physical and organizational assets that cannot be sold for cash quickly and without a major loss in value – assembled to pursue long-term growth and innovation, to a “financial” conception in which the NFC is seen as a “portfolio” of liquid subunits that home office management must continually restructure to maximize the stock price at every point in time. The second is a fundamental change in management’s reward structure, from one that links pay to the long-term success of the firm, to one that links to short-term stock price movements. In the context of an advanced economy like the US, Palley (2007) noted the following empirical features of financialization and the accumulation of finance


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capital in an aggregate economy. They are as follows (also cited in Dasgupta, 2013): 1. A significant increase in the financial sector debt to total debt in the economy vis-à-vis the non-financial sector debt to total debt. This is true for India also as far as the RBI data indicates. 2. A remarkable increase in the debt-revolving credit compared to the GDP growth. This signifies Ponzi financing in practice which means borrowing afresh to pay past debt. This is true for the NFCs in India, also as Sen and Dasgupta (2018) shows. 3. An increase in the share of mortgage debt in GDP and consequent creation of derivative instruments on such debt. The Global Crisis of 2008 shows how vulnerable the investment in mortgage-backed securities is as the value of these assets all of a sudden came down to zero level when the mortgages lost their values. 4. An increase in household debt as a percentage of GDP. 5. A fall in NFC debt for real investment as a proportion of total non-financial debt. NFCs in India during the last two decades, as shown by Sen and Dasgupta (2018), has followed Ponzi financing to meet their short-term or current liabilities and therefore, their Ponzi-type borrowings from the money market and the other financial institutions went up sharply, which put into question the long-term financial sustainability of these NFCs. 6. An increase in household debt as a proportion of domestic non-financial debt. 7. An increase of FIRE as a proportion of GDP, which we have already mentioned above. 8. A decrease in gross investment spending as a share of GDP. In the Indian context, what is alarming is the decrease in the share of public investment in gross capital formation. 9. An increase in labour productivity and stagnating real wage growth or compensation. This is also the case with India, as Dasgupta (2019) indicates. 10. An increase in financial innovations, with new forms of derivatives being introduced almost every day. 11. An increase in debt creation, through the financial sector, in terms of different vehicles of debt. In recent time, i.e. over the last two decades Indian public sector banks’ non-performing assets (NPAs) in the form of bad loans have increased significantly. And loans of huge amount have been managed by few borrowers (mostly personifying capitalists) from the banks, which later remained unpaid. With regard to financialization in a study of the distribution of GDP in the OECD during 1960–2000, as cited in Sen and Dasgupta (2018), “financialization and shareholder orientation of firms … has gone with the ‘rising share of interest and dividends in profits of non-financial business’, confirming the emergence of 40

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rentiers who live on past rather than on current activities” (Epstein and Power, 2003). This holds true for India as well, as Sen and Dasgupta (2018) indicate. One of the notable signifiers of financialization and the accumulation of finance capital in the neoliberal age is the deregulation of finance. NFCs in India thus seem to follow a path of short-termism in the face of the uncertainty encountered in the deregulated financial markets, with searches for quick returns on the high-risk short-term assets. As we point out, the above considerably dampens the prospects of further investments in physical assets, a familiar Minskyan paradigm where uncertainty in deregulated capital markets under financialization generates instability with short-termism in investments. (Sen and Dasgupta, 2018) As noted in Sen and Dasgupta (2018) at the micro level the assets of the NFCs can be classified into five broad parts, which include (a) net fixed assets, (b) capital work in progress, (c) financial investment, (d) loans and advances, and (e) cash and bank balances. Of the above, component (b) consists of funds used to build fixed assets, the completion of which is still outstanding. Components (a) and (b) can thus be clubbed together to define “physical assets”. It thus follows that Total Asset = Physical Asset (a + b) + Financial Investment (c) + Loans and Advances (d) + Cash and Bank Balances (e). Accordingly, Total Assets – Physical Assets = Financial Assets. And as indicated in that paper, investment in financial assets, particularly in different types of financial securities (mostly short-term), including secondary market equity shares and derivatives products, has increased tremendously over the last two decades. There is a trade-off between investment in physical assets and investment in financial assets, with the latter weighing heavily over the former. As we have mentioned already, what is discernible in the case of the NFCs in India is that there is Ponzi financing being used to meet the current liabilities of these NFCs, which include interest payments to their creditors and dividends payments to their shareholders. So, what is at stake is the real growth of the firms while there are speculative bubbles in these firms in terms of their burgeoning values based on their financial investments. This, in a way, signifies the hegemony of finance capital and the related fictitious capital a la Marx over industrial capital. Dasgupta (2013) noted that “the real sector operates in the interests of the financial sector and in the process it causes the real sector to become subjugated to the financial sector”. As noted in Dasgupta (2013), the stagnation of wages and changes in personal income distribution are accompanied by changes in the functional distribution of income. 41

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Following Palley (2007), the functional distribution of national income is presented as follows in a hypothetical economy:

Y = CS + WS (1) where Y is national income, CS and WS stand for capital share and wage share. Now the wage share (WS) is distributed between managers (MS) and workers (LS):

WS = MS + LS (2) Capital share (CS) is distributed between interest (I) and profit (Π):

CS = I + P (3) Profit (Π) is further distributed between the financial sector (ΠF) and non-financial sector (ΠNF):

P = PF + PNF (4) So, putting (2), (3), and (4) into (1) we get:

Y = MS + LS + I + PF + PNF (5) The interest of finance lies in ensuring an increase in the shares of interest (I) and financial profit (ΠF). But it also needs a rise in MS compared to LS, as the managers play the pivotal role, in both financial and non-financial companies, in ensuring as large a market value of shares as possible and, hence, they need to be given adequate incentive to do their jobs.

This is also indicated by Sen and Dasgupta (2018) in their the firm-level study of NFCs in India over the last two decades. Therefore, financialization and the process of the accumulation of finance capital signify the following in a neoliberal economy of today: (i) Managers’ share to increase at the cost of wage share in total wage share, which is to a certain extent indicated by Dasgupta (2019); (ii) Hence, a rise in managers’ share vis-à-vis wage share in national income; (iii) A rise in capital share vis-à-vis wage share in national income; (iv) An increase in the share of interest in national income which was demonstrated in the firm-level study by Sen and Dasgupta (2018); (v) An increase in the share of profit of the financial sector in total profit; 42

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(vi) An increase in the share of profit of the financial sector in the national income; (vii) A fall in the share of profit of the non-financial sector in national income. These facts are supported by statistics and information concerning the US economy over the last four decades, and emerging economies like India also register the same trends as mentioned above (Dasgupta, 2013; Sen and Dasgupta, 2018). Therefore, the following three points merit our attention as far as neoliberal global capitalism and the accumulation of finance capital through the process of financialization is concerned at both the macro and micro level of a country, including India: (a) Initial investment in the financial sector may be sourced from national savings, and therefore the age of finance put a larger emphasis on augmenting the national savings rate. (b) Investment in financial instruments is also sourced from corporate surplus, as indicated by Sen and Dasgupta (2018). (c) Also, investment in the financial sector or in financial instruments may be sourced from the reinvestment of a large part of the surplus accumulated in the financial sector itself. In the Indian context, financial institutions, like large banks, and including public sector banks and NFCs, are involved in the concentration of financial assets in their portfolios against physical assets given the less liquid nature of the latter. So, banks and NFCs in India are today in an overdetermined relationship, both causing the accumulation of a financial surplus. Now, the question is whether this is sustainable in the long run? Or whether there lies the clue to the periodic crisis of global capitalism as is happening frequently since 1990?

Finance capital and its conflicts with the real economy (industrial capital) and periodic crisis Circulation of finance capital in the financial sector is as we understand the circulation of capital in the economy in terms of Marx’s Capital (Vol. 2). In fact, there is a contest between the real sector and other social needs in society on the one hand and the finance sector on the other. To understand this, we take recourse to the class-focused Marxist approach. The masterstroke of Marx is his concept of “surplus labour”, which in the capitalist class process becomes “surplus value”. Class, as per Marx, is the process of the performance, appropriation, distribution and receipt of surplus labour (Resnick and Wolff, 1987). The site of the economy is disaggregated and decentred in various class processes. The total surplus generated in an economy from different capitalist and noncapitalist class processes can be divided into production surplus and social surplus (Chakrabarti, Dhar and Cullenberg, 2012). Production surplus consists of 43

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the subsumed payments needed to meet the conditions of existence of the class processes, a part of which is used for (industrial) capital accumulation. In the capitalist class process, a part of the surplus is used for capital accumulation. Social surplus targets the socially determined needs of the people (such as relating to poverty, environment, childhood, unemployment, old age and even entertainment, to name a few), which provide no direct conditions of existence per se to any fundamental or subsumed class process. Given the empirical evidence of money capital circulating within the financial sector in several financial instruments, and also of growing corporate investments in financial instruments, we can say that a part of the total surplus, generated at least in capitalist class processes, goes to the financial sector which we may dub as financial surplus. This financial surplus is used in the financial sector as self-valorizing initial money capital and causes accumulation of finance capital instead of industrial capital. Marx presented the concepts of industrial capital, the capitalist production process and the accumulation of industrial capital. This neoliberal age of finance has demonstrated how finance capital gets accumulated globally within the circuits of finance. This means the following in terms of the total surplus generated in an economy from different class processes:

TS = PS + SS + FS

where TS refers to total surplus, PS the production surplus, SS the social surplus and FS the financial surplus. In our rendition, we attach the financial surplus to global capitalist class processes. The financial surplus in the economy will increase more than the production surplus and social surplus if the rate of change in the financial surplus over time is greater than the rate of change in the production and social surplus, respectively. This is what is happening in this age of finance regarding advanced capitalist economies like the US and the emerging capitalist economies like India. Real growth in the economy is related to the accumulation of industrial capital, and since the accumulation of industrial capital is associated with the generation of a production surplus in the economy, real growth is hampered if the rate of change in the production surplus is low, if not negative, which may happen with a crisis in global capitalism. In terms of the accounting framework of a global capitalist enterprise, we noted the following above:


The left-hand side of the equation refers to the revenue generated to a global capitalist enterprise from different class and non-class processes. The right-hand side of the equation signifies expenditure or distribution of revenue generated from


Capital accumulation and finance capital

different class and non-class processes. In times of crisis in global capitalism, the above equality turns out to be inequality as follows:


This means the surplus or revenue generated from different class and non-class processes falls short of the required distribution of that revenue (Resnick and Wolff, 2006). In an accounting sense, this signals a crisis point in global capitalism, which is subject to periodic crises from time to time. Similarly, we can argue in favour of a global financial crisis. We have stated above that for financial enterprises, which are capitalist enterprises also with finance capitalists being unproductive capitalists, the above equality becomes:


Once again, the left-hand side of the equation represents the revenue generated from different subsumed and non-class processes, and the right-hand side represents the distribution of such revenue. Now, a financial crisis is also periodic like global capitalist crises and the above equality turns out to be an inequality when financial crisis erupts as follows:


At the point of crisis, financial enterprises do not have enough revenue for the required distribution within the financial and non-financial sectors. In the case of a financial crisis, there are different asset bubbles that burst periodically and cause a financial crisis which in turn gives way to a crisis of capitalism in the real economy. There is a contest between the production surplus and social surplus on the one hand and financial surplus on the other hand. A unique example of this contest can be found in the Union Budget (Budget of Government of India, 2020). Issues like unemployment and underemployment, along with the poverty faced by large sections of people and the ongoing recession and stagnation in the real economy, while much discussed, do not prioritize policymaking. Allocation of budgetary funds to MGNREGA has been downsized. There is hardly any increase in the allocation of government social sector schemes. All these signify, in classfocused terms, the lowering of the distribution of social surplus. And a contest also remains between production surplus and financial surplus. So, in this age of finance, financial surplus, as noted above, remains in contradiction with production and social surplus. If financial surplus outweighs production and social surplus, then both real economic growth and the fulfilment of different social needs remain adversely affected. One notable example during this COVID-19 context is the fact that while real growth is negative or falling steeply, the stock market


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remains in a buoyant mood despite the fall in real growth, which goes on to testify to the hegemony of finance over the real economy.

Conclusion This chapter has attempted to delineate the process of finance capital and also that of its accumulation in the current age of neoliberal global capitalism, where finance capital plays the role of a power block. To quote Harvey (2006): The concept of finance capital has a peculiar history in Marxian thought. Marx himself did not use the term, but bequeathed a mass of not very coherent writings on the process of circulation of different kinds of money capital. The implied definition of finance capital is of a particular kind of circulation process of capital which centres on the credit system. Later writers have tended to abandon this process viewpoint and treat the concept in terms of a particular configuration of factional alliances within the bourgeoisie – a power bloc which wields immense influence over the process of accumulation in general. Yet, apart from Hilferding’s basic work on the subject and the influential replication of some of his ideas in Lenin’s seminal essay on imperialism, the concept has remained quite unanalysed. In this chapter, we offered our viewpoint of this power bloc, which is finance capital in the present context of neoliberal global capitalism, with special reference to the emerging capitalist economy of India.

References Chakrabarti, Anjan, Anup Dhar and Stephen Cullenberg (2012), World of the Third and Global Capitalism. Delhi: World View. Crotty, James (2003), “The Neoliberal Paradox: The Impact of Destructive Product Market Competition and Impatient Finance on Non-financial Corporations in the Neoliberal Era”. Research Brief 2003–2005. Amherst: Political Economy Research Institute (PERI), University of Massachusetts. Dasgupta, Byasdeb (2013), “Financialization, Labour Market Flexibility and Global Crisis”. In Byasdeb Dasgupta (edited) Non-Mainstream Dimensions of Global Political Economy. London: Routledge. Dasgupta, Byasdeb (2019), “Finance Capital in Marxian Perspective”. In Achin Chakraborty, Anjan Chakrabarti, Byasdeb Dasgupta and Samita Sen (edited) “Capital” in the East: Reflections on Marx. Singapore: Springer. Epstein, G. and D. Power (2003), “Rentier Incomes and Financial Crises: An Empirical Examination of Trends and Cycles in Some OECD Countries”. Canadian Journal of Development Studies, 24(2), 229–48. Harvey, David (2006), Limits to Capital. London: Verso. Hilferding, Rudolph (1910), Finance Capital. A Study of the Latest Phase of Capitalist Development. Edited Tom Bottomore. London: Routledge & Kegan Paul, 1981, courtesy


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of Routledge. Also available at Marxist Internet Archive. https://www​.marxists​.org​/ archive​/hilferding​/1910​/finkap​/index​.htm Palley, Thomas I. (2007), “Financialization: What It Is and Why It Matters”. Working Paper No. 525. New York: Levy Economics Institute of Bard College, December 2007. Resnick, Stephen A. and Richard D. Wolff (1987), Knowledge and Class: A Marxist Critique of Political Economy Chicago: University of Chicago Press. Resnick, Stephen A. and Richard D. Wolff (2006), “The Reagan-Bush Strategy: Shifting Crises from Enterprises to Households”. In Stephen A. Resnick and Richard D. Wolff (edited) New Departures in Marxian Theory. London: Routledge (Special Indian Edition). Sen, Sunanda (2004), Global Finance at Risk: On Real Stagnation and Instability. New Delhi: Oxford University Press. Sen, Sunanda and Zico Dasgupta (2018), “Financialisation and Corporate Investments: The Indian Case”. Review of Keynesian Economics, 6(1), 96–113.



Satyaki Roy The current conjecture of late capitalism features rising debt, the slowing down of growth, particularly in advanced capitalist countries, deindustrialization and rising inequality. A financialized capitalism bearing the “signs of autumn” as Fernand Braudel suggested to be evident in the terminal phase of the cycles of accumulation,1 showing a spectacular boom in credit to non-financial private corporates, a burgeoning of financial instruments, the rising weight of incomes through interest, dividends, rents, capital gains on stocks and real-estate revenues, and characterizing the speculation of profit as increasingly divorced from the realm of production. The share of financial activities has increased in GDP both in advanced and in developing countries. In some way it is the reign of the “takers” over that of the “makers”, and the realm of production seem to be increasingly subverted by concerns for maximizing shareholders’ returns. The growth–profit trade-off and the primacy of short term returns apparent in the corporate decision-making process manifests a much deeper chasm between the capital in finance and the capital that creates and appropriates surplus in the process of accumulation. Such a scenario often prompts us to believe that finance is an epiphenomenon, a parasite that grows through extracting values created by others. Speculation on assets created through continuously redefining property rights on wealth, yet to be produced, becomes the lifeline of the capitalist growth process in the current financialized phase. Rent and interest in various forms expand, suffocating the profit of enterprise, which is supposed to be the return for organizing productive capital. Speculations often create violent convulsions in the economy, slipping it into a crisis, which involves the gross destruction of accumulated value. But is finance superimposed upon the real economy? Is it an outgrowth that can only be comprehended as a result of greed and lack of regulation or failures of budget and monetary policies that require an institutional fix and control, or is it something that follows from the internal dynamics of capitalism where values in monetary 48

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terms have the intrinsic tendency to become independent from the concrete determinations of production. This chapter focuses on the circuit of capital as integral to production and circulation. We identify the production and appropriation of surplus and its distribution through profits, interests and rents in various phases of circulation. The proliferation of rents involves redefining property rights. And that is not necessarily confined to physical assets such as land and various forms of fixed capital but entails a wider spectrum of propertied access over physical and mental resources, spanning across various domains of natural and ideational content. Innovations, technological upgradations and logistics involve a wide range of activities for deriving rent from the created surplus. Perpetual differentiation of production from the average capital, creating propertied access through monopoly rights or attaining a global scale advantage, defines the layers of exploitation and extraction in global production circuits. Finance and the architecture of “fictitious capital”, the return to which is independent of the valorization of productive capital, emerges within the circuit of capital itself; it thrives through redistributing surplus in its favour and creates a greater leverage for the capital involved in the realm of production. We see this as an integrated circuit where financial speculation and asset driven growth is not something inimical to normal capitalism but a natural outgrowth of wealth inequality that gives rise to huge markets for assets. Capital in this process loses the heterogeneity attached to its concrete existence and emerges as class power that defines the norms of accumulation. But in the same process it also energizes the competition of liquid assets that not only subvert labour’s return but allocates social labour according to what is considered to be “socially necessary” by capital. In the following section, we revisit the notion of a “production boundary” as it evolved in the course of economic theories and how we locate finance vis-à-vis the idea of productive activity. In this context, in the third section, we discuss the concept of “fictitious capital” and bring out the inherent tendency of capital to move towards dematerialized self-augmenting value as finance, which is intrinsic to the system and assumes particular roles in the process of circulation. The fourth section discusses the convergence of global labour arbitrage, distribution of rents defined by a global monopoly in the realm of production, rising inequality and the emergence of a financialized capitalism. In the fifth section, we see the realm of production and finance as integral parts of the totality of the circuits of capital and how productive capital and finance are mutually constitutive in a contradictory way. Finally, we discuss how finance mystifies the capital–labour relation and reifies the necessities of profit-making as universal norms of economic governance.

Production boundary and finance Growth of profit in the realm of finance, together with the slowing down of productive investment and overall growth and employment in the economy, marks an apparent divorce of the financial sector from the real economy. This once again 49

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justifies the flight of capital from painstaking productive activities to financial intermediations manifested in the rising share of these activities in the GDP and, since they are often fomented by speculation, bears the risk of crash landing into a crisis. This invokes once again looking into the productive boundary of the economy as it conceptually evolved in various phases of economic thought. Instead of going into the long history of the evolution of this notion of a productive boundary, what seems important for the current discussion is the distinction between the “makers” and the “takers” of the economy or the “industrial community” and the external class. Ricardo’s idea of absolute rent includes landowners rent on land and capitalist’s profit, both of which are not rewards for some real contribution to the economy but an extraction derived from private property and the means of production. Although he did make a distinction between capitalists, who do not waste these returns on luxurious consumption, and landlords, who do, these are still essentially expropriations of the wealth produced by others.2 The classical notion is that the only source of wealth is labour and shares of wealth created by them are expropriated by the landowners and capitalists by dint of their possession of scarce resources, which are “scarce” not because of some natural limits but only because they are privately owned. The idea of an “absentee owner” and the realization of wealth by non-contributing members gained further traction with the rise of joint-stock companies, which marked a separation between managers, owners and the various layers of financial intermediaries. The capital market grew with the expansion of the credit market, and access to credit and the dominance of financial activities also grew over time. Keynes identified these financiers as “functionless investors” or the rentiers as an unproductive social class. They are external to the boundaries of productive activity and derive interests because they are owners of capital, just as landowners receive rents for their property.3 Interest is primarily a reward for parting with liquidity, and hence liquid assets accumulate in the hands of rentiers who derive gains out of speculation based on second-order observations. The financial outcomes hardly reflect the real economy, as Keynes argued in his famous “beauty contest” example where perceptions about average responses decide the game rather than judgements based on intrinsic values.4 Such indirect perceptions on various levels often create imaginary economic outcomes that are increasingly delinked from the real economy. This is precisely the reason Keynes believed that only illiquid markets could be efficient, and since our knowledge of the factors influencing investment decisions are slight and negligible, it is unrealistic to assume that expectations embodied in investment decisions could be efficient. He made a distinction between speculation as “the activity forecasting the psychology of the market” and enterprise activity as “the activity of forecasting the prospective yield of assets over their whole life”, and hence the dominance of the latter by the former creates a distortion in the allocation of resources.5 In advanced capitalism, enterprises rely on borrowed funds, and rentiers tend to keep interest rates high, and the marginal efficiency of capital equates this rate of interest at below the full employment level. Capital is therefore kept scarce by financiers 50

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maintaining a high rate of interest, and labour and resources remaining unemployed further dampens productive investment. Therefore, Keynes argued that lowering interest rates and achieving full employment requires the “euthanasia” of the rentier. Neoclassical theories on efficient capital markets, on the other hand, resemble the idea of a comparative advantage in terms of information regarding the underlying entities of financial securities. In other words, this is a method of price arbitrage in asset markets, where rational agents instantaneously respond to publicly available information through stock prices, and therefore the missing information gets captured in asset prices, finally reducing the comparative information advantage. Hence, this process reduces the discrepancy between the “intrinsic” value of assets and their market price, and even if some discrepancy exists, it is random and not systemic, because if it had been so, prices would have responded to eliminate such revealed patterns.6 In this framework, financial instruments are nothing but essential vehicles to transform current consumption into future investments in the form of financial assets, and the traders who are absentee owners, through the purchase and sale of stocks, make information of the economic fundamentals available that helps to reduce the gap between intrinsic values and the actual prices of assets. Therefore, in this view, the more observers are distant from the industrial community, the more effective their intervention is in bridging the gap. Hence, financial intermediation provides an efficient solution for determining the optimal choice of investment decisions. The production boundary in Marxian analyses is much more complex than in classical, Keynesian and neoclassical premises. Its apparent resemblance with classical theory and that of Keynes’s is the idea that labour is the only source of wealth, but the production, appropriation and distribution of surplus value for Marx lays bare the fundamental expropriation of value by the capitalist through labour’s creation. And then it draws attention to the determination of profit, rent and interest between the multiple forms of capital that actively participate in the production and appropriation of surplus value and therefore create the preconditions for the reproduction of the capitalist circuit. In the Marxian scheme, the distinction between productive and unproductive has no reference to particular sectors of economic activity and modes of output, as has been the case in classical theories where industry and agriculture were considered to constitute the productive boundary. On the contrary, in Marx, productive activity is considered something which is paid out of capital and creates surplus value, while the rest is unproductive and is paid out of revenue.7 First of all, it does not make any reference to the physical nature of the output, material or immaterial, and second, those activities which are beyond the productive boundary in this scheme are not considered to be parasitic or non-essential in any sense; rather, they are considered to be internal to the circuit of capital. This becomes evident when we recognize the difference between the classical notion of value, particularly the Ricardian labour theory of value, and that of Marx’s. The value of a commodity for Marx is not determined by the actual amount of labour embodied in that 51

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commodity but depends on the socially sanctioned amount of labour allocated to its production. And this allocation of social labour involves a process of circulation which is mediated by money and credit, assuming the various forms of interest-bearing capital in successive phases of the capitalist circuit. Therefore, the rise of finance is intrinsic to the circuit and can’t be seen as a result of regulatory failure or some parasitic outgrowth; rather, finance considerably influences capital accumulation by not just extracting productive resources but also by creating additional room for leveraging credit or by reducing the turnover time that ultimately results in higher profits.

Finance as “fictitious capital” The evolution of money as a general equivalent of exchange and its attaining autonomy beyond circulation marks the conflicts that are internal to the dynamics of capitalism, which is essentially a society defined by the production of commodities. The emergence of value as a social relation of exchange is considered to be trans-historic in the classical political economy, while in Marx’s analyses it is very much internal to capitalism where labour congealed in products has to be first of all stripped of their concreteness, and values are measured in terms of abstract labour. The magnitude of the abstract labour that is supposed to measure the value of a commodity can only be determined by the act of exchange and hence this presupposes a society that is predominantly based on the production of commodities, which is capitalism. Undoubtedly the rise of money as a general equivalent predates the emergence of advanced capitalism, but in the course of the history of money the cardinal dynamics of this evolution are driven by a continuing conflict between the use and exchange values of commodities.8 Values are in search of autonomy from the concrete material uses of commodities and assume various forms of money, performing the role of exchange, the medium of circulation, means of payment and as a store of value. But in this metamorphosis, money does not remain restricted to the role of general equivalence but emerges as a socially accepted expression of private labour, attaching prices to products that are independent of the material value of a money commodity. It rather becomes a dematerialized expression of value in the form of prices that valorize the socially accepted allocation of labour through markets. In capitalism, therefore, we end up with a state of money which is not only a medium of circulation or means of payment but as an end in itself as capital, which is self-expanding. The autonomy of money from the process of circulation is actualized by a time-lapse between sale and purchase, allowing for the possibilities of hoarding of money and of breaks in the circulation contrary to what has been conceived in a Say’s law-driven world, where every sale has to be followed by a corresponding purchase. However, the relative autonomy of money as a store of value does not end the contradiction in any way but gives rise to various forms of monetary transactions and capital relations that aggravate the conflicts into frequent convulsions in an advanced capitalist economy. 52

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Marx discusses in detail the rise of credit money or interest-bearing capital.9 Credit money is “a promise to pay”, which allows a buyer to purchase something without a preceding realization of the value of their own products meant for sale or being supported by a corresponding saving. The mobilization of idle capital and the socialization of loanable funds through banks provides access to such credit where a promise to pay, backed by the nation-state or some authority, can enter into circulation as a means of payment. This facilitates purchases beyond the already existing value or in addition to the money in circulation. The return to parting with liquidity assumes the form of return to be capital property, and interest becomes an expression of the valorization of capital. This is the most fetishized form of capital, a mystification of capital where capital receives a return independent of any mediation of productive activity. In Capital III, Marx says, Money as such is potentially self-expanding value and is loaned as such, and loaning is the form of sale for this peculiar commodity. It becomes a faculty of money to generate value and yield interest, just as it is a faculty of a pear tree to bear pears. And the money lender sells his money as such an interest-bearing thing. But that is not all. The actually invested capital, as we have seen, presents itself in such a light, that it seems to yield the interest, not as a capital performing its function, but as a capital in itself, as money-capital.10 In this process, the capital emerging from various sources of production assumes a uniform nature independent of the concrete intervention of an individual capitalist. Interest is a return on money capital that emerges from the surplus value produced in the productive circuit of capital. In this process, earning interest appears to be an intrinsic quality of capital, where the process of surplus creation through exploiting wage labour becomes completely invisible. And since interest rates or returns on money capital have nothing to do with the specifics of capitalist production, it is as uniform as a return to capital, as capital in a generic sense that does not therefore confront wage labour in its realization. On the other hand, the return to productive activity organized by individual functioning capitalists, termed the “profit of enterprise”, also displaces labour from its conceptual frame. The profit net of interest appears to reward a capitalist’s effort as an entrepreneur rather than being created by exploiting the labour in the production process. In other words, interest appears as a return to capital as property which is uniform for all property owners employing it as capital, the capitalist class as a whole, while profit of enterprise is specific to the individual capitalist and its production process and manifests the return to efforts of the particular capitalist. But money capital or finance need not always be a pejorative term, as it is often seen as an unwanted parasite superimposed on the real economy, deriving returns without contributing to the creation of surplus value. The circuit of “industrial capital” in a Marxian analysis involves circuits of money capital, productive capital and commodity capital. Money capital attains autonomy from the particular 53

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circuit of industrial capital and emerges as interest-bearing capital or credit, which performs the role of reducing interruptions in the process of circulation. Commodity capital implies produced commodities that are embodied with surplus value and need to be realized through the involvement of commercial or merchant capital, which also evolves as a specialized role for a particular form of capital. In spite of the fact that the production of surplus value only takes place in the fundamental class process within the circuit of productive capital, money capital and commercial capital also play a significant role in the realization of surplus value. In fact, they are not “productive” as they do not create surplus value but useful and necessary, creating the preconditions for the production, appropriation and distribution of surplus value. These constitute subsumed class processes that are equally important for the reproduction of the circuit of industrial capital.11 Credit reduces the amount of reserve required for individual capital and allows the capitalist to expand the process of accumulation of capital beyond the already realized value. On the other hand, it puts idle capital to use, which is mobilized as loanable funds and could be used to purchase inputs even though the realization of surplus value in the previous cycle has not been completed. Credit also reduces the turnover time of capital and helps to increase the rate of profit. It is also important that the easy flow of credit money from one sector to another speeds up the process of equalizing the rate of profit across sectors. Particularly, innovation and new technology, where the high expectations of profit require an initial investment, and finance provides the resources for new ventures. Credit predates capitalism and industrial capital as it existed in the form of usury, but with the development of capitalism the rhythms of supply and demand of commercial capital and money became subservient to the circuits of industrial capital. In the Theories of Surplus Value III, Marx underlines the transition of usury to credit as linked to the social metabolism of capital: The commercial and interest-bearing forms of capital are older than industrial capital, which, in the capitalist mode of production, is the basic form of the capital relations dominating bourgeois society – and all other forms are only derived from it or secondary … In the course of its evolution, industrial capital must therefore subjugate these forms and transform them into derived or special functions of itself. It encounters these older forms in the epoch of its formation and development … Where capitalist production has developed all its manifold forms and has become the dominant mode of production, interest-bearing capital is dominated by industrial capital, and commercial capital becomes merely a form of industrial capital, derived from the circulation process. But both of them must first be destroyed as independent forms and subordinated to industrial capital. Violence (the State) is used against interest-bearing capital by means of compulsory reduction of interest rates … But this is a method characteristic of the least developed stages of capitalist production. The real way in which industrial capital subjugates 54

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interest-bearing capital is the creation of a procedure specific to itself – the credit system.12 The speculative nature of interest-bearing capital is intrinsic to capitalist accumulation rather than being the result of some historical aberration. The loanable funds, which are the reserves of industrial or merchant capital held in banks, emerge as interest-bearing capital. Idle capital is deployed in return for interest without being involved in any act of production. The circuit assumes the form M–M–C–M’–M’, which can be reduced to an autonomous form of M–M’, where making a profit is not mediated by an act of value creation. Hence capital assumes the most externalized and fetishized form as a self-expanding value that is freed from all scars of its origin. In the case of merchant capital, profit appears to emerge from the act of sale or at least as a result of some social relation, but for an interest-bearing capital return in the form of interest it appears as the quality of capital as a thing, as some intrinsic value, and buries the real stories of the social relations and the exploitative acts of the creation of surplus value entirely. Marx comprehends Thus the fetish form of capital and the conception of a fetish capital are perfect. In M–M’ we have the void form of capital, the perversion and individualisation of the relations of production in their highest degree. The interest-bearing form is the simple form of capital, in which it is assumed to be antecedent to its own process of reproduction. It is the faculty of money, or of a commodity, to expand its own value independently of reproduction, a mystification of capital in its most flagrant form.13 Interest-bearing capital acquires various forms of “a promise to pay” such as government bonds, private securities such as corporate bonds, shares, debentures, bill of exchange and derivatives representing mere claims to certain payments based on values to be realized in future. The values of such claims, however, have nothing to do with the original amount paid for these claims. And the returns on these capitals assume forms of interest and capital gain that are independent of the circuit of industrial capital. Titles of future revenues are illusory in nature as their value includes expectations that are yet to be realized. But the right of future revenues when transacted in financial markets appear to be assets that represent real values and their prices have independent movements. To the individual owners these assets are real, but for the society as a whole these are completely illusory or fictitious.14 Growth in the public debt of nation-states, rising trade deficits or current account deficits, instability in currency exchange markets and rises in interest rates facilitate the proliferation of speculative activity and hence fictitious capital. Speculation is parasitic as it derives profit without being involved in any way in the production or circulation process. As the return from the sphere of speculation increases, the prices of financial assets also increase, giving rise to 55

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an increased flow of funds from the productive sphere to the realm of finance. This in turn increases the asset prices, further brewing a bubble of expectation of a future rise in prices. Since money capital is in search of higher returns, independent of its uses, there is in fact no mechanism internal to the logic of capital that resists capital flowing towards speculative investment. But as demand for the extraction of surplus in the form of financial gains increases with the expansion of the unproductive sphere, the surplus created in the realm of production poses structural limits to such fictitious expansion, eventually leading to an explosion of the financial bubble.

Offshore outsourcing and finance Externalizing production through arms-length contract with producers located in developing countries, engaging cheap labour and natural resources, attracting low wage and low environmental costs, has emerged as a dominant strategy for multinational corporations over the past three decades. This also coincides with the period of the dominance of finance. Two apparently disparate trends in the globalization process have been treated as autonomous and discussed separately in the literature on financialization and global production networks. William Milberg was one of the few who underlined the mutual constitution of these two processes in the context of the US, showing that the increasing externalization of production has been associated with a rising corporate governance emphasizing shareholder value.15 Notable is the fact that even though there has been a protracted slowing down of growth in advanced capitalist countries, the profitability of the big corporates could be retained, and the profit share in GDP increased in most of the countries in the world during the period of globalization. This was possible largely by reducing input costs and deriving rents out of redefined property rights. Globalization has reduced the space for monopoly or oligopoly pricing and of making profits through an enhanced market price, but it has given rise to oligopsonic structures where global corporates source inputs and intermediate goods from developing countries. They hugely leverage labour arbitrage and take advantage of control over the entire production network. Imports from developing countries have increased during this period, backed by a significant relocation of production towards low wage segments of the world. Studies suggest that offshoring reduces costs to the firm to the tune of 40 to 60%, allowing network lead firms to garner huge profits.16 This largely appears to be the result of rising productivity in the parent company, but actually it is the value produced in the developing countries and captured in the advanced centres of corporate power. This process has negatively impacted the share of workers in value-added across the globe. Rising profits and the cheapening of capital due to globalization has increased capital intensity in manufacturing to replace labour with capital where the elasticity of the capital–labour substitution is relatively high and relocating production facilities to countries where labour is cheap in the case of product lines where the elasticity of substitution is relatively low.17 This put immense pressure 56

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on the bargaining power of workers both in advanced and developing countries. In advanced countries, it is primarily because of the declining demand for domestic labour, and for developing countries the relocation did not greatly increase demand for workers because the product lines relocated were labour intensive according to advanced country standards but relatively capital intensive according to the average factor intensities of industries in the host countries. On top of that, developing countries compete with each other as the suppliers of manufactured products, giving rise to a “race to the bottom”, which tend to push wages further down as supply price reduces over time due to increased competition.18 In other words, the process of externalizing production exacerbates inequality between advanced North and developing South on the one hand and, more importantly, between the property owners and workers across the globe, on the other. Global production networks are often projected as an alternative route to industrialization for developing countries, where they can specialize in supplying particular components to global networks instead of investing resources for long term industrial development. Integrating production space to global networks has often been considered as a virtue in itself, although for most developing countries their value share of the aggregate tends to decline over the years. The seminal smile curve shows that manufacturing activities accrue the lowest share of global value-added in the entire network, while activities related to conceptualizing and designing products or those related to logistics, branding or marketing derive the larger share of total value-added.19 The global production network literature, however, focuses on the gains that developing country producers can derive from innovating and upgrading particular tasks and therefore can realize a higher share of global value-added. But there is hardly any empirical evidence that suggests any strong absolute upgrading of tasks is happening in the production stages located in developing countries. The reason for this is structural and it does not only depend on efforts undertaken by the producers in particular stages of production. Value capture from low waged countries can be analyzed in three interlinked layers of exploitation and expropriation.20 First, if we assume a competitive scenario where there is no existence of a monopoly and no option of using labour below the value of labour power, then the profit accrued by capitalist producers depends on their relative contribution to the aggregate capital. This follows from Marx’s determination of the general rate of profit and the transformation of values into prices. The value of a produced commodity is determined not by the actual expenditure of the labour embodied in it but by the allocation of labour which is considered socially necessary. More importantly, the determination of what is socially necessary emerges through competition between various types of capital in the same production line, working with different organic compositions of capital. The cost of production is primarily the sum of constant and variable capital that is advanced by the capitalist initially to undertake the production process. The price of production is profit added to the particular cost of production. But the profit added would be equal to the contribution of surplus value of the particular capitalist only when it is the average capital for the particular production line. 57

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Otherwise, the individual profit accrued is the product of capital advanced by the particular capitalist and the general rate of profit. In other words, profit included in individual prices of production is proportional to the relative contribution of a particular capitalist in the total pool of social capital advanced in the process of surplus creation. Therefore, the capital of the developing South that generally operates at a relatively low organic composition of capital would be receiving a profit share that is proportionately less than their contribution in the aggregate surplus value produced. This also implies that production facilities in the developing South may undergo innovation, but if the pace of innovation for the average capital in that production line moves faster than the producer of the South, then even if the latter innovates they would be receiving a declining share of the profit. The distribution of profit along the value chain hence not only depends on the efforts of a particular capital in a stage of the value chain but also on the structural process of value distribution and the relative dynamics of the various types of capital involved in the value chain. Second, if we assume a more realistic scenario where a monopoly over the market as well on particular resources exists then the surplus value produced in the process of production could be appropriated in the form of rent. Rents may arise anywhere in the production network depending on how particular resources are made inaccessible to other types of capital and are hence kept aside from the competitive equalization of the rate of profit. There are certain resources which are location specific and cannot be accessible to all and therefore property rights on such resources generate a permanent flow of monopoly rent. Even if the resources are not very exotic, simple property ownership allows one to garner absolute rent by excluding others. But the most common source of rent is not something that arises due to some difference of a permanent nature but rather that evolves for a particular quality which is added through innovation and reduces the cost of production for a particular capital below the average capital. Innovation or existence of a particular quality or resource is only the necessary condition for rents arising out of that, while the sufficient condition is to make that particular quality or resource inaccessible to competing capitals through establishing property rights. Therefore, the appropriation of rent not only depends on innovation but also upon an institutional structure that either protects special rights to the privileged access of certain resources or protects property rights on cumulative innovation, which becomes the source of rent. In the context of globalization we encounter an asymmetric distribution of property rights where resources that are in abundance in the North and accumulated over the past are adequately protected through property rights as patents, royalties and stringent intellectual property rights, while resources that are easily available in the South such as labour and natural resources are made easily accessible through global capital. Finally, in the conceptual world of pure capitalism, we assume that exchange takes place on the basis of the equivalence of value, and hence the average wages at which labour power is purchased cannot be less than the value of that labour power. This is the classical premise of which Marx argued in Capital that even 58

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if the worker is paid a wage equivalent to the value of the labour power, surplus value emerges by the exploitation of the worker as the value of the commodities produced by the expenditure of labour is higher than the value of labour power. But this conceptual frame does not rule out the fact of the real world today that the workers are paid below the value of their labour power, and this gives rise to super-profits emerging out of labour arbitrage on a global scale, and differential wages are maintained through delegitimizing workers’ rights through informalizing, subcontracting and disenfranchising workers within the domestic labour market.21 Capturing value by externalizing production and under reproducing labour has become the hallmark of neoliberal globalization. This has resulted in rising inequality with the stagnating or declining real income of the vast majority, while a handful of rich amasses a huge amount of wealth. The declining share of wages in national income put pressure on consumer demand as a shift in income share from wage earners to profit earners essentially shifts income away from people who generally have a high marginal propensity to consume. This faltering effective demand is responded to by debt-financed consumption mediated through credit instruments, and the debt dependence of working people increases because of the contraction of the public provision of services, such as health, pension and education. On the other hand, profit accumulated by the corporates as well as the rich enhances demand not for goods and services for consumption but of financial instruments that are securities on future incomes. Profits based on asset price inflation fuelled by speculative demand drives the growth process, and the flip side of such growth is that it is increasingly delinked from production and employment. Financialization influences investment patterns where in the global production network a larger chunk of capital shifts to high valued activities, such as “knowledge production”, logistics and coordination. Increasing concerns for maximizing shareholder value and minimizing risks prompt global players to work with a few big players giving rise to new waves of concentration. This is primarily aimed towards deriving rents out of redefined property rights and finance capital conditions the allocation of resources as well as the distribution of value-added. Manufacturing activities receive the lowest share of value-added and become subservient to the shareholder’s return.

Integrating capital in finance and production In the context of past and contemporary episodes of capitalist crisis or at least the protracted slowing down of growth or stagnation, the relationship of finance with productive capital has come under scrutiny both in the Marxian and post-Keynesian literature. In the late 1960s, the major focus was on the rise of monopoly capitalism, with the excess surplus being appropriated into the hands of the few in advanced capitalist countries, but they are not finding enough scope to invest in productive activities due to relative shrinking of demand . This led to stagnation, and an escape route to displace the impending crisis was precisely of taking 59

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refuge in unproductive consumption.22 Financialization is rooted in this conjuncture of the stagnation of production where the weight of surplus in a sense had resulted in a slowing down of productive activity. One of the responses of capital in such a scenario used to be moving away from the realm of production to that of circulation. This implies that “monied” capital was appropriating a larger share of the produced surplus. In other words, capital in the realm of finance increased its share as subsumed class payments vis-à-vis surplus appropriated by functional capitalists. This has often prompted a misinterpretation of looking into finance and productive capital as independent entities functioning as autonomous subjects. But essentially, they are part of the integrated circuit of “industrial capital”, which includes the sphere of production and circulation as equally important in the reproduction of capital. One concern that emerged within the Marxist tradition was to appraise the financial crisis as having roots in the realm of production, mostly comprehended as the falling tendency of the rate of profit. But we can see that some of the episodes of the recent crisis actually originated in the realm of finance and gradually affected the real economy through financial channels, and not the other way round. But it is also important to comprehend financial crises beyond the immediate cause of regulatory failure, and even if the expansion of finance has been identified as the major cause of the rising speculative bubble and the subsequent crisis, analytically what determines these critical thresholds is not very clear in these theories. The rise of finance has also been seen as an expression of an excess demand for securities originating from rising global inequality.23 Inequality might not only manifest in terms of a deficient demand for goods and services but also as an excess demand for financial assets as accumulated wealth in only a few hands that needs to be channelized as a claim for future income. The problem of deficient demand is partly mitigated by credit-based consumption. Unlike the market for goods in the case of financial assets, the flow of funds increases the price of different forms of structured risk instead of the decrease expected in the case of the goods market. This rise in the price of financial assets, on the contrary, increases the expectation of further price rises and the consequent gains from this. This creates an excess demand for speculative assets, which ultimately leads to the undervaluing of risk and an impending crisis. Keynes explicitly challenged the mainstream proposition that financial markets are the desired mechanism to optimize the inter-temporal choice of present and future consumption. In fact, it was based on a fundamental critique of the ergodic axiom that suggests that one can convert uncertainty into calculable risk or predict future outcomes with a high degree of statistical accuracy based on a sample of past or present data. He rejects the assumption that uncertainty of the future can be measured by forecasts based on historical data, but rather he believed that the capitalist economy is continuously moving from an irrevocable past to an unpredictable future.24 And in this context, expectations became important determinants of investment decisions as well as liquidity preferences. It is finance supplied by financial institutions, rather than a priori savings, that is required to keep the real activity going. The rhythm of capitalist accumulation depends on 60

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the entrepreneur’s willingness to take up projects based on future expectations of profit and that of financiers to part with liquidity to meet contractual obligations related to the production process. Excessive expansion of financial markets, however, makes the agents believe that liquidity will expand indefinitely, providing fast exit options, and this euphoria of speculation may ultimately lead to a crash. The boom or euphoric state, which has a tendency to explode, was further analyzed by Minsky, who makes a distinction between a booming phase and stable growth in the fundamental instability hypothesis, primarily explaining how in the aftermath of sustained expansion, financial interactions and institutions ultimately lead to a collapse of investment which is driven by endogenous factors. Debtenhancing finance is integral to capitalist operations and market mechanisms, and instead of giving rise to a self-sustaining, stable price full employment equilibrium, end up in a collapse in asset values and consequently of profit from capital assets and overall investments.25 In the context of relations between real and financial sectors and relevant to the current discussion, post-Keynesian literature provides a deeper understanding of the conflict between productive and finance capital. The mediation of finance and the dominant concern with maximizing shareholders’ returns leads to a growth–profit trade-off where long term concerns of growth and employment are increasingly compromised as remuneration for managers, and sometimes even that of workers are made to align to the fluctuations of short term financial gains. But the dominance of finance has its impact on the real economy through wealth effects articulated by debt-led consumption. The redistribution of debt towards households may temporarily displace the problems of deficient demand, particularly in advanced countries where the degree of the dependence of households on equity-based incomes is high and the marginal propensity to consume from such incomes is also high. But as the interest burden increases, a reverse flow of funds takes place from the segments with a high to low propensity and borrowing based purchases ultimately leads to indebtedness.26 For the economy as a whole, this literature provides important insights into how the process of financialization is linked to a decline in the share of wages. As profitability declines with a rising share of interest in corporate cash flow, the response of the capitalist class is to push down wages. In spite of the fact that heterodox analyses show how the dominance of finance is essentially linked to the accumulation process of capitalism and financial mediation, the expansion of liquidity through financial markets and related gains through the increasing layers of speculative assets led to an inevitable collapse. However, in this analysis, the rentiers who gain from financial speculation are seen as an autonomous faction repressing industrial capitalists’ productive activity. This somewhat links specific features to the groups of capital defined by their roles in the accumulation process. This implicit feature of capitalists tends to undermine the metamorphosis of capital which needs to be seen as an integral whole. Marx talked about various forms of capital as productive capital, commercial capital, money dealing capital, and interest-bearing capital and also on the conflicts between interest, rent and profit in the process of distribution of 61

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surplus value, but interest-bearing capital emerging as fictitious capital is according to Marx the most generic form of capital: capital sui generis. In other words, capital as finance is the most general form of capital that is independent of its concrete uses. Hence making a distinction between capital involved in production and finance as bearing intrinsic features does not capture the realities of today’s world, where there are many global manufacturing brands who actually produce nothing and also separating corporate profits as emerging out of production and financial transactions is largely impossible. Undoubtedly the profit of enterprise declines as interest paid to finance increases, but it is also important to acknowledge that the rate of profit increases with leverage so long as the rate of profit is higher than the rate of interest, and more importantly leverage makes the rate of profit of enterprise higher than the average rate of profit. Therefore, financial and non-financial capital mutually constitute each other negatively and should be seen as a putative split between productive and parasitic capital.27 The features of financialization have also undergone a change. Large monopoly corporates relying on bank capital for short term circulating credit and long term investment credit, and in the process accelerating the concentration and centralization of capital, was the crux of Hilferding’s characterization of twentieth-century finance capital.28 Corporates these days do not rely on bank capital but rather depend heavily on retained profits, and external finance, if needed, is mobilized through capital markets. Banks, on the other hand, have largely become mediators in financial markets, earning fees, commissions, trading margins and also mediating and handling individual financial assets for similar returns. In developing countries, features of financialization have also crept in, although not exactly with similar features. In the case of India, the relative weight of finance and business services in GDP has increased, the operating profit of financial companies have been consistently higher than that of non-financial companies, the share of industry in non-food commercial credit issued by commercial banks has declined, the share of bank borrowing to total borrowing fell sharply for non-financial companies, and debt to equity ratio has increased significantly over the years. Moreover, industrial securities showed a marked decline while that of financial securities sharply increased in corporate finance over the recent past.29 But contrary to what has happened in more advanced countries, India’s corporate finance largely depends on external finance, and because of a low per capita income, and since a relatively low share of people transact in the financial markets, household savings as a share of disposable income continues to increase. Hence, the mediation of finance varies over time and space, and the relationship between the financial sector and the real economy largely depends on the concrete nature of banks, industries, institutions and the levels and composition of household credit in a particular country.

Financial architecture as reified capital relations Policy regimes that argue in favour of the deregulation of financial markets also propose deregulation of the labour market. This is not a mere coincidence but 62

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rather a concerted effort of capital to attain flexibility by cushioning the rise of interest burden through pushing down wages. Financial profits accrue through several channels. One could be “profit upon alienation”, which is limited to the realm of circulation or a zero-sum-game where one asset holder gains because the other loses out, while the total amount of profit to be distributed remains the same, as happens in stock markets. There can be a redistribution of income and wealth favouring the capitalist class articulated through a restructuring of property rights in financial dealings. For instance, in the case of a corporate takeover, there can be a redefining of wages or employment; there may be a reduction in tax burden or the government forcing payment of debt obligations by a community. The other important channel by which financial gains are increasing these days is the expropriation of workers’ savings in exchange for loans that are meant to acquire use-values. The return that finance receives here is not out of a surplus produced through an act of production; instead, it is a transfer of consumption funds from the worker to the financiers. This can be in the form of mortgages or loans, or insurance payments related to consumption of health or education services, or fees for managing pension funds. In these transactions, financial agents or lenders are in a much better position to access and process information, and hence the entry of a large number of small investors creates huge profits for financial agents. Stagnating real wages together with the privatization of public provisions of health, education and other services have forced workers to get involved in financial markets, both for the lending and borrowing of funds. This allows financial institutions to extract profit through the transfer of personal revenue as “financial expropriation”.30 The dominance of the capitalist class, as a whole, increases over the workers in the process of financialization, and the conflict between finance and productive capital has always been subservient to the overriding cause of subverting workers’ share. This has been manifested by a declining share of wages in national income both in advanced and in developing countries during the phase of financialized capitalism. In fact, the architecture of finance involves a process of reification of norms conducive to capitalist accumulation and creates a market that quantifies concrete risks in commensurate abstract forms. The claims upon a future stream of income are commodified as packaged risks that are traded in financial markets. Risks are defined as the probabilistic chance of realizing a future return and hence depend on the subjective perceptions and concrete assessment of returns that are supposedly individual in nature. In order to be traded, risks are to be assessed and processed according to a socially accepted norm around which a quantified statistical assessment makes them tradable.31 The implicit norm that defines the distribution of risks is nothing but the fetishization of preconditions ensuring profitability for capital. The financial market ensures an alignment of the interests of various investors towards a smooth realization of the fundamental capitalist norm of profit-making. It is objectified as the universal good, and any deviation that may hamper the process is considered to be a credible threat to the future stream of income. In other words, financial architecture imposes a governance 63

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structure that reifies the preconditions of capitalist accumulation as socially necessary. Increasingly it not only draws in capitalist investors but by involving workers, managers, consumers, students and so on in this grand game of financial mediation, effectively homogenizes risk and universalizes the norms of the ideal functioning of the capitalist economy as independent of class conflict. As profits accrue more in financial transactions rather than in that of production, the act of profit-making appears to be some intrinsic property of capital rather than the outcome of exploitation and expropriation.

Notes 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 29 30 31

Braudel (1984, 246). Sotiropoulos et al. (2013). Keynes (1973, 376). ibid: 156. ibid: 158. Fama (1965, 1970); LeRoy (1989). Marx (1957, 401). Marx (1958). Marx (1959), Part 5. Marx (1959, 384). Resnick and Wolff (1987). Marx (1971, 468). Marx (1959, 384). Carcanholo and Nakatani (2019). Milberg (2007). McKinsey Global Institute (2003). IMF (2017, 129). Roy (2019). Ye, Meng and Wei (2015). Roy (2017, 2020). Smith (2016). Baran (1973); Baran and Sweezy (1968). Lysandrou (2011). Sen (2020). Michailidou (2016). Boyer (2011), Palley (1994, 1996). Lapavitsas (2013). Hilferding (1981). Sen and Dasgupta (2015). Lapavitsas (2013). Sotiropoulos et al. (2013).

References Baran, Paul A. (1973) The Political Economy of Growth, Harmondsworth: Penguin (first published in 1957). Baran, Paul A. and Paul Sweezy (1968) Monopoly Capital, Harmondsworth: Penguin (first published in 1966).


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Boyer, Robert (2011) “Is a Finance-Led Growth Regime a Viable Alternative to Fordism? A Preliminary Analysis”. Economy and Society 29(1): 111–45. Braudel, Fernand (1984) Civilization and Capitalism, 15th–18th Century: The Perspective of the World, Berkeley: University of California Press, p. 246. Carcanholo, Reinaldo Antonio and Paulo Nakatani (2019) “Parasitic Speculative Capital: A Theoretical Precision on Financial Capital, Characteristic of Globalization”, in Gustavo Moura de Cavalcanti Mello and Mauricio de Souza Sabadini (eds.) Financial Speculation and Fictitious Profits: A Marxist Analysis. Switzerland: Palgrave Macmillan, pp. 117–38. Fama, E. F. (1965) “Random Walks in Stock Market Prices”. Financial Analysts Journal 21(5): 55–59. Fama, E. F. (1970) “Efficient Capital Markets: A Review of Theory and Empirical Work”. Journal of Finance 25(2): 383–417. Hilferding, R. (1981) Finance Capital: A Study of the Latest Phase of Capitalist Development. London: Routledge and Kegan Paul, 1st German edition 1910. IMF (2017) World Economic Outlook April 2017: Gaining Momentum? Washington, DC: International Monetary Fund. Keynes, J. M. (1973) The General Theory of Employment Interest and Money. Cambridge: Cambridge University Press. Lapavitsas, Costas (2013) “Financialised Capitalism: Crisis and Financial expropriation”, in C. Lapavitsas (ed.) Financialisation in Crisis. Chicago: Haymarket Books, pp. 15–50. Lapavitsas, Costas (2013) Profiting without Producing: How Finance Exploits Us All. London: Verso. LeRoy, S. F. (1989) “Efficient Capital Markets and Martingales”. Journal of Economic Literature 27(4): 1583–621. Lysandrou, Photis (2011) “Global Inequality, Wealth Concentration and the Subprime Crisis: A Marxian Commodity Theory Analysis”. Development and Change 42(1): 183–208. Marx, Karl (1957) Capital II. Moscow: Foreign languages Publishing House. Marx, Karl (1958) Capital I. Moscow: Foreign Languages Publishing House. Marx, Karl (1959) Capital III. Moscow: Foreign languages Publishing House. Marx, Karl (1971) Theories of Surplus Value, Vol III. Moscow: Progress Publishers. McKinsey Global Institute (2003) “Offshoring: Is It a Win-Win Game?” Report. August. Michailidou Domna, M. (2016) The Inexorable Evolution of Financialisation: Financial Crises in Emerging Markets. London: Palgrave Macmillan. Milberg, William (2007) “Shifting Sources and Uses of Profits: Sustaining U.S. Financialization with Global Value Chains”. Working Paper 2007–9, December, W Schwartz Center for Economic Policy Analysis, Department of Economics, The New School for Social Research, New York. Palley, T. (1994) “Debt, Aggregate Demand, and the Business Cycle: An Analysis in the Spirit of Kaldor and Minsky”. Journal of Post Keynesian Economics 16(3): 371–90. Palley, T. (1996) Post Keynesian Economics: Debt, Distribution and the Macro Economy. Basingstoke: Macmillan. Resnick, Stephen A. and Richard D. Wolff (1987) Knowledge and Class. Chicago: Chicago University Press. Roy, Satyaki (2017) “Rent and Surplus in GPN Framework: Identifying ‘Value Capture’ from the South”. Agrarian South: Journal of Political Economy 6(1): 32–52.


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Roy, Satyaki (2019) “Financialisation in India: Nature and Implications with Special Focus on Corporate Sector”. ISID, ICSSR sponsored Research Report. Roy, Satyaki (2019) “Global Production Network: The New Template of Power and Profit in the Regime of Empire”, in Achin Chakraborty, Anjan Chakraborty, Byasdeb Dasgupta and Samita Sen (eds.), Capital in the East: Reflections on Marx. Singapore: Springer, pp. 87–102. Roy, Satyaki (2020) Contours of Value Capture: India’s Neoliberal Path of Industrial Development. Cambridge, United Kingdom: Cambridge University Press. Sen, Sunanda (2020) “Investment Decisions under Uncertainty” Journal of Post Keynesian Economics 43(2): 267–80. Sen, Sunanda and Zico Dasgupta (2015) “Financialization and Corporate Investments: The Indian Case”. Working Paper No. 828, Levy Economics Institute of Bard College, Annandale-On-Hudson. Smith John (2016) Imperialism in the Twenty-First Century: Globalisation, Super Exploitation and Capitalism’s Final Crisis, New York: Monthly Review Press. Sotiropoulos, Dimitris P., John Milios and Spyros Lapatsioras (2013) A Political Economy of Contemporary Capitalism and Its Crisis: Demystifying Finance. London: Routledge. Ye, Ming, Bo Meng and Shang-jin Wei (2015) “Measuring Smile Curves in Global Value Chains”. IDE Discussion Paper No. 530, Institute of Developing Economies, Japan External Trade Organization (JETRO).


4 THE FINANCIAL SECTOR IN THE INDIAN ECONOMY Some reflections using Hyman Minsky’s lens Sukanya Bose The Basic disequilibriating tendencies of capitalist finance will once again push the financial structure to the brink of fragility. When that occurs a new era of reforms would be needed. There is no possibility that we can set things right once and for all; instability put to rest by one set of reforms will after a time, emerge in a new guise. (Minsky, 1986, 370) Following the global financial crisis, there has been a renewal of interest in Hyman Minsky’s work worldwide.1 Minsky’s prophetic writings, “Can ‘It’ Happen Again?”, drew attention to the conditions that will drive the capitalist economy to a situation where another crisis of the intensity and scale of the great depression is possible.2 It also drew attention to why the three decades following the New Deal didn’t see any major financial and economic crises in advanced capitalist countries. The financial sector in the Indian economy is going through a deep and prolonged malaise, with the public sector banks at the centre of the crisis. In what ways can the Minskian analysis of the capitalist economy with a sophisticated financial system and the inherent tendencies for instability be useful to analyze the present predicament of the Indian financial system? What vantage point and policy lessons does it offer?

The essential Minsky Building on the ideas of Keynes, Fisher, Schumpeter and Kalecki, Hyman Minsky’s work rejects the mainstream equilibrium view of the economy. Instead, Minsky combines historical analysis and an understanding of the working of the institutions in a macro-economic framework where uncertainty and instability are key features. The internal dynamics of our modern economy are not equilibrium seeking. And if ever the economy did reach “equilibrium”, the internal dynamics would push it away – the system is not stable. Stability is destabilizing. Entrepreneurial Decision and Uncertainty: Keynes’ ideas on investment under uncertainty are the essential building blocks on which Minsky’s theory of financial 67

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instability develops (Wray, 2016).3 Keynes’s approach begins with a focus on the entrepreneurial decision – each firm produces what it expects to sell – rather than on the consumer who maximizes utility through time. That entrepreneurial decision is based on a comparison between the costs incurred to produce now against the proceeds expected to be received in the future. Production will not be undertaken unless the expected proceeds exceed by a sufficient margin the costs. For Keynes, the most important decision is the one made by entrepreneurs to invest because this decision is by its very nature forward-looking towards an unknowable future. As worries about future prospects rise, investment falls, which means lower employment. Thus, Keynes had an “endogenous” theory of the cycle – it is in the nature of capitalism to cycle due to “whirlwinds” of optimism and pessimism. The cycle is thus related to the investment decision, which depends on expectations about an inherently uncertain future. Uncertainty for Keynes was different from the probabilistic calculation of risk (Keynes, 1937; Sen, 2020a). Financing of Investment: Minsky extends Keynes’ investment theory to include the role of the financial system to finance investment. Capital development of a capitalist economy is accompanied by an exchange of present money for future money. Minsky expounds on what Keynes would have meant by “interposition of this veil of money between the real asset and the wealth owner as a specially marked characteristic of the modern world” (Keynes, 1972, 151, cited in Minsky, 1992). The Keynes veil implies that money is connected to financing through time. Initially, the exchanges are for the financing of investment, and subsequently the exchange fulfils the prior commitments stated in the financing contract. Expectations of business profits determine both the flow of financing contracts to business and the market price of existing financing contracts. Profit realizations determine whether the commitments in financial contracts are fulfilled. Minsky incorporates both Kalecki’s (1965) and Levy and David’s (1983) view of profits, in which the structure of aggregate demand determines profits. An economy’s ability to produce profits comes down to the net expansion of the aggregate balance sheet in the macro accounting identity; there has to be a net positive investment as a whole for there to be profits.4 Investment takes place now because businessmen and their bankers expect investment to take place in the future. As Minsky put it, The peculiar circularity of a capitalist economy – that sufficient investment to assure the economy does well now will be forthcoming only as it is believed that sufficient investment to assure the economy does well will be forthcoming in the future – has a banking and financialsystem corollary. Not only must the banking and financial system maintain favourable asset prices and conditions for investment financing now, but the banking and financing system also must be expected to maintain favourable asset prices and conditions for investment financing in the 68

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future. Because such normal functioning of the banking and financial system is a necessary condition for the satisfactory operation of a capitalist economy, disruption of the system will lead to malfunctioning of the economy. (Minsky, 1986, 227) Among the substantial body of Minsky’s works, Financial Instability Hypothesis (FIH) is the most cited. Investment can proceed only if the demand price, which emerges out of the asset price system, exceeds the supply price, which emerges out of the current output price system. Minsky distinguished between a price system for current output and one for asset prices. Current output prices can be taken as determined by the cost plus the mark-up set at a level that generates profits. In the case of investment goods, the current output price is effectively a supply price of capital, which ordinarily would also include the cost of external finance. If the firm has to borrow funds, then the supply price of capital also includes external finance costs. In that case, supply price increases because of “lender’s risk” – the additional cost associated with borrowing funds from the lender. Turning to the demand for capital assets, the demand price would be determined by the expectations of the net revenue that the asset can generate. However, if the asset is externally financed, as is often the case, the amount one is willing to pay depends on the amount of external finance required. Greater borrowing exposes the buyer to a higher risk of insolvency and bankruptcy. That is why “borrower’s risk” must also be incorporated into demand prices. These adjustments add a margin of safety in case the future turns out to be worse than expected (Wray, 2016; Minsky, 1992). Optimism and reduced uncertainty would tend to raise the demand price for capital assets. At the same time, optimism would lower both lender’s risk and borrower’s risk, which lowers the supply price and reinforces the demand price. This feeds investment and growth. Pessimism and rising uncertainty work in the opposite direction. Margins of safety differ across economic units. Three distinct income–debt relations for economic units can be identified with decreasing margins of safety: hedge, speculative and Ponzi finance. Hedge financing units (HFUs) are those which can fulfil all of their contractual payment obligations with their cash flows: the greater the weight of equity financing in the liability structure, the greater the likelihood that the unit is a HFU. Speculative financing units are units that can meet their payment commitments on their liabilities, even as they cannot repay the principal from income cash flows. For Ponzi financing units (PFU), the cash flows from operations are not sufficient to fulfil either the repayment of principal or the interest due on outstanding debts. The debtor must borrow in order to pay interest. The economy has financing regimes under which it is stable, and financing regimes under which it is unstable (first theorem of FIH). If HFUs dominate, then the economy may well be an equilibrium-seeking and containing system. In contrast, the greater the weight of speculative and PFU, the greater the likelihood 69

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that the economy is a deviation amplifying system. Over periods of prolonged prosperity, the economy transits from financial relations that make for a stable system to financial relations that make for an unstable system (second theorem of FIH). When that happens, a big government and a big central bank would be required to stabilize the system. Thus, what Minsky proposes is an appropriate model which can explain the observed instability of both finance and the economy. An appropriate model of our type of economy needs to demonstrate that the observed instability and the contained instability of both finance and the economy are the consequences of behaviour of self-seeking banks, firms and households and the interventions of the governmental units. (Minsky, 1992a, 5) The rise of financialization, which Minsky termed as money managerial capitalism, presents a more fertile ground for the instability to play out. The financial structures became increasingly fragile in the past three decades or more. Shadow banks pushed financial practice to new frontiers, and the commercial banks followed suit (McCulley, 2009). The structure of incentives and rewards changed such that risky bets, high leverage ratios and short-term profits were promoted over long-term returns. Financial speculation became an accepted way of making a short-term return, whereas everything that could be securitized was securitized. The rise in financial fragility makes it likely that stagnation or even a deep depression is possible, at any time, as the global financial crisis amply demonstrated (Kregel, 2008, 2010; Wray, 2009; Keen, 2013). Stabilizing the Economy: If stabilization policy is to be successful, it must stabilize profits (Minsky, 1982). Expansion can take place only as expected profits are sufficient to induce increasing expenditures on investments. Current profits provide the cash flows that enable a business to meet their debt payments, even as expected profits determine the ability of a business to issue debt to both finance expenditures and roll over maturing debt. Big governments and big banks are key to stabilization strategy. When a crisis threatens, the central bank can intervene strongly to refinance organizations. When income declines, the federal government can run a deficit (Minsky, 1982, 30). Thus, managing a financial crisis and a recession involves two distinct steps: one is refinancing the markets or institutions whose perilous position defines the crisis, and the other is assuring that the aggregate of business profits does not decline by raising aggregate demand. Central bank interventions and the stabilization of profits by government deficits, however, mean that liability structures that derive from innovations in finance during periods of expansion are validated during crises and recessions (Wray, 2016). To the extent that the policies of the government and central bank intervention help to return the economy to stability, they are also destabilizing! Financial market participants will adjust their expectations to include government 70

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bailouts should anything go wrong. So ironically, the success of the interventions encourages more risk-taking. While instability requires a big government response, in a world dominated by globalized financial flows of a short terms nature, the ability of the nation-states to follow discretionary fiscal policies stands compromised. This is a particularly severe constraint for the developing regions as the global capital demands greater deregulation and less state intervention. Globalization requires the conformity of institutions across national lines and in particular the ability of creditors to capture assets that underlie the securities. There is a symbiotic relation between the globalization of the world’s financial structure and the securitization of financial instruments. (Minsky, 1987, 2) This conformity of institutions across nations as part of the global financial network is what happened in countries like India.

The Indian financial sector in historical perspective In the advanced capitalist economies, the three decades following the New Deal didn’t see any major financial crisis. This period of managed capitalism (the Golden age) with financial regulation stands out for its relative stability and low unemployment rate. Many of the institutions constrained the financial system. In the US, this included Regulation Q, which limited interest payments on deposits, and the Glass–Steagall Act’s separation of commercial banking from riskier investment banking. In the case of India, the regulated financial structure served the development agenda with its focus on growth and equity. This is not to say that it was a perfect system. The monetary policy of the Reserve Bank of India (RBI) and of the government was directed primarily to assist the economy in reaching the goals set out in plans for the four decades after independence. Also, the Reserve Bank had to operate on a very modest amount of international reserves.5 The principal macroeconomic concerns in India have been aggregate growth and its distribution, inflation and the balance of payments. In most years, credit aggregates were accorded greater importance than monetary growth both as indicators of the thrust of monetary policy and as proximate targets of the monetary authority.6 With credit as the intermediate target, rather than monetary aggregates, the instruments for monetary control were such that the impact was to be felt on aggregate credit as well as provide directions for its allocation. In an administered interest rate structure with concessional rates of interest on several categories of lending, with minimum and ceiling rates defined for deposit and lending rates, respectively, the interest rate was used only occasionally for monetary management. A potent instrument of credit policy was the cash reserve ratio (CRR), 71

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used extensively in certain periods, as was the statutory liquidity ratio (SLR) prescribed by the monetary authorities. In a supply rationed credit market, refinance or recourse to the central bank by the commercial and cooperative banks was a flexible and important tool for credit management, which could be adjusted from the busy season to the slack season. A wide spectrum of selective credit control was used with the aim of preventing the speculative piling up of goods in short supply and especially for articles of daily consumption. There were strict regulations on trade credit. Large-scale industry and trade had to accept the credit discipline implied by the Credit Authorisation Scheme and the inventory and working capital norms. Credit planning of the kind that emerged in India required radical institutional changes in the pre-existing structures. The most significant step in this direction was the nationalization of the 14 big private sector banks in 1969, which symbolized the ideas of social control and social responsibility. Once public ownership was established, branch expansion was significant, especially in rural areas. Important sectors of the economy previously neglected were pronounced as priority sectors with mandatory stipulations of credit for these sectors. Between June 1969 and March 1990, the ratio of priority sector advances to net bank credit rose from 14 to 43%. The total number of commercial bank offices rose from 8,262 to 60,294. The number of rural branches increased its share to 57 from 22% in 1969. As a ratio of national income at current prices, bank deposits expanded during the period, from 15.2 to 50% (Malhotra, 1990). Rural lending was pursued with much vigour. A state-led development process implied large expenditure for the government, not all of which were covered by the revenues the state raised, with the result that recourse to deficit financing was inevitable. Sale of government securities was to a captive market consisting of commercial banks, provident funds and life insurance institutions for whom it was mandatory to hold a specified portion of their portfolio as government securities. Like the rest of the interest rates, the yield on government securities was administered. After the nationalization of the banks in 1969, the SLR was steadily increased. Government ownership of banks raised the pre-emption of government on banking resources. Monetization of the deficit was a fiscal decision, and to an extent the central bank’s independence as the monetary authority was curtailed because of the automatic monetization of the deficit. The BOP crisis and beyond The mid-1980s marked the beginning of a shift in respect to several of the objectives that had underlined policymaking in India over the previous decades, a transformation that was to continue at an accelerated pace during the 1990s subsequent to the balance of payments crisis of 1991. Pro-market reforms in the financial sector were a part of the IMF conditionality following the 1991 balance of payment crisis. In the banking sector, which dominates India’s financial system, the reforms in the 1990s revolved around the following major sets of 72

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initiatives. First, those aimed at increasing the credit-creating capacity of banks through reductions in the SLR and CRR. This was combined with greater flexibility in determining the structure of interest rates on both deposits and loans. The second was to increase competition. While the existing nationalized banks were permitted to sell equity to the private sector, private investors were permitted to enter the banking arena. Further, foreign banks were given greater access to the domestic market, both as subsidiaries and branches, subject to the maintenance of a minimum assigned capital and being subject to the same rule as domestic banks. Also, a degree of “broadbanding” of financial services was permitted, which would ultimately lead to a form of universal banking (Chandrasekhar and Ghosh, 2000). Third, to render this competition effective for influencing bank functioning, banks were provided with greater freedom in determining their asset portfolios. Priority sector lending targets could be overlooked. Banks were permitted to cross the firewall that separated the banking sector from the stock market and invest in equities. Fourth, “reforms of the regional rural banks largely followed the same format as that of the commercial banks, irrespective of the fact that their very role in the society required a special status and a different set of policies” (Bose, 2005, 4). The Narasimham Committee-II (RBI, 1998) recommended that the development finance institutions (DFIs) should either convert themselves into universal banks or non-banking financial companies. ICICI, IDBI, UTI, the prominent DFIs, were thus converted into banks. These changes in policy undermined the institutions that lent stability to the financial system, as we shall see below.

The consolidation of finance and rising instabilities The present slowdown in the Indian economy has been the longest in several decades (Dasgupta, 2020). The unemployment rate has reached the highest level in four decades.7 The economy is locked in a downward spiral, with real growth pulling down the financial sector and vice versa (Subramanian and Felman, 2019). Increasingly, companies are turning Ponzi. A large number of corporate firms are insolvent. The reflection of the deteriorating corporate balance sheet can be seen in the banks’ assets which have seen rising non-performing assets, stressed assets and debt write-offs. Non-performing assets to gross advances ratio of scheduled commercial banks (SCBs) are in double digits compared to around 2% in the late 2000s (Figure 4.1). Debt write-off has gone up steadily (RBI’s Trend and Progress of Banking in India, December 2019). Credit deployment is at an all-time low. A number of large NBFCs have defaulted on their debt commitments, bringing in fear of contagion and full-blown financial crisis.

The high growth years and procyclical finance The genesis of the crisis, as Minsky had stressed, arose in periods of economic boom. 73

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100 9






7 64


10 8 6




30 3





8 3 22 16 3 2


3 15











Growth in Gross Advances (%) Gross NPAs to Gross Advances Rao (%) RIGHT AXIS


















4 2 0

Growth in Gross NPAs (%)

Figure 4.1  Worsening Non-Performing Assets of Scheduled Commercial Banks. Source: RBI, Database on the Indian Economy. Notes: As on 31st March.

1951-52 1953-54 1955-56 1957-58 1959-60 1961-62 1963-64 1965-66 1967-68 1969-70 1971-72 1973-74 1975-76 1977-78 1979-80 1981-82 1983-84 1985-86 1987-88 1989-90 1991-92 1993-94 1995-96 1997-98 1999-00 2001-02 2003-04 2005-06 2007-08 2009-10 2011-12 2013-14 2015-16 2017-18 2019-20

40 35 30 25 20 15 10 5 0

Figure 4.2  Growth in credit disbursed by scheduled commercial banks (%). Source: RBI, Database on the Indian Economy.

The period since 2003–4 saw one of the largest credit cycles in the entire postindependence era as credit growth reached unprecedented levels (Figure 4.2). With buoyant export growth, expansionary demand conditions in the economy, private investment growth was high. Gross capital formation to GDP of the private (nonhousehold) sector increased from 6.5% in 2003–4 to 17.3% in 2007–8. Investment growth was largely debt-financed. Even though the boom since 2003–4 was led by private corporate investment, the amount of capital mobilized from the primary equity market touched merely 1% of the GDP at its peak in 2007–8 (Azad et al., 2017). Bank lending financed a large part of the boom. Public sector banks (PSBs) led the credit boom, with new credit from PSBs averaging around 6.7% of the GDP between 2004 and 2009. Credit growth dipped during the global financial crisis only to recover swiftly and increase in the aftermath, which saw a coordinated fiscal stimulus package from major countries, including India. 74

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Bank credit growth during this period has typical characteristics of a Minsky cycle where the financial cycle accentuated the investment cycle. During the course of an economic boom that is led by investment spending, private firms stretch liquidity, borrowing more so that income flows are leveraged by debt, and the ratio of safe assets to liabilities falls. This leads to increasingly fragile financial positions. “Because bankers live in the same expectational climate as businessmen, profit-seeking bankers will find ways of accommodating their customers; this behaviour by bankers reinforces dis-equilibrating pressures” (Minsky, 1986, 255). The money supply expands as bankers accommodate the demands of their customers in a procyclical manner. The procyclical behaviour of bank lending amplifies the business cycle, increasing the thrust towards instability. For Minsky, the modern business cycle is a financial cycle. Consider bank lending to the real estate sector. The onsite inspections of banks revealed lax underwriting due to the euphoria, notes Shyamala Gopinath, then Deputy Governor of the RBI (Gopinath, 2011). Euphoria is the notion where you think a downturn is impossible. There were emerging signs of the under-pricing of risks as real estate prices were spiralling, fuelled by ample liquidity and the stock market boom. On the demand side, mortgages for investment purposes were on the rise; at the same time, the inventory build-up of completed commercial and residential units was also increasing. The real estate companies monetized the huge land banks aided by bank lending for commercial real estate, and supported by the stock market boom. The macro-environment was supportive of the real estate binge. The real interest rates had come down significantly, lowering the demand price and the supply price of the assets, real estate, in this case. Liquidity was in abundance, contributed by the rising tide of international capital flows. Credit was easy to come by for many other capital-intensive sectors as well. Bank loans were concentrated in sectors such as infrastructure, iron and steel, aviation, etc. In the past, the needs of long-term investment had typically been financed by DFIs in India. These institutions were very different from banks, and were modelled along the lines of the Kreditbanken in Germany during its industrial take-off (Gerschenkron, 1962, cited in Chandrasekhar, 2014). The DFIs were created to finance long-term investment, including lending to capital-intensive sectors. Having lent long, they are very often willing to lend more in the future. Since such lending often leads to higher than normal debt to equity ratios, development banks to safeguard their resources, closely monitor the activities of the firms they lend to, resulting in a special form of relationship banking. Importantly, the DFIs in India had access to low-cost funds from the government through the RBI. The demise of the DFIs since the early 2000s as an outcome of financial reforms created a void in financing the private corporate sector, which the PSBs were to fill through syndicated lending. The new arrangement created a financial system far more fragile than what existed before. While financial instability is an endogenous response in a capitalist system, it is not as if all are equally prone to financial instability; some structures are more prone, more fragile. 75

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Between 2007 and 2012, borrowings of the top ten large corporate groups was equivalent to 13% of bank loans (up from 6% in 2007) and 98% of the banking system’s net worth (Credit Suisse, 2012). As the business environment deteriorated, corporate insolvency loomed large. The average group debt to earnings ratio for the ten large groups was 7.6x, whereas four of the ten large groups had an interest coverage ratio of less than 1 by 2012. Stressed firms were clearly on the rise, and they contributed a significant proportion to banks’ lending portfolio.8 Among the various sectors, infrastructure (particularly power), iron and steel, aviation and textiles, mining, textiles which accounted for about a fourth of the gross advances of the banking system, contributed over half of the overall stressed advances. With rising uncertainty and pessimism on future profitability, the demand price of capital fell below the supply price, reducing investment and today’s profits below the level necessary to validate past expectations. The margins of safety that had been included in borrower’s and lender’s risk were revised upwards. This reduction of investment and the upward revision of safety margins fuelled a downturn as aggregate demand fell. Corporate stress was not reflected in problem loans by the banks for another few years until the Asset Quality Review 2015, by the RBI forced a reclassification of assets. Rather banks continued to extend their lending to the stressed firms in the typical manner of Ponzi financing. Regulatory forbearance was writ large. There were attempts to keep insolvent firms afloat through the evergreening of loans, debt write-off and by tolerating debt defaults. As Dasgupta, 2020 perceptively notes, in the absence of any effective policy instrument which can boost demand, credit supply becomes a policy lever to keep firms afloat despite being insolvent or retain private investments by increasing leverage ratios. Instead of any anomaly, here, financial fragility becomes a logical necessity. Former RBI Governor, Raghuram Rajan (2014) applies the term riskless capitalism to describe the impunity of the private borrowers (cited in Azad et al., 2017). Faced with this asymmetry of power, banks are tempted to cave in and take the unfair deal the borrower offers. The bank’s debt becomes a junior debt, and the promoter’s equity becomes super equity. The promoter enjoys “riskless capitalism”. It is not surprising that the mainstream macro-economic policy establishment blames the “irresponsibility” of public sector banks for the crisis. There is a major push for the privatization of banks, citing incompetence, whereas the problem, as we see it, lies in the system and not individual pathologies. Inept banking supervision by the central bank and the government’s own policies in this regard need as much scrutiny. These institutions can attenuate the boom–bust cycle, as Minsky had clearly argued. There are obvious changes in government 76

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policy that has meant that nationalized banks have been left holding the can, notes Patnaik (2019). Infrastructure investment has steadily been financed through private investment instead of being government-financed. The large infrastructure projects, which require lumpy investment and have long gestation periods and therefore involve high risks, are now left to be implemented by the private corporate sector, rather than the public sector as previously. The other policy shift, as we have seen, relates to the end of DFIs, which forced PSBs to lend to these projects, a policy designed to fail.

The growing gap between growth in finance and the real sector Minsky was particularly concerned with the problem of money-manager capitalism and the excessive financialization of the economy. The emergence of money-manager capitalism means that the financing of the capital development of the economy has taken a back seat to the quest for short-term returns (Minsky, 1992, 32). In this form of capitalism, the dominant financial players are “managed money” – shadow banks such as pension funds, hedge funds, sovereign wealth funds – with huge pools of funds searching for the highest returns. Innovations by financial engineers encouraged the growth of private debt relative to income and increased reliance on volatile short-term finance. India became a party to the financialization in the world economy, and this has led to accumulation as well as a concentration of financial assets, providing sources of rentier income that are higher than those obtainable from physical assets (Sen, 2014, 2020). Largely driven by deregulation, the financialization process works to make assets in sectors, especially in finance, real estate and commodity trade, relatively attractive compared to other assets, offering both better returns and potential capital gains. Financialization shows up in the rising income share of finance in the economy and the growth of the volume of financial claims on the balance sheet of firms and financial intermediaries. Indian corporates have shown a tendency to hold financial assets as a rising proportion of their portfolio. There has been a steady rise in financial securities as a proportion of total assets held by Indian corporates from less than 60 to 70% (and higher), whereas industrial securities dropped from 40% in 2002–3 to 15% by 2011–12 and thereafter (Sen and Dasgupta, 2015). The Financial Stability Report (RBI, June 2020) notes that incremental borrowings were used by the Indian firms towards creating financial assets (loans and advances to subsidiary/other companies and financial investments) and not for capex formation. Corporate investments in equities, including derivatives, are primarily transacted in the secondary market for stocks. Resources mobilized from the primary market are comparatively small, and when compared to the total turnover in the derivatives market, they are but a tiny fraction. Similarly, a substantial portion of the revenue of banks comes from non-interest incomes. Service charges on deposit accounts (such as failure to meet minimum balance, early withdraw or closure fee, dormant account, ATM Charges), 77

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trading revenues (off-balance-sheet derivatives contracts, revaluation to carrying value of assets and liabilities due to marking to market, foreign exchange and equity derivatives) and other fee incomes (e.g. bill collection, insurance sale) generate significant earnings for the banks. Even without including the secondary market activity in financial products, the chasm between the real economy and the financial sector is wide and growing. In a previous paper (Bose and Kumar, 2018), we noted that the forward linkage from finance, real estate and business services (FINREBS) to the rest of the economy is below average compared to the rest of the sectors of the economy, and backward linkage from FINREBS is amongst the lowest.9 Backward and forward linkages from FINREBS weakened between 2003–4 and 2007–8, the period corresponding to the initial boom in this sector. This is witnessed in the growing share of FINREBS in overall GDP from 12% in the mid-1990s to about 22% by 2014. The gap in the growth rate of FINREBS and the rest of the sectors becomes more prominent after the global financial crisis (Figure 4.3). The autonomous growth of FINREBS witnessed in Figure 4.3 arises from tendencies of financialization, including real estate lending with which finance has a self-reinforcing loop.10

Shadow banking Minsky had worried that “all too often it seems as if the Federal Reserve authorities have been surprised by changes in financial practices” (Minsky, 1975, 150). There is a radical suspension of disbelief. Indeed, the Fed and most mainstream economists missed the rise of the shadow banking system in the US and other developed economies (Wray, 2016; Krugman, 2009). The same is the case vis-àvis the NBFCs in India. 3

Growth in %

2.5 2 1.5


2001q1 2001q3 2002q1 2002q3 2003q1 2003q3 2004q1 2004q3 2005q1 2005q3 2006q1 2006q3 2007q1 2007q3 2008q1 2008q3 2009q1 2009q3 2010q1 2010q3 2011q1 2011q3 2012q1 2012q3 2013q1 2013q3 2014q1 2014q3



GDP excluding FINREBS

Figure 4.3  The widening gap: quarterly growth rate (%). Source: Bose and Kumar (2018). Notes: Rolling Quarterly Growth Rate.


The financial sector in the Indian economy

Lack of regulatory requirements or lower regulatory standards makes the shadow banking system more competitive than the formal banking system. Banks appear to require a substantial spread between interest earned on assets less rates paid on liabilities. This spread covers the normal rate of return on bank capital plus the required reserves imposed on banks plus costs of servicing customers in the form of staffing the bank, etc. In contrast, shadow banks can operate with much lower spreads precisely because they are exempt from reserve ratios, regulated capital requirement and much of the costs of relationship banking. In India, since the majority of NBFCs do not hold the deposits of ordinary households, these institutions were not considered to be entities that need strict regulation. Typically, NBFCs work with thin margins of safety. Short-term funds from banks, mutual funds, insurance companies, etc., are used to provide longmaturity loans with the expectation that they would be able to roll over much of these loans. The strong dependence on refinancing to meet financial obligations makes these institutions more vulnerable to the market downside. Rating companies play an instrumental part in rating these as safe investments. The asset side of NBFC balance sheets has exposure to small and medium enterprises, agriculture, real estate sector, etc. The pattern of lending is again concentrated in only some sectors and entities rather than being broad-based. NBFCs accounted for about half of the Rs. 5 lakh crore outstanding in the real estate sector (Subramaniam and Felman, 2019). By the end of March 2018, the NBFI sector was 20% of the SCBs taken together in terms of balance sheet size.11 Credit growth for NBFCs was 32% (2017–18) compared to bank credit growth of 10%. Credit growth in the economy, particularly in the period beyond 2014–15, was clearly propelled by the NBFC sector rather than banks.12 It suited the banks in two ways (Chandrasekhar, 2020). It helped the banks to transfer risks out of their own books. And by lending to NBFCs, it allowed the banks to use their liquidity even when they themselves were stretched and could not discover, scrutinize and monitor new borrowers. Infrastructure Leasing and Financial Services (IL&FS), the behemoth infrastructure development and finance company with 256 group companies, defaulted on its outstanding payments (September 2018). Soon other NBFCs – who were not as highly leveraged as the IL&FS – also faced liquidity constraints with rolling over their existing debt. There was a downgrade of their instruments. Several NBFCs defaulted/came close to default. Share prices of many NBFCs dropped drastically, and they had to sell their stakes in a variety of projects to honour their payments. RBI stepped in to push liquidity into the system. Among other things, it raised refinance limits for the National Housing bank. SCBs were asked to lend more to NBFCs by raising exposure limits for single entities. The situation has somehow contained. Credit growth in the economy, which was already faltering, came to a standstill affecting the sectors dependent on NBFC credit badly. The NBFC crisis added to the problem of debt overhang, which the corporates and the banks were already facing. If anything, the location of the crisis in the 79

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NBFC sector made it worse. Banks at least have backstops. Bank depositors have deposit insurance, though the limit is very low. PSBs have recapitalization opportunities through budgetary support. Scheduled commercial banks are subject to regulatory oversight and can access the credit lines from RBI in case of liquidity or other crisis. NBFCs do not have these facilities. Moreover, NBFCs are the ones with maximum payables to the rest of the system (Figure 4.4). In terms of inter-sectoral exposure, NBFCs, followed by housing finance companies, are the major receivers of funds. Bank credit to the NBFC sector stood at 7.13 lakh crore (October 2019). Defaults on payments by NBFCs therefore had a ripple effect throughout the financial system. RBI was aware of the dangers of the shadow banking system, since the 2008 crisis was not of a garden variety, and shadow banks were in the thick of things. Taking cognizance of the systemic risks, the RBI began to publish the Financial Stability Report, every half year since 2010. The prudential regulation framework had acknowledged the limitations of micro-prudential regulation and instead shifted focus to macro-prudential regulation. As Gopinath (2011, 179) notes, The real concern from the interconnectedness perspective arises from the NBFC which is regulated by regulators for respective Sectors … Many of the NBFIs which do not accept public deposits are significantly larger and through their interaction with other financial market segments could pose systemic risk. For such entities a stricter prudential framework on the lines of banks has been put in place. And yet, the NBFC crisis followed. The fault lines were exposed once again. 10 8 6 4 2 0 -2 -4 -6 -8 -10



4.7 1.1

0.1 0.0



-4.8 -8.0

AMCs & Insurance MFs



UCBs Sep-18

AIFIs Sep-19

Foreign Banks


Pvt Banks NBFCs


Figure 4.4  Net receivables (+ve)/payables (–ve) by institutions (In Rupees Crores).

Source: RBI, Financial Stability Report, various issues. Note: Urban Cooperative Banks (UCBs), Asset Management Companies – Mutual Funds (AMCMFs), Non-Banking Financial Companies (NBFCs), Housing Finance Companies (HFCs), Pension Funds (PFs) and All India Financial Institutions (AIFIs).


The financial sector in the Indian economy

Stabilizing an unstable economy: agenda for reform The crisis in finance and the economy calls for a serious revaluation of the current set of policies. In this section, we turn to ideas for reforming the system, drawing on Minsky’s agenda for reforms. Any reform has to be approached with the knowledge that instability addressed by one set of reforms will emerge in a new guise after a time. There is no possibility that we can set things right once and for all. One can, however, develop a system where the flaws are less evident. What kind of reforms was Minsky suggesting? As we delve into the relevant parts of Minsky’s agenda for reforms focusing on the financial sector reforms and the role of the government, a critique of the present policy direction is also presented.

Reform of the financial structure •

One way of addressing the instabilities caused by the shadow banking sector is to have regulation by function rather than institution. If a shadow bank offers a financial product that is subject to regulation when offered by a bank, the shadow bank must also be regulated. This approach would impose similar costs and reduce “the race to the bottom”. The RBI, in recent times, has acknowledged that the regulation of the NBFC sector is weak. It has begun to move slowly in the direction of greater regulation for NBFCs.13 How successfully, only time will tell. On banking, Minsky favoured a policy to support small- to medium-sized banks as he believed that bank size is related to the size of firms with which they do business. He wanted the smaller size banks to provide a broader range of services required by their smaller customer base (Minsky, 1986). He didn’t, however, recommend such a structure for large banks and preferred a separation of the Glass–Steagall Act kind. Unfortunately, the Indian banking system is headed in a completely wrong direction. Take the issue of bank size. As part of banking reforms, PSBs have been merged to create fewer and larger banks. The short-term imperative is to showcase a better balance sheet, while the long-term aspiration is to be globally competitive.14 What is being ignored is that these too big to fail institutions are a threat to stability as the central bank would be forced to bail them out, fearing contagion and systemic risks. Large banks and financial institutions exaggerate the negative externalities and correlated exposures within the financial system. Their scale, complexity and interconnectedness imply that their resolvability becomes extremely difficult and hence the “too big to fail” conundrum. In direct contrast, there are no proper regulatory mechanisms and backstops for some of the smaller banks in the present system.15 The number of cooperative banks that fail every year presents a telling picture as to what extent the financial system is serving the public purpose.16 This is not unique to India. After each financial crisis, the biggest institutions are bigger than they had been, and they have far less competition. The government would 81

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backstop the “too big to fail” banks, which actually increases their competitive advantages, but would let smaller banks fail. Minsky believed that decentralization and favouring smaller institutions, plus maintaining exposure to risk (instead of originating to distribute model), could reorient institutions back towards relationship banking. He proposed a community development bank since many communities, lower-income consumers and smaller start-up firms are inadequately provisioned with these services. For the Indian banking system, which has a rich history of rural banking institutions, this is a familiar territory that needs to be revived. The experience of the past two decades of long-term capital financing through commercial bank lending shows that the strategy is fundamentally flawed. Since the bulk of the resources of the commercial banks are raised through short-term deposits, such long-term financing means unwarranted risks to the commercial banking sector. The unprecedented accumulation of NPAs on the balance sheets of PSBs indicates that there is a need to revive and strengthen the DFI model. The model of universal banking seems to have failed in the Indian context, where the public sector commercial banks lack the requisite skills and diligence required for financing long-term projects (Azad et al., 2017). A keen observer of banking behaviour, Minsky was in favour of banks borrowing at the discount window where the balance sheet of the borrowing bank is under the central bank’s direct scrutiny, rather than borrowing through the inter-bank market or borrowing through open market operations. By choosing which assets are eligible for “discounting”, a bank submits assets that the Fed “discounts” – that is, lends reserves against. This method would allow the central bank to more closely supervise banks and to favour safer bank activities by choosing what could serve as collateral against loans of reserves (Wray, 2016, 30). More generally, a closer scrutiny and supervision by the central bank is key to pre-empt procyclical credit cycle and build-up of financial risks. Finally, the overall size of the financial sector requires downsizing. Downsizing finance is necessary to ensure that capital development of the economy can be well done (Wray, 2016). Multilateral agencies who till recently were vigorously pushing financial liberalization and capital account convertibility have admitted the excesses of the present systems.17

Role of the government Stabilization of the financial sector in a downturn requires an expansionary fiscal push to stabilize aggregate demand, investment and profits (and vice versa in a boom). This understanding, unfortunately, has completely escaped policymakers in India. While the government has contributed to the recapitalization of PSBs, the role of the government in macro-stabilization is completely neglected. Instead, the recommended fiscal stance is almost always contractionary, as the 82

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government is bound by fiscal rules.18 The fear of crowding out private investment is one reason extended. What is overlooked is that crowding out is almost impossible when the economy is in a recession, liquidity is abundant and there is neither demand nor supply of credit from the banks to the private sector. Rather holding safe government debt would stabilize and strengthen the portfolio of the banks. The other reason provided against government borrowing relates to the accumulation of debt. Higher borrowing today would raise the debt servicing burden for future generations, which is partly true, but the alternative to not spending and stabilizing the economy is far worse. It must be stressed here that over the years the public debt to GDP ratio (centre and states combined) in India has climbed down while the private corporate debt has gone up substantially (Figure 4.5). Even household debt has gone up as a proportion to GDP. As Minsky argued, private debt is much riskier than government debt. A government-spending-led expansion would allow the private sector to expand without creating fragile balance sheets. Indeed, government deficits would boost profits and add safe government debt to private portfolios. Unfortunately, the fiscal responsibility and budget management target expects government debt to come down to 60% of the GDP in the medium term, which means that the government’s fiscal stance will remain conservative and procyclical. This in turn will mean that FINREBs will be called upon to provide an expansionary push to the rest of the economy, even as it maintains its autonomous character through financialization and other means. Downsizing finance and strengthening countercyclical fiscal policy is thus an antithesis to the current






























































60 40 20 0

General Government

Non-financial corporates


Figure 4.5  Total debt to GDP and its composition across sectors (%).

Source: Reserve Bank of India (RBI), Central Statistics Office (CSO), Bloomberg, NBFCs/HFCs company reports, CEIC, MOFSL as cited in Ecoscope, Motilal Oswal Group.


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policy dispensation and its economic logic. Minsky’s writings help us underline the fallacies in the mainstream arguments. What should be the nature of government spending? Minsky advocated targeted spending rather than pump-priming in general. Wray (2016) notes that Minsky never believed that the “rising tide lifts all boats”. He wanted targeted spending New-Deal-style job creation and support for consumption by workers. According to him, reducing unemployment, poverty and inequality would help to promote financial stability. Minsky saw the employer of last resort programme as a stabilizing alternative to the typical 1960s Keynesian approach that relied on a combination of “pump-priming” policy to encourage investment, plus welfare policies for the poor (Wray, 2016, 36). The employer of the last resort programme was to provide guaranteed employment at the minimum wage. The minimum wage would make no sense unless the government guaranteed employment at the minimum wage. Just like the lender of last resort role of the central bank, an employer of last resort (ELR) is necessary to set a base level for minimum wages. Such a policy would simultaneously ensure that a major part of the growth would be consumption-based, and therefore less unstable than an investment-led growth. In an economy assailed by unemployment such as ours, a policy of ELR covering both rural and urban needs immediate implementation. Another area of targeted public spending is infrastructure spending. A large part of the bad debt problem in the Indian financial sector is due to infrastructure loans to the private corporate sector. The private corporate sector was encouraged to invest in the infrastructure industry, particularly during the 11th plan period (2007–8 to 2011–12), whereas, previously, infrastructure and other core sector investments were made by the public sector. The poor record of the past decade should alert policymakers to the pitfalls of private-sector-led infrastructure spending. Rather, as Minsky and several others have argued, this is an arena that requires public investment. For India, the capital expenditure multiplier, which measures the response of GDP to a unit increase in capital expenditure, exceeds 2 (Bose and Bhanumurthy, 2015). Investment in public infrastructure can be an effective instrument for raising demand and releasing supply bottlenecks.

Conclusion Financial deregulation of the Indian economy has created conditions where the cycle of boom and bust could play out much more easily, even as finance consolidates its position through greater financialization. The transition from a stable to unstable debt structure as witnessed by the Indian economy confirms Minsky’s FIH. Since 2003–4, buoyant export growth, abundant foreign capital inflows and investment growth was accommodated by procyclical credit creation by public sector banks. It produced one of the largest credit booms in post-independence history, with credit concentrated in certain sectors and groups. Because of the euphoria (and certain government policies), PSBs financed investments that would otherwise be considered high risk. As many projects failed and the 84

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corporate stress rapidly increased, banks continued Ponzi financing. Eventually, with rising sub-standard and non-performing assets on the PSBs’ books, the economy transited to a phase of a prolonged slowdown in credit and economic activity. The weakness in the formal banking system shifted the locus of credit activity to an under-regulated shadow banking system, a sector which, by its very design is even more fragile and vulnerable to crisis. The NBFC crisis therefore comes as no surprise. The costs in terms of output and employment loss have been heavy. According to Minsky, big governments and big banks (central banks) are essential to contain instability. Drawing on relevant parts of Minsky’s writings, we have discussed the necessary reforms of the financial structure and fiscal policy for India. Along with the regulation of shadow banks, there is a need for greater supervision and monitoring of balance sheets of all financial institutions, including PSBs by the central bank. Small banks on universal banking models and large banks on narrow banking models are the logical solutions that would serve the development goals and help contain instability. There is a need to restore the DFIs to meet the long-term capital financing needs of the economy. We also argued that the central bank’s lender of last resort facility needs to extend to all types of banks rather than only the TBTF institutions. Stabilization of the financial sector in a downturn requires an expansionary fiscal push to stabilize aggregate demand, investment and profits (and vice versa in a boom). Among the fiscal policy reforms, the ELR function of the government is crucial, especially for a country like ours. Infrastructure financing is another important area for public spending. A government-spending-led expansion would allow the private sector to expand without creating fragile balance sheets.

Acknowledgements I am deeply thankful to Prof. Sunanda Sen for her comments on the first draft of the chapter.

Notes 1 Hyman P. Minsky (1991–96) is regarded as one of the most insightful theorists of the twentieth century in heterodox circles, whereas the mainstream world continues to ignore him or pays lip service. Minsky had a deep understanding of financial institutions and financial markets, using which he developed an alternative approach, diametrically opposite to the dominant neoclassical paradigm. 2 Minsky (1982). 3 Randall Wray, Minsky’s student and one of the leading post-Keynesian economists, presents Minsky’s ideas in plain, accessible language in a book titled, Why Minsky Matters? (Wray, 2016). We have dipped generously into this book to discuss Minsky’s theory. 4 In the Kalecki–Keynes view profits are not the result of the technical productivity of capital but are due to the types and sources of financed demand. 5 Goldsmith (1983). 6 Bose (2002).


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7 As per the Periodic Labour Force Survey, 2017–18, National Statistical Office. 8 Stressed firms are those firms whose profit income is less than the interest payment, i.e. the interest coverage ratio is less than 1. 9 Finance alone accounts for the major share of the GDP of FINREBS, based on 2004–5 NAS definition. 10 See Bose and Kumar, 2018 for more details. 11 AIFIs – NABARD, EXIM, NHB, SIDBI – constituted 23% of total assets, while NBFCs constituted the rest. A total of 9,642 NBFCs are registered with the RBI (as of 30 September 2019). In 2018–19 and 2019–20 (HY), the certificate of registration of more than 2000 NBFCs was cancelled by the RBI. Eighty-two of these are deposit-taking institutions (NBFCs-D) permitted to mobilize and hold deposits. Of the large number of non-deposit-taking NBFCs (NBFCs-ND), only 274 are identified as being systemically important (NBFCs-ND-SI) with asset size of Rs. 500 crore or more. 12 This period has seen major policy-induced shocks in demonetization (November 2016) and introduction of GST (July 2017). 13 RBI to steadily tighten NBFC regulations: Shaktikanta Das, 17 December 2019, Mint https://www​.livemint​.com​/companies​/news​/rbi​-to​-steadily​-tighten​-nbfc​-regulations​ -shaktikanta​-das​-11576604028452​.html. 14 To be part of the league of 100 top banks in the world is the aspiration that the government has articulated in its recent Economic Survey (GoI, 2019–20). 15 The collapse of the Punjab and Maharashtra Cooperative Bank and the freeze on depositors money has brought the issue to the forefront. 16 For details, see Deposit Insurance and Credit Guarantee Corporation website. 17 How much Finance is Too much: Lessons for Emerging Markets by Ratna Sahay, Presentation at the Indian Council for Research on International Economic Relations, New Delhi, 9 March 2016. This is based on http://www​.imf​.org​/external​/pubs​/ft​/sdn​ /2015​/sdn1508​.pdf. 18 Subramanian and Felman (2019), in analyzing the present financial crisis in terms of twin balance sheets, argue that there is no room for any fiscal stimulus.

References Azad, R., P. Bose and Z. Dasgupta (2017) “‘Riskless Capitalism’ in India: Bank Credit and Economic Activity”, Economic & Political Weekly, 52(31), 85–98. Bose, S. and N.R. Bhanumurthy (2015) “Fiscal Multipliers for India”, Margin: The Journal of Applied Economic Research, 9(4), 379–401. Bose, Sukanya (2002) Money and Finance in Deregulated Economies: The Indian Experience, PhD Thesis, http://hdl​.handle​.net​/10603​/29286 Bose, Sukanya (2005) “Regional Rural Banks: The Past and the Present Debate”, http:// www​.macroscan​.org​/fet​/jul05​/pdf​/RRB​_Debate​.pdf Bose, Sukanya and Abhishek Kumar (2018) “Financialisation in Contemporary Capitalism: An Inter-Sectoral Approach to Trace Sources of Instability in Finance, Real Estate and Business Services in India”, in The Changing Face of Imperialism: Colonialism to Contemporary Capitalism, edited by Sunanda Sen and Maria Cristina Marcuzzo, London and New York: Routledge, pp. 267–294. Chandrasekhar, C.P. (2014) “Development Finance in India”, https://in​.boell​.org​/sites​/ default​/files​/uploads​/2014​/03​/development​_finance​_in​_india​.pdf Chandrasekhar, C.P. (2020) “Revisiting the NBFC Crisis”, Economic & Political Weekly, 55(2), 10–11.


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Chandrasekhar, C.P. and J. Ghosh. (2000) “Financial Liberalisation and Bank Fragility”, https://www​.macroscan​.org​/the​/finance​.htm Credit Suisse (2012) “India Financial Sector”, https://research​-doc​.credit​-suisse​.com​/ docView​?language​=ENG​&source​=emfromsendlink​&format​=PDF​&document​_id​ =991849241​&extdocid​=991849241​_1​_eng​_pdf​&serialid​=6D4​Mc02​hhUT​0waP​ccTs​ ATBcZQ​%2bzXynsFIAQCD3qJ44E​%3d Dasgupta, Zico (2020) “Economic Slowdown and Financial Fragility: The Structural Malaise of India’s Growth Process”, Economic and Political Weekly, 55(13), 46–53. Gerschenkron, A. (1962) Economic Backwardness in Historical Perspective: A Book of Essays, Cambridge: MA: Harvard University Press. GoI (2020) Economic Survey, 2019–20. New Delhi: Ministry of Finance, GoI. Goldsmith, R.W. (1983) Financial Development of India: 1860–1977, Delhi: Oxford University Press. Gopinath, Shyamala (2011) “Macroprudential Approach to Regulation: Scope and Issues”, in Growth and Finance: Essays in Honour of Dr. C. Rangarajan, edited by Sameer Kochhar, New Delhi: Academic Foundation, pp. 163–181. Jay, L.S. and A. David (1983) Profits and the Future of American Society, New York: Harper and Row. Kalecki, Michael (1965) Theory of Economic Dynamics, London: Allen and Unwin. Keen, S. (2013) “A Monetary Minsky Model of the Great Moderation and the Great Recession”, Journal of Economic Behavior and Organization, 86 (Feb., 2013), 221–35. Keynes, J.M. (1937) “The General Theory of Employment”, The Quarterly Journal of Economics, 51(2) (Feb., 1937), 209–23. Keynes, J.M. (1972) Essays in Persuasion, The Collected Writings of John Maynard Keynes, Vol. IX, London: MacMillan, St. Martin’s Press. Kregel, Jan (2008) “Using Minsky’s Cushions of Safety to Analyze the Crisis in the U. S. Subprime Mortgage Market”, International Journal of Political Economy, 37(1), 3–23, Kregel, Jan (2010) “Is this the Minsky Moment for Reform of Financial Regulation?”, Levy Economics Institute Working Paper No. 586. Krugman, Paul R. (2009) The Return of Depression Economics and the Crisis of 2008, New York: W.W. Norton. Malhotra, R.N. (1990) The Evolving Financial System in 50 Years of Central Banking: Governor’s Speak, Mumbai: RBI, 1997. McCulley, Paul (2009) “The Shadow Banking System and Hyman Minsky’s Economic Journey”, in Insights into the Global Financial Crisis, edited by Laurence B. Siegel, Charlottesville, VI: Research Foundation of CFA Institute, 224–56. Minsky, Hyman P. (1975) “Suggestions for a Cash Flow-Oriented Bank Examination”, Hyman P. Minsky Archive, 17. https://digitalcommons​.bard​.edu​/hm​_archive​/17 Minsky, Hyman P. (1982) Can “It” Happen Again?, Armonk, NY: M. E. Sharpe. Minsky, Hyman P. (1986) Stabilizing an Unstable Economy, London and New Haven: Yale University Press. Minsky, Hyman P. (1987) “Securitization”, https://digitalcommons​.bard​.edu​/hm​_archive​/15 Minsky, Hyman P. (1992) “The Financial Instability Hypothesis”, Working Paper No. 74, The Jerome Levy Economics Institute of Bard College. Minsky, Hyman P. (1992a) “Financial Instability and APT Bank Supervision”, Hyman P. Minsky Archive, 470. https://digitalcommons​.bard​.edu​/hm​_archive​/470 Patnaik, Prabhat (2019) “Fifty Years after Bank Nationalization”, https://www.networkideas. org/news-analysis/2019/07/fifty-years-after-bank-nationalization/#:~:text=Fifty%20


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years%20ago%20on%20July,private%20banks%20had%20been%20 nationalized.&text=From%20the%20very%20beginning%20however,financial%20 sector%20from%20its%20hands. Rajan, Raghuram G. (2014) “Saving Credit”, speech byRBI Governor at the Third Dr Verghese Kurien Memorial Lecture at IRMA, Anand, 25 November, viewed on 8 February 2016, https://rbi​.org​.in​/scripts​/BS​_SpeechesView. aspx?Id=929 RBI (1998) Committee on Banking Sector Reforms (Chairman: M. Narasimham). RBI (2019) Trend and Progress of Banking in India. RBI (2020) Financial Stability Report, June. Sen, Sunanda (2014) Dominant Finance and Stagnant Economy, New Delhi: Oxford University Press. Sen, Sunanda (2020) “Financialization, Speculation and Instability”, in The International Handbook of Financialization, edited by Philip Mader, Daniel Mertens and Natascha van der Zwan. London: Routledge, pp. 448–57. Sen, Sunanda (2020a) “Investment Decisions under Uncertainty”, Journal of Post Keynesian Economics, 43(2), 267–80. Sen, Sunanda and Zico DasGupta (2015) “Financialization and Corporate Investments: The Indian Case”, Economics Working Paper Archive wp_828, Levy Economics Institute. Subramanian, Arvind and Josh Felman (2019) “India’s Great Slowdown: What Happened? What’s the Way Out?”, CID Faculty Working Paper No. 370, December 2019. Wray, L. Randall (2009) “The Rise and Fall of Money Manager Capitalism: A Minskian Approach”, Cambridge Journal of Economics, 33(4), 807–28. Wray, L. Randall (2016) Why Minsky Matters? An Introduction to the Work of a Maverick Economist, Princeton and Oxford: Princeton University Press.



Saumita Paul and Malabika Roy Introduction The present chapter analyzes whether there is any possible link between non-performing assets (NPA) and priority sector lending in the Indian banking industry. According to the RBI’s definition, “An asset, including a leased asset, becomes non-performing when it ceases to generate income for the bank”. From 31 March 2004, the RBI started the “90 days’ overdue” norm1 for identifying NPA and adopting international best practices to ensure greater transparency (Master Circular – Prudential Norms on Income Recognition, Asset Classification and Provisioning – Pertaining to Advances, 1 September 2001 and 1 July 2015, RBI). The loans and advances that the banks are disbursing to the various segments of society are the sources of NPA. This lending can be divided into two broad categories: priority sector lending and non-priority sector lending. The idea of priority sector lending was introduced in 1970 to ensure credit facilities to those weaker sectors and segments of society that could least afford to pay high rates of interest. The definition of the priority sector has always been changed from time to time. The latest definition of the priority sector includes: agriculture; micro, small and medium enterprises; export credit; education; housing; social infrastructure; renewable energy; and others (Master Direction – Priority Sector Lending-Targets and Classification, RBI, Updated as of 5 December 2019). So the priority sector is considered important for the inclusive development of the country. In this respect, the RBI mandates that all scheduled commercial banks must ensure that 40% of the adjusted net bank credit goes to the total priority sector advances and the rate of interest is directed by the RBI. Priority sector lending involves small and medium-sized loans, and the borrowers are typically the producers who are most vulnerable to shocks, and hence their creditworthiness is suspect. It is argued that they will face difficulty when repaying time-bound loans. However, priority sector lending is mandatory


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under RBI rules. Hence, it can be potentially considered as one of the main reasons for higher NPA in India. So, in our chapter, we are going to address this issue: whether the mounting NPA is related to priority sector lending. The chapter is organized as follows. The next section contains a literature review. This is followed by a description of the database and methodology of the present study. The subsequent section shows both a graphical representation and the results of the Spearman correlation test between two variables: the GNPA to gross advances ratio and the priority sector advances to gross advances ratio. The ensuing section presents the results of a panel regression to show whether the priority sector advances ratio is responsible for a higher GNPA ratio. The last section concludes the chapter.

Review of the literature The problem with the non-performing assets of Indian commercial banks is a well-researched area, and several studies have considered the issue of priority sector lending. Shajahan (1999) compared the proportion of total NPA in the priority sector and non-priority sector of public sector banks from 1995 to 1999. The study found that the non-priority sector bears around a 74% share of the incremental NPA, whereas it is only 23% for the priority sector, confirming that the non-priority sector was responsible for the higher NPA. Ramesh (2017) and Mahesha (2019) showed that the share of priority sector NPA to total NPA was higher up until 2011, but after 2011 the ratio of non-priority sector NPA to total NPA started to increase in public sector banks. Addressing the same issue, Pandey et al. (2013) found that from 2009 to 2012 NPA of the priority sector was higher than the NPA of the non-priority sector for public sector banks compared to private sector banks. Kaur and Kumar (2018) examined the NPA data for ten public sector banks and ten private sector banks from 2001–2 to 2013–14. They divided the entire period into two parts: the pre-crisis period (2001–2 to 2007–8) and the post-crisis period (2008–9 to 2013–14). They found that both the public and private sector banks experienced a higher level of priority sector NPA during the precrisis period. The non-priority sector NPA was decreasing for the public sector banks and increasing for the private sector banks. However, during the post-crisis period, the priority sector NPA was stable, unlike the NPA of the non-priority sector. Goyal et al. (2016) performed both a pooled and panel regression analysis to examine the effects of priority sector lending (PSL) on NPA from 2001 to 2013, utilizing the group data of public and private sector banks together. The pooled regression results showed that aggregate PSL, agriculture PSL and SSI PSL factors had a significant positive impact on NPA, and the two-way fixed-effect panel regressions showed that public sector banks were less efficient at controlling NPA than private sector banks. Das (2002) investigated the effect of NPA on the productivity of public sector banks and the effect of priority sector lending on NPA for 1995–96 to 2000–1. By using a two-stage least square regression, he observed an insignificant negative impact of NNPA on the bank’s productivity along with 90

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an insignificant impact of the priority sector loan ratio on NNPA. Sen and Ghosh (2005) analyzed the composition of NPA in the priority and non-priority sectors of public sector banks in India from 1997 to 2004. They found a gradual declining share of priority sector NPA at an aggregate level over the period. Bhattacharya (2013) showed that in spite of an increased demand and the evidence of stable NPA in the MSME sector the credit flow declined during the post-reform period of the last two decades, since the banks consider giving credit to the MSE sector much riskier than to the corporate sector, and the situation did not improve even after the regulatory and administrative directives of financial inclusion, after 2010. The report “Who Are the Non-Performers?” in the Economic & Political Weekly (25 July 2000) also confirmed that it’s very likely that priority sector lending is not responsible for an increase in NPA. Rao et al. (2006) analyzed the reason for declining commercial bank lending to the small scale industry from 39% to 24% during the period of 1992 to 2004. They observed that the banks were found to reduce the credit disbursement to SSIs after experiencing a deficiency in the banks’ return on assets (RoA) and sometimes to evade the problem of non-performing SSI loans. Finally, several studies identified factors other than priority sector or non-priority sector lending as a reason for the higher NPA. Shajahan (1998) observed that by changing the definition from gross NPA in 1996 to net NPA in 1997, the RBI report on Trend and Pattern of Banking in India (1997) misrepresented the real decline in NPA during the period 1996–97, and this also played a crucial role in over-reporting the proportion of priority sector in total NPA in 1997. Banerjee and Duflo (2004) showed that the amount of NPA that the Indian banks have is not the result of their sectoral lending policies. Rather it comes from the banks’ mistaken judgement. Sometimes a higher NPA is the result of reducing loans to borrowers who really require them and can utilize them better. While NPA has been a widely discussed issue, our study differs from the existing studies in several ways. In our study, we considered data from the individual public and private sector banks in operation during the entire period, whereas the earlier studies either analyzed the banks’ group-wise or used a number of selected banks. So our study is more encompassing as well as more comprehensive. Further, we used the relative value of GNPA, and priority sector advances, i.e. the GNPA to gross advances ratio and priority sector advances to gross advances ratio, as the two main variables to offset the effects of gross advances, and this makes our study distinct from all others. Also, there was a sudden rise in NPA from 2015–16 in the Indian banking sector. Our analysis covers this crucial period as we considered the period 2005 to 2019 in our study. Finally, we develop our hypothesis from an exploratory data analysis and then check the hypothesis using rigorous regression analysis. This approach was not also adopted in earlier studies.

Database and methodology The period under consideration for the analysis was 2005 to 2019. The bankwise annual data were obtained from the Statistical Tables Relating to Banks 91

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in India2 (2005–19) – Reserve Bank of India: Database for the Study Period “2005–2019”. During the time periods considered, some banks started operation, some banks stopped operation, while some merged with one another. For consistency of the calculations, our panel included only those banks which continued to operate during the entire period. The total number of banks under consideration was 39, of which 21 were public sector banks and 18 were private sector banks. For the rank correlation test, we used a total of 585 data points, considering all the private and public sector banks together, whereas a panel regression model was estimated using a total of 507 data points. The details are discussed later in the model specification section. We used Spearman’s rank correlation test to check whether the banks’ GNPA ratio (GNPA to gross advances ratio) and priority sector advances ratio (priority sector advances to gross advances ratio) were correlated to each other (i.e.) to see whether there existed any possible links between them. The panel regression analysis was performed with 39 banks to check whether the gross NPA ratio was affected by the priority sector advances ratio. More precisely, we wanted to examine whether the rising GNPA resulted from the augmented priority sector lending in the Indian economy in the period of 2005 to 2019.

An exploratory analysis of NPA and Priority Sector Lending In this section, we carry out an exploratory analysis of the pattern of NPA and priority sector lending in India for the period 2005–19. A graphical representation of the variables In Figure 5.1, both the aggregate GNPA and aggregate GNPA to gross advances ratio are plotted, taking all the public and private sector banks simultaneously 1200000



12 10







200000 0 2004

2 2006





Figure 5.1  GNPA and GNPA ratio: 2005–19. Source: Statistical Table Relating to Banks, RBI.




GNPA ratio


0 2020

Priority sector lending and high NPA 

from 2005 to 2019 to get the trend of the GNPA and GNPA ratio (GNPA to gross advances ratio) for this time period. Figure 5.1 shows that the aggregate GNPA had an increasing trend starting from 2008 to 2018, whereas the GNPA ratio (GNPA to gross advances ratio) had a downward trend initially that lasted up until 2009 and then it started to increase from 2012. There was a sudden fall both in GNPA and GNPA ratio in 2019, after increasing at a higher rate from 2016 to 2018. The trends of the aggregate GNPA and GNPA ratio are different because a higher amount of bank lending comes with a larger number of defaulters, which implies that an increase in gross advances leads to an increase in GNPA (Ramesh, 2017; Mahesha, 2019). However, higher bank loans are a precondition for a growing and productive economy. Hence, efficient management requires to control the NPA without reducing the amount of gross advances. Therefore, the GNPA to gross advances ratio is the variable of our choice to neutralize the effect of higher advances. Figure 5.2 depicts the trend of aggregate priority sector advances, and priority sector advances to gross advances ratio from 2005 to 2019, considering all 39 banks together. From the graph below, we find that priority sector advances show an upward trend indicating a smooth increase during the entire period. In contrast, the priority sector advances to gross advances ratio show ups and downs together with an overall linear downward trend until 2013 and then a more or less stable trend for the rest of the period. So it can be inferred that although the priority sector advances increase in absolute values, they actually decrease as a ratio in gross advances. More specifically,

3500000 3000000

35 30







1000000 500000

10 5

0 2004








Priority sector advances Priority sector advances ratio Linear (Priority sector advances ratio)

Figure 5.2  Priority sector advances and priority sector advances ratio: 2005–19. Source: Statistical Table Relating to Banks, RBI.


0 2020

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the rate of increase in gross advances over time is higher than that of priority sector advances. Hence, from the above figures, it can be said that as a whole the GNPA increases more rapidly than priority sector advances between 2005 and 2019. So, apparently, a higher GNPA ratio did not result from priority sector lending. Spearman rank correlation test We use Spearman’s test to check whether the GNPA ratio and priority sector advances ratio are correlated to each other. The Spearman test is a non-parametric (distribution-free) rank statistic proposed by Spearman in 1904 to measure the strength of the associations between two ranks. This rank correlation coefficient is defined by:

r = 1-

6 å di 2 n(n 2 - 1)

where d is the difference in rank under two different rankings of variables under consideration, and N is the total number of observations. In this case, the difference in rankings refers to two rankings in two different variables. Here we ranked all the banks together for each year from 2005 to 2019 according to these two variables: the GNPA ratio and priority sector advances ratio. Then we calculated the mean rank for each of the banks based on these two variables for the entire time period, i.e. from 2005 to 2019, and performed the Spearman rank correlation test. Table 5.1 shows the results. We calculated the rank correlation coefficients for all the banks together, as well as for public sector banks and private sector banks separately. The results are reported in Table 5.1. The results from Table 5.1 show that the correlation value is very low irrespective of whether we take all the banks together or we take the private sector and public sector banks separately. For the public sector banks, the rank correlation actually takes a negative value. These findings corroborated that the GNPA ratio may be independent of the priority sector lending ratio. In the next section, we check this hypothesis using a panel regression analysis.

Table 5.1  Spearman rank correlation: GNPA ratio and priority sector advances ratio Category of Bank

No. of observations

Spearman’s rho

Prob> |t|

Public sector banks Private sector banks All banks

21 18 39

–0.0222 0.2261 0.0381

0.9240 0.3670 0.8179

Source: Author’s calculation


Priority sector lending and high NPA 

NPA and priority sector lending: a panel regression analysis Based on the exploratory analysis above, we had a null hypothesis as follows: Hypothesis: When taken as a ratio of gross advances, changes in NPA is not driven by changes in priority sector lending. Following Shajahan (1999), we focused on the relationship between increments or the changes in the two ratios, as the growth or changes in the values are more relevant than the level values. The level values could reflect historical effects rather than current effects. In this section, we define the regression relation and report the results. The model specification In this subsection, we develop the following panel regression model to analyze whether rising GNPA results from augmented priority sector lending:

Yit = ai + b1 X1it -1 + b2 X 2it + b3 X 2it * d1 + b4 X 3it + U it (5.1)

Here і = 1…….39, and t = 1……13. The panel regression model (Gujarati, 2004) represents either a fixed-effect model or a random-effect model. Equation (5.1) can represent a fixed-effect model with the assumption that the slope coefficient is constant, but the intercept varies by individual banks. For the fixed-effect model, the intercept term αi identifies the time-invariant cross-sectional variation. However, in the random-effect model, we can define the term αi as (α1 + Vi). Where α1 represents the common mean value of the intercept and Vi represents the individual differences in the intercept value of each bank. Substituting the value of αi in Equation (5.1), we can obtain the following equation:

Yit = a1 + b1 X1it -1 + b2 X 2it + b3X 2it * d1 + b4 X 3it + Vi + U it

This can be rewritten as:

Yit = a1 + b1 X1it -1 + b2 X 2it + b3 X 2it * d1 + b4 X 3it + Wit (5.2)

where, Wit = Vi + Uit represents the composite error term. Vi represents the individual error component, and Uit is the combined time series and cross-section error component. Here we want to check the relation between changes in GNPA ratio and the changes in priority sector lending ratio. So the main variables of interest are: Y: Percentage change in GNPA ratio.3 X1: Lag of percentage change in priority sector lending ratio.4 95

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Here we used one period lagged value of the priority sector lending ratio because there could be a problem of simultaneity in our model in the following way: as the priority sector lending increases, it can potentially raise the GNPA (Goyal et al., 2016), and at the same time when a bank’s GNPA increases its management can try to reduce priority sector lending in an attempt to control its GNPA level, because the bank may find it difficult to ensure repayment of priority sector loans (Bhattacharya, 2013). This leads to the endogeneity problem, which can be taken care of by replacing this independent variable with the lagged value as the instrumental variable. Besides addressing the endogeneity problem, we also performed the VIF (variance inflation factor) test to check the multi-collinearity among the variables. The result showed that there was no multi-collinearity problem in this model. We also needed to control for other factors that could potentially affect GNPA. So we included the following variables in the regression equation to control for other effects. X2: The size of the bank. Here we used the logarithm of the bank’s asset size as a proxy variable for bank size. Regehr and Sengupta (2016) found a significant positive effect between bank size and a bank’s profitability using the logarithm of the bank’s total assets as a proxy variable of bank size. Udegbunam (2004) examined the effect of bank size (measured by logarithm of the bank’s asset size) on the expected return on equity. He showed that an increase in bank size leads to an increase in the expected return on equity, which significantly reduces the market risk of the bank’s stock return. Abor and Biekpe (2007) inferred that firm size had a significant positive relation to the bank–debt ratio by representing the firm size as a logarithm of total assets. Kishan and Opaila (2000) concluded that in the presence of contractionary monetary policy the large banks with adequate capital size were less affected and retained their loan growth and thus maintained various alternative sources for raising funds. Taking into account the above studies, we found that bank size could be an important determinant of NPA. The reason is that the larger the size of the bank, the larger is its loan portfolio. We have already argued that a larger loan portfolio can potentially increase the extent of NPA. So this variable is expected to have a positive effect on changes in NPA. We also used here an interactive dummy variable to allow for the marginal effect of a bank’s asset size on the changes in GNPA ratio, as it is different for the public sector banks and the private sector banks. So, the model allowed us to see whether the marginal effects of bank size were different between these two groups. In other words, whether the effect of larger asset size was more pronounced for public sector banks, which traditionally follow developmental goals rather than a profit motive. d1 is the dummy variable for a bank’s ownership type: whether the bank is a private sector or public sector bank. d1 = 1, for public sector banks = 0, otherwise 96

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X3: Percentage change in the bank’s management quality, where the management quality is defined as total expenses divided by total assets. The bank’s expenses/assets ratio was used as the proxy variable of management quality (Udegbhnam, 2004) in our model to control for its possible effect on GNPA. Generally, the less the expenses to assets ratio, the more efficient the management quality, which leads to a lower GNPA ratio through more efficient management. The rationale behind this variable was that the ability of the bank’s employees to monitor and control the loan account could control the extent of NPA and thus increase its profits as well. Using a similar concept, Dhar and Bakshi (2015) used profit per employee as a measure for management efficiency of the banks, which was also used under the CAMELS model (M stands for management) by the RBI, to see the effects of management efficiency on controlling the NPA. However, Udegbhnam (2004) came up with a different result that showed a significant positive effect of a higher expenses to assets ratio on the bank’s stock returns. According to him, one plausible reason for this may be that the increase in the bank’s expenditure in terms of employee salaries, information technology, etc., leads to an increase in the bank’s operational efficiency and profitability. However, a bank can achieve a lower expenses to assets ratio simply by either decreasing its expenses or by increasing its assets. But an increase in assets also calls for an increase in loans and advances, which in turn leads to higher NPA, and hence is a requirement of management efficiency, which enables the bank to control its NPA level without sacrificing its asset size. Although our total dataset was from 2005 to 2019, the model was taken from 2007 to 2019 for the purpose of our analysis for two reasons. First, we considered the percentage change for both the variables, i.e. GNPA ratio and priority sector advances ratio in each year with respect to the previous year. Due to the unavailability of data prior to 2005, the percentage change was calculated from 2006 onwards. Second, we took one lag period data of the percentage change in the priority sector lending ratio, corresponding to each year’s percentage change in GNPA ratio. Since our data set of the percentage change was limited to 2006–19 in the model, we arrayed the percentage change in GNPA ratio data in 2007 against the percentage change in priority sector lending ratio data in 2006 and so on. Regression results The null hypothesis we wanted to establish was that a higher GNPA ratio did not result from the priority sector lending ratio. In this section, we present the results of the regression analysis. The Hausman test supports a fixed-effect model. The results of the fixed-effect panel regression model are reported in Table 5.2. In Table 5.3 we also report the results of the random-effect model. We got the same results from both the fixed-effect and the random-effect model that the growth or changes in the GNPA ratio was not driven by the changes in 97

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Table 5.2  Fixed-effect model Independent variables

Coefficients (Std. Err.)

Lag of % change in priority sector lending ratio Ln asset size Ln asset * d1 % change in Management quality Constant No. of observations No. of banks Chi-Sq statistic (Hausman test) Prob > chi-Sq

–0.091 (0.073) 13.682***(4.815) 35.684***(8.18) 1.031***(0.238) –377.09***(50.279) 507 39 1006.06 0.00

Note: *** indicates 1% level of significance

Table 5.3  Random-effect model Independent variables

Coefficients (Std. Err.)

Lag of % change in priority sector lending ratio Ln asset size Ln asset * d1 % change in Management quality Constant No. of observations No. of banks

–0.036 (0.071) 7.079***(2.102) –0.46 (0.479) 0.849*** (0.241) –63.649***(22.625) 507 39

Note: *** indicates 1% level of significance

the priority sector lending ratio. Since the Hausman statistic supported the fixedeffect model, we interpreted the results as in Table 5.2. The bank size, interactive dummy variable and the percentage change in management quality had a significant impact on the percentage change in the GNPA ratio. The coefficient of the bank size showed its marginal effect on changes in the GNPA ratio for the private sector banks, whereas the coefficient of the interactive term indicated the differential effects on public sector banks. The positive coefficient of the bank size corroborated the fact that the larger the size of the bank, the higher the change in its GNPA ratio. A larger asset size means a larger portfolio of loans, which in turn results in a larger growth in the GNPA ratio. So at some level the result seems to suggest more loans give rise to larger growth in the GNPA ratio. The significantly positive value of the dummy implied that the marginal effect of size was larger for public sector banks than for private sector banks, as we had expected. The loans given by public sector banks were more driven by government intervention and development motives than profit motives. Possibly this aspect of the operation of public sector banks drives the marginal effect of the increase in asset size being more pronounced for the public sector banks than for the private sector banks. 98

Priority sector lending and high NPA 

In the result, a significant positive coefficient of the percentage change in management quality indicated that a positive change in the expenses to asset ratio raised the GNPA ratio of a bank. Our consulted literature also supported this view because the increase in a bank’s expenditure to asset ratio is a sign of deteriorating management quality, which in turn leads to an increase in NPA. The coefficient of the constant term in the fixed-effect model captured the individual-specific variation keeping the time-invariant. In our model, this variation may have resulted from the unique qualities of each bank, for example, the working atmosphere inside the bank, the relationship between employer and employee, etc. However, the significant negative impact of the constant term on the percentage change in the GNPA ratio expanded the importance of the bank’s individual special features.

Conclusion The fallacy regarding priority sector lending as the factor responsible for higher NPA impedes credit growth in essential sectors of the Indian economy. Despite contributing a significant share in GDP and generating substantial employment, the priority sector suffers from inadequate attention. Even after adopting a policy of financial inclusion, poor farmers still have to borrow money from informal sources rather than the nationalized banks to meet their credit requirements, compelling them to pay a higher interest rate. In our chapter, we examined whether priority sector lending was truly responsible for higher NPA, or whether this was just a myth. We used several methods for this analysis, and in every case the result supported our hypothesis that growth in NPA was not driven by growth in priority sector lending. The graphical representation of the aggregate GNPA and aggregate priority sector advances from 2005 to 2019 shows that the trend in priority sector lending was steeply increasing for the entire period, whereas in the GNPA it was fluctuating, implying a disparity between the trends of these two variables. The trends of GNPA ratio and priority sector advances ratio are also incompatible with each other. As a result, no such relationship between these variables can be established from this graphical analysis. Also, we find no correlation between the mean ranks of the bank’s GNPA ratio and priority sector advances ratio when we compute the ratio for the public and private sector banks separately as well as jointly, which also establishes that there is no relationship between the two variables. Finally, the panel regression analysis offers an insignificant coefficient for the percentage change in priority sector advances both in the fixed-effect and the random-effect model, implying that the changes in priority sector advances were not liable for the mounting NPA. Based on the fixed-effect model, the other explanatory variables, i.e. the bank’s size, the interactive dummy variable (bank’s size multiplied by the bank’s ownership dummy) and the percentage change in bank’s management quality had a significant positive impact on the percentage 99

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change in GNPA ratio. The interactive term indicates that the public sector banks were more prone to a higher GNPA ratio due to increased asset size. The coefficient of the percentage change in managerial quality once again confirms that it was the operational efficiency of the banks that controlled the level of GNPA ratio. Moreover, the coefficient of the constant term shows a significant effect of each bank-specific quality in reducing the GNPA ratio. Therefore, we can conclude that it is not priority sector lending that jeopardizes the Indian banking system with its increasing NPA level. Some of the literature suggests that non-priority sector NPA is much higher than priority sector NPA. So, it is time to stop blaming the priority sector for this crisis and to channel adequate funds to all those sectors which require it the most so that the economy can experience a more equitable and inclusive as well as a higher development process. As the problem of NPA is increasing day by day and results in a weak financial system in the Indian economy, further research is necessary to identify the factors that are actually accelerating the growth of NPA and prevent them from causing any further damage.

Appendix    Table 5.A1  List of the public sector banks No.

Name of the bank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Allahabad Bank Andhra Bank Bank of Baroda Bank of India Bank of Maharashtra Canara Bank Central Bank of India Corporation Bank Dena Bank IDBI Bank Ltd.* Indian Bank Indian Overseas Bank Oriental Bank of Commerce Punjab and Sind Bank Punjab National Bank State Bank of India Syndicate Bank UCO Bank Union Bank of India United Bank of India Vijaya Bank

*IDBI Bank was privatized on 21 January 2019. In this chapter, we have continued the consideration of IDBI Bank as a public sector bank because our data period ends on 31 March 2019.


Priority sector lending and high NPA 

Table 5.A2  List of the private sector banks No.

Name of the bank

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

Axis Bank Ltd. Catholic Syrian Bank Ltd. City Union Bank Ltd. DCB Bank Ltd. Federal Bank Ltd. HDFC Bank Ltd. ICICI Bank Ltd. Indusind Bank Ltd. Jammu and Kashmir Bank Ltd. Karnataka Bank Ltd. KarurVysya Bank Ltd. Kotak Mahindra Bank Ltd. Lakshmi Vilas Bank Ltd. Nainital Bank Ltd. RBL Bank Ltd. South Indian Bank Ltd. Tamilnad Mercantile Bank Ltd. Yes Bank Ltd.

Notes 1 Prior to 31 March 2004, the overdue period was four quarters in 1993, three quarters in 1994 and 180 days from 31 March 1995 onwards. The overdue norm of 90 days is considered for the recognition of NPA in case of the term loan, the bills purchased and discounted, the accounts in respect of an overdraft/cash credit, derivative transaction, an account in case of interest payment and any amount of liquidity facility. However, for the short duration crop and the long duration crop, the overdue periods are two crop seasons and one crop season, respectively. 2 Statistical Tables Relating to Banks are available on the Reserve Bank of India website, https://dbie​.rbi​.org​.in​/DBIE​/dbie​.rbi​?site= publications, accessed on 5 May 2020. 3 Percentage change in GNPA ratio = [(GNPA ratio in year t – GNPA ratio in year t – 1)/ GNPA ratio in year t – 1]*100. 4 Lag of percentage change in priority sector lending ratio = [(Priority sector lending ratio in year t – 1 – priority sector lending ratio in year t – 2)/ Priority sector lending ratio in year t – 2]*100.

References Abor, Joshua and Nicholas Biekpe (2007), “Small Business Reliance on Bank Financing in Ghana”, Emerging Markets Finance and Trade, Volume 43, No. 4; pp. 93–102. Banerjee, Abhijit and Esther Duflo (2004), “What Do Banks (Not) Do?”, Economic and Political Weekly, Volume 39, No. 38; pp. 4212–13. Bhattacharya, Achintan (2013), “Credit Retrogression in the Micro and Small Enterprise Sector”, Economic and Political Weekly, Volume 48, No. 35; pp. 105–14. Das, Abhiman (2002), “Risk and Productivity Change of Public Sector Banks”, Economic and Political Weekly, Volume 37, No. 5; pp. 437–48. Dhar, Satyajit and Avijit Bakshi (2015), “Determinants of Loan Losses of Indian Banks: A Panel Study”, Journal of Asia Business Studies, Volume 9, No. 1; pp. 17–32.


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Economic and Political Weekly (2000), Who Are the Non-Performers? Volume 35, No. 49; p. 4292. Goyal, Neha, Agrawal, Rachna and Renu Aggarwal (2016), “Two Way Fixed Effect of Priority Sector Lending (Sector Wise) on Non Performing Assets of Indian Commercial Banks”, International Journal of BRIC Business Research, Volume 5, No. 1; pp. 1–15. Gujarati, Damodar N (2004), Basic Econometrics, New York: McGraw-Hill. Kaur, Manvir and Rohit Kumar (2018), “Sectoral Analysis of Non Performing Assets during Pre and Post Crisis Period in Selected Commercial Banks”, Pacific Business Review International, Volume 11, No. 3; pp. 34–41. Kishan, Ruby P. and Timothy P. Opiela (2000), “Bank Size, Bank Capital, and the Bank Lending Channel”, Journal of Money, Credit and Banking, Volume 32, No. 1; pp. 121–41. Mahesha, N. M. (2019), “An Analysis of NPAs in Priority and Non-Priority Sectors with respect to Public Sector Banks in India”, International Journal of Applied Research, Volume 5, No. 1; pp. 179–82. Pandey, Shruti J., Tilak, Vishakha G. and Bipin Deokar (2013), “Non-Performing Assets of Indian Banks: Phases and Dimensions”, Economic and Political Weekly, Volume 48, No. 24; pp. 91–93. Ramesh, Kandela (2017), “Analysis of Priority and Non-Priority Sector NPAs of Indian Public Sectors Banks”, Special Issue of IOSR-JBM International Conference on Paradigm Shift in Taxation, Accounting, Finance and Insurance, Volume 5; pp. 56–61. Rao, K. S. R., Das, Abhiman and Arvind Kumar Singh (2006), “Commercial Bank Lending to Small-Scale Industry”, Economic and Political Weekly, Volume 41, No. 11; pp. 1025–33. Regehr, Kristen and Rajdeep Sengupta (2016), “Has the Relationship between Bank Size and Profitability Changed?”, Economic Review, Volume 101, No. 2; pp. 49–72. Reserve Bank of India (2001), “Master Circular – Prudential Norms on Income Recognition, Asset Classification and Provisioning – Pertaining to Advances”, Reserve Bank of India, Department of Banking Operations & Development, Central Office, Mumbai; DBOD No. BP.BC/20/21.04.048/2001-2002. Reserve Bank of India (2015), “Master Circular – Prudential Norms on Income Recognition, Asset Classification and Provisioning Pertaining to Advances”, Reserve Bank of India, Department of Banking Regulation, Central Office, Mumbai; RBI/2015-16/101, DBR. No.BP.BC.2/21.04.048/2015-16. Reserve Bank of India (2019), “Master Direction – Priority Sector Lending – Targets and Classification”, Reserve Bank of India, Financial Inclusion Development Department, Central Office, Mumbai; RBI/FIDD/2016-17/33, Master Direction FIDD. CO.Plan.1/04.09.01/2016-17. Sen, Sunanda and Soumya Kanti Ghosh (2005), “Basel Norms, Indian Banking Sector and Impact on Credit to SMEs and the Poor”, Economic and Political Weekly, Volume 40, No. 12; pp. 1167–80. Shajahan, K. M. (1998), “Non-Performing Assets of Banks: Have They Really Declined? And on Whose Account?”, Economic and Political Weekly, Volume 33, No. 12; pp. 671–74. Shajahan, K. M. (1999), “Priority Sector Bank Lending: How Useful?”, Economic and Political Weekly, Volume 34, No. 51; pp. 3572–74. Udegbunam, Ralph I. (2004), “Asset Portfolio Composition, Size, and Bank Stock Risk: Evidence from Nigerian Commercial Banks”, African Review of Money, Finance and Banking, Volume 18; pp. 5–30.


6 AN EMPIRICAL EXPLORATION OF THE INDIAN STOCK MARKET Investigating the interface of return, sentiment and exchange rate Kuntal Chakraborty Introduction The stock and foreign exchange markets are two important financial barometers in a market economy. Regarding daily volatility, these two markets are a very sensitive domain in the macro-economy. Since the modern economy is based on the dynamics of the demand and supply system, these two indicators, for policymakers as well as for the central banks, are of utmost significance for monetary and fiscal policy measures. For developed and emerging economies, the stock market index is a vital indicator for attracting foreign investment. In the case of India, the LPG (liberalization, privatization and globalization) model was introduced in 1991. Since then, the Indian stock market has witnessed an enormous growth of cash inflow through FII (foreign institutional investment). After implementing the Capital Issue Control Act in the early 1990s, firms could introduce stock concerns into the capital market to raise funds for their upcoming projects by disclosing their financial reports to investors. Since then, the Indian stock market has witnessed exponential growth in its equity segment. Traditional finance is based on the EMH (efficient market hypothesis), which holds that for a particular time a stock price contains all the available information; therefore, it is practically impossible for the stock to outperform the market. The basic feature of this theory is that it accepts that all investors are rational in terms of their trading and investment patterns, but several crashes in the history of the financial markets have already shown the weakness of this theory. Therefore, since the late 1980s, behavioural finance has come into play which prorogates the role of noise traders in the financial markets. Behavioural finance theory argues that arbitrage opportunities are limited across the financial market as arbitrage is not completely cost-free, and there is the existence of psychological biases among investors, which may be called noise trader sentiment. Therefore, in the field of behavioural finance, there is a strong argument that investor sentiment has a significant effect on the return and volatility of the stock market. 103

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As said earlier, the foreign exchange rate is also an important indicator for a country of its balance of payments situation. After the Cold War, the US economy has dominated the international financial markets; therefore, the US dollar has become an international currency in the field of international trade. In the early 1970s the floating exchange rate system was introduced across the world. In the case of India, after 1992, international trade increased significantly due to the opening of the economy. In the year 1997–98, the Asian currency crisis affected stock market returns across the world, from which it was evident that there was an underlying relationship between the stock market and exchange rate return. In this regard, two theories, the flow-oriented model and stock oriented model, may be mentioned to strengthen the relationship between the stock market and foreign exchange market. The flow-oriented model argues that the determination of the exchange rate is based on the equilibrium condition of current accounts, which is ultimately related to the macro-economic situation and indirectly to stock market returns. The stock oriented model is based on the equilibrium condition of the capital account of the country, which controls the flow of capital across the international market and indirectly affects stock market returns. In the existing literature, there are many studies that explore the relationship between macro-economy and stock market, but research on the relationship between investor sentiment and stock market returns and the macro-economy in the Indian context are rare. In the present study, we investigated the nexus among stock market returns (represented by Nifty50), investor sentiment (represented by VIX) and exchange rates (represented by EXR, i.e. value of Indian rupee with respect to the US dollar). Here, India VIX is taken as a proxy to represent investor sentiment since, as per the white paper published by NSE, India VIX represents the expected fluctuation of the stock market over the next 30 days, which is based on Nifty based option prices. According to NSE India, VIX (volatility index) is a measure of market expectations calculated with Nifty index option prices. As per Sarwar (2012), VIX is one of the most important proxies for investor sentiment for the US as well as emerging economies.

Related work Baker and Wurgler (2006) studied investor sentiment in the US stock market by taking various proxy variables, including the volatility index (VIX). Chen et al. (2013) also studied the role of investor sentiment on the portfolio return of different industry segments. They had found the relationship between portfolio return and investor sentiment depended on the specific region and particular time interval. They found a strong co-movement of investor sentiment and market return over the long term. Mitra (2017) had found that the volatility of Indian stock market returns and exchange rates was interdependent. Shen and Zhao (2017) found that macro-economic variables had a significant effect on aggregate investor sentiment. Chiu, Harris, Stoja and Chin (2018) applied the VAR model to examine the interaction between the stock market, investor sentiment and macro-economic 104

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variables of emerging economies. The result of this study showed that the nexus among these variables differed between countries. Tang and Yao (2018) also found that the country-specific economic situation had a significant impact on defining the interaction between stock market returns and exchange rates in the case of developing countries. Nagahisarchoghaei et al. (2018) showed that there was a strong relationship between exchange rate movements and financial performances of Indian firms, which indirectly related to the price/earnings ratio over the long term. Research questions 1) Whether there is any significant relationship among the underlying variables? 2) Whether the previous changes are important to explore the present and future changes in financial variables? 3) Whether only these three variables are enough to define the equilibrium relationship among them? Objective 1) To find out the optimum number of lag for exploring the relationship among these variables. 2) To explore both the short-term and long-term relationship among these variables.

Research methodology The current research study was conducted with monthly time series data collected from the RBI and NSE websites. To conduct further statistical analysis, the data was transformed into a natural log return of each time series. First, a descriptive test and normality test were conducted to understand the characteristics of the observed dataset. To test the stationarity of each series, a unit root test (Augmented Dickey-Fuller) was applied, as it was found that each series became stationary after the first difference. Finally, we conducted the Granger causality test and Johansen cointegration test to explore the short-term and long-term relations, respectively. To select the optimal lag order for these two tests, we applied an unrestricted VAR model. The optimal lag order in the case of the VAR model was selected from a few lag order selection criteria, a stability test as well as an LM test (for checking serial correlation in the residuals).

Empirical analysis At first, each of the time series variables was taken as a natural log transformation of the monthly changes, i.e. ln(xt/xt–1), where xt = current month observation and xt–1 = previous month observation. 105

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Then the descriptive tests were conducted to understand the basic characteristic of the variables under study.   From the above tables it was found that none of the series followed the normal distribution. In an ideal case, the value of the skewness and kurtosis is 0. But here in each case they were non-zero, and from the normality test it was observed that every series was non-normal. Then the ADF (augmented Dickey-Fuller) test was applied to check the stationarity of each time series variable. Since it was found that each time series had a unit root (non-stationarity), the first difference was taken for each time series variable. Here we applied the ADF test in three forms: the trend, trend and intercept and none. In every case, it was observed that after taking the first difference of the log return of each time series it became stationary. The result of the ADF test after taking the first difference of each series is presented as follows.   In the above case, we chose six criteria for the optimum VAR model. We tested up to 12 lags. Here most of the criteria were fulfilled in the case of the VAR model with 8 lags. Therefore, the unrestricted VAR equation with eight lags was chosen for investigating the equilibrium relationship between the variables. To check the efficiency of the VAR model, we further checked the serial correlation, i.e. with the LM statistic test in this case, up to 8 lags (Table 6.5). We found p < 0.01, except for lag 3 where there was no serial correlation in the residuals. In the case of lag 8, it was 29.26%. Using Table 6.6, we checked the stability of the Table 6.1  Descriptive test Descriptive statistics Mean Variable Statistic NIFTY VIX EXR

Std. deviation Skewness Statistic

1.00118461 .005047122 .99831809 .046807467 1.00586220 .116187856


Statistic Std. Error Statistic Std. Error –.043 .641 7.172

.222 .222 .222

3.080 1.923 82.765

.440 .440 .440

Source: Author’s estimation

Table 6.2  Normality Test Tests of Normality Kolmogorov-Smirnova










.080 .104 .460

119 119 119

.061 .003 .000

.951 .961 .141

119 119 119

.000 .001 .000

a. Lilliefors Significance Correction Source: Author’s estimation


An exploration of the Indian stock market

Table 6.3  Augmented Dickey-Fuller test Trend and intercept




t-statistic Prob.* t-statistic Prob.* t-statistic Prob.*

NIFTY Test Critical Value       VIX  


0.0000 –8.356

0.0000 –8.383


–3.489 –2.887 –2.58 –9.022


–4.041 –3.45 –3.15 –8.984


–2.585 –1.943 –1.614 –9.047



–3.49 –2.887 –2.58 –9.209


–4.043 –3.451 –3.15 –9.167


–2.986 –1.943 –1.614 –9.252


–3.489 –2.887 –2.58


–4.041 –3.45 –3.15


–2.585 –1.943 –1.614



ADF test statistic 1% level 5% level 10% level ADF test statistic Test Critical 1% level 5% level Value 10% level   ADF test statistic Test Critical 1% level 5% level Value 10% level

* one-sided p value Source: Author’s estimation The main goal of our study to find out the short- and long-term relationship of the underlying variables; therefore, we have to perform the Granger causality and Johensen cointegration test. Therefore, to select an optimum number of lags to form the equation, an unrestricted VAR (Vector Autoregression) model is applied.

Table 6.4  VAR lag order selection criteria Lag







0 1 2 3 4 5 6 7 8 9 10 11 12

567.4317 619.1952 656.5988 667.6432 700.7254 711.1806 722.0836 727.5783 742.7116 749.8335 757.4994 764.5388 769.8799

NA 99.62033 69.86719 20.00498 58.04980 17.75415 17.89732 8.708584 23.12823* 10.48134 10.84797 9.562978 6.953562

4.76e-09 2.12e-09 1.24e-09 1.20e-09 7.62e-10 7.44e-10 7.21e-10 7.75e-10 6.97e-10* 7.30e-10 7.59e-10 8.02e-10 8.78e-10

–10.64965 –11.45651 –11.99243 –12.031 –12.48538 –12.51284 –12.54875 –12.48261 –12.59833* –12.5629 –12.53772 –12.50073 –12.4317

–10.57427 –11.15499 –11.46477 –11.2772 –11.50544* –11.30676 –11.11652 –10.82424 –10.71382 –10.45225 –10.20093 –9.9378 –9.642624

–10.6191 –11.3343 –11.77857 –11.72548 –12.08821* –12.02401 –11.96826 –11.81046 –11.83453 –11.70744 –11.59061 –11.46196 –11.30127

* indicates lag order selected by the criterion Where LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error, AIC: Akaike information criterion SC: Schwarz information criterion, HQ: Hannan-Quinn information criterion Source: Author’s estimation


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Table 6.5  VAR residual serial correlation LM test Lags



1 2 3 4 5 6 7 8

 16.59447  6.941569  22.58306  15.72663  6.966280  14.38098  10.43995  10.75862

 0.0555  0.6432  0.0072  0.0728  0.6406  0.1094  0.3161  0.2926

*Probs from chi-square with 9 df. Source: Author’s estimation

Table 6.6  VAR model stability test Roots of characteristic polynomial Endogenous variables: NIFTY VIX EXR  Exogenous variables: C  Root


0.660530 + 0.647136i 0.660530 – 0.647136i –0.073170 – 0.902565i –0.073170 + 0.902565i –0.431225 – 0.790619i –0.431225 + 0.790619i 0.113533 + 0.882285i 0.113533 – 0.882285i –0.367520 – 0.782215i –0.367520 + 0.782215i –0.715492 – 0.475390i –0.715492 + 0.475390i 0.502335 – 0.689407i 0.502335 + 0.689407i –0.765970 + 0.346968i –0.765970 – 0.346968i –0.758413 – 0.102559i –0.758413 + 0.102559i 0.406756 – 0.626632i 0.406756 + 0.626632i 0.384596 – 0.451350i 0.384596 + 0.451350i –0.430770 + 0.370614i –0.430770 – 0.370614i

0.924708 0.924708 0.905526 0.905526 0.900574 0.900574 0.889560 0.889560 0.864251 0.864251 0.859026 0.859026 0.853008 0.853008 0.840891 0.840891 0.765316 0.765316 0.747073 0.747073 0.592985 0.592985 0.568259 0.568259

Source: Author’s estimation


An exploration of the Indian stock market

Table 6.7  VAR Granger causality/block exogeneity Wald tests Dependent variable: NIFTY Excluded




VIX EXR All Dependent variable: VIX Excluded NIFTY EXR All Dependent variable: EXR Excluded NIFTY VIX All

 7.326512  7.381221  14.58709

8 8 16

 0.5018  0.4961  0.5551

Chi-sq  5.389110  1.481015  6.875566

8 8 16

Chi-sq  33.11217  12.06009  52.72487

8 8 16


Prob.  0.7153  0.9930  0.9756


Prob.  0.0001  0.1485  0.0000

Source: Author’s estimation

Table 6.8  Johensen’s cointegration test Hypothesized number of CE(s)

Trace Eigenvalue Statistic

None At most 1 At most 2

 0.177382  0.113088  0.086978

0.05 Critical Value

0.05 Max-Eigen Critical Prob.** Statistic Value

 44.68946  29.79707  0.0005  23.21054  15.49471  0.0028  10.00947  3.841466  0.0016

 21.47892  13.20107  10.00947


 21.13162  0.0447  14.26460  0.0731  3.841466  0.0016

**MacKinnon-Haug-Michelis (1999) p values Source: Author’s estimation

VAR model. The estimated VAR model was generally considered to be stable if all roots had a modulus of less than one and therefore lay inside the unit circle. In this case, the number of roots was 24, i.e. the number of endogenous variables multiplied by the number of lags. In all cases, our estimated VAR model satisfied the stability condition.  From Table 6.6, it is observed that the p value was statistically insignificant (p > 0.05) in most of the cases. Only when the dependant variable is the exchange rate, the model stability is going down, which means the Nifty and all other altogether can well explain the movement of the exchange rate.  From the Johensen cointegration test, it was observed that there was a contradiction between the trace statistics and the Max-Eigen value statistics. The trace statistics indicated that there was no cointegration among the variables, whereas the Max-Eigen statistics indicated that there was at most one cointegrating equation. But the previous literature guides us that if there is a contradiction between 109

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the trace statistics and Max-Eigen value statistics, we have to accept the trace statistics result, which indicates that there would be no long-term relationship between the three variables.

Findings From the analysis of our study, it was found that the optimum lag for defining a stable relationship was 8 lag for the respective time period. In the short term, stock market returns and investor sentiment together may have a significant effect on exchange rate movement, but it is not necessarily always true.. Further, the study reveals that there is no significant level of a long-term relationship between the variables.

Conclusion The absence of any significant relationship, especially in the long term, indicates that there may be other explanatory variables, which might be effective to define the underlying equilibrium relationship between these three variables. Future researchers in this field may explore the relationship by undertaking other explanatory variables further.

References Baker, M. and Wurgler, J. (2006), “Investor sentiment and the cross-section of stock returns”. The Journal of Finance, 61(4); pp. 1645–80. Chen, M. P., Chen, P. F. and Lee, C. C. (2013), “Asymmetric effects of investor sentiment on industry stock returns: Panel data evidence”. Emerging Markets Review, 14; pp. 35–54. Chiu, C. W. J., Harris, R. D., Stoja, E. and Chin, M. (2018), “Financial market volatility, macroeconomic fundamentals and investor sentiment”. Journal of Banking & Finance, 92; pp. 130–45. Mitra, P. K. (2017), “Dynamics of volatility spill over between Indian stock market and foreign exchange market return”. Academy of Accounting and Financial studies Journal, 21(2); pp. 1–11. Nagahisarchoghaei, M., Nagahi, M. and Soleimani, N. (2018), “Impact of exchange rate movements on Indian firm performance”. International Journal of Finance and Accounting, 7(4); pp. 108–21. Sarwar, G. (2012), “Is VIX an investor fear gauge in BRIC equity markets?” Journal of Multinational Financial Management, 22(3); pp. 55–65. Shen, J., Yu, J. and Zhao, S. (2017), “Investor sentiment and economic forces”. Journal of Monetary Economics, 86; pp. 1–21. Tang, X. and Yao, X. (2018), “Do financial structures affect exchange rate and stock price interaction? Evidence from emerging markets”. Emerging Markets Review, 34; pp. 64–76.


Part 2


7 THE DYNAMICS OF GLOBAL DEMAND, INVESTMENT AND TRADE DEFICIT A model of India’s external dependence Zico Dasgupta Rethinking India’s growth story Not long ago, the Indian economy was described as the “growth miracle” and the “emerging giant” whose higher growth trajectory was attributed to its embracing economic liberalization (Panagariya, 2008; Bhagwati and Panagariya, 2013). For many of its proponents, the trend in India’s gross domestic product (GDP) growth rate could be explained simply in terms of a binary between the “triumph of liberalization” and the obstacles arising from “reforms” – with episodes of high growth attributed to the successful implementation of liberalization policies and episodes of slowdown explained as “reform was halted” (ibid). These claims were primarily based on India’s successful growth story of the 2000s, the period when the average GDP growth rate remained significantly higher than that of the 1980s and 1990s. The prolonged economic slowdown that has gripped the Indian economy since 2016–17 and has been particularly severe since 2019, however, has dealt a severe blow to the credibility of such interpretations of India’s growth story. Despite continuing with the same set of liberalization policies as before, the present episode of slowdown has continued unabated and even before the emergence of the COVID-19 crisis, it seems to be the most prolonged slowdown since the 1990s (Dasgupta, 2020a). Similarly, the brief recoveries that the economy registered during the first half of the 2010s could also be attributed to external factors such as the softening of international oil prices and hence, to events that were independent of domestic policies (ibid). Beyond the specificity of the present episode of slowdown and the experiences of the 2010s, however, there exist at least two other limitations to the explanations attributing high growth phases exclusively to liberalization policies. The first limitation is theoretical, whereas the second is empirical in nature. The theoretical root of such explanations can be located within the supply-side framework, the defining feature of which is the presence of a production function 113

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and the existence of an adjustment mechanism where ex ante savings necessarily generate an equivalent level of investments under the assumption of perfect price flexibility. India’s recent growth story has been explained within this framework by referring to the phenomenon of the higher growth rate of total factor productivity (Bosworth et al., 2007; Basu and Maertens, 2007), which in turn, has been perceived as a logical corollary of the successful implementation of liberalization policies (Panagariya, 2008; Bhagwati and Panagariya, 2013). The central limitation of the supply-side framework pertains to its restrictive and unrealistic assumption that investment necessarily adjusts to changes in ex ante savings. While any increase in the productivity growth rate would involve an increase in what Harrod (1939) termed the “natural” growth rate, it is precisely this restrictive assumption regarding investment behaviour that ensures within the supply-side framework that any rise in the “natural” growth rate automatically generates a higher “actual” growth rate through an appropriate increase in the output–capital ratio and the savings–capital ratio (or the “warranted” growth rate). However, as pointed out by Sen (1970), even if one retains the assumption of price flexibility, such an automatic adjustment process between the “actual” and the “natural” growth rate will break down once the role of expectations in investment behaviour is introduced by dropping the unrealistic assumption that all savings are necessarily invested. In the absence of such an automatic adjustment process, the explanation for the rise in India’s investments and output growth rate during the liberalization period itself remains incomplete within this framework. The second limitation involves the proposition of a causal relationship between economic liberalization and the output growth rate. Not only has the relationship between the degree of trade openness and the output growth rate been found to be statistically insignificant in various cross-country regression analyses (Rodrik, 1999; Rodriguez and Rodrik, 1999), but also, in itself, it falls short of explaining one key aspect of India’s growth process. While the formal inauguration of liberalization policies in India came about as early as 1991 with the implementation of new economic policies and a structural adjustment programme, the output growth rate during the entire decade of the 1990s remained roughly similar to that of the 1980s (Chandrasekhar and Ghosh, 2002) and it was primarily due to the high growth rates achieved during the 2000s that the period of economic liberalization as a whole registered a higher average output growth rate as compared to that of the 1980s (Azad et al., 2017). This specificity of the 2000s remains largely unaddressed within these explanations. Notwithstanding the limitation of the supply-side view, however, the broader question remains – How can India’s high growth phases during the post-liberalization period along with its downturn be conceptualized? If it is not the extent or the pace of the implementation of reforms, then does any specific factor exist, the presence or the absence of which can demarcate the high growth phases from the slowdowns? What would be an alternative theoretical framework to address these questions? 114

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The alternative to the supply-side view can be located within what can be termed the demand-side view, where ex ante investment decisions are analytically distinct from savings decisions and it is the ex ante investments that generate an equivalent level of savings. The central mechanism through which the output growth rate is affected within this framework is through changes in the autonomous components of aggregate demand. However, in sharp contrast to the 1980s when government expenditures played a stimulating role in the Indian economy, the post-liberalization period was characterized by fiscal orthodoxy where the role of government expenditures in generating demand remained severely curtailed except for a few exceptional years. Further, while the income share of labour registered a drastic decline reflecting workers’ weakened bargaining strength, such changes in income distribution, ceteris paribus, were found to have a negative impact on various components of aggregate demand during this period (Dasgupta, 2020b). If the Indian economy registered a higher demand in high growth phases despite such stagnationist tendencies, what drove demand during this period? What has been common to all demand-side explanations that have been provided in the recent literature is the central role played by private investments. The high growth phases have been explained in terms of higher investment rates, whereas the stimulus for higher investments in turn has been argued to have been provided by changes in the structure of domestic demand (Patnaik, 2007; Chandrasekhar, 2011) or a rise in housing loans (Ghosh and Chandrasekhar, 2009; Nagaraj, 2013) or higher exports (Dasgupta, 2020a). Similarly, various episodes of growth slowdown during the present decade, including the present episode, have been explained through lower investments and, inter alia, a sharp reduction in exports (ibid). While the trend in investments has been explained in various ways, what appears to be the central factor in demarcating the phases of high investment rates from those of low investments is the trend in exports. This close relationship between investments and exports during the liberalization period is reflected in Figure 7.1. As evident from Figure 7.1, both the investment–GDP ratio as well as the export–GDP ratio registered a sharp rise during the 2000s, the period that can be described as India’s high growth phase. Similarly, the slowing down of the investment ratio during the latter half of the 2010s was also associated with a reduction in the export ratio. Although the co-movement of investments and exports is nothing unique when compared to many other developing countries, what is specific to India’s growth story is the relationship between investments, exports and its trade balance. Although investments and exports moved more or less in a similar direction throughout the liberalization period, nonetheless, investments and net exports moved in opposite directions (see Figure 7.1). The period of higher investments and exports during the 2000s was associated with a sharp deterioration in the trade balance, whereas a relatively lower level of investments and exports during the 2010s was associated with an improvement in the trade balance. Such a relationship between investments, exports and the trade balance stands in sharp contrast 115

Zico Dasgupta 50.0 40.0 30.0 20.0 10.0


1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20




Net Exports

Figure 7.1  Share of gross capital formation, exports and net exports in GDP (%).

Source: Linked GDP series, National Statistical Commission and National Account Statistics, CSO.

to the experience of many East Asian countries, including China, where higher investments were accompanied by improvements in both exports and net exports. Further, the manner in which the Indian economy witnessed higher exports during the relevant period was fundamentally different from various East Asian economies where the export structure significantly changed towards more technologically sophisticated goods due to state policies. Such changes in export structure can be argued to increase the export growth rate even at a given level of global demand, since the growth rate of demand for these goods in the global market would be greater as compared to other goods (Patnaik and Chandrasekhar, 1996). The state policies of transforming the export structure in East Asian countries have comprised of initiatives to increase export-oriented foreign direct investments (FDIs) (Palley, 2011), to enforce technological transfer from the metropolitan capital (Rodrik, 2006; Hutchet, 2014) and to undertake higher domestic investments in the technologically more advanced sectors (Storm and Naastepad, 2005). In sharp contrast, India did not receive export-oriented greenfield foreign direct investments (Rao et al., 2014), nor did it impose any conditions for technology transfer (Chaudhuri, 2015), nor did it witness any substantial rise in R&Drelated expenditures. In the absence of any significant change in its export structure, India’s exports were primarily driven by exogenous changes in the level and structure of global demand (Dasgupta, 2020b). With the real effective exchange rate having little impact, both merchandize and the aggregate export growth rate of India followed the trend in the exogenously given import growth rate of the rest of the world (ibid). Since the data for the merchandize sector is updated to 2019–20 and it


Dynamics of global demand, investment and trade deficit 

captures the period of the current slowdown, Figure 7.2 reports this relationship for merchandize exports. As evident from Figure 7.2, the nominal growth rate of India’s merchandize exports followed precisely the same trend as that of the exogenously given merchandize import growth rate of the rest of the world throughout the period. In a nutshell, the Indian economy can be described by three stylized facts as follows: (1) it is solely dependent on exogenous changes in global demand to maintain its export growth rate; (2) it registered higher investments during periods of higher exports and a reduction in investments during periods of low exports; and (3) investments and net exports moved in opposite directions. Accordingly, in one’s attempt to explain India’s growth story within the demand-side framework, one confronts the following theoretical questions: How are these stylized facts related to each other? How is the diverging trend in investment and export on the one hand and net export on the other explained? Can one draw a causal relation between exogenously given exports and investment despite a deterioration in the trade balance? By laying bare the relation between exports and investment in a dynamic demand-side growth model, this chapter attempts to argue that an exogenous rise (fall) in global demand can simultaneously lead to higher (lower) investments as well as a deterioration (improvement) in the trade balance. Although the Indian economy registered an inverse relation between net exports and investment, the primary stimulus for investment during the liberalization period is argued to have been provided by exogenously given exports. The rest of the chapter is organized as follows:

1992 - 1993 1993 - 1994 1994 - 1995 1995 - 1996 1996 - 1997 1997 - 1998 1998 - 1999 1999 - 2000 2000 - 2001 2001 - 2002 2002 - 2003 2003 - 2004 2004 - 2005 2005 - 2006 2006 - 2007 2007 - 2008 2008 - 2009 2009 - 2010 2010 - 2011 2011 - 2012 2012 - 2013 2013 - 2014 2014 - 2015 2015 - 2016 2016 - 2017 2017 - 2018 2018 - 2019 2019 - 2020

50 40 30 20 10 0 –10 –20 –30 –40 –50

India Merchandise Export

RoW Merchandise Import

Figure 7.2  Nominal growth rates of merchandize imports of the rest of the world and India’s merchandize exports (%). Source: WITS, COMTRADE.


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The second section discusses the theoretical relation between export and investment in the context of the Kalecki–Luxemburg debate. The third section constructs a theoretical model to produce the specific condition and investment behaviour that can explain India’s growth story. The fourth section empirically tests the investment function for the Indian economy, which is laid out in the theoretical model. The final section provides the concluding remarks.

Exports as the exogenous stimulus: A theoretical framework In a demand-constrained capitalist economy, there are two possible routes through which the external sector can be perceived to provide a stimulus for investment in the domestic economy. The first route is where an increase in net exports (export net of import) leads to a rise in the effective demand and output of the domestic economy through the conventional multiplier, which in turn, can lead to a further increase in the investment rate as capitalists expect higher realized profit in the future period on the basis of the improved demand conditions of the current period (Kalecki, 1971). Here, the external sector provides a stimulus for investment by acting as a location for the realization of surplus or ex ante savings for the domestic economy. Accordingly, the necessary condition by which exports provide stimulus to investment using the first route is an improvement in net exports. An obvious example of such a growth process would be the experience of various East Asian economies which simultaneously witnessed higher investment and improvement in net exports during the decades of the 1980s, 1990s and 2000s. The second route pertains to a situation where the gross export in itself acts as an inducement to invest in the domestic economy, over and above the stimulus provided by the conventional accelerator (Patnaik, 1972). It is the possibility of this second route which we shall now explore in detail in our attempt to explain the accumulation process during the post-liberalization period. Our specific enquiry regarding the relationship between exports and investment in India, however, in a way brings us back to the broader discussion on the interlinkage between the external sector and the accumulation process in a capitalist economy, a theme which was once explored by Rosa Luxemburg in the context of explaining the dynamics of capitalist development. Starting from the assumption of a closed capitalist economy where wages and profits are distributed among two antagonistic classes – the workers and the capitalists – the central question that Luxemburg (1951) tried to address was: What was the “starting point of accumulation” or the inducement to invest in such an economy? In the case of expanded reproduction, i.e. when a part of the surplus value is transformed into additional capital to expand the production base, the necessary condition for the accumulation process to proceed was found in the “expansion of effective demand for commodities”. Thus, the enquiry pertaining to the inducement to invest boiled down to the question of “[w]here is this continually increasing demand to come from?” (ibid, p. 131). In Luxemburg’s analytical framework, the solution to this question was found in the expansion of the 118

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external market, whereas such an external market was perceived to comprise the “strata of buyers outside capitalist society” (ibid, p. 351) or “social organization or strata whose own mode of production is not capitalistic” (ibid, p. 352). The primary mechanism of expansion of this external market was located in the imperialistic conquest of non-capitalist economies and the condition of “continually increasing demand” in a closed capitalist economy was sought to be met through higher exports from the capitalist sector to the non-capitalist sector. The emphasis of our discussion, however, is neither on the specificities of Luxemburg’s assumptions (where any realization of surplus value in the domestic economy was a priori deemed impossible) nor on the specific manner in which she perceived the relationship between the capitalist sector and the non-capitalist sector. Rather, what would be relevant for our present discussion is Luxemburg’s notion of exports to the external market and the manner in which they can be perceived to act as a stimulus for investment in the domestic economy. The notion of exports in Luxemburg’s analysis, it can be noted, did not make any distinction between gross and net categories. Independent of the relative level of imports from the non-capitalist sector, what remained as the central component of the accumulation process was simply the presence of an external sector, where capitalists could export in order to realize their surplus value. This lack of distinction between gross and net exports was precisely the point of criticism made by Kalecki (1971) vis-à-vis Luxemburg. Kalecki’s critique was based on the fact that “imported goods absorb purchasing power just like those home-produced, and thus to the extent that exports are offset by imports they do not contribute to the expansion of the markets” (ibid, p. 152). Thus, instead of gross exports, what would be relevant for the realization of a surplus in a capitalist economy, according to Kalecki, was solely the trend in the net export. Kalecki’s own conclusion, accordingly, was based on the assumption of what we earlier described as the first route, where the external sector only acts as the location for the realization of surplus value and that it is necessarily by changing the level of aggregate demand that the external sector affects the accumulation process in the capitalist sector (Patnaik, 1972). However, for reasons discussed earlier, Kalecki’s conclusion ceases to hold once one takes into consideration the concrete possibility that the external sector itself provides the impetus to investment in the domestic sector (ibid). But why should the external sector itself provide such a stimulus to investment? In the specific context of Luxemburg’s analysis, Patnaik (2008) pointed out the historic role played by the non-capitalist sector as a “market on tap” for the capitalist sector. Since such markets can be potentially encroached upon as and when needed, either through direct conquests or simply through imposing a process which would involve deindustrialization and dispossession in the non-capitalist sector, the very presence of such non-capitalist sectors as “reserve markets” can instil among capitalists the confidence to undertake investment without fear of a possible realization crisis during a downturn. Thus, independent of actual encroachment into the non-capitalist sector, or what Luxemburg termed the 119

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“expansion of the market”, the very presence of such an external sector as a “market on tap” can act as an inducement to invest in the domestic economy. While Luxemburg’s analysis can be regarded as one specific example, nonetheless, the possibility of the external sector acting as a separate stimulus for investment is not dependent on the “market on tap” argument alone. As argued by Patnaik (1972), even to open up an external market or to get access to it, the capitalists of the domestic economy may need to incur additional investment expenditures over and above the level they would invest on account of the stimulus provided by the existing state of demand. In other words, what can be argued from the foregoing discussion is that the external market would act as a separate stimulus for investment if there existed any structural difference between the domestic market and the external market, such that for a given level of demand, the capitalists perceive the two markets differently while taking investment decisions. Given the specific nature of our enquiry, namely, examining the relationship between gross exports and investment in the Indian economy, the “external sector” in our analysis indicates the foreign market and the “domestic sector” comprises the entire national economy. Accordingly, the question that needs to be addressed in the Indian context is whether there exists any such structural difference between the domestic market and the foreign market such that global demand acts as a separate stimulus for investment from the perspective of domestic capitalists, particularly the big capital which undertakes the bulk of corporate investment. For historical reasons, the perceived structural difference between the two markets can remain even in a globalized world. Firstly, the corporate houses of the domestic economy may have already emerged as “trusted brands”, built extensive distribution networks and developed experience to cater to the domestic economy according to local tastes and preferences by the time of economic liberalization on account of decades of protection received from the state. Such opportunities for domestic capital are simply missing in the foreign markets where they have limited footholds and lack adequate technological capabilities to break into these markets. Similarly, the domestic corporates may have to compete with those foreign corporate groups that have “first mover’s advantage” in the foreign market where the latter have already established their own footholds and emerged as “trusted brands”. In short, compared to the domestic markets, the scope of the domestic corporates of a developing country carving out their niche markets visà-vis foreign capital can be considerably reduced. Secondly, the demand structure of the external market can be different from that of the domestic market in terms of being more biased towards capital-intensive goods, because of which catering to one unit of demand in the external market may call forth higher investments as compared to that in the domestic market. Thus, either on account of the different intensity of competition or the different demand structure, catering to one unit of demand in the foreign market can be fundamentally different from catering to one unit of demand in the domestic economy. Such a structural difference between the two markets can affect investment 120

Dynamics of global demand, investment and trade deficit 

decisions because maintaining a given market share in the external market would compel domestic corporates to incur additional investment expenditure over and above what is induced by the given state of aggregate demand. In the midst of such a relationship between investment and the external market, an exogenous rise in export demand would require domestic capitalists to incur higher investments in order to gain access or cater to the larger market even at a given level of aggregate demand. Again, since maintaining idle capacity over and above the targeted level involves incurring additional cost from the perspective of the capitalists, an exogenous decline in export demand would involve cutting down investments in the external market. Export demand in this analytical framework would play the same role as does innovations in Kalecki, whereby similar to the case of getting access to new innovations, catering to the external market itself requires incurring additional investment expenditures over and above the stimulus provided by the existing state of demand. Gross exports in such cases would act as a separate stimulus for investment for a given level of aggregate demand. Accordingly, instead of net exports, the key indicator for identifying an export-driven growth process turns out to be the trend in gross exports along with its relationship with the investment rate. The following theoretical model attempts to lay bare the dynamics of such a process.

The model This sections attempts to highlight the relationship between gross exports, investment and net exports for what can be termed as a general form of an exportinduced investment-led growth process. The central objective of this exercise is to show that an improvement in net exports is one possible outcome for an exportinduced growth process but not a necessary condition. By examining the dynamics of investment and net exports following an exogenous rise in gross exports, it is shown that higher exports can simultaneously lead to a higher steady-state growth rate as well as a deterioration in the net exports. While the dynamics of the general form of the export-induced investment-led growth process is described by assuming that exports act both as a mechanism for realizing ex ante savings as well as a separate stimulus for investment, the model can be used to examine the specific case where only the former mechanism operates. The following subsection describes the basic model. The basic equations We assume a one-commodity world with an oligopolistic set-up where investment is undertaken solely by oligopolistic firms and prices are fixed according to a given markup. The only constraint facing this model economy is the level of effective demand. For the sake of simplicity, the depreciation rate is assumed to be zero. The price of foreign goods and the nominal exchange rate are assumed to be fixed. 121

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Investment function Although the nature of the investment function has been one of the most widely debated and contested issues, nonetheless, what is common within all demandside models and theories is the emphasis on the causal relationship from aggregate demand to the desired investment rate of capitalists in the long run. Taking a cue from Hein (2014), this causal relationship is described through an investment function which is positively affected by the capacity utilization rate of firms. The specificity of the investment function of the export-induced investment-led growth process, however, would be the exports acting as a separate stimulus for investment over and above that provided by the capacity utilization rate or the aggregate demand. Such an investment function is described by Equation (7.4), where “gd” is the desired investment rate or the growth rate of capital stock, “u” is the capacity utilization rate, “x” is the export–capital ratio and “a”, “b” and “c” are positive parameters. For the sake of convenience, we shall henceforth call the parameters “b” and “c” the output coefficient and the export coefficient, respectively.

g d = a + bu + cx (7.1)

where a > 0; b > 0; c > 0. The actual investment rate can diverge from the desired investment rate in the short run as investment decisions are made at the beginning of the period. However, any divergence between the desired investment rate and the actual investment rate brings about an adjustment in the actual investment rate in the long run according to Equation (7.2) with “θ” indicating the speed of the adjustment and “g” reflecting the exogenously given short-run investment rate.

g = q ( g d - g ) (7.2)


g = growth rate of capital stock =

g =

q > 0


dg dt

Plugging Equation (7.1) into Equation (7.2), the investment function then transforms to

g = q ( a + bu + cx - g ) (7.3) 122

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Export function Since changes in the exchange rate did not play any significant role in India’s growth story (Dasgupta, 2020b), its impact on exports is dismissed for the sake of simplicity. At any given level of global demand, the export–capital ratio can be perceived to be exogenously given in the short run. However, in the long run, exports can be perceived to be affected by two other factors over and above global demand. Firstly, exports may be positively affected by higher investment as the latter can bring about a change in the composition of exports in favour of technologically sophisticated goods, the growth rate of which in the export market is higher than other commodities (Patnaik and Chandrasekhar, 1996). Thus, by changing the export structure of an economy, a higher investment may lead to higher exports at any given level of global demand (ibid). Secondly, to accommodate the role of historical time, exports of any given period can be perceived to be positively related to its lagged value, indicating that higher (lower) exports of the past would be associated with relatively higher (lower) levels of current exports. To be realistic, it is further assumed that a rise in exports in any given period by one unit, ceteris paribus, is associated with a less than proportionate rise in exports in the next period. A close approximation of the above arguments can be described by Equation (7.4) which is given in the form of discrete time. The first term on the right-hand side (RHS) reflects the positive impact of exogenously given global demand “w” on exports, where “c1” is a positive parameter. The second term describes the relationship between investment and export during a given period, where the parameter “c2” can be either positive or zero. The third term simply captures the effect of lagged value, where “c3” is a positive fraction.

x t = c1w t + c 2g t + c3x t -1 (7.4)

where c1 > 0; c2 ≥ 0; 0  0; e = 2 ³ 0; f = c3 c3 c3 Savings and import function

Similar to any canonical demand-side model, the savings function and the import function are described by Equations (7.6) and (7.7), respectively, where “S” is the aggregate domestic savings, “M” is the aggregate import, “s” is the exogenously given savings propensity, “m” is the exogenously given import propensity and “β” is the technological output–capital ratio.

S = sub (7.6) K

M = mub (7.7) K

where 0  0

It can be noted that since (∂ġ/∂g) = q , the sign of parameter “q” indicates nothing but the Harrodian stability condition. A positive “q” implies that the system is Harrod stable, whereas a negative “q” indicates Harrodian instability. In order to focus on the question at hand, the possibility of Harrodian instability is assumed away throughout the exercise. Since θ > 0, q > 0 if the term (1–(b/(s + m)β)) > 0. In other words, Harrodian stability would be guaranteed if the sensitivity of the sum of the savings and imports with respect to the capacity utilization rate (s + m)β is greater than the sensitivity of the desired investment rate with respect to the capacity utilization rate (b). This assumption is maintained throughout the chapter. The Jacobian matrix of the two dynamic equations (Equations 7.10 and 7.11) is derived as follows:

é -q J=ê ëe

r ù (7.12) -f úû

Since f > 0, the trace of the Jacobian matrix is negative and is given by Equation (7.13):

trJ = -q - f < 0 (7.13) 125

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Thus, the dynamic stability of the economy would be ensured if the determinant of the Jacobian is positive, i.e.

J = fq - er > 0 (7.C.1a)

With θ  >  0, the sufficient condition for J > 0 can also be written as condition (7.C.1b) by plugging in the value of q and r:

( s + m ) b ( f - ce ) + b ( e + f ) > 0 (7.C.1b)

With the value of all other parameters positive, condition (7.C.1b) would be guaranteed if

0. Equation  (7.C.2) calculates this difference. Since r  >  0 and e  ≥  0, the RHS of Equation (7.C.2) is positive. Thus, the roots are real and distinct, indicating that the system has a stable node.

( trJ )


- 4 J = ( q - f ) + 4re (7.C.2) 2

In the long run, both the investment rate (g) and the gross export–capital ratio (x) adjust according to Equations (7.3) and (7.4), respectively. The long-run steady-state values of “g” and “x” can be derived by solving for Equations  (7.10) and (7.11) at ġ  =  0 and ẋ  =  0. Accordingly, the long-run steady-state values of the investment rate (g*) and the export ratio (x*) are given by Equations (7.14) and (7.15). Since J > 0 by hypothesis (condition 7.C.1c), g* > 0 and x* > 0.

g* =

fp + hwr (7.14) J

x* =

ep + hwq (7.15) J 126

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The long-run steady-state level of net exports (n*) can be derived by plugging the value of x* and g* into Equation (7.9b) and described as

n* =

p ( es - mf ) + hw ( sq - mr ) (7.16) (s + m ) J

The relationship between the steady-state equilibrium level of the investment rate (g*), gross exports (x*) and net exports (n*) is graphically shown in Figure 7.3. The right panel measures the investment rate “g” in the vertical axis and the gross export ratio (x) in the horizontal axis. The straight line, g0, is the isocline for the investment function (Equation 7.10) along which ġ = 0. The intercept and slope of the isocline g0 are given by p/q and dg / dx g = 0 = r / q , respectively. Assuming e > 0 in Figure 7.3,1 the straight line, x0, is the isocline for the export function (Equation 7.11) along which ẋ = 0. The intercept and slope of the isocline x0 are given by –(hw/e) and dg / dx x = 0 = f / e, respectively. The slope of g0 is flatter than that of x0 due to the stability condition (7.C.2). The steady-state values of “g” and “x” are determined by the intersection of g0 and x0 at E0 and are denoted as “g*” and “x*”, respectively. The steady-state equilibrium at E0 is a stable node, as indicated by the arrows. The horizontal axis of the left-hand panel measures the (positive) values of net exports, whereas the straight line, n0, shows the relationship between the steady-state net exports and the steady-state investment rate. The net export function in the “g” and “n” space can be depicted by n0 and is derived by plugging the value of x* into Equation (7.9b). The intercept and the slope of the net export function are, respectively, ( ep + hwq / J ) (1 - ( m / s + m ) )

Figure 7.3  Determination of the steady-state investment rate, gross exports and net exports.


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and –(m/s + m). The steady-state value of net exports, n*, is determined as g* intersects n0 at F0. Having described the steady-state solutions here, the next section examines the impact of an exogenous change in global demand on the steady-state investment rate, gross exports and net exports. Comparative dynamics The impact of an exogenous change in global demand on the steady-state investment rate and gross exports is calculated by taking the partial derivatives of Equations (7.14) and (7.15) w.r.t. “w”. The partial derivatives are shown in Equations (7.17) and (7.18). Similar to any demand-side model, the partial derivatives are unambiguously positive.

¶g * hr = > 0 (7.17) J ¶w

¶x * hq = > 0 (7.18) J ¶w

The impact of an exogenous change in global demand on steady-state net exports is calculated by taking the partial derivatives of Equation (7.16) w.r.t. “w”. After plugging in the value of q and r, the partial derivative is shown in Equation (7.19). In sharp contrast to Equations (7.17) and (7.18), the sign of the partial derivative in Equation (7.21) is ambiguous since the sign of the third bracketed term of the numerator (sβ – b – cmβ) is ambiguous whereas the denominator is positive (by condition 7.C.2).

h ( s + m ) ( sb - b - cmb ) ¶n * = (7.19) ¶w ( s + m ) b ( f - ce ) - b ( e + f )

Accordingly, the sign of the above partial derivatives will be determined by the sign of the numerator and is given by the following conditions:

¶n * > 0 if m < m c (7.C.3) ¶w


¶n * £ 0 if m ³ m c (7.C.4) ¶w


mc =

( sb - b ) (7.20) cb


Dynamics of global demand, investment and trade deficit 

Thus, the sign of the above partial derivative of net exports w.r.t. global demand depends on the precise value of import propensity (m) as compared to a certain critical value of import propensity (mc). The critical value of import propensity, as described by Equation (7.20), is defined as that level of import propensity at which the impact of an exogenous change in global demand on net exports is zero. With the actual import propensity “m” being positive by hypothesis, the sign of the partial derivative would be ambiguous as there is no guarantee that the import propensity would be lower than this critical value. The economic explanation for the above result is the following: Any rise (fall) in global demand has two opposite effects on net exports: (i) it increases (reduces) net exports by increasing (reducing) exports and (ii) it reduces (increases) net exports by increasing (reducing) imports through a higher (lower) investment rate at a given import propensity. Thus, the net impact of higher global demand on net exports would depend on the relative strength of these two effects. The higher the level of import propensity, the greater the relative strength of the change in imports vis-à-vis exports for any given change in global demand. These two effects can be calculated by substituting x* and g* for “x” and “g” in Equation (7.9) and taking a partial derivative w.r.t. “w”. Thus, a change in net exports on account of a change in global demand can be decomposed into two opposite effects and expressed as a difference between two terms with ∂n*/∂w = T1 – T2, where T1 = (1 – (m/s + m))∂n*/∂w and T2 = (m/s + m)∂g*/∂w. The first term, T1, reflects the positive effect of higher gross exports on net exports and is given by (1- ( m / s + m ) ) hq / J . The second term, T2, reflects the negative effect of a higher investment rate on net exports and would be equal to mhr / ( s + m ) J . The net impact of higher global demand on a change in net exports is equal to T1 – T2 = (h(s + m)(sβ – b – cmβ))/((s + m)β(f –ce) – b(e + f)). Thus, the critical level of import propensity (mc) at which the numerator becomes 0 is the value of the import propensity at which the positive effect of higher exports is equal to the negative effect of higher investment rates on net exports, i.e. T1 = T2. At m  T2, whereas at m > mc, it is just the opposite with T1  0. Figure 7.4a shows the impact of an exogenous rise in global demand on steadystate investment rates, gross exports and net exports when m  g0* and x1* > x0*. Similarly, the net export function n0 shifts leftwards to n1 as it intercepts the term ( ep + hwq / J ) (1 - ( m / s + m ) ) and increases. The new steady-state investment rate (g1*) intersects the net export function n1 at F1 to determine the steady-state net exports n1*. Here, higher global demand leads to an improvement in net exports as the new steady-state net exports n1* > n0*. 130

Dynamics of global demand, investment and trade deficit 

Figure 7.4b shows the case where m > mc. As compared to Figure 7.4a, here the slope of g0 is flatter, the leftward shift of the net export function is smaller and the slope of the net export function is steeper. Similar to Figure 7.4a, the steady-state investment rate and export ratio increase on account of higher global demand. However, in sharp contrast to Figure 7.4a, here an exogenous rise in global demand leads to a deterioration in net exports with n1*  sβ.

h ( s + m ) ( sb - b ) ¶n * = (7.21) ¶w c = 0 ( s + m ) bf - b ( e + f )

The existence of a positive export coefficient, however, opens up the possibility of the above partial derivative becoming negative even at relatively lower values 131

Zico Dasgupta

Table 7.1  Sign of ∂n*/∂w for different parametric values b  0. By executing the regression for both 1993–94 and 2009–10, we aim to estimate the coefficients to investigate whether the per capita consumption is positively linked with inequality and whether the neoliberal regime has actually strengthened this relationship. Table 8.8 presents the regression results. For the early 1990s, we did not find any statistically significant positive impact of inequality on consumption. However, this had changed by the late 2000s. For both rounds, the influence of the growth-driving sectors is statistically significant, but oddly, it does not show much change over time. Nevertheless, the coefficients are, as expected, positive. The coefficients of the state dummies are also positive, but they were significant only in 2009–10. This could imply that it is during the period of market-oriented reforms that the state-specific regional divergence in per capita consumption became more pronounced, since it is contingent upon the level of development in a state (or the lack of it). Thus, we find that higher consumption is positively linked with higher inequality and, while this was not the case in the early 1990s 159

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Table 8.8  Regression results Coefficients with robust standard errors Independent variables



Inequality (×1) Growth drivers (×2) State dummy (d1) State dummy (d2) Constant No. of observations F-value R-squared

0.017 (0.309) 0.545*** (0.125) 0.020 (0.023) 0.022 (0.030) 2.309*** (0.082) 51 10.94*** 0.451

0.742*** (0.217) 0.546*** (0.103) 0.066** (0.027) 0.087*** (0.022) 2.534*** (0.060) 30.38*** 0.718

Note: Robust standard errors are shown in parentheses. Significance at 1% is denoted by *** and ** denotes significance at 5%.

owing to the equalizing forces in the 1980s, it is certainly true after almost two decades of market-oriented reforms.

Investment and inequality linkages The foregoing discussion establishes that rising inequality in post-reform India seems to contribute to (per capita) consumption – the largest source of domestic demand. In this section, we empirically demonstrate that investment – the other significant source of domestic demand – is also linked with rising inequality. Here, unlike in the previous section, increasing inequality refers to the rising capital–labour divide, which is reflected in increasing profit shares. Using the conceptualization provided by Foley and Michl (1999), we estimate the investment function considering all the sectors without “agriculture and allied activities” and “public administration and defence”. Through this empirical exercise, we intend to verify whether accumulation, measured by I/K, is positively impacted by rising profit share. Additionally, this also enables us to highlight the importance of capacity utilization in driving accumulation. The analysis of the relationship between accumulation and the functional distribution of the income (between capital and labour) share has been a matter of intense academic debate for a long time. The discourse of the demand-led growth regime has produced various conceptualizations of the investment function. In this regard, most of the scholarly endeavours were inspired by the seminal work of Bhaduri and Marglin (1990).8 In the Indian context, the profit share is found to be a (statistically) significant factor in determining accumulation in the organized manufacturing sectors, along with the capacity utilization rate and the capacity– capital ratio (Basu and Das, 2016). However, unlike their analysis, we incorporate the data of the manufacturing sector as well as various other sectors. The period of analysis for us is 1980–81 to 2012–13. The factor share data is obtained from the Central Statistics Office (CSO). For the period between 1980–81 160

India’s recent slowdown and neoliberal regime of accumulation

and 1999–2000, we utilize the data from the National Accounts Statistics, Factor Incomes (Base Year 1999–2000), published by the CSO, Department of Statistics, Ministry of Statistics and Programme Implementation in 2008. For the remaining years (2000–01 to 2012–13), we consider the data from the NAS (2008, 2013 and 2014). The factor income data between 1980–81 and 1999–2000 is at constant prices while after 2000–01, the estimates are at current prices. Since our concern is the share, rather than the absolute value, of the operating surplus and the compensation to employees in the net (sectoral) output, the price effect is not a major concern. In India, the total produce cannot be easily segregated into capital share and labour share for all the sectors because of the presence of “mixed income” in the unorganized sector. We implement a method suggested by Mohan (2003). According to him, “the CE proportion in the Mixed Income is the same as that of the proportion of reported CE to Net Domestic Product (NDP)” (p. 648). The following method is deployed to segregate the mixed income into operating surplus and wage component:

( CE / NVA ) * ( MI ) = CE MI ( CE component in MI ) and MI - CE MI = OSMI

The data on the real net domestic product, investment (net capital formation) and net capital stock is collected from the EPWRFITS. These estimates are in 2004–05 prices. We use panel data analysis for our empirical exercise, where the concerned variables are (1) accumulation – ratio of net capital formation to net capital stock; (2) profit share (h) – the ratio of total profit or operating surplus to actual output (Y), i.e. net value added; (3) capacity utilization index (u) – the ratio of actual output to potential output (Y/Y*), where we calculate the potential output using the Hodrick–Prescott filter; and (4) capacity–capital ratio (z) – the ratio of potential output to net capital stock.

I / K = f ( h, u, z ) (8.2)

For this analysis, we use the panels corrected standard error method. If the crosssectional units in a panel data set are geographical areas or states in a country, this will lead to spatial clustering in the empirical analysis. These clusters may have cross-sectional interdependence which, in turn, would lead to spatial autocorrelation resulting in contemporaneous correlation and panel heteroscedasticity. The standard practice in such a scenario is to employ the feasible generalized least square (FGLS) model. The weight in such a model is given to the matrix of variance–covariance of errors Ω–1 in order to correct the autocorrelation and heteroscedasticity issues. The estimated coefficient β is expressed as (X′Ω–1Y) (X′Ω–1X)–1, where Y is the dependent variables’ vector and X is the explanatory variables’ vector. The variance–covariance matrix of errors Ω–1 is considered in the inverse form to scale down the effect of autocorrelation and heteroscedasticity. However, when 161

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spatial autocorrelation arises in the panel data, the problems of contemporaneous correlation and panel heteroscedasticity cannot be scaled down by the FGLS method since it has both cross-sectional and time series effects. In this context, the panel corrected standard error (PCSE) model is the most suitable alternative. The standard panel data model may be expressed as

Yit = bX it + eit + di + ht

where Yit is the dependent variable of i cross-sectional at time t Xit refers to one or more independent variables of i cross-sectional at time t εit is the error of i cross-sectional at time t β is the parameter to be estimated Therefore, to take the time effect and cross-sectional effect into consideration, the dummies ηtand δi are introduced into the model; δi is the effect of one crosssectional unit, which will differ across individual cross-sectional entities across all time periods; and ηt is the time effect across all the cross-sectional units. When there are cross-sectional relationships which can affect the dependent variable Y, it can be safely assumed that the error of one cross-sectional εit is related to another cross-sectional εjt at the same time period t, referred to as a contemporaneous correlation. The errors within the cross-sectional units are not normally distributed, nor do they have constancy of variance. Hence, to solve this empirical problem, the panel corrected standard error model is used. Generally, in an OLS estimation, cov(β) = Σ(X′X)–1, where Σ = σ2I means no autocorrelation and no heteroscedasticity, where σ2 is the constant variance and I is the identity matrix. However, in the presence of spatial autocorrelation:

cov ( b ) = ( X¢X )


( X¢WX ) ( X¢X )


where Ω is the contemporaneous correlation and panel heteroscedasticity. This has an NT × NT block diagonal matrix with an N × N matrix along the diagonal of the contemporaneous covariance E. E can be computed as

ij E = å Tt =1 eit e jt / Tij or E = e¢e / T

where εit = the error of one cross-sectional at time t εjt = the error of another cross-sectional at the same time t E represents the error correlation between the different units and the variance within the same unit at the same time. Therefore, E is the matrix of variance and covariance of error. Further, Ω consists of E and an identity matrix I, such that

W = EÄI 162

India’s recent slowdown and neoliberal regime of accumulation

where Ω is the TN dimension of the matrix and E is the TN × TN matrix with the identity matrix whose diagonal has T element. Suppose cross-sectional n = 3 and time period t = 2. Here, the Ω Kronecker product will have a 6 × 6 dimension.

é µ12 ê ê0 2 êµ12 ê ê0 ê0 ê êë 0

0 µ12 0 2 µ12 0 0

µ221 0 µ22 0 µ223 0

0 µ221 0 µ22

0 0 2 µ32

0 µ223

0 µ32 0

0ù ú 0ú 0ú ú 2 µ32 ú 0ú ú µ32 úû

In the presence of spatial autocorrelation, the panel OLS cov(β) will be corrected through (X′X)–1(X′ΩX)(X′X)–1 and the square root of the block diagonal matrix Ω will then be taken. The cov(β) corroborates the PCSE model. The PCSE represents the correct confidence interval of estimator β, so as to accept or reject the suitable null hypothesis of β. The Monte-Carlo simulation shows that the PCSE method reduces the variability of the confidence interval of β by 50% or more, as compared to the Park test. The findings of this exercise show (see Table 8.9) that accumulation is positively linked with all three variables – profit share, capacity utilization and capacity–capital ratio. Evidently, in India, the increasing capital–labour divide boosts accumulation, although the capacity utilization rate becomes the most important determinant, as per our findings. In post-reform India, exports growth has been the most important source of growth and investment growth performance is strongly connected to it (Dasgupta, 2020a). The post-global crisis era and the recent slowdown is evidence of this. On the other hand, consumption growth is not really broad based as there is the underlying interplay of consumption and inequality. However, this might structurally hinder investment growth in turn Table 8.9  The PCSE result Dependent variable: Accumulation Variables


h u Z Constant No. of observations No. of groups Wald Chi-square

0.0190184*** (0.00599) 0.2127689** (0.0945425) 0.0051098** (0.0025427) –0.1566987* (0.0945801) 429 13 22.72***

Note: Standard errors are shown in parentheses. ***p  W. We have assumed that K is perfectly mobile among M, Y and I. All markets are competitive, and each production unit operates under the standard neo-classical technology of constant returns to scale and diminishing marginal productivity. The following notations are used to describe the equations of the model: Xi = product produced by the ith sector, i = A, I, M, Y PA(1 + s) = subsidy inclusive of the domestic price of good A PI = price of commodity I PM = world price of good M PM = PM (1 + t) = tariff inclusive of the domestic price of good M PY(1 + s) = subsidy inclusive of the domestic price of good Y W = competitive wage rate of unskilled labour W  = fixed wage rate of formal unskilled labour in the manufacturing sector WH = wage of skilled labour in the service sector r = common rate of return to capital R = return to land K = economy’s aggregate capital stock T = total amount of land L = economy’s total supply of unskilled labour H = endowment of skilled labour in the economy aji = input coefficients (j = L, K, H, T and i = A, I, M, Y) t = ad valorem rate of tariff on the import of commodity M s = ad valorem rate of subsidy on the export of commodity A and Y λji  =  employment share of jth factor/input in the production of ith commodity; j = L, K, H, T and i = A, I, M, Y Λ = proportional change θji = distributive share of the jth input in the ith commodity The competitive equilibrium conditions in the product market give us the following equations:

Wa LA + Ra TA = PA (1 + s ) (10.1)

Wa LI + ra KI = PI (10.2)

Wa LM + ra KM = PM (1 + t ) (10.3)


Reformatory policies and factor prices

WH a HY + ra KY = PY (1 + s ) (10.4)

The unskilled labour and the specific skilled labour endowment equations are

a LA X A + a LI X I + a LM X M = L (10.5)

a HY X Y = H (10.6)

Full employment conditions for K and T can be expressed as

a KI X I + a KM X M + a KY X Y = K (10.7)

a TA X A = T (10.8)

So, there are eight unknown variables in the system, W, r, R, WH, XA, XI, XM and XY, with eight independent equations. Thus, the system can be solved. From Equation (10.3), we determine the value of r. Plugging the value of r into Equations (10.1), (10.2) and (10.4), W, R and WH are obtained, respectively. The input coefficients, ajis denote the input requirement per unit of output. Once the factor prices are determined, we can calculate all ajis that are not variables. But one has to determine the values of ajis to solve for outputs. Thus, , XI, and XY are simultaneously solved from Equations (10.5)–(10.8).

Effects of reformatory policies Effects of labour market reform Labour market reform is one of the most important structural reforms. Such reform calls for deregulating labour laws with a focus on relaxing wage rigidity. Hence, wage flexibility may generate jobs for workers in the formal sector and workers can move freely between the informal and formal sectors.4 In reality, freely mobile capital between the formal and informal sectors under flexible labour market conditions increases the return to capital in the formal sector. Thus, capital is drawn into the formal sector from the informal sector and the informal wage rate is reduced due to the supply-side response. So, in this model we want to scrutinize the effect of labour market reform defined by lowering W . To do so, we assume unchanged t and s. Also note that commodity prices are considered fixed by virtue of the small open economy assumption. A decline in the formal wage rate W would increase the return to capiˆ > 0 as W ˆ < 0. An increase in r depresses the infortal r: rˆ = - ( qLM / qKM ) W mal wage (W) and skilled wage (WH) because of higher capital costs and fixed commodity prices due to the small country assumption. These are as follows: ˆ S = ( qLM qKY / qKM qHY ) W ˆ < 0 and W ˆ = ( qLM qKI / qKM qLI ) W ˆ < 0 as W ˆ < 0. W 199

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As agricultural workers and informal workers earn identical wage W, a reduction in W leads to an increase in R, a return to the specific factor T in A: ˆ < 0. Therefore, on the output front, ˆ > 0 as W Rˆ = - ( qLM qKIqLA / qKM qLIqTA ) W depending on the factor intensity comparison and the elasticity of substitution, we may have various possibilities. Based on these outcomes, we have the following propositions. Proposition 1: Labour market reform leads to: (i) a decline in W and WH and an increase in r and R; (ii) a contraction of I and Y; (iii) an expansion of A and M. Explanation Labour market reform or a fall in the negotiated wage in the formal sector would cause a lowering of the cost of production in the formal manufacturing sector for a given cost of capital and PM. As M is more capital intensive than I and Y, this sector may appropriate this cost advantage by investing more on capital and producing more output. Thus, M expands and the demand for capital increases which helps to increase the return to capital r.5 So, capital will be redirected from other sectors to M as capital is now mobile among M, I and Y. Subsequently, this lowers the return to both the unskilled workers (W) and the skilled workers (WH).6 Following this change in factor prices, the output effects of different sectors depend solely on factor substitutability. A change in Y is shown in Equation (10.A.7) in Appendix A.1. Labour market reform increases r and decreases WH which helps producers to economize on the use of K and substitutes K with H. And as H is fixed in supply, Y contracts. On the other hand, when W decreases, the agricultural sector may appropriate this by investing more in land and hence the return to land increases and results in an expansion of A.7 On the other hand, an increase in r enhances the cost of using capital in the production of informal goods. Consequently, I may substitute K with L. But the expansion of A requires more L. This produces a shortage of labour supply for I. Hence, I contracts. Proposition 2: Labour market reform decreases the wage gap between skilled and unskilled labour in the economy. Explanation As mentioned before, this model successfully explains why both skilled and unskilled wages decline due to labour market reform. However, the skilled– unskilled wage disparity crucially depends on the factor intensity assumption and thus the rates of change in WH and W. The expression for the wage gap is given ˆ H -W ˆ = qLM ( qKY qLI - qKIqHY ) / qLIqKM qHY W ˆ . So, if Y is more capital by W


) (


intensive than I, i.e., θKY > θKI, then the wage gap decreases owing to labour marˆ H -W ˆ < 0. This is due to the fact that the factor intensity ranking ket reform W



may ensure a larger increase in the return to capital in Y than in I which, in turn, explains why we may experience a bigger fall in WH compared to W. 200

Reformatory policies and factor prices

Effect of tariff reduction Tariff liberalization is another important reformatory policy for developing nations. So, we attempt to examine the effects of tariff liberalization on factor prices and output. Consequent upon a liberalized trade policy in the form of declining tariffs, r reduces as formal manufacturing labour is unionized and obtains a predetermined wage W . The exact effect is shown as ˆr = ( a / qKM ) ˆt < 0 as ˆt < 0. This leads to an improvement in the wages earned by informal labour and skilled labour. The mathematical results are as follows: ˆ H = - ( aqKY /qKM qHY ) ˆt > 0 and W ˆ = - ( aqKI /qKM qLI ) ˆt > 0 as ˆt < 0. Again, W when W goes up, R must fall. This is apparent from Equation (10.1) asPA is given. Therefore, Rˆ = aqKIqLA qKM qLIqTA ˆt < 0 as ˆt < 0. In what follows, output effects depend on the elasticity of substitution between factors used in production. These results can be summarized in the following proposition. Proposition 3: Tariff liberalization leads to (i) an increase in W and WH and a decrease in r and R; (ii) an expansion of I and Y and a contraction of both A and M; (iii) an increase in the skilled–unskilled wage gap if θKY > θKI. Explanation When t is slashed, M contracts due to a less protectionist effect on M.8 The resources are redirected from M to other sectors. Since M is capital intensive compared to other sectors (I and Y), released K per unit of L from M is higher than K demanded per unit of L in I and Y. This indicates an excess supply of capital in the economy, causing a reduction in r. When r decreases, the cost of using capital in production is lower and such a cost reduction can be appropriated by employing more labour in the informal sector through the factor substitution and profit-maximizing behaviour of producers. This helps to increase the informal wage rate W, and hence I expands. Changes in I are shown in Equation (A.10.19) in Appendix A.2. Again, a reduction in r makes capital constraint more binding and an increase in W makes labour constraint less binding. Subsequently, the output effects of A and Y depend on factor substitutability.9 As A uses unskilled labour and land, an increase in W causes a cost enhancement in the agricultural sector. This depresses the return to T, and thus A must contract. This is because of the specificity of the use of T in A. Again, when r decreases, WH goes up for the unchanged commodity price of Y. H being the specific factor, Y inflates because WH is now higher, leading to a fall in aHY. Both skilled and unskilled labour benefit due to the tariff reduction but the skilled– ˆ H -W ˆ hinges on the magnitude of the increase in W and unskilled wage gap W H ˆ H -W ˆ = a ( qKIqHY - qKY qLI ) / qLIqKM qHY ˆt . W. The wage gap is given by W Higher capital intensity in Y compared to I leads to a higher return to capital in Y than I. This causes WH to rise more than W. So, tariff liberalization worsens the skilled–unskilled wage gap, if θKY > θKI.




) (



Mandal, Ghosh and Chaudhuri

Effect of a reduction in export subsidy A subsidy is generally described as the opposite of a tax. It signifies the difference between domestic market prices and world prices, and the prime objective of a subsidy is to create a wedge between consumer prices and producer costs. Export subsidies are thought of as internal price supports to enhance production in the home country. Thus, subsidies may be provided in the form of reduced tax liability, low interest government loans, etc. Export subsidies can also be spent on wage hikes demanded by workers. Again, exports of subsidized products also affect domestic production of the same product in the importing country. So, export subsidy reduction is an important trade policy reform in a liberalized regime. In line with this view, in this model we want to check the effect of a reduction in subsidies on the agricultural and service sectors. In our model, we have introduced export subsidy (s) in two exportable sectors A and Y. So, a change in s will be directly appropriated by R and WH. Note that there will be no change in W and r since these are already determined from Equations (10.2) and (10.3) for given PI and PM. Because of the constant returns to scale assumption, the output of I and M would not change. So, from Equations (10.1) and (10.4), it is apparent that the return to skilled labour (WH) and land (R) would decrease as the subsidy rate is reduced. The expressions are ˆ H = ( b / qHY ) sˆ < 0 and Rˆ = ( b / qTA ) sˆ < 0 as sˆ < 0 .10 Subsequently, using W the concept of the elasticity of substitution, we derive the following expressions ˆ A = bsA ( qLA / qTA ) sˆ and for the output effect on A and Y. These are given by X ˆ Y = bsY ( qKY / qHY ) sˆ , respectively. Based on these outcomes, we have the folX lowing proposition. Proposition 4: Y and A contract along with a decrease in WH and R owing to export subsidy reduction. Subsidy reform also lowers the skilled–unskilled wage gap. Explanation When a subsidy11 goes down, the effective price of A and Y decreases. Producers intend to produce less and this lowers the demand for factors. We have already mentioned that W and r remain unchanged due to the non-changing commodity prices and tariff rate. So, A wants to substitute labour for land in production. As land is specifically used in A, the fixed supply of this factor results in a contraction of this sector. For similar reasons, Y also shrinks due to subsidy reduction. On the wage disparity front, the skilled–unskilled wage difference (WH – W) depends on the rate of changes of WH and W. Although subsidy reform decreases ˆ H, W ˆ remains unchanged. We mentioned earlier the the skilled wage rate W underlying reasons for such effects. Since we know about the ranking of wages of different categories (WH > W), the wage gap between skilled and unskilled workers is lowered due to subsidy reduction. 202

Reformatory policies and factor prices

Extended model with corrupt informal sector The informal sector plays a crucial role in employment generation in developing economies. This argument is quite well accepted by now, and does not require much clarification. The problems of the informal sector, nevertheless, arise because of its unrecorded, non-tax-paying, extralegal characteristics. In such cases, informal production units need to pay an extra cost in the form of a bribe or an extortion fee to local political leaders or government officials to protect unbridled informal production from legal troubles. Government bureaucracy could also be a part of this negotiation process.12 Since the informal sector does not abide by all government rules and regulations because of being lubricated by the process of the intermediation of extortionists, our model assumes the existence of such workers who take care of these problems related to informal production. We call them unproductive workers13 (Lc) in informal production activities because without them the quantity of output would not change. However, the survival of the informal sector depends on the existence of such a sector where extortionists are paid an extortion fee which is defined as “u”. This is the fraction of the value of the informal output that is lost due to such activities. Now, we modify the structure of the basic model by introducing the cost of corruption “u” into the informal of its characteristics, it pays some extra costs for its survival. We also assume that these workers are paid an identical wage to informal workers. In a competitive set-up, total expenditure on Lc has to be equal to the lost14 value of output (uPIXI), which is essentially paid to extortionists. Therefore, Equations (10.2) and (10.5) of the original model are now modified as

Wa LI + ra KI = PI (1 - u ) (10.2A)

a LA X A + a LI X I + a LM X M = L - Lc (10.5A)

We mentioned earlier that Lc are also paid at the rate W which is the same as the amount earned by informal productive workers. The total value of output lost in I is uPIXI. In a competitive set-up, this must be equal to the payment made to such people engaged in intermediation-related extortion activities. Thus, the value– cost equality of corruption is

uPI X I = WLc (10.9)

Other equations and assumptions of the model remain the same as in the basic model. So, we have nine unknown variables in the system, W, r, R, WH, XA, XI, XY, XM and Lc, with nine independent equations. From Equation (10.3), for a given tariff rate, formal wage W and r remain unchanged. When r is unchanged, from Equation (10.4), WH also does not change for given s. So, W can be calculated in terms of uˆ from Equation (10.2), and R would be solved from Equation (10.1). 203

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Thus, all ajis are determined through a constant returns-to-scale (CRS) assumption. Then, using endowment equations we solve the outputs of different sectors. Hence, Equations (10.6) and (10.8) give us the value of Y and A as the endowment of H and T are constants. Thus, M and I are simultaneously solved from Equations (10.5) and (10.7). Consequently, Lc is solved through Equation (10.9). Detailed mathematical results are provided in Appendix B, which helps us to understand the effect of a decrease in the cost of corruption on the factor prices and outputs of different sectors along with the wage gap between skilled and unskilled workers.

Effect of bureaucratic reform on informal wage and output In order to consider the effect of bureaucratic reform, we assume that for some reason the cost of corruption or the extortion fee (u) falls. This extortion fee may be reduced because of the qualitative improvement in administrative officials, the effective delivery of services and better local governance. A decrease in u leads to an increase in the effective price of I which is represented as PI(1 – u). From Equation (10.3) it is evident that r must remain unchanged. Hence, the wage rate ˆ = - ( d /qLI ) uˆ > 0 as of unskilled workers W would increase as u decreases: W uˆ < 0. When W increases, from Equation (10.1) R goes down as PA is given. Subsequently, using the concept of the elasticity of substitution, we solve for the value of A.15 The magnitude of the effect on I and M can be expressed as

ˆ I = l KM D1uˆ > 0 and X ˆ M = - l KI D 2uˆ < 0 as uˆ < 0 X l l

where æ ö ö d ÷ qLA > 0, D 2 = ç l u + q + l LA D1 ÷ > 0 LI ø è ø

æ q + qLA D1 = sA ç TA è qTA qLI

l = éël LM l KI - ( l LI + l u ) l KM ùû < 0

Thus, we have the following proposition: Proposition 5: Due to bureaucratic reform: (i) W increases; (ii) the wage gap (Wˆ H - Wˆ ) falls; (iii) the informal output expands. Explanation When the cost of corruption decreases, informal producers pay less money to extortionists to sustain their production. Hence, this sector takes advantage of this cost reduction and invests in other factors, and tries for output expansion following the optimization principle. Thus, W increases. Interestingly, though, 204

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we incorporate the cost of corruption only into the informal sector in this model, which also induces changes in WH and R. WH remains constant and R falls. As T is the specific factor in A, a fall in R implies an increase in aTA. Hence, A shrinks (from Equation [10.8]), releasing L that moves to I, and I expands. ˆ , whereas W is unchanged. Therefore, it is Bureaucratic reform raises W H ˆ H -W ˆ ) is also apparent that the wage gap between skilled and unskilled labour ( W reduced. Effect on number of extortionists In this extended part of the model, extortion or corruption is an important issue. Extortionists or unproductive workers (Lc) are involved in informal production to combat legal hassles. We have already mentioned that these workers get a wage (W) equal to the unskilled wage. Equation (10.9) has described the payment made to those engaged in corruption or intermediation activities. A reduction in the cost of corruption increases W and naturally leads to an expansion of I. From the factor price effects, it is understandable that both A and M contract and release some L who are either employed in I or in extortion activity. So, it is not unambiguous if Lc would rise, which is corroborated by Equation (10.9) as well. Detailed mathematical calculations are provided in Appendix B (see Equations [10.B.4] and [10.B.7] for further clarification). One interesting point to note is that although I expands, Lc may not rise. This is because extortionists are now more efficient, which is shown by an increased W. Such an increase in productivity to match the higher wage rate may drive this interesting outcome. Therefore, the effect on Lc is uncertain. The desired mathematical expression ˆ is Lc = l u l KM / l + l u + ( d /qLI ) uˆ . Thus, we have the following proposition:




Proposition 6: Bureaucratic reform leads to an ambiguous effect on the number of extortionists.

Conclusion Reformatory policies are quite common and are supposed to have desirable results in developing economies. In order to examine the veracity of such claims, this chapter starts with a four-sector general equilibrium model in which both formal and informal sectors are embedded. The economy has a formal sector that uses skilled labour as a specific factor whereas unskilled labour is assumed to be perfectly mobile among the remaining three sectors. Capital is also mobile between two formal sectors and the informal one. Against this backdrop, it has been shown that labour market reform and tariff reform produce distinctly opposite outcomes on the factor returns and output. Labour market reform leads to a contraction of both the informal and the service sectors with decreasing unskilled and skilled wage rates, but manufacturing and agricultural output expands. A tariff reduction 205

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policy, on the other hand, comes up with distinctly opposite results. A reduction in subsidies yields effects similar to the results of labour market reform in respect of skilled labour and similar to a tariff cut on the capital front. However, skilled– unskilled wage disparity is reduced owing to both labour market reform and subsidy reduction, whereas a tariff cut worsens it. Later on, the basic model is extended to incorporate corruption in the informal sector, and it is found that a decrease in the cost of corruption helps to increase the informal wage along with expanding informal output, but the effect on the number of extortionists is ambiguous.

Acknowledgements We thank Saibal Kar, Bishakha Ghosh, Byasdeb Dasgupta and Dibyendu Maiti for many insightful comments. We also gratefully acknowledge the comments received from the editors of the volume. Those comments were very helpful in refining our arguments. The usual disclaimer applies.

Notes 1 Specifically 82% in South Asia, 66% in sub-Saharan Africa, 65% in East and South East Asia, 45% in the Middle East and North Africa and 51% in Latin America of total employment is considered as the informal labour force (Vanek et al., 2014). According to ILO India Labour Market Update (2016) and NSSO data (2011–12), more than 90% of employment in the agricultural sector and close to 70% in the non-agricultural sector fall under the informal category. 2 Extortion is often referred to as payment to intermediators who arrange for intermediation with government in order to protect extralegal informal production units. Sometimes, such negotiation is done by politically supported intermediaries. We may call them “extortionists”. 3 Special training includes higher education, technical training, computer literacy, software knowledge, etc. 4 A policy of labour reform in M comes with a lower labour wage in M. This increases the demand for labour in M as the sector, now earning super-normal profits, wants to expand. Such extra demand should be matched by labour supply from informal units. Hence, this exodus of labour should actually raise the wage in the informal sector. But capital is also released from informal production units. Therefore, the informal wage will reduce only if the effect of capital outflow outweighs the effect of labour outflow. 5 See Equation (10.A.11) in Appendix A.1 6 The factor intensity assumption of M is very important here. 7 This produces a sort of Rybczynski effect. As A is land intensive. See Equation (10.A.9) in Appendix A.1. 8 See Equation (10.A.20) in Appendix A.2. 9 The exact effect is shown in Equations (10.A.17) and (10.A.18) in Appendix A.2. 10 Where β = s/(1 + s) > 0. 11 Note that for a small open economy we don’t need to distinguish between ad valorem and a specific or per unit subsidy because of the fixity of commodity prices. 12 Bureaucracy is an administrative institution managed by government officials which works according to government policies, rules and regulations. Bureaucrats provide services such as policy formulating, coordinating and monitoring or they provide


Reformatory policies and factor prices



15 16

incentives to limit corruption. Therefore, it is likely that they are involved in such negotiation processes. Intermediaries are unproductive in that their marginal productivities in terms of the volume of output are zero though they achieve positive returns for their work. In our model, we have used this sort of intermediation as directly unproductive profit-seeking activities (Bhagwati, 1982) to reflect on the concept of corruption. By the word “lost” we only indicate loss from the value of production that does not directly come back to the productive factors of production. This “lost” value is, however, very much needed for production in the informal sector. Notice that some workers are also paid by the “lost” value. So, this is not truly “lost” from the economy (Mandal, 2018), it is very much within the system. See Equation (10.B.3) in Appendix B. Y uses capital more intensively than I.

References Agenor, R. and Montiel, P. (1996), Development Macroeconomics, 2nd edition, Princeton, NJ: Princeton University Press. Anderson, J. and Marcouiller, D. (2002), “Insecurity and the Pattern of Trade: An Empirical Investigation”, Review of Economics and Statistics, vol. 84(2), pp. 342–352. Bhagwati, J. (1982), “Directly Unproductive Profit Seeking (DUP) Activities”, Journal of Political Economy, vol. 90(5), pp. 988–1002. Bhagwati, J. (1988), Protectionism, 1st edition, Cambridge, MA: Massachusetts Institute of Technology (MIT) Press. Carruth, A. and Oswald, A. (1981), “The Determination of Union and Non-Union WageRates”, European Economic Review, vol. 16(2), pp. 285–302. Charmes, J. (2000), “Informal Sector, Poverty and Gender: A Review of Empirical Evidence”, Background Paper for the World Development Report 2001, Washington, DC: World Bank. Chaudhuri, S. (2000), “Rural-Urban Migration, the Informal Sector, Urban Unemployment and Development Policies: A Theoretical Analysis”, Review of Development Economics, vol. 4(3), pp. 353–364. Dixon, H. (1998), “Controversy: The Macroeconomics of Unemployment in the OECD”, Economic Journal, vol. 108, pp. 779–781. Dutta, N., Kar, S. and Roy, S. (2013), “Corruption and Persistent Informality: An Empirical Investigation for Indian States”, International Review of Economics and Finance, vol. 27, pp. 357–373. Fiess, N., Fugazza, M. and Maloney, W. (2002), “Exchange Rate Appreciations, Labor Market Rigidities, and Informality”, World Bank Policy Working Paper 2771. Fiess, N., Fugazza, M. and Maloney, W. (2008), “Informality and Macroeconomic Fluctuations”, IZA Discussion Papers 3519. Goldberg, P. K. and Pavcnik, N. (2003), “The Response of the Informal Sector to Trade Liberalization”, Journal of Development Economics, vol. 72(2), pp. 463–496. Helpman, E. and Krugman, P. (1989), Trade Policy and Market Structure, Cambridge, MA: Massachusetts Institute of Technology (MIT) Press. International Labour Organization (2016), India Labour Market Updates, Geneva: ILO. Jones, W. R. (1971), “A Three Factor Model in Theory, Trade, and History”, in J. Bhagwati et al. (Eds.), Trade, Balance of Payments and Growth (pp. 3–21), Amsterdam: North-Holland.


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Mandal, B. (2018), “Tax on Traded Goods, and Corrupt Non-Traded Goods Sector: Implications for Intermediation Activities”, Review of Economics, vol. 69(1), pp. 1–15. Mandal, B., Marjit, S. and Beladi, H. (2018), “Reform, Informal Sector and Extortion”, Economics and Politics, vol. 30(1), pp. 106–123. Marjit, S. and Kar, S. (2005), “Pro-Market Reform and Informal Wage: Theory and the Contemporary Indian Perspective”, India Macroeconomics Annual, pp. 130–156. Marjit, S. and Kar, S. (2011), The Outsiders: Economic Reform and Informal Labour in a Developing Economy, New Delhi: Oxford University Press. Marjit, S. and Kar, S. (2015), “Broader Implications of Labor Market Reforms in India: A General Equilibrium Perspective”, Indian Economic Review, vol. 49(1), pp. 27–35. Marjit, S., Kar, S. and Maiti, D. (2009), “Labor Market Reform and Poverty: The Role of Informal Sector”, in B. Dutta, E. Somanathan and T. Ray (Eds.), New and Enduring Themes in Development Economics (pp. 229–240), Singapore: World Scientific. Marjit, S. and Maiti, D. (2006), “Globalization, Economic Reform and Informal Labor”, in B. Guha-Khasnobis and R. Kanbur (Eds.), Informal Labor Markets and Development (pp. 9–28) New York: Palgrave-MacMillan. Marjit, S. and Mandal, B. (2012), “Domestic Trading Costs and Pure Theory of International Trade”, International Journal of Economic Theory, vol. 8(2), pp. 165–178. Marjit, S., Mukherjee, V. and Kolmar, M. (2006), “Poverty, Taxation and Governance”, Journal of International Trade and Economic Development, vol. 15(3), pp. 325–333. National Sample Survey Organization (2011–12), “National Sample Survey (68th Round)”, Employment and Unemployment Situations in India, Report No. 554(68/10/1). Paz, L. (2012), The Effect of Trade Liberalization on Payroll Tax Evasion and Labor Informality, Mimeo: Syracuse University. Salunkhe, H. A. and Deshmukh, B. B. (2016), “A Study of Agricultural Subsidy and its Impact on Agriculture Sector with Reference to Jalgaon District”, International Journal of Sciences, Spirituality, Business and Technology, vol. 5(1), pp. 6–10. Schneider, F. (2007), “Shadow Economies and Corruption All Over the World: New Estimates for 145 Countries”, Economics – The Open-Access, Open-Assessment E-Journal, vol. 1, pp. 2007–2009. www​.economicsejournal​.org​/economics​/ journalarticles​/2007​-9. Tokman, V. E. (2001), “Integrating the Informal Sector into the Modernization Process”, SAIS Review, vol. 21(1), pp. 45–60. Vanek, J., Chen, M., Carre, F., Heintz, J. and Hussmanns, R. (2014), “Statistics on the Informal Economy: Definitions, Regional Estimates and Challenges”, WIEGO Working Paper (Statistics) No. 2.

Appendices Appendix A Appendix A.1: Labour market reform or a reduction in W Differentiating Equation (10.3) and manipulating, we get

ˆ qLM + rˆqKM = 0 (10.A.1) W


Reformatory policies and factor prices

From Equation (10.A.1)

q ˆ > 0 as W ˆ < 0 (10.A.2) rˆ = - LM W qKM

Differentiating Equations (10.2) and (10.4) and substituting the expression for ˆr we get

ˆ H = qLM qKY W ˆ < 0 as W ˆ < 0 (10.A.3) W qKM qHY

ˆ = qLM qKI W ˆ < 0 as W ˆ < 0 (10.A.4) W qKM qLI

The wage gap between skilled and unskilled wages is derived from Equations (10.A.3) and (10.A.4):

( Wˆ


ˆ = qLM ( qKY qLI - qKIqHY ) W ˆ < 0 as W ˆ < 0 -W qLIqKM qHY


if (θKY > θKI).16 ˆ , we obtain Again, differentiating Equation (10.1) and substituting W

q q q ˆ ˆ < 0 (10.A.5) Rˆ = - LM KI LA W > 0 as W qKM qLIqTA

Equation (10.7) yields

ˆ Y = -aˆ HY (10.A.6) X

By definition, the elasticity of substitution between H and K in Y is given by

sY =

aˆ KY - aˆ HY ˆ H - ˆr W

Using the expression for the elasticity of substitution (Jones, 1971) between H and K in Y, we have



ˆ H - ˆr qKY aˆ HY = -sY W

Substituting the values from Equations (10.A.3) and (10.A.2) in the foregoing equation, we obtain

æ q q + qLM qHY aˆ HY = -sY ç LM KY qKM qHY è

ö ˆ ÷ qKY W ø 209

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Using Equation (10.A.6) and simplifying, we obtain

ˆ Y = A1 W ˆ < 0 as W ˆ < 0 (10.A.7) X

where A1 = σY(θLMθKY + θLMθHY/θKMθHY)θKY > 0 Again, from Equation (10.8) we get

ˆ A = -aˆ TA (10.A.8) X

The elasticity of substitution between L and T in A is expressed as ˆ - Rˆ . sA = aˆ TA - aˆ LA /W The application of the envelope theorem and zero profit condition ensures ˆ - Rˆ qLA . aˆ TA = sA W










above ˆ . = sA ( qLM qKIqTA + qLM qLA qKI /qKM qTA qLI ) qLA W




aˆ TA Again Equation (10.A.8) can be rewritten as

ˆ A = -A 2 W ˆ > 0 as W ˆ < 0 (10.A.9) X

where A2 = σA(θLMθKIθTA + θLMθLAθKI/θKMθTAθLI)θLA > 0. Differentiating Equations  (10.5) and (10.6) and substituting the values of Equations (10.A.7) and (10.A.9):

ˆ I + l LM X ˆ M = l LA A 2 W ˆ l LI X

ˆ I + l KM X ˆ M = -l KY A1 W ˆ l KI X

Rearranging the above equations in matrix form, we solve for

ˆ I = 1 A3 W ˆ (10.A.10) X l

ˆ M = - 1 A4 W ˆ (10.A.11) X l

where, A 3 = ( l LA l KM A 2 + l LM l KY A1 ) > 0 , A 4 = ( l LIl KY A1 + l LA l KI A 2 ) > 0, l = ( l LIl KM - l LM l KI ) > 0 if I is more labour intensive than M in comparison with capital. 210

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Appendix A.2: Tariff reform or a reduction in t From Equation (10.3), we get

rˆqKM = aˆt (10.A.12)

where α = (t/1 + t) > 0. Equation (10.A.12) can be rewritten as

a ˆ rˆ = t < 0 as ˆt < 0 (10.A.13) qKM

From Equations (10.2) and (10.4), the values of change in WH and W can be expressed as

ˆ H = - aqKY ˆt > 0 as ˆt < 0 (10.A.14) W qKM qHY

ˆ = - aqKI ˆt > 0 as ˆt < 0 (10.A.15) W qKM qLI

So, the expression for the wage gap between the skilled and unskilled labour is ˆ H -W ˆ = a ( qKIqHY - qKY qLI ) /qLIqKM qSY ˆt > 0 as ˆt < 0 if θ  > θ . W


) (




Again, from Equation (10.1): aqKIqLA ˆ Rˆ = t < 0 as ˆt < 0 (10.A.16) qKM qLIqTA

Differentiating Equation (10.7), using the expression for the elasticity of substitution and using Equations (A.13) and (A.14), we obtain

ˆ Y = -aB1ˆt > 0 as ˆt < 0 (10.A.17) X

where B1 = σY(θKY + θHY/θKMθHY)θKY > 0. Similarly, differentiating Equation (10.8):

ˆ A = -aB2ˆt > 0 as ˆt < 0 (10.A.18) X

where B2 = σA(θKIθTA + θLAθKI/θKMθTAθLI)θLA > 0. Differentiating Equations  (10.5) and (10.6) and then solving them in matrix form yields the following sets of equations:

ˆ I = - 1 B3ˆt > 0 (10.A.19) X l 211

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ˆ M = a B4ˆt < 0 (10.A.20) X l

where B3  =  (λLAλKMB2  +  λLMλKYB1) > 0, B4  =  (λLIλKYB1  +  λLAλKIB2) > 0, l = ( l LIl KM - l LM l KI ) > 0. Appendix A.3: Removal of export subsidy or a reduction in s Differentiating Equations (10.2) and (10.3) and simplifying, we obtain no change in r and W:

ˆ = ˆr = 0 W

ˆ = ˆr = W ˆ = 0 . Factor substituNote that nothing would happen to I and M as W tion is not permitted due to non-changing factor prices. Moreover, unchanged factor supply confirms the constancy of I and M. ˆ , we get Differentiating Equation (10.1) and substituting W

b ˆ Rˆ = s < 0 as sˆ > 0 (10.A.21) qTA

where β = s/(1 + s) > 0. Again, differentiating Equation (10.4) and substituting ˆr , we get

ˆ H = b sˆ < 0 as sˆ > 0 (10.A.22) W qHY

Differentiating Equation (10.7) and using the expression for the elasticity of substitution and Equation (10.A.22), we obtain

ˆ Y = bC1sˆ < 0 as sˆ > 0 (10.A.23) X

where C1 = σY(θKY/θHY) > 0. Similarly, Equation (10.8) yields

ˆ A = bC2sˆ < 0 as sˆ > 0 (10.A.24) X

where C2 = σA(θLA/θTA) > 0. Appendix B Effects of bureaucratic reform Bureaucratic reform is considered in the form of a decrease in the cost of corruption. Corruption is incorporated in the informal sector only. 212

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Differentiating Equations (10.3) and (10.4) and simplifying, we obtain

ˆ H = rˆ = 0 W

Differentiating Equation (10.2) and substituting ˆr , we get

ˆ = - d uˆ > 0 as uˆ < 0 (10.B.1) W qLI

where δ = (u/1 – u) > 0. Again, differentiating Equation (10.1) and substituting Equation (10.B.1):

dqLA Rˆ = uˆ < 0 as uˆ < 0 (10.B.2) qLIqTA

Differentiating Equation (10.8) and using the expression for the elasticity of substitution, we obtain

ˆ A = dD1uˆ < 0 as uˆ < 0 (10.B.3) X

where D1 = σA(θTA + θLA/θTAθLI)θLA > 0. Differentiating Equation (10.9):

ˆ I + æ l u + d ö uˆ (10.B.4) Lˆ c = l u X ç qLI ÷ø è

Differentiating Equations (10.5) and (10.6):

( l LI + l u ) Xˆ I + l LM Xˆ M = -D2uˆ

ˆ I + l KM X ˆ M = 0 l KI X

Rearranging the above equations in matrix form and manipulating them, we derive the expressions for

ˆ I = l KM D 2uˆ > 0 (10.B.5) X l

ˆ M = - l KI D 2uˆ < 0 (10.B.6) X l



where D 2 = l u + ( d qLI ) + l LA D1 > 0, l = éël LM l KI - ( l LI + l u ) l KM ùû < 0. ˆ I in Equation (10.B.4) yields Substituting the value of X

æl l d ö Lˆ c = ç u KM + l u + ÷÷ uˆ (10.B.7) ç l q LI è ø 213

11 IMPACT OF TRADE LIBERALIZATION ON INFORMAL EMPLOYMENT A theoretical approach Debabrata Roy Introduction It is a well-recognized fact that a significant share of the economic activities in developing economies takes place in the informal sector. According to Agenor (1996), the share of the informal sector in economic activities varies from 70 to 80 % in many developing countries. This share was more than 90% in India during the 1990s. Consequently, the share of informal employment in total employment is also high in India. According to Chandrashekhar and Ghosh (2013), the share of the informal sector in the Indian economy increased from 84.7 to 86.8% between 1999–2000 and 2004–05. The total workforce expanded by 60 million between 1999–2000 and 2004–05. But formal employment declined and the total increase was due to an increase in informal employment during the same period. It is worth mentioning here that the growth rate in formal employment was negative, but total employment grew at a positive rate as the growth rate in informal employment surpassed the negative growth rate in formal employment. This was very prominent for some industries, particularly manufacturing and trade. It is observed that overall employment grew at only 0.21% per annum between 2004–05 and 2009– 10. If a formal job contract is taken as an indicator of informal employment, then there is a clear trend towards increased informal employment with an increase in the number of the wage employed without any written job contract, or a job contract of less than one-year duration. On the basis of the coverage of social security, it is observed that the number of workers with social security coverage declined (Srivastava, 2012). So, the trend of both of these indicators points towards the informalization of the workforce. At the start of the 2000s, although total employment had increased marginally, the organized sector witnessed an improvement in employment primarily driven by increments in informal employment (Ghose, 2012; Mehrotra et al., 2012). Chandrashekhar and Ghosh (2014) have also shown that the share of informal workers in both public and private firms in the organized sector in non-farming activities is rising. They have also confirmed that informalization has taken place though increasing numbers of contract workers in registered manufacturing. 214

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The informal sector is linked to the formal sector either vertically or horizontally. In other words, the informal sector produces either intermediate goods that are used as a factor input in production in the formal sector, or homogeneous goods in the formal sector, which are competing with the formal sector. In this study, informal employment is defined as workers with no restriction on profitable retrenchment (no adjustment cost/severance pay). More precisely, this segment of workers is not protected by labour laws, trade unions, etc. Informal employment is generated through informal contracts. Firms can employ informal labour through subcontracting production to the informal sector. In recent years, particularly in the post-reform period, it has been observed that informalization of the workforce has taken place in many developing economies (Agenor, 1996). In other words, the share of informal employment in total employment has substantially increased. Ghosh and Srivastava (2012) show that this phenomenon is more visible in the traded goods sector (export and importcompeting sector) in the post-trade reform period in India. Therefore, trade liberalization has an impact on the informalization of the workforce in India. Trade liberalization may either increase or decrease the size of informal employment. However, developing countries have surplus labour, a perfectly elastic labour supply. Hence, a change in labour demand or the size of informal employment in developing countries does not have any significant effect on wages. This is evident from the empirical literature that in developing countries, trade liberalization is one of the key factors responsible for the informalization of the workforce. In the post-reform period, an export boom took place in India during the early years of the 21st century along with positive and significant growth in employment in those sectors. This could be attributed to buoyant global demand during this period. Despite the decline in formal employment, total employment grew with a sharp increase in the low-wage informal employment of women in the export sectors. Later, global demand became bleak due to the global economic crisis of 2008. As a result, the export sector boom came to an end and even witnessed a contraction in output and employment. Most of the informal jobs created during the export boom have withered away in the post-crisis period (Chowdhury, 2011). Further, Ghose (2012) observed that the growing export orientation of the economy has helped to improve employment conditions not because it has brought India’s presumed comparative advantage in unskilled labour-intensive products into play but because it has stimulated the overall growth of the organized sector. Shembavnekar (2019) found that tariff reductions were not associated with significant employment shifts in informal enterprises, a finding that may be attributable to the fact that these enterprises rarely engage in international trade in India. Shembavnekar did not find any linkage between trade and the quality of employment because of selection of particular industries. Similar work carried out on evidence from other developing countries shows similar results. Selwaness and Zaki (2013) show that trade reforms increased 215

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labour informality in Egypt. The study by Akbulut and Eğen (2020) analyzed the effects of import tariff changes on the formal and informal labour market in Turkey. They found that while there was a positive relationship between formal labour employment and import tariff rates, a negative relationship occurred between informal employment and tariff rates. Soares (2005) did not find robust evidence that trade liberalization had a substantial effect on the fall in the proportion of registered workers in Brazil. A reduction in domestic import tariffs showed that the direction of the effect on the share of informal employment depended on the initial labour market conditions. A cut in trading partner import tariffs decreased the share of domestic informal employment and increased the average formal wage. This was empirically established using data for Brazil during 1989–2001 (Paz, 2014). In Mexico, a significant, direct connection can be found between the growth of maquila exports and informal employment, in the sense that the growth of maquila exports did not reverse or reduce the increase in the weight of informal employment (Puyana and Romero, 2006). Reductions in industry tariffs increase labour informality, and the effect is differentially stronger in industries with a larger share of small-size firms for crosssection data. However, a time series analysis found that the fall in the average national tariff decreased aggregate informality in the manufacturing sector but increased it in the non-traded sector in Argentina (Cruces et al., 2018). While informal output increases with deeper trade liberalization, informal employment falls (Fugazza and Fiess, 2010). It can be seen empirically that trade liberalization has a mixed impact on informal employment across the world. It would be interesting to look at this theoretically. However, before we look at it from the theoretical perspective, it is worthwhile critically examining the existing theoretical literature.

Literature review As mentioned in the previous section, large firms subcontract. In the recent past, increased participation by giant multinational corporations (MNCs) along with the growth of small businesses, predominantly family controlled, has been observed throughout the developing world. Many of these small enterprises are vertically integrated into giant firms through subcontracting and horizontal linkages among themselves (Rutten and Upadhya, 1997). A theoretical explanation of trade liberalization and its impact on informal employment has been highlighted in Maiti and Marjit (2008) who focus on the export sector and Goldberg and Pavnic (2003) who focus on the import-competing sector. According to Maiti and Marjit (2008), trade liberalization provides access to international markets to reap the benefits of higher prices in the international market. Producers in the formal sector find it profitable to focus on marketing by subcontracting production to the informal sector. Thus, employment is relocated from the formal sector to the informal sector. Hence, the informalization of the 216

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workforce takes place. Goldberg and Pavnic (2003) showed that trade liberalization allows more competitive international firms to enter the domestic market, whereby domestic firms may lose their market share of the domestic market with a consequential increase in the probability of job losses. Irrespective of their efforts, the formal workers will lose their jobs due to a decline in the market share of the domestic firm with the entry of more efficient international firms. Hence, workers choose to shirk. But firms will offer higher wages not to shirk. This will increase the marginal cost of hiring a formal worker. Hence, firms prefer to choose more low-cost informal workers relative to formal labour to retain their profit level. However, there are some limitations to these studies. Maiti and Marjit explain their results by assuming that the international price is always higher than the domestic price. Thus, they rule out the problem of demand deficiency for exports. But international product demand is volatile which is reflected in the price change (see the Appendix). The model does not consider the volatility in demand in the international market. It was observed that many farmers and artisans lost their jobs when the domestic market was exposed to price volatility in the international market with the opening up of trade. Again, when formal sector producers shift their efforts from production to marketing, this impacts on the productivity of formal labour. This chapter tries to overcome both shortcomings by introducing demand uncertainty. The model could not explain the post-global crisis situation in the recent past because it could not predict what would happen if global prices were less than domestic prices. Goldberg and Pavnic (2003) have addressed the problem of demand to a certain extent by introducing falling price with the entry of international firms in the domestic market. However, the possibility of rise of demand is missing. They assume that there is no supervisor in the formal sector while there is a supervisor in the informal sector. However, it is unclear how a firm can catch a worker who shirks without a supervisor. The introduction of demand uncertainty will give the same result without assuming any difference in supervision across the sectors (Rebitzer and Taylor, 1991). Moreover, workers know that they will get compensation at the time of lay off if they do not shirk and that they will get nothing at the time of lay off if they are caught shirking. Therefore, workers should consider the cost of being unemployed and the benefit from shirking. This aspect is missing. This study attempts to resolve this problem by introducing the concept of demand uncertainty. This study offers another theoretical explanation for trade liberalization and its impact on the nature of employment for both the export and the import-competing sectors. Again, this study distinguishes between formal and informal workers by labour institutions such as labour laws, whereas Rebitzer and Taylor (1991)1 distinguished between formal and informal workers by effort level and wage rate. When an economy is closed because it is protected by trade barriers, product demand is less uncertain. In other words, domestic firms know the market demand and their share in the market demand because there is a bar on the entry of more competitive international firms and they are delinked from the international market, but 217

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demand may increase/fall due to other reasons in a closed economy. The demand uncertainty in a closed economy may arise due to political instability, war, natural calamity, etc. When trade opens up to the rest of the world, there is a possibility of domestic firms in the import-competing sector losing their market share due to the entry of more competitive international firms. Thus, the domestic firms’ product demand becomes more uncertain (it may remain the same or change). Again, it is observed that the international market is volatile (Cashin and McDermott, 2002; and see the Appendix). In the case of the export sector, it is exposed to the volatile international market with the opening up of the economy. Hence, trade liberalization leads to more demand uncertainty (Kali and Reyes, 2010; Gamberoni et al., 2010). One may think that a country can export even when it does not liberalize/ open up its import-competing sector to the rest of the world. However, in reality, other countries do not provide market access to a country that protects its importcompeting sector by imposing various trade barriers. After trade liberalization, product demand for domestic firms in the export sector emanates from domestic consumers and demand from the rest of the world (Rakshit, 2010; Bhaduri, 1986). Similar to demand uncertainty in the domestic market, demand in the rest of the world is also uncertain. Thus, product demand becomes more uncertain with the liberalization of trade. Under an uncertain demand regime, a firm will choose its input combination of formal and informal workers to maximize its profit. Under a highly uncertain demand regime, a firm will choose more informal labour and less formal labour to maximize its expected profit as compared to that in a less uncertain demand situation. Thus, trade liberalization leads to the informalization of the workforce.

Model In this study, workers are categorized into the following types: formal worker (L1) and informal worker (L2). The formal worker is characterized by a long-term contract and firms have to pay compensation for retrenching within a contract period. On the contrary, the informal worker could be retrenched at any point in time without having to be paid compensation or severance pay. Further, it has been assumed that (i) unit labour costs (W)2 are the same for both types of workers and (ii) productivities are the same for both types of workers, i.e. homogeneous.3 A producer in the formal sector employs an informal worker either through subcontracting to the informal sector or as a service provider (piece rate) such that the employer–employee relationship changes to a business relationship.4 In this model economy, the production function is f(L) which is a concave production function, i.e. f > 0, f″  0 and f ¢¢ < 0 (11.6)

Hence, the second-order condition is satisfied and it is profitable for firms to employ L* formal workers. There may be either boom or recession, so demand is uncertain. Therefore, the expected profit function is

E ( P ) = j éëqH f ( L*, L 2 ) - WL * - WL 2 ùû + (1 - j ) éë qLf ( L *) - WL *ùû (11.7)

Since E(Π(L*)) ≥ E(Π(L1)) for all L1. 220

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In order to maximize profit by choosing L2, the first-order condition is

dP / dL 2 = 0 => f ¢ = W / qH (11.8)

The second-order condition is f″  0 since qL , f ¢ > 0, f ¢¢ < 0

Similarly, from Equation (11.8) we get

dL 2 / dqH > 0

From the first condition (Equation 11.5), we can see that if θL falls, the marginal cost of hiring a labourer is greater than the marginal productivity of the labourer because the right-hand side will be higher and the left-hand side will remain the same. Therefore, firms would like to lay off some workers until f′ = W/θL. Hence, L*falls as θL falls, i.e. dL*/dθL > 0. Again if θH rises, the marginal cost of hiring a labourer is less than the marginal productivity of the labourer. Therefore, it would be profitable for firms to employ more informal labour until f′ = W/θH. Hence, dL2/dθH > 0. Proposition: Demand uncertainty increases with the opening up of trade. With rising demand uncertainty, firms choose relatively more informal labour as compared to formal labour to maximize their profit. So far, it is assumed that demand remains unchanged over time or regime. This assumption can be relaxed, i.e. expected demand may either increase or decrease with a large variance in price in an open economy as compared to that in a closed economy. (a) Expected demand increases Total employment increases with the rising expected demand. It has already been seen that the size of formal employment depends on the price of the output in a recession, θL. θL will be lower as the economy is opened up. Therefore, formal employment in an open economy will be lower than that in a closed economy. Hence, the size of informal employment will increase both in absolute terms as well as in relative terms. (b) Expected demand declines Total employment will be lower when expected demand is lower. Formal employment will decline in absolute terms as a result of increasing demand uncertainty. 221

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However, its relative situation in the economy will depend on the ratio of change in formal employment to change in total employment and this is ambiguous.

Conclusion This study offers a theoretical explanation for the relationship between trade liberalization and a rise in the share of informal employment in a labour-surplus developing economy. It has been observed that product demand becomes more uncertain with the opening up of trade. When an economy opens up, domestic firms in the export sector are exposed to volatile prices in the international market. Again, domestic firms in the import-competing sector are facing competition from international firms. Increasing uncertainty of demand ushered in by trade reforms induces firms to opt to employ less formal labour and more informal labour to maximize their profits. This increase in informal employment can take place either through subcontracting to the informal sector or through offering more informal contracts within the formal sector. Thus, trade liberalization results in the informalization of the workforce.


Figure 11.1  Free market commodity prices, monthly, January 1960–May 2013. Source: UNCTADSTAT.


Impact of trade liberalization on informal employment

Notes 1 This study explains how demand uncertainty pushes firms to choose informal labour over formal labour in order to be competitive in the market for their respective effort level and wage rate. Thus, increasing demand uncertainty leads to the informalization of the workforce. 2 Introducing wage differential between the two types of workers strengthens the result. 3 There is no technological difference between the sectors as these are not very skillintensive industries. 4 Although it is observed that there is a wage differential between informal and formal workers, formal sector producers employ informal labour through a service provider company/intermediaries by paying the same wage cost to hire and fire without paying any severance pay. The difference between wages is retained by the service provider company/intermediaries. This model does not capture the above-mentioned story explicitly, the introduction of which will only complicate the analysis, but the results will remain unaltered. 5 For the rising expected demand. 6 Firms have to employ a certain number of formal labourers because of institutional obligations. 7 Subcontracting is not illegal/violating labour law because it does not come under the purview of labour law as it is a pretend business relationship rather than an employer– employee relationship.

References Agenor, P. (1996): The Labor Market and Economic Adjustment. IMF Staff Papers, 43(2), 261–335. Akbulut, H. and Eğen, H. T. (2020): Import Tariffs and Informal Labour Market: A Computable General Equilibrium (CGE) Analysis for Turkey. Ten Years of the Review of Economic Analysis, 12(2), 129–132. Bhaduri, A. (1986): Macroeconomics: The Dynamics of Commodity Production. New Delhi: Macmillan India Ltd. Cashin, P. and McDermott, C. J. (2002): The Long-Run Behavior of Commodity Prices: Small Trends and Big Variability. IMF Staff Papers, 49(2), International Monetary Fund, 175–199. Chandrasekhar, C. P. and Ghosh, J. (2013): India’s Informal Economy. Business Line, October 28. Chandrasekhar, C. P. and Ghosh, J. (2014): Contract Workers in Manufacturing. Business Line, April 28. Chowdhury, S. (2011): Employment in India: What Does the Latest Data Show? Economic & Political Weekly, 46(32), 23–26. Cruces, G., et al. (2018): Trade Liberalization and Informality in Argentina: Exploring the Adjustment Mechanisms. Working Papers 0229, CEDLAS, Universidad Nacional de La Plata. Fugazza, M. and Fiess, N. (2010): Trade Liberalization and Informality: New Stylized Facts. UNCTAD Blue Series Papers 43, United Nations Conference on Trade and Development. Gamberoni, E., et al. (2010): The Role of Openness and Labour Market Institutions for Employment Dynamics during Economic Crises. Employment Working Paper No. 68, International Labour Office, Employment Sector, Trade and Employment Program.


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Ghose, A. K. (2012): Employment: The Fault Line in India’s Emerging Economy, Palgrave Macmillan. Association for Comparative Economic Studies, 54(4), 765–786. Goldberg, P. and Pavnick, N. (2003): The Response of the Informal Sector to Trade Liberalisation. Journal of Development Economics, 72(2), 463–496. Kali, R. and Reyes, J. (2010): Financial Contagion on the International Trade Network. Economic Inquiry, 48(4), 1072–1101. Maiti, D. and Marjit, S. (2008): Trade Liberalization, Production Organization and Informal Sector of the Developing Countries. The Journal of International Trade & Economic Development: An International and Comparative Review, 17(3), 453–461. Marjit, S., et al. (2007): Informality, Corruption and Trade Reform. European Journal of Political Economy, 23(3), 777–789. Mehrotra, S., et al. (2012): Joblessness and Informalization: Challenges to Inclusive Growth in India. Occasional Papers 9/2012, Institute of Applied Manpower Research, Planning Commission, Government of India. Paz, L. S. (2014): The Impacts of Trade Liberalization on Informal Labor Markets: A Theoretical and Empirical Evaluation of the Brazilian Case. Journal of International Economics, Elsevier, 92(2), 330–348. Puyana, A. and Romero, J. (2006): Trade Liberalization in Mexico: Some Macroeconomic and Sectoral Impacts and the Implications for Macroeconomic Policy. Paper presented at the International Development Economics Associates (IDEAS) and United Nations Development Programme (UNDP) Conference on “Post Liberalisation Constraints on Macroeconomic Policies”, Chennai, 27–29 January. Rakshit, M. (2010): Global Downturn and Cross-Border Trade: Some Theoretical and Policy Perspectives. Economic & Political Weekly, 45(18), 43–56. Rebitzer, J. B. and Taylor, L. J. (1991): A Model of Dual Labor Markets When Product Demand is Uncertain. The Quarterly Journal of Economics, 106(4), 1373–1383. Rothschild, M. and Stiglitz, J. E. (1970): Increasing Risk: I. A Definition. Journal of Economic Theory, 2(3), 225–243 . Rutten, M. and Upadhya, C., eds. (1997): Small Business Entrepreneurs in Asia and Europe – Towards a Comparative Perspective. New Delhi: SAGE. Selwaness, I. and Zaki, C. (2013): Assessing the Impact of Trade Reforms on Informality in Egypt. Working Papers 759, Economic Research Forum, revised Jun 2013. Shembavnekar, N. (2019): Economic Reform, Labour Markets and Informal Sector Employment: Evidence from India. Economies, MDPI, Open Access Journal, 7(2), 1–42. Soares, F. V. (2005): The impact of trade liberalisation on the informal sector in Brazil. Working Papers 7, International Policy Centre for Inclusive Growth. Srivastava, R. (2012): Employment, Wages and Mobility: Interpreting Changes in Labour Markets in India over Three Decades. Unpublished.



Debottam Chakraborty Introduction The process of economic liberalization, privatization and globalization of the Indian economy in 1991 and the subsequent emergence of the World Trade Organization (WTO) in synergizing global trade policies have totally changed the ways and practices of businesses and trade taking place nationally, regionally and globally. It has had major implications for all sectors of the Indian economy in general and West Bengal in particular in the agriculture, industry and services sectors. In a world characterized by the rising pace of globalization, fast-growing foreign trade in commodities and services for countries such as India and a greater flow of cross-border capital, the role of the WTO has become extremely important. Decision-making in the WTO is based on negotiations and consensus, and the signatories to WTO agreements are bound to abide by these agreements. However, WTO compliance often entails major changes to an economy, with high adjustment costs if proper policies and strategies are not adopted in a proactive manner. The desired roles of central government and state governments in the federal set-up in India have to undergo strategic shifts, and the active role of the state government is an absolute necessity. For example, agriculture comes under state jurisdiction, while the WTO is under central jurisdiction. Similarly, much of the services sector (e.g. financial services) comes under the purview of the central government, and industry is subject to regulation by both central government and state government. In agriculture-related issues, while the central government takes a pan-India view, proper representation of state-specific issues is required in deciding India’s stand on and strategies in WTO negotiations (or negotiations related to free trade agreements [FTAs]). The same is applicable with respect to services and industry. Here, another issue is to identify specific areas of interest to the state and prepare the ground for intervention in these areas, so that the concerns of the aforementioned sectors are adequately reflected in the central government’s stand on and strategies in WTO negotiations. 225

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With respect to India’s economy (both agriculture and industry), the major issues of concern are not the lowering of import duties or Indian exports facing high or increasing tariffs. The more important concerns include non-tariff barriers (NTBs), such as the sanitary and phytosanitary restrictions (SPS) affecting Indian agricultural exports, and the technical barriers to trade (TBTs) affecting Indian manufacturing exports. Whenever NTBs are changed in the Indian export markets, the domestic production sector catering to exports undergoes some sort of adjustment. The adjustment cost can be quite high as demonstrated by the experiences of different countries (shrimp export by Thailand, chicken export by China, textiles export to the US and the European Union [EU] by various developing countries and so on). A state government needs to play a significant role in this respect in view of the structure of federalism in India. In this context, this chapter discusses some of the major commodities of interest to the state of West Bengal in the wake of changes in the international environment and the comparative advantages the state possesses. WTO issues with respect to these commodities are also highlighted. The second section highlights the relevant literature which explores the area of trade potential. The third section proposes a methodology which is mostly independent of regional trade data. The fourth section presents the results. The fifth section highlights some WTO issues with respect to the trade potential of West Bengal and the sixth section analyzes trade dispute cases. The seventh section concludes the chapter.

Literature review Wu (2003) applied an extended Heckscher–Ohlin model to compare the export performance among Chinese regions. Variables such as government spending, non-state sector development and foreign direct investment (FDI) have been included in the model and it has been observed that they positively affect the intensity of imports. Infrastructure development and government spending also have a positive influence on export efficiency. The state sector also plays an important role in boosting regional export potential, but foreign direct investment does not necessarily have any positive influence on export efficiency. It is found that Chinese regions achieved, on average, above 70% of their export potential during 1992–2001. Regional export efficiency indices were calculated, showing that Chinese regions, in general, performed better in 1998–2001 than the period pre-1998. In Reddy et al. (2003), nominal protection coefficients (NPCs), effective protection coefficients (EPCs) and domestic resource cost (DRC) were computed to measure trade competitiveness. Using these three measures for rice in India, trade competitiveness was estimated using the data from Karnataka on the basis of the importable hypothesis for the two periods: pre-liberalization (1985–86 to 1991– 92) and post-liberalization (1996–97 to 2000–01). The trade competitiveness of a commodity reveals whether a country has an opportunity to engage in export 226

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trade. It was found that rice, which is the major crop in Karnataka state, had been largely competitive on an importable basis with its NPC values below unity during the reference period. The EPC estimates showed that, in only 5 years during the 17-year reference period, it was more than 1, indicating that the state had protected the crop only in those years. However, for the reference period, the average EPC revealed that Karnataka is an efficient producer of rice. The estimates of DRC revealed that the state had a comparative advantage in rice production. Barua and Chakraborty (2010) attempted to find the relationship between inter-regional inequality and trade openness in the case of India. They found that regional inequality in India has been increasing in all components of income except for the primary sector. In these circumstances, while openness had initially led to a rise in both income and manufacturing inequalities, there was clear evidence of a decreasing tendency of inequality as openness had increased. In the case of agriculture, this relationship is just the opposite. Again, any imbalance in infrastructural development within the country would result in a sustained increase in inter-regional inequality in this framework. However, all these results have been drawn on the basis of the generalized openness of a country and not its regional openness. Marjit et al. (2007) proposed a regional trade openness index (RTOI) based on a comparison of the production proportion of a state and the export/import shares of India. The states had been ranked according to the rank correlation for a particular year for a particular state; in the case of exports and imports the same methodology was followed, but an inverse rank was computed. A composite rank was calculated from these two ranks (through the arithmetic mean of the two ranks), and this rank was actually the RTOI. This index was further used to find its relationship with regional disparity. It was found that states with relatively high levels of income are also those with greater exposure to trade and such a relationship has grown stronger over time. Helmers and Pasteels (2006) carried out an analysis based on a decision tree using four indicators: (1) trade potential at the sector level, based on the gravity equation specification; (2) trade flow analysis at the commodity level; (3) trade costs at the commodity level; and (4) supply and demand conditions at the commodity level. It measured the trade potential at the sector level using the International Trade Centre’s (ITC) TradeSim gravity model. A trade flow analysis at the commodity level indicates different parameters such as current trade, indicative trade potentials (measured through the complementarity of trade between countries) and other parameters such as average annual growth rates and unit value. It also takes into account competitors in the exporting countries. The trade cost takes into account import tariffs, trade policy instruments and transportation costs. To assess the supply/demand conditions at the commodity level, Helmers and Pasteels (2006) took into account the quantitative production data, other production variables (such as the rate of utilization of production capacity and production efficiency), product characteristics and consumer preferences, FDI, etc. They identified a few products where all the criteria had been met. Using 227

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this approach, they did not arrive at single numbers, indicating the precise magnitude of export potentials, but at broad qualitative conclusions. Nevertheless, these qualitative assessments allow for the identification of products that have potential and to narrow down the products under analysis. Dotterweich and Hipple (1997) calculated the export attainment index, export potential index and export performance index. Export performance indices were used to indicate the relative level of export attainment versus its potential for each of the eight metropolitan areas in Appalachia. It was found that only one of the eight regions has attained exports in excess of the amount predicted by the export potential index. The figures for two other metro areas seem to suggest that they are both exporting at nearly the national average and may have little room for more export development. The other five metro areas have significantly less export activity than the export potential index would suggest. The degree of deficiency ranges between 6 and 8% less than the national average for the other five metro areas. Krakoff (2003) used trade flow analysis to find potential products and their markets. Different non-tariff barriers and ad valorem duties were used to measure the real barrier to trade for South African exporters. The import penetration ratios (import/gross domestic product [GDP]) of different countries were also estimated to identify the markets. A range of methods and variables have thus been used to find the export potential at the regional or country level. In this chapter, we propose a methodology which was originally used by the WTO Cell, the Government of West Bengal (2007) and later by Chakraborty (2019). We replicated the methodology for a different time span but have obtained fairly similar results. Through this methodology, we can find export potential at the state level through minimal use of published state-level data on exports (due to inaccuracies).

Methodology There is virtually no data or research reports on the WTO issues specific to the state of West Bengal. The situation is further complicated by the fact that the number of WTO-related issues relevant to any sector or a commodity within it is very large. So much so, that any attempt to take a short-term look at the exhaustive set of issues pertaining to all sectors of interest to West Bengal is almost impossible. The only feasible option then is to take sectors and commodities in turn. A reasonable first step in that direction is to identify sectors or commodities that are of immediate interest to West Bengal. There are clearly two sets of such sectors and/or commodities. The first set consists of the sectors/commodities that are already important, in the sense that West Bengal has a sizeable production/ export and production/import of these goods. The second set of sectors/commodities consists of those that are not yet important but have the potential to be so in the near future, especially with respect to exports. Biotechnology and food processing are good examples of such sectors. 228

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To initiate a preliminary discussion on the WTO issues pertinent to West Bengal, a two-step procedure is used. In the first step, the sectors or commodities that are important to West Bengal and have a high degree of outward orientation, either in terms of export or import, are identified. In the second step, small subsets of the huge set of issues that have been debated in the WTO with respect to these sectors are considered. In the first step, we have considered the production data for West Bengal and the trade data from India. Commodities that are of high importance in production have been matched with commodities that have a high trade growth rate. As we are more interested in the potential trade basket and not in the existing one, commodities with a high trade growth rate have been considered rather than commodities that have a high share in export or import. The specific reason for doing this exercise is that the state export data is not accurate. Since the state of origin of an export consignment or the destination of an import consignment does not make any difference to traders, they are not inclined to provide accurate data. So, in most cases, the state export or import data is very difficult to trust. Hence, using production and trade data, we have removed our dependency on the state trade data. This methodology has a serious defect in that it does not incorporate state consumption data or domestic trade data. Since the census data on these items is not available, it is not possible to incorporate them. A solution to this issue would be to carry out a survey; however, such a survey was not incorporated into this work, which is a major drawback of this methodology.

Identification of sectors Based on production figures provided by the Annual Survey of Industries (ASI) and an economic review of the Government of West Bengal, Table 12.1 illustrates the major products and crops (in terms of value of production) in the state in 2015–16. All these products are generally termed traditional products of the state. High steel production in the state is due to the availability of raw materials and the presence of some of the earliest steel plants in the country. The same is true for the chemical industry. The main reason for the growth of the petroleum and petrochemicals industry in West Bengal is the upstream and downstream linkages developed by the oil refining and petrochemical units located in the state. West Bengal’s heritage of textiles is legendary. The exquisite texture of Baluchari Sarees and silk and tussar textiles from Murshidabad, Birbhum, Bankura, Hoogly and Nadia districts have become the choice of the century. The fascinating handloom textiles of the same regions mentioned are now attracting worldwide attention. The availability of skilled manpower has led to the development of the textile industry in the state. As far as agriculture and allied production are concerned, the soil conditions, the availability of water, etc., have helped the state to become the top producer of many of the crops in 229

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India. Jute and tea are traditional cash crops grown in this part of India due to its conducive weather conditions. In order to identify the products that would increasingly be brought under the purview of WTO rules, the growth rate of exports from India between 2010–11 and 2015–16 is first calculated and the result is matched with the production of major commodities in West Bengal. The commodities that are produced in large quantities in value terms in West Bengal and also have a high growth rate in terms of exports from India are thus identified. Table 12.2 lists these commodities along with their export growth rates. The list in Table 12.2 forms the preliminary set of commodities for which WTO rules are relevant for West Bengal. Although this list gives an indication of India’s upcoming export items that are of relevance to West Bengal, it does not account for the exports of other states that are routed through West Bengal. To overcome this limitation, the study has listed the set of commodities, a large (>10%) proportion of which passes through Kolkata Port (Table 12.3). As data regarding export from West Bengal is not available, this is considered the best approximation to account for exports from West Bengal as well as other states passing through the ports of West Bengal. Secondly, there is no port data at ASI/ NIC classification levels for cross verification. Hence, the result is indicative but not analytically sound and this is also a limitation. Matching Tables 12.1 and 12.3, it is clear that the commodities for which export growth for India is high and are of interest to West Bengal are coal, rice, fruit, vegetables, jute manufacturing and textiles. In the next step, in order to smoothen the absolute production figure of the state which is considered in the above list, India’s production of all three-digit categories and the proportion accounted for by West Bengal is calculated after normalizing the aggregate production figures of both India and West Bengal. In other words, the proportion of a particular commodity in West Bengal’s total production is calculated and divided by the proportion of that commodity in India’s production. Obviously, if the resulting figure is greater than 1 then West Bengal has an advantage in producing it in the sense that its share in West Bengal’s production is higher than its share in India’s production. To economists, this is simply the Balassa index of revealed comparative advantage (Balassa, 1965) with the trade figures replaced by the production figures: Yiw



PA iw =







where Yiw is the value of the production of commodity i in West Bengal YiI is the value of the production of commodity i in India 230

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If the value of the production advantage (PA) for commodity i is more than 1, this implies that West Bengal has a production advantage in that commodity compared to the rest of India. This shows the importance of the commodity for West Bengal with respect to the rest of India (Table 12.4). Continuing with the above methodology, commodities that are common to both Tables 12.1and 12.4 are • • • •

Rice Fruit Vegetables Jute manufacturing

Interestingly, all the items (except jute manufacturing) are agricultural items. Thus, these products are produced in bulk in West Bengal and have a high rate of export from India. Further, West Bengal has a considerable share in India’s exports as well as its production. Disregarding the Indian scenario and matching only the production advantage data in Table 12.4 and West Bengal’s export share data in Table 12.3 gives the set of products that appear important, keeping only the scenario in West Bengal (Table 12.5). These commodities are • • • • • • •

Jute manufacturing Leather goods Tea Fresh vegetables Iron and steel Fresh fruit Metal products

Thus far, two sets of commodities (those in bullets) have been identified based on two different criteria. A combination of these two sets of commodities is export items of special interest to West Bengal. International trade laws, including those propounded by the WTO, regional trading agreements and bilateral trade agreements pertaining to these commodities, are therefore important for the export economy of West Bengal. •

Agricultural goods • Rice • Fresh vegetables • Fresh fruit Industrial goods • Jute products • Leather goods • Tea 231

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• •

Iron and steel Metal products

For export, the products have been separated out as per their potential. Based on tariffs, non-tariff barriers in 2016 and the import penetration ratio (imports divided by GDP in 2015–16), this classification is made. Table 12.6 shows this classification with respect to different destinations. As has already been noted, these goods do not include goods that have the potential to become important in the future. The methodology for imports is similar to that of exports. However, an analysis of import items passing through the ports of West Bengal is omitted as they do not indicate the threat of foreign competition faced by domestic producers. Accordingly, the following broad categories of products are important for West Bengal and are likely to face competition from other countries (based on Tables 12.7 and 12.8): • • • •

Chemicals Iron and steel Petroleum, oil and lubricants Edible oils

WTO issues in the identified products The basic tenet of the WTO as far as the commodity sector is concerned is that all member countries must reduce tariffs (or customs duties) on all commodities to WTO-specified rates called “bound rates”. The bound rate may represent either a tariff reduction or a commitment not to raise an existing tariff rate. Forced to commit themselves to these rates and unable to manipulate tariffs to restrict the import of foreign goods into their country, most countries have resorted to tacit means of blocking other countries’ goods. These measures other than tariffs to restrict trade flows are called non-tariff barriers. The most common NTBs are • • • •

Quotas Voluntary export restraints Anti-dumping (AD) duties Countervailing duties

Apart from these NTBs, countries have devised numerous mechanisms to stop the inflow of goods into their market, including erecting technical barriers to trade such as specifying the exact technical specifications of goods to be imported and sanitary and phytosanitary measures such as blocking a good due to health concerns. It should be noted that the WTO recognizes the fact that these are important issues. For example, the WTO encourages the import of food items that are safe for consumption. Such “safety” is subject to interpretation by 232

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the member countries and hence, has effects of different dimensions on trade. If product standards imposed by the importing country are deemed “unfair” by the exporting country then disputes arise. The aggrieved party then goes to “court”, in this case the disputes settlement body of the WTO, as a “complainant”, while the country that has imposed the standard defends its case as a “respondent”. A long process of argument and counterargument follows. Finally, the new standard is accepted or rejected. So numerous are these NTB cases in the WTO that many see the WTO as synonymous with these disputes and their settlement, “fair” or “unfair”. Several of the cases that are relevant to the products identified for West Bengal are listed below. As a prelude to these cases, some of the non-tariff barriers faced by Indian exports in general and for some of the products in particular are briefly indicated. The products on which NTBs are predominantly imposed by the US are woven apparel (19%), knit apparel (7%), textile floor coverings (9%), edible fruit and nuts (5%), fish and sea foods (3%), cotton yarn and fabric (2%), iron and steel products (2%) and vehicles (2%). Similarly in the EU, Indian products facing the highest percentage of NTBs are vegetable products followed by chemical and allied products (27%), prepared food and beverages etc. (19%), base metals and articles of base metals (13%) and machinery, mechanical appliances (15%) and electrical equipment (18%). Among the products that are relevant to West Bengal, vegetables and chemical products face major NTBs in EU countries. Textiles and ready-made garments face NTBs in the US. Figures 12.1 and 12.2 categorize these NTBs according to products for the US and the EU markets.

Others (51%)

Woven Apparel (19%)

Knit Apparel (7%)

Textile Floor Coverings (9%)

Edible Fruits and Nuts (5%)

Fish and Sea Food (3%)

Cotton Yarn Fabric (2%)

Iron & Steel Products (2%)

Vehicles (2%)

Figure 12.1  NTBs facing Indian imports into the United States.

Source: Mehta, R (2005), “Non-tariff Barriers Affecting India’s Exports”, Research and Information System for Developing Countries, New Delhi, Discussion Paper No. 97.


Debottam Chakraborty

Vegetable Products (34%) Products of Chemical & Allied Industries (23%) Prepared Foodstuffs;Breverages, Spirits and Vinegar; Tobacco and Manufactured Tobacco Substitutes (19%) Base Metals and Articles of Base Metal (13%) Machinery and Mechanical Appliances; Electrical Equipment;(4%) Live Animals (2%) Okastucs abd Articles thereof; Rubber and Articles Thereof (2%) Mineral Products (1%) Textiles and Textile Articles (1%) Vehicles, Aircraft, Vessels and Associated Transport Equipment (1%)

Figure 12.2  NTBs faced by Indian imports into the EU.

Source: Mehta, R (2005), “Non-tariff Barriers Affecting India’s Exports”, Research and Information System for Developing Countries, New Delhi, Discussion Paper No. 97.

Trade dispute cases involving India in the products identified for West Bengal In order to provide a basic idea of the nature of the cases brought to the WTO, some sample cases on the products that are important for West Bengal are cited below. Although the state government cannot take part directly in these cases, it may be able to provide relevant input to the central government. It should be noted that there are numerous references to different articles in the WTO in these cases. Cases involving India as a complainant Rice CASE: EUROPEAN COMMUNITIES: RESTRICTIONS ON CERTAIN IMPORT DUTIES ON RICE

On 28 May 1998, India requested consultations with the European Community (EC) in respect of the restrictions allegedly introduced by an EC regulation establishing a so-called cumulative recovery system (CRS), for determining certain import duties on rice, with effect from 1 July 1997. India contended that the measures 234

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introduced through this new regulation would restrict the number of importers of rice from India, and would have a limiting effect on the export of rice from India to the EC. India alleged violations of Articles I, II, III, VII and XI of the General Agreement on Tariffs and Trade (GATT) 1994; Articles 1–7, 11 and Annex I of the Customs Valuation Agreement; Articles 1 and 3 of the Import Licensing Agreement; Article 2 of the TBT Agreement; Article 2 of the SPS Agreement; and Article  4 of the Agreement on Agriculture. India also claimed nullification and impairment of benefits accruing to it under the various agreements cited. Steel CASE 1: US – ANTI-DUMPING AND COUNTERVAILING MEASURES ON STEEL PLATE FROM INDIA

On 4 October 2000, India requested consultations with the US concerning: •

• •

Final affirmative determinations of sales of certain cut-to-length carbonquality steel plate products from India at less than fair value by the US Department of Commerce (DOC) on 13 December 1999 and affirmed on 10 February 2000. Interpretation and use of provisions relating to facts available in the antidumping and countervailing duty investigations by DOC. Determination and interpretation by the US International Trade Commission (ITC) of negligibility, cumulation and material injury caused by the said Indian steel imports.

India considered that these determinations were erroneous and based on deficient procedures contained in various provisions of the US anti-dumping and countervailing duty law. According to India, these determinations and provisions raised questions concerning the obligations of the US under GATT 1994, the AntiDumping Agreement (ADA), the SCM Agreement and the agreement establishing the WTO (WTO Agreement). India considered that the provisions of these agreements with which these measures and determinations appeared to be inconsistent include, but were not limited to, the following: Articles VI and X of GATT 1994; Articles 1, 2, 3 (especially 3.3), 5 (especially 5.8), 6 (especially 6.8), 12, 15, 18.4 and Annex II of the Anti-Dumping Agreement; Articles 10, 11 (especially 11.9), 15 (especially 15.3), 22 and 27 (especially 27.10) of the SCM Agreement; and Article XVI of the WTO Agreement. The panel ruled that the US did not act inconsistently with Article 15 of the AD Agreement with respect to India in the anti-dumping investigation underlying this dispute. CASE 2: EUROPEAN COMMUNITIES – ANTI-DUMPING DUTIES ON CERTAIN FLAT ROLLED IRON OR NON-ALLOY STEEL PRODUCTS FROM INDIA

On 5 July 2004, India requested consultations with the European Communities concerning the imposition of definitive anti-dumping measures on imports of 235

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certain flat-rolled products of iron or non-alloy steel, of a width of 600 mm or more, not clad, plated or coated, in coils, not further worked other than hot rolled (“HR coils”) from India. According to the Indian request, the EC violated Article 9.2 of the AntiDumping Agreement, which requires that an anti-dumping duty shall be collected on a non-discriminatory basis on imports of the product from all sources found to be dumped and causing injury. India claimed that, while anti-dumping measures were in force against imports into the community of HR coils from India, no measures were in force against imports of the same product concerned from Egypt, Slovakia and Turkey, notwithstanding that the products imported from the latter three countries were also found by the commission to be dumped and caused injury to the community industry. India also considered that the anti-dumping measures concerned violated certain other provisions of the Anti-Dumping Agreement, including, but not limited to, Article 3, especially Articles 3.4 and 3.5, and Article 4.1. On 22 October 2004, India and the European Communities notified the dispute settlement board (DSB) that they had reached an agreement with respect to the matter raised by India in its request for consultations. According to the notification, the European Communities agreed to terminate the measure at issue. Jute CASE: BRAZIL: ANTI-DUMPING DUTIES ON JUTE BAGS FROM INDIA

On 9 April 2001, India requested consultations with Brazil concerning the determination by the Brazilian government to continue to impose anti-dumping duties on jute bags and bags made of jute yarn from India, based on an allegedly forged document regarding the dumping margin attributed to a non-existing Indian company: • • • •

Its refusal to reconsider the decision to continue anti-dumping duties on Indian jute products despite the fact that the non-existence of that company was brought to the notice of the authorities. Non-consideration of the fresh evidence regarding cost of production, domestic sales prices, export prices, etc., of Indian jute manufacturers, and refusal to initiate a review of the decision to impose anti-dumping duties. The general practice of Brazil regarding a review and the imposition of antidumping duties. Brazilian anti-dumping laws and regulations, including, but not limited to, Article 58 of Decree No. 1.602 of 1995.

According to India, the provisions with which these determinations and legal provisions appeared to be inconsistent included, but were not limited to, Articles  VI and X of GATT 1994; Articles  1, 2, 3, 5, 6 (especially 6.6, 6.7, 236

Trade potential and WTO issues for West Bengal

6.8 and Annex II, 6.9, 6.10), 11, 12, 17.6(i), 18.3, 18.4; and Article XVI of the WTO Agreement. In addition, the determination to continue the anti-dumping duties allegedly nullified and impaired the benefits accruing to India under, or otherwise impeded the attainment of the objectives of, the cited agreements. Tea CASE 1: UNAUTHORIZED USE AND REGISTRATION OF DARJEELING TEA AND LOGO IN JAPAN (VIOLATION OF GEOGRAPHICAL INDICATION [GI] PROTECTION)

The Tea Board of India filed an invalidation action against International Tea KK, a Japanese company, over the registration of the Darjeeling logo mark, namely, Darjeeling women “serving tea/coffee/coca/soft drinks/fruit juice” in the Japanese Patent Office (JPO) on 29 November, the registration in Japan of the identical Darjeeling logo mark by the Tea Board of India, with the trademark registration number 2153713, dated 31 July 1987. The Tea Board also filed a non-use cancellation action. On 28 August 2002, the JPO board of appeal held that the pirate registration 1996 with the trademark registration number 3221237 was invalid. The impugned registration was made notwithstanding and was invalid because it was contrary to public order and morality. With regard to the Tea Board’s non-use cancellation action, the JPO decided that International Tea KK had not furnished sufficient evidence to substantiate its use of the registration and thereby allowed the appeal of the Tea Board. CASE 2: DEFENDING GI AGAINST DEVELOPED COUNTRIES (NON-RECIPROCITY IN GI PROTECTION)

While the Indian system protects French GIs, France on the other hand does not extend similar or reciprocal protection to Indian GIs. Thus, French law does not permit any opposition to an application for a trademark similar or identical to a GI if the goods covered are different from those represented by the GI. The owner of the GI can take appropriate judicial proceedings only after the impugned application has proceeded to registration. The net effect of such a provision has been that despite India’s protests, Darjeeling has been misappropriated as a trademark in respect of several goods in class 25, namely, clothing, shoes and headgear. Even though the French examiner found evidence in favour of the Tea Board of India (i) on sufficient proof of the use of “Darjeeling” tea in France, and that (ii) the applicant had slavishly copied the name Darjeeling in its application, he held that the respective goods “clothing, shoes, headgear” and “tea” are not of the same nature, function or intended use, are produced in different places and are sold through different networks. The examiner also held that even if the applicant had slavishly copied the Tea Board’s Darjeeling logo (being the prior mark), the difference in the nature of the respective goods was sufficient to hold that the 237

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applicant’s mark may be adopted without prejudicing the Tea Board’s rights in the name “Darjeeling”. Cases involving India as a respondent Chemicals CASE: INDIA: ANTI-DUMPING MEASURES ON CERTAIN PRODUCTS FROM THE SEPARATE CUSTOMS TERRITORY OF TAIWAN, PENGHU, KINMEN AND MATSU

On 28 October 2004, Chinese Taipei requested consultations with India concerning the provisional and definitive anti-dumping measures imposed by India on the following seven products: acrylic fibres, analgin, potassium permanganate, paracetamol, sodium nitrite, caustic soda and green veneer tape. According to the request for consultations from Chinese Taipei, India violated its WTO obligations in a number of ways, including: • •

• •

• • • •

The rejection of the information provided by exporters without providing reasons; and the lack of satisfaction as to the accuracy and reliability of the information provided by the domestic industry. The initiation of the investigations and the imposition of the anti-dumping duties, despite no imports of the product concerned from Chinese Taipei into India during the period of investigation, and despite the insufficiently substantiated petitions for the initiation on the existence of dumping and injury. The lack of correct determination of the normal value and export price. The determination of injury not based on positive evidence or an objective examination and without examining all injury factors mentioned by the AntiDumping Agreement; and the determination of the threat of material injury not on facts but on allegation, conjecture or remote possibility. The lack of demonstration that the dumped imports were causing the alleged injury, and the failure to ensure that the alleged injury caused by other factors was not attributed to dumping. The lack of providing interested parties with the full opportunity for the defence of their interests, and the lack of informing the interested parties of the essential facts under consideration which form the basis for the decision. Provisional measures imposed for more than the period of time allowed under the ADA. The notice of initiation of investigations lacking in all the grounds that support dumping and injury, and the notice of definitive findings lacking in all relevant information of facts and law and reasons which led to the imposition of the anti-dumping measures.

Chinese Taipei considered that these Indian measures were inconsistent with, inter alia, Article VI:1 and VI:2 of GATT 1994 and Articles 1, 2, 3.1, 3.2, 3.3, 3.4, 3.5, 3.7, 3.8, 4, 5, 6 (including Annex II), 7.4, 12.1 and 12.2 of the ADA. 238

Trade potential and WTO issues for West Bengal


On 4 October 1996, the US requested consultations with Argentina concerning the imposition of specific duties on these items in excess of the bound rate and other measures by Argentina. The US contended that these measures violated Articles  II, VII, VIII and X of GATT 1994; Article  2 of the TBT Agreement; Articles 1–8 of the Agreement on the Implementation of Article VII of GATT 1994; and Article 7 of the Agreement on Textiles and Clothing. On 9 January 1997, the US requested the establishment of a panel. At its meeting on 22 January 1997, the DSB deferred the establishment of a panel. Further to a second request to establish a panel by the US, the DSB established a panel at its meeting on 25 February 1997. The EC and India reserved their third-party rights. On 4 April 1997, the panel was composed. The report of the panel was circulated on 25 November 1997. The panel found that the minimum specific duties imposed by Argentina on textiles and apparel were inconsistent with the requirements of Article II of GATT, and that the statistical tax of 3% ad valorem imposed by Argentina on imports was inconsistent with the requirements of Article VIII of GATT. On 21 January 1998, Argentina notified its intention to appeal certain issues of law and legal interpretations developed by the panel. The report of the appellate body was circulated to members on 27 March 1998. The appellate body upheld, with some modification, the panel’s findings and conclusions. The appellate body’s report and the panel’s report, as modified by the appellate body, were adopted by the DSB on 22 April 1998. Steel CASE: US – SUNSET REVIEW OF ANTI-DUMPING DUTIES ON CORROSIONRESISTANT CARBON STEEL FLAT PRODUCTS FROM JAPAN

On 30 January 2002, Japan requested consultations with the US in respect of the final determinations of both the US Department of Commerce and the US International Trade Commission in the full sunset review of the anti-dumping duties imposed on imports of corrosion-resistant carbon steel flat products from Japan. These determinations were issued on 2 August 2000 and 21 November 2000, respectively. • •

Japan claimed that these determinations were erroneous and based on deficient rulings, procedures and provisions pertaining to the US Tariff Act of 1930, as amended (“the Act”) and related regulations. Japan further claimed that the procedures and provisions of the Act and related regulations as well as the above determinations were inconsistent 239

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with, inter alia, Articles VI and X of GATT 1994; Articles 2, 3, 5, 6 (including Annex  II), 11, 12, and 18.4 of the Anti-Dumping Agreement; and Article XVI:4 of the WTO Agreement. Further to a second request from Japan, the DSB established a panel at its meeting on 22 May 2002. Brazil, Canada, Chile, the EC, India, Korea, Norway and Venezuela reserved third-party rights to participate in the panel proceedings. The panel ruled against Japan in its report in August 2003. On 15 September 2003, Japan sent its notification of an appeal to the DSB and filed the Notice of Appeal with the appellate body. The appellate body did not make any finding that the US had acted inconsistently with its obligations under the Anti-Dumping Agreement or the WTO Agreement. Fresh fruit and vegetables CASE: AUSTRALIA – CERTAIN MEASURES AFFECTING THE IMPORTATION OF FRESH FRUIT AND VEGETABLES

On 18 October 2002, the Philippines requested consultations with Australia on certain measures affecting the importation into Australia of fresh fruit and vegetables, including bananas, which include, but are not limited to, • • • •

Section 64 of the Quarantine Proclamation 1998 promulgated under the Quarantine Act 1908. Regulations, requirements and procedures issued pursuant thereto. Amendments to any of the foregoing. Their application.

The Philippines considered that these measures were inconsistent with the obligations of Australia under GATT 1994, the SPS Agreement and the Agreement on Import Licensing Procedures. The relevant provisions of these agreements included, but were not limited to, Articles XI and XIII of GATT 1994; Articles 2, 3, 4, 5, 6 and 10 of the SPS Agreement; and Articles 1 and 3 of the Agreement on Import Licensing Procedures. On 1 November 2002, the EC and Thailand requested to join the consultations. On 7 November 2002, Australia informed the DSB that it had accepted the request of the EC and Thailand to join the consultations. On 7 July 2003, the Philippines requested the establishment of a panel. At its meeting on 21 July 2003, the DSB deferred the establishment of a panel. Further to a second request to establish a panel by the Philippines, the DSB established a panel at its meeting on 29 August 2003. China, the EC, Ecuador, India, Thailand and the US reserved their third-party rights. On 4 September 2003, Chile reserved its third-party rights.


Trade potential and WTO issues for West Bengal

Conclusion The various aspects discussed in this chapter clearly indicate the pivotal role that governments need to play in the WTO regime to explore the potential of the different sectors and to safeguard them from various WTO obligations. This becomes more challenging with the “inclusive growth” envisaged for social welfare. Handling WTO issues at the national level may not be enough, as different states have different priority sectors. A state authority to handle WTO issues may highlight all such issues affecting the interests of the state of West Bengal to sensitize Indian negotiators at the WTO to protect the interests of the state and to pave the way for maximizing the benefits that can accrue in the liberalized trade environment.

References Annual Survey of Industry (2015–16): Ministry of Statistics and Programme Implementation, Government of India. Balassa, B. (1965): “Trade Liberalisation and Revealed Comparative Advantage”, The Manchester School, 33, 99–123. Barua, A. and P. Chakraborty (2010): “Does Openness Affect Regional Inequality? A Case Study for India”, Review of Development Economics, 14(3), 447–465. Chakraborty, D. (2019): “Trade Potential of West Bengal under WTO Regime”, Journal of Commerce, Arts and Science, 2(1), 26–34. Dotterweich, D. P. and F. Steb Hipple (1997): “Measuring the Export Potential of Urban Regions: A Case Study from Appalachia, USA”, USA College of Business East Tennessee State University, Johnson City, TN. Economic Review (2015–16): Department of Planning and Statistics, Government of West Bengal. Economic Survey (2010–11 and 2015–16): Ministry of Finance, Government of India. Helmers, Christian and Jean-Michel Pasteels (2006): “Assessing Bilateral Trade Potential at the Commodity Level: An Operational Approach”, International Trade Centre Working Paper. Krakoff, Charles (2003): “SADC: Key Potential Export Markets and the Market Access Barriers Facing Exporters”, paper presented to the Southern Africa Trade Regional Network Symposium, Maputo, Mozambique. Marjit, Sugata, Saibal Kar and Dibyendu S. Maiti (2007): “Regional Trade Openness Index and Income Disparity – A New Methodology and the Indian Experience”, Centre for Social Sciences, Kolkata, India. Mehta, R. (2005): “Non-Tariff Barriers Affecting India’s Exports”, Research and Information System for Developing Countries, New Delhi, Discussion Paper No. 97. Reddy, B. V., M. S. R. Chinnappa and L. Achoth (2003): “Global Competitiveness of Medium-Quality Indian Rice: A PAM Analysis” in Impact of Globalization on Rice Farmers, Food and Agriculture Organisation, United Nations Trade Data Collected from Directorate General of Commercial Intelligence and Statistics, Kolkata Office, 2015–16, by this author Website of Ministry of Commerce and Industries, Government of India, accessed on 19.11.2019. https://commerce​.gov​.in​/trade​-statistics/


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World Bank Website accessed on 12.11.2019. https://data​.worldbank​.org/ WTO Cell, Government of West Bengal at IIFT Kolkata (2007): Sectors of Importance for West Bengal under WTO Regime (A Preliminary Investigation). WTO Website accessed on 29.10.2019. https://www​.wto​.org​/english​/res​_e​/statis​_e​/statis​ _e​.htm Wu, Yanrui (2003): “Export Potential and Its Determinants among the Chinese Regions”, School of Economics and Commerce, University of Western Australia, Crawley, WA.

Appendix Table 12.1  Products that have high production value in West Bengal Manufacturing


Basic iron and steel Refined petroleum products Basic chemicals Textiles, including spinning, weaving and finishing Other food products Grain mill products Metals (casting) Other fabricated metal products Tobacco products

Paddy Vegetables Potato Fruits Wheat Tea Jute Condiments and spices

Sources: Annual Survey of Industries, Government of India (2015–16); Economic Review, Government of West Bengal (2015–16).

Table 12.2  Products with high production levels in West Bengal and high export growth rates from India Products

Trade growth value (%)

Mineral fuels and lubricants (incl. coal) including petro products Machinery, transport and metal products including iron and steel* Rice Chemicals and allied products Fruit, vegetables and pulses (excl. processed fruits and juices) Jute products including twist and yarn Ready-made garments from all textile materials

270 148 134 103 70 35 18

Sources: Annual Survey of Industries, Government of India (2015–16); Economic Survey, Government of India (2010–11 and 2015–16).


Trade potential and WTO issues for West Bengal

Table 12.3  Products for which ports in West Bengal have a high (>10%) export share Products

Export share (%)

Jute Shellac Wheat Mica Leather goods Tea Other cereals Ferro alloys Fresh vegetables Pulses Natural silk yarn, fabrics Floriculture products Coal Computer software in physical form Aluminium other than products Primary and semi-finished iron and steel Rice (other than basmati) Fruit or vegetable seeds Fresh fruit Plastic and linoleum products Woollen yarn fabrics Spirit and beverages Metal products

93 87 83 81 58 41 40 35 31 28 22 21 21 13 12 11 11 10 10 10 10 10 10

Source: DGCI&S, Kolkata (2015–16).


Debottam Chakraborty

Table 12.4  Products for which West Bengal has production advantage Manufacturing processes


Manufacture of railway and tramway locomotives and rolling stock Saw milling and planing wood Manufacture of bodies (coach work) for motor vehicles; manufacture of trailers and semis Manufacture of accumulators, primary cells and primary batteries Tanning and dressing leather; manufacture of luggage bags, saddlery and harnesses Casting of metals Manufacture of coke oven products

Jute Other drug Potato

Manufacture of products from wood, cork, straw and plaiting materials Manufacture of tobacco products Manufacture of electric lamps and lighting equipment Publishing Manufacture of basic iron and steel Manufacture of basic chemicals Manufacture of other fabricated metal products; metal working service activities Manufacture of structural metal products, tanks, reservoirs and steam generators Spinning, weaving and finishing of textiles Manufacture of footwear Manufacture of furniture Printing and related service activities Manufacture of glass and glass products Manufacture of optical instruments and photographic equipment Manufacture of other food products

Tea Paddy Sesamum Vegetables and fruit

Source: Calculated on the basis of the Balassa index from Annual Survey of Industry, Government of India (2015–16); Economic Review, Government of West Bengal (2015–16).

Table 12.5  Important export items passing through ports in West Bengal in which West Bengal has a production advantage Commodities

Share of India’s exports (%)

Jute Leather goods Tea Fresh vegetables Iron and steel Fresh fruit Manufacture of metals

94 56 49 39 18 14 10

Source: Based on Tables 12.3 and 12.4.


Trade potential and WTO issues for West Bengal

Table 12.6  Potential of different exportables from West Bengal High potential

Medium potential

Low potential

Fruit (UAE, Oman) Vegetables (South Africa) Leather (Singapore, Japan)

Jute (US, UK, Brazil) Tea (Ukraine, Hungary) Rice (Indonesia, Vietnam)

Metal (Vietnam) Iron and steel (Australia)

Source: Based on World Bank Database (2015–16); WTO Database (2015–16).

Table 12.7  Products with high import growth rates – India Products

Import growth (%)

Non-ferrous metals including gold and silver Chemical elements and compounds Transport equipment Iron and steel Crude rubber Capital goods Plastic material, regenerated Electrical machinery Metal products Dyeing, tanning and colouring Non-electrical machinery Pearls (precious and semi-precious) Raw wool Petroleum (oil and lubricants) Cashewnuts (unprocessed) Fertilizers and fertilizer manufacturing Medicines and pharmaceuticals Edible oils Pulp and wastepaper Paper, paper board and related products

2265 1854 376 249 165 162 160 141 139 112 101 99 93 90 90 88 85 84 73 61

Source: DGCI&S, Kolkata (2015–16).

Table 12.8  Products for which West Bengal has high production levels/production advantage and that have high import growth rates for India Products

Import growth (%)

Non-ferrous metals including gold and silver Chemical elements and compounds Transport equipment Iron and steel Plastic material (regenerated) Non-electrical machinery Pearls (precious and semi-precious) Petroleum, oil and lubricants Edible oils

2289 1876 359 240 163 99 95 91 88

Source: Annual Survey of Industries, GOI (2010–11 and 2015–16); Economic Review, Government of West Bengal (2015–16); DGCI&S, Kolkata (2015–16).



animal spirits 21 asymmetry 19, 20, 23, 25 bank capital 31 behavioural finance 103 bureaucratic reform 204–205 capacity utilization 123–125 capital account liberalization 22–23 capital accumulation 29, 32, 35, 48, 118 capitalist class enterprise 32–33 capitalist class process 30 circuits of finance 32, 34–36, 43 circuits of global capital 33, 35, 36, 49, 51 class-focused Marxist approach 43 class process 30 comparative dynamics 128–131 conservative monetary policy 32 constant capital 30–31 contagion effects 20 credit money 53 debt-led consumption 61 demand decomposition 142–143 demand-side view 115, 141, 144 demand uncertainty 221–222 derivatives 24, 33 economic liberalization 113–115, 195 economic slowdown 113, 115, 140, 142, 144 effective demand 118 efficient market 19, 20, 24, 25, 50; hypothesis 103 ergodic probability 21 expanded reproduction 31, 118 exports 114–117, 119–121, 131, 141, 151, 154; function 123–124; subsidy 202

fictitious capital 31, 49, 62 finance capital 29–32, 36, 38, 46, 62 financial crisis 20, 23, 45, 60, 67, 73–74, 76 financial de-regulation 20, 22, 24, 25, 32, 77, 84 financial instability 68, 81; hypothesis 69 financial markets 19, 21 financial sector 19, 20 financial surplus 44 financialization 20, 22–24, 29–30, 32, 34, 39, 48, 56, 59–60, 62, 70, 77 FIRE 29, 38, 40 Foreign Direct Investment (FDI) 173–178, 183–187 Fundamental class process 33 Glass-Steagall Act 71, 81 global capitalism 29–30, 32, 34, 36, 43, 58 global value-added 57 globalization 29, 30, 56, 59, 71 hedge financing units (HFUs) 69 heterodox school 21, 23, 61 Hilferding 29, 31, 38, 62 India’s Dream Run 144, 151 industrial capital 31, 32, 53–54 inequality and: consumption 155–160; investment 160–164 informal sector 195–197, 199, 203–204, 214–216, 219 interest-bearing capital 31, 51, 53–55, 62 job-less growth 156 Keynes 21–22, 50–51, 60, 67, 68


I ndex

labour market reform 200, 205 labour power 30 Lenin 29, 31, 46

rational choice 21 real economy 19–20, 24, 31, 49, 50, 61 rentier class 38

market capitalization 20 market on tap 119–120 Marx 29, 31, 38, 51–54, 57–58, 61 Minsky 23, 61, 67–69, 75–77, 81–82, 84–85 monopoly 58

shadow banking 78–80 social surplus 44 speculation 48–50, 55 stabilization 70, 82 stock market 19–21, 51, 103–104 subsumed class process 33 supply-side framework 114 surplus labour 30, 43 surplus value 30, 31, 34, 43, 51, 62

neoliberal economics 20–23, 25, 32 neoliberalism 30, 43, 59, 142 Non-Banking Financial Companies (NBFCs) 79–80 non-performing assets (NPAs) 73, 89–100 pharmaceutical industry 173 Ponzi finance 23, 40, 41, 69 post-Keynesians 22, 23, 59 priority-sector lending 89, 99–100 process patent 174 product patent 174 production surplus 43, 44 productivity spillovers 173–178, 183–187 protectionism 195

tariff liberalization 201 trade dispute cases 234–240 trade liberalization 215–216, 218 uncertain knowledge 22–23 uncertainty 21–25, 76 unproductive capitalists 38 variable capital 30–31 World Trade Organization (WTO) 225–228; and West Bengal 228–232