International Competitiveness, Investment and Finance: A Case Study of India (Routledge Studies in Development Economics, 33)

International Competitiveness, Investment and Finance

Studies on international competitiveness have tended to focus primarily on factors such as productivity, costs and technological capabilities, largely ignoring the role of financial factors. In this book the authors offer a fuller account of the determinants of international competitiveness. Using India as a case study, this well-written, concise book covers everything one needs to know to understand how a country becomes internationally competitive. Showing that reforms that pertain to the real sector alone, such as industrial deregulation and trade reforms, are not enough to enhance a country’s competitiveness, the text makes a compelling case for complementary financial sector reforms. Of interest to academics studying international trade, industrial economics and development economics, this book is also guaranteed to be extremely useful for professional economists and those involved with policy making in developed and developing countries. A.Ganesh-Kumar is Associate Professor at the Indira Gandhi Institute of Development Research, Mumbai, India. Kunal Sen is Senior Lecturer in Economics at the School of Development Studies, the University of East Anglia in the UK. Rajendra R.Vaidya is Associate Professor at the Indira Gandhi Institute of Development Research, Mumbai, India.

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Edited by Geoff Harris 15 Small Enterprises and Economic Development The dynamics of micro and small enterprises Carl Liedholm and Donald C.Mead 16 The World Bank New agendas in a changing world Michelle Miller-Adams 17 Development Policy in the Twenty-First Century Beyond the post-Washington consensus Edited by Ben Fine, Costas Lapavitsas and Jonathan Pincus 18 State-Owned Enterprises in the Middle East and North Africa Privatization, performance and reform Edited by Merih Celasun 19 Finance and Competitiveness in Developing Countries Edited by José Maria Fanelli and Rohinton Medhora 20 Contemporary Issues in Development Economics Edited by B.N.Ghosh 21 Mexico Beyond NAFTA Edited by Martin Puchet Anyul and Lionello F.Punzo 22 Economies in Transition A guide to China, Cuba, Mongolia, North Korea and Vietnam at the turn of the twenty-first century Ian Jeffries 23 Population, Economic Growth and Agriculture in Less Developed Countries Nadia Cuffaro 24 From Crisis to Growth in Africa? Edited by Mats Lundal 25 The Macroeconomics of Monetary Union An Analysis of the CFA Franc zone David Fielding 26 Endogenous Development Networking, innovation, institutions and cities Antonio Vasquez-Barquero 27 Labour Relations in Development Edited by Alex E.Fernández Jilberto and Marieke Riethof 28 Globalization, Marginalization and Development Edited by S.Mansoob Murshed 29 Programme Aid and Development Beyond conditionality

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Howard White and Geske Dijkstra 30 Competitiveness Strategy in Developing Countries A manual for policy analysis Edited by Ganeshan Wignaraja 31 The African Manufacturing Firm An analysis based on firm surveys in sub-Saharan Africa Dipak Mazumdar and Ata Mazaheri 32 Trade Policy, Growth and Poverty in Asian Developing Countries Edited by Kishor Sharma 33 International Competitiveness, Investment and Finance A case study of India A.Ganesh-Kumar, Kunal Sen and Rajendra R.Vaidya

International Competitiveness, Investment and Finance A case study of India

A.Ganesh-Kumar, Kunal Sen and Rajendra R.Vaidya

LONDON AND NEW YORK

First published 2003 by Routledge 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 Routledge is an imprint of the Taylor & Francis Group This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” © 2003 A.Ganesh-Kumar, Kunal Sen and Rajendra R.Vaidya 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. 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 Ganesh-Kumar, A. (Anand) 1966– International competitiveness, investment and finance : a case study of India/A.Ganesh-Kumar, Kunal Sen & Rajendra R.Vaidya. p. cm.—(Routledge studies in development economics ; . 33) Includes bibliographical references and index. 1. India—Commercial policy. 2. Competition, International. 3. Investments, Foreign—Government policy—India. 4. Corporations— India—Finance. 5. Balance of payments—India. 6. International economic relations—India. I. Sen, Kunal 1961–II. Vaidya, Rajendra R. III. Title. IV. Series. HF1589.G36 2003 382′.0954−dc21 2002036645 ISBN 0-203-48078-3 Master e-book ISBN

ISBN 0-203-34505-3 (Adobe eReader Format) ISBN 0-415-31232-9 (Print Edition)

To my late father Anandam, mother Nalinakshi and wife Lakshmi AGK To my sisters Malini and Sravani KS To my wife Jayshree and daughter Kaveri RRV

Contents

List of figures

x

List of tables

xi

Preface 1

xiii

Competitiveness, investment and finance: analytical links

1

1.1

The theoretical framework

3

1.2

India as a case study

6

1.3

Organisation of the book

7

The policy environment in India

9

2.1

Macroeconomic policy

9

2.2

Trade policy

10

2.3

Exchange rate policy

11

2.4

Industrial policy

12

2.5

Financial sector policy

13

2.6

Labour laws

16

2.7

Exit policy

16

2.8

An assessment

18

The balance of payments and national competitiveness

20

3.1

The evolution of the external sector

21

3.2

The determinants of BOP-constrained growth

23

3.3

Trends and patterns in aggregate competitiveness

30

3.4

Concluding remarks

40

The determinants of sectoral competitiveness

42

4.1

The analytical framework

42

4.2

Data and variable description

43

2

3

4

ix

4.3

Trends and patterns in export shares

45

4.4

Productivity and the price determinants of international competitiveness

45

4.5

Regression results

47

4.6

Concluding remarks

50

Outward orientation: a firm-level analysis

52

5.1

A review of the literature on firm-level exporting behaviour in India

53

5.2

Modelling a firm’s decision to export

55

5.3

Data and variable description

57

5.4

Industry characteristics

58

5.5

The characteristics of exporting firms

62

5.6

The lumpiness of investment

69

5.7

The determinants of exporting behaviour

71

5.8

Concluding remarks

75

6

Finance constraints, persistent exporting and investment

78

6.1

Theories of asymmetric information and finance constraints

78

6.2

Testing for the source of the finance constraint

82

6.3

The sources of funds for Indian firms— patterns and trends

84

6.4

Empirical results

85

6.5

Concluding remarks

94

Conclusions and policy implications

96

5

7

Appendix

101

Notes

123

References

127

Index

132

Figures

3.1 3.2 3.3 3.4 3.5 3.6 3.7 4.1 4.2 4.3 5.1 5.2 5.3

The current account balance as percentage of GDP Exports, imports and the trade balance as percentage of GDP The nominal effective exchange rate and the real effective exchange rate Openness (exports plus imports as percentage of GDP) India’s share of manufacturing exports in world exports India’s manufacturing trade ratio CMS decomposition of India’s annual export growth Labour productivity, real wage and unit labour costs (ULCs) Relative unit labour costs (ULCs) Profitability of the manufacturing sector Persistence in exporting Investment frequency Effect of investment spike

21 22 22 24 24 24 32 47 48 48 68 70 76

Tables

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4.1 4.2 4.3 4.4 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12

Estimation results of the BOP-constrained growth model CMS decomposition of India’s export growth SITC two-digit commodity codes and names Importance of different commodities Trends in commodity shares Regions and country details Importance of different regions/markets Trends in market shares of different regions/markets Industry-wise export shares Descriptive statistics Correlation matrix of explanatory variables Determinants of sectoral competitiveness Frequency count of exports to manufacturing sales ratio Industry characteristics Firm characteristics Average profitability Exporter premia Firm transition rates in the export market Long-run export persistence Export sequences Firm characteristics: non-exporters, occasional exporters and persistent exporters Summary statistics on the distribution of the investment rate Investment-spike sequences Determinants of decision to export Frequencies of actual and predicted outcomes Number of firms in sample classified by categories Financial characteristics of firms Number of firms by type Sources and uses of funds—all firms Sources and uses of funds—by firm type Summary statistics for variables in equation (6.2) Correlation amongst explanatory variables in equation (6.2) Estimation results for equation (6.2) Summary statistics of explanatory variables in equations (6.3) and (6.5) Correlation amongst explanatory variables in equation (6.5) Frequency of use of different external sources of funds Estimation results for equation (6.3)

29 31 33 35 36 37 39 40 45 48 49 49 58 59 63 64 64 65 66 67 67 69 71 74 75 81 81 82 84 85 87 87 89 89 90 91 92

xii

6.13 A.1 A.2 A.3 A.4 A.5 A.6 A.7 A.8 A.9 A.10

Estimation results for equation (6.5) Commodity-wise shares (%) in total world exports Commodity-wise shares (%) in India’s total exports India’s share (%) in commodity-wise total world exports Trends in commodity-wise shares in total world exports Trends in India’s share in commodity-wise total world exports Share (%) of market in world total import Share (%) of market in India’s total export India’s share (%) in total imports of each market Trends in market-wise shares in total world imports Trends in India’s share in total imports of each market

92 101 106 111 115 117 119 120 120 121 122

Preface

Why have some developing countries been successful in exporting manufactured goods when many others have not been is an issue that has engaged academicians and policy-makers for many years. In the postWTO (World Trade Organisation) era of increasing globalisation, becoming internationally competitive is all the more important for developing countries. Many of the previous studies on international competitiveness have looked at ‘real’ determinants such as productivity, costs and technology. Quite independently, there is also a vast and rich literature that examines the role of finance in economic development. However, there is very little research integrating real and financial factors in a comprehensive manner to understand the determinants of international competitiveness. This book attempts to fill this gap. Our interest in this area arose from our involvement in a multi-country research project funded by the International Development Research Centre (IDRC), Canada, in 1997–98. Subsequent to our involvement in this project, we felt there was a need to write a book that undertakes a detailed investigation of this issue for a developing country undergoing economic reforms such as India. We would like to thank José Maria Fanelli and Rohinton Medhora as leaders of the IDRC-funded project for getting us interested in this area. Many others in the project provided detailed comments, which helped us enormously. We would like to thank in particular José Maria Fanelli, Paolo Guerrieri, Ari Kuncoro and Mustapha Nabli. IDRC has been especially encouraging in allowing us to use the project funds for supporting the research in this book. Much of the book was written while Ganesh-Kumar was visiting the School of Development Studies at the University of East Anglia, on a Commonwealth Fellowship. He would like to thank the Association of Commonwealth Universities and the British Council for providing this fellowship. He would also like to thank the School of Development Studies at the University of East Anglia for hosting him under this fellowship. Comments on one of the chapters received in a seminar at the School of Development Studies, the University of East Anglia were useful. We would also like to thank three anonymous reviewers of Routledge for detailed and incisive comments on an earlier draft of the book, which have improved the final version considerably. All remaining errors are solely our responsibility. A.Ganesh-Kumar Kunal Sen Rajendra R.Vaidya September, 2002

1 Competitiveness, investment and finance Analytical links

International competitiveness generally refers to the ability of a country to expand its share in world markets. It is well known that most of the developing countries export mainly primary commodities (agricultural products and minerals), which have been facing a long-run decline in prices in the world market (this is the Prebisch-Singer thesis; see Sarkar and Singer 1991; Bleaney and Greenaway 1993; Sapsford and Chen 1998 for recent evidence). Thus, it is being increasingly recognised both among academicians and policy-makers that the capacity of a country to increase its standard of living in the long term depends on the competitiveness of the manufacturing sector (Singh and Howes 2000; Fanelli and Medhora 2002). At a fundamental level, the competitiveness of a country in a particular commodity depends on the price at which it delivers the commodity in a foreign market in comparison with the price offered by competing countries for that commodity in the same market. At an analytical level, the evolution of overall competitiveness of a country over time depends on both macroeconomic and microeconomic factors. The most important macroeconomic variable influencing international competitiveness is the real exchange rate. In the standard neo-classical model, given its assumption of complete wage-price flexibility, any disequilibrium in balance of payments (BOP) in the country in question (possibly arising from differences in the ability of countries to compete in world markets) can only be resolved by adjustments in the real exchange rate. At the micro level, traditional trade theories have seen competitiveness in terms of factor endowments (labour, capital, natural resources, etc.) of a country and have argued that unit labour costs are the key determinants of international competitiveness. New trade and technology-based theories, on the other hand, have stressed the importance of non-price factors such as investment, technological capability and quality as being more important than price factors in the ability of an industry or firm to gain international competitiveness. A thread common to both the traditional and new trade theories is that all the factors identified as influencing competitiveness are ‘real’ factors. Both these sets of theories, and the empirical literature following these two approaches, ignore important financial factors that can affect the capacity of a firm to compete internationally. Yet, modern views on finance, originating from the asymmetric information/agency costs framework, argue that the financial environment could impose constraints on firms to obtain investible funds and thus constrain their ability to under-take costly investments required to compete in international markets. The relationship between finance and international competitiveness has remained under-researched in the literature on finance and development. In the presence of asymmetric information/agency costs in financial markets, a country’s level of financial development may have a significant impact on its ability to compete in manufacturing exports. This is because the manufacturing sector is characterised by two features that make firms in this sector depend greatly on external funds for financing their investments. First, the

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manufacturing sector is often characterised by increasing returns to scale, as compared to the agricultural sector, which is characterised by constant or decreasing returns to scale (Young 1928; Kaldor 1967). Second, typically, firms in this sector need to obtain credit for working capital purposes prior to the production process. Due to asymmetric information/agency costs, financial intermediaries will incur transaction costs when channelling savings to entrepreneurs. A more efficient financial system leads to a decrease in these transaction costs and consequently, an easier access to external finance. This would imply that the structure of incentives for production can easily shift to the sector that relies more on external finance for investment needs, that is, the manufacturing sector (Kletzer and Bardhan 1987; Beck 2002). Thus, economies with a strong or well-developed financial system will be more able to channellise savings to firms in the manufacturing sector and thereby, overcome finance constraints. This provides a robust positive link between the level of financial development in a particular country and the ability of the country to compete in manufacturing goods in world markets. There have been few empirically grounded country-level studies of the determinants of international competitiveness that examine the latter within an analytical framework that stresses financial and real constraints to international competitiveness.1 This study attempts to fill this gap in a developing country context. From a policy perspective, especially in the context of a developing country, it is important to study the links between the real and financial factors in affecting international competitiveness, as reforming the financial sector may have implications for export performance if the level of financial development is an important determinant of the country’s international competitiveness. We posit the relation between the real factors and the financial factors through the volume of investments undertaken by a firm. That is, productivity growth, technological upgradation and the capacity to deliver the required quantities (i.e. the scale of operation) are firm-level factors that affect the ex-factory competitiveness and these are directly influenced by the firm’s investment decisions. The decision to invest would of course depend on the firm’s perception of the future returns that the investment would yield. However, how much is actually invested depends on the availability and the cost of funds. The latter in turn depends on the institutional setting in the financial sector and the efficiency of this sector in making funds available at the right cost to those sectors and firms where the potential for growth is the highest. A weak institutional setting in the financial sector, which is typical of most developing countries, can raise the cost of investible funds for a firm, and thus reduce the amount that a firm actually invests, which in turn would restrict its ability to compete in international markets. We test our hypothesis empirically using India as a case study. Past studies that empirically examined international competitiveness have been at an aggregate level in terms of overall export performance, and/or at the sectoral level, where export performance of individual sectors was related to various real factors such as unit labour costs, investment, measures of technology, etc. (Fagerberg 1988; Amendola et al. 1993; Magnier and Toujas-Bernate 1994; Agenor 1997). The modern literature on empirical industrial organisation has emphasised the presence of significant inter-firm heterogeneity within a sector/industry, which if unaccounted for may lead to incomplete understanding of the underlying phenomena and in the worst case even misleading conclusions.2 For example, many studies in various contexts have stressed the importance of differences in firm size, which a study at the sectoral/ industry level would be unable to take into account. In our empirical investigations, we attempt to account for heterogeneity in some selected firm characteristics, including firm size. As we shall see later, accounting for firm heterogeneity enriches our understanding of the determinants of international competitiveness. In this book, we study international competitiveness at the aggregate level, at the sectoral (industry) level and at the firm level. We test our hypothesised link between investments and exports at both sectoral and firm levels, and then proceed further to examine the link between financial factors and firmlevel

COMPETITIVENESS, INVESTMENT AND FINANCE

3

investments, thus completing the chain of reasoning. We provide below the theoretical framework used to establish the above mentioned links. 1.1 The theoretical framework BOP constraint, international competitiveness and economic growth Perhaps the clearest statement on the importance of international competitiveness in exerting a positive impact on economic growth comes from Thirlwall (1979). In Thirlwall’s framework, a country’s ability to move to a higher growth path would depend on whether it can relax the BOP constraint on economic growth, that is, a higher growth path would inevitably lead to an increase in imports and a worsening of the balance of trade (and consequently, the BOP). For the country to observe rapid economic growth without coming up against the BOP constraint, it would need to increase its export growth. This would depend both on world income growth (which would determine the overall growth of exports) and on the ability of the country to move into the more dynamic commodities and markets in world trade. Given that world income growth is exogenously determined, it is critical for the country’s policy-makers to increase the international competitiveness of its commodities in order to observe faster growth in exports, and thus, of output. In Thirlwall’s model, an increase in the international competitiveness of a country’s commodities would come about by an increase in the income elasticity of world demand for the country’s exports. Such an increase would be through both price and non-price channels, the latter including infrastructural improvements, technological upgradation and faster capital accumulation. The increase in the income elasticity of world demand can manifest itself as a change in the export basket towards commodities whose world demand is relatively more rapidly growing, and/or towards markets that are growing faster than the world average. The constant market shares (CMS) model provides a framework for tracing the changes in a country’s export basket and market focus, by decomposing growth in exports into four components, namely a world trade effect, commodity composition effect, market distribution effect and a competitiveness effect. This analysis would enable us to evaluate export performance at the sectoral level setting the stage for identifying the factors affecting sectoral competitiveness. Investment and sectoral competitiveness Most analyses of sectoral competitiveness distinguish between price and non-price factors. Evolution of sectoral unit labour costs is the most critical price determinant of international competitiveness. Among nonprice determinants, perhaps the variables most widely discussed in the literature are investment and technology. Much of the empirical studies on international competitiveness, mostly in the developed country context, have concentrated on the role of technology in determining the latter, with technology being measured by patents, research and development activity, scientists and engineers in total population, etc. (Dosi et al. 1990; Grossman and Helpman 1991; Lall 1998). There is little doubt that technological progress has been a crucial determinant of economic growth in the advanced market economies (Fagerberg 1994). Furthermore, the ability of manufacturing firms in the newly industrialising countries to close the ‘technology gap’ between these countries and ‘technology leaders’ in the West can explain to a great extent the increasing international competitiveness demonstrated by the manufacturing sectors of these countries (Amsden 1989, 2001; Wade 1990). However, it is arguable whether technology catchup factors such as the ability to innovate or to invest in research and development can play an important role in influencing the

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international competitiveness of the majority of developing countries, many of whom have primitive institutional structures for research and development activity and a weak legal framework relating to intellectual property rights (Evenson 1995).3 In this book, we instead emphasise capital accumulation as the crucial non-price determinant of international competitiveness in developing countries. In so arguing, we follow Fagerberg (1988) who had originally put forward the thesis that ‘the growth in market shares for a country at home and abroad does not only depend on technology and prices, but also on its ability to deliver’ (Fagerberg 1988:359). As Fagerberg argues, this ability to deliver is in turn determined by the country’s investment rate. To the extent that investment in new plant and machinery embodies new technology, this variable in fact could partly capture some of the technology-related factors stressed in the literature mentioned earlier. We apply Fagerberg’s hypothesis to industry-level data in India for the period 1989–904 to 1997–98. In our empirical analysis, we try and disentangle the role of price factors from investment in determining sectoral competitiveness, as measured by the industry’s share in world total exports of the commodity. Exporting behaviour at the firm level The use of industry as the unit of analysis has significant conceptual weakness as argued earlier. We take our analysis of international competitiveness one step further by examining this issue at the level of the firm. Traditionally, the analysis of export performance at the firm level has borrowed from theories of industrial economics and the literature on multinational corporations. Based on the various theories of firm performance (especially the structure-conduct-performance paradigm) researchers have incorporated size/ scale and market structure as variables that explain inter-firm variations in export performance. Firm size is used to indicate scale economies in producing and exporting and the capacity to overcome the inherent risks and information costs involved in exporting. Market structure variables (typically measures of concentration and entry barriers) are included to account for inter-industry differences in the incentive to export (White 1974; Glejser et al. 1980). The literature on multinational corporations has emphasised the need to understand how international comparative advantage is acquired by the firm. This approach recognises that skills, technology and in fact scale economies are firm specific because they have been created, at least partly, by the firms themselves by their past effort and investment. The set of variables that this approach stresses include expenditure on research and development (R&D), royalty payments, expenditure on advertising/marketing and managerial and technical skills (Caves 1996). We review in Chapter 5, some of the empirical work in this regard with respect to India. In our own empirical work, we borrow from two strands in the recent literature. The first emphasises the presence of sunk costs which a nonexporting firm must incur to enter export markets for the first time (Dixit 1989a,b; Krugman 1989). The two most important sunk costs include the cost of gathering information about demand conditions in foreign markets and the cost of establishing or gaining access to a distribution system in foreign markets. It is generally assumed that these sunk costs recur in full if the firm exits the export market and wishes to re-enter those markets. These one time sunk costs and uncertainty (which is inherent in the production decision) would induce persistence in a firm’s exporting status. In other words, a firm that has already incurred the sunk costs in the past would be more likely to export in the current period. An implication of this at the aggregate level is that export supply function becomes sensitive to the nature and number of firms that are exporting at any given point in time. If the number of exporting firms changes very slowly over time then it automatically restricts the responsiveness of exports to changing relative prices, growth in world trade and the policy environment (the so-called hysteresis in trade flows).5

COMPETITIVENESS, INVESTMENT AND FINANCE

5

The second strand of literature that we use relates to the lumpiness of investment activity arising due to the non-convex cost of adjusting capital stock. This approach stresses that small adjustments to capital stock are not feasible because almost all investment projects of consequence (e.g. investment in plant and machinery) are not available in small quantities (Cooper et al. 1999; Cooper and Haltiwanger 2000). Consequently, investments at the firm level are likely to be characterised by bursts of investment activity (investment spikes) followed by relatively long periods of inactivity. This is in stark contrast to the traditional neo-classical model of investment/capital accumulation that assumes convex cost of adjusting capital stock leading to smooth adjustment of capital stock to its optimal level. The recent empirical studies on exporting behaviour at the firm level have restricted themselves to analysing the role of sunk costs (Roberts and Tybout 1997; Bernard and Jensen 1999, 2001). Our empirical analysis of the exporting behaviour of firms integrates the above two strands of literature, which we believe adds substantially to our understanding of exporting behaviour. Moreover, it provides us a link to analyse the role of financial factors in influencing investments and ultimately export behaviour. Investment and finance The relationship between investment and finance has remained a controversial issue in the literature on economic development. Much of the earlier debate in this area has revolved around the arguments of McKinnon (1973) and Shaw (1973) who postulated a strong complementarity between the accumulation of financial assets and physical capital. Recent developments in the theories of asymmetric information and contract enforcement as applied to financial markets provide further support for the view that the availability of loanable funds has a strong positive relationship with investment expenditures of firms, independent of other determinants of the latter. According to this literature, external finance, if available at all, may be more costly than internal finance because of transaction costs, contract enforcement (agency cost) problems and asymmetric information.6 The argument rests on the distinction between ‘insiders’ (the firm’s owners/ managers) who have full information about a particular firm’s investment prospects, and ‘outsiders’ who may correctly perceive the prospects for an industry but cannot distinguish the quality of individual firms within an industry. In particular, there may exist certain firms that face high information costs in financial markets, and there would be others who face negligible information costs. An important empirical implication of this literature is that the availability of finance may constrain the investment decisions of ‘high-information-cost’ firms, while ‘low-information-cost’ firms are less likely to be constrained by the availability of finance in their investment decisions. While problems of asymmetric information and contract enforcement are likely to be present in most, if not all, financial markets (whether in developed or developing countries), it can be argued that these problems will be more severe in developing countries where greater heterogeneity among firms exist, capital markets are quite segmented (so that informational flows between suppliers of funds are limited) and there is a lack of a well-developed system of contract enforcement procedures, such as an adequate legal system and a sound bankruptcy code (Aivazian 1998). A well-functioning financial system (which includes financial markets, intermediaries and instruments) minimises these transaction costs and enables firms to be as close as possible to their desired/optimal capital stock. Historically, different countries have evolved different financial systems in their attempts to overcome this fundamental problem. The Anglo-Saxon system is one in which capital markets play a dominant role while in the German-Japanese system, commercial banks play a vital role. There has been considerable debate in recent years on what should be the ‘optimal’ financial structure for developing countries. Mayer (1990) and Singh (1993) have argued that, to the extent developing countries have a choice, they should

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attempt to foster bank-based financial systems than to establish and encourage stock markets. Mayer and Singh argue that possible negative aspects of stock markets like short-termism, speculation and the tendency of large firms to expand via mergers rather than new investments would, in all probability, outweigh possible positive aspects like encouragement of savings, more efficient allocation of investment resources and the discipline of corporate management via the market for corporate control. Within bank-based systems, several authors have advocated the creation of development finance institutions (World Bank 1993; Bhatt 1995). This position is based on the observation that scarcity of long-term finance is a key impediment to greater investment and growth in developing countries (Caprio and Demirguc-Kunt 1998). Furthermore, the experiences of Japan and Korea with specialised financial institutions suggest that these institutions furthered the growth of the financial sector by establishing procedures for project financing and monitoring that commercial banks then copied (World Bank 1993:350). The question that arises in this context is which markets/institutions impose the least finance constraints on firms? It could be argued that it is not as useful to know that finance constraints exist on the investment decisions of some firms as it is to know which markets or institutions are the cause of the constraint. Unlike most developed countries, financial markets and institutions in a particular developing country may function under quite disparate regulatory regimes and institutional constraints, and thus, some markets or institutions may work better than others. The role of public policy in this case is to identify why these constraints exist and to find ways to make these markets or institutions work better. In our empirical analysis, we follow the approach adopted by us in our earlier work (Ganesh-Kumar et al. 2001, 2002) wherein we use exporting behaviour of firms as a sorting criteria to distinguish between highand low-information-cost firms. This helps to establish the link between exporting, investments and finance. Further, we also attempt to trace the source of the finance constraint, which helps us to link the particular characteristics of the financial system to investment and export performance. 1.2 India as a case study As mentioned earlier, this book attempts to study the linkages between international competitiveness, investment and finance from a developing country perspective using India as a case study. India’s policy history and past economic performance have been quite similar to many other developing countries of Asia, Africa and Latin America.7 Like many other developing countries India, following independence in 1947, adopted an inward-looking development strategy based on centralised planning that accorded a dominant role for the public sector with emphasis on self-reliance and import substitution. Under this strategy, investments in basic and heavy industries were considered essential for achieving self-reliance and investments in consumer goods were given a much lower priority. By the early 1980s, India had developed a fairly large and diversified industrial base, though its capacity to manufacture consumer goods was somewhat limited. Moreover, the inward-looking policies did not foster a competitive environment in domestic markets. As a result, though India had a large industrial base, its capacity to compete in world markets was restricted to a few traditional industries like textiles, garments, tea, spices and metallic and nonmetallic mineral ores. As in the case of most other developing countries that followed such an inwardlooking strategy, the overall economic growth in India was quite slow and was clearly inadequate to lift the well-being of a rapidly growing population. India, like many other developing countries, gradually started to reorient this inward-looking development strategy since the mid-1980s to a more market friendly approach. The big change came in 1991 in the form of the structural adjustment programme undertaken in the wake of the BOP crisis in that year. These

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reforms have more or less abandoned centralised planning and seek to integrate the Indian economy with the global economy in a phased manner. Consequently, competitive pressures on Indian industry have increased dramatically in recent times. Export growth in the new policy environment is widely recognised as the key to achieving higher growth rates of national income. It then follows that forces that aid or inhibit the ability of Indian industry to expand its share in world markets merits detailed empirical investigation. The insights that this study will bring at the end, we hope, will be relevant and useful to other similarly placed developing countries. Data and period of analysis In this study we have used data from various sources. Data on macroeconomic variables pertaining to India have been collated from various issues of the Economic Survey, Ministry of Finance, Government of India, and the Report on Currency and Finance, Reserve Bank of India. Data on international trade was taken from the International Economic Databank, maintained by The Australian National University, and the World Trade Database from Statistics Canada. Data on industry characteristics for India were taken from various issues of the Annual Survey of Industries, Central Statistical Organisation, Government of India and also from the Industrial Statistics Database—2001 CDROM of the UNIDO. Other international statistics such as the price indices for the USA, etc. were taken from the IMF’s International Financial Statistics. The firmlevel data used in our analysis are from the database called PROWESS provided by Centre for Monitoring Indian Economy (CMIE), Mumbai. As we have taken data from various sources, we have not been able to maintain a common period of analysis for each of our empirical exercises. The analysis at the aggregate and the sectoral levels uses data pertaining to both the pre- and post-reforms period. The firm-level analysis confines itself to the post-reform period. Restricting the analysis at the firm level to the post-reform period, we feel, is not a major limitation of the present study. First of all, it is worth recalling that the book is a study on the determinants of competitiveness and the role of finance therein (especially at the firm level) and is not an evaluation of the effectiveness of reforms. Second, as will be seen later in Chapter 2, in the post-reform period most of the direct incentives to export (such as export subsidies) that prevailed in the pre-reform period were removed. Consequently, exporting by firms in the post-reform period is largely determined by firm characteristics, which is what the study seeks to identify. Finally, from an analytical point of view, the framework to study finance constraints is not appropriate to the pre-reform period when wide-spread controls on the financial intermediaries (banks, development finance institutions, capital markets) were prevalent. 1.3 Organisation of the book The book is organised as follows: In Chapter 2, we provide a brief overview of the important policies with regard to macroeconomic, international trade, exchange rate, industries, the financial sector, labour and exit procedures. The objective is to provide a snapshot of the important phases in the evolution of these policies (keeping the details to the bare minimum possible) and their implications for the theories that we propose to test in the rest of the book. The empirical analysis begins in Chapter 3 by first examining the evolution of India’s external sector. We then develop a simple BOP constrained growth model, which we estimate econometrically to establish the importance of export performance for the growth of the economy. This provides an empirical rationale for

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the subject matter, namely India’s international competitiveness. We then examine the evolution of India’s aggregate competitiveness over the period 1970–98. Using the CMS model, we decompose India’s export growth into four components, namely a world trade effect, commodity composition effect, market distribution effect and a competitiveness effect. This latter analysis helps us identify the strengths and weaknesses in India’s export basket and market focus. In Chapter 4, we examine international competitiveness at the sectoral level. Competitiveness at the sectoral level is defined as India’s share in total world exports of that sector’s output. We begin with a review of Fagerberg’s model that relates investment to export performance and set up an empirical model of India’s export competitiveness. The empirical analysis begins with an examination of the trends and patterns in disaggregated export shares, aggregate movements in labour productivity, real wages and unit labour costs in the Indian manufacturing sector. We then econometrically estimate our empirical model for a panel of seventy eight Indian industries over the period 1989–90 to 1997–98. In Chapter 5, we study the exporting behaviour of firms. We begin with a brief review of the related literature in the Indian context, followed by a discussion of the more recent theoretical developments on this issue. The empirical exercise begins by examining the characteristics of exporting and non-exporting firms and their investment patterns using a sample of 672 firms over the period 1992–93 to 1997–98. We then proceed to empirically model the decision to export at the firm level in light of the theoretical framework that emphasises the role of sunk entry costs and the lumpy nature of investments discussed earlier. In Chapters 4 and 5, we establish that investment is crucial for exports, both at the sectoral and the firm level. As we discussed earlier, financial markets and institutions are important in influencing investment and facilitating technological innovation by identifying and funding those firms with the greatest chances of success. In Chapter 6, we explore the determinants of investment and in particular the relationship between financial factors and firms’ investment behaviour. Using the same sample of firms studied in Chapter 5, we look for the presence of finance constraints on the investment decisions of firms in our sample, especially the outward-oriented firms. Further, we try to trace the source of these finance constraints to the particular institutional setting found in the Indian financial sector. The concluding chapter summarises the key findings of the analysis in the book and examines their implications for policies for export promotion.

2 The policy environment in India

Since the First Five-year Plan (beginning in 1951) till the major reform initiative in 1991, India followed a policy of planned industrialisation. ‘Self-reliance’ as an explicit policy objective was introduced in the Third Plan. Policies governing international trade, exchange rate, industry and finance were designed to complement each other so as to achieve this objective. Given the generally pessimistic assessment of export prospects the planners regarded import substitution as the prime means of achieving self-reliance. This is amply borne out by the nature of the policies followed in this period. Major reforms to the overall policy environment were initiated in 1991, though some of them had roots already in a series of partial reform measures taken up during the mid-1980s. In this chapter, we provide a brief overview of the important policies that have a bearing on international competitiveness, from the early 1970s to the late 1990s. These are macroeconomic policy, trade policy, exchange rate policy, industrial policy, financial sector policy, labour policy and exit policy. Detailed accounts of these policies and their impact on the Indian economy can be found in several earlier studies, notable amongst which are Bhagwati and Desai (1970), Bhagwati and Srinivasan (1975), World Bank (1989), Ahluwalia (1991), Pursell (1992), Joshi and Little (1994) and Agrawal et al. (1995). The overall implications of these policies for India’s international competitiveness are assessed in the concluding section of this chapter. 2.1 Macroeconomic policy For much of the 1970s and the early 1980s, India followed a conservative macroeconomic policy, underpinned by a strong political commitment to low inflation rates (Joshi and Little 1994).1 However, since the mid-1980s there has been a gradual erosion in macroeconomic discipline. This is evident from the increase in the fiscal deficit as a percentage of GDP from an average of 6.3 in 1980–84 to 8.2 in 1985–89. This in turn was caused by an increase in the revenue deficit, which is the difference between current expenditures and current revenues. The increase in the revenue deficit was primarily caused by a sharp increase in current expenditures relating to interest payments and subsidies (Agrawal et al. 1995). From the mid-1980s till about 1991, the increasing deficits of the central government were financed both by monetary expansion and through borrowings from the public. The balance of payments crisis of 1991 could be linked to the large and growing fiscal imbalances witnessed in India since the mid-1980s. Simultaneously, the postmid-1980s period witnessed an upward trend in the inflation rate culminating in an inflation rate of 13.8 per cent in 1991. With the structural adjustment programme initiated in 1991, the fiscal deficit fell from 7.0 per cent of GDP in 1990–91 to 5.0 per cent of GDP in 1991–92. Correspondingly, there was a fall in the revenue deficit from 3.5 per cent of GDP in 1990–91 to 2.6 per cent of GDP in 1991–92. Subsequently, the fiscal deficit has

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remained at around 4–5 per cent of GDP while the revenue deficit has inched up to 3.7 per cent of GDP in 1997–98. The lack of success in reducing the revenue deficit and the use of borrowings to cover this deficit have contributed to high real interest rates in the 1990s. With regard to inflation, there has been substantial progress with the inflation rate touching a record 18-year old low of 2.0 per cent by July 1999. Much of this progress could be attributed to the nature of the monetary policy followed since 1991. 2.2 Trade policy2 In the beginning of the 1970s, India had a highly restrictive trade regime. Nearly all imports were subject to discretionary import licensing or were ‘canalised’ by government monopoly trading organisations. The only exceptions were commodities listed in the Open General License (OGL) category. Capital goods were divided into a restricted category and the OGL category. While import licenses were required for restricted capital goods, those in the OGL could be imported without a license (but of course only after paying customs duty) subject to several other conditions. The most important of these were that the importing firm had to be the ‘actual user’ of the equipment and could not sell the latter for 5 years without the permission of the licensing authorities and that the resulting change in capacity must be compatible with the capacity approved by the industrial licensing authorities. Intermediate goods were also classified into the banned, restricted and limited permissible categories plus an OGL category. OGL imports of intermediate goods were also governed by the ‘actual user’ condition. The import of consumer goods was, however, banned (except those which were considered ‘essential’ and could only be imported by the designated government canalising agencies). Like imports, exports were also subject to an elaborate licensing regime. There were goods whose export was ‘not allowed’, goods whose exports were considered on a case-by-case basis, goods whose export was allowed within an export quota announced and allocated each year, goods whose export was canalised, and goods on the OGL list that could be exported subject to prescribed conditions. The aim of this licensing system was essentially threefold: first, to ensure that an ‘essential’ commodity was not exported when domestic demand was greater than domestic supply; second, to guard against adverse price movements in those international markets in which India was a key player; and third, to restrict the exports of intermediates in favour of ‘high-value-added’ downstream products (Agrawal et al. 1995). Furthermore, there were a large number of export incentives for manufactured goods. Special import licenses allowed exporters to import raw materials more easily. The Cash Compensatory Support Scheme (which was introduced in 1964) provided cash assistance to exporters in respect of certain items. This was intended to compensate exporters for indirect taxes (most importantly, sales tax) on inputs imported or domestically produced. The Duty Exemption Scheme (introduced in 1976) granted customs duty exemption to registered exporters for import of specified raw materials against an advance license for the manufacture of specified export products and imposed upon the exporters an export obligation in return. Beginning with the export—import policy of 1977–78, there was a slow but sustained relaxation of import controls. Several capital goods that were not allowed to be imported without an import license were steadily shifted to the OGL category. The number of capital goods on the OGL list increased from 79 in 1976 to 1170 in April 1988. These changes were made with the intention of allowing domestic industries to modernise. Moreover, during the 1980s the import licensing of capital goods in the restricted list was administered with less stringency (Pursell 1992). As a consequence, the import penetration ratio in the capital goods sector increased from 11 per cent in 1976–77 to 18 per cent in 1985–86 (Goldar and Renganathan 1990). In the case of intermediate goods too, there was a steady shift of items from the

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restricted and limited permissible categories to the OGL category. However, in practice a capital or an intermediate good was placed in the OGL list only if it was not being domestically produced. Thus, import liberalisation during this period may not have led to immediate direct competition to established producers of intermediate and capital goods in India (though in several instances, the goods that were allowed to be imported were imperfect substitutes of domestically produced goods). Furthermore, the average effective tariff rate for capital goods increased from 37 per cent in 1973–74 to 63 per cent in 1988–89. Thus, the liberalisation of the 1980s through expanding the OGL list merely freed the importer from the bureaucratic hassle of obtaining an import license in advance. That itself was a big gain. The pace of the trade reforms—in particular, the shift from quantitative import controls to a protective system based on tariffs—initiated in the mid1970s was considerably quickened by the new government (led by Rajiv Gandhi) that came into power in 1984. Restrictions on the import of capital goods were further eased to encourage technological modernisation. Also, beginning in the mid-1980s, there was a renewed emphasis by the new administration on export promotion. The number and value of incentives offered to exporters were increased and their administration streamlined. The allotment of REP licenses—tradable import entitlements awarded to exporters on a product-specific basis—became increasingly generous (Agrawal et al. 1995). Finally, the duty exemption scheme for imported inputs was extended to cover all imported inputs for both direct and indirect exporters. In 1991, as a part of the structural adjustment programme, quotas on the imports of most machinery and equipment and manufactured intermediate goods were removed. REP licences were abolished and a large part of the import licensing system was replaced by tradable import entitlements (named ‘Eximscrips’) linked to export earnings. Furthermore, the ‘actual user’ criterion for the imports of capital and intermediate goods was removed. There was also a significant cut in tariff rates, with the peak tariff rate reduced from 300 to 150 per cent and the peak duty on capital goods cut to 80 per cent. There was, however, little change in trade policy with respect to consumer goods which remained in the ‘negative’ (banned) list. With regard to exports, the Cash Compensatory Scheme was withdrawn from 1992 with little change in other aspects of export promotion.3 2.3 Exchange rate policy In the period 1947–71, the rupee was pegged to the sterling pound. After a brief period when the rupee was pegged to the US dollar, there was a switch back to a sterling peg from December 1971. The link to a weak currency was in part deliberate. In spite of the oil shock of 1973 and the severe droughts of 1972 and 1974, with the sterling falling in value in relation to other currencies in the early 1970s, there was a real effective devaluation of the rupee during this period. In September 1975, the peg was altered to a basket of currencies with undisclosed weights, though the sterling continued to be the currency of designation and intervention. For the first two years from September 1975, the nominal exchange rate (NER) was kept fixed, but was allowed to fall in 1978 and 1979 as the dollar was weak at that time (Joshi and Little 1994). Keeping the NER stable would have led to a large appreciation against the dollar. In fact, the real exchange rate (RER) fell by about 17 per cent during this period as inflation in India was low (we discuss in Chapter 3 the behaviour of the RER in more detail). From 1980 to 1982, the NER was again kept fixed leading to an appreciation of the RER, with inflation in India being high during this period. During 1983–85, the NER was made more flexible with a view to keeping the RER at its 1982 level. With the emphasis on export promotion from the mid-1980s, there was a

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steady devaluation of the Indian rupee during this period. Effectively, India operated an ‘active’ crawling peg from 1986 onwards to produce a sharp real depreciation of the rupee in the period 1986–90. In July 1991, as a part of the stabilisation package, the rupee was devalued against the dollar by 18 per cent. From 1991 to 1993, India moved gradually to full current account convertibility of the exchange rate, first in March 1992, replacing the tradable import entitlements, that is, the Eximscrips, by a dual exchange rate system, under which exporters could sell 60 per cent of their export earnings to authorised foreign exchange dealers at a ‘market-determined’ rate (the remaining 40 per cent at the official exchange rate); and then, in March 1993, moving to a unified ‘market-determined’ exchange rate system (i.e. a managed float). However, strict controls over the capital account, especially capital outflows remain. This substantially mutes the influence of movements in the capital account on the exchange rate. Further, even on the current account, the ‘market-determined’ exchange rate is not completely free of intervention by the Reserve Bank of India (RBI). In line with the reforms, the nature of RBI intervention has undergone a qualitative change. Unlike in the past when the RBI used to ‘announce’ the official exchange rate, it now influences to a great extent the ‘market-determined’ exchange rate through direct market operations as a seller or a buyer of foreign exchange and also through other policy instruments such as the interest rate. Indeed, it may be argued that in the recent past the RBI has been protecting the rupee through a combination of market operations and interest rate changes. 2.4 Industrial policy Three basic sets of legislation provided the legal and operational framework for strict control of the operations and growth of non-financial firms according to priorities set out in the various five-year plans. They are: 1 Industries (Development and Regulation) Act, 1951, and the Industrial Policy Resolution, 1956, 2 The Monopolies and Restrictive Trade Practices Act (MRTP), 1969, 3 Foreign Exchange Regulation Act (FERA), 1974. The Industries (Development and Regulation) Act stipulated that no new units could be set up nor substantial expansion be made to existing units without a license from the Central Government. The Act also empowered the government to exempt any class of industrial firms from all or any of the provisions of the Act. Under this provision the government exempted all firms with investment of less than 1 million rupees (called small-scale industries) in land, building and machinery. This exemption limit has been progressively raised and currently stands at 10 million rupees. The Industrial Policy Resolution, 1956, classified industries into three categories based on the role which the state was expected to play in each category. The divisions were: 1 Industries in Schedule A, mostly public utilities, basic and strategic industries, which were exclusively reserved for the state to develop. 2 Industries listed in Schedule B, mostly heavy industries that were to be progressively owned by the state but private firms were also allowed to enter. 3 Industries outside the Schedules A and B which were open to private firms.

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The MRTP Act stipulated that all firms with a capital base of over 20 million rupees were to be classified as MRTP firms and were permitted to enter only selected industries and that too on a case-by-case basis. In addition to industrial licensing all investment proposals by these firms required separate approvals from the Department of Company Affairs. The FERA provided the regulatory framework for commercial and manufacturing activities of branches of foreign companies in India and Indian joint-stock companies with a foreign equity holding of over 40 per cent. The Act specified a list of industries where such firms would be allowed to operate and all new investments and substantial expansions required separate approval from the Department of Company Affairs. As a result of these laws, industry in India was subject to rather formidable legal barriers to entry. Investments, both in terms of expansion of capacity of existing firms and establishment of new firms, was controlled by the government through its licensing policies that were in turn determined according to plan priorities. Following an initial attempt in 1975–76, in 1985–86, the liberalisation of industrial controls gathered momentum when some industries were taken out of the purview of industrial licensing provisions of the Industries (Development and Regulation) Act. Furthermore, modernisation of equipment resulting in an increase of up to 49 per cent of license capacity no longer required an additional license. Expansion of capacity up to a mandated efficient scale was also permitted in industries where economies of scale were considered to be significant. Finally, the definition of the MRTP Act was relaxed to exclude firms whose assets were less than 1 billion rupees. On 24 July 1991 a path-breaking Industrial Policy Statement was tabled in Parliament. There were a few more announcements made in subsequent years. As a result, the current position with respect to the abovementioned acts is: 1 Industrial licensing stands abolished irrespective of the level of investment except for 16 industries. 2 The number of industries reserved for the public sector presently is six. 3 The MRTP Act was amended to remove the threshold limit of assets. This Act now only seeks to curb anti-competitive behaviour on the part of firms. 4 Under the new policy guidelines on foreign investment automatic permission is granted for foreign equity participation up to 51 per cent in a specified list of high technology and high investment priority industries. Separate clearances are still required for foreign firms to be able to operate in other areas.4 2.5 Financial sector policy5 The financial sector in India consists of commercial banks, non-bank financial institutions (NBFIs) and the capital market. Two important features of financial markets and financial intermediaries in India are, (a) Credit markets are highly segmented with commercial banks being the dominant players in the short-term credit market (i.e. credit for less than 1 year, mainly used to finance inventories), and NBFIs are the dominant players in the long-term credit market (mainly used to finance investments in plant and machinery) and (b) Stock markets in India are relatively well developed when compared with other developing countries. Overall, the evolution of the financial sector policies beginning from 1969 can be divided into three distinct sub-periods: first, a period of increasing financial repression from the early 1970s to the mid-1980s; second, a period of mild reforms till 1991; and finally, from 1991, a period of an increasingly liberalised

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financial sector. We discuss next the policies with regard to commercial banks, non-bank financial institutions and the capital markets in turn. Commercial banks In the two decades since independence, banks in India operated in a relatively liberal environment The nationalisation of 14 commercial banks in 1969 was a turning point in the evolution of banking sector policies in India. The RBI now began to play a more direct and active role with banking policies re-oriented to act ‘as an active instrument of growth’ (Reserve Bank of India 1983:7) and to meet social objectives such as the reduction in inequalities of income and the concentration of economic power. As a consequence, policies of financial repression—interest rate controls, directed credit programmes, etc.—increased in magnitude during this period. The social control of banking and the push by the government for banks to open branches in rural areas led to significant growth in the commercial banking system both in geographical coverage and amount of resources mobilised, with a sevenfold increase in the number of bank branches and a trebling of the ratio of bank deposits to national income from 15.3 per cent in 1969 to 51.8 per cent in 1994 (Sen and Vaidya 1997). However, there was increasing recourse to the banking sector by the government to finance its escalating deficits in the 1980s. This was done by the means of mandatory requirements for banks to invest in government securities and cash reserve ratio requirements, both of which increased steadily in the 1980s. At the same time, there were severe restrictions placed on commercial banks by the government regarding both the pricing and allocation of credit and mandatory lending requirements to the ‘priority sectors’ (such as small-scale industry, exports and agriculture). In 1991, as a part of the economic reform package, there was considerable relaxation on the entry of new private banks and existing public sector banks were allowed to issue fresh capital to the public through the capital market. However, the ownership of the dominant commercial banks in the Indian financial sector remained in government hands. Mandatory requirements to invest in government securities and hold cash were scaled down drastically. This increased the ability of banks to lend, but there was very little change in the restrictive environment with regard to the deployment of credit; that is, there was no reduction in the priority sector lending requirements. With respect to regulations on the pricing of credit, most of the interest rate controls were lifted by 1994 in a gradual manner. The most significant change in the banking environment from the viewpoint of the private corporate sector was the interest rate deregulation. Banks were now allowed to charge differential interest rates for firms based on their risk perceptions, which was not the case prior to the deregulation. In the restrictive environment before 1991, credit rationing occurred as an outcome of the credit policy of the government. Banks faced fixed targets for lending to particular sets of borrowers irrespective of their risk-return characteristics. Indeed, official documents of the government6 itself recognise that there was evidence of growing political and administrative intervention in the credit decision-making process. In this milieu, some firms would have faced a situation of being denied credit arbitrarily. Following the reforms, lending decisions now rest with the banks themselves and political and administrative interventions have dramatically reduced. Non-bank financial institutions Non-bank financial institutions in India can be classified into three groups, namely the insurance sector, mutual funds and development finance institutions (also known as term lending institutions in India). The insurance firms and the mutual funds provide funds to firms primarily through the capital market by

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subscribing to new issues of shares, bonds, debentures and fixed deposits. They are also the most dominant players on the stock market. The insurance sector is as yet completely government owned following its nationalisation in 1956. Major reforms in the insurance sector such as allowing the entry of domestic and foreign private insurance companies, have been initiated only in 2000. The mutual funds sector is still dominated by the Unit Trust of India (UTI) (established in 1964) which is a government owned mutual fund. Financial liberalisation of this sector has proceeded via a removal of legal barriers to entry and a strengthening of regulations aimed at ensuring transparency to investors. This sector has since seen a large number of new private entrants. The development finance institutions (DFIs) have traditionally been the most important source of longterm borrowings for private corporate firms. The two largest DFIs are (i) the Industrial Development Bank of India (IDBI) which was set up by the RBI7 in collaboration with other financial institutions and (ii) the Industrial Credit and Investment Corporation of India (ICICI).8 Prior to the reforms, the government controlled the operations of the DFIs with regard to both the sources and uses of their funds. The government provided subsidised credit to these DFIs. These institutions were not allowed to choose the firms (either new or established ones) to whom they could lend. They were instead ‘directed’ to lend at a fixed rate of interest9 to those private firms that had acquired a licence either to create new capacity in an industry or to expand its existing capacity. These licences were issued by the government in accordance with its plan priorities. The plans had both industry-specific real capacity targets and a financial plan to ensure the realisation of these targets. In such a scenario, where DFI lending was based on licensing policies and at an interest rate that was invariant across firms with projects of varying risk-return profiles, these DFIs had very limited screening role to perform. Financial liberalisation has brought about three important changes to the operations of DFIs. First, both IDBI and ICICI have seen a dramatic reduction in their access to subsidised funds from the government. Consequently, these institutions have increasingly raised funds through the capital market by issuing new shares, bonds, debentures and fixed deposits. Second, interest rate deregulation in this sector took place as early as August 1991 when the government permitted all DFIs to charge interest rates in accordance with the perceived risks inherent in the projects funded subject to a minimum lending rate. Finally, with the abolition of industrial licensing and the re-orientation of the economy away from planning industry specific capacity targets to market-driven allocation of resources, long-term credit provided by the DFIs is no more ‘directed’ by the government. In this scenario, the DFIs are key financial intermediaries who would channelise investments based on market signals and as a result of their screening function. Capital markets Unlike other developing countries, India has had fairly well-developed stock markets and their role in the overall financial system has dramatically increased since the 1980s.10 Prior to 1992, the primary issues market was very closely regulated by the government in almost every aspect. The government put in place various regulations with regard to the pricing, quantum and timing of new issues and also forced certain industry-specific debt/equity ratio norms on firms, leaving little leeway for firms to choose their capital structure. Indeed, the government through the Capital Issues (Controls) Act, 1947, sought to ensure that investments do not take place contrary to the objectives of the five-year plans. The Office of the Controller of Capital Issues was in fact created for this purpose and empowered with the authority to ‘decide’ on the price, timing and quantum of new issues. Such severe restrictions discouraged corporations from using new issues to raise funds. More importantly, such administratively determined price of new issues completely lacked any information content for investors.

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In 1992, there was a substantial deregulation of the stock market especially with respect to the new issues market, with the abolition of the Office of the Controller of Capital Issues. Price controls on the issue of new shares were lifted and new guidelines that were less restrictive than those in the pre-1991 period were put in place. Also, in September 1992 the Government of India allowed unrestricted entry in terms of volumes of investments in both primary and secondary markets to reputed Foreign Institutional Investors such as pension funds, mutual funds, investment trusts and asset management companies. Following these reforms, the three years from 1992 to 1995 saw a boom in the primary market as large number of firms raised new equity, bonds and debentures through the stock market after which there was a distinct lull. 2.6 Labour laws11 Industrial relations in India are primarily governed by three central government Acts although some variations exist in laws across states since the state governments can also legislate on industrial relations. The three central government Acts are: 1 The Trade Unions Act, 1926, 2 The Industrial Employment Act, 1946, and 3 The Industrial Disputes Act, 1946. The Trade Unions Act regulates the formation and registration of unions and provides immunity from civil and criminal liability to trade union executives and members for bonafide trade union activity. The Industrial Employment Act regulates the conditions of employment such as recruitment, discharge, disciplinary action, etc., for workers in industrial organisations employing at least 100 workers. This Act was subsequently extended to all firms employing at least 50 workers. The Industrial Disputes Act provides for the settlement of industrial disputes, including individual disputes in case of termination of services. In cases of strikes and lock-outs it provides channels for voluntary arbitration, or at the government’s discretion, compulsory adjudication if a mutually satisfactory agreement between employer and employees is not forthcoming within a specified time period. Under this Act, in the unorganised sector, small firms (employing less than 100 workers) may retrench workers but they are required to give one month’s notice in writing to workers who have been employed for more than 1 year. A retrenchment compensation equal to 15 days pay for each year worked with that particular employer should also be paid in such cases. However, in the organised sector, firms are not permitted to retrench any worker who has been employed with the firm for at least 1 year without permission from the government. If such permission is granted, the firm must give three month’s notice to the worker as well as retrenchment compensation as in the case of smaller firms. However, such permission is rarely granted. In spite of reforms in almost all sectors, there has been very little change in the labour laws. Thus, almost complete job security continues to exist for workers in the organised sector. 2.7 Exit policy12 The exit process in India is a very complicated and time consuming process. There exist several legal barriers to exit. The three most important ones are:

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1 The industrial Disputes Act, 1947: According to Chapter V-B of this Act, no large industrial unit is allowed to retrench workers or close down the firm without the permission of the central government. 2 Urban Land (Ceiling and Regulation) Act, 1976: This legislation prevents firms from selling land without the permission of the concerned state government. This permission has nearly always been declined by state governments and thus becomes a barrier to any restructuring or even formal exit of firms. 3 The Sick Industrial Companies (Special Provisions) Act (SICA), 1985: This Act was amended in 1992 and according to it a unit is said to be sick if (a) the unit has been registered under the Factories Act for five years, and (b) it has accumulated losses which are in excess of its net worth. The Act provided for the setting up of the Board for Industrial and Financial Reconstruction (BIFR). Any firm that becomes sick is required to make a reference (within 60 days of its audited accounts being published) to BIFR for determination of the measures to be adopted with reference to the said company. Once a reference is made to BIFR, it uses a consensus approach to approve a suggested rehabilitation package (which necessarily involves more loans being sanctioned to the sick unit), which needs to meet with the approval of all the parties concerned (management, trade unions, creditors and share-holders). In very few cases does the BIFR actually suggest the winding up (i.e. liquidation) of a company. All this entails an inordinate amount of time spent between a unit becoming sick and a rehabilitation package being adopted, or the unit being wound up. During this period (which may exceed 5 years) the unit stays in a state of ‘suspended animation’, that is, production is stopped and workers and creditors are not paid. In the event that the BIFR recommends winding up a sick unit, the winding up procedures could take as long as 10 years. The winding up of a firm is guided by the Companies Act, 1956. Winding up operations are under-taken by the relevant High Court, which passes a winding up order and appoints an official liquidator, who is required to take physical custody of the firm and collect all financial records and prepare an account of available assets. With regard to the priority of claimants on the firm’s assets, Section 529A of the Companies Act states that payments of (a) workmen’s dues, and (b) debts due to secured creditors shall have priority over all other debts. The experience has been that given the phenomenal delays involved in each step of this process, very little can be realised from the sale of the sick unit. This implies that the controlling interests of the firm bear a very low cost of bankruptcy while the major claimants (workers and secured creditors) bear a high cost (Anant et al. 1992). This inefficient functioning of the exit process with its large delays clearly has important implications on bankruptcy costs. The need for streamlined legal and administrative procedures for exit has been recognised by the government. An important first step in the reform of bankruptcy laws and exit procedures in India was taken by the government with the setting up of a Committee (Goswami Committee) to study various aspects of industrial sickness and corporate restructuring (Ministry of Industry 1993). The two most important recommendations, amongst many, of the Committee are: 1 The responsibility of industrial and corporate reorganisation must shift from secured creditors and the state to the defaulting debtor firms. 2 BIFR should make use of the winding up provisions of SICA more frequently so as to facilitate the sale of unviable units. Though the Committee submitted its report in 1993, no real reforms to the bankruptcy laws were initiated till 1998.13

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2.8 An assessment After four decades of import-substituting industrialisation, the Indian economy has been going through radical economic reforms since 1991. The 1991 reforms have followed on from the more gradualist and piecemeal reforms initiated in 1984. In the trade, exchange rate and industrial policy regime prior to the mid-1980s, protection was automatically extended to all industries, regardless of cost, efficiency and comparative advantage. This served to divorce marketdetermined investment decisions from any guidelines that international opportunity costs might have otherwise provided (Bhagwati and Srinivasan 1975). In this environment, there was little incentive for firms to export, given the high profitability of producing for the domestic market. Furthermore, the industrial licensing system, which became increasingly complex over time, led to the elimination of the possibility of competition ‘in any meaningful sense of the term’ (Bhagwati and Desai 1970:272), and contributed to high levels of inefficiency in the industrial sector. The gradual move from quotas to tariffs in the trade regime pertaining to intermediate and capital goods since 1984 increased the potential of firms across all sectors to compete effectively. Furthermore, it brought in competition albeit in a limited manner for firms in the intermediate and capital goods sector. The focused export promotion policies along with an active exchange rate policy effectively countered the historical anti-export bias of the earlier policies and may have induced many firms to enter into export markets. Two comprehensive studies by Ahluwalia (1991) using industrial level data and Srivastava (1994) using firm level data find a clear increase in total factor productivity growth since the mid-1980s. Srivastava, in particular, argues that the improved productivity performance does not appear to be related to increased competition within the industry or better exploitation of economies of scale but to improved access to imported inputs and improved utilisation of labour. The 1991 reforms would have gone a much longer way in providing firms with access to imported inputs and also to bring in competition across all sectors. The reforms have also led to greater flexibility on the part of firms with respect to investment timing, location, scale and technology. Though quantitative estimates of the impact of the 1991 reforms on productivity of Indian manufacturing firms are not yet available, arguing along the above lines, one may expect much greater positive impact since 1991. With respect to financial sector policies prior to 1991, these were very much designed to complement the restrictive industrial and trade policy regime. The plans had both industry-specific real capacity targets and a financial plan to ensure the realisation of these targets. Control was exercised on the financial side by public ownership of financial institutions providing long-term loans to the private corporate sector. The government provided subsidised credit to these financial institutions, which were in turn directed to the private corporate sector at a fixed rate of interest implying that these institutions had a limited screening role to perform. Private corporate firms faced severe restrictions on the pricing, quantum and timing of new issues and the government also forced certain industry-specific debt/equity ratio norms on firms, leaving little leeway for firms to choose their capital structure. In 1991, the financial sector also witnessed significant reforms. There was a substantial deregulation of the stock market especially with respect to the new issues market in 1992. Also, foreign portfolio investors have been allowed to invest in the stock market. Controls on the lending rates of banks and term-lending institutions have been lifted. Simultaneously, term-lending institutions have witnessed a substantial reduction in their access to subsidised credit. Entry barriers in the banking sector have been lifted. All these changes have increased competition in the financial sector leading to a reduction in the cost of financial intermediation. In this new deregulated environment it can be argued that, largely market forces now guide financial decisions of both firms and financial intermediaries.

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However, while there has been a significant reduction in barriers to entry to new firms, there remain severe impediments for old firms to reorganise their labour force in response to increased competition and in extreme conditions, exit from a particular industry. This is because of strong governmental control of the labour and land markets that yet remain in place and a costly bankruptcy procedure. The labour laws and exit restrictions not only limit the ability of firms to restructure themselves so as to compete effectively in world markets, but also have serious implications for the efficient allocation of resources across industries and across firms. More flexible labour laws would go a long way in preventing firms from falling into financial distress. Resources blocked in firms that are financially distressed show up as non-performing assets in the financial sector and adversely affect the financial health of the institutions lending to sick firms. Clearly these resources could be better utilised if sick firms could be liquidated fairly easily.

3 The balance of payments and national competitiveness

National competitiveness can be defined as the ‘ability to produce goods and services that meet the test of international competitiveness, while (the country’s) citizens enjoy a standard of living that is both rising and sustainable’ (Tyson 1992, p. 1). Thus, a competitive country is one that is able to produce tradable goods that are in sufficient demand both in home and overseas markets such that trade will be in balance without the country having to continuously depreciate its currency or to operate at a level of activity below its potential (Howes and Singh 2000). Clearly, large and persistent deficits in the country’s current account are one important indicator of structural problems with respect to the country’s relative competitiveness, though such deficits may also be caused by macroeconomic disequilibrium or inappropriate exchange rate policies. Large current account deficits often trigger balance of payments (BOP) crises for developing countries and may necessitate the use of adjustment measures such as a contraction in aggregate demand in the country in question or large exchange rate adjustments. For a developing country that is attempting to pursue high rates of economic growth, such a growth strategy could lead to widening current account deficits and effectively constrain the long-run growth rate of the economy. A higher long-run growth rate that is consistent with a sustainable BOP situation is only possible with a rapidly growing export sector. As we have argued in Chapter 1, given that agricultural commodities often face declining prices in world markets, the manufacturing sector must necessarily be the leading sector in the country’s export drive. Therefore, the key policy issue at the national level is how to increase the international competitiveness of the country’s manufacturing sector. The purpose of this chapter is threefold. In Section 3.1, we provide a brief overview of the evolution of India’s external sector for the period 1970–98, focussing on the current account, the trade balance and the real exchange rate (RER). In this section, we also look at some summary measures of the Indian manufacturing sector’s international competitiveness. In Section 3.2, we develop a theory of BOPconstrained growth derived from Thirlwall (1979) for application to India. This theory will allow us to examine the relative importance of world income growth and movements in the RER in explaining India’s BOPconstrained equilibrium growth path. We estimate this model using annual data for 1960–98 and then seek to establish whether the economic reforms of 1984 and 1991 have had any effect on this equilibrium growth path. In Section 3.3, we examine trends and patterns in India’s aggregate competitiveness using the constant market share (CMS) methodology. This methodology will enable us to see whether India’s export sector has been moving towards the more rapidly growing commodities and/or markets, which would be a clear indication of the increasing international competitiveness of the country’s tradable sector. Section 3.4 concludes.

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Figure 3.1 The current account balance as percentage of GDP. Source: Economic Survey, Ministry of Finance, Govt. of India, various issues.

3.1 The evolution of the external sector It is evident from Figure 3.1 that it is only from the early 1980s onwards that India had large and persistent deficits in the current account, at around 2–3 per cent of GDP. In the 1990s, while India still had a deficit in its current account, the current account deficit to GDP ratio was considerably lower than in the 1980s. The widening current account deficits of the 1980s cannot be linked to rising deficits in the trade account; in fact, India had a rapidly falling deficit in its trade balance from the early 1980s (Figure 3.2). The latter was primarily due to strong export growth, with the exports to GDP ratio increasing from 4.0 per cent in the mid-1980s to around 10 per cent in the late 1990s. As we have discussed in the previous chapter, during this period, India had followed a policy of steadily depreciating the rupee in real terms in combination with other export promotion measures to boost exports. As is clear from Figure 3.3, there was a steady depreciation of the real effective exchange rate since the mid-1980s. Clearly then, the worsening current account deficit in the 1980s cannot be attributed to a weakly performing export sector or an inappropriate exchange rate policy. The latter point with respect to exchange rate policy is indeed supported by Elbadawi (1994) who estimates the degree of misalignment in India’s RER for the period 1965–88. The degree of misalignment is defined as the deviation of the actual RER from the equilibrium RER, which is the level of the RER which allows the economy to simultaneously attain internal equilibrium (i.e. the non-tradable market clears, the budget is balanced and portfolio equilibrium holds) and external equilibrium (the current account is in balance). Elbadawi develops a model of the equilibrium RER where the latter is determined by domestic absorption and government expenditures (both as ratios of GDP), terms of trade and a measure of the degree of ‘openness’ of the economy and finds that except for 1965 and 1986, which witnessed episodes of overvaluation of 16.3 and 10.6 per cent respectively, the period is characterised by single-digit RER misalignments, most of which are actually quite small. Thus, one can argue that India’s historically weak

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Figure 3.2 Exports, imports and the trade balance as percentage of GDP. Source: Economic Survey, Ministry of Finance, Govt. of India, various issues.

performance in the export sector may be more due to restrictive microeconomic policies rather than RER misalignment. Joshi and Little (1994) argue that the increase in the current account deficit to GDP ratio in the 1980s could be linked to an increase in the investment-savings gap caused by the widening fiscal deficits of the central government. The public investment-savings gap increased from 7.1 per cent of GDP in 1982–84 to 8.4 per cent of GDP in 1985–89. The structural adjustment programme of 1991 led to some correction in this imbalance and hence, a more sustainable current account deficit. With the fiscal retrenchment initiated in 1991, there was a narrowing of the investment-savings gap in the 1990s and a consequent decrease in the current account deficit to GDP ratio. Yet it is interesting to note that the current account deficit has again shown signs of widening in the mid-1990s. This time around, it seems to be linked to a widening deficit in the trade account, primarily due to a weak performance in exports since 1995. One important reason for the slow growth in exports since the mid-1990s is the slight appreciation one observed in the real effective exchange rate (REER), in spite of favourable movements in the nominal effective exchange rate. An important development in the Indian economy since the mid-1980s has been the rapidly increasing openness of the economy, which has gone up from around 11 per cent of GDP in the mid-1980s to over 21 per cent in the late 1990s (Figure 3.4). This increasing openness can clearly be linked to the export promotion policies of the 1980s along with the limited but crucial import liberalisation in the same period. Much of the improved performance in the export sector is due to the increase in manufacturing exports, which had become four times as large as non-manufactured exports in 1995, whereas they were roughly the same in the 1970s. India’s manufacturing exports as a ratio of world manufacturing exports show a significant increase since the mid-1980s, although India’s market share in manufacturing exports still remains at less then 1 per cent in the late 1990s (Figure 3.5).

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Figure 3.3 The nominal effective exchange rate (NEER) and the real effective exchange rate (REER). Source: Economic Survey, Ministry of Finance, Govt. of India, various issues.

India’s manufacturing trade ratio (i.e. its ratio of manufacturing exports to manufacturing imports) also shows a large increase since the mid-1980s (Figure 3.6). Both the above ‘stylised facts’ suggest that India’s manufacturing sector has been increasingly internationally competitive in the 1980s and 1990s. We explore the trends and patterns of competitiveness at the aggregate level for India’s exports in greater detail in Section 3.3. But first we examine whether the favourable developments evident in India’s trade sector since the mid-1980s have had any discernible positive effect on the country’s long-run rate of growth of output. 3.2 The determinants of BOP-constrained growth It is evident that in the long run, a country cannot grow faster than the rate of growth of output consistent with BOP equilibrium on the current account. The analytical framework pioneered by Thirlwall (1979) provides a simple but powerful way by which to link changes in both the composition and growth of the trade sector to the growth rate of the economy that is consistent with BOP equilibrium. We first set out the theory of BOP-constrained growth, as proposed by Thirlwall.1 The theory of BOP-constrained growth The BOP equilibrium on current account measured in units of the home currency can be expressed as (3.1)

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Figure 3.4 Openness (exports plus imports as percentage of GDP). Source: Economic Survey, Ministry of Finance, Govt. of India, various issues.

where Pd is the price of exports in home currency, Pf is the price of imports in foreign currency, E is the exchange rate (i.e. the home price of foreign currency), X is the quantity of exports and M is the quantity of imports. In a growing economy, this condition for BOP equilibrium will become (3.2) where lower-case letters represent rates of change of the variables. We can write the import demand function as (3.3) , Pf is the foreign price of where a is a constant, φ is the price elasticity of demand for imports imports, Pd is the domestic price of import substitutes, Y is domestic income, and π is the income elasticity of the demand for imports The rate of growth of imports may be written as (3.4) where lower-case letters represent rates of change of the variables. Similarly, the export demand function can be written as (3.5) ), Pf is the foreign price of goods where b is a constant, η is the price elasticity of demand for exports ( competitive with exports, Pd is the domestic price of exports, Z is world income and ε is the income ). elasticity of the demand for exports (

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Figure 3.5 India’s share of manufacturing exports in world exports. Source: International Economic Databank, The Australian National University.

The rate of growth of exports may be written as (3.6) where lower-case letters represent rates of change of the variables. Substituting equations (3.4) and (3.6) in (3.2), we can solve for the rate of growth of income consistent with BOP equilibrium. Denoting this to be yB,we can write (3.7) Equation (3.7) represents the basic BOP-constrained equilibrium growth rate. We can simplify the ). In that case, expression by denoting t as the change in the RER ( 1 A depreciation of the exchange rate ( ) will raise yB as long as the sum of the price elasticities of ). The latter is demand for exports and imports is greater than unity in absolute value (i.e. commonly termed the Marshall-Lerner condition, where a currency depreciation improves the trade balance. 2 Domestic prices rising faster than foreign prices will worsen the BOP-equilibrium growth if the sum of the price elasticities of demand for exports and imports is greater than unity in absolute value. 3 A faster rate of growth of world income (z) will raise the BOP-constrained equilibrium growth rate. 4 The higher the income elasticity of the demand for imports (π), the lower will be the BOP-constrained equilibrium growth rate. 5 The higher the income elasticity of export demand (ε), the higher is the BOP-constrained equilibrium growth rate.

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Figure 3.6 India’s manufacturing trade ratio (manufacturing exports as a ratio of manufacturing imports). Source: International Economic Databank, The Australian National University.

It should be noted that it is possible to generalise equation (3.7) to allow for the possibility that for a country receiving persistent capital imports, the trade balance does not have to be in equivalence. However, such a modification may not be very relevant for India, which has historically received capital from abroad in the range of 0.7–2.1 per cent of GDP. Furthermore, actual output growth may be less than the BOP-constrained equilibrium growth rate if a country starts to accumulate BOP surpluses. Again, this has not been the case with India in the period of the study. However, there is a modification that one needs to make to the Thirlwall model in the case of India. Since the mid-1970s, there has been a significant increase in inward remittances by expatriate Indians, mainly the Indian workers who have migrated to the Gulf countries in response to the oil boom.2 Clearly, this large inflow of remittances would have significantly eased the BOP constraints on growth of output for India since the 1970s. We make appropriate modifications in the Thirlwall model to reflect this reality. An extension: remittance income Let R be the value of remittances measured in domestic currency. Then the BOP equilibrium in the current account is In rate of change terms, we get where θ is the share of exports in total receipts of the country, and r is the rate of change of remittances. So the BOP-constrained equilibrium growth path is

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(3.8) In this case, as long as r is positive and is greater than Pd, yB would be higher in an economy which is observing a positive growth in remittance income than in an economy where there is zero or negative growth in remittance income. The estimation of equation (3.8) will enable us to quantify the extent to which changes in the RER, remittance income and world income economic growth has eased the BOP constraint in India, and consequently, influenced the rate of growth of output. Furthermore, if India’s trade structure has changed in the period under study, leading to changes in the income elasticities of import and export demand, this will also impinge on the equilibrium rate of economic growth. Specifically, if the trade and industrial policy reforms of 1984 and 1991 have led to greater specialisation by India’s exporting sector into commodities and markets that are growing faster than the world average, this will be reflected as an increase in the income elasticity of demand for exports (Thirlwall and Gibson 1992; Meliciani 2002). As is clear from equation (3.8), a higher income elasticity of demand for exports (ε) resulting from increasing specialisation by a country in the more dynamic commodities and markets will lead to a higher growth rate of the economy in the long run. However, it is not clear whether the increase in the income elasticity of demand for exports brought about by trade reforms will necessarily lead to a higher long-run growth rate if such reforms lead simultaneously to an increase in the income elasticity of import demand with greater import liberalisation—an increase in π will lead to a fall in yB. In estimating the Thirlwall model for India, we see whether the reforms of 1984 and 1991 have led to a higher equilibrium growth path by bringing about an upward shift in the income elasticity of demand for India’s exports, over and above the possible increase in the income elasticity of import demand following these reforms. The estimation procedure Equation (3.8) is estimated using annual data from 1960 to 1998. While empirically implementing the Thirlwall model, it should be noted, however, that equation (3.8) provides the equilibrium or steady-state rate of growth of the economy and thus, may not hold at a particular point of time. In the short run, it may be possible for actual output growth to be greater or less than the BOP-constrained equilibrium growth rate. Thus, for example, it may be possible for an economy to witness actual output growth (y) as being less than yB, if there are weather-related supply failures in the economy, or a domestic demand-induced recession. It could also be possible for the economy to grow faster than yB if it receives temporary capital inflows, whether private or official (aid). However, in the long run, it can be argued that in the Indian context, y will return to yB. For y to be less than yB on a persistent basis, India is accumulating BOP surpluses, which does not seem to be an accurate portrayal of India’s recent macroeconomic experiences (Joshi and Little 1994: Chapter 3). For y to be greater than yB, a BOP crisis can be triggered at any time, especially when India has not been a net capital importer for a sufficient length of time. Thus, both the theory and India’s macroeconomic history would suggest that there would be inevitable pressures on the economy to converge to the BOP-constrained growth rate in the long run. A simple yet elegant way to separate the short-run out-of-equilibrium adjustment dynamics from the longrun steady-state properties of the model is to estimate equation (3.8) using the general to specific modelling strategy. The essence of the general-to-specific modelling procedure is to embed the relationship being investigated within a sufficiently complex dynamic specification, including lagged dependent and independent variables so that a parsimonious specification of the model can be uncovered (Hendry et al. 1985; Hendry 1996).

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Under this procedure, estimation starts with an over-parameterised autoregressive distributed lag (ADL) specification of an appropriate lag order (3.9) where a is a vector of constants, Yt is a vector of endogenous variables, Xt is a ( vector of and ) matrices of parameters. explanatory variables, and Ai and Bi are Equation (3.9) can be reparameterised in terms of differences and lagged levels so as to separate the short- and long-run multipliers of the system as follows (3.10) where

and where the long-run multipliers of the system are given by Equation (3.10), which is known as the error-correction modelling (ECM) representation of the model, constitutes the ‘maintained hypothesis’ of the specification search. The estimation procedure involves first estimating the unrestricted equation (3.10) and then progressively simplifying it by restricting statistically insignificant coefficients to zero and reformulating the lag patterns where appropriate in terms of levels and differences to achieve orthogonality. To be acceptable, the final equation must satisfy various diagnostic testing procedures. In applying this estimation procedure, we set the initial lag length on all variables in the general ADL equation at two periods. This is the established practice in modelling with annual data. Results Column 1 of Table 3.1 reports the final parsimonious estimated equation, together with a set of commonly used diagnostic statistics and long-run elasticities relating to the key explanatory variables. The variables and capture the short-run effects of the RER and real remittances on actual output , and along with the lagged dependent variable incorporate growth while the variables the long-run impact of the explanatory variables on output growth. We see that the long-run coefficients on world income (z), the RER (t) and remittances (r) are of the right sign and in the case of z and r, are statistically significant. The long-run elasticities indicate that a 1 per cent increase in world income leads to a 0.72 increase in India’s output growth rate in the long-run. On the other hand, a rise in real remittances by 1 per cent leads to an increase in output growth by 0.073 per cent (Table 3.1). We then attempt to separately assess the effect of the 1984 and 1991 reforms on the growth rate of the economy by changes in the income elasticities of demand for exports and imports. We do this by introducing slope dummies on the coefficient of world income growth, by letting the latter interact with the dummies for 1984 and 1991. We label these dummies DUM84 and DUM91, respectively, and introduce them into the regression specification individually.3 As is clear from the results in Columns 2 and 3, both the 1984 and 1991 reforms have had a positive impact on the coefficient on world income, raising the equilibrium BOP-constrained growth path of the economy. However, the interaction dummy for the 1984 reforms is statistically significant, while the interaction dummy for the 1991 reforms is not. The significance

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of the 1984 interaction dummy is consistent with the finding of the previous section that there has been a clear increase in the outward orientation of the Indian economy, and in particular, the international competitiveness of the manufacturing sector since the mid-1980s. The lack of significance of the 1991 interaction dummy could be due to the fact that the radical trade reforms of 1991 would have led to both an increase in the income elasticity of demand for exports and imports, with the sweeping liberalisation of the import regime and the complete abolition of export quotas. Overall, one can argue that the economic reforms of 1984, and to a limited extent, the 1991 reforms, seem to have had the desired effect of increasing India’s export competitiveness, and consequently, the economy’s equilibrium growth path consistent with a sustainable current account deficit. How has this increase in India’s export competitiveness come about? Is it through a specialisation of India’s export basket towards the more dynamic commodities and/or through a reorientation towards the more dynamic markets? We explore this in detail in the next section. Table 3.1 Estimation results of the BOP-constrained growth model Explanatory variables

Column (1)

Column (2)

Column (3)

∆t(−1)

DUM84*z(−2)

−0.12 (1.35) 0.04* (1.68) −0.93*** (5.39) 0.67*** (2.94) −0.20 (1.56) 0.07* (2.02) —

−0.13 (1.51) 0.03 (1.40) −0.99*** (5.57) 0.68*** (3.05) −0.21* (1.70) 0.05 (1.53) —

DUM91*z(−2)

—

−0.09 (1.09) 0.04* (1.72) −1.03*** (5.80) 0.58** (2.59) −0.16 (1.27) 0.07** (2.20) 0.56* (1.77) —

R-squared S.E. of regression Serial correlation—Chi-square Functional form—RESET F Statistic Normality of residuals—Chi-square Long-run elasticities

0.56 0.0356 2.91 0.09 2.34

0.60 0.0344 2.03 0.004 1.13

∆r(−1) y(−1) z(−2) t(−2) r(−2)

Notes a ∆y is the dependent variable. b t, z and r are log-first differences of the real exchange rate, world income and real remittances.

0.61 (1.34) 0.58 0.0352 3.09 0.29 1.18

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Explanatory variables Column (1) Column (2) Column (3) c Domestic and foreign prices used in the construction of the RER are the Wholesale Price Index of India and the Consumer Price Index of the United States respectively and is obtained (along with the Rupee/US$ nominal exchange rate) from the IMF’s International Financial Statistics. d z is the growth in World Income and is obtained from the IMF’s International Financial Statistics. e Data on output and remittances (private transfers) is obtained from the Economic Survey, Ministry of Finance, Govt. of India. f * significant at the 10 per cent level; ** significant at the 5 per cent level; *** significant at the 1 per cent level.

3.3 Trends and patterns in aggregate competitiveness To study the trends and patterns in competitiveness at the aggregate level, we use the CMS analysis. The CMS method allows us to decompose the increase in a country’s exports into four components: (a) standard growth effect—that proportion of growth in a country’s exports that can be attributed to the overall growth in world exports; (b) commodity composition effect—the extent to which a country’s export basket consists of those commodities that dominate world exports; (c) market distribution effect—the extent to which a country’s exports are focussed on those markets that are growth centres for world demand and (d) a residual effect which may be termed ‘competitiveness effect’—which captures the net gain or loss in the country’s market share in the world after all other effects have been accounted for. Algebraically, the CMS model is specified as follows

where ∆E is the change in total exports of focus country (between time 1 and time 0); S0, share of focus , share of focus country in country in total world exports (in time 0); ∆W, change in total world exports; , share of focus total world exports of ith commodity; ∆Wi, change in world exports of ith commodity; country in the total exports of ith commodity to jth country; ∆Wi,j, change in total exports of ith commodity total exports of ith commodity to jth country (in time 1); ∆Si,j, change in share of focus to jth country; country in the total exports of ith commodity to jth country. The first bracketed term in the above equation is the standard growth effect, the second bracketed term is the commodity composition effect, the third bracketed term is the market distribution effect and the last bracketed term is the residual representing competitiveness. A positive commodity composition term would indicate that the country’s exports consist mainly of those goods for which world import growth has been particularly strong. Likewise, a positive market distribution term would suggest that the country’s exports concentrate mainly on the rapidly growing markets in the world. The residual term captures the net gain/loss in market shares (after accounting for the other three effects), presumably due to changes in the relative price and/or quality of the product, marketing effort and skill of the exporters. We use the CMS methodology to decompose the annual change in India’s total exports (all commodities), over the period 1970–96. The data set used is the World Trade Database from Statistics Canada, which is based on the trade data from the United Nations Statistical Office. This database

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reports commodity-wise bilateral trade flows for all the countries in the world, as per the Standard International Trade Classification (SITC), Revision 2.4 In order to estimate the CMS effects, one should specify a ‘standard’ against which India’s exports to the focus destination is to be matched. Earlier studies for India (Biswas 1982; Leela 1985; Tiwari 1986; Marjit and Raychaudhuri 1997), that have used the CMS methodology, have used exports to the OECD countries as the ‘standard’ in such analysis, presumably because the OECD countries account for a bulk of India’s exports. In this study, however, we have used the world as the standard in order to exploit the full richness of our data set described above. Data on the annual growth in India’s export over the period 1970–96 and its decomposition into the four CMS effects are reported in Table 3.2. A graphical plot of these are presented in Figure 3.7. It can be seen that Table 3.2 CMS decomposition of India’s export growth Year

Annual change in India’s exports (billion US$)

1971 −0.025 1972 0.395 1973 0.464 1974 0.964 1975 0.617 1976 0.934 1977 0.727 1978 0.486 1979 0.898 1980 0.540 1981 −0.957 1982 2.015 1983 0.581 1984 0.415 1985 −0.580 1986 0.797 1987 2.187 1988 1.554 1989 1.751 1990 2.685 1991 −0.248 1992 2.919 1993 1.577 1994 3.454 1995 5.509 1996 2.620

World trade effect (%)

Commodity composition effect (%)

Market distribution effect (%)

Competitiveness effect (%)

−805.3 98.9 206.4 130.2 37.6 58.3 115.4 221.7 186.5 261.4 16.1 −19.6 −48.1 136.5 −49.5 105.2 79.6 116.1 57.4 78.8 −221.6 51.4 32.9 69.9 90.6 65.0

208.5 2.5 −8.7 −60.9 −0.1 3.6 16.1 9.3 −32.6 −67.3 28.2 3.3 16.6 −7.4 28.4 79.0 −3.0 −11.6 4.6 −45.2 −50.5 5.8 −7.2 8.3 −16.4 −25.2

156.0 21.0 −4.9 18.4 63.0 −26.2 21.0 41.5 −3.7 41.4 −3.8 0.7 −16.7 16.0 −50.8 −49.3 −1.0 −6.5 12.4 −8.0 93.3 −7.3 38.3 −2.3 1.1 −5.6

540.8 −22.4 −92.7 12.4 −0.5 64.4 −52.5 −172.5 −50.2 −135.5 59.6 115.7 148.3 −45.1 171.9 −34.9 24.4 2.0 25.7 74.4 278.7 50.1 36.0 24.0 24.7 65.8

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Figure 3.7 CMS decomposition of India’s annual export growth.

India’s exports are explained largely by the movements in world exports and by the residual term. On the other hand, both the commodity composition effect as well as the market distribution effect account for a very small portion of the growth in India’s exports. Looking first at the residual term, it is seen that there is a discernible rising trend since about 1985, albeit with some large fluctuations, compared to the pre-1985 situation when the residual term was more or less constant. As mentioned earlier, the residual term in the CMS analysis is often interpreted as some kind of a composite ‘competitiveness’ measure. Thus, if one were to follow this interpretation, both the 1984 and 1991 sets of reforms have had the desirable effect on India’s competitiveness, confirming our earlier findings. What exactly is the source of this competitiveness is, however, not obvious. It could refer to movements in both price and nonprice factors, and both macroeconomic and microeconomic factors affecting competitiveness. A clear and straightforward interpretation is not possible. What is, however, clear from the CMS analysis is that both the commodity composition effect as well as the market distribution effect account for a very small portion of the growth in India’s exports. In fact both the commodity composition and market distribution effects are often negative suggesting that (a) India’s export basket probably includes (does not include) commodities that have been relatively unimportant (important) in terms of growth in world exports and (b) India’s exports are probably not focussed on some of the fast growing markets in the world. We explore both these aspects further. Commodity composition We assess the relative importance of various commodities at the SITC two-digit level in total world exports and in India’s total exports. This is done (i) by examining the share of each commodity in total world exports and in India’s total exports over our period of analysis and (ii) by comparing the trends in commodity shares over the period under study.5 Details of the commodities at the SITC two-digit level are presented in Table 3.3.

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33

From the commodity shares reported in Table 3.4, the weak commodity composition of India’s exports emerges in two ways. First, commodities that are relatively unimportant in world exports account for relatively larger share in India’s total exports. These include, Fish, crustaceans, molluscs, (SITC 03), Coffee, tea, cocoa, spices (07), Feeding stuff for animals (08), Metalliferous ores and metal scrap (28), Crude animal and vegetable materials n.e.s. (29), Dyeing, tanning and colouring materials (53), Leather, leather manufactures n.e.s and dressings (61), Textile yarn, fabrics, (65), Non-metallic mineral manufactures n.e.s (66), Articles of apparel and clothing accessories (84) and Footwear (85). It may be noted in passing here that these commodities are either primary products or semi-finished/finished manufacturing products that are not technologically sophisticated. Moreover, many of these primary commodities are generally considered to have a low elasticity of demand with respect to (world) income. Second, commodities with a relatively high share in world exports account for relatively lower share in India’s total exports. These include, Live animals chiefly for food (01), Artificial resins, plastic materials, cellulose esters (58), Chemical materials and products n.e.s (59), Paper, paperboard, articles of paper, paper pulp (64), Non-ferrous metals (68), various types of electrical and transport machinery (SITC 71–79), Professional, scientific and controlling equipment (87), and Photographic apparatus, optical goods, watch (88). Again it may be noted in passing here that most of these products (SITC 71–79, 87 and 88) are usually considered to be technologically sophisticated, both in terms of product design and in their manufacturing processes. Turning to the trends in commodity shares, Table 3.5 summarises the results of ordinary least squares (OLS) estimates of linear trend equations over the period 1970–96 for (a) commodity-wise shares in total world exports and (b) India’s market share in total world exports of each commodity.6 These results show a clear mismatch in the two sets of trends for many commodities. Of the 23 commodities that have witnessed a rise in Table 3.3 SITC two-digit commodity codes and names 00

–

Live animals chiefly for food

65

–

Textile yarn, fabrics, made—upart., related products

01 02 03

– – –

Meat and meat preparations Dairy products and birds’ eggs Fish, crustaceans, mollucs, preparations thereof

66

–

Non-metallic mineral manufactures, n.e.s.

04 05 06 07

– – – –

08 09

– –

11 12

– –

67 – Cereals and cereal preparations 68 – Vegetables and fruit 69 – Sugar, sugar preparations and honey 71 – Coffee, tea, cocoa, spices, manufactures thereof 72 – Feeding stuff for animals Miscellaneous edible products and preparations 73 – 74 – Beverages Tobacco and tobacco manufactures 75 –

21

–

Hides, skins and furskins, raw

Iron and steel Non-ferrous metals Manufactures of metal, n.e.s. Power generating machinery and equipment Machinery specialised for particular industries Metalworking machinery General industrial machinery and equipment Office machines and automatic data processing eqpt.

34

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

22

–

Oil seeds and oleaginous fruit

23 24 25 26 27 28 29 32 33 34 35 41 42 43

– – – – – – – – – – – – – –

Crude rubber Cork and wood Pulp and waste paper Textile fibres (except wool tops) Crude fertilizers and crude materials Metalliferous ores and metal scrap Crude animal and vegetable materials, n.e.s. Coal, coke and briquettes Petroleum, petroleum products Gas, natural and manufactured Electric current Animal oils and fats Fixed vegetable oils and fats Animal—vegetable oils—fats, processed

51 – Organic chemicals 52 – Inorganic chemicals 53 – Dyeing, tanning and colouring materials 54 – Medicinal and pharmaceutical products 55 – Essential oils and perfume materials 56 – Fertilizers, manufactured 57 – Explosives and pyrotechnic products 58 – Artif. resins, plastic mat., cellulose esters 59 – Chemical materials and products, n.e.s. 61 – Leather, leather manuf, n.e.s. 62 – Rubber manufactures, n.e.s. 63 – Cork and wood manufactures (excl. furniture) 64 – Paper, paperboard, artic. of paper, paper—pulp

76

–

Telecommunications and sound recording apparatus

77

–

Electrical machinery, apparatus and appliances

78

–

Road vehicles (incl. air cushion vehicles)

79 – 81 – 82 – 83 –

Other transport equipment Sanitary, plumbing, heating and lighting fixtures Furniture and parts thereof Travel goods, handbags and similair containers

84

Articles of apparel and clothing accessories

–

85 – 87 –

Footwear Professional, scientific and controling instruments

88

–

Photographic apparatus, optical goods, watches

89

–

Miscellaneous manufactured articles, n.e.s.

91 – 93 –

Postal packages not classified Special transactions and commod., not classified

94 – 95 –

Animals, live, n.e.s., incl. zoo—animals Armoured fighting vehicles, arms of war

96 97 99

Coin (other than gold) not being legal tender Gold, non-monetary Non-identified products

– – –

their shares in total world exports, only in 10 of them have India’s shares in total world exports witnessed a rise. In other words, India has not been able to take advantage of the rise in world demand for as many 13 of these commodities. A similar picture is seen in the case of the 27 commodities that have witnessed a decline in shares in total world exports. As many as 18 of

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

Table 3.4 Importance of different commodities—1970–96 Share of different Share of different commodities in total world exports commodities in India’s total exports >5% >5%

1–5%

<1%

SITC33(I+) SITC77(W+) SITC78

1–5% SITC65 SITC66 SITC84(I+) SITC04 SITC05 SITC51(I+) SITC54(W+, 1+) SITC67 SITC69 SITC89 SITC93(I+) SITC01 SITC58 SITC59(W+) SITC64 SITC68 SITC71 SITC72 SITC74 SITC75 SITC76 SITC79 SITC87 SITC88

<1%

SITC03 SITC07(W−, I−) SITC08 SITC28(W−, I−) SITC29 SITC53(I+) SITC61(I−) SITC85(I+) SITC00 SITC02 SITC06 SITC09 SITC11 SITC12(I−) SITC21 SITC22 SITC23 SITC24(W−) SITC25 SITC26(W−, I−) SITC27 SITC32 SITC34 SITC35* SITC41 SITC42 SITC43 SITC52 SITC55 SITC56 SITC62 SITC63 SITC73

35

36

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

Share of different Share of different commodities in total world exports commodities in India’s total exports >5%

1–5%

<1% SITC81 SITC82 SITC83 SITC94 SITC95 SITC97* SITC99*

Notes 1 W+ or W− in brackets indicates significant rise/fall in the share of a commodity in total world exports during the 1990s compared to the situation earlier. 2 I+ or I− in brackets indicates significant rise/fall in the share of a commodity in India’s total exports during the 1990s compared to the situation earlier. 3 Only cases warranting a reclassification of a commodity is shown using the above codes. In such cases, the situation during the 1990s is reported in the table. 4 * Indicates no exports by India of that commodity. Table 3.5 Trends in commodity shares—1970–96 India’s share in world total share of commodity in total world exports exports of a commodity Rising

Stable

Rising

Stable

Falling

SITC51 SITC54 SITC58(I*) SITC59 SITC62 SITC75(I*) SITC83 SITC84 SITC87 SITC93 SITC03 SITC55 SITC71 SITC76 SITC78 SITC81 SITC85 SITC89

SITC11 SITC43 SITC53 SITC66

SITC0l SITC02 SITC04 SITC27(I*) SITC52(I*) SITC56 SITC72

SITC33 SITC34 SITC63 SITC64 SITC74

SITC00 SITC08 SITC22 SITC23 SITC26 SITC32 SITC41 SITC42 SITC65(W*)

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

India’s share in world total share of commodity in total world exports exports of a commodity Rising

Falling

Stable

SITC09 SITC61 SITC77 SITC82 SITC88

Falling SITC67 SITC68 SITC69 SITC73(W*) SITC05 SITC06(I*) SITC07 SITC21 SITC24 SITC25 SITC28(I*) SITC29 SITC94(W*) SITC95

SITC12 SITC79

Notes 1 Based on linear time trend, estimated by ordinary least squares with correction for auto-correlation where required. 2 I* in brackets indicates weak trend in case of India’s share in the world total exports of the commodity (trend coefficient is insignificant at even 10%, but t−value > 1). 3 W* in brackets indicates weak trend in case of share of commodity in total world exports (trend coefficient is insignificant at even 10%, but t−value > 1). Table 3.6 Regions and country details America

Africa

Asia and Pacific

Europe

CUS—Canada and USA

SSA—Sub-Saharan Africa

JP—Japan

Canada

Benin Br Ind Oc Tr Burkina Faso Burundi Cameroon

Malawi Mali

Japan

Mauritania

CH—China

WE— Western Europe Austria BelgiumLux Denmark

Mauritius Mozambiqu e Niger

China

EEC nes EFTA nes

Netherlands Norway

EAT—East Asian Tigers

Finland

Nigeria

Hong kong

Singapore

Other Africa nes Reunion

Korea Rp

Taiwan

Fm German Dm Rp France

Other Eur nes Portugal

USA

RAM—Rest of the Americas Argentina Guatemala Bahamas Guyana Barbados

Haiti

Belize

Honduras

Central Afr Rep Ceuca nes

Bermuda

Jamaica

Chad

Bolivia

Laia nes

Comoros

Germany

Iceland Ireland Italy

Spain Sweden

37

38

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

America Brazil Cacm nes

Africa

Europe

Rwanda Senegal

RE A—Rest of East Asia Asia cpe nes Malaysia

Gibraltar Greece

Switzerland Turkey

Seychelles

Brunei

Greenland

UK

Panama

Congo Congo Dem Rep Cote D’Ivoire Eq Guinea

Sierra Leone Cambodia

Myanmar

Paraguay

Ethiopia

Somalia

Philippines

Colombia

Peru

Fr So Ant Tr

Costa Rica

Rest America nes St Kitts Nev

Gabon Gambia

South Africa Korea D P Rp St Helena Laos P Dem R Sudan

St Pierre Miqu Surinam Trinidad Tbg Turks Caicos Isl Uruguay

Ghana

Tanzania

RSA—Rest of South Asia

Guinea GuineaBissau Kenya

Togo Uganda

Afghanistan Bangladesh

Nepal Pakistan

Zambia

Bhutan

Sri Lanka

Liberia

Zimbabwe

Maldives

Caribbean nes Cayman islds Chile

Cuba

Dominican Rp Ecuador El Salvador Falkland Isl French Guiana Guadeloupe

Mexico Neth Antilles Nicaragua

Asia and Pacific

Indonesia

Mongolia

Thailand Vietnam

Venezuela NAF— North Africa Algeria Djibouti Egypt Libya Malta

Morocco North Africa nes Tunisia Western Sahara

ME— Middle East Bahrain Cyprus Fm Dem Yemen Fm Yemen Iran Iraq Israel Jordan Kuwait OC Oceania

Lebanon Middle East nes Oman Qatar Saudi Arabia Syrn Arab Rp Untd Arab Em Yemen

EE—Eastern Europe and Fm USSR Albania Fm Yugoslavia Bulgaria Hungary Fm Czechoslov ak Fm Eur cpe nes Fm USSR

Poland

Romania

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

America

Africa

Asia and Pacific Areas nes Australia Fiji Kiribati New Caledonia

39

Europe

New Zealand Oth Oceania nes Papua N Guinea Solomon Islds Unknown partner

these commodities, have actually witnessed a rise/no change in India’s shares in total world exports. In other words, India has been putting greater effort in exporting those commodities that have witnessed a decline in their importance in world exports. These type of mismatches in the trends in commodity shares clearly brings forth the weak commodity composition of India’s exports as revealed by the CMS analysis. Market distribution We carry out a similar analysis with regard to the share of each market in total world imports and in India’s total exports. For this we group the countries into 13 regions/markets, as shown in Table 3.6. The relative importance of these markets in terms of shares in total world imports and in India’s total exports are presented in Table 3.7.7 These results indicate that the market focus of Indian exports may not be as serious an issue of concern as its commodity composition, especially during the 1990s. The 1990s has seen some important corrections in India’s market focus. Particular mention may be made of the case of Eastern Europe (EE), East Asian Tiger economies (EAT), and the Rest of East Asia (REA), where the importance of these markets in India’s exports have witnessed some major reorientation in line with their relative importance in world trade. If anything, the importance Table 3.7 Importance of different regions/markets–1970–96 Share of different regions/ Share of different regions/markets in total world imports markets in India’s total exports >10%

>10%

5–10%

CUS WE

EAT(W+, I +)

5–10%

JP(I−)

<5%

RAM

<5%

REA(I+) ME SSA NAF EE(I−) CH RSA OC(W−)

40

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

Share of different regions/ Share of different regions/markets in total world imports markets in India’s total exports >10%

5–10%

<5%

Notes 1 W+ or W− in brackets indicates significant rise/fall in the share of a region/market in total world imports during the 1990s compared to the situation earlier. 2 I+ or I− in brackets indicates significant rise/fall in the share of a region/market in India’s total exports during the 1990s compared to the situation earlier. 3 Only cases warranting a reclassification of a region/market is shown using the above codes. In such cases, the situation during the 1990s is reported in the table. Table 3.8 Trends in market shares of different regions/ markets—1970–96 India’s share in total imports of different regions/ markets

Share of different regions/markets in total world imports Rising

Stable

Falling

Rising

CH EAT

SSA(I*) OC

Stable

REA

CUS(I*) RAM WE RSA ME NAF EE JP

Falling

Notes 1 Based on linear time trend, estimated by ordinary least squares with correction for autocorrelation where required. 2 I* in brackets indicates weak trend in case of India’s share in the market (trend coefficient is insignificant at even 10%, but t-value >1). 3 In the case of India’s share in total imports by China, the trend is over 1982–96.

of EAT and REA in India’s exports has increased much faster than the growth in imports into these two sets of important markets. The trends in market shares summarised in Table 3.88 too present a similar picture. That is during the 1990s India has been reasonably successful in reorienting its market focus along the lines of the relative importance of the different regions/markets in total world trade. 3.4 Concluding remarks Both in the 1980s and the 1990s, India has witnessed persistent deficits in its current account. The factors that have contributed to this imbalance in the external account seem to be different in the two decades. In the 1980s, the causal factor seems to be macroeconomic in nature, linked to the widening investment-saving gap in the public sector. In the 1990s, the contributing factors were a weakly performing export sector along with unfavourable movements in the RER. Thus, it is difficult to disentangle macroeconomic factors from structural explanations of India’s current account deficits of the past two decades.

BALANCE OF PAYMENTS AND NATIONAL COMPETITIVENESS

41

Notwithstanding the stagnancy in India’s exports in the late 1990s, the period since the mid-1980s has been characterised by strong growth in India’s total exports. This period has also seen significantly greater degrees of integration of the Indian economy with the rest of the world as compared to the 1970s and early 1980s. Much of the growth in total exports during this period can be attributed to a strong export performance of India’s manufacturing sector, which seems to have become increasingly competitive in world markets, as evident by an increase in its share of world manufacturing exports and in the manufacturing trade ratio. The estimation of the BOP-constrained equilibrium growth model for India suggests that while RER depreciation and real growth in remittances have a positive effect on economic growth, world income growth provides the strongest positive impact on the growth rate of the economy. The results also suggest that the 1984 and 1991 reforms seem to have a positive impact on the responsiveness of economic growth in India to world income growth. This may indicate that the income elasticity of demand for India’s exports may be rising, possibly due to greater specialisation of India’s exporters in the more rapidly growing commodities and/or markets in the world economy. The main points that come out of the CMS analysis is that India’s export performance over the period 1970–96, can be largely explained by movements in total world trade. Further, the CMS analysis shows that the increase in the income elasticity of demand for India’s exports since the mid-1980s is mainly due to a reorientation of India’s exports towards the more rapidly growing markets in the world, rather than due to greater specialisation in the more dynamic commodities. While historically, the market focus of India’s exports was not in tune with the world trade patterns, a major correction has taken place during the 1990s. As a result, India’s market focus seems to be less of a problem by the end of the 1990s. The main weakness, however, continues to be the commodity composition of India’s exports, dominated as it is by primary products, semi-finished intermediaries, and finished products involving simple technologies, all with low value addition and facing low world (income) elasticity of demand. Most of these commodities are not the fast growing commodities in world trade. Although manufacturing commodities dominate India’s export basket, what is lacking is a specialisation within manufacturing towards the more dynamic commodities. How can India achieve a transformation in the commodity mix in its export basket towards more dynamic commodities? To answer this question, we need to understand the determinants of India’s competitiveness at the commodity/ sectoral level. This is the subject matter of the next chapter.

4 The determinants of sectoral competitiveness

Sectoral competitiveness refers to the ability of an industry or sector in a given country to expand its share of exports in world markets. There is a rich empirical literature that has examined the determinants of sectoral competitiveness for developed countries (Soete 1981; Dosi et al. 1990; Amendola et al. 1993; Magnier and Toujas-Bernate 1994). However, there is no study that one is aware of which has done so for developing countries. In this chapter, we analyse the determinants of sectoral competitiveness (as measured by industry-level export shares) using panel data for 78 Indian industries over the period 1989–90 to 1997– 98. The key objective of the empirical studies in this area is to disentangle the role of price from non-price factors in influencing sectoral competitiveness. Among the non-price factors, the two variables most commonly emphasised in these studies are technological innovation and the investment rate. In our empirical analysis, we focus on the investment rate rather than on technological innovation for reasons given below. The analytical framework guiding the empirical analysis in this chapter is derived from Fagerberg (1988). In this framework, capital accumulation has an unambiguous positive effect on international competitiveness, independent of other factors. This chapter is in six sections. Section 4.1 discusses the Fagerberg model and its relevance for the Indian case. Section 4.2 provides a description of the data and the variables used in the analysis. As a prelude to the empirical analysis, Sections 4.3 and 4.4 examine trends and patterns in disaggregated export shares, and aggregate movements in labour productivity, real wages and unit labour costs (ULCs) in the Indian manufacturing sector, respectively. Section 4.5 discusses the regression results. Section 4.6 concludes. 4.1 The analytical framework It is well recognised that both price and non-price factors are important in influencing competitiveness.1 Fagerberg (1988) has proposed an empirical model which integrates both price and non-price determinants of international competitiveness in a single framework. In Fagerberg’s model, export shares of a country or sector are a function of ULCs, the technological level, and the investment rate.2 Both ULCs and the technological level are taken relative to the country’s main competitors. ULCs—the ratio of wages to labour productivity—capture the effects of price factors on export shares. If ULCs increase, then the price of the product will increase in world markets (for a given exchange rate), and consequently, the country or sector will lose market share. Technological level and the investment rate will capture non-price factors in international competitiveness.3 Technological factors are also stressed in the evolutionary-technology gap theories of international competitiveness and in the new trade theories.4 Fagerberg’s main innovation is to introduce the investment rate—that is, net investment as a ratio of capital stock—as an additional

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

43

determinant of international competitiveness. The argument here is that lack of competitiveness in terms of technology or prices may be compensated by an ability to meet world demand, if other countries face a capacity constraint. Thus, the country or sector’s ability to compete in world markets depends critically on what Fagerberg terms ‘the ability to deliver’. The argument that investment is crucial to international competitiveness has particular relevance to lowincome developing countries. In such countries, capacity constraints may act as a serious impediment to expanding market shares in exports even when price factors are favourable. Also given the lack of institutional structures that promote research and development in these countries, much of technological progress occurs through embodied technology in plant and machinery, for which again investments are critical.5 In applying Fagerberg’s model to the Indian manufacturing sector, we need to make two important modifications. First, we will omit the level of technological development as an explanatory variable in the empirical modelling of industry-level export shares. This is due to the lack of data on measures of technology at the industry level such as research and development expenditures, number of scientists and engineers, etc. We will however, attempt to capture intrinsic time invariant technological differences across industries that may impact on sectoral competitiveness by an appropriate econometric methodology. Second, we do not normalise unit labour costs of a particular sector by the weighted ULCs of the same sector in other countries that compete with India in that product. This is again due to lack of available data on sectoral ULCs for competitor countries. Thus, the empirical specification used is (4.1) where i denotes industry, t denotes time and export share is the share of the industry’s export in world exports. It is possible to argue that the components of ULCs—real wages and labour productivity—may differ in their impact on export shares. We also estimate a more disaggregated version of the above model that allows for this possibility. (4.2) We have noted in Chapter 2 that Indian trade and industrial policy have differed widely across consumer goods, intermediate goods and capital goods sectors. Furthermore, it is widely believed that India has a greater comparative advantage in many of the consumer goods sector, with many industries in this sector being more labour intensive than industries in the intermediate and capital goods sectors. To see if there are systematic differences in export competitiveness of industries across these three sectors, we add dummy variables for industries located in the intermediate goods and capital goods sectors in equations (4.1) and (4.2). We also add time dummies to capture cyclical changes in world demand conditions. 4.2 Data and variable description Data and period of analysis We estimate equations (4.1) and (4.2) using panel data for 78 industries over the period 1989–90 to 1997– 98. The period of analysis is constrained by the lack of industry data for the years following 1997–98, and the lack of data on investment prior to 1989–90.6 The industries are at the International Standard Industry

44

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

Classification (ISIC) 4-digit level of disaggregation, and the industry data is obtained from the 2001 Industrial Statistics Database (available in CD-ROM format) of the United Nations Industrial Development Organisation (UNIDO). This industry data is in turn obtained from the Annual Survey of Industries, published by the Indian Central Statistical Office (CSO), the agency responsible for the preparation of the data on industries in India. It should be noted that the 3–digit National Industries Classification Code (NIC), which is the classification code used by the CSO coincides with that of the ISIC 4-digit level and is the maximum level of disaggregation that the data will permit. In our empirical analysis, it can be argued that the level of disaggregation matters. First, the greater the level of disaggregation, the more reliable are export shares as a measure of international competitiveness as the industry’s product would be more similar between India and the world. Second, the more aggregated ISIC 3-digit classification clubs consumer and capital/intermediate goods sectors into the same ISIC 3-digit industry. In the Indian case, this would be highly inappropriate as factors that impact on international competitiveness (such as factor endowments and trade and industrial policies) would differ widely across these sectors for the same ISIC 3-digit industry.7 Finally, the use of ISIC 4-digit level industries as the unit of analysis allows us to construct a somewhat larger panel of 702 observations. To compute export shares, we use the export data for India and the world, available from the International Economic Databank, The Australian National University. This database maps the Standard International Trade Classification (SITC) commodity listing at the 5-digit level to that of the ISIC (a classification by industrial origin) at the 4-digit level (Revision 2), using the UN commodity concordance (UN 1994). The exports data were tabulated from United Nations trade data tapes. The reason for using the ISIC classification instead of the more commonly used SITC classification (as in the previous chapter) here is that it would be necessary in the empirical analysis of industry-level competitiveness to match the export shares so computed from the export data to the industry data contained in the UNIDO database. Variable construction The sectoral ULC is defined as total wage bill (at current price) divided by total value added (at current price). Labour productivity is defined as the ratio of real value added (deflated by sectoral wholesale price index (WPI)) to total number of workers employed. Real wage rate is defined as the ratio of total real wage bill (deflated by WPI) to total number of workers employed. We estimate the beginning-of-the-period real capital stock at the sectoral level using a method similar to Athey and Laumas (1994).8 The following assumptions are made: 1 All the capital in sector i has an identical useful life Li. 2 For the first year in the sample, the end-of-period capital stock equals the reported net fixed assets (NFA) in current rupees. 3 A straight-line method of depreciation is used and actual depreciation is exponential with depreciation 1/Li. 4 All investments are made at the beginning of the year and all depreciation is subtracted at the end of the year. Thus, we estimate the beginning-of-the-period capital stock by

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

45

where, P is the WPI of capital goods. Two sets of data related problems arose while constructing the above variables. First, data on sectoral wage bill, value added and employment are at ISIC 4-digit level and were taken from the International Economic Databank, maintained by the Australian National University. This database, however, does not report sectoral WPI. Hence, the data on WPI for the manufacturing sector published by the Reserve Bank of India (RBI) was mapped on to the ISIC 4–digit level, and this mapped WPI data was used in our analysis.9 A similar problem arose while constructing the sectoral capital stock measure used in our analysis. The data on sectoral NFA are from the Annual Survey of Industry (ASI), which was then mapped on to ISIC 4-digit level before using to construct the capital stock measure.10 4.3 Trends and patterns in export shares We have already observed in Section 3.1 that India’s manufacturing sector has witnessed an increase in its market share of world exports since the mid-1980s. In this section, we take a closer look at the behaviour of the export shares of India’s manufacturing sector both across industries and over time. In Table 4.1, we provide the export shares only for those industries whose share in world exports exceeds an average of 0.3 per cent for the period 1976–98. There are 41 industries that meet this criterion, and these industries comprise nearly 89 per cent of total manufacturing exports over the period 1976–98. It should be noted that among these 41 industries, only 17 have a market share that exceeds 1 per cent, indicating that the majority of the industries in Indian manufacturing exhibit relatively little export competitiveness. We find that there are significant changes in the export shares of certain industries in the 1990s as compared to the 1980s and late 1970s. Industries that have seen a dramatic decline in export shares in the 1990s are textile goods (excluding wearing apparel) and leather tanneries and leather finishing. Industries that could be termed ‘winners’ in the 1990s are carpets and jewellery. It is interesting to note that even for the 1990s, there is wide variation in export competitiveness for any given industry, with most industries seeming to lose export shares since the mid-1990s. 4.4 Productivity and the price determinants of international competitiveness In this section, we provide an overview of the behaviour of labour productivity, real wages and ULCs for the manufacturing sector as a whole for the period 1970–97. As is clear from Figure 4.1, ULCs showed a dramatic Table 4.1 Industry-wise export shares Industries/ISIC 4–digit level

1976– 80 1981– 85 1986– 90 1991– 95 1996– 98 Average 1976–98

3111-SLGHTRG, PREP, PRESERV MEAT 3113-CANNG, PRES FRUITS VEGS 3114-CAN, PRES, PRS OF FISH, CRUS 3115-MANUF VEG, ANL OILS+FATS 3116-GRAIN MILL PRODUCTS 3118-SUGAR FACTORIES REFINERS

0.33

0.45

0.31

0.35

0.32

0.35

0.43 0.22 2.64 6.79 4.73

0.61 0.26 1.53 12.54 1.72

0.28 0.10 1.98 11.41 0.23

0.22 0.07 3.84 14.24 1.39

0.36 1.73 3.68 6.38 1.69

0.38 0.48 2.73 10.27 1.95

46

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

Industries/ISIC 4–digit level

1976– 80 1981– 85 1986– 90 1991– 95 1996– 98 Average 1976–98

3121-MANUF OF FOOD PRODS NEC 3140-TOBACCO MANUFACTURES 3211-SPINNG, WEAVG, FINSHG TEXTS 3212-MAN MDUP TXT GDS EX WEARG APP 3213-KNITTING MILLS 3214-CARPETS 3215-CORDAGE ROPE, TWINE INDS 3219-MANUF OF TEXTILES, NEC 3220-MANUF WEARG APP EX FTWR 3231-TANNERIES, LTHER FINISHNG 3233-MAN PRODS LTER EXC FWR, APP 3240-MAN FTWR EX RUBBR, PLASTC 3511-BASIC IND CHEMS EXC FERT 3512-FERTILISERS PESTICIDES 3521-PAINTS, VARNISH LACQUERS 3522-DRUGS+MEDICINES 3523-SOAP, CLNS PRPS, PERF, COSM 3529-CHEMICAL PRODUCTS NEC 3530-PETROLEUM REFINERIES 3551-TIRE+TUBE INDUSTRIES 3692-CEMENT, LIME AND PLASTER 3699-NON-MET MINL PRODS NEC 3710-IRON+STEEL BAS INDS 3811-CUTLY, HAND TLS, GEN HDWRE 3813-STRUCTURAL METAL PRODUCTS 3819-FAB MET PRD EX MACH EQP NEC 3821-ENGINES+TURBINES 3831-ELEC IND MACH+APPARATUS 3833-ELEC APPLNCS+HOUSEWARES 3839-ELEC APPAR+SUPPLIES NEC 3842-RAILROAD EQUIPMENT 3844-MOTOR CYCLES+BICYCLES 3901-JEWELRY+RELATED ARTICLES 3903-SPORTING+ATHLETIC GOODS 3909-MANUF INDUSTRIES NEC

1.24 0.51 2.71

1.18 1.04 2.19

0.87 0.55 1.91

0.81 0.31 2.91

1.13 0.13 3.91

1.05 0.51 2.72

8.29

4.41

1.84

1.23

1.82

3.52

0.10 4.10 0.66 0.59 1.89 14.26 1.35

0.10 7.60 0.25 0.44 2.25 9.85 2.56

0.22 7.04 0.30 0.37 2.54 7.00 3.43

0.31 8.63 1.12 0.39 2.90 3.28 4.38

1.06 9.91 1.13 0.19 2.81 2.69 3.03

0.36 7.46 0.69 0.40 2.48 7.41 2.95

3.24 0.22 0.04 1.34 0.61 1.09 0.40 0.06 0.47 2.27 1.04 0.77 0.96

7.62 0.18 0.15 1.75 1.27 2.62 0.32 0.28 0.43 0.12 0.60 0.13 0.80

10.67 0.44 0.43 1.39 1.23 1.48 0.30 0.90 0.64 0.18 0.43 0.21 0.62

10.84 0.73 0.80 0.31 1.26 0.98 0.28 0.52 1.27 1.41 1.21 0.61 0.77

4.50 1.01 0.34 0.04 0.47 0.23 0.41 0.36 0.68 1.00 1.82 0.72 0.72

7.38 0.52 0.35 0.97 0.97 1.28 0.34 0.42 0.70 1.00 1.02 0.49 0.77

0.66

0.46

0.18

0.22

0.21

0.34

0.75

0.55

0.35

0.45

0.70

0.56

0.51 0.51 0.78 0.70 1.32 1.44 3.01 1.62 0.36

0.43 0.32 0.43 0.70 0.62 1.07 4.89 1.20 0.23

0.31 0.24 0.12 0.47 0.30 1.18 6.95 0.52 0.17

0.45 0.35 0.16 0.23 0.14 1.61 8.11 0.42 0.34

0.28 0.29 0.12 0.20 0.16 0.73 8.51 1.67 0.57

0.40 0.34 0.32 0.46 0.51 1.20 6.30 1.09 0.33

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

47

Figure 4.1 Labour productivity, real wage and unit labour costs (ULCs).

decline from the mid-1980s onwards. The improvement in ULCs in Indian manufacturing has occurred even in relation to ULCs in the manufacturing sector of the United States, as is evident from Figure 4.2. This implies that India has gained in price competitiveness with the United States, one of its major trading partners, from the mid-1980s onwards. Much of the improvement in ULCs has been driven by increases in labour productivity, perhaps due to the economic reforms initiated in the same period. Ahluwalia (1991), Srivastava (1994) and Chand and Sen (2002) also find an improvement in total factor productivity growth in Indian manufacturing since the mid-1980s, which they show are related to the trade and industrial policy reforms of this period. Finally, the decline in ULCs also led to an improvement in the profitability of the manufacturing sector (Figure 4.3), as measured by the ratio of the WPI of manufacturing to ULCs (see Agenor 1997 for a discussion of this measure). In the empirical analysis, we will see whether the apparent correlation between an increase in the manufacturing sector’s external competitiveness along with a decline in the ULCs that we observe at the aggregate level is also supported by disaggregated industry-level data. 4.5 Regression results Table 4.2 summarises the descriptive statistics of the variables used in the regression. We find that there is wide variability in export shares, ULCs (particularly labour productivity) and in the investment rate. Table 4.3 provides the correlation matrix of the explanatory variables. It is clear from Table 4.3 that the correlation coefficients between the explanatory variables are not large (except in the case of real wage and real labour productivity, which have a common denominator) so that multicollinearity is not expected to be a problem in the regression analysis.

48

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

Figure 4.2 Relative unit labour costs (ULCs).

Equations (4.1) and (4.2) are estimated using panel data for the 78 industries in our sample over the period 1989–90 to 1997–98. We employ the fixed-effects estimator to allow for intrinsic differences across industries with respect to the rate of technological progress that may impact on international competitiveness. Fixed-time effects would have to be included to control for aggregate business cycle influences on investment. With regard to the industry specific effects, we estimate the model in the first difference form in order to conserve degrees of freedom. To capture the time effects we use time dummies for each year in the sample. Finally, we include the investment rate lagged by one year to allow for gestation lags in capital accumulation. In Column (1) of Table 4.4, we include only the ULCs in the regression. In Column (2), we add the investment rate as an explanatory variable. The Table 4.2 Descriptive statistics Variables

Mean

Standard deviation

Minimum

Maximum

Export shares Unit labour costs Real wage Labour productivity Investment rate

0.007 0.467 156.16 534.18 172.38

0.013 2.072 105.03 879.06 904.27

0.0000 0.059 11.07 5.36 −3716.84

0.093 55.11 902.75 11,136.07 18,522.36

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

Figure 4.3 Profitability of the manufacturing sector. Note Profitability is measured by WPI-manufacturing as a ratio of ULCs. Table 4.3 Correlation matrix of explanatory variables Variables

Export shares

Unit labour costs

Real wage

Labour productivity

Investment rate

Export shares Unit labour costs Real wage Labour productivity Investment rate

1.00 −0.031 −0.126 −0.023 −0.053

1.00 0.047 −0.051 0.006

1.00 0.655 0.026

1.00 0.009

1.00

Table 4.4 Determinants of sectoral competitiveness Variables

(1)

(2)

(3)

(4)

(5)

(6)

Constant

0.0000075 (0.010) —

0.0000079 (0.010) 0.0000013 (2.50)** −0.000103 (−0.45) —

0.0029 (2.48) 0.00000128 (2.44)** −0.000101 (−0.45) —

0.0000104 (0.014) 0.00000133 (2.54)** —

0.0028 (2.42)** 0.00000129 (2.47)** —

0.00284 (1.299) 0.0000013 (2.46)** —

0.000015

0.0000145

0.0000146

Investment (-1) Unit labour costs Real wage rate

−0.000107 (−0.47) —

49

50

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

Variables

(1)

(2)

(3)

Labour productivity

—

—

—

Dummy for capital goods

—

—

Dummy for intermediate goods

—

Year dummies R-square

No 0.00041

— (−3.31)*** No 0.012

−0.0037 (1.85)* −0.0056 No 0.031

(4)

(5)

(6)

(2.06)* 0.0000018 (2.35)** —

(1.93)* 0.00000178 (2.29)** −0.0036 (−1.79)* −0.0055 (−3.23)*** No 0.042

(1.92)** 0.00000178 (2.27)** −0.0036 (−1.78)* −0.0055

— (−3.25)*** No 0.022

Yes 0.042

Notes *, ** and *** indicate statistical significance at the 10, 5 and 1 per cent level, respectively.

latter has a positive and statistical significant effect on export shares, while ULCs seem to have no impact. In Column (3), we add the dummies for intermediate and capital goods sectors. The negative and statistically significant coefficient on the two dummy variables indicate that export shares in industries that belong to these two groups observed a lower growth relative to industries in the consumer goods sector. Decomposing ULCs into real wage and labour productivity, we see that both these variables play an important role in determining international competitiveness. Increases in the real wage have a clear negative effect on export shares while increases in the labour productivity have a positive effect (Column 4). In Column (5), we add dummies for intermediate and capital goods sectors, with little change in our results. So far we had not included year dummies. In the estimates for Column (6), we add year dummies, with almost identical results to that provided in Column (5). Thus, the observed relationship between investment and export competitiveness seem to be robust to alternative specifications. Our results indicate that both price factors and the investment rate are important in determining international competitiveness at the industry level. 4.6 Concluding remarks Sectoral competitiveness depends on both price and non-price factors. We have argued in this chapter that among non-price factors, capital accumulation could be considered to be the most important for most developing countries. Analysing patterns in India’s sectoral international competitiveness (measured by industry export shares) using disaggregated 4-digit ISIC industry data for the period 1976–98, we find significant variability in industry export shares both over time and across industries. While India’s manufacturing sector has gained international competitiveness in the aggregate since the mid-1980s, industries such as textile goods (excluding wearing apparel) and leather goods (excluding footwear) have lost international competitiveness in a significant manner, particularly since the 1990s. Industries that have gained world market share in the 1990s are carpets and jewellery. The improvement in overall international competitiveness of the manufacturing sector seems to be driven by a fall in ULCs since the late 1980s. The fall in ULCs since the late 1980s is also observed relative to ULCs in the United States. There has also been an improvement in overall manufacturing profitability in the period since the late 1980s.

THE DETERMINANTS OF SECTORAL COMPETITIVENESS

51

To examine the determinants of international competitiveness at the sectoral level, we estimate a panel data model for 78 industries over the period 1989–90 to 1997–98, with the investment rate and ULCs as the key explanatory variables. We find that the investment rate has a positive and significant effect on industry export shares, thus establishing a clear role for the investment rate in influencing sectoral competitiveness, as argued by Fagerberg. While ULCs in the aggregate do not seem to influence sectoral competitiveness, decomposing labour costs into its components—real wage and labour productivity—allows for a negative effect of the real wage and a positive effect of labour productivity on sectoral competitiveness. Thus, price factors also do matter in determining international competitiveness at the industry level. We also find that industries that belong to the intermediate and capital goods sectors have a lower international competitiveness in the aggregate than industries in the consumer goods sectors. This could possibly be due to the high degree of protection enjoyed by these two sectors till the late 1980s. It could also be related to the possible lack of comparative advantage India has in these sectors, as compared to the consumer goods sector. In this chapter, we have introduced industry-level heterogeneity in under-standing international competitiveness. This, as we have seen, has allowed us to identify investment as a key variable in influencing export competitiveness. We take this analysis one step further in the next chapter where we introduce firm-level heterogeneity.

5 Outward orientation A firm-level analysis

In this chapter we conduct an analysis of export competitiveness at the firm level. Prior to conducting the analysis, it is necessary to ask what would be the relevant definition of ‘export competitiveness’ (or equivalently, ‘international competitiveness’) at the level of the firm. As we have seen in the previous chapter, ‘international competitiveness’ for an industry is conventionally defined to be the share of the industry’s product in the total world exports of that product. Clearly, such a definition is not meaningful at the firm level as the use of world total exports as the denominator for calculating the firm’s export share would yield quite insensible results. A more feasible definition is to calculate the firm’s export share using total exports of the industry in which the firm is located in as the appropriate denominator. However, such a measure may provide biased estimates of the firm’s export competitiveness if the firm is gaining export share in an industry which itself is losing export markets. In this case, it is debatable whether the increase in the share of the firm in total industry exports can be termed as an increase in the firm’s export competitiveness. Rather than defining export competitiveness as a gain in export shares, we take the very act of exporting by a firm as a measure of the firm’s ability to compete in export markets. That is, we take the firm’s export status as a measure of the firm’s export competitiveness. The focus on the firm’s export status as a measure of ‘outward orientation’ is consistent with the arguments presented in Chapter 1 that sunk entry costs are a significant feature of export markets. If this is the case then, the act of exporting can be seen as a signal by the firm that it is confident it will be able to compete effectively in international markets as it is willing to pay these initial non-recoverable costs in return for future expected profits from exporting. In this chapter, we first attempt to understand whether there are systematic differences between exporting and non-exporting firms using a sample of 672 Indian manufacturing firms for the period 1992–93 to 1997–98. This we do by presenting several empirical regularities with respect to the exporters in our sample, compared to the non-exporters, on a wide range of firm characteristics—size, wage-intensity of production, use of imported capital goods and raw materials and profitability. We then examine the factors influencing the firm’s export decision. In the empirical analysis, we draw from two distinct theoretical strands. The first is the proposition that capital accumulation is a fundamental determinant of the ability to export. Thus, the empirical analysis in this chapter follows on from the industry-level analysis conducted in the previous chapter where we have found that industries that observe faster rates of capital accumulation are able to gain a greater share of world exports. Modelling investment at the firm level necessitates a different approach than that used at the industry level. This is because investment activity is lumpy at the firm level, proceeding with discrete jumps, rather than the smoothness in investment behaviour that one tends to observe at the industry or macro level. We will incorporate this aspect of investment behaviour at the firm level when we model the effect of investment on the firm’s export status.

OUTWARD ORIENTATION

53

The second theoretical strand is the sunk cost literature discussed in Chapter 1. This literature makes strong predictions on the importance of prior exporting experience in determining which firms export and which firms do not. The literature argues that the presence of sunk costs will lead to a large degree of persistence in the firm’s export status and that exporting is not a random phenomenon. However, studies by Roberts and Tybout (1997) and Bernard and Jensen (2001) also point out the importance of other firm- and industry-specific attributes such as firm size, foreign ownership, access to imported raw materials and capital goods and industry spillovers in influencing the firm’s export decision. In our empirical analysis, we attempt to control for these factors when we estimate the effect of sunk costs and investment on the firm’s decision to export. The chapter is divided into eight sections. In Section 5.1, we provide a brief overview of the relevant literature on the determinants of exporting behaviour at the firm level in India, and highlight the variables commonly discussed in the literature. The next section outlines a simple model of the firm’s decision to export which incorporates sunk entry costs to exporting and investment in the model. Section 5.3 describes the data and the variable construction while Section 5.4 describes the industry characteristics of the firms in our sample. Section 5.5 explores the differences between the exporting and non-exporting firms. Section 5.6 characterises the special properties of investment in the firms in our sample, namely the lumpy and discrete nature of firm-level investment. Section 5.7 discusses the empirical specification of the model used in the econometric analysis of the firm’s decision to export and the results obtained. Section 5.8 provides some concluding remarks. 5.1 A review of the literature on firm-level exporting behaviour in India There have been a number of papers that have attempted to study export behaviour at the firm level using Indian data. These studies span quite a large time period pertaining only to the pre-reform period. One of the earliest papers, Lall and Kumar (1981) attempts to identify the factors influencing export activity of the largest 100 engineering enterprises in the years 1966–68 and 1976–78. The study uses data from a variety of sources including (i) ‘Top 100 Engineering Companies’ 1979 published by the Association of Indian Engineering Industries, New Delhi, (ii) ‘Top 300 Companies’ 1979 published by the Federation of Indian Chambers of Commerce and Industry, New Delhi and (iii) Handbook of Export Statistics 1977–78 published by the Engineering Export Promotion Council, Calcutta. The authors find that there exists enormous inter-firm variability in export performance and generally speaking exporting is a marginal activity. This they attribute to two factors: (a) the structure of incentives that makes domestic markets more attractive and (b) proliferation of firms that are uncompetitive in world markets presumably due to the policy regime of the time. Their empirical results indicate that exports are negatively correlated with profitability and technological activity. With respect to profitability they argue that while ‘exporting is not remunerative for most firms, those firms that are expanding their exports the fastest are also enjoying rapid increases in profitability (P457)’. The results with respect to the size and export performance relationship were ambiguous. For the years 1976–78, the results indicated that larger firms tended to export more in absolute terms but at very large sizes this relationship is reversed (thus, there is an inverted U relationship between firm size and export performance). Lall (1986) analyses the export performance (export to sales ratio) at the firm level using data pertaining to 100 engineering and 45 chemical firms over the period 1978–80. Subsidies and advertising expenditures positively influence export performance. The impact of in-house research and development (R&D) expenditures was ambiguous across the two industries.

54

OUTWARD ORIENTATION

Keshari and Saggar (1989) study the export behaviour of firms in the machinery and transport equipment (SITC 7) industry, over the years 1980–84 and find that financial collaborations are detrimental to export performance but technical collaborations actually improve export performance. Singh (1989) analyses export and import propensities and balance of trade at firm level, while focusing on size, foreign ownership and degree of foreign ownership impacts. The period of analysis is 1973–74 to 1976–77 and the data are collected from annual reports of 93 firms from the pharmaceutical industry. Singh comes to the following conclusions: (a) small firms are more likely to be non-exporters and (b) foreign-owned firms are more likely to be exporters and have a larger export intensity as measured by the export to sales ratio. Pant (1993) is perhaps the first study to distinguish between the probability of being an exporter and the actual export performance of firms. Pant studies 420 firms in the chemical and electrical and non-electrical industries over the period 1985–90, using data provided by the Centre for Monitoring Indian Economy (CMIE), Mumbai. Pant uses the data to first estimate a discrete choice (logit) model to obtain the probability of being an exporter depending on various firm characteristics and then attempts to explain the export to sales ratios of exporting firms again by using firm characteristics. The results indicate that larger firms have a higher probability of being exporters and this probability is not influenced by profitability and transnationality. The results regarding export intensity of exporting firms support, the author argues, a ‘market imperfections hypothesis’ as there is a negative relationship between firm size and export intensity; that is, larger firms prefer to sell in more profitable domestic market while smaller fringe firms rely more on export sales. The author also finds that the export effort is critically influenced by the import intensity of sales. Kumar and Siddharthan (1994) analyse the export behaviour of 406 companies belonging to 13 industries for the 3 years 1987–88 and 1989–90 using discrete choice (tobit) models. The data source is the Reserve Bank of India’s ‘Finances of Large Public Limited Companies in the Private Sector’. The models are estimated separately for each industry and not for the pooled sample as a whole. There is thus an enormous diversity in the results for the various industries. Generally speaking the following conclusions emerge: 1 Technological activities like R&D influence export behaviour only in low and medium technology industries. In high technology industries, in-house R&D has no influence on exporting behaviour. 2 Firm size—export behaviour relationship is predominantly inverted U shaped. The usual argument is provided as justification. 3 Indian affiliates of multinationals do not exhibit significantly different export behaviour. 4 Tax concessions offered to exporters appear to have limited success in influencing exports. Bhavani and Tendulkar (2000) use data pertaining to 310 small-scale units in the garments and apparel industry located in Delhi. They use data from the census of small-scale units carried out during 1987–88. An export decision function and an export performance function is estimated following Pant (1993). The estimates of the export decision function indicates that the probability of exporting is the highest for public limited companies followed by large partnerships and proprietary firms. Scale economies also turn out to be very important in determining whether a firm is an exporter or not. In the export performance function too scale economies are important, as is the role of sales expenditures. The authors conclude that the existing policy of reservation for small-scale industries hinders exports. A common result of these studies is the positive influence that firm size has on export behaviour. However, there is some ambiguity with regard to the influence of foreign ownership on export behaviour. It is interesting to note that none of these studies have examined the implications of sunk entry costs into the

OUTWARD ORIENTATION

55

export market in analysing the determinants of exporting behaviour. As we will show in this chapter, this could be considered a significant omitted variable in all these studies and thus, inferences drawn from these studies should be treated with caution. 5.2 Modelling a firm’s decision to export Consider a profit-maximising firm that needs to take a decision in any given period whether to export or not. Following Bernard and Jensen (2001), we treat the decision to export as analogous to the decision to market a new product, so that the firm receives a new set of revenues from the export good.1 In taking this decision, the firm considers expected profits today and in the future from the decision to enter the foreign market net of any fixed costs. if it We assume that the firm is always able to produce at the profit-maximising level of exports, enters the foreign market. In the one-period case with no entry costs, the firm’s profits (π) are: (5.1) where pt is the price of goods sold abroad, and cit is the variable cost of producing Factors exogenous to the firm affecting profitability are denoted by Xt while firm-specific factors are denoted by Zit. Apart from shocks to demand, exogenous factors may be exchange rate movements, credit-market conditions and industry spillovers. Firm characteristics that might increase the probability of exporting include size, foreign ownership and access to imported capital goods and raw materials. If expected profits are greater than zero, then the firm will export. The export status of firm i in period t is given by Yit taking the value one if the firm exports; zero otherwise. (5.2) In the multi-period case with no entry costs, expected profits are: (5.3) where δ is the discount rate. It would seem thus far that exporting in any given period is a random phenomenon depending on whether the firm expects to receive a sufficient amount of revenue in that period to cover costs. There are two possible ways by which we can link the decision to export today to the decision to export tomorrow. First, production of the export good today may lower costs tomorrow—this is the standard ‘learning by doing’ argument applied to exporting (Arrow 1962). Thus, we have and Second, as we have argued in Chapter 1, firms face significant sunk entry costs when entering the foreign market for the first time. These costs may be the costs of information about demand conditions abroad or costs of establishing a distribution system. To simplify the analysis, we assume that these entry costs recur in full if the firm exits the export market for any amount of time. We denote these entry costs by N. The firm will export if current and expected revenues are greater than costs. That is,

56

OUTWARD ORIENTATION

(5.4) is current revenue and discounted expected future profits. where The presence of sunk entry costs has important implications for the firm’s exporting behaviour. First, due to the presence of such costs, firms will only enter the export market if they are confident that current and expected future revenues will be sufficient to cover the one-off fixed costs associated with entry and the variable costs associated with producing the export good in the current and subsequent periods. This would mean that there is a pay-off to waiting, and the firm will postpone the decision to enter the export market if it feels that the conditions are not conducive to exporting in the current period. However, once the firm enters the export market by paying these entry costs, the firm will attempt to remain in the export market at least for some more periods. This implies that exporting would not be a once and for all event, and there would be a distinct long-run persistence in the firm’s export status; that is, exporting is no longer a random phenomenon. We will investigate whether we observe such persistence in export status using simple descriptive statistics in Section 5.4. More rigorous methods of testing for persistence using econometric methods are carried out in Section 5.5. While sunk entry costs may explain why current exporters are more likely to remain exporters in future periods, it will not by itself explain why some firms are more likely to be exporters. As we have already seen in Chapter 4, the investment rate is a significant determinant of export competitiveness at the industry level. We would also like to establish whether investment is also an important determinant of the likelihood of exporting at the firm level. Thus, we bring in the investment rate as an important firm-level attribute contained in the vector Zit, which may help explain the probability of a firm being an exporter. The modelling of investment at the firm level is not as straightforward as modelling of investment at the industry- or economy-wide level. Recent literature that has examined the behaviour of investment at the plant level finds evidence of significant non-convex costs of adjusting capital stock (Cooper et al. 1999; Cooper and Haltiwanger 2000). During periods of investment activity, firms incur fixed adjustment costs, which are proportional to their stock of capital. These fixed adjustment costs represent the need for plant restructuring, worker retraining and organisational restructuring during periods of intensive investment. Given these fixed costs to investment, firms will tend to adjust their capital stock in discrete jumps rather than continuously. This would imply that for firms undertaking positive investment activity, periods of inaction would be followed by periods of investment bursts. Unlike the observed behaviour of investment at the industry or economy-wide level, which would tend to be smooth and continuous, investment at the firm level would be expected to be lumpy and non-continuous. We incorporate this aspect of investment behaviour when we model the effect of investment on the firm’s exporting behaviour. A characterisation of investment behaviour among the firms in our sample is available in Section 5.6. A fuller discussion of empirical specification used in modelling the firm’s export decision is provided in Section 5.7, along with a discussion of other factors that may influence the firm’s decision to export.

OUTWARD ORIENTATION

57

5.3 Data and variable description Choice of firms The database used is the firm-level data called PROWESS provided by CMIE, Mumbai. This is a database containing time series information (from 1987–88 onwards) on financial performance (income-expenditure statement, balance sheet and other details from the annual reports) of firms which are listed on various stock exchanges in the country. Thus, the unorganised/small-scale firms who form about 30 per cent of the manufacturing sector as a whole in India are not covered in this database. The database covers all industries in the manufacturing sector. To the extent that a particular industry is dominated by the unorganised/smallscale firms, it is under-represented in the database. The sample period for analysis considered here is from 1992–93 to 1997–98. Only those firms for which data are available for all the years of our sample period are considered for the analysis here. The choice of a balanced panel is motivated by the fact that the empirical model involves the estimation of a dynamic specification involving lagged endogenous variable. Though the database provides information on corporate firms from 1987–88 onwards, we choose to confine our analysis to the period 1992–93 to 1997–98. The coverage in the database in terms of the number of firms has been increasing over the years as a result of which a balanced panel beginning from 1987–88 has too few firms. By confining ourselves to the post-1992–93 period, we have expanded dramatically on the cross section across firms but have ended up losing 4 years’ data. Moreover, the period between 1990 and 1992 witnessed serious macroeconomic imbalances and dramatic changes in the policy environment, which may possibly contaminate econometric results drawn from a sample that includes these 3 years.2 In this balanced panel, only those firms satisfying the following criteria were included: 1 The firm is a private corporate firm. The theoretical literature that we draw upon presumes profit maximising behaviour on part of privately owned firms. Moreover, it is well known that Indian public sector enterprises are controlled directly by various Central and State government ministries, who are in charge of major decisions like exporting, investments, etc. Arguably such decisions cannot be analysed in the theoretical framework of this study. 2 Values for net worth, manufacturing sales and capital stock3 were positive for the firm for all years. Thus firms undergoing restructuring and/or bankruptcy are kept out of the analysis. 3 Manufacturing sales contribute at least 75 per cent of the total sales of the firm for at least two-thirds of the sample period. That is, we exclude firms whose primary activity is not manufacturing. This provides us with 672 firms that met the above three criteria. These sets of firms were drawn from 21 industries and encompass intermediate, capital and consumer goods sectors. Choice of export variable We define an exporting firm to be one that has reported any magnitude of exports. We use export status as the relevant firm characteristic and use a dummy variable, Yit, for exporting in our analysis. Thus, this dummy variable takes the value 1 if the ith firm has exported in year t; zero otherwise. An alternative approach is to use the export to manufacturing sales ratio as the appropriate measure of the firm’s export status. However, there are two reasons why a discrete variable—that is, a dummy variable of the firm’s export

58

OUTWARD ORIENTATION

status—may be a more meaningful way of measuring a firm’s outwardorientation than a variable that is continuous in nature—that is, the export to sales ratio. First, as we have discussed in Section 5.1, there are significant sunk costs involved in the decision to enter the export market at the firm level, and the very act of exporting can be seen to reflect the decision by the firm to surmount these large entry-level costs and a signal that the firm expects to compete effectively in world markets. Second, as is clear from Table 5.1, among the firms who export, the majority of firms (over 55 per cent) in our sample export less than 10 per cent of total manufacturing sales. This would imply that a continuous variable of a firm’s exporting behaviour such as the export to sales ratio would be highly skewed towards zero and have less information content than a dummy variable on the firm’s export status.4 Table 5.1 Frequency count of exports to manufacturing sales ratio Range

1992–93

1993–94

1994–95

1995–96

1996–97

1997–98

=0.00 0.00≤0.01 0.01≤0.10 0.10≤0.20 0.20≤0.30 0.30≤0.40 0.40≤0.50 0.50≤0.60 0.60≤0.70 0.70≤0.80 0.80≤0.90 0.90≤1.00 >1.00 Total firms

244 71 190 76 37 9 12 6 4 2 4 12 5 672

198 77 216 75 38 20 13 7 1 4 5 15 3 672

174 80 215 92 41 23 12 5 4 5 7 12 2 672

166 97 202 80 44 33 15 6 4 3 8 9 5 672

166 81 207 82 45 29 22 10 7 6 6 9 2 672

169 73 211 73 45 33 16 13 10 7 11 9 2 672

Note In the data base that we use, total sales is defined to include manufacturing sales and trading sales, while the exports figures do not make this distinction. Hence, in a few cases, we get exports to manufacturing sales>1.

Table 5.1 also makes clear that the number of non-exporters in our sample has steadily decreased from 1992–93 to 1995–96, and remained stable since then. By 1997–98, around 75 per cent of firms in our sample were exporters, up from 64 per cent in 1992–93. More interestingly, there was a large increase in the number of firms that exported more than one-fifth of their output, from 14 per cent in 1992–93 to 22 per cent in 1997–98. Thus, the overall picture is of greater outward-orientation of the Indian manufacturing sector, both in terms of the number of firms entering the export market and the larger proportion of output that firms are selling in overseas markets. 5.4 Industry characteristics In Table 5.2(a, b), we present industry characteristics of the firms in our sample on the percentage of sales and exports, average firm size of exporters and non-exporters and average output exported by exporting firms, for the years 1992–93 and 1997–98, respectively.5 In 1992–93, large industries, those accounting for

OUTWARD ORIENTATION

59

more than 5 per cent of total sales, are (in descending order) ferrous metals, dyes and pigments, electrical machinery, cement, drugs and pharmaceuticals, synthetic textiles and tobacco products (Column 3 of Table 5.2(a)). With the exception of electrical machinery, cement and synthetic textiles, these industries also dominate total exports (Column 7 of Table 5.2(a)). Three industries have shares in total exports exceeding their respective shares in total sales—these are cotton-blended textiles, diamonds and jewellery and cotton cloth. Industries with the largest shares of exporting firms are drugs and pharmaceuticals, ferrous metals and cotton-blended textiles—each of these three industries have more than a 10 per cent share of exporting firms (Column 5 of Table 5.2(a)). Eight industries are highly export oriented with more than 75 per cent of firms in these industries involved in exporting (Column 4 of Table 5.2(a)). These are ready-made garments, cotton cloth, diamonds and jewellery, drugs and pharmaceuticals, bicycles and two wheelers, leather and leather products, dyes and pigments and tobacco products. However, barring the sugar industry, at least 40 per cent of firms export in all industries. Thus, there is evidence of widespread export activity among firms that cuts across industries and sectors, though clearly some industries (such as diamonds and jewellery, leather and leather products and tobacco products) are significantly more export oriented than others. There is also a significant variation in the export to sales ratio across industries, with a low of 1.8 per cent in paints and varnishes and a high of 97.1 per cent in diamonds and jewellery (Column 8 of Table 5.2(a)). It is interesting to observe that in all industries, exporters are larger than non-exporters, as measured by the average size of their capital stock (Columns 6 and 9 of Table 5.2(a)). Table 5.2 Industry characteristics Industry name

(1) (a)

1

2 3 4 5

For the year 1992–93 Readymade garments Cotton cloth Electrical machinery Industrial chemicals Diamonds and jewellery

No. affirms

Sectoral No. of exporters (%) share in total sales (%)

Exporters

Nonexporters average size*

Within each sector

Over all sectors

Average size*

Share in total exports (%)

Average exports sales (%)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

4

0.2

75.0

0.7

10.7

1.0

62.0

3.9

26

3.9

76.9

4.7

54.9

7.1

19.4

11.9

69

9.3

56.5

9.1

26.4

4.0

5.2

17.6

55

4.6

72.7

9.4

69.9

3.1

7.5

18.7

5

0.7

100.0

1.2

4.0

7.3

97.1

0.0

60

OUTWARD ORIENTATION

Industry name

(1) 6

7 8

9 10 11

12

13 14 15

16

17 18

19 20 21

Drugs and Pharmace uticals Ferrous metals Rubber and rubber products Auto ancillaries Cement Bicycles and two wheelers Leather and leather products Paints and varnishes Sugar Vegetable oils and fats Cottonblended textiles Dyes and pigments Other food products Synthetic textiles Tea Tobacco products Totals

No. affirms

Sectoral No. of exporters (%) share in total sales (%)

Exporters

Nonexporters average size*

Within each sector

Over all sectors

Average size*

Share in total exports (%)

Average exports sales (%)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

59

6.7

81.4

11.2

24.5

7.8

11.7

12.5

94

13.6

48.9

10.8

177.1

16.5

14.8

22.0

19

4.2

63.2

2.8

48.8

4.7

12.0

29.5

63

3.3

69.8

10.3

15.2

2.2

8.0

8.3

23 9

6.9 4.0

52.2 88.9

2.8 1.9

209.8 71.1

2.3 2.0

3.7 5.3

41.8 4.5

3

0.1

100.0

0.7

6.9

1.3

91.4

0.0

9

1.8

66.7

1.4

22.7

0.3

1.8

10.8

22 21

1.9 2.2

13.6 47.6

0.7 2.3

48.3 15.5

0.1 2.5

4.1 15.5

30.5 5.0

65

4.7

70.8

10.8

25.9

8.7

20.8

9.4

37

13.5

86.5

7.5

142.1

11.7

8.8

127.5

31

4.1

51.6

3.7

25.8

3.0

9.6

10.4

32

6.4

68.8

5.1

99.5

3.3

5.5

11.6

23 3

1.4 6.3

43.5 100.0

2.3 0.7

52.6 151.7

3.1 8.0

24.1 12.1

7.2 0.0

672

68,087.8*

428.0

6585.2*

OUTWARD ORIENTATION

Industry name

(1) (b)

1

2 3 4 5

6

7 8

9 10 11

12

13 14

For the year 1997–98 Readymade garments Cotton cloth Electrical machinery Industrial chemicals Diamonds and jewellery Drugs and Pharmace uticals Ferrous metals Rubber and rubber products Auto ancillaries Cement Bicycles and two wheelers Leather and leather products Paints and varnishes Sugar

No. of firms

Sectoral No. of exporters (%) share in total sales (%)

Exporters

Nonexporters average size*

Within each sector

Over all sectors

Average size*

Share in total exports (%)

Average exports sales (%)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

4

0.3

75.0

0.6

72.7

1.1

55.7

7.8

26

4.4

88.5

4.6

250.8

8.2

26.0

887.9

69

9.0

66.7

9.2

123.5

5.9

9.5

34.3

55

4.4

81.8

9.0

176.6

2.9

9.2

43.4

5

0.5

100.0

1.0

10.2

3.8

90.5

0.0

59

7.1

94.9

11.1

117.4

12.9

23.1

7.4

94

15.8

71.3

13.3

480.2

16.6

14.3

67.4

19

4.2

84.2

3.2

103.4

2.3

9.2

145.3

63

3.8

79.4

9.9

51.3

2.2

9.1

35.1

23 9

6.0 5.0

43.5 88.9

2.0 1.6

783.1 237.9

1.7 2.1

4.5 5.4

162.4 15.4

3

0.1

100.0

0.6

19.8

0.2

61.7

0.0

9

1.6

88.9

1.6

72.1

0.1

0.6

35.9

22

2.0

13.6

0.6

209.8

0.1

3.3

105.0

61

62

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Industry name

(1) 15

16

17 18

19 20 21

Vegetable oils and fats Cottonblended textiles Dyes and pigments Other food products Synthetic textiles Tea Tobacco products Totals

No. of firms

Sectoral No. of exporters (%) share in total sales (%)

Exporters

Nonexporters average size*

Within each sector

Over all sectors

Average size*

Share in total exports (%)

Average exports sales (%)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

21

2.3

57.1

2.4

269.1

1.6

13.1

11.7

65

5.0

86.2

11.1

84.3

11.2

31.3

46.5

37

12.1

83.8

6.2

550.2

12.1

13.7

320.5

31

4.4

67.7

4.2

86.7

3.5

11.4

38.6

32

5.4

78.1

5.0

2361.6

5.4

14.0

73.7

23 3

1.2 5.5

52.2 100.0

2.4 0.6

87.7 507.9

2.5 3.7

27.1 8.7

9.3 0.0

672

1,42,982. 8*

503.0

18,102.6*

* Rupees 10 million.

The figures on the number of exporters and the average export to sales ratio in each industry for the year 1997–98 in Table 5.2(b) (Columns 4 and 7) confirm the finding from Table 5.1 that there has been greater signs of exporting behaviour across most industries, both in terms of the number of firms entering the export market and the proportion of output exported over the period 1992–93 to 1997–98. We find little change in the relative ranking of an industry in terms of sales or exports in the total sample, though there has been a large increase in the contribution of the drugs and pharmaceuticals industry to total exports from 7.8 per cent in 1992–93 to 12.9 per cent in 1997–98. Except for cotton cloth and rubber and rubber products, exporters in each industry are larger than non-exporters in 1997–98 (as was found to be the case in 1992–93). 5.5 The characteristics of exporting firms Table 5.3 gives the averages of exporting and non-exporting firms for seven characteristics: net fixed assets (NFA), manufacturing sales, wages and

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63

Table 5.3 Firm characteristics, average over 1992–93 to 1997–98

All firms Net fixed assets (NFA)* Manufacturing sales* Wages and salary/GVA (%) Investment/capital stocks: Imported capital goods/NFA (%) Imported raw materials/manuf. sales (%) Profitability (%) Small firms Net fixed assets (NFA)* Manufacturing sales* Wages and salary/GVA (%) Investment/capital stocks Imported capital goods/NFA (%) Imported raw materials / manuf. sales (%) Profitability (%) Large firms Net fixed assets (NFA)* Manufacturing sales* Wages and salary/GVA (%) Investment/capital stocks Imported capital goods/NFA (%) Imported raw materials/manuf. sales (%) Profitability (%)

Exporters

Non-exporters

118.10 197.44 36.67 0.21 3.50 7.67 15.00

36.75 62.70 47.83 0.18 1.83 4.33 15.00

10.99 52.09 36.50 0.19 2.17 8.00 13.17

8.45 31.42 56.83 0.16 1.17 3.67 12.83

187.10 290.66 36.50 0.22 3.83 7.33 16.00

92.87 124.64 27.17 0.22 2.50 5.83 18.50

* Rupees 10 million.

salaries as a percentage of gross value added, investment as a ratio of capital stock, imported capital goods as a percentage of NFA, imported raw materials as a percentage of manufacturing sales and profitability. This is also done by size category, with small firms defined to be ones with a capital stock of less than Rs. 250 million.6 Perhaps the most striking difference between exporters and non-exporters is their size disparity; exporters are substantially larger than non-exporters both in terms of NFA and sales. On average, exporters are more than three times larger than non-exporters both in terms of NFA and sales. This difference persists even within size categories. Exporting firms have a lower wage bill as a proportion of value added than non-exporting firms on average, and among small firms in particular. We also find that on average, exporting firms have a higher investment rate than non-exporting firms, though investment rates do not differ between exporters and nonexporters for the larger firms. There is, however, a clear difference between exporters and non-exporters regardless of size on the use of imported capital goods and raw materials, both the imported capital goods to NFA and imported raw materials to sales ratios being significantly higher for exporting firms than for nonexporting firms. Interestingly, profitability does not seem to differ between exporters and non-exporters, and

64

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in fact, profitability of large domestic firms is higher than large exporting firms. The latter finding could be linked to the high degree of protection that firms producing for the domestic market have received in the Indian context in the past. However, as is clear from Table 5.4, the profitability difference between large exporters and nonexporters, which was large to begin with in 1992–93, had considerably narrowed by 1996–97. The convergence of the profitability of large non-exporters to that of large exporters could be due to increased competition that large Indian firms faced with the onset of the reforms. Table 5.4 Average profitability (in per cent) Year 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98

All firms

Small firms

Large firms

Exporters

Non-exporters

Exporters

Non-exporters

Exporters

Non-exporters

16.0 16.0 16.0 16.0 14.0 12.0

17.0 17.0 16.0 16.0 10.0 14.0

13.0 14.0 14.0 14.0 13.0 11.0

15.0 14.0 14.0 15.0 16.0 13.0

17.0 17.0 17.0 17.0 15.0 13.0

20.0 21.0 21.0 19.0 16.0 14.0

The evidence so far points to significant differences between exporters and non-exporters across a whole range of indicators, yet it is not clear whether these are more related to differences across industries than to differences across firms. To estimate the difference between exporters and non-exporters more precisely, we calculate the percentage differences for selected indicators after controlling for the industries in which the firms are located. We also control for cyclical (time-varying) factors that may influence exporters differently than non-exporters. Finally we control for firm size. Table 5.5 reports the percentage differences in the characteristics after controlling for these factors. Rows (a) and (b) report the export premia estimated from a regression of the form where Yit is a dummy for current export status and INDUSTRYi and YEARt are industry and year dummies, respectively. Rows (c) to (f) report export premia after adding an additional control for firm size, as measured by log NFAs. It is clear that even after controlling for industry groups, exporters are significantly larger than nonexporters (over 110 per cent). Within the same industry, exporters also invest 25 per cent more as a fraction of their capital stock than non-exporters, and have a higher proportion of imported capital goods (as a per cent of NFA) than non-exporters. However, there Table 5.5 Exporter premia Variables (a) (b) (c) (d) (e)

Exporter premia (%) Net fixed assets (log) Manufacturing sales (log) Investment/capital stock Imported capital goods/net fixed assets Imported raw materials/manuf. sales

114.2*** 113.1*** 25.8*** 0.42* 0.67

OUTWARD ORIENTATION

Variables

65

Exporter premia (%)

(f)

Profitability

−0.89

Notes 1 Numbers in the Exporter premia column for rows (a) and (b) are coefficients on an export dummy of the form: 2 Numbers in the Exporter premia column for rows (c) to (f) are coefficients on an export dummy of the form: 3 Variables: Subscript i indicates the firm. Subscript t indicates the year. Yit=1 if the firm is an exporter in time t. INDUSTRY is a set of industry dummies. YEAR is a set of year dummies. 4 *, *** indicates significance at the 10% and 1%, respectively.

seems to be no difference between exporters and non-exporters with respect to imported raw materials as percentage of sales and profitability. Persistence of exporting The theoretical discussion on the determinants of a firm’s decision to export stressed the importance of sunk entry costs. This would imply that entry and exit from the export market would not be random, and that once firms have made costly and irreversible investment decisions to enter the export market, they would attempt to stay in this market for future years to recover all or part of their initial costs. To understand the magnitude of movement in and out of exporting, we look at the transition rates for our sample of firms. As Table 5.6 indicates, exporting is not a once and for all phenomenon. On average over the period, 19.2 per cent of non-exporters begin to export in any given year while 4.6 per cent of exporters stop exporting. At the same time, there is substantial persistence in exporting behaviour. Among the firms that did export in time t−1, more than 95 per cent of them also exported in time t. Similarly, for firms that did not export in time t−1, more than 75 per cent of them did not export in time t. Interestingly, the probability that a nonexporting firm will continue to remain so has increased over the period 1992–93 to 1997–98, suggesting that by the end of the period, there was increasing self-selection on the part of firms that were not exporting in earlier years not to enter the export market in the current year. The pattern of movements in and out of exporting that is evident from Table 5.6 suggests significant nonrandomness in export behaviour. This is borne out in Table 5.7, which reports the predicted rates of persistence if exits and entries were chosen randomly using the calculated annual transition rates. At all horizons, the predicted persistence is substantially lower than that observed in the sample. We can conclude, therefore, that there is a substantial amount of re-entry by former exporters; that is, they have Table 5.6 Firm transition rates in the export market From

To

Exporter Exporter Total no. of exporters Non-exporter Non-exporter Total no. of non-exporters

Exporter Non-exporter

1992– 93

428 Exporter Non-exporter 244

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

96.73 3.27 474 24.59 75.41 198

96.20 3.80 498 21.21 78.79 174

94.98 5.02 506 18.97 81.03 166

94.47 5.53 506 16.87 83.13 166

94.66 5.34 503 14.46 85.54 169

66

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Table 5.7 Long-run export persistence—number of 1992–93 firms with the same export status

Year: 1993–94 Exporter Non-exporter Total Year: 1994–95 Exporter Non-exporter Total Year: 1995–96 Exporter Non-exporter Total Year: 1996–97 Exporter Non-exporter Total Year: 1997–98 Exporter Non-exporter Total

Transition probabilities

Actual no. of firms

Estimated no. of firms

Exporter

Non-exporter

Exporter

Non-exporter

Exporter

Non-exporter

0.96729 0.24590

0.03271 0.75410

414 60 474

14 184 198

414 60 474

14 184 198

672

456 42 498

18 156 174

412 52 464

16 192 208

672

473 33 506

25 141 166

407 46 453

21 198 219

672

478 28 506

28 138 166

404 41 445

24 203 227

672

479 24 503

27 142 169

405 35 440

23 209 232

672

0.96203 0.21212

0.94980 0.18966

0.94466 0.16867

0.94664 0.14458

0.03797 0.78788

0.05020 0.81034

0.05534 0.83133

0.05336 0.85542

Total

Notes Total number of exporters in 1992–93=428. Total number of non-exporters in 1992–93=244.

higher probabilities of exporting after having exited the export market. Similarly, former non-exporters have a higher propensity to stop exporting. The observed persistence of exporting behaviour may not necessarily be due to the presence of sunk costs but could also result from the attributes of the firms themselves—that is, some firms are more export oriented than others are. Such firm-level attributes could be greater managerial capabilities, more skilled employees, greater access to newer technology and so on. To better understand the importance of sunk costs in explaining persistent exporting, we look at the distribution of firms across all the possible 64 sequences of exporting and non-exporting for the 6 years from 1992–93 to 1997–98 (Table 5.8). There is a small proportion of firms that do not export at all (14.6 per cent). However, 381 out of the 672 firms export in all 6 years— that is, the majority of firms in our sample are persistent exporters. We further find that runs of exporting and non-exporting are more common

OUTWARD ORIENTATION

Table 5.8 Export sequences S. no. No. of 1s Sequenc e

No. of firms

S. no. No. of 1s Sequenc e

No. of firms

S. no. No. of 1s Sequenc e

No. of firms

1 2 3 4 5 6 7

0 1 1 1 1 1 1

000000 100000 010000 001000 000100 000010 000001

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

110000 101000 100100 100010 100001 011000 010100 010010 010001 001100 001010 001001 000110 000101 000011

23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 41 42

2 1 0 1 1 0 0 1 51 0 5 0 0 0 0 2 4 2 0 13 33

43 44 45 46 47 48 49 50 4 52 53 54 55 56 57

4 4 4 4 4 4 4 4 101011 4 4 4 4 4 4

11100 11010 111001 10110 10101 10011 101110 101101 1 100111 011110 011101 011011 010111 001111

5 0 2 0 1 1 0 0

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

98 2 2 7 4 3 11 29 31 3 3 1 0 0 5 1 0 1 2 0 1 4 1 17 39

58 59 60 61 62 63

5 5 5 5 5 5

111110 111101 111011 110111 101111 011111

64

6

111111

9 2 3 3 4 34 55 381

3 3 3 3 3 3 3 3 100101 3 3 3 3 3 3 3 3 3 3 3

111000 110100 110010 110001 101100 101010 101001 100110 1 100011 011100 011010 011001 010110 010101 010011 001110 001101 001011 000111

0 6 1 1 2 17 37

Table 5.9 Firm characteristics: non-exporters, occasional exporters and persistent exporters No. of years exported No. of firms NFA* Manufacturing sales* Investment/ capital stock Imported capital goods/NFA Imported raw materials/ manuf. sales Profitability * Rupees 10 million.

0

1

2

3

4

5

6

98 29.08 50.86 0.16 0.01 0.03 0.14

29 33.71 60.19 0.21 0.01 0.08 0.14

39 35.94 59.51 0.20 0.02 0.05 0.19

33 72.82 105.56 0.20 0.02 0.07 0.15

37 46.05 90.11 0.19 0.03 0.05 0.15

55 66.86 131.15 0.19 0.03 0.07 0.14

381 135.87 223.45 0.21 0.04 0.08 0.15

67

68

OUTWARD ORIENTATION

Figure 5.1 Persistence in exporting: (a) 2-period exporting; (b) 3-period exporting; (c) 4-period exporting. Note Sequence of continuous exporting status is indicated through dark shading.

than sequences where exporting is interspersed with non-exporting. Sequences with runs such as 111100

OUTWARD ORIENTATION

69

and 011110 are more prevalent than those without runs, such as 101010 or 010101. More importantly, sequences with runs at the end of the period are far more likely than sequences with runs at the beginning or the middle of the period. This is most evident in Figures 5.1a-c which report the probability that a firm has followed a given sequence, conditional on the fact that it has exported 2, 3 and 4 out of 6 years, respectively. It is clear from these figures that sequences such as 000011, 000111 and 001111 have a significantly higher probability of occurrence as compared to sequences with similar counts of exporting behaviour. It is striking to note that for the case where firms have exported 4 out of the 6 years, the sequence 001111 is four times more likely to occur than any other sequence. This strongly suggests that once firms have taken a decision to enter the export market, they are not likely to leave. The results thus provide strong support for the argument that sunk entry costs are extremely influential in explaining a firm’s decision to export. Given the large proportion of persistent exporters (i.e. firms which have a sequence of 111111) in our sample, it would be useful to see whether there are any clear differences between occasional exporters and persistent exporters. In Table 5.9, we report characteristics of firms across the full range of exporting behaviour—that is, we categorise firms by the number of years that they have exported, including firms who have not exported at all. We see that firms who are persistent exporters are significantly larger than occasional exporters or non-exporters. With respect to the investment rate, imported raw materials (as a ratio of sales) and profitability, there is no obvious correlation between these variables and the persistence of exporting behaviour. In the case of imported capital goods as percentage of NFA, we see that persistent exporters have a higher value of the latter than occasional exporters or non-exporters. 5.6 The lumpiness of investment We have argued in Section 5.1 that a firm’s ability to enter the export market is predicated on the firm being able to increase its capital stock. We have also argued that investments that increase capital stock are lumpy and take the form of spikes (i.e. discrete jumps in the investment rate) rather than a continuous adjustment of the capital stock. In Figure 5.2, we plot the distribution of the net investment rate for our sample of firms for each of the 5 years from 1993–94 to 1997–98.7 It is clear that for all years, the investment rate distribution is non-normal having a considerable mass around zero, fat tails and is heavily skewed to the right. Table 5.10 summarises the main features of the distribution of investment rates. We derive three points about the investment activity of firms from the table. First, if we define investment inaction to be an investment rate less than 5 per cent in absolute value, we find that 28.57 per cent of the (672 firm times 5 years=3360) observations are in this category. Thus, a large proportion of firms exhibit significant inaction in terms of capital adjustment. Second, there is clear asymmetry in investment rates with a positive investment rate in 75.06 per cent of the observations. Third, defining Table 5.10 Summary statistics on the distribution of the investment rate Variable

Per cent

Fraction of observations with negative investment Inaction rate investment Spike rate: positive investment Spike rate: negative investment

24.94 28.57 29.29 1.04

Notes Inaction rate is when the investment rate is less than 5 per cent in absolute

70

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Figure 5.2 Investment frequency. Variable Per cent value; spike rate is when the investment rate is more than 20 per cent in absolute value.

investment spikes to be episodes of investment rates in excess of 20 per cent in absolute value, there is clear evidence of periods of significant positive adjustment of capital stock by firms, with investment rates exceeding 20 per cent in 29.29 per cent of the observations. Table 5.11 reports sequences of investment spikes observed in our sample of firms for the period 1992– 93 to 1997–98. From this table, it is clear that for any given firm, investment spikes are infrequent and far between— the majority of firms (51.79 per cent) exhibit investment spikes for 1 or 2 years in the 6 years under consideration. Only 9 firms (out of a total of 672 firms) had investment spikes for 5 out of the 6 years while no firm had spikes in all 6 years. There is, therefore, strong support for the hypothesis that there are significant non-convexities in the costs of adjusting capital stock, with the process of investment being lumpy and discontinuous rather than smooth and continuous. Firms adjust capital stock in discrete jumps, rather than continuously, with investment bursts, if they do occur at all, followed by prolonged periods of inaction. We incorporate this aspect of investment behaviour when we examine the relationship between exporting and investment in the next section.

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71

5.7 The determinants of exporting behaviour The empirical specification In order to understand the determinants of exporting behaviour we estimate a relationship of the following type Yit=F(sunk costs, investment behaviour, and certain control factors) Since the dependent variable in the above relationship is a qualitative variable, the model essentially describes the probability that Yit=1 (i.e. firm i exports in period t). We estimate the above relationship as a probit model.8 Table 5.11 Investment-spike sequences S. No. No. of 1s Sequenc No. of e firms

S. No. No. of 1s Sequenc No. of e firms

S. No. No. of 1s Sequenc No. of e firms

1

0

000000

170

2 3 4 5 6 7

1 1 1 1 1 1

100000 010000 000100 000100 000010 000001

8 9 10 11 12 13 14 15 16 17 18 19

2 2 2 2 2 2 2 2 2 2 2 2

110000 01000 100100 100010 100001 011000 010100 010010 010001 001100 001010 001001

0 44 53 49 17 26 189 0 0 0 0 0 56 8 6 5 39 5 6

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

11100 11010 11001 10110 10101 10011 101110 101101 101011 100111 011110 011101 011011 010111 01111

0 0 0 0 0 0 0 0 0 0 13 9 2 3 6 33

20 21 22

2 2 2

000110 000101 000011

58 59 60 61 62 63

5 5 5 5 5 5

111110 111101 111011 110111 101111 011111

0 0 0 0 0 9 9

64

6

111111

0

20 7 7 159

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

111000 110100 110010 110001 101100 101010 101001 100110 100101 100011 011100 011010 011001 010110 010101 010011 001110 001101 001011 000111

0 0 0 0 0 0 0 0 0 0 54 5 11 5 2 2 17 3 5 8 112

72

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The theoretical framework as well as the descriptive statistics presented earlier, stress the importance of sunk costs in determining the decision to export on the part of the firms. We capture this by using a oneperiod lagged value of Y. Thus, we attempt to establish the extent to which being an exporter in the last period influences the probability of being an exporter in the current period. As explained earlier, while the literature so far has stressed the importance of sunk costs, we believe that investment behaviour on the part of the firms is an additional but critical determinant of exporting behaviour. Our maintained hypothesis in the following regressions is that the decision to export on the part of a firm depends on its ability to increase its capital stock. However, as seen in the previous section, investment behaviour at the firm level is lumpy and discontinuous rather than smooth and continuous. We, therefore, use a dummy variable capturing investment spikes as the explanatory variable rather than the investment rate per se. This dummy variable takes the value 1 when the investment rate for the firm exceeds 20 per cent in a given year, zero otherwise. Lagged values of the investment spike dummy are used as an explanatory variable in our regressions. Besides sunk costs and investment spikes, we consider five other explanatory variables as controlling factors in our regressions. Earlier, we saw that exporting firms make a greater use of imported capital goods and imported raw materials than non-exporting firms. Hence we consider lagged values of imported capital goods (as a ratio to NFA), and imported raw materials (as a ratio to manufacturing sales) as additional explanatory variables. We also control for firm size. This we do for several reasons. First, as is evident from our review of the literature on the determinants of exporting in India in Section 5.1, firm size has been found to be an important explanatory variable of exporting behaviour by several previous studies. Second, we have already seen in Section 5.3 that even within the industry, exporters tend to be larger than non-exporters. Finally, firm size capture scale-based effects on exporting (Krugman 1984; Roberts and Tybout 1997) as well as serving as a proxy for productivity. The hypothesis being that more efficient firms tend to grow faster than inefficient ones. We measure firm size by the current total sales. Apart from sunk costs, the literature has also stressed the role of spillover effects of exporting behaviour within industries. A non-exporting firm in an industry characterised by widespread exporting behaviour is more likely to become an exporter than in an industry where exporting is a peripheral activity. This is because externalities of this form reduce the cost of access to foreign markets (Aitken et al. 1997). We measure spillover effect by the ratio of number of exporters in an industry to the total number of firms in that industry. We use lagged values of the spillover variable in our regressions.9 We consider the extent of foreign ownership in a firm as another explanatory variable in our regressions. Firms with significant foreign ownership are likely to benefit in many ways, such as through greater access to capital, modern technology, management skills, knowledge about markets in which the foreign partner has operations, the marketing and distribution networks of the foreign partner, etc. (see Caves 1996 for a review of the relevant literature). These benefits of foreign ownership are likely to influence a firm’s exporting behaviour and hence we need to take this factor into account. We model the extent of foreign ownership in two ways: (a) as Foreign Share, a continuous variable going from 0 to 100 per cent, and (b) through two dummy variables, DUM2651 (taking the value 1 if foreign ownership in a firm is between 26 and 51 per cent, zero otherwise) and DUM51 (taking the value 1 if foreign ownership exceeds 51 per cent, zero otherwise). These two dummy variables capture the ability of the foreign owner to exercise operational control over its domestic affiliate. Under the Indian Companies Act of 1956, if the foreign owner has a share of less than 26 per cent in the firm in question, the foreign owner does not have the ability to block special resolutions that may be detrimental to both the owner’s and the firm’s interests. Thus, this threshold

OUTWARD ORIENTATION

73

of share holding is critical in the Indian context. In the case of 51 per cent ownership, the foreign owner clearly has unambiguous control over the firm’s decisions. Two sets of econometric issues arise in the context of probit models using panel data. First is heteroscedasticity. We follow Harvey’s (1976) approach, wherein we relate the variance of the error term to firm size as measured by their NFA. That is, where ε is the error term in probit model, NFA is net final asset and y is a coefficient to be estimated along with the model parameters. A significant non-zero value for y is taken to indicate the presence of heteroscedasticity. Likelihood ratio tests for homoscedasticity were then performed, and these did not reject the null hypothesis of homoscedasticity (Greene 2000). The second econometric issue relates to the possible presence of firm-specific effects, related to unobserved heterogeneity in firm-level attributes that may have an influence on exporting behaviour. As is well known, for a probit model only a random effects specification can be estimated (Greene 2000). Such a specification was rejected by our data in favour of the standard probit model. We also test for systematic industry differences between exporters and non-exporters, linked to market structure or industry-specific differences in the trade regime (such as export subsidies targeted to industries, or import duties concessions for exporting firms in a given industry) by the addition of industry-level dummies. We also test for period effects linked to changes in export profitability over time common to all firms in a given year, such as changes in credit market conditions or the exchange rate, by the addition of year dummies. Both industry and year dummies turned out to be insignificant. Results Table 5.12 reports the final estimated probit models. In Specification 1, we report the estimated equations, with the foreign ownership share as the variable measuring the influence of foreign ownership on the firm’s exporting behaviour. This is substituted with the two dummy variables, capturing foreign ownership between 26 and 51 per cent, and foreign ownership exceeding 51 per cent, in Specification 2. Both specifications explain firm-level exporting behaviour very well, with the goodness of fit of the models evident by the close match between the actual and predicted outcomes in Table 5.13. The results strongly support our hypothesis that sunk costs (and thus, prior exporting experience) is an important determinant of the decision to export on the part of the firms. Examining the results for Specification 1 first, we see from the column of marginals for the probit regressions that having exported in the last period increases the probability of exporting in the current period by 55 per cent.10 We also find that the investment spike is an important determinant of the probability of exporting, investment bursts in the last period are associated with a higher likelihood of exporting behaviour in the current period. There is also strong evidence that industry spillover and firm size positively affects the firm’s likelihood of exporting. These results thus support the findings of previous studies both for India and other countries. Besides these variables, access to imported raw materials and foreign ownership (both variants) are also found to significantly influence exporting behaviour. With regard to the latter, it is interesting to note that in Specification 2, the dummy variable corresponding to a foreign ownership share above 51 per cent is significant, while the dummy variable corresponding to a foreign ownership share between 26 and 51 per cent is not. This result indirectly supports the finding of Chibber and Majumdar (1999) which also noted that only when property rights devolve to foreign owners, at ownership levels of 51 per cent or more providing

74

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unambiguous control, do firms in which there is foreign ownership display relatively superior performance. Surprisingly, imported capital goods is the only insignificant variable in these regressions. Table 5.12 Determinants of decision to export Explanatory variable

Specification 1

Specification 2

Specification 3

Specification 4

Coefficients Marginals Coefficients Marginals Coefficients Marginals Coefficients Marginals Constant n[−1] Investment spike [−1] Imported capital goods [−1] Imported raw materials [−1] Spillover [−1] Sales Foreign ownership (%)

−1.9654 (−10.83) 2.3586 (30.32) 0.1456

−0.4583 (−9.79) 0.5499 (19.98) 0.0339

−1.9648 (−10.77) 2.3649 (30.42) 0.1569

−0.4576 (−9.75) 0.5508 (19.95) 0.0365

−1.9703 (−10.88) 2.3598 (30.36) 0.1556

−0.4595 (−9.83) 0.5503 (19.98) 0.0363

−1.9706 (−10.82) 2.3666 (30.46) 0.1691

−0.4590 (−9.79) 0.5513 (19.96) 0.0394

(1.75) 0.2567

(1.74) 0.0599

(1.87) 0.3139

(1.87) 0.0731

(1.95)

(1.95)

(2.12)

(2.11)

(0.39) 0.6680

(0.39) 0.1558

(0.48) 0.7103

(0.48) 0.1654

0.6718

0.1567

0.7155

0.1667

(1.85) 1.3298 (5.27) 0.0007 (2.71) 0.0073

(1.85) 0.3101 (5.21) 0.0002 (2.82) 0.0017

(1.98) 1.3480 (5.30) 0.0008 (2.95)

(1.98) 0.3140 (5.24) 0.0002 (3.08)

(1.86) 1.3380 (5.32) 0.0007 (2.73) 0.0074

(1.86) 0.3120 (5.26) 0.0002 (2.85) 0.0017

(1.99) 1.3582 (5.36) 0.0008 (2.98)

(1.99) 0.3164 (5.30) 0.0002 (3.12)

(2.55)

(2.56)

(2.61)

(2.62) 0.0600

0.0140

(0.42) 0.5222

0.1216

Foreign ownership— dummy 26– 51%

0.0574

(0.40) Foreign ownership— dummy>51% (2.19) LR-test for significance of coefficients

1651.24

Note T-values are reported in brackets.

(0.40) 0.5121

(2.20) 1649.83

0.0134

(0.42) 0.1193

(2.23) 1651.08

(2.25) 1649.60

OUTWARD ORIENTATION

75

Table 5.13 Frequencies of actual and predicted outcomes Actual Specification 1 0 1 Total Specification 2 0 1 Total Specification 3 0 1 Total Specification 4 0 1 Total

Predicted 0

1

Total

576 126 702

99 1887 1986

675 2013 2688

575 126 701

100 1887 1987

675 2013 2688

576 126 702

99 1887 1986

675 2013 2688

575 126 701

100 1887 1987

675 2013 2688

We also report in Specifications 3 and 4 estimated equations with imported capital goods omitted from the probit regressions. We find that the statistical significance of the coefficient on the lagged investment spike improves when we omit imported capital goods, with the coefficient now significant at the 5 per cent level. The finding that investment spikes are important determinants of exporting behaviour at the firm level supports the finding of the previous chapter that investments are important determinants of sectoral or industry-level competitiveness. The link between investment and exporting at the firm level is clear from Figure 5.3(a) and (b). The figures plot the probability of exporting over the range of sales observed in the sample, evaluated at the sample average for the other variables, when the firm experiences an investment spike in the previous period. Figure 5.3a plots these probabilities for non-exporters and Figure 5.3b does the same for exporters. We note that the influence of investment spikes is much more pronounced for nonexporters than for exporters. For both exporters and non-exporters, the strong influence of size on the probability of exporting is seen. In the case of non-exporters, the probability approaches 1 for firms with sales exceeding Rs. 45 billion. For exporters, however, this happens for firms with sales exceeding just Rs. 25 billion. 5.8 Concluding remarks This chapter has set out to examine the determinants of outward orientation at the firm level using a sample of 672 firms over the period 1992–93 to 1997–98. The firm-level data points to an increasing outwardorientation of the Indian manufacturing sector, both in terms of the number of new firms entering the export marker and existing exporters expanding the proportion of output exported. Examining the characteristics of

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Figure 5.3 Effect of investment spike: (a) non-exporters and (b) exporters.

exporting firms relative to non-exporting firms, we find that exporters differ significantly from nonexporters, even within the same industry, with exporting firms having a disproportionate weight in total

OUTWARD ORIENTATION

77

capital stock and output. Exporting firms also have a higher investment rate, higher use of imported capital goods (as a per cent of NFS) and imported raw materials (as a per cent of sales) than non-exporting firms. This remains true even when we control for industry and year effects. Furthermore, there is clear evidence on the persistence of exporting behaviour, with firms that enter the export market tending to stay on for several more years. Even though exporting is not an once and for all phenomenon, with year-to-year transition rates from exporting to non-exporting (and vice versa) quite large, the majority of firms who were exporting at the beginning of the period remained so at the end of the period. The analysis of the determinants of exporting at the firm level suggests the importance of prior exporting experience as a variable that significantly increases the probability of a firm exporting in any given period. The results indicate that having exported in the previous period increases the probability of exporting in the current period by 55 per cent. The significance of the lagged export status dummy in the regression results provide further support for the argument that firms face large sunk entry costs in entering the export market, leading to a high degree of persistence in exporting behaviour. We also find that large firms, foreign-owned firms and firms in industries where other firms are exporting, are more likely to be exporters. Finally, investment and exporting is strongly correlated at the firm level, as firms that have exhibited investment bursts in the previous period are more likely to be exporters in the current period. The importance of investment in determining firm-level outward orientation provides us with a basis for exploring (in the next chapter) the role of financial factors in influencing investments and hence export competitiveness.

6 Finance constraints, persistent exporting and investment

In the previous two chapters, we have established that investments are crucial for exports, both at the sectoral and firm level. As we discussed in Chapter 1, financial markets and institutions are important in influencing investment and facilitating technological innovation by identifying and funding those firms with the greatest chances of success (King and Levine 1993). In this chapter we explore the determinants of investment and in particular the relationship between financial factors and firms’ investment behaviour. Specifically, we look for the presence of finance constraints on the investment decisions of firms in our sample (as defined in the previous chapter), and further we try to trace the source of these finance constraints. The rest of this chapter is organised into five sections. Section 6.1 presents the theoretical framework that is commonly used to test for finance constraints on the firm’s investment decisions. In Section 6.2, we discuss our proposed extension of this framework to test for the sources of finance constraints across different providers of external funds. Section 6.3 discusses the key features of the financial intermediation process in India, with particular reference to the sources and uses of funds of the firms in our study. Section 6.4 discusses the empirical results. Section 6.5 concludes. 6.1 Theories of asymmetric information and finance constraints Heterogeneity in firms’ financing patterns has attracted a great deal of attention in both theoretical and applied research on financial markets. The ‘traditional’ view is that firms compare the costs and benefits of debt and equity which helps them to choose an optimal leverage ratio. Distinction between who provides the debt and/or equity are not made in this view, and the optimal leverage ratio depends purely on the costs and benefits associated with debt and equity. These costs and benefits can be affected by factors such as bankruptcy costs, taxes, etc. The ‘modern’ view takes a slightly broader view of financing patterns and emphasises costs associated with different providers of funds. It stresses the lack of substitutability between internal sources (retained profits and depreciation) and external sources (different types of debt and new equity) of funds. This imperfect substitutability arises primarily due to asymmetric information between the suppliers and users of funds and incentive problems between managers and owners of the firm. It has generally been argued that these information asymmetries and incentive problems make external funds more costly than internal funds. In the new equity markets, this manifests as a ‘lemons premia’ (Myers and Majluf 1984) and in credit markets as credit rationing or loan mispricing (Stiglitz and Weiss 1981). Further, this view contends that the cost differential between internal and external funds would vary across firms depending upon the extent of information asymmetry. Besides, this view also suggests that transaction costs might also vary across firms. Thus, one important implication of a higher cost of external funds is that investments would be constrained

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79

by the availability of internal funds. At an empirical level, we need to estimate an investment function to test the finance constraint hypothesis, which leads us to the question of specification of the investment function. Investment function specification—theoretical and empirical issues1 Traditional neo-classical models of investment implicitly assume a world characterised by perfect capital markets; that is, a world where all the Modigliani-Miller (1958) assumptions hold. In this approach, the optimal capital stock is determined at the point where the demand schedule for capital intersects the supply schedule (which is a horizontal straight line at the interest rate r), where the expected marginal profitability of capital equals the interest rate. Consequently, financial factors are not a determinant of the actual capital stock (which in fact would be equal to the optimal capital stock). Empirical representation of the neoclassical investment function generally has followed Tobin’s q-theory approach, in which changes in investment opportunities surrounding a firm (i.e. shifts in the demand schedule for capital) can be summarised by the market valuation of the firm’s capital stock. The modern theories of investment, as mentioned earlier, relax the assumption of perfect capital markets and allow for information asymmetries and incentive problems. Consider a situation where information costs are high; that is, a situation where the quality and possibly the amount of information about profitable investment opportunities are different between firm insiders and outsiders. In such a situation, even if the observed Tobin’s q rises it does not lead to a rise in investment as would be predicted by the traditional neoclassical model. This is because, firm insiders would find it difficult to communicate the true investment opportunity to firm outsiders or because incentive problems require a commitment of internal financing to carry out investment.2 Thus, financial factors become an important determinant of the actual capital stock. To the extent that the actual capital stock is less than the optimal capital stock, a finance constraint is said to be binding on the firm. To the extent information costs vary across firms the sensitivity of investments to financial factors would vary across firms— the so-called ‘excess sensitivity’ hypothesis. In terms of empirical specification, the investment function for a panel of firms is typically specified as follows: (6.1) where I is investment, K is capital stock, Q is an estimate of Tobin’s q, IF is internal funds (cash flow) and ε is the error term, i is the firm subscript and t is the time subscript. If in the above specification the estimated coefficient α2 turned out to be positive and significant, it is taken as the rejection of the neo-classical model in favour of the finance constraint hypothesis, provided measured Q adequately controls for shifts in investment opportunities. Numerous practical difficulties arise in measuring Tobin’s q, especially in the developing country context. In the empirical work within this framework in the developing country context the Tobin’s q is replaced by a traditional sales-accelerator model of investment (Athey and Laumas 1994; Harris et al. 1994).3 In order to test the excess sensitivity hypothesis firms are grouped into ‘high-’ and ‘low-informationcost’ categories based on some a priori criteria. A higher value for the estimated coefficient α2 for the ‘highinformation-cost’ group points to the excess sensitivity of this group to financing constraints.4 Thus, the starting point of the empirical analysis is to identify a priori the types of firms that are likely to belong to the ‘high-’ or ‘low-information-cost’ categories, with the sorting criteria based on some aspect of the firm’s characteristics (such as age, size, etc.). The empirical literature for developed countries that has studied the presence of finance constraints on investment have used a variety of firm’s attributes to demarcate firms into ‘high-’ and ‘low-information-

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FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

cost’ categories. Among these variables, perhaps the most widely used is the ‘size’ of the firm, with the hypothesis that smaller firms are more likely to face finance constraints than the larger, more mature firms (see the seminal study by Fazzari et al. 1988 for the United States.). The argument here is that larger firms can offer a larger amount of collateral to the lender, thus, providing less incentive on the part of the borrower to default on the loan. Small firms rely more on bank credit than large firms (who can also access impersonal centralised bond and equity markets) and in periods of tight credit, small borrowers are often denied loans in favour of large borrowers. The prevalent practice in the empirical literature on finance constraints in using the size of the firm in distinguishing between firms facing different information costs is problematic in the Indian context. The key assumption here is that the collateral that a firm offers is ‘marketable’ so that larger firms, with more fixed assets, could liquidate these assets to repay the loan, if required. However, the absence of welldeveloped bankruptcy laws and exit procedures imply that the process of initiating the closure of a firm and the subsequent liquidation of a firm’s assets may be very complicated and time-consuming on the part of the creditors.5 Furthermore, the lack of secondary markets for various types of fixed assets (such as land) could also make the costs of bankruptcy extremely high. In this institutional context, the size of the firm may convey little information to the lender in assessing whether the firm will be able to repay its loan in the future. Thus, in the Indian case, there may not be significant differences in the cost of external finance between large and small firms (in contrast to what has been hypothesised for developed countries). It can be argued that a characteristic of the firm more revealing to the suppliers of funds in the Indian context is the firm’s outward orientation. In an economy that had allowed high degrees of firm-level xinefficiency to exist in the past (by protecting domestic firms from external competition and via the industrial policy regime allowing very little domestic competition), attributes of the firm such as profitability, age and size may not be good indicators of its potential to grow in the future. Here, the clearest signal to the suppliers of funds of the firm’s quality may perhaps be how successful the firm has been in competing in international markets. Firms which have managed to sell successfully in world markets would be expected to be efficient and producing high-quality output to be effective competitors.6 Thus, in the new liberalised environment where one may witness the decline of several of the older and more established firms (and some of the new entrants), firms which are persistent exporters have demonstrated an ability to face competition in international markets and hence suppliers of funds may perceive them to have a higher probability of surviving/doing well in the new liberalised environment. For this set of reasons we will use persistent exporting as the attribute of the firm that lenders may consider in distinguishing ‘highinformation-cost’ firms from ‘low-information-cost’ firms. Earlier, in Chapter 5, we have shown that the ‘real’ characteristics of persistent exporters differ in some important respects from those of occasional exporters and non-exporters (see Chapter 5, Table 5.8). We also have noted that persistent exporters in the Indian context are found in almost all industries and that they are a significant proportion (56.7 per cent) of the total number of firms in our sample (see Table 6.1). In Table 6.2, we present some financial characteristics for the full and the two sub-samples of firms. It is clear that for persistent exporters all firm-financial characteristics such as proportion of investments in fixed assets, total sources/uses of funds, total stock of assets, the proportion of liquid assets to total assets and profitability are higher than for occasional/non-exporting firms (and in the case of total sources/uses of funds and total assets, significantly higher). This gives credence to our argument that persistent exporting by a firm is a useful yard-stick to categorise firms into high- and low-information-cost categories. At this juncture it is worth noting that though persistent exporters are on average larger in terms of net fixed assets (see Chapter 5, Table 5.9), the

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Table 6.1 Number of firms in sample classified by categories Industry name

All firms

Persistent exporters

Occasional/ non-exporters

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

1 26 69 55 5 59 94 19 63 23 9 3 9 22 21 65 37 31 32 23 3 672

3 19 33 35 4 44 42 10 42 9 8 3 6 1 9 42 27 15 18 8 3 381

1 7 36 20 1 15 52 9 21 14 1 0 3 21 12 23 10 16 14 15 0 291

Ready-made garments Cotton cloth Electrical machinery Industrial chemicals Diamonds and jewellery Drugs and pharmaceuticals Ferrous metals Rubber and rubber products Auto ancillaries Cement Bicycles and two wheelers Leather and leather products Paints and varnishes Sugar Vegetable oils and fats Cotton-blended textiles Dyes and pigments Other food products Synthetic textiles Tea Tobacco products Total

Table 6.2 Financial characteristics of firms All firms Investment/total sources/uses of funds Total sources/uses of funds* Total assets* Liquid assets/total assets Profitability Persistent exporters Investment/total sources/uses of funds Total sources/uses of funds* Total assets* Liquid assets/total assets Profitability Occasional/non-exporters Investment/total sources/uses of funds

Average

Standard deviation

Maximum

Minimum

0.4350 38.2 202.4 0.0569 0.1495

8.1849 120.4 567.4 0.0713 0.1727

252.0000 2347.7 10,640.7 0.8245 4.4286

−400.8889 −775.5 1.1 −0.0054 −5.1429

0.4419 53.9 284.7 0.0581 0.1501

3.2722 150.1 726.8 0.0665 0.1393

55.0000 2347.7 10,640.7 0.6854 0.8571

−83.7778 −184.0 2.2 −0.0054 −4.5985

0.4260

11.8631

252.0000

-400.8889

81

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FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Total sources/uses of funds* Total assets* Liquid assets/total assets Profitability

Average

Standard deviation

Maximum

Minimum

17.7 94.7 0.0553 0.1487

57.3 177.0 0.0770 0.2086

986.3 2409.0 0.8245 4.4286

−775.5 1.1 0.0000 −5.1429

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the Centre for Monitoring Indian Economy (CMIE), Mumbai. * Rupees 10 million. Table 6.3 Number of firms by type

Small firms Large firms Total

Persistent exporters

Occasional/ non-exporters

Total

136 245 381

176 115 291

312 360 672

Note Large firms are defined to be those with a capital stock exceeding Rs. 250 million.

classification of firms based on the persistence of their exporting status is by no means the same as the classification based on firm size. This comes out clearly from the four-way count of firms by size and exporting status reported in Table 6.3. The presence of a significant number of large occasional/ nonexporters and small persistent exporters in our sample suggests that the two sorting criteria are distinctly different from each other. In order to test for the presence of finance constraints, we use an empirical specification along the lines of Devereux and Schiantarelli (1990) and Harris et al. (1994). We relate investment in fixed assets to a sales accelerator, internal funds, stock of long-term debt and stock of liquid assets. The debt and liquid assets terms are included to control for the effects of leverage and collaterisable assets on investments.7 Our empirical specification is as follows: (6.2) and where, I is investment in fixed assets, K is capital stock, S is sales, IF is internal funds, D is stock of longterm debt, L is stock of liquid assets, v is error term, λ is the time invariant firm specific effect, η is a common time effect, ε is the idiosyncratic component of the error term, i is firm subscript and t is time subscript. Positive and significant estimates of α2 indicate the presence of finance constraints. Tests of the excess sensitivity hypothesis can be done as described earlier. 6.2 Testing for the source of the finance constraint The finding that the coefficient α2 is significant, positive and larger for ‘high-information-cost’ firms than for ‘low-information-cost’ firms simply establishes that finance constraints exist on investment for these

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83

firms. In order to determine the source of the finance constraint, one needs to begin by looking for the sensitivity of investments to the availability of external funds. Indeed, the problem of information asymmetries inherent in capital markets really concerns external providers of funds whose concern is to develop contracts that align incentives and ensure repayment. For a group of firms which have low information costs the contracts which attempt to align incentives and ensure repayment will not cause the actual capital stock to fall far short of the desired capital stock because the wedge between costs of external and internal funds would not be very large. On the other hand, firms with high information costs will fall far short of their desired capital stocks indicating that these firms are finance constrained. In such a scenario, investments of the group of firms with low information costs will be much less sensitive to the availability of external sources than the group of firms with high information costs. The empirical literature on finance constraints in the context of advanced countries emphasises the role of internal funds possibly because of the stylised fact that internal funds are the most important (the contribution of external sources being negligible in many cases) sources of funds that are used to finance investment (Mayer 1990). In an underdeveloped country context in general and for India in particular it is in fact external sources that are the most important source of funds (Singh and Hamid 1992; Sen and Vaidya 1997). Indeed this is the case with our sample of firms, as will be seen later. A possible reason for this could be that firms find it impossible to finance such investments using internal sources due to the inherent lumpiness of investments in plant and machinery (as we have discussed in Chapter 5). In such a situation, even though imperfections in capital markets would make external funds more expensive than internal funds, our focus should be on external funds and not on internal funds. It should be noted here that in many underdeveloped countries (including India) development financial institutions (DFI) were set up precisely because it was perceived that lumpy investments required to spur growth could not be expected to be funded through internal sources. With this in mind, we replace IF with external funds in the investment function specification as follows: (6.3) where EF is total external funds. The replacement of IF with EF in the above equation can also be justified by invoking the flow of funds identity (6.4) where OUF is other uses of funds. Thus, if in equation (6.3), the coefficient is greater for high-informationcost firms than for lowinformation-cost firms, it can be taken as equivalent evidence in favour of the excess sensitivity of investments to the availability of external funds across different firm types. The advantage of using equation (6.3) to test for finance constraints is that it is possible to decompose EF into its constituents, and to identify the source(s) of external funds that is responsible for the finance constraint. For example in the Indian context, we could decompose EF into five components, namely fresh capital including share premium (FC), fixed deposits, debentures and bonds (FDB), bank borrowings (BB), borrowings from development finance institutions (FIB) and other borrowings (OB). As will be seen later, these five items are all important in the external sources of funds for both domestic and exporting firms. Of these five components, FC and FDB are routed through capital markets. OBs include inter-corporate loans and trade credit. Accordingly, equation (6.3) can be written as

84

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

(6.5)

In Section 6.4, we report the estimates of equations (6.2), (6.3) and (6.5) for the full sample and also for the two sub-samples in order to establish the presence of finance constraints and then to identify its source. But first we look at the sources and uses of funds to understand the trends in the financing patterns of the firms in the Indian corporate sector. 6.3 The sources of funds for Indian firms— patterns and trends Firms in India obtain their funds from three main sources—commercial banks, non-bank financial institutions (NBFIs) and the capital market. The reforms, outlined in Chapter 2, have had a profound impact on the relative importance of the various sources of funds tapped by firms to finance their investments. Financial sector reforms made it possible for firms to respond to changes in the cost of funds from various sources. This is reflected in the consolidated sources and uses of funds statements of the firms in our full sample presented in Table 6.4. Two points stand out clearly. First, the predominant dependence on external sources is a distinctive feature of these firms, especially when compared with firms in developed countries (Mayer 1990; Singh and Hamid 1992). Second, in 1992–93, 1993–94 and 1994–95, the share of funds raised through capital markets rose dramatically in response to the reforms mentioned above. Thereafter, due to the sluggishness in the secondary markets the importance of this source has declined. In Table 6.5 we present the sources and uses of funds statements separately for persistent exporters and occasional/non-exporters. The quantum of funds raised by persistent exporters is far larger than that by the occasional/ non-exporters, with the total sources/uses of funds per firm in the case of persistent exporters being more than double than that in the case of occasional/ non-exporters. However, it is interesting to note that the broad financing patterns are more or less similar for the two groups. Both groups of firms invest a similar proportion of total funds raised in gross fixed assets. With respect to the sources of funds, both groups of firms on an average over Table 6.4 Sources and uses of funds—all firms

Sources of funds Internal sources Retained profits Depreciation External sources Capital markets Fresh capital (incl. share premium) Fixed deposits/ debentures/bonds Borrowings Bank borrowings

1992–93

1993–94

1994–95

1995–96

1996–97

1997–98

26.8 11.7 15.1 73.2 37.0 22.4 14.5 36.2 12.2

26.6 17.1 9.4 73.5 39.0 26.3 12.7 34.5 2.5

26.1 17.6 8.5 73.9 31.1 28.2 2.9 42.8 17.4

33.7 21.0 12.8 66.3 15.8 13.8 2.0 50.5 22.1

36.0 14.6 21.4 64.0 21.3 8.4 13.0 42.7 10.2

31.9 9.6 22.3 68.4 15.4 5.8 9.6 53.0 20.0

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Financial institutions Other borrowings Uses of funds Gross fixed assets Financial investments Current assets including inventories Total sources/uses of funds* Total sources/uses of funds per firm* No. of companies in the panel

1992–93

1993–94

1994–95

1995–96

1996–97

1997–98

16.6 7.4

6.5 25.5

7.1 18.3

9.3 19.2

15.6 17.0

12.1 20.9

59.1 1.6 39.3 15,656 23.3 672

52.5 13.5 34.0 23,108 34.4 672

46.5 12.4 41.1 34,730 51.7 672

63.1 0.9 36.0 30,663 45.6 672

74.5 4.5 21.0 24,175 36.0 672

66.9 6.5 26.8 25,878 38.5 672

85

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Note Individual items of sources and uses of funds are reported as percentages of the total over all firms. * Rupees 10 million.

time raise about 65–75 per cent of their funds from external sources. With respect to the disaggregated sources of funds we notice a somewhat higher reliance on borrowings and within it from banks and financial institutions in particular on part of the occasional/non-exporters as compared to persistent exporters. We explore in the following section whether this apparent heterogeneity in financing patterns have any implications for finance constraints across financial markets/institutions for the two groups of firms. 6.4 Empirical results In this section we will begin by establishing the presence of finance constraints on firms’ investment decisions. This we will do by estimating equation (6.2) Table 6.5 Sources and uses of funds—by firm type Persistent exporters

Sources of funds Internal sources Retaine d profits Depreci ation External sources Capital markets

Occasional/non-exporters

1992– 93

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

1992– 93

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

25.8

26.1

26.1

35.3

37.2

34.0

31.6

28.3

25.9

27.6

31.6

24.7

11.2

16.4

17.8

22.1

15.9

11.2

13.7

20.2

16.5

16.6

10.1

3.9

14.6

9.7

8.3

13.2

21.4

22.7

17.9

8.2

9.5

11.0

21.5

20.8

74.2

73.9

73.9

64.7

62.8

66.4

68.4

71.7

74.1

72.4

68.4

75.3

37.9

41.1

30.6

14.4

21.9

13.8

32.4

30.1

33.3

21.0

19.3

21.0

86

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Persistent exporters

Fresh capital (incl. share premiu m) Fixed deposi ts/ debent ures/ bonds Borrow ings Bank borro wings Financ ial institut ions Other borro wings Uses of funds Gross fixed assets Financia l investme nts Current assets includin g inventori es Total sources/ uses of funds* Total sources/ uses of

Occasional/non-exporters

1992– 93

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

1992– 93

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

23.1

25.2

28.8

13.3

6.6

3.1

19.2

30.9

25.5

15.6

14.9

15.0

14.8

15.9

1.8

1.1

15.3

10.7

13.2

−0.8

7.9

5.4

4.5

5.8

36.3

32.8

43.3

50.3

40.9

52.6

36.0

41.6

40.8

51.3

49.1

54.3

11.6

2.2

17.3

22.9

9.4

19.9

15.0

3.4

18.3

18.8

13.0

20.7

17.4

5.8

6.4

8.2

15.3

9.7

12.8

9.4

9.8

13.3

16.7

20.1

7.2

24.7

19.6

19.1

16.3

23.0

8.1

28.7

12.7

19.2

19.4

13.6

58.6

54.1

46.0

64.4

76.0

66.8

61.6

45.7

49.0

58.1

69.2

67.6

1.9

15.2

12.7

0.2

4.3

7.9

0.3

6.5

11.1

3.5

5.5

1.8

39.6

30.7

41.3

35.4

19.8

25.7

38.2

47.8

39.9

38.4

25.3

30.6

12, 931

18, 595

28, 452

24, 270

18, 945

20, 052

2724

4513

6278

6393

5230

5826

33.9

48.8

74.7

63.7

49.7

52.6

9.4

15.5

21.6

22.0

18.0

20.0

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Persistent exporters 1992– 93 funds per firm* No. of 381 compani es in the panel

87

Occasional/non-exporters

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

1992– 93

1993– 94

1994– 95

1995– 96

1996– 97

1997– 98

381

381

381

381

381

291

291

291

291

291

291

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Note Individual items of sources and uses of funds are reported as percentages of the total over all firms. * Rupees 10 million.

for the full sample and for the two sub-groups. Further, we attempt to trace the sources of finance constraints by estimating equations (6.3) and (6.5). The presence of finance constraints In Table 6.6, we report the sample means, standard deviation, maximum and minimum values for the variables in equation (6.2). All the variables show substantial variation with the coefficient of variation exceeding 1, though the ratio of investment to capital stock exhibits the smallest coefficient of variation at 1.168. Table 6.7 presents the correlation matrix for the explanatory variables. It is seen that these correlations are by and large small and multicollinearity is unlikely to be an issue in our estimation. Two sets of issues have to be handled while estimating equation (6.2). The first relates to the firm specific and time effects in the model and the second relates to the possible endogeneity of the regressors. Firm specific Table 6.6 Summary statistics for variables in equation (6.2)—all firms

Investment/capital stock D-sales/capital stock Internal sources/capital stock Long-term debt/capital stock Liquid assets/capital stock

Average

Standard deviation

Maximum

Minimum

0.2006 0.3398 0.1749 0.1029 0.2281

0.2343 1.1378 0.2919 0.2572 0.7256

3.7385 14.7804 6.0985 4.3050 18.7002

−4.0562 −20.1010 −2.7475 −1.0529 −0.0393

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Note D-sales: First difference in sales. Table 6.7 Correlation amongst explanatory variables in equation (6.2)—all firms

D-sales/capital stock Internal sources/capital stock Liquid assets/capital stock

D-sales

Internal sources

Liquid assets

Long-term debt

1.0000 0.1656 0.0433

0.1656 1.0000 0.2241

0.0433 0.2241 1.0000

−0.0066 0.0093 0.0459

88

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Long term debt/capital stock

D-sales

Internal sources

Liquid assets

Long-term debt

−0.0066

0.0093

0.0459

1.0000

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Notes All variables are in first difference. D-sales: First difference in sales.

effects need to be included to account for unobserved time-invariant links between investment and the explanatory variables. Fixed time effects would have to be included to control for aggregate business cycle influences. With regard to the firm specific effects, we estimate the model in the first difference form in order to conserve degrees of freedom. To capture the time effects we use time dummies for each year in the sample. We estimate the model first by the OLS method. Given the fact that the regressors in our model may be endogenous we also estimate the model using the two-staged least squares method (2SLS). This endogeneity arises because current cash flow, debt and current liquid assets and investment may all be simultaneously determined. One period lagged values were used as the instrumental variables in the 2SLS estimation. Table 6.8 reports the estimation results. We also report the Wald test statistic for the significance of the time effects and for the joint significance of coefficients. The OLS estimates suggest the presence of finance constraints for the full sample (as indicated by the statistical significance of the coefficient of internal sources). However, the results for the two sub-samples suggest that persistent exporters are not subjected to finance constraints while occasional/non-exporters are. Furthermore, the coefficient of internal sources is distinctly smaller for persistent exporters than for occasional/ non-exporters—the OLS estimate of the coefficient of internal sources for persistent exporters is 0.0059 as compared to 0.2255 for occasional/non-exporters. The 2SLS estimates also support the hypothesis that persistent exporters face less finance constraints than occasional/non-exporters. Interestingly, the 2SLS estimates seem to indicate the lack of finance constraint for the full sample. Turning to the other regressors in our model, the coefficient of the accelerator term is significant for all categories of firms (except in the case of the OLS estimates for persistent exporters). The coefficient of long-term debt is positive and significant for all categories of firms. With regard to liquid assets, we find that this variable has a significant negative influence on investment for all firms (except the 2SLS estimates for occasional/ non-exporters). As for the time effects, the time dummy for 1996–97 is significant for all categories of firms, whereas the time dummy for 1997–98 is significant only for the full sample and for occasional/non-exporters. We also introduced industry specific dummies, all of which turned out to be insignificant suggesting that industry specific factors were not an important determinant of investment. Sources of finance constraints Having established the presence of finance constraints, we now seek to trace its sources. In Table 6.9 we report the sample means, standard deviation,

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

89

Table 6.8 Estimation results for equation (6.2) Explanatory variables Constant D-sales Internal sources Long-term debt Liquid assets Year 1997 dummy Year 1998 dummy Wald—Year 1997 dummy Wald—Year 1998 dummy Wald—Joint significance No. of observations

All firms

Persistent exporters

Occasional/non-exporters

OLS

2SLS

OLS

2SLS

OLS

2SLS

−0.0422 (−3.86) −0.0133 (−3.55) 0.0705

−0.0546 (−4.17) −0.0377 (−4.68) 0.0325

−0.0383 (−2.76) −0.0023 (−0.37) 0.0059

−0.0540 (−3.44) −0.0374 (−2.19) −0.0135

−0.0461 (−2.68) −0.0221 (−4.48) 0.2255

−0.0516 (−2.23) −0.0421 (−4.32) 0.1906

(2.98) 0.0576 (4.39) −0.1140 (−6.78) −0.0443

(−0.57) 0.0546 (3.47) −0.2398 (−2.38) −0.0460

(0.21) 0.0269 (1.52) −0.1411 (−5.63) −0.0334

(-0.22) 0.0270 (1.29) −0.2982 (−2.55) −0.0372

(5.35) 0.0899 (4.66) −0.0919 (−3.91) −0.0495

(1.54) 0.0810 (3.42) −0.1435 (−0.80) −0.0541

(−2.84) 0.0271

(−2.72) 0.0364

(−1.67) −0.0003

(−1.67) 0.0150

(−2.03) 0.0587

(−2.04) 0.0600

(1.77) 8.09

(2.11) 7.38

(−0.02) 2.80

(0.69) 2.79

(2.42) 4.11

(2.17) 4.14

3.12

4.46

0.01

0.47

5.87

4.72

139.19

96.94

75.84

54.51

101.40

62.94

2016

2016

1143

1143

873

873

Notes All variables are ratios to capital stock. The estimation uses 3 years’ data only. One year’s data are lost in constructing D-sales; another year’s data are lost in first-differencing; and a third year’s data are lost in using lagged values as instruments, T-values are reported in brackets. D-sales: First difference in sales. Table 6.9 Summary statistics of explanatory variables in equations (6.3) and (6.5)

All firms External sources/capital stock Fresh capital and share premium/capital stock Fixed deposits debentures bonds/capital stock Bank borrowings/capital stock Borrowings from financial institutions/capital stock Other borrowings/capital stock

Average

Standard deviation

Maximum

Minimum

0.3124 0.0945 0.0170 0.0721 0.0241 0.1047

0.8646 0.4104 0.1337 0.3587 0.1363 0.6496

18.8930 11.9444 2.4226 9.6852 1.4953 18.2134

−12.2981 −12.2955 −0.9617 −5.5817 −0.9572 −13.5112

90

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Average

Standard deviation

Maximum

Minimum

Persistent exporters External sources/capital stock 0.3279 0.6618 7.7714 −5.1929 Fresh capital and share premium/capital stock 0.1008 0.3956 11.9444 −1.4956 Fixed deposits debentures bonds/capital stock 0.0194 0.1298 2.1530 −0.9617 Bank borrowings/capital stock 0.0775 0.3178 5.0311 −4.7037 Borrowings from financial institutions/capital stock 0.0242 0.1249 0.9236 −0.8320 Other borrowings/capital stock 0.1061 0.4856 8.0176 −9.0914 Occasional/non-exporters External sources/capital stock 0.2921 1.0736 18.8930 −12.2981 Fresh capital and share premium/capital stock 0.0862 0.4290 6.3031 −12.2955 Fixed deposits debentures bonds/capital stock 0.0138 0.1386 2.4226 −0.7224 Bank borrowings/capital stock 0.0651 0.4060 9.6852 −5.5817 Borrowings from financial institutions/capital stock 0.0241 0.1498 1.4953 −0.9572 0.1029 0.8160 18.2134 −13.5112 Other borrowings/capital stock Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai.

maximum and minimum values of the explanatory variables appearing in equations (6.3) and (6.5). For all the external sources, the minimum values observed in the sample are negative indicating that some firms extinguished their debt obligations without raising any new debt from a particular source and also that some firms reduced their capital base. Table 6.10 presents the correlation matrix for the explanatory variables appearing in equation (6.5). It is seen that these correlations are by and large low and multicollinearity is unlikely to be an issue in our estimation. Table 6.10 Correlation amongst explanatory variables in equation (6.5)—all firms

D-sales FC FDB FIB BB OB

D-sales

FC

FDB

FIB

BB

OB

1.0000 0.1530 0.0294 0.0115 0.1442 0.0905

0.1530 1.0000 −0.1382 −0.0251 −0.3029 −0.0552

0.0294 −0.1382 1.0000 −0.0417 −0.0174 −0.0128

0.0115 −0.0251 −0.0417 1.0000 0.0035 −0.0080

0.1442 −0.3029 −0.0174 0.0035 1.0000 −0.0996

0.0905 −0.0552 −0.0128 −0.0080 −0.0996 1.0000

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Notes All variables are in first difference, and are all ratios to stock of capital. D-sales First difference in sales. FC Fresh capital including share premium. FDB Fixed deposit/debentures/bonds. FIB Borrowings from financial institutions. BB Borrowings from banks. OB Other borrowings.

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

91

A similar problem of endogeneity of the regressors occurs while estimating equations (6.3) and (6.5). In this specific case, the endogeneity problem refers to the possibility that some of the external sources of funds may be closely related to the current level of operations of the firm represented by sales (amongst other variables). Here we need to note that three of the external sources, namely FC, FDB and FIB, are long-term in nature, are infrequently used and are thus unlikely to be related to the current sales. Moreover, the frequency and extent of use of these three sources are likely to be influenced by factors completely external to the firm such as the conditions on the stock market and the level of interest rates prevailing in the economy. In Table 6.11, we report the frequency with which each of the external sources of funds was used by the firms in our sample. Well over 70 per cent of the firms have used these three sources for less than half the number of years in the sample. Thus, these three sources are unlikely to suffer from the endogeneity problem. On the other hand, BB and OB are primarily short-term in nature. In the Indian context, bank lending is predominantly short-term in nature intended to finance inventories, wages, salaries and other current expenditure. Banks also give long-term loans. Other borrowings usually consist of short-term trade credit that the firm receives against its purchase of material inputs. Both these sources are thus likely to be influenced by current sales. Table 6.11 suggests that these two sources were indeed used significantly more Table 6.11 Frequency of use of different external sources of funds—all firms No. of years accessed

FC

FDB

FIB

BB

OB

0 1 2 3 4 5 6 Total no. of firms

110 129 142 134 92 53 12 672

232 90 98 91 87 49 25 672

128 96 128 126 103 69 22 672

5 24 57 137 213 167 69 672

1 6 36 151 233 195 50 672

Source: Authors’ calculations using firm-level data from PROWESS database, provided by the CMIE, Mumbai. Notes FC Fresh capital including share premium. FDB Fixed deposit/debentures/bonds. FIB Borrowings from financial institutions. BB Borrowings from banks. OB Other borrowings.

frequently by the firms. Thus it is likely that BB and OB would suffer from an endogeneity problem. The endogeneity of all these variables was investigated econometrically using the Durbin—Wu— Hausman tests (Davidson and MacKinnon 1993). The test results8 confirmed our above expectations regarding the exogeneity of FC, FDB and FIB and the endogeneity of BB and OB. This test was conducted for the total external funds also and it showed that EF does not suffer from endogeneity problem. We estimate equations (6.3) and (6.5) using both OLS and 2SLS. The instruments for BB and OB used in the 2SLS procedure were their lagged values. Additionally, lagged values of ∆S/K (the accelerator) was used as its instrument. Equations (6.3) and (6.5) were estimated in first-differences with time dummies to take into account firm specific and time effects. Tables 6.12 and 6.13 report the estimation results for equations (6.3) and (6.5), respectively.

92

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

The results presented in Table 6.12 confirm our earlier finding in Table 6.8, namely that persistent exporters face a far lesser degree of finance constraints than occasional/non-exporters (in the case of OLS estimates, the coefficient on external sources in the case of persistent exporters is approximately half that of occasional/non-exporters). We also find that the year dummy for 1996–97 is significant for all categories of firms, and year dummy for 1997–98 is significant in the case of occasional/non-exporters. As expected, the sales accelerator term—D-sales—is highly significant for both groups of firms. Table 6.12 Estimation results for equation (6.3) Explanatory variables Constant D-sales External sources Year 1997 dummy Year 1998 dummy Wald—Year 1997 dummy Wald—Year 1998 dummy Wald—Joint significance No. of observations

All firms

Persistent exporters

Occasional/non-exporters

OLS

2SLS

OLS

2SLS

OLS

2SLS

−0.0227 (−2.11) −0.0233 (−6.20) 0.0937

−0.0370 (−3.27) −0.0400 (−4.99) 0.0296

−0.0214 (−1.51) −0.0192 (−2.93) 0.0562

−0.0409 (−2.66) −0.0290 (−1.53) −0.0212

−0.0315 (−1.90) −0.0244 (−5.26) 0.1146

−0.0421 (−2.43) −0.0416 (−4.64) 0.0489

(11.60) −0.0422

(1.99) −0.0479

(4.28) −0.0403

(−0.74) −0.0401

(11.07) −0.0404

(2.72) −0.0508

(−2.79) 0.0032

(−3.05) 0.0155

(−2.05) −0.0192

(−1.92) −0.0033

(−1.73) 0.0372

(−2.08) 0.0474

(0.21) 7.79

(0.99) 9.32

(−0.98) 4.19

(−0.16) 3.69

(1.59) 2.98

(1.94) 4.32

0.04

0.97

0.95

0.03

2.52

3.75

210.83

88.25

57.21

39.08

167.98

62.93

2016

2016

1143

1143

873

873

Notes All variables are ratios to capital stock. The estimation uses 3 years’ data only. One year’s data are lost in constructing D-sales; another year’s data are lost in first-differencing; and a third year’s data are lost in using lagged values as instruments, T-values are reported in brackets. D-sales: First difference in sales. Table 6.13 Estimation results for equation (6.5) Explanatory variables Constant D-sales

All firms

Persistent exporters

Occasional/non-exporters

OLS

2SLS

OLS

2SLS

OLS

2SLS

−0.0126 (−1.20) −0.0236

−0.0147 (−1.38) −0.0440

−0.0151 (−1.09) −0.0116

−0.0190 (−1.31) −0.0329

−0.0122 (−0.77) −0.0259

−0.0130 (−0.81) −0.0416

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

Explanatory variables

FC FDB FIB BB OB Year 1997 dummy Year 1998 dummy Wald—Year 1997 dummy Wald—Year 1998 dummy Wald—Joint significance No. of observations

All firms

Persistent exporters

Occasional/non-exporters

OLS

2SLS

OLS

2SLS

OLS

2SLS

(−6.44) 0.1036 (7.08) 0.1705 (4.80) 0.5272 (14.64) 0.1071 (7.57) 0.0568 (6.02) −0.0452

(−5.39) 0.1034 (5.72) 0.1749 (4.78) 0.5282 (14.50) 0.0681 (2.54) 0.0447 (2.95) −0.0558

(−1.76) 0.0652 (3.52) 0.0822 (1.81) 0.4465 (8.81) 0.0906 (4.24) 0.0020 (0.11) −0.0362

(−2.03) 0.0636 (1.95) 0.0955 (1.79) 0.4291 (8.27) 0.0489 (0.92) −0.0200 (−0.58) -0.0480

(−5.63) 0.1634 (6.00) 0.2843 (4.92) 0.5929 (11.62) 0.1103 (5.60) 0.0779 (6.69) −0.0510

(−4.86) 0.1646 (5.91) 0.2768 (4.73) 0.6069 (11.75) 0.0722 (2.54) 0.0666 (3.98) −0.0592

(−3.08) −0.0122

(−3.68) −0.0119

(−1.86) −0.0291

(−2.34) −0.0268

(−2.30) 0.0098

(−2.62) 0.0083

(−0.83) 9.49

(−0.80) 13.56

(−1.51) 3.46

(−1.36) 5.45

(0.44) 5.28

(0.37) 6.84

0.69

0.64

2.27

1.85

0.19

0.14

394.08

325.23

133.23

115.33

297.46

254.36

2016

2016

1143

1143

873

873

93

Notes All variables are ratios to capital stock. The estimation uses 3 years’ data only. One year’s data are lost in constructing D-Sales; another year’s data are lost in first-differencing; and a third year’s data are lost in using lagged values as instruments. T-values are reported in brackets. D-sales: First difference in sales; FC: Fresh capital including share premium; FDB: Fixed deposit/debentures/bonds; FIB: Borrowings from financial institutions; BB: Borrowings from banks; OB: Other borrowings.

The results in Table 6.13 clearly indicate that the source of financing matters. The sensitivity of investment to different sources of finance varies remarkably across different external sources of funds for both persistent exporters and occasional/non-exporters. The size of the coefficient on the different sources of external funds suggests that investments are most sensitive to FI borrowings. On the other hand, this sensitivity is remarkably lower with regard to the other external sources. This is true across the full and the two sub-samples. Another significant result is that persistent exporters face a lower degree of finance constraints than occasional/non-exporters across all the external sources of funds. Focusing on persistent exporters, we find that the OLS coefficient of FIB is 0.4465 compared to the next highest coefficient of 0.0906 on BB. That is, the degree of finance constraint originating from the DFIs is at

94

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

least five times more than that originating from any other external source of funds. Therefore, among the key players in India’s financial sector, the DFIs which provide long-term finance to Indian corporate firms seem to be a ‘weak link’ in allocating resources to arguably the more dynamic firms in the Indian manufacturing sector. At this point we need to take note of the potential influence of priority sector lending requirements9 on the coefficient of BB for exporting firms. There are plausible reasons to believe that this coefficient is not significantly influenced by such lending requirements. First of all, the difference in the coefficient of BB is not very large across persistent exporters and occasional/non-exporters. Second, the ranking of the different sources in terms of their coefficient estimates indicate that BB is the second highest source of finance constraint for persistent exporters whereas it is the fourth highest source of finance constraint for occasional/ non-exporters. Clearly if the priority sector lending requirements were driving the results such a ranking would be highly unlikely. What explains the weakness in the lending criteria of the DFIs as compared to the capital markets and commercial banks? A complete answer to this question can only be provided with a detailed examination of the type of projects that the DFIs provide funds for. This requires detailed information on specific projects funded by the DFIs and such information are not readily available. Here, we offer an answer based on our reading of the policy history of DFIs, and the changes in the environment faced by these institutions brought about by deregulation. As discussed in Chapter 2, a large proportion of the commercial banks and all DFIs are government owned, either in part or in whole. Thus, public ownership per se cannot explain the difference in the lending behaviour of commercial banks versus DFIs. However, there are two important differences between these two sets of institutions with regard to the nature of the lending undertaken by these institutions in the pre- and post-reform periods. First, the DFIs provide long-term finance for investment while commercial banks provide mainly working capital finance. As argued by Bhatt (1995: 117), ‘commercial banks are in a position to identify the problems of the enterprise in time, as they are dealing with the enterprise on a day-to-day basis and the performance of the enterprise is reflected in their transactions with the commercial banks’.10 This is not the case with the DFIs, and may explain why their lending behaviour differs from that of commercial banks in the Indian case. Second, as we have noted in Chapter 2, in the pre-reform period the development finance institutions were merely passive conduits of funds, with little active involvement in the screening of firms. With deregulation, the lack of expertise in a core activity for financial intermediaries may have constrained the performance of the DFIs in the postreform period. 6.5 Concluding remarks This chapter examined the role of financial factors in influencing investment decisions at the firm level. Using the same sample of 672 firms identified in Chapter 5, we find that finance constraints affect firm-level investments, and that persistent exporters are subjected to a lower degree of finance constraints than occasional/non-exporters. This seems to indicate that suppliers of funds in India use the firm’s exporting history as a signal of the strength of the firm, and that persistent exporters are less constrained by the availability of internal funds than firms which export occasionally or sell only to domestic markets. Further this chapter examined whether key financial markets and intermediaries have played their part in allocating investible funds to the persistent exporters. We find that the degree of the ‘finance constraint’ differs significantly across external suppliers of funds. Our results suggest that investments are most sensitive to borrowings from the DFIs. This sensitivity is remarkably lower with regard to funds from

FINANCE CONSTRAINTS, PERSISTENT EXPORTING AND INVESTMENT

95

capital markets and commercial banks. Another significant result is that persistent exporters face a lower degree of finance constraints than occasional/non-exporters across all the external sources of funds, with the degree of finance constraint originating from the DFIs being the highest. The finding that occasional/non-exporters are subjected to a greater degree of finance constraint than persistent exporters combined with the results of Chapters 4 and 5 that investments are critical for export performance suggests a kind of ‘poor-export-performance-trap’. Since firms need finance for investment to be successful in exporting and lenders use past export performance as a signal of firm strength, there seems to be a trap which the occasional/non-exporters (especially non-exporters) are in. Given their poor past export performance, they are finance constrained and hence they cannot make the necessary investment in order to become successful exporters. It is in such a situation that the DFIs are usually expected to play a key role in ensuring that investible funds are easily available for all firms with a good project. But our results show that amongst all external suppliers of funds, the DFIs indeed impose the maximum finance constraint on both persistent exporters as well as occasional/non-exporters. Thus, among the key players in India’s financial sector, DFIs which provide long-term finance to Indian corporate firms seem to be a ‘weak link’ in allocating resources to arguably the more dynamic firms in the Indian manufacturing sector. Moreover, by imposing higher finance constraints on non-exporters, the functioning of the DFIs is no different from the rest of the financial intermediaries. Given that policy would like to encourage nonexporters to become exporters, the entire role of the DFIs in the allocation of resources remains ambiguous.

7 Conclusions and policy implications

A country can achieve a standard of living that is both rising and sustainable if it is able to produce goods and services that meet the test of international competitiveness. Using India as a case study, in this book we have made an attempt to analyse the evolution of competitiveness of the manufacturing sector, both at the aggregate level and various levels of disaggregation. In addition, we have also identified the factors that affect competitiveness at various levels. The Indian case is particularly relevant from a developing-country viewpoint given the complexity of controls that prevailed in the economy for about four decades and the large changes in policy that have taken place in the 1990s. Till the 1980s, as in many other developing countries, Indian policy-makers had a generally pessimistic assessment of export prospects. As a consequence import substitution was considered the prime means of achieving self-reliance which was one of the primary objectives of planning. This is amply borne out by the nature of industrial, trade and financial policies followed in this period. While generally conservative macroeconomic policies were followed till the mid-1980s there was a gradual erosion in macroeco-nomic discipline subsequently. The resulting deficits of the central government were financed both by monetary expansion and through borrowings from the public. The balance of payments (BOP) crisis of 1991 could be linked to the large and growing fiscal imbalances witnessed in India since the mid-1980s. The structural adjustment programme initiated in 1991 involved a significant change in the emphasis placed on market forces as opposed to centralised planning in allocating resources. The elaborate controls on industry, trade and the financial system were gradually dismantled. In the new regime international trade in general and exports in particular were given much greater emphasis. Self-reliance was no longer a major policy goal and the emphasis shifted to achieving higher rates of growth of GDP. Even though the period between 1985 and 1990 saw a large erosion of fiscal discipline this period was marked by a significant rise in exports. This was due to the fact that India operated an ‘active’ crawling peg from 1986 onwards to produce a sharp real depreciation of the rupee in the period 1986–90. From 1991 to 1993, India moved gradually to full current account convertibility of the exchange rate. In this scenario though the central bank does have a major influence on the exchange rate, a policy of sustained depreciation is no longer feasible as it is likely to be inconsistent with other macro policy objectives. Consequently export competitiveness has to be rooted in micro fundamentals in general and plant- and firm-level efficiencies in particular. Conceptually, two sets of factors can impede firms from effectively competing in world markets. The first relates to the incompleteness of the reform process so far. The reforms programme initiated in 1991 has been gradual. These reforms have been extremely far reaching with respect to the financial markets and the trade regime. Though entry barriers in the commodity markets arising from licensing requirements have been done away with, important aspects of the economy are still subject to restrictions and rigidities. Two

CONCLUSIONS AND POLICY IMPLICATIONS

97

such critical areas are bankruptcy laws and labour laws. Both these are critical for efficient allocation of resources, which is a precondition for improving competitiveness. The second set of factors is more structural in nature and has so far not attracted much attention in the context of export competitiveness. These relate to distortions and imperfections inherent in particular markets. Entry into export markets may be impeded by the presence of significant sunk costs.1 Another such structural factor is the imperfections in financial markets arising due to asymmetric information and transaction costs and the ability/inability of the financial intermediaries and markets to effectively overcome these problems. The analysis presented in this book has brought out the significance of these structural factors. If India’s ability to produce goods that meet the test of international competitiveness is limited, then this has an immediate bearing on the behaviour of the trade balance and the current account balance. Historically, India has experienced widening current account deficits only since the mid-1980s. The behaviour of major macroeconomic variables over the period 1970–98 suggests that the worsening current account deficit in the 1980s cannot be attributed to a weakly performing export sector or an inappropriate exchange rate policy but to an increase in the investment-savings gap caused by the widening fiscal deficits of the central government. On the other hand the widening current account deficits seem to be linked to a widening deficit in the trade account, primarily due to a weak performance in exports since 1995–96. Purely macroeconomic considerations suggest that a country cannot grow faster than the rate of growth of output that is consistent with BOP equilibrium on the current account. Our estimation of the BOPconstrained equilibrium growth model for India suggests that while real exchange rate depreciation and real growth in remittances have had a positive effect on economic growth, world income growth provides the strongest positive impact on the growth rate of the economy. The results also suggest that the 1984 and 1991 reforms seem to have a positive impact on the responsiveness of economic growth in India to world income growth. This may indicate that the income elasticity of demand for India’s exports may be rising, possibly due to greater specialisation of India’s exporters in the more rapidly growing commodities and/or markets in the world economy. To investigate this issue empirically we estimated a CMS model. The main point derived out of the CMS analysis is that India’s export performance over the period 1970–96 can be largely explained by movements in total world trade. While historically, the market focus of India’s exports was not in tune with the world trade patterns, a major correction has taken place during the 1990s. As a result, India’s market focus seems to be less of a problem by the end of the 1990s. The main weakness, however, continues to be the commodity composition of India’s exports, dominated by primary products, semi-finished intermediaries, and finished products involving simple technologies, all with low-value addition and low-income elasticity of demand. How can India achieve a transformation in the commodity mix in its export basket towards technologically more sophisticated and high-value added products? To answer this question, we need to understand the determinants of India’s competitiveness at the commodity/sectoral level. The relative importance of price versus non-price factors in determining a country’s international competitiveness has remained controversial in the literature. Among the non-price factors, capital accumulation could be considered to be the most important for most developing countries. Analysing patterns in India’s sectoral international competitiveness (measured by industry export shares) using disaggregated 4-digit ISIC industry data for the period 1976–98, we find significant variability in industry export shares both over time and across industries. While India’s manufacturing sector has gained international competitiveness in the aggregate since the mid-1980s, industries such as textile goods (excluding wearing apparel) and leather goods (excluding footwear) have lost international competitiveness

98

CONCLUSIONS AND POLICY IMPLICATIONS

in a significant manner, particularly since the 1990s. Industries that have gained world market share in the 1990s are carpets and jewellery. The improvement in overall international competitiveness of the manufacturing sector seems to be matched by a fall in unit labour costs since the late 1980s. This fall in unit labour costs seems to be due to an improvement in labour productivity, rather than a decline in the real wage rate. The fall in unit labour costs since the late 1980s is also observed relative to unit labour costs in the United States. There has also been an improvement in overall manufacturing profitability in the period since the late 1980s. To examine the determinants of international competitiveness at the sectoral level, we estimated a panel data model for 78 industries over the period 1989–90 to 1997–98, with the investment rate and unit labour costs as the key explanatory variables. We find that the investment rate has a positive and significant effect on industry export shares, thus establishing a clear role for the investment rate in influencing sectoral competitiveness. While unit labour costs in the aggregate do not seem to influence sectoral competitiveness, decomposing labour costs into its components—real wage and labour productivity—allows for a negative effect of the real wage and a positive effect of labour productivity on sectoral competitiveness. Thus, price factors also do matter in determining international competitiveness at the industry level. We also find that industries that belong to the intermediate and capital goods sectors have a lower international competitiveness in the aggregate than industries in the consumer goods sectors. This could possibly be due to the high degree of protection enjoyed by these two sectors till the late 1980s. It could also be related to the possible lack of comparative advantage India has in these sectors, as compared to the consumer goods sector. The analysis here shows clearly the importance of industry-level heterogeneity in understanding international competitiveness and allowing us to identify investment as a key variable in influencing export competitiveness. We take this analysis one step further by introducing firm-level heterogeneity. We examined the determinants of outward-orientation at the firm level using a sample of 672 firms over the period 1992–93 to 1997–98. The firm-level data points to an increasing outward-orientation of the Indian manufacturing sector, both in terms of the number of new firms entering the export market, and existing exporters expanding the proportion of output exported. Examining the characteristics of exporting firms relative to non-exporting firms, we find that exporters differ significantly from nonexporters, even within the same industry, with exporting firms having a disproportionate weight in total capital stock and output. Exporting firms also have a higher-investment rate, higher use of imported capital goods (as a per cent of net-fixed assets) and imported raw materials (as a per cent of sales) than non-exporting firms. This remains true even when we control for industry and year effects. Furthermore, there is clear evidence on the persistence of exporting behaviour, with firms that enter the export market tending to stay on for several more years. Even though exporting is not an once and for all phenomenon, with year to year transition rates from exporting to non-exporting (and vice versa) fairly large, the majority of firms who were exporting at the beginning of the period remained so at the end of the period. The analysis of the determinants of exporting at the firm level suggests the importance of prior exporting experience as a variable that significantly increases the probability of a firm exporting in any given period. The results indicate that having exported in the previous period increases the probability of exporting in the current period by 55 per cent. We find clear evidence supporting the argument that firms face large sunk entry costs in entering the export market, leading to a high degree of persistence in exporting behaviour. We also find that large firms, foreign-owned firms, and firms in industries where other firms are exporting, are more likely to be exporters. Finally, investment and exporting is strongly correlated at the firm level, as firms that have exhibited investment bursts in the previous period are more likely to be exporters in the current period. The importance of investment in determining firm-level outward-orientation provides us with a basis for exploring the role of financial factors in influencing investments and hence export competitiveness.

CONCLUSIONS AND POLICY IMPLICATIONS

99

Using the same sample of 672 firms we studied the investment-cash flow relationship in order to understand the role of financial factors in influencing investment decisions. We find that the availability of internal funds (cash flow) constrains firm-level investments, and that persistent exporters face a lower degree of finance constraints than occasional/non-exporters. This seems to indicate that suppliers of funds in India use the firm’s exporting history as a signal of the strength of the firm, and that persistent exporters are less constrained by the availability of internal funds than firms which export occasionally or sell only to domestic markets. Further we examined whether key financial markets and intermediaries have played their part in allocating investible funds to the persistent exporters. We find that the degree of the ‘finance constraint’ differs significantly across external suppliers of funds. Our results suggest that investments are most sensitive to borrowings from the DFIs. This sensitivity is remarkably lower with regard to funds from capital markets and commercial banks. Another significant result is that persistent exporters face a lower degree of finance constraints than occasional/non-exporters across all the external sources of funds, with the degree of finance constraint originating from the DFIs being the highest. The above results seem to indicate the possible presence of a poorexport-performance-trap for occasional/ non-exporters. Given their poor past export performance, they are finance constrained and hence they cannot make the necessary investment in order to become successful exporters. It is in such a situation that development finance institutions are expected to overcome the market failures that are associated with financial markets/ intermediaries. But our results show that the functioning of the DFIs is no different from the rest of the financial intermediaries in imposing the maximum finance constraint on both persistent exporters as well as occasional/ non-exporters. Thus, among the key players in India’s financial sector, DFIs which provide long-term finance to Indian corporate firms seem to be a ‘weak link’ in allocating resources to arguably the more dynamic firms in the Indian manufacturing sector. Thus far we have summarised the conclusions at each stage of our analysis, namely at the aggregate level, at the sectoral level and at the firm level. A theme that emerges clearly across all these stages of analysis is that heterogeneity matters in the understanding of international competitiveness. We get a vastly richer perspective when we attempt to account for heterogeneity at each stage of analysis. The analysis at the aggregate level masks the heterogeneity in export performance at the sectoral level and the role of investments in shaping export competitiveness is not immediately obvious. It is only when we attempt to analyse the inter-industry differences in competitiveness that the role of investment stands out. The analysis of firm-level export behaviour re-emphasises the importance of investment. However, accounting for interfirm differences in investment has necessitated a new approach to modelling the role of investment in influencing export behaviour. We have used a class of models that emphasise the lumpiness of investment arising due to non-convex costs of adjusting capital stock. Firms witness bursts of investment followed by periods of inactivity and it is these investment bursts (spikes) which drive exporting at the firm level. The analysis of exporting behaviour at the firm level, has also high-lighted the role of various firm specific attributes, such as spillovers, ownership and size. Perhaps the most significant finding at the firm level concerns the persistence of exporting, which arises due to the presence of sunk entry costs to exporting. Firm specific heterogeneity are also evident in the financing of investment. Export promotion policies that ignore this heterogeneity are unlikely to yield desired results. Policy implications In this section, we attempt to derive some policy implications of our study for developing countries in general. At the macroeconomic level, a key implication of our analysis is that the response of the BOP to

100

CONCLUSIONS AND POLICY IMPLICATIONS

adjustments in the exchange rate is far more complex than what neo-classical models suggest. Our analysis suggests that export supply functions need to take account of the number and type of firms already exporting, and the perceived permanence/transitory nature of changes in the exchange rate. Thus, the response of aggregate or sectoral exports to changes in the exchange rate would be country- and periodspecific. Much of the focus of export promotion policies in developing countries has been on expanding the export volumes. Our analysis suggests that there is a need to distinguish between policies that are aimed at (i) expanding export volumes of existing exporters and (ii) promoting the entry of new exporters. If it is more difficult for a firm to enter into export markets than for an exporting firm to increase exports, then a more effective export promotion policy would be to reduce the sunk cost of entering the export market for the first time along with uncertainty related to profitability from exporting in future periods. Measures to reduce the sunk entry cost may include providing information about potential markets, and developing export infrastructure. A stable macroeconomic and policy environment that reduces uncertainty surrounding returns to exporting is also crucial in muting the effects of sunk costs. It could be argued that from a policy viewpoint it is not enough to know that substantial sunk entry costs to exporting exist. What is needed is for a deeper understanding of the nature and extent of these costs. There is a growing literature that points out the importance of global commodity chains in explaining the growth of labour intensive manufacturing exports from East Asia in the 1970s and 1980s, and China in the 1990s. This global commodity chains are buyer-driven networks where retailers and marketers in the United States and Europe rely on foreign pro-ducers for supplying products such as apparel, footwear, toys and sporting goods. Thus, sunk entry costs in these industries would refer to the ability of a firm to gain access to such a network (Gereffi 1999). It is plausible that other industries could face a different type of sunk entry cost. Further research on this issue may go a long way in understanding the nature of sunk costs and if indeed governments have any direct role to play in reducing them. The other possible barrier for firms to enter into export markets arises from the functioning of the financial sector. Our results have empirically demonstrated the operation of a poor-export-performance-trap in India, wherein firms (especially occasional/non-exporters) desiring to invest in order to become successful exporters are subjected to a high degree of finance constraint, because lenders use past export performance as a signal of firm strength. In other words, given their poor past export performance, occasional/non-exporters are finance constrained and hence they cannot make the necessary investment in order to become successful exporters. It is an open question whether such a poor-exportperformance-trap exists in other developing countries as well. If indeed such a trap exists in other developing countries also, then it poses a challenge to both policy-makers and academicians, quite analogous to other such development traps. Whatever the situation, the policy regime has to recognise the role of investment in determining export performance. Our analysis highlights the importance of an efficient financial sector capable of channelling resources to the more dynamic firms/sectors in the presence of information asymmetries and agency costs. This implies that reforms that pertain to the real sector alone (such as industrial deregulation and trade reforms) may not be sufficient to enhance the country’s competitiveness if complementary financial sector reforms are not undertaken.

Appendix

Table A.1 Commodity-wise shares (%) in total world exports Year

SITC 00

SITC 01

SITC 02

SITC 03

SITC 04

SITC 05

SITC 06

SITC 07

SITC 08

SITC 09

SITC 11

SITC 12

SITC 21

SITC 22

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

0.46 0.47 0.59 0.50 0.30 0.36 0.32 0.30 0.33 0.29 0.28 0.29 0.31 0.29 0.26 0.27 0.30 0.27 0.28 0.26 0.24 0.24 0.25 0.23 0.22 0.20

1.52 1.54 1.69 1.75 1.04 1.13 1.13 1.10 1.16 1.14 1.05 1.08 1.11 1.05 0.93 0.94 1.03 1.03 1.00 1.03 1.02 1.06 1.08 1.04 1.01 0.95

0.74 0.87 0.85 0.80 0.66 0.72 0.69 0.67 0.70 0.67 0.67 0.72 0.75 0.68 0.62 0.60 0.65 0.66 0.71 0.67 0.61 0.63 0.67 0.68 0.61 0.61

0.73 0.78 0.84 0.82 0.61 0.65 0.72 0.75 0.83 0.81 0.70 0.76 0.80 0.83 0.78 0.85 1.04 1.08 1.08 1.03 1.02 1.08 1.06 1.06 1.05 0.96

2.45 2.39 2.36 3.03 3.07 3.09 2.57 2.08 2.15 2.11 2.23 2.46 2.21 2.24 2.28 1.86 1.50 1.28 1.42 1.56 1.33 1.27 1.34 1.21 1.10 1.16

1.95 1.93 1.99 1.84 1.41 1.54 1.53 1.52 1.51 1.42 1.36 1.42 1.46 1.42 1.43 1.42 1.54 1.54 1.46 1.44 1.50 1.58 1.52 1.50 1.49 1.42

0.75 0.79 0.91 0.82 1.12 1.53 1.00 0.88 0.77 0.46 0.70 0.70 0.50 0.46 0.39 0.32 0.35 0.34 0.34 0.34 0.35 0.33 0.34 0.32 0.32 0.36

1.76 1.53 1.43 1.32 1.09 1.08 1.42 1.93 1.66 1.43 1.19 0.91 0.99 1.10 1.19 1.20 1.35 0.96 0.84 0.72 0.63 0.60 0.55 0.58 0.70 0.67

0.69 0.68 0.64 0.83 0.56 0.49 0.59 0.65 0.58 0.56 0.54 0.59 0.58 0.71 0.56 0.48 0.55 0.52 0.58 0.54 0.46 0.47 0.49 0.49 0.45 0.41

0.18 0.19 0.18 0.17 0.17 0.19 0.18 0.19 0.20 0.19 0.19 0.20 0.22 0.22 0.22 0.23 0.25 0.26 0.23 0.23 0.24 0.28 0.31 0.35 0.35 0.35

0.69 0.70 0.73 0.72 0.50 0.54 0.52 0.52 0.58 0.57 0.52 0.52 0.54 0.52 0.50 0.53 0.58 0.59 0.59 0.59 0.63 0.65 0.67 0.68 0.69 0.64

0.56 0.55 0.57 0.49 0.43 0.46 0.44 0.43 0.47 0.41 0.38 0.42 0.48 0.45 0.42 0.44 0.43 0.43 0.42 0.45 0.51 0.55 0.57 0.53 0.52 0.47

0.41 0.39 0.48 0.45 0.27 0.24 0.32 0.30 0.32 0.37 0.25 0.24 0.24 0.24 0.28 0.28 0.29 0.31 0.28 0.23 0.20 0.15 0.14 0.14 0.16 0.15

0.72 0.71 0.70 0.87 0.77 0.63 0.61 0.68 0.65 0.61 0.52 0.58 0.56 0.56 0.57 0.47 0.44 0.40 0.40 0.35 0.31 0.29 0.28 0.27 0.28 0.26

102

Year

SITC 00

SITC 01

SITC 02

SITC 03

SITC 04

SITC 05

SITC 06

SITC 07

SITC 08

SITC 09

SITC 11

SITC 12

SITC 21

SITC 22

1996

0.18

0.91

0.57

0.94

1.25

1.35

0.34

0.62

0.46

0.34

0.62

0.50

0.14

0.30

Year

SITC 23

SITC 24

SITC 25

SITC 26

SITC 27

SITC 28

SITC 29

SITC 32

SITC 33

SITC 34

SITC 35

SITC 41

SITC 42

SITC 43

1970 1971 1972 1973 1974

0.73 0.60 0.51 0.64 0.59

1.44 1.37 1.42 1.71 1.28

0.84 0.71 0.64 0.62 0.73

1.69 1.58 1.67 1.83 1.37

0.72 0.68 0.63 0.57 0.65

2.49 2.03 1.78 1.85 1.82

0.48 0.48 0.48 0.46 0.39

0.91 0.86 0.83 0.73 0.92

0.26 0.31 0.35 0.32 0.40

0.09 0.09 0.11 0.11 0.09

0.15 0.15 0.11 0.12 0.13

0.47 0.53 0.43 0.44 0.61

0.08 0.08 0.07 0.08 0.09

1975

0.45

0.96

0.66

1.10

0.67

1.70

0.37

1.16

0.59

0.09

0.10

0.50

0.06

1976

0.52

1.16

0.63

1.18

0.56

1.61

0.38

1.00

0.70

0.11

0.10

0.39

0.06

1977

0.51

1.16

0.53

.09

0.54

1.41

0.40

0.87

0.78

0.11

0.11

0.47

0.07

1978

0.52

1.15

0.46

1.06

0.52

1.30

0.40

0.80

0.80

0.11

0.10

0.47

0.07

1979

0.55

1.27

0.50

0.97

0.51

1.54

0.38

0.80

0.99

0.13

0.10

0.48

0.07

1980

0.51

1.13

0.54

0.95

0.53

1.59

0.36

0.90

1.41

0.13

0.08

0.41

0.07

1981

0.43

0.89

0.53

0.92

0.53

1.40

0.36

0.86

1.69

0.17

0.08

0.38

0.06

1982

0.39

0.87

0.47

0.87

0.50

1.29

0.37

0.92

1.87

0.16

0.08

0.37

0.06

1983

0.46

0.91

0.48

0.88

0.49

1.26

0.38

0.82

1.84

0.17

0.08

0.41

0.06

1984

0.46

0.88

0.54

0.93

0.49

1.33

0.37

0.84

1.83

0.18

0.09

0.54

0.08

1985

0.41

0.83

0.46

0.88

0.50

1.31

0.38

0.94

1.84

0.19

0.08

0.51

0.08

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

0.41 0.41 0.41 0.34 0.27 0.25 0.25 0.24 0.26 0.28

0.86 0.92 0.92 0.95 0.86 0.78 0.79 0.86 0.86 0.76

0.50 0.58 0.63 0.66 0.53 0.43 0.42 0.33 0.40 0.55

0.79 0.81 0.82 0.81 0.67 0.61 0.54 0.47 0.53 0.53

0.48 0.42 0.43 0.42 0.38 0.36 0.32 0.30 0.35 0.28

1.16 1.09 1.25 1.29 1.07 0.98 0.88 0.81 0.77 0.89

0.43 0.45 0.44 0.43 0.42 0.43 0.43 0.40 0.39 0.37

0.84 0.65 0.63 0.61 0.58 0.58 0.53 0.48 0.41 0.41

7.47 8.39 8.19 9.04 17. 93 15. 87 16. 45 16. 93 14. 37 16. 74 21. 02 20. 81 19. 44 17. 78 16. 58 13. 61 8.81 8.74 5.83 6.85 8.54 7.39 6.32 5.98 4.99 5.36

1.38 0.89 0.77 0.77 0.85 0.97 0.86 0.82 0.61 0.65

0.18 0.18 0.16 0.18 0.17 0.16 0.16 0.16 0.16 0.16

0.05 0.04 0.05 0.04 0.03 0.03 0.03 0.03 0.04 0.04

0.32 0.30 0.32 0.31 0.29 0.30 0.30 0.29 0.37 0.38

0.07 0.06 0.07 0.07 0.06 0.06 0.06 0.07 0.08 0.09

103

Year

SITC 23

SITC 24

SITC 25

SITC 26

SITC 27

SITC 28

SITC 29

SITC 32

SITC 33

SITC 34

SITC 35

SITC 41

SITC 42

SITC 43

1996

0.27

0.73

0.35

0.49

0.27

0.81

0.36

0.42

6.71

0.97

0.17

0.03

0.35

0.07

Year

SITC 51

SITC 52

SITC 53

SITC 54

SITC 55

SITC 56

SITC 58

SITC 59

SITC 61

SITC 62

SITC 63

SITC 64

SITC 65

SITC 66

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

1.49 1.45 1.45 1.46 1.91 1.58 1.68 1.62 1.65 1.84 1.67 1.73 1.72 1.93 2.04 2.12 2.12 2.19 2.32 2.26 2.09 2.10 2.06 2.08 2.18 2.23 2.10

0.94 0.95 0.90 0.87 0.95 0.94 0.95 0.98 1.05 0.96 0.94 0.91 0.97 0.97 1.00 1.04 1.06 1.04 0.92 0.85 0.77 0.77 0.75 0.75 0.67 0.67 0.63

0.50 0.53 0.56 0.55 0.50 0.42 0.47 0.45 0.45 0.44 0.41 0.40 0.42 0.43 0.41 0.44 0.51 0.54 0.58 0.56 0.57 0.56 0.58 0.59 0.60 0.57 0.55

0.93 0.98 0.95 0.90 0.78 0.83 0.79 0.78 0.85 0.77 0.77 0.80 0.92 0.90 0.87 0.94 1.07 1.06 1.07 1.04 1.10 1.20 1.33 1.44 1.46 1.45 1.47

0.40 0.41 0.41 0.40 0.38 0.37 0.38 0.38 0.41 0.40 0.40 0.41 0.42 0.43 0.43 0.45 0.50 0.52 0.54 0.57 0.61 0.63 0.67 0.72 0.74 0.72 0.73

0.44 0.44 0.51 0.53 0.71 0.76 0.44 0.45 0.48 0.47 0.53 0.49 0.44 0.47 0.55 0.56 0.48 0.44 0.48 0.44 0.40 0.40 0.37 0.31 0.30 0.32 0.35

1.24 1.27 1.30 1.37 1.51 1.20 1.36 1.30 1.33 1.48 1.42 1.33 1.36 1.50 1.55 1.57 1.79 1.94 2.10 2.01 1.98 2.02 1.96 1.95 2.09 2.20 2.03

0.89 0.90 0.86 0.84 0.86 0.85 0.82 0.82 0.89 0.97 0.99 0.99 1.04 0.95 0.96 1.00 1.06 1.08 1.08 1.05 1.06 1.08 1.09 1.12 1.16 1.16 1.16

0.34 0.35 0.43 0.39 0.28 0.29 0.33 0.33 0.35 0.40 0.32 0.30 0.31 0.32 0.36 0.37 0.40 0.44 0.41 0.41 0.42 0.40 0.43 0.45 0.49 0.46 0.46

0.58 0.63 0.64 0.60 0.59 0.65 0.62 0.62 0.63 0.61 0.61 0.64 0.60 0.62 0.63 0.64 0.71 0.73 0.76 0.74 0.73 0.74 0.77 0.77 0.77 0.76 0.77

0.51 0.53 0.59 0.65 0.47 0.44 0.50 0.51 0.51 0.50 0.46 0.46 0.44 0.47 0.44 0.44 0.48 0.53 0.53 0.53 0.54 0.53 0.57 0.62 0.62 0.59 0.59

1.75 1.73 1.69 1.59 1.68 1.48 1.47 1.39 1.37 1.35 1.36 1.39 1.39 1.42 1.48 1.56 1.72 1.81 1.89 1.86 1.89 1.89 1.85 1.76 1.81 2.01 1.81

3.94 4.11 4.10 4.11 3.36 3.08 3.16 3.01 3.10 3.00 2.83 2.82 2.78 2.87 2.89 2.95 3.26 3.39 3.20 3.23 3.27 3.37 3.42 3.32 3.38 3.24 3.07

1.89 1.99 2.13 2.21 1.74 1.77 1.88 2.08 2.36 2.26 2.20 1.82 1.80 1.89 1.81 1.80 1.99 2.04 2.19 2.26 2.18 2.14 2.11 2.12 2.25 2.16 2.04

Year

SITC 67

SITC 68

SITC 69

SITC 71

SITC 72

SITC 73

SITC 74

SITC 75

SITC 76

SITC 77

SITC 78

SITC 79

SITC 81

SITC 82

1970 1971 1972

4.93 4.66 4.50

3.64 2.74 2.67

2.22 2.19 2.11

1.83 1.97 1.98

3.99 3.98 4.07

0.85 0.86 0.85

3.84 3.98 4.08

1.38 .37 1.37

1.57 1.66 1.79

2.68 2.68 2.83

6.93 7.79 7.88

2.76 3.07 3.21

0.23 0.25 0.26

0.38 0.42 0.48

104

Year

SITC 67

SITC 68

SITC 69

SITC 71

SITC 72

SITC 73

SITC 74

SITC 75

SITC 76

SITC 77

SITC 78

SITC 79

SITC 81

SITC 82

1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

4.66 5.35 5.00 4.29 3.88 4.13 4.09 3.71 3.68 3.64 3.28 3.41 3.60 3.38 3.12 3.45 3.58 3.14 3.05 2.85 2.93 2.89 3.01 2.72

2.83 2.82 2.01 2.06 2.02 1.97 2.18 2.58 1.97 1.83 2.10 2.03 1.99 1.90 1.94 2.32 2.44 2.18 1.99 1.82 1.76 1.79 2.02 1.85

2.02 1.97 2.10 2.05 2.17 2.21 2.01 1.98 2.01 2.08 2.00 1.87 1.88 1.01 1.98 1.91 1.98 1.99 2.03 2.08 2.04 2.04 2.06 2.01

1.77 1.51 1.85 1.84 1.84 2.07 1.84 1.79 1.96 2.13 2.12 2.09 2.18 2.26 2.27 2.34 2.43 2.37 2.40 2.47 2.50 2.49 2.36 2.37

3.91 3.56 4.21 3.87 3.63 3.74 3.45 3.45 3.62 3.58 3.24 3.16 3.49 3.74 3.68 3.65 3.79 3.79 3.67 3.60 3.62 3.58 3.53 3.51

0.77 0.68 0.82 0.76 0.69 0.79 0.74 0.75 0.77 0.69 0.62 0.60 0.69 0.85 0.80 0.79 0.82 0.82 0.76 0.67 0.63 0.63 0.68 0.71

3.85 3.53 4.11 3.78 3.81 3.45 3.16 3.13 3.14 3.18 3.01 2.89 3.09 3.36 3.35 3.58 3.63 3.65 3.68 3.72 3.71 3.79 3.80 3.80

1.29 1.08 1.09 1.06 1.08 1.22 1.24 1.36 1.44 1.65 2.09 2.56 2.83 3.15 3.43 3.66 3.76 3.71 3.88 4.01 4.29 4.49 4.72 5.01

1.69 1.45 1.60 2.03 1.95 2.18 1.98 2.03 2.23 2.24 2.47 2.67 2.79 3.07 3.05 3.04 3.07 3.12 3.26 3.31 3.53 3.88 3.77 3.70

2.82 2.59 2.72 2.95 3.05 3.51 3.26 3.24 3.29 3.48 3.93 4.33 4.28 4.81 5.04 5.44 5.62 5.60 5.95 6.37 7.06 7.85 8.54 8.43

7.20 5.96 6.87 7.24 7.46 7.85 7.22 6.79 7.05 7.36 7.64 7.96 8.82 9.86 9.81 9.44 9.20 9.24 9.31 9.61 9.33 9.38 8.89 8.79

3.17 2.71 3.10 3.08 2.85 2.78 2.51 2.66 3.05 3.31 3.29 3.19 3.20 2.91 2.62 2.67 2.88 3.12 3.45 3.39 3.08 2.81 2.39 2.42

0.25 0.20 0.20 0.20 0.22 0.24 0.22 0.22 0.21 0.21 0.21 0.20 0.21 0.25 0.27 0.29 0.29 0.30 0.32 0.35 0.36 0.35 0.33 0.33

0.50 0.42 0.46 0.51 0.54 0.59 0.57 0.56 0.57 0.59 0.60 0.60 0.65 0.76 0.80 0.83 0.85 0.88 0.90 0.94 0.96 0.98 0.95 0.97

Year

SITC83 SITC84 SITC85 SITC87 SITC88 SITC89 SITC93 SITC94 SITC95 SITC97 SITC99 Total

1970

0.10

1.78

0.58

1.17

1.14

2.65

0.93

0.01

0.33

0.00

6.76

1971

0.11

1.95

0.64

1.16

1.18

2.71

0.95

0.01

0.33

0.00

5.69

1972

0.12

2.12

0.70

1.15

1.24

2.78

0.90

0.01

0.25

0.00

4.95

1973

0.12

2.00

0.60

1.07

1.20

2.64

0.84

0.01

0.19

0.00

5.21

1974

0.10

1.65

0.50

0.94

1.05

2.19

0.90

0.01

0.17

0.00

3.93

324, 138, 151 355, 592, 073 422, 615, 626 586, 683, 936 837, 929, 189

105

Year

SITC83 SITC84 SITC85 SITC87 SITC88 SITC89 SITC93 SITC94 SITC95 SITC97 SITC99 Total

1975

0.11

1.80

0.57

1.07

1.10

2.22

1.01

0.01

0.25

0.00

4.15

1976

0.13

1.97

0.58

1.03

1.16

2.19

0.90

0.01

0,22

0.02

4.31

1977

0.14

1.95

0.59

1.05

1.23

2.27

1.01

0.01

0.22

0.04

4.62

1978

0.16

2.05

0.63

1.09

1.35

2.33

1.11

0.01

0.26

0.23

4.59

1979

0.16

1.98

0.63

1.08

1.24

2.33

1.25

0.01

0.24

0.50

4.64

1980

0.15

1.97

0.61

1.10

1.25

2.36

1.84

0.01

0.21

0.46

0.67

1981

0.16

2.07

0.61

1.19

1.28

2.41

1.61

0.01

0.24

0.44

0.54

1982

0.17

2.17

0.68

1.28

1.24

2.57

1.53

0.01

0.36

0.37

0.65

1983

0.18

2.22

0.71

1.34

1.29

2.61

1.71

0.01

0.32

0.32

0.70

1984

0.18

2.40

0.73

1.37

1.29

2.72

1.71

0.01

0.27

0.36

0.39

1985

0.19

2.46

0.78

1.52

1.35

2.87

1.30

0.01

0.25

0.39

1.41

1986

0.22

2.88

0.87

1.67

1.54

3.31

1.34

0.01

0.22

0.69

1.26

1987

0.24

3.13

0.85

1.64

1.54

3.48

1.61

0.01

0.25

0.40

1.72

1988

0.24

3.05

0.82

1.65

1.47

3.63

1.79

0.01

0.20

0.51

2.80

1989

0.26

3.12

0.81

1.68

1.45

3.87

1.44

0.01

0.19

0.44

1.41

887, 738, 423 994, 699, 328 1,147, 828, 669 1,348, 163, 195 1,687, 032, 140 2,013, 836, 384 1,976, 241, 018 1,865, 763, 275 1,809, 616, 840 1,915, 109, 990 1,969, 663, 908 2,141, 205, 894 2,500, 181, 583 2,858, 988, 977 3,063, 461, 563

106

Year

SITC83 SITC84 SITC85 SITC87 SITC88 SITC89 SITC93 SITC94 SITC95 SITC97 SITC99 Total

1990

0.25

3.19

0.85

1.68

1.44

4.03

1.96

0.01

0.19

0.42

0.72

1991

0.26

3.45

0.86

1.73

1.49

4.04

2.01

0.01

0.16

0.46

0.49

1992

0.29

3.68

0.89

1.78

1.49

4.31

2.14

0.01

0.17

0.41

0.50

1993

0.30

3.65

0.94

1.78

1.51

4.33

2.37

0.01

0.16

0.54

0.31

1994

0.31

3.55

0.91

1.76

1.48

4.28

1.96

0.01

0.16

0.59

0.35

1995

0.30

3.25

0.83

1.76

1.44

4.09

2.46

0.01

0.14

0.42

0.37

1996

0.29

3.24

0.82

1.80

1.39

4.03

2.41

0.01

0.16

0.51

0.42

3,469, 610, 629 3,569, 732, 012 3,862, 838, 722 3,957, 481, 578 4,378, 896, 378 5,214, 774, 301 5,493, 900, 565

Note Total world exports in US$ ’000 are reported in the last column. Table A.2 Commodity-wise shares (%) in India’s total exports Year

SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC1 SITC1 SITC2 0 1 2 3 4 5 6 7 8 9 1 2 1

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

0.00 0.00 0.00 0.00 0.00 0.01 0.03 0.14 0.07 0.06 0.14 0.31 0.09 0.08 0.04 0.02

0.21 0.10 0.14 0.23 0.20 0.20 0.37 0.44 0.44 0.68 0.88 1.32 0.80 0.74 0.70 0.68

0.00 0.01 0.03 0.00 0.00 0.00 0.01 0.04 0.02 0.02 0.05 0.09 0.05 0.03 0.03 0.03

2.02 2.32 2.93 3.24 2.40 3.07 3.56 3.19 3.87 4.63 3.25 4.86 4.13 3.70 3.26 3.74

0.44 0.24 1.51 2.25 0.73 0.36 0.42 0.44 2.80 2.85 2.56 2.09 3.28 1.29 1.69 2.35

4.88 4.92 4.43 3.93 3.95 3.69 3.47 3.75 2.47 2.96 3.43 2.70 3.51 2.97 3.08 3.69

1.23 2.23 0.74 1.04 6.30 12.4 5.63 1.06 1.66 2.46 0.58 0.66 1.10 1.74 0.30 0.13

13.84 13.46 11.92 10.30 9.71 10.10 9.21 15.98 12.36 10.25 11.71 7.72 7.47 8.32 10.10 10.78

3.75 3.13 3.10 7.10 4.01 2.54 4.11 3.77 1.92 3.16 2.13 3.00 2.14 2.05 1.47 1.41

0.14 0.14 0.11 0.1 0.08 0.17 0.11 0.13 0.11 0.12 0.13 0.09 0.17 0.14 0.13 0.11

0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.01 0.01 0.03 0.01 0.02 0.01 0.01 0.01 0.01

2.15 2.78 3.34 2.83 2.69 2.87 2.03 2.29 2.22 2.00 2.26 1.43 2.97 1.82 1.51 1.55

0.34 0.05 0.05 0.06 0.02 0.01 0.01 0.01 0.00 0.01 0.01 0.03 0.00 0.00 0.00 0.00

107

Year

SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC1 SITC1 SITC2 0 1 2 3 4 5 6 7 8 9 1 2 1

1986 0.01 1987 0.00 1988 0.00 1989 0.06 1990 0.00 1991 0.00 1992 0.00 1993 0.01 1994 0.00 1995 0.01 1996 0.01

0.63 0.57 0.47 0.31 0.44 0.53 0.49 0.5 0.48 0.58 0.59

0.03 0.03 0.02 0.02 0.01 0.05 0.05 0.03 0.05 0.04 0.09

4.24 3.36 3.08 2.91 2.90 3.26 3.23 3.64 4.27 3.17 3.37

1.98 2.53 1.76 1.40 1.59 2.12 1.93 1.98 1.61 5.01 3.80

4.23 3.11 2.51 2.07 2.22 2.57 2.35 2.45 2.47 2.15 1.99

0.02 0.08 0.03 0.20 0.11 0.34 0.58 0.26 0.08 0.47 0.92

9.26 7.47 5.73 2.75 4.71 4.15 3.01 2.91 3.02 3.08 2.82

1.74 1.29 1.96 2.26 1.86 2.02 2.78 3.32 2.21 2.22 2.99

0.14 0.09 0.06 0.06 0.05 0.07 0.10 0.07 0.05 0.07 0.10

0.01 0.01 0.01 0.01 0.04 0.04 0.05 0.05 0.06 0.04 0.04

1.49 0.87 0.60 0.38 0.79 0.85 0.88 0.66 0.30 0.41 0.63

0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Year

SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC3 SITC3 SITC3 SITC4 SITC4 2 3 4 5 6 7 8 9 2 3 4 1 2

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

0.40 0.43 0.34 0.53 1.25 1.23 1.66 0.30 0.14 0.40 0.38 1.15 0.68 0.40 0.39 0.22 0.25 0.08 0.24 0.53 0.46 0.25 0.24 0.38 0.32

0.01 0.01 0.01 0.03 0.05 0.00 0.10 0.14 0.07 0.01 0.02 0.08 0.00 0.01 0.01 0.01 0.00 0.00 0.01 0.02 0.01 0.02 0.05 0.02 0.03

0.42 0.44 0.51 0.63 0.34 0.34 0.35 0.35 0.30 0.13 0.16 0.11 0.02 0.01 0.01 0.01 0.00 0.00 0.01 0.05 0.00 0.01 0.00 0.00 0.00

0.06 0.03 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.97 1.81 2.53 1.69 1.86 1.13 1.59 0.23 0.21 1.38 2.38 1.76 1.35 1.85 0.62 0.69 1.75 0.81 0.40 0.66 2.82 0.84 0.49 1.01 0.29

1.55 1.39 1.34 0.83 1.00 0.85 0.84 0.83 0.90 0.60 1.09 1.39 0.90 0.77 0.93 1.01 0.86 1.04 1.09 1.22 0.98 0.96 0.90 0.88 0.95

9.63 7.98 7.10 6.07 5.48 6.98 6.07 5.60 4.99 5.79 6.21 7.85 4.56 4.5 4.56 6.25 4.93 3.94 4.73 5.66 4.21 3.99 2.67 2.63 2.10

2.75 3.09 2.68 2.64 3.67 2.51 2.12 2.31 2.27 2.19 2.43 3.40 2.05 1.64 2.05 2.12 1.74 1.92 1.53 1.31 1.18 1.14 1.06 0.94 0.86

0.27 0.11 0.11 0.17 0.15 0.41 0.28 0.25 0.16 0.04 0.05 0.10 0.06 0.00 0.04 0.09 0.05 0.03 0.04 0.02 0.02 0.02 0.08 0.07 0.13

0.65 0.44 0.55 1.17 0.58 0.60 0.39 0.32 0.22 0.31 0.38 0.35 14.56 16.2 15.5 5.94 3.32 4.10 2.51 2.88 2.87 2.33 2.60 1.79 1.62

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.34 0.21

0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.40 0.40 1.00 1.63 1.24 0.84 0.89 0.50 0.21 0.65 0.34 0.49 0.30 0.30 0.47 0.43 0.24 0.09 0.04 0.39 0.22 0.31 0.22 0.36 0.47

108

Year

SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC3 SITC3 SITC3 SITC4 SITC4 2 3 4 5 6 7 8 9 2 3 4 1 2

1995 1996

0.49 0.56

Year

SITC4 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC6 SITC6 SITC6 SITC6 3 1 2 3 4 5 6 8 9 1 2 3 4

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

0.01 0.01 0.01 0.01 0.02 0.02 0.04 0.07 0.08 0.14 0.10 0.07 0.01 0.12 0.02 0.01 0.01 0.02 0.01 0.02 0.04 0.07 0.05 0.05 0.10 0.13 0.09

Year

SITC6 SITC6 SITC6 SITC6 SITC6 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 5 6 7 8 9 1 2 3 4 5 6 7 8

1970 1971 1972 1973

22.79 26.01 25.74 23.35

0.03 0.04

0.16 0.21 0.17 0.10 0.28 0.20 0.22 0.17 0.26 0.28 0.23 0.28 0.30 0.30 0.28 0.27 0.38 0.72 1.20 1.49 1.30 .72 1.55 1.64 2.12 2.27 2.71

3.05 3.68 4.46 4.60

0.00 0.01

0.27 0.27 0.27 0.28 0.55 0.51 0.38 0.34 0.31 0.29 0.30 0.33 0.20 0.19 0.29 0.21 0.25 0.23 0.30 0.28 0.33 0.33 0.36 0.30 0.32 0.35 0.38

6.52 3.37 2.07 2.29

0.00 0.00

0.40 0.40 0.49 0.38 0.66 0.53 0.61 0.74 0.56 0.69 0.87 0.40 0.73 0.69 0.68 0.68 0.97 1.23 1.24 0.84 1.30 1.45 1.45 1.39 1.44 1.13 1.31

0.78 0.69 0.53 0.43

0.27 1.42

0.55 0.58 0.47 0.56 0.68 0.65 0.46 0.55 0.93 1.08 1.45 0.32 1.26 1.60 1.99 1.45 1.29 1.51 1.84 0.54 2.53 2.72 2.09 2.18 2.22 2.29 2.43

1.54 1.81 1.55 1.31

0.87 0.83

0.48 0.54 0.52 0.44 0.53 0.42 0.34 0.45 0.36 0.43 1.13 0.38 1.69 0.45 0.62 0.62 0.63 0.63 0.81 0.3 1.34 1.44 0.51 0.68 0.71 0.53 0.54

0.42 0.48 0.61 0.57

2.18 2.08

0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.02 0.08 0.09 0.07

0.70 0.65 0.49 0.43

0.98 1.06

0.15 0.09 0.03 0.05 0.06 0.02 0.05 0.06 0.04 0.02 0.02 0.08 0.03 0.01 0.03 0.05 0.08 0.12 0.14 0.10 0.15 0.16 0.21 0.25 0.62 0.70 0.66

0.15 0.17 0.11 0.10

0.09 0.07

0.11 0.08 0.07 0.09 0.25 0.14 0.10 0.12 0.14 0.14 0.11 0.14 0.11 0.1 0.19 0.31 0.27 0.22 0.44 0.09 0.43 0.60 0.52 0.44 0.58 0.73 0.84

0.55 0.45 0.46 0.55

1.47 1.47

4.71 5.50 7.76 8.08 5.09 4.63 5.42 4.71 5.44 8.17 5.44 4.16 4.56 4.42 5.36 5.98 5.90 6.02 5.63 3.56 4.66 3.66 3.03 2.44 2.61 2.27 1.85

0.13 0.16 0.07 0.16

0.10 0.00

0.44 0.48 0.45 0.30 0.40 0.30 0.43 0.45 0.41 0.29 0.41 0.29 0.55 0.42 0.62 0.76 0.68 0.54 0.49 0.30 0.73 0.61 1.13 1.09 1.04 0.91 0.91

0.18 0.19 0.16 0.14

0.00 0.00

0.10 0.09 0.07 0.13 0.27 0.15 0.20 0.25 0.24 0.27 0.25 0.40 0.20 0.13 0.12 0.12 0.14 0.11 0.08 0.17 0.07 0.07 0.06 0.21 0.15 0.11 0.11

0.75 0.88 0.92 0.77

0.68 0.48

0.36 0.24 0.21 0.24 0.27 0.19 0.15 0.17 0.14 0.09 0.09 0.09 0.07 0.06 0.10 0.06 0.05 0.04 0.06 0.06 0.08 0.10 0.14 0.15 0.27 0.36 0.30

1.62 1.47 1.31 1.15

109

Year

SITC6 SITC6 SITC6 SITC6 SITC6 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 5 6 7 8 9 1 2 3 4 5 6 7 8

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

21.14 13.81 13.96 14.77 13.03 14.08 15.30 12.91 11.44 9.78 11.88 11.58 11.34 13.99 11.95 9.66 12.19 14.11 14.21 13.16 14.58 13.79 14.79

Year

SITC7 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 S1TC8 SITC9 SITC9 SITC9 Total 9 1 2 3 4 5 7 8 9 3 4 5

1970

0.62

0.03

0.04

0.04

1.80

0.76

0.06

0.43

1.74

0.37

0.06

0.03

1971

0.50

0.03

0.04

0.04

2.14

0.79

0.08

0.39

1.67

0.25

0.06

0.03

1972

0.34

0.03

0.04

0.12

2.79

0.71

0.08

0.34

1.74

0.21

0.07

0.03

1973

0.31

0.06

0.05

0.13

3.41

0.53

0.06

0.28

1.78

0.25

0.06

0.02

1974

0.25

0.09

0.09

0.11

4.20

0.61

0.07

0.25

2.07

0.19

0.04

0.02

4.12 3.83 5.73 8.86 12.41 8.11 8.42 11.20 6.97 12.86 10.20 13.37 16.38 16.45 21.28 21.48 15.67 14.49 15.95 17.6 16.73 16.15 13.67

2.41 2.61 6.85 6.19 4.26 2.26 1.13 1.12 0.72 0.49 0.63 0.51 0.45 0.79 1.35 1.87 1.54 1.93 2.57 3.63 2.93 3.24 3.16

2.28 4.71 4.14 1.83 1.88 0.28 0.23 0.21 0.25 0.22 0.15 0.38 0.19 0.15 0.45 0.48 0.52 0.73 1.02 0.59 0.66 0.50 0.67

1.94 2.01 2.34 2.89 3.52 3.39 2.89 3.22 2.44 1.99 1.67 1.36 1.30 1.42 1.68 1.87 1.86 1.80 2.33 2.08 1.84 1.79 1.91

0.84 0.92 0.76 0.94 1.32 1.03 1.17 0.90 1.07 0.71 0.69 0.64 0.67 0.57 0.66 0.63 0.70 0.71 0.75 0.77 0.57 0.54 0.66

0.73 1.24 0.71 0.71 0.87 0.85 0.87 0.95 0.77 0.91 1.09 1.08 1.18 1.06 1.17 0.46 1.36 1.08 0.92 0.74 0.84 0.80 0.90

0.17 0.19 0.25 0.34 0.32 0.49 0.42 0.33 0.34 0.34 0.30 0.60 0.55 0.48 0.45 0.11 0.31 0.25 0.23 0.20 0.19 0.18 0.17

0.71 0.82 0.64 0.77 0.87 0.73 0.87 1.24 0.72 0.70 0.75 0.66 0.62 0.63 0.61 0.61 0.68 0.60 0.66 0.66 0.66 0.61 0.74

0.17 0.14 0.07 0.03 0.02 0.01 0.02 0.20 0.02 0.08 0.03 0.21 0.41 0.35 0.69 0.32 0.58 0.32 0.35 0.51 0.59 0.70 0.86

0.18 0.22 0.20 0.17 0.15 0.11 0.13 0.18 0.21 0.08 0.05 0.05 0.10 0.13 0.16 0.15 0.17 0.20 0.21 0.21 0.26 0.43 0.40

1.15 1.33 1.19 1.27 1.34 1.19 1.48 0.90 1.53 1.03 1.03 1.32 1.32 1.52 1.45 0.46 1.34 1.50 1.00 1.01 1.10 1.32 1.56

1.43 1.86 1.51 1.78 2.25 1.85 2.72 1.30 2.10 1.40 1.35 1.36 1.31 1.53 1.58 0.77 1.90 2.48 2.76 2.55 2.72 2.76 2.60

2, 098, 149 2, 072, 865 2, 468, 013 2, 932, 263 3, 896, 522

110

Year

SITC7 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 S1TC8 SITC9 SITC9 SITC9 Total 9 1 2 3 4 5 7 8 9 3 4 5

1975

0.59

0.05

0.07

0.17

4.49

0.55

0.07

0.26

1.75

0.20

0.04

0.01

1976

0.43

0.05

0.08

0.12

6.25

0.60

0.07

0.23

1.87

0.23

0.04

0.01

1977

0.13

0.07

0.11

0.15

5.72

0.45

0.08

0.24

2.06

0.24

0.03

0.01

1978

0.20

0.17

0.10

0.23

6.68

0.49

0.07

0.26

2.73

0.42

0.01

0.01

1979

0.48

0.14

0.08

0.22

7.77

0.48

0.11

0.37

3.01

0.35

0.00

0.00

1980

0.45

0.13

0.08

0.23

7.93

0.60

0.21

0.44

2.94

0.35

0.00

0.00

1981

0.43

0.62

0.14

0.48

11.64

0.74

0.23

0.23

2.36

0.46

0.01

0.02

1982

0.20

0.10

0.06

0.31

6.76

0.39

0.16

0.37

2.90

0.22

0.00

0.00

1983

0.12

0.06

0.06

0.32

7.63

0.35

0.12

0.47

2.90

0.25

0.00

0.00

1984

0.25

0.06

0.05

0.36

8.41

0.41

0.16

0.32

2.71

0.24

0.00

0.00

1985

0.31

0.07

0.04

0.45

10.24

0.46

0.18

0.34

2.83

0.16

0.00

0.00

1986

0.30

0.03

0.02

0.57

11.34

0.65

0.26

0.33

2.78

0.31

0.00

0.00

1987

0.10

0.03

0.01

0.70

12.46

0.93

0.41

0.27

1.27

1.94

0.00

0.00

1988

0.10

0.07

0.03

0.64

11.39

0.78

0.31

0.34

1.67

2.10

0.00

0.00

1989

0.28

0.15

0.08

0.82

12.89

0.72

0.22

0.11

1.67

0.78

0.01

0.01

4, 513, 354 5, 446, 915 6, 173, 444 6, 659, 495 7, 557, 064 8, 097, 313 7, 140, 093 9, 155, 222 9, 736, 200 10, 150, 981 9, 570, 667 10, 367, 654 12, 554, 777 14, 108, 901 15, 859, 504

111

Year

SITC7 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 S1TC8 SITC9 SITC9 SITC9 Total 9 1 2 3 4 5 7 8 9 3 4 5

1990

0.28

0.08

0.02

0.75

14.18

1.04

0.27

0.16

1.91

2.01

0.00

0.00

1991

0.25

0.07

0.03

0.91

14.22

1.02

0.26

0.18

2.50

1.57

0.00

0.00

1992

0.08

0.07

0.05

1.04

15.07

1.13

0.17

0.17

2.55

1.73

0.00

0.00

1993

0.07

0.08

0.05

0.90

13.44

.26

0.20

0.16

3.33

1.64

0.00

0.00

1994

0.15

0.09

0.04

0.97

14.15

1.31

0.20

0.18

3.72

1.70

0.00

0.00

1995

0.06

0.09

0.04

0.98

13.05

1.09

0.20

0.18

3.58

1.62

0.00

0.00

1996

0.18

0.09

0.04

0.73

12.67

1.04

0.22

0.21

3.41

1.76

0.00

0.00

18, 544, 645 18, 296, 490 21, 215, 543 22, 792, 387 26, 246, 620 31, 755, 862 34, 376, 018

Note India’s total exports in US$ ’000 are reported in the last column. Table A. 3 India’s share (%) in commodity-wise total world exports Year

SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC1 SITC1 S1TC2 0 1 2 3 4 5 6 7 8 9 1 2 1

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

0.00 0.00 0.00 0.00 0.00 0.01 0.06 0.26 0.10 0.09 0.20 0.39 0.15 0.15 0.08 0.04

0.09 0.04 0.05 0.07 0.09 0.09 0.18 0.22 0.19 0.27 0.34 0.44 0.35 0.38 0.40 0.35

0.00 0.01 0.02 0.00 0.00 0.00 0.01 0.03 0.01 0.01 0.03 0.04 0.03 0.02 0.02 0.03

1.79 1.73 2.03 1.99 1.82 2.42 2.72 2.29 2.31 2.56 1.85 2.31 2.52 2.41 2.21 2.13

0.12 0.06 0.37 0.37 0.11 0.06 0.09 0.11 0.65 0.61 0.46 0.31 0.73 0.31 0.39 0.61

1.62 1.48 1.30 1.07 1.30 1.22 1.24 1.33 0.81 0.93 1.01 0.69 1.18 1.12 1.14 1.26

1.07 1.65 0.48 0.64 2.62 4.11 3.07 0.65 1.06 2.42 0.34 0.34 1.07 2.05 0.40 0.19

5.09 5.14 4.89 3.89 4.15 4.75 3.54 4.46 3.68 3.21 3.97 3.05 3.70 4.06 4.49 4.36

3.50 2.69 2.81 4.28 3.32 2.63 3.82 3.14 1.62 2.53 1.58 1.84 1.82 1.55 1.39 1.43

0.49 0.43 0.38 0.28 0.20 0.45 0.33 0.35 0.27 0.28 0.28 0.16 0.38 0.34 0.31 0.24

0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.01 0.02 0.01 0.01 0.01 0.01

2.46 2.92 3.39 2.91 2.92 3.20 2.50 2.85 2.34 2.21 2.43 1.24 3.05 2.17 1.90 1.72

0.53 0.07 0.06 0.07 0.03 0.02 0.01 0.02 0.01 0.01 0.02 0.05 0.00 0.00 0.00 0.00

112

Year

SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC0 SITC1 SITC1 S1TC2 0 1 2 3 4 5 6 7 8 9 1 2 1

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

0.02 0.01 0.00 0.12 0.00 0.00 0.01 0.02 0.01 0.02 0.02

Year

SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC3 SITC3 SITC3 SITC4 SITC4 2 3 4 5 6 7 8 9 2 3 4 1 2

1970 0.36 1971 0.35 1972 0.28 1973 0.31 1974 0.76 1975 0.99 1976 1.48 1977 0.23 1978 0.11 1979 0.30 1980 0.29 1981 0.72 1982 0.6 1983 0.39 1984 0.36 1985 0.23 1986 0.28 1987 0.10 1988 0.30 1989 0.79 1990 0.79 1991 0.44 1992 0.46 1993 0.82 1994 0.67

0.29 0.28 0.23 0.16 0.23 0.25 0.25 0.28 0.28 0.37 0.40

0.01 0.01 0.01 0.03 0.04 0.00 0.11 0.15 0.06 0.01 0.01 0.07 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.04 0.02 0.03 0.10 0.04 0.08

0.02 0.02 0.01 0.01 0.01 0.04 0.04 0.02 0.05 0.04 0.10

0.19 0.19 0.21 0.18 0.12 0.18 0.17 0.16 0.13 0.05 0.06 0.05 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.03 0.00 0.01 0.00 0.00 0.00

1.97 1.56 1.41 1.46 1.52 1.54 1.68 1.98 2.43 2.01 2.24

0.04 0.03 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.64 0.99 0.61 0.47 0.64 0.85 0.79 0.94 0.88 2.63 1.90

0.75 0.67 0.89 0.46 0.63 0.52 0.74 0.11 0.10 0.64 1.01 0.69 0.76 1.13 0.36 0.38 1.08 0.50 0.24 0.42 2.27 0.70 0.49 1.24 0.32

1.33 1.02 0.84 0.74 0.79 0.83 0.85 0.94 1.00 0.92 0.92

1.40 1.20 1.25 0.73 0.71 0.65 0.82 0.83 0.86 0.53 0.83 0.95 0.88 0.85 1.00 0.97 0.87 1.25 1.25 1.51 1.38 1.38 1.54 1.67 1.62

0.02 0.12 0.05 0.30 0.17 0.54 0.93 0.46 0.16 0.79 1.69

2.50 2.29 2.32 1.64 1.40 2.09 2.06 2.14 1.90 1.69 1.57 2.02 1.74 1.92 1.82 2.32 2.05 1.82 1.88 2.26 2.11 2.09 1.66 1.88 1.63

3.33 3.90 3.39 1.97 4.01 3.53 2.99 2.92 2.60 2.79 2.84

3.73 3.77 3.25 2.87 4.37 3.43 3.03 3.13 2.80 2.61 2.74 3.40 2.75 2.32 2.92 2.69 1.97 2.14 1.71 1.58 .51 .36 1.36 1.35 1.30

1.53 1.25 1.67 2.17 2.18 2.20 3.10 3.93 2.94 3.27 4.02

0.19 0.08 0.08 0.12 0.07 0.18 0.15 0.15 0.10 0.02 0.02 0.04 0.03 0.00 0.03 0.05 0.03 0.02 0.03 0.02 0.02 0.02 0.08 0.09 0.19

0.27 0.18 0.13 0.13 0.11 0.14 0.17 0.12 0.09 0.13 0.18

0.06 0.03 0.04 0.06 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.37 0.49 0.50 0.21 0.18 0.24 0.21 0.22 0.18 0.16 0.23 0.17 0.19

0.01 0.01 0.01 0.01 0.03 0.03 0.04 0.05 0.05 0.04 0.04

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.24 0.21

1.68 1.02 0.72 0.44 0.82 0.80 0.85 0.72 0.34 0.53 0.78

0.01 0.02 0.02 0.10 0.01 0.05 0.01 0.00 0.02 0.04 0.02 0.00 0.02 0.42 0.07 0.02 0.01 0.05 0.01 0.00 0.02 0.01 0.02 0.01 0.02

0.00 0.00 0.00 0.03 0.00 0.01 0.00 0.01 0.00 0.00 0.00

0.55 0.44 1.37 1.85 0.94 0.85 1.25 0.57 0.22 0.60 0.33 0.46 0.41 0.4 0.46 0.41 0.36 0.16 0.07 0.65 0.41 0.53 0.41 0.72 0.77

113

Year

SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC2 SITC3 SITC3 SITC3 SITC4 SITC4 2 3 4 5 6 7 8 9 2 3 4 1 2

1995 1996

1.17 1.17

Year

SITC4 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC5 SITC6 SITC6 SITC6 SITC6 3 1 2 3 4 5 6 8 9 1 2 3 4

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

0.04 0.07 0.09 0.08 0.11 0.20 0.33 0.51 0.59 0.85 0.57 0.39 0.11 1.03 0.12 0.08 0.09 0.13 0.06 0.16 0.32 0.66 0.46 0.41 0.76 0.93 0.81

Year

SITC6 SITC6 SITC6 SITC6 SITC6 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 5 6 7 8 9 1 2 3 4 5 6 7 8

1970 1971 1972 1973

3.75 3.69 3.66 2.84

0.07 0.09

0.07 0.08 0.07 0.03 0.07 0.06 0.07 0.06 0.08 0.07 0.05 0.06 0.09 0.08 0.07 0.06 0.09 0.16 0.25 0.34 0.33 0.42 0.41 0.45 0.58 0.62 0.81

1.04 1.08 1.22 1.04

0.00 0.01

0.18 0.16 0.18 0.16 0.27 0.28 0.22 0.19 0.15 0.13 0.13 0.13 0.10 0.10 0.15 0.10 0.12 0.11 0.16 0.17 0.23 0.22 0.26 0.23 0.29 0.32 0.38

0.86 0.42 0.27 0.25

0.00 0.00

0.52 0.44 0.51 0.35 0.62 0.64 0.71 0.88 0.62 0.71 0.85 0.36 0.86 0.86 0.87 0.76 0.92 1.14 .06 0.77 1.23 1.32 1.37 1.35 1.44 1.20 1.48

0.14 0.15 0.12 0.08

0.32 1.82

0.38 0.35 0.29 0.31 0.41 0.40 0.32 0.38 0.54 0.63 0.76 0.15 0.67 0.95 1.21 0.75 0.59 0.71 0.85 0.27 1.23 1.16 0.86 0.87 0.91 0.96 1.04

0.45 0.48 0.43 0.33

1.91 1.95

0.76 0.76 0.74 0.55 0.66 0.57 0.49 0.63 0.43 0.48 1.13 0.34 1.97 0.57 0.77 0.67 0.61 0.60 0.74 0.27 1.17 1.17 0.41 0.55 0.57 0.45 0.46

0.15 0.14 0.18 0.16

1.50 1.60

0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01 0.03 0.16 0.17 0.12

0.11 0.09 0.07 0.06

1.60 1.85

0.08 0.04 0.01 0.02 0.02 0.01 0.02 0.03 0.01 0.01 0.01 0.02 0.01 0.00 0.01 0.02 0.02 0.03 0.03 0.03 0.04 0.04 0.06 0.08 0.18 0.19 0.20

0.12 0.12 0.07 0.06

0.13 0.11

0.08 0.05 0.05 0.05 0.13 0.08 0.07 0.08 0.08 0.06 0.04 0.05 0.05 0.06 0.10 0.15 0.12 0.10 0.20 0.05 0.22 0.28 0.26 0.23 0.30 0.38 0.45

0.09 0.07 0.07 0.07

0.17 0.14

8.84 9.17 10.54 10.28 8.41 8.20 8.99 7.59 7.75 9.14 6.84 5.07 7.18 7.33 8.01 7.90 7.11 6.95 6.71 4.48 5.95 4.69 3.91 3.11 3.22 3.01 2.53

0.06 0.07 0.03 0.06

0.09 0.00

0.48 0.45 0.41 0.25 0.31 0.23 0.37 0.39 0.32 0.21 0.27 0.17 0.44 0.37 0.52 0.57 0.47 0.37 0.32 0.21 0.54 0.42 0.80 0.81 0.80 0.73 0.74

0.07 0.06 0.05 0.04

0.00 0.02

0.12 0.10 0.07 0.10 0.26 0.17 0.22 0.27 0.24 0.24 0.22 0.32 0.22 0.14 0.15 0.14 0.14 0.10 0.07 0.17 0.07 0.07 0.06 0.19 0.14 0.11 0.12

0.18 0.19 0.19 0.14

1.07 0.86

0.13 0.08 0.07 0.07 0.07 0.07 0.06 0.06 0.05 0.03 0.03 0.02 0.02 0.02 0.04 0.02 0.01 0.01 0.01 0.02 0.02 0.03 0.04 0.05 0.09 0.11 0.10

0.15 0.11 0.10 0.08

114

Year

SITC6 SITC6 SITC6 SITC6 SITC6 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 SITC7 5 6 7 8 9 1 2 3 4 5 6 7 8

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

2.93 2.28 2.42 2.64 2.07 2.10 2.17 1.65 2.02 1.83 2.18 1.90 1.68 2.07 1.84 1.55 1.99 2.14 2.28 2.28 2.58 2.59 3.01

Year

SITC7 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC9 SITC9 SITC9 Total 9 1 2 3 4 5 7 8 9 3 4 5

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982

0.15 0.09 0.06 0.05 0.04 0.10 0.08 0.03 0.04 0.09 0.07 0.05 0.03

1.10 1.10 1.67 2.29 2.59 1.61 1.54 2.23 1.90 3.67 2.98 3.62 3.99 4.05 4.80 4.93 3.83 3.47 4.15 4.77 4.46 4.55 4.19

0.07 0.07 0.06 0.12 0.20 0.13 0.13 0.18 0.35 0.29 0.24 1.07 0.24

0.21 0.27 0.87 0.86 0.51 0.25 0.12 0.11 0.10 0.08 0.10 0.07 0.06 0.13 0.19 0.27 0.26 0.32 0.49 0.71 0.61 0.65 0.73

0.07 0.06 0.04 0.05 0.10 0.08 0.09 0.11 0.08 0.06 0.06 0.09 0.05

0.38 1.19 1.10 0.49 0.47 0.06 0.04 0.04 0.07 0.06 0.04 0.09 0.05 0.04 0.10 0.10 0.13 0.19 0.31 0.19 0.22 0.15 0.23

0.29 0.21 0.59 0.55 0.54 0.77 0.53 0.58 0.73 0.64 0.62 1.11 0.89

0.46 0.49 0.62 0.72 0.79 0.76 0.59 0.58 0.58 0.54 0.47 0.35 0.31 0.36 0.43 0.49 0.50 0.45 0.62 0.59 0.54 0.53 0.59

0.65 0.64 0.77 0.85 1.19 1.27 1.74 1.58 1.61 1.76 1.62 2.03 1.53

0.26 0.25 0.23 0.27 0.32 0.25 0.26 0.17 0.25 0.18 0.17 0.14 0.14 0.13 0.14 0.14 0.16 0.15 0.17 0.18 0.14 0.14 0.17

0.85 0.72 0.59 0.44 0.57 0.49 0.57 0.41 0.39 0.34 0.40 0.44 0.28

0.10 0.15 0.10 0.11 0.12 0.11 0.10 0.09 0.11 0.15 0.18 0.15 0.15 0.14 0.16 0.06 0.19 0.15 0.14 0.12 0.14 0.14 0.16

0.03 0.04 0.04 0.03 0.03 0.04 0.04 0.04 0.03 0.05 0.07 0.07 0.06

0.12 0.12 0.18 0.27 0.20 0.30 0.22 0.15 0.24 0.30 0.26 0.42 0.32 0.30 0.28 0.07 0.21 0.17 0.19 0.19 0.18 0.16 0.15

0.24 0.19 0.16 0.12 0.11 0.12 0.11 0.11 0.1 0.13 0.14 0.07 0.15

0.09 0.10 0.09 0.11 0.12 0.10 0.11 0.14 0.11 0.13 0.14 0.10 0.09 0.09 0.08 0.09 0.10 0.08 0.10 0.10 0.10 0.10 0.12

0.43 0.36 0.37 0.34 0.44 0.40 0.47 0.49 0.58 0.58 0.50 0.35 0.55

0.07 0.07 0.03 0.02 0.01 0.00 0.01 0.05 0.01 0.02 0.01 0.04 0.06 0.05 0.09 0.04 0.08 0.04 0.05 0.07 0.08 0.09 0.11

0.26 0.16 0.13 0.15 0.10 0.10 0.14 0.13 0.19 0.13 0.08 0.10 0.07

0.06 0.07 0.05 0.05 0.03 0.02 0.03 0.03 0.05 0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.03 0.03 0.03 0.03 0.04 0.07 0.07

4.09 2.50 2.93 2.31 2.11 2.45 2.36 1.88 0.80 0.15 0.05 0.53 0.14

0.21 0.25 0.22 0.22 0.19 0.16 0.18 0.10 0.22 0.14 0.13 0.15 0.13 0.15 0.13 0.04 0.13 0.13 0.09 0.08 0.08 0.09 0.12

0.06 0.06 0.06 0.05 0.05 0.03 0.03 0.02 0.01 0.00 0.00 0.02 0.00

0.11 0.14 0.11 0.13 0.14 0.11 0.16 0.07 0.14 0.10 0.09 0.07 0.06 0.08 0.08 0.04 0.11 0.14 0.16 0.16 0.17 0.19 0.19

0.65 0.58 0.58 0.50 0.47 0.51 0.55 0.54 0.49 0.45 0.40 0.36 0.49

115

Year

SITC7 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC8 SITC9 SITC9 SITC9 Total 9 1 2 3 4 5 7 8 9 3 4 5

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

0.02 0.04 0.05 0.05 0.02 0.02 0.05 0.05 0.04 0.01 0.01 0.03 0.01 0.05

0.15 0.17 0.15 0.06 0.06 0.13 0.26 0.14 0.11 0.12 0.13 0.15 0.16 0.18

0.05 0.04 0.03 0.01 0.01 0.02 0.05 0.01 0.02 0.03 0.03 0.03 0.02 0.03

0.95 .05 1.15 1.25 1.45 1.30 1.66 1.62 1.79 1.98 1.75 1.89 2.00 1.58

1.84 1.86 2.02 1.90 2.00 1.85 2.14 2.38 2.11 2.25 2.12 2.39 2.45 2.44

0.27 0.30 0.29 0.36 0.55 0.47 0.46 0.66 0.61 0.69 0.78 0.86 0.80 0.79

0.05 0.06 0.06 0.08 0.13 0.09 0.07 0.09 0.08 0.05 0.07 0.07 0.07 0.08

0.19 0.13 0.12 0.10 0.09 0.11 0.04 0.06 0.06 0.06 0.06 0.07 0.07 0.09

0.6 0.53 0.48 0.41 0.18 0.23 0.22 0.25 0.32 0.32 0.44 0.52 0.53 0.53

0.08 0.07 0.06 0.11 0.6 0.58 0.28 0.55 0.40 0.44 0.4 0.52 0.40 0.46

Note The last column reports India’s share in total world exports. Table A.4 Trends in commodity-wise shares in total world exports–1970-96 Commodity Constant

t-value Time

t-value

Rho

t-value DW

Adj. R sq

SITC00 SITC0l SITC02 SITC03 SITC04 SITC05 SITC06 SITC07 SITC08 SITC09 SITC11 SITC12 SITC21 SITC22 SITC23 SITC24 SITC25 SITC26 SITC27 SITC28

13.80 14.03 2.86 10.08 13.55 14.96 7.17 11.03 26.93 6.54 9.96 10.03 13.47 28.61 26.60 18.37 13.01 16.07 41.72 18.24

−5.03 −3.32 −3.99 3.04 −4.97 −2.43 −3.32 -4.48 −5.54 4.61 −0.20 −0.32 −5.37 −12.09 −10.28 −5.69 −3.59 −7.46 −14.79 −7.45

0.471 0.522 0.367 0.667 0.625 0.739 0.578 0.595 — 0.817 0.753 0.770 0.449 0.318 0.233 0.401 0.507 0.629 0.285 0.600

2.72 3.12 2.01 4.57 4.08 5.60 3.62 3.78 — 7.22 5.84 6.16 2.56 1.71 1.22 2.23 3.00 4.12 1.51 3.82

0.676 0.486 0.529 0.624 0.785 0.323 0.606 0.672 0.533 0.806 −0.033 −0.038 0.701 0.912 0.857 0.708 0.476 0.865 0.931 0.864

44,765.0 142,550.0 77,541.0 69,447.0 282,910.0 179,940.0 96,361.0 168,240.0 68,438.0 15,312.0 61,770.0 49,809.0 41,084.0 80,610.0 65,218.0 140,560.0 71,948.0 161,300.0 70,029.0 212,850.0

−1001.7 −2067.8 −663.7 1262.6 −6279.4 −1735.4 −2720.0 −4155.5 −878.8 621.1 −71.5 −94.2 −1007.6 −2108.5 −1564.1 -2684.9 −1217.9 −4533.1 −1539.3 −5282.4

1.82 1.95 1.50 1.46 1.44 1.84 1.85 1.24 1.98 1.26 1.93 1.72 1.63 1.87 1.67 1.71 1.76 1.72 1.79 1.27

0.22 0.14 0.16 0.17 0.24 0.17 0.71 0.03 0.00 0.00 0.00 0.02 0.00 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.01 0.00 0.00 0.01

0.54 0.53 0.49 0.48 0.5 0.49 0.52 0.53 0.51 0.55 0.58 0.60 0.61 0.63

116

Commodity Constant SITC29 SITC32 SITC33 SITC34 SITC35 SITC41 SITC42 SITC43 SITC51 SITC52 SITC53 SITC54 SITC55 SITC56 SITC58 SITC59 SITC61 SITC62 SITC63 SITC64 SITC65 SITC66 SITC67 SITC68 SITC69 SITC71 SITC72 SITC73 SITC74 SITC75 SITC76 SITC77

t-value Time

45,796.0 12.98 101,890.0 12.27 1,205,900.0 2.56 46,137.0 1.02 9591.2 5.85 14,117.0 24.68 51,431.0 14.84 7463.7 16.20 146,690.0 19.31 101,830.0 10.95 47,250.0 9.08 78,986.0 5.44 35,620.0 6.08 56,371.0 11.18 112,980.0 11.97 82,338.0 27.25 31,300.0 11.18 56,893.0 24.66 48,504.0 11.20 160,130.0 9.45 372,490.0 11.17 193,990.0 17.10 493,780.0 29.47 286,680.0 12.08 211,940.0 40.64 170,700.0 22.97 387,970.0 31.06 80,974.0 17.82 366,740.0 12.39 93,381.0 1.76 129,570.0 12.37 192,700.0 2.62

−340.3 −1972.9 −12,819.0 2497.2 330.2 −453.6 −774.9 −30.1 3006.4 −1126.0 267.9 2135.3 1261.4 −733.5 3610.7 1215.8 484.7 744.1 274.1 624.7 −2751.3 678.6 −8537.8 −4407.0 −549.8 2889.2 −1460.9 −451.0 −330.2 14,166.0 9068.5 22,099.0

t-value

Rho

t-value DW

Adj. R sq

−1.70 −3.93 −0.49 1.04 3.38 −12.78 −3.61 −1.05 6.41 −2.19 0.91 2.76 4.44 −2.37 6.29 6.63 2.84 5.33 1.05 0.66 −1.46 0.98 −8.28 −3.04 −1.72 6.31 −1.90 −1.62 −0.19 5.82 14.12 5.94

0.845 0.631 0.861 0.902 0.719 0.252 0.312 0.301 0.390 0.870 0.845 0.899 0.939 0.465 0.597 0.612 0.550 0.641 0.651 0.867 0.849 0.542 0.441 0.567 0.516 0.427 0.442 0.518 0.807 0.950 0.501 0.926

8.05 4.15 8.64 10.66 5.27 1.33 1.68 1.61 2.16 9.02 8.05 10.49 13.88 2.68 3.80 3.94 3.36 4.26 4.37 8.85 8.18 3.29 2.50 3.51 3.07 2.41 2.52 3.08 6.96 15.57 2.95 12.49

0.052 0.650 0.195 0.040 0.624 0.908 0.472 0.024 0.769 0.379 0.210 0.644 0.806 0.363 0.830 0.854 0.450 0.773 0.070 0.281 0.095 0.059 0.849 0.416 0.155 0.781 0.188 0.157 −0.022 0.884 0.951 0.885page type="natural" move="word" number="140" id="p20008b459980140"/>

1.13 1.58 1.74 0.82 2.25 2.03 1.68 1.83 1.09 1.57 1.46 1.52 1.15 1.78 1.99 1.60 1.65 1.91 1.71 1.96 1.50 1.48 1.57 1.70 1.75 1.84 1.98 1.70 1.09 3.54 1.82 1.33

Commodity Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC78 SITC79 SITC81 SITC82

12.68 13.72 6.41 9.28

9718.9 −725.4 422.1 2359.0

3.04 −0.55 2.37 10.65

0.693 0.638 0.880 0.725

4.91 4.23 9.46 5.37

1.33 1.24 1.02 1.44

0.616 −0.023 0.574 0.942

677,480.0 301,130.0 20,831.0 34,529.0

117

Commodity Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC83 SITC84 SITC85 SITC87 SITC88 SITC89 SITC93 SITC94 SITC95 S1TC97 SITC99

7.44 6.93 11.85 8.48 21.03 5.94 6.41 5.78 8.94 0.88 6.86

831.6 6649.7 1244.6 2736.3 1403.2 5925.2 5872.1 −12.7 −500.4 1723.2 −23,044.0

13.28 4.92 4.47 4.05 4.31 2.87 8.67 −1.30 −2.47 2.64 −4.57

0.621 0.794 0.660 0.877 0.581 0.915 0.282 0.804 0.604 0.401 0.641

4.05 6.66 4.48 9.29 3.64 11.54 1.50 6.89 3.86 1.96 4.25

1.62 1.34 1.41 1.54 1.56 1.14 2.02 1.07 1.70 2.20 1.82

0.958 0.843 0.764 0.848 0.716 0.740 0.825 0.228 0.324 0.351 0.707

7686.8 160,760.0 54,695.0 104,090.0 112,520.0 236,790.0 70,051.0 977.7 29,824.0 10,484.0 572,420.0

Notes 1 The table reports the results of linear time trends, estimated by ordinary least squares, with correction for autocorrelation where required. 2 The shares have all been scaled up by a factor of 10,000,000.0. The coefficients have to be interpreted accordingly. 3 In the case of SITC97, the trend is over 1976–96. Table A.5 Trends in India’s share in commodity-wise total world exports—1970–96 Commodity

Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC00 SITC01 SITC02 SITC03 SITC04 SITC05 SITC06 SITC07 SITC08 SITC09 SITC11 SITC12 SITC21 SITC22 SITC23 SITC24 SITC25 SITC26 SITC27 SITC28 SITC29 SITC32

7643.7 9149.9 −147.5 205,850.0 −12,388.0 136,610.0 165,570.0 476,710.0 269,290.0 41,404.0 −519.9 339,620.0 20,730.0 34,182.0 1868.7 18,141.0 2759.4 51,218.0 90,700.0 213,770.0 388,260.0 11,543.0

1.27 1.18 −0.14 7.72 −0.61 12.72 3.04 21.92 3.65 15.32 −0.84 19.89 2.45 1.63 0.80 5.70 2.27 2.68 2.97 14.03 25.62 2.63

−112.2 1092.1 193.7 −197.7 5396.6 −2041.6 −4472.1 −7414.9 42.2 −1157.4 152.7 −11,315.0 −1028.5 1551.3 143.9 −787.1 −123.3 1427.8 2662.4 −1503.7 −9862.8 −218.5

−0.30 2.43 3.02 −0.12 4.31 −3.08 −1.33 −5.46 0.01 −6.86 4.05 −10.62 −2.01 1.21 1.00 −4.18 −1.83 1.20 1.52 −1.60 −10.43 −0.82

0.520 0.811 0.439 0.637 0.319 0.412 0.388 — 0.686 — 0.628 — 0.649 0.456 0.437 0.752 0.871 — 0.827 0.366 — 0.654

3.11 7.06 2.49 4.21 1.72 2.30 2.15 — 4.81 — 4.11 — 4.35 2.62 2.48 5.82 9.05 — 7.51 2.01 — 4.41

2.05 1.69 2.06 2.02 1.96 2.06 1.66 1.99 2.17 1.65 2.29 1.77 0.54 1.77 1.86 2.03 1.01 2.14 2.26 1.96 1.68 1.90

−0.021 0.381 0.354 0.012 0.534 0.393 0.142 0.526 −0.031 0.639 0.652 0.811 0.166 0.040 0.013 0.760 0.155 0.016 0.462 0.084 0.806 0.015

118

Commodity

Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC33

4596.5

0.50

665.7

1.19

0.672

4.62

1.44

0.202

Commodity Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC34 SITC41 SITC42 SITC43 SITC51 SITC52 SITC53 SITC54 SITC55 SITC56 SITC58 SITC59 SITC61

−0.45 1.44 3.07 0.67 −0.32 1.91 5.74 2.95 6.54 −1.48 0.37 −0.25 17.99

280.3 −65.0 −810.8 1887.4 2771.0 597.3 3788.5 2894.8 −230.8 733.9 472.4 1200.1 −25662.0

1.16 −0.33 −0.54 1.91 3.65 1.53 9.88 5.26 −0.34 2.10 1.51 3.69 −7.24

0.642 — 0.507 0.397 0.946 0.848 — — −0.235 0.648 0.923 0.623 0.366

4.27 — 3.00 2.21 14.90 8.17 — — −1.23 3.41 12.23 4.06 2.00

1.09 1.88 1.87 2.09 1.53 2.02 2.07 2.02 1.97 1.46 1.32 2.21 1.93

0.233 −0.036 0.031 0.163 0.682 0.107 0.788 0.507 −0.036 0.424 0.325 0.600 0.795

2.55 4.25 2.22 5.19 2.09 1.87 1.44 4.82 6.08 7.80 2.79 7.22 1.69 2.72 15.90 4.02 7.27 2.16 6.34 2.61 6.78 3.11 3.32

1390.5 −232.4 −104.2 −3103.3 15600.0 −179.5 −557.6 278.2 −187.7 244.9 257.5 69.6 147.3 −37.6 −485.4 129.4 −262.6 −54.8 −237.9 6589.0 6372.0 24.2 190.6

2.20 −0.87 −0.47 −0.92 7.71 −0.12 −0.44 0.47 −0.89 3.40 0.75 0.90 1.32 −0.34 −5.68 0.82 −3.55 −0.10 −3.01 16.38 8.38 0.02 3.16

0.560 0.518 0.935 0.853 0.413 0.787 0.652 0.765 0.607 — 0.556 0.574 0.524 0.840 — 0.557 0.263 0.195 0.502 — 0.430 0.884 0.428

3.45 3.09 13.47 8.32 2.31 6.50 4.39 6.06 3.89 — 3.41 3.57 3.14 7.89 — 3.42 1.39 1.02 2.96 — 2.43 9.66 2.41

2.27 2.16 0.92 2.45 1.91 1.14 1.46 1.44 2.46 1.96 2.25 2.01 2.38 1.66 1.96 2.36 1.80 2.05 2.04 1.63 2.47 2.01 1.77

0.365 0.065 0.011 0.189 0.835 −0.038 0.034 −0.040 0.161 0.289 0.034 0.018 0.108 0.050 0.546 0.031 0.409 −0.039 0.487 0.911 0.848 0.039 0.444

SITC62 SITC63 SITC64 SITC65 SITC66 SITC67 SITC68 SITC69 SITC71 SITC72 SITC73 SITC74 SITC75 SITC76 SITC77 SITC78 SITC79 SITC81 SITC82 SITC83 SITC84 SITC85 SITC87

−1822.2 4594.1 75,239.0 10,777.0 −5134.0 13,191.0 35,277.0 26,017.0 71,939.0 −10,270.0 2278.0 −1360.7 1,032,100. 0 26,363.0 18,638.0 9995.4 311,110.0 68,726.0 47,917.0 30,170.0 48,124.0 21,009.0 8989.5 15,734.0 9145.2 3087.4 5306.5 21,770.0 10,449.0 8667.2 19,590.0 8169.2 16,843.0 83,765.0 63,295.0 3248.5

119

Commodity Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

SITC88 SITC89 SITC93 SITC94 SITC95

7.94 4.45 0.67 4.71 4.31

−436.6 91.1 1412.5 −14159.0 −213.6

−3.20 0.16 2.74 −3.65 −3.12

0.469 0.723 0.465 0.799 0.721

2.71 5.34 2.68 6.78 5.31

2.01 1.66 2.06 2.01 2.35

0.446 −0.033 0.432 0.678 0.576

17,648.0 41,897.0 5643.4 314,010.0 4946.6

Notes 1 The table reports the results of linear time trends, estimated by ordinary least squares, with correction for autocorrelation where required. 2 The shares have all been scaled up by a factor of 10,000,000.0. The coefficients have to be interpreted accordingly. 3 In the case of SITC56, the trend is over 1980–96. Table A. 6 Share (%) of market in world total import Year

CUS

RAM

SSA

NAF

EE

WE

JP

CH

EAT

REA

RSA

ME

OC

Total

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

17.0 18.0 18.1 16.2 16.0 14.8 15.9 16.7 16.8 16.0 15.4 16.9 16.3 18.5 21.6 22.0 21.7 20.7 19.6 19.4 18.4 17.6 17.7 19.2 19.5 18.4 18.5

5.8 5.8 5.5 5.3 6.3 6.3 5.6 5.7 5.3 5.4 6.2 6.3 5.6 4.5 4.6 4.1 3.8 3.7 3.7 4.1 3.9 4.4 5.0 5.3 5.6 5.3 5.4

3.4 3.6 3.0 2.9 3.0 3.4 3.1 3.1 2.9 2.4 2.8 3.1 2.8 2.2 1.9 2.0 1.7 1.6 1.5 1.4 1.4 1.5 1.5 1.4 1.3 1.4 1.3

1.2 1.2 1.2 1.2 1.6 1.1 1.8 1.9 1.9 1.6 1.7 2.1 2.0 2.0 1.9 1.7 1.4 1.2 1.2 1.2 1.2 1.1 1.0 1.1 1.0 1.0 0.9

3.7 3.7 5.0 5.1 4.8 6.1 5.4 5.0 4.9 4.4 5.3 5.3 5.2 5.2 4.9 5.1 4.8 4.2 3.0 3.5 3.0 2.6 2.6 3.0 3.0 3.3 4.2

46.0 46.1 46.6 46.8 44.5 43.3 44.2 42.7 42.5 44.3 44.9 41.1 41.6 41.2 39.7 40.8 43.8 45.3 45.5 45.6 47.8 47.5 46.1 42.0 41.3 41.3 40.2

5.0 4.7 4.8 5.8 6.9 6.1 6.0 5.7 5.6 6.3 6.7 6.9 6.8 6.8 6.8 6.0 5.7 5.8 6.0 6.3 6.4 5.9 5.4 5.3 5.6 6.0 6.0

0.5 0.5 0.5 0.7 0.7 0.7 0.5 0.5 0.7 0.8 0.9 0.9 0.8 1.0 1.3 2.0 1.9 1.7 2.0 1.9 1.6 2.0 2.4 3.3 3.2 3.2 3.3

3.1 3.2 3.2 3.6 3.3 3.2 3.4 3.5 4.0 4.3 4.4 4.9 5.1 5.4 5.6 5.1 5.3 6.0 6.8 7.0 7.4 8.3 8.6 9.0 9.4 9.7 9.5

2.0 1.9 1.8 1.8 2.1 2.0 1.8 1.9 2.0 2.0 2.2 2.3 2.6 2.6 2.3 2.0 1.8 2.0 2.2 2.5 2.9 3.1 3.3 3.7 4.1 4.5 4.6

0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

2.1 2.4 2.5 2.5 3.1 4.3 4.6 5.0 5.1 4.4 4.7 5.6 6.1 5.7 4.8 4.1 3.3 2.8 2.7 2.7 2.6 2.9 3.1 3.0 2.7 2.5 2.5

9.7 8.6 7.5 7.7 7.3 7.2 7.3 7.9 7.8 7.8 4.4 4.2 4.7 4.5 4.0 4.6 4.3 4.6 5.5 3.9 3.0 2.8 2.9 3.2 2.9 3.1 3.4

324,138,151 355,592,073 422,615,626 586,683,936 837,929,189 887,738,423 994,699,328 1,147,828,669 1,348,163,195 1,687,032,140 2,013,836,384 1,976,241,018 1,865,763,275 1,809,616,840 1,915,109,990 1,969,663,908 2,141,205,894 2,500,181,583 2,858,988,977 3,063,461,563 3,469,610,629 3,569,732,012 3,862,838,722 3,957,481,578 4,378,896,378 5,214,774,301 5,493,900,565

Note

120

Year CUS RAM SSA NAF EE WE JP CH EAT Total world exports in US$ ’000 are reported in the last column.

REA

RSA

ME

OC

Total

Table A. 7 Share (%) of market in India’s total export Year

CUS

RAM

SSA NAF

EE

WE

JP

CH

EAT

REA RSA ME

OC

Total

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

15.3 18.7 16.9 14.9 14.5 12.3 13.0 12.7 14.3 12.8 12.3 19.8 14.0 25.4 25.0 22.3 22.1 22.1 21.0 23.6 15.8 17.6 20.2 19.2 20.3 18.4 20.3

0.5 1.0 0.6 0.4 0.8 0.5 0.4 0.4 0.3 0.2 0.5 0.7 0.2 0.2 0.4 0.2 0.2 0.2 0.6 0.4 0.4 0.6 0.7 0.9 1.2 1.1 1.7

4.7 6.2 4.6 3.0 4.0 4.6 3.7 4.1 4.4 3.8 4.5 2.6 3.2 2.1 1.7 1.6 1.7 1.6 1.7 0.1 1.8 2.3 3.0 2.8 3.1 4.5 4.0

20.6 21.3 22.8 20.2 18.6 18.2 14.8 16.4 12.6 12.4 20.4 3.0 24.9 15.2 18.5 21.0 18.2 15.9 15.8 2.0 17.5 11.0 4.3 4.3 3.0 3.8 3.3

22.2 21.6 24.5 26.3 25.9 24.2 29.1 28.8 28.6 32.5 26.9 32.8 24.9 21.8 22.0 21.3 25.8 28.8 27.8 32.1 31.3 31.2 32.3 30.2 30.4 29.4 27.7

14.3 12.2 11.5 13.6 10.7 10.7 11.3 10.2 10.2 11.0 10.1 14.8 9.2 9.6 9.1 11.4 11.2 11.1 10.9 12.5 9.5 9.6 8.1 8.4 8.1 7.3 6.3

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.4 0.4 0.0 0.1 0.1 0.1 0.3 0.2 0.2 0.5 0.6 0.3 0.3 0.8 1.4 0.8 1.0 1.7

2.2 2.8 1.9 2.9 2.8 2.9 3.5 3.1 3.7 3.5 3.8 5.5 3.4 6.7 4.0 4.6 6.2 6.4 7.2 7.3 7.0 7.9 8.4 10.1 10.7 11.6 11.4

3.1 2.5 1.6 2.0 3.1 3.1 3.9 3.2 4.4 4.3 3.4 3.3 2.5 1.8 1.6 1.4 2.0 1.8 2.4 3.6 3.7 4.4 4.4 5.4 4.8 6.3 6.3

2.1 2.7 2.6 3.6 3.4 2.2 1.9 2.2 2.0 2.0 2.2 2.4 5.5 2.9 4.5 2.2 1.9 1.7 2.3 12.0 3.8 3.2 3.8 2.8 2.6 2.1 2.3

2,098,149 2,072,865 2,468,013 2,932,263 3,896,522 4,513,354 5,446,915 6,173,444 6,659,495 7,557,064 8,097,313 7,140,093 9,155,222 9,736,200 10,150,981 9,570,667 10,367,654 12,554,777 14,108,901 15,859,504 18,544,645 18,296,490 21,215,543 22,792,387 26,246,620 31,755,862 34,376,018

3.5 2.1 1.6 1.0 1.3 1.6 2.1 1.7 2.1 1.4 1.3 1.8 1.0 1.6 1.1 1.3 0.9 0.7 0.7 0.7 0.5 0.7 0.8 0.8 0.7 0.7 0.8

4.5 3.2 5.8 7.0 2.9 3.4 2.4 3.0 3.7 3.8 2.9 2.2 1.9 3.4 3.6 3.8 3.1 2.7 2.5 1.6 2.6 3.0 4.0 3.7 4.7 4.7 5.0

6.9 5.5 5.6 5.1 12.1 16.2 13.7 14.1 13.2 11.9 11.4 11.0 9.2 9.3 8.4 8.7 6.5 6.7 6.6 3.7 6.0 8.2 9.2 9.9 9.5 9.1 9.4

Note India’s total exports in US$ ’000 are reported in the last column. Table A.8 India’s share (%) in total imports of each market Year

CUS

RAM

SSA

NAF

EE

WE

JP

CH

EAT

REA

RSA

ME

OC

1970 1971 1972 1973

0.59 0.61 0.55 0.46

0.06 0.10 0.06 0.04

0.90 1.03 0.89 0.51

1.88 1.05 0.79 0.41

3.63 3.37 2.66 1.98

0.31 0.28 0.31 0.28

1.85 1.52 1.41 1.18

0.00 0.00 0.00 0.00

0.46 0.51 0.35 0.40

1.04 0.78 0.53 0.56

5.90 4.86 9.22 9.57

2.13 1.37 1.32 1.02

0.14 0.18 0.21 0.23

121

Year

CUS

RAM

SSA

NAF

EE

WE

JP

CH

EAT

REA

RSA

ME

OC

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

0.42 0.42 0.45 0.41 0.42 0.36 0.32 0.43 0.42 0.74 0.62 0.50 0.50 0.54 0.53 0.63 0.46 0.52 0.63 0.58 0.63 0.61 0.69

0.06 0.04 0.04 0.04 0.03 0.02 0.03 0.04 0.02 0.02 0.05 0.02 0.03 0.03 0.08 0.05 0.05 0.07 0.08 0.10 0.14 0.13 0.19

0.62 0.69 0.67 0.71 0.74 0.72 0.64 0.31 0.57 0.52 0.48 0.39 0.46 0.49 0.55 0.03 0.68 0.81 1.07 1.11 1.45 2.01 1.99

0.38 0.40 0.64 0.50 0.54 0.39 0.31 0.32 0.26 0.44 0.32 0.36 0.31 0.31 0.28 0.30 0.25 0.32 0.44 0.45 0.41 0.42 0.53

1.82 1.54 1.51 1.77 1.28 1.26 1.56 0.21 2.35 1.58 2.02 2.02 1.87 1.90 2.61 0.30 3.13 2.18 0.92 0.83 0.59 0.70 0.50

0.27 0.29 0.36 0.36 0.33 0.33 0.24 0.29 0.30 0.29 0.30 0.26 0.29 0.32 0.30 0.37 0.35 0.34 0.39 0.42 0.44 0.44 0.43

0.72 0.90 1.03 0.96 0.90 0.79 0.61 0.79 0.67 0.77 0.71 0.93 0.96 0.98 0.90 1.03 0.79 0.84 0.82 0.92 0.87 0.74 0.66

0.00 0.00 0.00 0.01 0.29 0.23 0.17 0.00 0.09 0.05 0.03 0.07 0.04 0.06 0.12 0.18 0.09 0.09 0.18 0.24 0.16 0.19 0.32

0.39 0.46 0.57 0.48 0.45 0.37 0.34 0.40 0.33 0.67 0.38 0.44 0.57 0.53 0.52 0.54 0.51 0.49 0.54 0.65 0.69 0.73 0.76

0.69 0.82 1.18 0.92 1.12 1.00 0.63 0.51 0.49 0.36 0.37 0.35 0.52 0.48 0.56 0.73 0.68 0.72 0.75 0.84 0.70 0.85 0.86

3.25 3.56 3.67 4.10 4.09 3.56 2.33 1.65 1.79 3.56 3.47 3.50 3.08 2.92 3.12 1.99 3.59 3.71 5.34 5.10 6.91 7.03 7.98

1.83 1.91 1.64 1.54 1.27 1.21 0.99 0.71 0.75 0.89 0.94 1.03 0.96 1.23 1.22 0.71 1.24 1.48 1.67 1.92 2.16 2.25 2.40

0.22 0.15 0.15 0.15 0.13 0.12 0.20 0.21 0.58 0.34 0.60 0.24 0.22 0.18 0.21 1.60 0.67 0.58 0.73 0.50 0.53 0.42 0.44

Table A.9 Trends in market-wise shares in total world imports–1970–96 Market

Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

CUS RAM SSA NAF EE WE JP CH EAT REA RSA ME OC

16.64 5.69 3.58 1.56 4.70 45.70 5.60 0.01 2.30 1.51 0.47 2.97 8.86

12.07 9.01 22.04 4.78 6.45 24.05 11.70 0.03 3.91 2.61 10.89 2.40 15.14

0.0948 −0.0329 −0.0921 −0.0158 −0.0332 −0.1442 0.0218 0.1090 0.2540 0.0983 −0.0017 0.0043 −0.2430

1.16 −0.89 −9.25 −0.85 −0.77 −1.28 0.76 5.91 7.75 3.40 −0.68 0.07 −6.79

0.754 0.786 0.503 0.840 0.763 0.745 0.701 0.738 0.864 0.929 0.682 0.914 0.531

5.86 6.47 2.97 7.88 6.02 5.70 5.01 5.58 8.77 12.82 4.76 11.47 3.20

1.34 1.54 1.68 1.71 2.03 1.47 1.40 1.52 1.59 0.71 1.75 0.87 1.68

0.234 0.223 0.892 0.189 0.355 0.038 −0.016 0.847 0.928 0.642 −0.034 0.009 0.822

Notes

122

Market Constant t-value Time t-value Rho t-value DW Adj. R sq 1 The table reports the results of linear time trends, estimated by ordinary least squares, with correction for autocorrelation where required. 2 The shares are all in percentage. The coefficients have to be interpreted accordingly. Table A.10 Trends in India’s share in total imports of each market–1970–96 Market

Constant

t-value

Time

t-value

Rho

t-value

DW

Adj. R sq

CUS RAM SSA NAF EE WE JP CH EAT REA RSA ME OC

45,070.0 1999.3 49,106.0 139,030.0 251,270.0 26,994.0 151,040.0 −17,818.0 36,496.0 82,019.0 440,750.0 148,140.0 7825.9

7.57 0.50 1.09 3.13 8.03 8.40 6.01 −2.94 8.12 4.43 2.64 2.88 0.74

553.4 416.1 3548.3 −4462.2 −5751.4 460.5 −3507.1 1528.2 994.1 −534.9 2363.1 1112.5 2069.0

1.51 1.81 1.41 −1.88 −2.94 2.37 −2.44 5.16 3.56 −0.49 0.24 0.38 3.13

0.478 0.836 0.862 0.897 — 0.652 0.827 — 0.234 0.725 0.674 0.836 —

2.78 7.78 8.66 10.36 — 4.38 7.49 — 1.23 5.37 4.65 7.77 —

2.09 2.37 2.04 0.78 1.60 2.01 1.91 1.66 2.14 1.70 1.87 1.76 1.84

0.163 0.224 0.124 0.230 0.228 0.402 0.288 0.647 0.406 −0.015 −0.039 0.001 0.253

Notes 1 The table reports the results of linear time trends, estimated by ordinary least squ ares, with correction for autocorrelation where required. 2 The shares have all been scaled up by a factor of 10,000,000.0. The coefficients have to be interpreted accordingly. 3 In the case of CH, the trend is over 1982–96.

Notes

1 Competitiveness, investment and finance: analytical links 1 A notable exception is Fanelli and Medhora (2002). 2 The earliest firm-level study of export behaviour that demonstrates the presence of significant inter-firm heterogeneity within an industry is Lall and Kumar (1981). 3 Furthermore, it should be noted that, apart from some technology-intensive industries such as engineering, electronics and drugs and pharmaceuticals, most other industries in low-income developing countries use relatively low-end technologies that can be easily purchased ‘off the shelf’. In these industries, technological factors such as innovation and research and development may play a relatively less important role in determining their international competitiveness. 4 1989–90 corresponds to the Indian fiscal year 1 April 1989 to 31 March 1990. We use this notation throughout the book. 5 Notice that an analysis done only at the sectoral/industry level ignoring firm heterogeneity would not have captured this aspect. 6 See, for example, Jensen and Meckling (1976), Stiglitz and Weiss (1981), Myers and Majluf (1984) and Bernanke and Gertler (1990). Two useful surveys of this literature are Gertler (1988) and Hubbard (1998). 7 See, for example, Bhagwati and Desai (1970), Balassa and associates (1971), Krueger (1974, 1978, 1993), Leith (1974), Baldwin (1975), Behrman (1975), Bhagwati and Srinivasan (1975), Diaz-Alezandro (1975), Hansen and Nashashibi (1975), Michaely (1975), Balassa (1987), Corbo and de Melo (1987), World Bank (1986, 1987, 1988, 1989b, 1991, 1995), IMF (1988, 1992), Easterly et al. (1994), Slawinski (1996) and Teranishi (1997).

2 The policy environment in India 1 Historically the inflation rate in India has been fairly low by developing country standards with an annual average of 6.1 per cent for the period 1951–90. 2 The discussion of India’s trade and exchange rate policy draws heavily from World Bank (1989), Pursell (1992), Joshi and Little (1994) and Agrawal et al. (1995). 3 With India being a signatory to the World Trade Organisation (WTO) agreements, most of the quantitative restrictions (QRs) to imports have been removed by April 2001. The QRs have been replaced with tariffs, most of which are also fairly low, much below the bound rates that India submitted to the WTO. 4 Progressively, the list of industries eligible for foreign investment through the automatic route has been expanded. 5 We do not provide a comprehensive account of Indian financial sector policies but only those that directly relate to the tradable sector. For the former, see Sen and Vaidya (1997).

124

NOTES

6 See Reserve Bank of India (1985), Chakravarty committee report. 7 IDBI was taken over by the Government in 1975 and converted into a separate organisation. 8 ICICI was set up as a private sector DFI in 1955. Initially, it was owned by the World Bank, Indian commercial banks and insurance companies. Presently, a large part of its share capital is held by the government-owned financial institutions including commercial banks. 9 The rate of interest remained at 14 per cent per annum for many years. 10 In 1993, India ranked 22nd in the world in terms of market capitalisation, 24th in terms of value traded and 2nd in listed domestic securities. 11 We draw from Agrawal et al. (1995). 12 We draw from Sen and Vaidya (1997). 13 Only recently, in the Annual Budget presented in February 2002, has the government indicated its intentions to bring forth drastic changes in the bankruptcy laws to facilitate restructuring/liquidation of sick firms. The proposed changes include the abolition of BIFR, and the consolidation and simplification of various related laws. These intentions are, however, still to transform into new/reformed laws governing bankruptcy.

3 The balance of payments and national competitiveness 1 Further details of Thirlwall’s model is provided in Thirlwall and Gibson (1992), Chapter 12 and McCombie and Thirlwall (1994). 2 According to the Indian balance of payments records, private transfers (which predominantly consist of remittances) increased from 0.2 per cent of GDP in the early 1970s to 1.5 per cent of GDP in the 1990s. 3 The dummy variable DUM84 takes the value of 1 for the years 1984 onwards and zero otherwise, and the dummy for 1991 (DUM91) takes the value of 1 for the years 1991 onwards and zero otherwise. 4 The data CD-ROM procured directly from Statistics Canada covers the period 1980–96 only. For the period 1970–79, the same database from Statistics Canada is available on CD-ROM from the NBER (Feenstra et al. 1997). The CD-ROM from NBER in fact reports the World Trade Database from Statistics Canada for the period 1970–92. Comparison of the data from the two CD-ROMs for the period 1980–92 showed very little difference. Hence, we use the data for the period 1970–79 from the NBER CD-ROM and for the period 1980–96 from the Statistics Canada CD-ROM. 5 Time series data for the period 1970–96 on (a) commodity-wise shares in total world exports, (b) commoditywise shares in India’s total exports and (c) India’s market share in total world exports of each commodity, are presented in Appendix Tables A.l–A.3, respectively. 6 The OLS linear trend estimates for (a) commodity-wise shares in total world exports and (b) India’s market share in total world exports of each commodity, are presented in Appendix Tables A.4 and A.5, respectively. 7 Time series data for the period 1970–96 on (a) market-wise shares in total world imports, (b) market-wise shares in India’s total exports and (c) India’s share in total imports of each market, are presented in the Appendix Tables A.6–A.8, respectively. 8 The OLS linear trend estimates for (a) market-wise shares in total world imports and (b) India’s share in total exports of each market, are presented in Appendix Tables A.9 and A.10, respectively.

4 The determinants of sectoral competitiveness 1 There are two broad aspects of non-price competitiveness. The first is the act of selling and marketing. The second is the characteristics of the product including design, ease of maintenance, quality, reliability, delivery time and after-sales service. See McCombie and Thirlwall (1994) for a review of theoretical and empirical studies on non-price competitiveness in international trade.

NOTES

125

2 Fagerberg also adds world trade as a determinant of export shares to capture cyclical movements in world demand conditions. As will be seen later, we control for cyclical factors by adding time dummies to the regression model. 3 Fagerberg measures the level of technological development in the country/ sector by expenditures on research and development and the number of patent applications. 4 An illustrative study under the technology gap approach is by Dosi et al. (1990) (also see Fagerberg 1994). A notable study under the new trade theory approach is by Grossman and Helpman (1991). 5 A separate set of studies (De Long and Summers 1991, 1992, 1993) have found that countries (particularly developing countries) with higher rates of investment in plant and machinery exhibit higher economic growth. This they argue is due to the positive externalities associated with investment in plant and machinery. 6 It should also be noted that there was a major classification change in 1987–88, which makes industry data at the 4-digit ISIC level in 1987–88 not strictly comparable to the pre-1987–88 data. 7 To take two examples, consider the 3-digit ISIC industries—Electrical Machinery (383) and Transport Equipment (384). In the first case, audio and video equipment (ISIC 3832)—a consumer goods industry—is clubbed together with several capital goods industries, such as electrical industrial machinery (ISIC 3831) and insulated wires and cables (ISIC 3839). Similarly, in the second case, another consumer goods industry, motor vehicles (ISIC 3843), is clubbed together with other capital goods industries, for example, railroad equipment (ISIC 3842). As we have noted in Chapter 2, Indian trade policy with respect to capital goods has differed significantly from that with respect to consumer goods. 8 The same procedure is followed in Chapter 5 to construct real capital stock at the firm level. 9 The mapping of WPI data from RBI classification to ISIC 4-digit level is available upon request. 10 The mapping of NFA data from ASI classification to ISIC 4-digit level is available upon request.

5 Outward orientation: a firm-level analysis 1 An alternate approach is to consider the profits from exporting a commodity as additional to those obtained from selling the same commodity to the domestic market. Such an approach is followed by Roberts and Tybout (1997). Both approaches lead to similar implications on sunk costs for exporting behaviour. 2 Perhaps the most important policy change relating to exporting behaviour relevant to our analysis was the removal of export subsidies with the reforms of 1991. Clearly, in the pre-1991 period, exporting could not be seen as a signal of international competitiveness as many firms might have exported to obtain such subsidies, even if exporting was not profitable per se. This would not be the case in the post-1991 period. 3 We follow the same procedure explained in Chapter 4 to construct a measure of capital stock at the firm level. 4 While it is possible to argue that the export to sales ratio may differ significantly across industries, we will find that industry (and time) dummies are not significant in the estimated regressions in explaining exporting behaviour. 5 The years 1992–93 and 1997–98 correspond to the beginning and end of the period of the analysis. 6 Note that the definition that the Indian Government uses for a firm to be classified as small is in terms of the original value of plant and machinery and has ranged between Rs. 1 million in 1978, Rs. 3.5 millions in 1985 and subsequently to Rs. 10 millions in 1990s. 7 One year’s data (i.e. for 1992–93) are lost when we first difference net fixed assets to obtain the net investment rate. 8 Further details of the probit model, which is in the class of limited dependent variable models, are found in Greene (2000). 9 Note that this variable may very well be capturing the industry’s comparative advantage. We, however, prefer to interpret it as capturing spillovers. 10 This impact of previous exporting experience on current export status is higher than the 36 per cent that was found by Bernard and Jensen (2001) for US firms.

126

NOTES

6 Finance constraints, persistent exporting and investment 1 We draw upon Hubbard (1998) while discussing the theoretical issues involved in the specification of the investment function. Discussion with respect to developing countries relies upon Athey and Laumas (1994) and Harris et al. (1994). 2 Notice the lack of perfect substitutability between internal and external finance that arises due to information asymmetry. 3 The sales-accelerator model of investment has limitations from an empirical standpoint as it is a reduced form specification and therefore, subject to the criticism that changes in the policy regime will affect the parameters of the equation (Lucas 1976). In its defence, one should note that several studies have shown that reduced form investment functions exhibit only modest parameter non-constancy in the presence of policy shifts (Oliner et al. 1996). Furthermore, there were no significant policy changes that may affect investment behaviour in the Indian economy in the period of our study. 4 Kaplan and Zingales (1997) have argued that testing for differences in investment-cash flow sensitivities across firms is not an adequate method of establishing which firms are finance constrained and which are not. Kaplan and Zingales favour a more direct way of ascertaining whether a particular firm is finance constrained by scrutinising the annual reports of firms for managerial statements indicating whether or not financial constraints are a problem. However, the latter methodology has been criticised on the grounds that it is more suitable for establishing which firms are financially distressed rather than those that are financially constrained (Hubbard 1998). 5 See Chapter 2, Section 2.7 for an overview of the exit policy in India. For a detailed discussion on the bankruptcy procedures and their effect on financial institutions see Anant and Goswami (1995). 6 It should be noted that firms that are exporting part of their output are not necessarily more efficient producers than firms that sell only to domestic markets. Using a game-theoretic model, Shy (2000) shows that firms may wish to export to signal to consumers and lenders that it is producing a high-quality product, even if selling internationally is less profitable than selling at home. 7 We have no a priori expectation on the signs of the coefficients of the debt and liquid assets terms. For a detailed discussion on this issue see Devereux and Schiantarelli (1990). 8 Not reported here, but would be made available upon request. 9 The priority sector lending requirements stipulate that 40 per cent of commercial banks total credit should be advanced to priority sectors. The identity of which sectors were classified as priority sectors has undergone some change over the years. In the context of our sample period the sectors so classified were agriculture and allied activities, small-scale industry and export credit. 10 In a different context, Fama (1985) argues that commercial banks have a comparative advantage over other lenders by virtue of lending short-term. As Fama argues, the advantages of short-term inside debt is that the renewal process triggers periodic evaluation of the firm’s ability to meet ‘low priority fixed payoff claims’, and provides the bank with valuable inside information.

7 Conclusions and policy implications 1 This is in addition to any trade barriers that other countries may impose.

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Index

accumulation of: financial assets 7; physical capital 7 agency costs 2, 7, 122 agricultural commodities 27 asymmetric information x, 2, 7, 93–4, 116 autoregressive distributed lag 37

commodity composition effects 4, 11, 40, 42–3, 48, 50, 117; competitiveness effects 4, 11, 40; market orientation effects 4, 11; world trade effect 4, 11 consumer goods 9, 13, 15, 53, 61–2, 70, 118, 149 contract enforcement 7 convex costs 6 credit rationing 19, 94 current account deficit 27–30, 38, 49, 116

balance of payments ix, xii, 1, 4, 9–10, 13, 27–8, 33–6, 38, 50, 115–16, 120, 147; constrained growth model xii bank-based financial systems 8 bankruptcy: costs 24, 93, 96; laws 24, 96, 116, 147; procedures 26, 151 BIFR 23–4, 147 bonds 20–1, 96, 100 BOP see balance of payments borrowings: from banks 100; from DFIs 100, 112–13, 119

debentures 20–1, 100 deregulation 19, 21, 25, 112–13, 122 devaluation 15–16 DFIs 20–1, 112–14, 119 directed credit programs 19 economic development xiv, 7 economic reforms xiv, 19, 24, 28, 38, 57 entry barriers 5, 116 error-correction modelling 37 excess sensitivity hypothesis 95, 99–100 exchange rate policy 12, 27, 29, 116, 146 exit policy 12, 151 export: demand function 34; premia 77; promotion 11, 15–16, 24, 29, 31, 120–1; quota 14, 38; shares x, xii, 11, 51–5, 57, 59, 61, 63, 117–18, 149; status 63–4, 67, 69, 71, 77, 92, 150; supply function 6, 120 external finance xiii, 2, 7, 94, 96, 99–102, 107–9, 112–13, 119, 150

capital goods 13–15, 24, 53–4, 59, 61–2, 64, 76, 86, 90, 118, 149 capital imports 34 capital market 7, 8, 10, 18–19, 20–1, 94, 99, 100–1, 112– 13, 119 capital stock 6–7, 52, 54–5, 69–70, 72, 76–7, 83–4, 86, 92, 94–5, 99, 104, 118, 120, 149–50 Cash Compensatory Support Scheme 14–15 cash reserve ratio 19 Chakravarty committee report 147 CMS see constant market shares commercial banks 8, 18–19, 101, 112–13, 119, 147, 151 constant market shares xi–xii, 4, 11, 28, 39–2, 48, 50, 117;

finance constraints x, 8, 10–11, 93–6, 99–102, 104–5, 109, 112–14, 119, 121 132

INDEX

financial characteristics xii, 97 financial intermediaries 2, 10, 18, 21, 25, 113–14, 116, 119 financial repression 18–19 financial sector policy 12, 18, 25, 147 financial systems 2, 7–8, 21, 115 financing patterns 93–4, 101 firm size 3, 64–7, 72, 77, 86–8, 98 firm specific effects 99, 105 fiscal deficit 12–13, 30, 116 fixed deposits 20–1, 100 foreign ownership 64–5, 67, 87–8 global commodity chains 121 globalisation xiv gross fixed assets 101 heterogeneity: financing patterns 102; firm-level 3, 62, 118, 146; industry-level 62, 118 hysteresis 6 import: demand function 33; licensing 13–15; substitution 9, 12, 115 imported: capital goods 64, 67, 76–7, 82, 86, 88, 90, 92, 118; raw materials 64, 76, 78, 82, 86, 88, 92, 118 incentive problems 94, 95 income elasticity of: export demand 34; import demand 36; world demand 4 increasing returns to scale 2 industrial: licensing 13, 17, 21, 24; policy 12, 24, 36, 53–4, 57, 96 industry specific effects 59 inflation 12–13, 16, 146 information costs 5, 7, 94–7, 99–100 inter-corporate loans 101 interest rate: controls 19; deregulation 19, 21 intermediate goods 13, 14, 15, 53 internal finance 94, 99, 100, 105 investment:

133

bursts see also investment spikes; in plant and machinery 6, 149; spikes x, xi, 6, 69, 83, 84, 86, 88, 90, 92, 100, 119, 120 investment rate xii, 5, 51–2, 59, 61, 69, 76, 82–4, 86, 92, 118, 150 investment-savings gap 30, 49, 116 inward-looking development strategy 9 labour: policy 12; productivity 11, 51–3, 55, 57, 59, 61–2, 117–18 legal barriers: to entry 17, 20; to exit 22 lemons premia 94 liquid assets 97, 99, 105, 151 loanable funds 7 loan mispricing 94 long-term debt 99, 105 long-term finance 8, 112–14, 119 lumpiness of investment see also investment spikes macroeconomic policy 12, 115 manufactured goods xiv manufacturing exports xi, 2, 31, 50, 55, 121 manufacturing trade ratio xi, 31, 50 market structure 5, 88 Marshall-Lerner condition 34 multinational corporations 5 net fixed assets 54–5, 75–7, 83, 86–7, 92, 97, 118, 150 new trade theories 1, 52, 149 nominal effective exchange rate xi, 31 nominal exchange rate 15 non-bank financial institutions 18, 101 non-convex costs 6, 69, 120 non-exporters xii, 64, 66, 72, 75–9, 82–3, 86, 88, 90, 92, 97–8, 101–2, 105, 109, 112–14, 118–19, 121 non-price factors 1, 4, 42, 51–2, 61, 117 occasional exporters xii, 82–3, 97 openness 29, 31 ordinary least squares 43, 105 outward orientation 38, 63, 92, 96, 119 panel data 51, 53, 59, 61, 87, 118 persistence of exporting behaviour 79, 82, 92, 118

134

INDEX

persistent exporters xii, 79, 82–3, 96–7, 99, 101–2, 105, 109, 112–14, 119 Prebisch-Singer thesis 1 price elasticity of: demand for export 34; demand for import 33 price factors 1, 5, 51–2, 61–2, 117–18 priority sector 19, 112, 151 probability of exporting 67, 88, 90, 92, 119 probit model 84, 87–8, 150 productivity xi, xiv, 2, 25, 54, 59, 61–2, 86, 118 profitability xii, 24, 57, 61, 64–7, 76, 78, 82, 88, 94, 96–7, 117, 121 random effects 87 RBI see Reserve Bank of India R&D see research and development real effective exchange rate xi, 29, 31 real exchange rate 1, 16, 27–30, 34–5, 38, 49–50, 116 real wages xi, 51, 53–5, 59, 61–2, 117–18 remittances 35, 38, 50, 117, 147 research and development 4–6, 52, 65–6, 146, 149 Reserve Bank of India 9, 16, 19, 55, 66, 147, 149 revenue deficit 13 sales accelerator 95, 99, 109, 150 scale economies 5–6, 67 sectoral competitiveness x, xii, 4−5, 51–2, 61–2, 118, 149 self reliance 9, 12, 115 sources/uses of funds 97 spillovers 64, 67, 86–8, 120, 150 stock market 8, 20–1, 25, 108 structural adjustment programme 9, 13, 15, 30, 115 sunk costs 6, 64, 71, 79, 84, 86, 88, 116, 121, 150 technological innovation 11, 51, 93 technological progress 4, 52, 59 technology gap 5, 52, 149 technology leaders 5 terms of trade 29 time effects 59, 99, 104–5, 109 total factor productivity 25, 57 trade balance xi, 27–8, 34–5, 116 trade credit 101, 108 trade policy 12, 15, 25, 149 transaction costs 2, 7, 94, 116 two-stage least squares 105 ULCs see unit labour costs

unit labour costs xi, 1, 3–4, 11, 51–5, 57, 59, 61, 117–18 uses of funds xiii, 93, 97, 100–1 world income growth 4, 28, 38, 50, 117 x-inefficiency 96